DISCLAIMER
This report is a product of the International Bank for Reconstruction and Development/the World Bank. The
findings, interpretations and conclusions expressed in this paper do not necessarily reflect the views of the
Executive Directors of the World Bank or the governments they represent. The World Bank does not
guarantee the accuracy of the data included in this work.
COPYRIGHT STATEMENT
The material in this publication is copyrighted. Copying and/or transmitting portions of this work without
permission may be a violation of applicable laws.
For permission to photocopy or reprint any part of this work, please send a request with the complete
information to either: (i) the Cluj County Council (Calea Dorobanților, No. 106, Cluj-Napoca, Romania); or
(ii) the World Bank Group Romania (Vasile Lascăr Street, No 31, Et 6, Sector 2, Bucharest, Romania).
This report was delivered in March 2020 under the Reimbursable Advisory Services Agreement on the Cluj
County Spatial Plan, concluded between the Cluj County Council and the International Bank for
Reconstruction and Development on 8 May 2019. It corresponds to Output 2. Intermediary Report on
activities carried out by the Bank under the above-mentioned agreement.
ACKNOWLEDGMENTS
This report is delivered according to the Reimbursable Advisory Services Agreement on the Cluj County
Spatial Plan and was developed under the guidance and supervision of David N. Sislen (Operational
Manager in the field of urban development for Europe and Central Asia) and Tatiana Proskuryakova
(Country Manager for Romania and Hungary). It was drafted by a team coordinated by Jozsef Benedek,
Ciprian Moldovan, Marius Cristea, Ștefana Varvari and Marcel Ionescu-Heroiu, and made up of Adina-Eliza
Croitoru, Răzvan Bătinaș, Sanda Roșca, Viorel Puiu, Titus Man, Bogdan-Eugen Dolean and Iulia Hărănguș,
benefiting from the technical support of the team comprising Cosmina Ursu, Oana Stănculescu, Ștefan
Teișanu, Oana Franț, Adina Vințan, Ioana Irimia, George Moldoveanu and Bianca Butacu. Călin Hințea,
Harika Masud and Cesar Niculescu made comments on this research study.
The team would like to express their gratitude for the excellent cooperation, guidance and support provided
by the representatives of Cluj County Council, particularly Alin Tișe (President of the County Council), Chief
Architect Claudiu Salanță, as well as the multitude of local and regional actors who helped with the
development of this report.
ABBREVIATIONS AND ACRONYMS
ABA Water Basin Administration
ANAR National Administration „Romanian Waters”
APSFR Areas with significant potential in case of floods
CAEN National Classification of Economic Activities
CCC Cluj County Council
CSP County Spatial Plan
EBRD European Bank for Reconstruction and Development
EU European Union
FDI Foreign Direct Investment
FS Feasibility Study
FUA Functional Urban Area
GIS Geographic Information System
ISU Inspectorate for Emergency Situations
LAG Local Action Group
NGO Non-Governmental Organization
NIS/INSSE National Institute of Statistics
PUG General Urban Plan (Plan Urbanistic General)
PUZ Zonal Urban Plan (Plan Urbanistic Zonal)
ROP Regional Operational Program
T/O Turnover
TAU Territorial Administrative Unit
TP Technical Project
WB World Bank
TABLE OF CONTENTS
1. DEMARCATION OF THE REVIEWED OBJECTIVE ...................................................................................................1
2. CRITICAL ANALYSIS OF THE CURRENT SITUATION...............................................................................................1
2.1. CLIMATE RISKS AND THEIR ASSOCIATED CHANGES............................................................................................................. 1
2.1.1. General information, data sources and methods used ................................................................................ 1
2.1.2. Indices on the average air temperature and extreme air temperatures ...................................................... 5
2.1.2.1. Average air temperature ......................................................................................................................................... 5
2.1.2.2. Maximum air temperature...................................................................................................................................... 6
2.1.2.3. Minimum air temperature ...................................................................................................................................... 9
2.1.2.4. Heat waves ............................................................................................................................................................ 12
2.1.2.5. Cold waves ............................................................................................................................................................ 18
2.1.2.6. Days with characteristic temperatures specific to the warm half-year................................................................. 23
2.1.2.7. Days with characteristic temperatures specific to the cold season of the year .................................................... 32
2.1.2.8. Agriculture-specific indices ................................................................................................................................... 39
2.1.3. Extreme rainfall indices .............................................................................................................................. 42
2.1.3.1. Frequency indicators ............................................................................................................................................. 42
2.1.3.2. Indicators of intensity ........................................................................................................................................... 50
2.1.3.3. Snow cover thickness ............................................................................................................................................ 62
2.1.4. Year-round weather events ........................................................................................................................ 63
2.1.4.1. Mist ....................................................................................................................................................................... 63
2.1.4.2. Fog......................................................................................................................................................................... 65
2.1.4.3. Squalls ................................................................................................................................................................... 66
2.1.5. Meteorological phenomena typical for the warm period of the year ........................................................ 67
2.1.5.1. Thunderstorms ...................................................................................................................................................... 67
2.1.5.2. Hail ........................................................................................................................................................................ 68
2.1.6. Meteorological phenomena typical for the cold period of the year ........................................................... 70
2.1.6.1. Hoar-frost .............................................................................................................................................................. 70
2.1.6.2. Blizzard .................................................................................................................................................................. 71
2.1.6.3. Freezing rain.......................................................................................................................................................... 72
2.1.6.4. Rime ...................................................................................................................................................................... 73
2.1.7. Conclusions ................................................................................................................................................. 75
2.2. WATER RISKS ......................................................................................................................................................... 79
2.2.1. High water. Floods. .................................................................................................................................... 80
2.2.2. Flood control measures .............................................................................................................................. 95
2.2.3. Winter occurrences on streams ................................................................................................................ 116
2.2.4. Excessive humidity.................................................................................................................................... 116
2.2.5. River depletion ......................................................................................................................................... 116
2.2.6. Conclusions ............................................................................................................................................... 117
2.3. LANDSLIDE RISKS ................................................................................................................................................... 118
2.3.1. Critical analysis of the current situation ................................................................................................... 118
2.3.2. Risks of landslides (areas affected and/or at risk, existing facilities to tackle landslides, condition,
investments etc.) ................................................................................................................................................ 119
2.3.3. General information, data sources and methods used ............................................................................ 125
2.3.4. Lithological coefficient (Ka) ...................................................................................................................... 127
2.3.5. Geomorphological coefficient (Kb) ........................................................................................................... 128
2.3.6. Structural coefficient (Kc) ......................................................................................................................... 129
2.3.7. Hydrological and climatic coefficient (Kd) ................................................................................................ 130
2.3.8. Hydrogeological coefficient (Ke) .............................................................................................................. 130
2.3.9. Seismic coefficient (Kf) ............................................................................................................................. 131
2.3.10. Forestry coefficient (Kg) ......................................................................................................................... 133
2.3.11. Anthropic coefficient (Kh) ....................................................................................................................... 135
2.3.12. Spatial likelihood of landslides ............................................................................................................... 135
2.4. SEISMIC RISK ........................................................................................................................................................ 143
2.5. SOIL EROSION ....................................................................................................................................................... 146
2.6. OTHER NATURAL RISKS (FOREST FIRES, SNOW AVALANCHES) - AFFECTED AREAS, EXISTING INFRASTRUCTURE, INVESTMENTS ETC.) 152
2.6.1. Forest fires ................................................................................................................................................ 152
2.6.2. Avalanches ............................................................................................................................................... 161
2.7. EMERGENCY SITUATIONS INFRASTRUCTURE AND MANAGEMENT ..................................................................................... 162
3. FLAWS AND INTERVENTION PRIORITIES......................................................................................................... 174
3.1. FLAWS AND INTERVENTION PRIORITIES IDENTIFIED CONCERNING EXTREME METEOROLOGICAL AND CLIMATE PHENOMENA AND
CLIMATE CHANGES ....................................................................................................................................................... 174
3.2. FLAWS AND PRIORITIES CONCERNING EXTREME HYDROLOGIC PHENOMENA ....................................................................... 174
3.3. FLAWS AND PRIORITIES RELATED TO LANDSLIDES .......................................................................................................... 178
3.4. FLAWS AND PRIORITIES REGARDING THE EMERGENCY RESPONSE INFRASTRUCTURE AND SERVICES .......................................... 179
4. PROPOSALS FOR SUPPRESSION/REDUCTION OF FLAWS ................................................................................. 180
4.1. PROPOSALS FOR SUPPRESSION/REDUCTION OF FLAWS CONCERNING EXTREME METEOROLOGICAL AND CLIMATE PHENOMENA AND
CLIMATE CHANGES ....................................................................................................................................................... 180
4.2. PROPOSALS FOR SUPPRESSION/REDUCTION OF FLAWS CONCERNING EXTREME HYDROLOGIC PHENOMENA............................... 182
4.3. PROPOSALS TO ELIMINATE/MITIGATE FLAWS RELATED TO LANDSLIDES ............................................................................. 183
4.4. PROPOSALS TO ELIMINATE/MITIGATE FLAWS REGARDING THE EMERGENCY RESPONSE INFRASTRUCTURE AND SERVICES .............. 184
5. PREDICTIVE RESEARCH, SCENARIOS OR DEVELOPMENT ALTERNATIVES.......................................................... 185
5.1. PREDICTIVE RESEARCH, SCENARIOS OR DEVELOPMENT ALTERNATIVES CONCERNING METEOROLOGICAL AND CLIMATE PHENOMENA
OBSERVED IN THEIR EVOLUTION ...................................................................................................................................... 185
5.2. PREDICTIVE RESEARCH, SCENARIOS OR DEVELOPMENT ALTERNATIVES CONCERNING EXTREME HYDROLOGICAL PHENOMENA ........ 188
5.3. FORECASTS, SCENARIOS OR ALTERNATE OPTIONS REGARDING LANDSLIDES ........................................................................ 190
5.4. FORECASTS, SCNEARIOS OR OPTIONS REGARDING THE EMERGENCY RESPONSE INFRASTRUCTURE AND SERVICES ........................ 196
ANNEX 1. LANDSLIDES EVENTS IN CLUJ COUNTY DURING 1972-2019 ................................................................. 197
BIBLIOGRAPHY .................................................................................................................................................. 210
LIST OF FIGURES
FIGURE 1 – MEAN ANNUAL AIR TEMPERATURE (TMED) IN CLUJ COUNTY (1961-2013) (°C) .............................................................. 5
FIGURE 2 – TREND IN MEAN ANNUAL AIR TEMPERATURE (TMED) IN CLUJ COUNTY (1961-2013)........................................................ 6
FIGURE 3 – MEAN MAXIMUM TEMPERATURE (TXM) IN CLUJ COUNTY (1961-2013) (°C) ................................................................. 7
FIGURE 4– THE HIGHEST TEMPERATURE (TXX) IN CLUJ COUNTY (1961-2013) (°C) ......................................................................... 8
FIGURE 5 – TREND IN MEAN MAXIMUM TEMPERATURE (TXM) IN CLUJ COUNTY (1961-2013) ........................................................... 8
FIGURE 6 – TREND IN THE HIGHEST ANNUAL TEMPERATURE (TXX) IN CLUJ COUNTY (1961-2013) ...................................................... 9
FIGURE 7 – MEAN MINIMUM TEMPERATURE (TNM) IN CLUJ COUNTY (1961-2013) (°C) ............................................................... 10
FIGURE 8 – THE LOWEST TEMPERATURE (TNN) IN CLUJ COUNTY (1961-2013) (°C) ...................................................................... 11
FIGURE 9 – TREND IN MEAN MINIMUM TEMPERATURE (TNM) IN CLUJ COUNTY (1961-2013) ......................................................... 11
FIGURE 10 – TREND IN THE LOWEST ANNUAL TEMPERATURE (TNN) IN CLUJ COUNTY (1961-2013) .................................................. 12
FIGURE 11 – MEAN ANNUAL NUMBER OF HEAT WAVES (HWN) IN CLUJ COUNTY (1961-2013) (EVENTS/YEAR) ................................. 13
FIGURE 12 – MEAN DURATION OF A HEAT WAVE EVENT (HWD) IN CLUJ COUNTY (1961-2013) (DAYS/EVENT) .................................. 14
FIGURE 13 – MEAN ANNUAL FREQUENCY (CUMULATIVE DURATION) OF HEAT WAVES (HWF) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR)
............................................................................................................................................................................. 15
FIGURE 14 – MEAN ANNUAL INTENSITY OF HEAT WAVES (HWA) IN CLUJ COUNTY (1961-2013) (°C) ............................................... 15
FIGURE 15 - TREND IN MEAN NUMBER OF HEAT WAVES (HWN) IN CLUJ COUNTY (1961-2013) ...................................................... 16
FIGURE 16 - TREND IN MEAN ANNUAL DURATION OF A HEAT WAVE EVENT (HWD) IN CLUJ COUNTY (1961-2013) .............................. 16
FIGURE 17 - TREND IN MEAN ANNUAL FREQUENCY (CUMULATIVE DURATION) OF HEAT WAVES (HWF) IN CLUJ COUNTY (1961-2013) .... 17
FIGURE 18 - TREND IN HEAT WAVE INTENSITY (HWA) IN CLUJ COUNTY (1961-2013) .................................................................... 17
FIGURE 19 - MEAN ANNUAL NUMBER OF COLD WAVES (CWN) IN CLUJ COUNTY (1961-2013) (EVENTS/YEAR) .................................. 18
FIGURE 20 - MEAN DURATION OF A COLD WAVE EVENT (CWD) IN CLUJ COUNTY (1961-2013) (DAYS/EVENT) ................................... 19
FIGURE 21 – MEAN ANNUAL FREQUENCY (CUMULATIVE DURATION) OF COLD WAVES (CWF) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR)
............................................................................................................................................................................. 20
FIGURE 22 – MEAN ANNUAL COLD WAVE INTENSITY (CWA) IN CLUJ COUNTY (1961-2013) (°C)..................................................... 20
FIGURE 23 – TREND IN MEAN ANNUAL NUMBER OF COLD WAVES (CWN) IN CLUJ COUNTY (1961-2013) .......................................... 21
FIGURE 24 – TREND IN MEAN ANNUAL DURATION OF A COLD WAVE EVENT (CWD) IN CLUJ COUNTY (1961-2013) ............................. 21
FIGURE 25 - TREND IN MEAN ANNUAL FREQUENCY (CUMULATIVE DURATION) OF COLD WAVES (CWF) IN CLUJ COUNTY (1961-2013) .... 22
FIGURE 26 - TREND IN COLD WAVE INTENSITY (CWA) IN CLUJ COUNTY (1961-2013) .................................................................... 22
FIGURE 27 - MEAN ANNUAL NUMBER OF SUMMER DAYS (SU25) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR) ................................... 24
FIGURE 28 – TREND IN MEAN ANNUAL NUMBER OF SUMMER DAYS (SU25) IN CLUJ COUNTY (1961-2013) ....................................... 24
FIGURE 29 - MEAN ANNUAL NUMBER OF TROPICAL DAYS (TXGE30) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR) .............................. 25
FIGURE 30 - TREND IN MEAN ANNUAL NUMBER OF TROPICAL DAYS (TXGE30) IN CLUJ COUNTY (1961-2013).................................... 25
FIGURE 31 – MEAN ANNUAL NUMBER OF HOT DAYS (TXGE35) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR)..................................... 26
FIGURE 32 – TREND IN MEAN ANNUAL NUMBER OF HOT DAYS (TXGE35) IN CLUJ COUNTY (1961-2013) .......................................... 27
FIGURE 33 – MAXIMUM ANNUAL NUMBER OF TROPICAL NIGHTS (TR) IN CLUJ COUNTY (1961-2013) (DAYS) ..................................... 28
FIGURE 34 – TREND IN MEAN ANNUAL NUMBER OF TROPICAL NIGHTS (TR) IN CLUJ COUNTY (1961-2013) ........................................ 29
FIGURE 35 - SHARE OF MEAN ANNUAL NUMBER OF WARM NIGHTS (TN90P) IN CLUJ COUNTY (1961-2013) (%) ............................... 30
FIGURE 36 - TREND IN SHARE OF MEAN ANNUAL NUMBER OF WARM NIGHTS (TN90P) IN CLUJ COUNTY (1961-2013) ......................... 30
FIGURE 37 – SHARE OF MEAN ANNUAL NUMBER OF VERY WARM DAYS (TX90P) IN CLUJ COUNTY (1961-2013) (%) ........................... 31
FIGURE 38 – TREND IN SHARE OF MEAN ANNUAL NUMBER OF VERY WARM DAYS (TX90P) IN CLUJ COUNTY (1961-2013) ................... 31
FIGURE 39 – MEAN ANNUAL NUMBER OF ICE DAYS (ID) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR) ............................................... 32
FIGURE 40 – TREND IN MEAN ANNUAL NUMBER OF ICE DAYS (ID) IN CLUJ COUNTY (1961-2013) .................................................... 33
FIGURE 41 – MEAN ANNUAL NUMBER OF FROST DAYS (FD) IN CLUJ COUNTY (1961-2013) (DAYS/YEAR) .......................................... 33
FIGURE 42 – TREND IN MEAN ANNUAL NUMBER OF FROST DAYS (FD) IN CLUJ COUNTY (1961-2013) ............................................... 34
FIGURE 43 – SHARE OF MEAN ANNUAL NUMBER OF COLD NIGHTS (TN10P) IN CLUJ COUNTY (1961-2013) (%) ................................. 35
FIGURE 44 – TREND IN MEAN ANNUAL NUMBER OF COLD NIGHTS (TN10P) IN CLUJ COUNTY (1961-2013)........................................ 36
FIGURE 45 – SHARE OF MEAN ANNUAL NUMBER OF COOL NIGHTS (TX10P) IN CLUJ COUNTY (1961-2013) (%).................................. 36
FIGURE 46 - TREND IN MEAN ANNUAL NUMBER OF COOL NIGHTS (TX10P) IN CLUJ COUNTY (1961-2013) ......................................... 37
FIGURE 47 – MEAN DIURNAL TEMPERATURE RANGE (DTR) IN CLUJ COUNTY (1961-2013) (°C) ...................................................... 38
FIGURE 48 – TREND IN MEAN DIURNAL TEMPERATURE RANGE (DTR) IN CLUJ COUNTY (1961-2013) ................................................ 38
FIGURE 49 – MEAN ANNUAL GROWING SEASON LENGTH (GSL) IN CLUJ COUNTY (1961-2013) (DAYS) ............................................. 39
FIGURE 50 – TREND IN MEAN ANNUAL GROWING SEASON LENGTH (GSL) IN CLUJ COUNTY (1961-2013) .......................................... 40
FIGURE 51 – MEAN ANNUAL GROWING DEGREE DAYS (GDDGROW10) IN CLUJ COUNTY (1961-2013) (°C)...................................... 41
FIGURE 52 – TREND IN MEAN ANNUAL GROWING DEGREE DAYS (GDDGROW10) IN CLUJ COUNTY (1961-2013) ................................ 41
FIGURE 53 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL NUMBER OF CONSECUTIVE DRY DAYS (CDD) IN CLUJ COUNTY (1961-2013)
(DAYS) .................................................................................................................................................................... 43
FIGURE 54 – TREND IN MEAN ANNUAL NUMBER OF CONSECUTIVE DRY DAYS (CDD) IN CLUJ COUNTY (1961-2013) ............................. 44
FIGURE 55 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL NUMBER OF CONSECUTIVE WET DAYS (CWDP) IN CLUJ COUNTY (1961-2013)
(DAYS) .................................................................................................................................................................... 45
FIGURE 56 – TREND IN MEAN ANNUAL NUMBER OF CONSECUTIVE WET DAYS (CWDP) IN CLUJ COUNTY (1961-2013) ......................... 46
FIGURE 57 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL NUMBER OF HEAVY PRECIPITATION DAYS (R10) IN CLUJ COUNTY (1961-2013)
(DAYS/YEAR) ............................................................................................................................................................ 47
FIGURE 58 – TREND IN MEAN ANNUAL NUMBER OF HEAVY PRECIPITATION DAYS (R10) IN CLUJ COUNTY (1961-2013)......................... 48
FIGURE 59 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL NUMBER OF VERY HEAVY PRECIPITATION DAYS (R20) IN CLUJ COUNTY (1961-
2013) (DAYS) .......................................................................................................................................................... 49
FIGURE 60 – TREND IN MEAN ANNUAL NUMBER OF VERY HEAVY PRECIPITATION DAYS (R20) IN CLUJ COUNTY (1961-2013) ................. 50
FIGURE 61 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL AMOUNT OF PRECIPITATION FALLEN IN WET DAYS (PRCPTOT) IN CLUJ COUNTY
(1961-2013) (MM/YEAR) ......................................................................................................................................... 51
FIGURE 62 – TREND IN TOTAL ANNUAL AMOUNT OF PRECIPITATION FALLEN IN WET DAYS (PRCPTOT) IN CLUJ COUNTY (1961-2013) .... 52
FIGURE 63 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL TOTAL AMOUNT OF DAILY PRECIPITATION (RX1DAY) IN CLUJ COUNTY (1961-
2013) (MM/DAY) ..................................................................................................................................................... 53
FIGURE 64 – TREND IN MAXIMUM TOTAL AMOUNT OF DAILY PRECIPITATION (RX1DAY) IN CLUJ COUNTY (1961-2013) ........................ 54
FIGURE 65 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL TOTAL AMOUNT OF PRECIPITATION FALLEN IN 3 CONSECUTIVE DAYS (RX3DAYS)
IN CLUJ COUNTY (1961-2013) (MM) .......................................................................................................................... 55
FIGURE 66 – TREND IN MAXIMUM TOTAL AMOUNT OF PRECIPITATION FALLEN IN 3 CONSECUTIVE DAYS (RX3DAYS) IN CLUJ COUNTY (1961-
2013) .................................................................................................................................................................... 56
FIGURE 67 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL TOTAL AMOUNT OF PRECIPITATION FALLEN IN VERY WET DAYS (RP95) IN CLUJ
COUNTY (1961-2013) (MM/YEAR) ............................................................................................................................. 57
FIGURE 68 – SHARE OF MEAN (UP) AND MAXIMUM (DOWN) ANNUAL TOTAL AMOUNT OF PRECIPITATION FALLEN IN VERY WET DAYS IN CLUJ
COUNTY (1961-2013) (%)........................................................................................................................................ 58
FIGURE 69 – TREND IN MEAN ANNUAL TOTAL AMOUNT OF PRECIPITATION FALLEN IN VERY WET DAYS (R95P) IN CLUJ COUNTY (1961-
2013) .................................................................................................................................................................... 59
FIGURE 70 – MEAN (UP) AND MAXIMUM (DOWN) ANNUAL TOTAL AMOUNT OF PRECIPITATION FALLEN IN EXTREMELY WET DAYS (R99P) IN
CLUJ COUNTY (1961-2013) (MM/YEAR)...................................................................................................................... 60
FIGURE 71 – SHARE OF MEAN (UP) AND MAXIMUM (DOWN) ANNUAL TOTAL AMOUNT OF PRECIPITATION FALLEN IN EXTREMELY WET DAYS IN
CLUJ COUNTY (1961-2013) (%) ................................................................................................................................ 61
FIGURE 72 – TREND IN MEAN ANNUAL TOTAL PRECIPITATION AMOUNT FALLEN IN EXTREMELY WET DAYS (R99P) IN CLUJ COUNTY (1961-
2013) .................................................................................................................................................................... 62
FIGURE 73 – MONTHLY AVERAGE THICKNESS (LEFT) AND MAXIMUM THICKNESS (RIGHT) OF THE SNOW COVER AT THE WEATHER STATIONS IN
CLUJ COUNTY (1969-2014) (CM) ............................................................................................................................... 63
FIGURE 74 – MONTHLY AVERAGE NUMBER (LEFT) AND MAXIMUM NUMBER (RIGHT) OF MISTY DAYS AT THE WEATHER STATIONS IN CLUJ
COUNTY (1969-2014) (DAYS) .................................................................................................................................... 64
FIGURE 75 – MONTHLY AVERAGE NUMBER (LEFT) AND MAXIMUM NUMBER (RIGHT) OF FOG DAYS AT THE WEATHER STATIONS IN CLUJ
COUNTY (1969-2014) (DAYS) .................................................................................................................................... 65
FIGURE 76 – MONTHLY AVERAGE NUMBER (LEFT) AND MAXIMUM NUMBER (RIGHT) OF SQUALLS DAYS AT THE WEATHER STATIONS IN CLUJ
COUNTY (1969-2014) (DAYS) .................................................................................................................................... 66
FIGURE 77 – MONTHLY AVERAGE NUMBER (LEFT) AND MONTHLY MAXIMUM NUMBER (RIGHT) OF THUNDERSTORM DAYS AT WEATHER
STATIONS IN CLUJ COUNTY (1969-2014) (DAYS) ............................................................................................................ 68
FIGURE 78 – MONTHLY AVERAGE NUMBER (LEFT) AND MONTHLY MAXIMUM NUMBER (RIGHT) OF HAIL DAYS AT WEATHER STATIONS IN CLUJ
COUNTY (1969-2014) (DAYS) .................................................................................................................................... 69
FIGURE 79 – MONTHLY AVERAGE NUMBER (LEFT) AND MONTHLY MAXIMUM NUMBER (RIGHT) OF HOAR-FROST DAYS AT WEATHER STATIONS
IN CLUJ COUNTY (1969-2014) (DAYS) ......................................................................................................................... 71
FIGURE 80 – MONTHLY AVERAGE NUMBER (LEFT) AND MONTHLY MAXIMUM NUMBER (RIGHT) OF BLIZZARD DAYS AT WEATHER STATIONS IN
CLUJ COUNTY (1969-2014) (DAYS) ............................................................................................................................. 71
FIGURE 81 – MONTHLY AVERAGE NUMBER (LEFT) AND MONTHLY MAXIMUM NUMBER (RIGHT) OF FREEZING RAIN DAYS AT WEATHER
STATIONS IN CLUJ COUNTY (1969-2014) (DAYS) ............................................................................................................ 73
FIGURE 82 – MONTHLY AVERAGE NUMBER (LEFT) AND MONTHLY MAXIMUM NUMBER (RIGHT) OF RIME DAYS AT WEATHER STATIONS IN CLUJ
COUNTY (1969-2014) (DAYS) .................................................................................................................................... 74
FIGURE 83 – MAP OF POTENTIALLY SIGNIFICANT FLOOD RISK AREAS ............................................................................................. 83
FIGURE 84 – FLOOD HAZARD MAP ASSOCIATED WITH THE LOW PROBABILITY SCENARIO - Q 0,1% ......................................................... 84
FIGURE 85 – FLOOD HAZARD MAP ASSOCIATED WITH THE AVERAGE PROBABILITY SCENARIO - Q 1% ..................................................... 85
FIGURE 86 – FLOOD HAZARD MAP ASSOCIATED WITH THE HIGH PROBABILITY SCENARIO - Q 10% ......................................................... 86
FIGURE 87 – FLOOD HIGH RISK MAP ASSOCIATED WITH THE HIGH PROBABILITY SCENARIO - Q 10% ....................................................... 87
FIGURE 88 – FLOOD AVERAGE RISK MAP ASSOCIATED WITH THE AVERAGE PROBABILITY SCENARIO - Q 1% ............................................. 88
FIGURE 89 – FLOOD LOW RISK MAP ASSOCIATED WITH THE LOW PROBABILITY SCENARIO - Q 0.1% ....................................................... 89
FIGURE 90 – FLOOD VULNERABILITY OF INTRA-URBAN AREAS ASSOCIATED WITH THE HIGH PROBABILITY SCENARIO - Q 10% ...................... 90
FIGURE 91 – FLOOD VULNERABILITY OF INTRA-URBAN AREAS ASSOCIATED WITH THE AVERAGE PROBABILITY SCENARIO - Q 1% .................. 91
FIGURE 92 – FLOOD VULNERABILITY OF INTRA-URBAN AREAS ASSOCIATED WITH THE LOW PROBABILITY SCENARIO - Q 0,1% ...................... 92
FIGURE 93 – NUMBER OF ISU INTERVENTIONS ON ADMINISTRATIVE SUBDIVISIONS IN CASE OF FLOODS (2007-2018)........................... 95
FIGURE 94 - MAP OF LANDSLIDES IN CLUJ COUNTYSOURCE DATA: DATA PROCESSED AFTER ISU CLUJ ............................................... 122
FIGURE 95 - MAP OF RISKS LINKED TO THE GEOMORPHOLOGICAL PROCESSES OCCURRING NEARBY THE TRANSPORT NETWORK ............... 123
FIGURE 96 - MAP OF CURRENT GEOMORPHOLOGICAL RISKS AFFECTING THE TRANSPORT NETWORK .................................................. 123
FIGURE 97 – ISU INTERVENTIONS PER TERRITORIAL ADMINISTRATIVE UNITS IN CASES OF LANDSLIDES ................................................ 124
FIGURE 98 – METHODOLOGICAL DIAGRAM OF THE APPLIED MODEL ............................................................................................ 126
FIGURE 99 – DISTRIBUTION PER GEOLOGICAL CLASSES OF LANDSLIDES IN CLUJ COUNTY.................................................................. 127
FIGURE 100 – LITHOLOGICAL COEFFICIENT MAP ..................................................................................................................... 128
FIGURE 101 – GEOMORPHOLOGICAL COEFFICIENT MAP ........................................................................................................... 129
FIGURE 102 – STRUCTURAL COEFFICIENT MAP ....................................................................................................................... 129
FIGURE 103 – HYDROLOGICAL AND CLIMATIC COEFFICIENT MAP ................................................................................................ 130
FIGURE 104 – HYDROGEOLOGICAL COEFFICIENT MAP .............................................................................................................. 131
FIGURE 105 – ROMANIAN TERRITORY ZONING IN TERMS OF PEAK TERRAIN ACCELERATION VALUES FOR AG DESIGNING FOR EARTHQUAKES
WITH AN AVERAGE IMR RECURRENCE INTERVAL = 100 YEARS. DESIGN CODE P100-1/2006 ................................................ 132
FIGURE 106 – SEISMIC ZONING OF THE ROMANIAN TERRITORY – MSK INTENSITY CATEGORIES, ACCORDING TO SR 11100–1:93 SEISMIC
ZONING. MACROZONING OF THE ROMANIAN TERRITORY ................................................................................................. 132
FIGURE 107 – SEISMIC COEFFICIENT MAP .............................................................................................................................. 133
FIGURE 108 – FOREST COEFFICIENT MAP .............................................................................................................................. 134
FIGURE 109 – ANTHROPIC COEFFICIENT MAP ......................................................................................................................... 135
FIGURE 110 – LIKELIHOOD OF LANDSLIDES AND THE RELATED RISK COEFFICIENT (KM) .................................................................... 136
FIGURE 111 – RELATIVE DISTRIBUTION OF TERRITORIAL ADMINISTRATIVE UNITS WITH LARGE AREAS FALLING UNDER THE MEDIUM LANDSLIDE
PROBABILITY CLASS .................................................................................................................................................. 136
FIGURE 112 – RELATIVE DISTRIBUTION OF TERRITORIAL ADMINISTRATIVE UNITS WITH LARGE AREAS FALLING UNDER THE MEDIUM-HIGH
LANDSLIDE PROBABILITY CLASS ................................................................................................................................... 137
FIGURE 113 – RELATIVE DISTRIBUTION OF TERRITORIAL ADMINISTRATIVE UNITS WITH LARGE AREAS FALLING UNDER THE LOW LANDSLIDE
PROBABILITY CLASS .................................................................................................................................................. 137
FIGURE 114 – DRAINAGE SITES IN CLUJ COUNTY .................................................................................................................... 140
FIGURE 115 – MAPPING THE TERRITORY OF CLUJ COUNTY BY CLASS OF SEISMIC RISK ..................................................................... 143
FIGURE 116 – ROMANIAN TERRITORY ZONING IN TERMS OF PEAK TERRAIN ACCELERATION VALUES ................................................... 144
FIGURE 117 – SEISMIC ZONING OF CLUJ COUNTY ................................................................................................................... 144
FIGURE 118 – STAGES OF THE SOIL EROSION DETERMINATION MODEL ........................................................................................ 146
FIGURE 119 – SOIL EROSION RISK MAP FOR CLUJ COUNTY ........................................................................................................ 147
FIGURE 120 – WORKS AIMED MITIGATING SOIL EROSION IN CLUJ COUNTY .................................................................................. 152
FIGURE 121 – SPATIAL DISTRIBUTION PER YEARS OF FOREST FIRES IN CLUJ COUNTY, 2009-2018 .................................................... 155
FIGURE 122 – NUMBER OF FOREST FIRES IN CLUJ COUNTY PER TERRITORIAL ADMINISTRATIVE UNIT (2009-2018) ............................. 156
FIGURE 123 – MAP OF INTERVENTION TIMES OF FIRE DEPARTMENTS IN CLUJ COUNTY ................................................................... 157
FIGURE 124 – CLASSIFICATION OF TERRITORIAL ADMINISTRATIVE UNITS PER CLASSES OF AVALANCHE RISK .......................................... 161
FIGURE 125 – AREAS OF COMPETENCE OF THE INSPECTORATE FOR EMERGENCY SITUATIONS .......................................................... 167
FIGURE 126 – FREQUENCY OF LANDSLIDES REQUIRING INTERVENTIONS BY AUTHORITIES DURING 1970-2018 ................................... 191
FIGURE 127 – LIKELIHOOD OF LANDSLIDES UNDER SCENARIO 1 ................................................................................................. 193
FIGURE 128 – LIKELIHOOD OF LANDSLIDES UNDER SCENARIO 2 ................................................................................................. 194
FIGURE 129 – LIKELIHOOD OF LANDSLIDES UNDER SCENARIO 3 ................................................................................................. 195
LIST OF TABLES
TABLE 1. EXTREME TEMPERATURE AND PRECIPITATION INDICES ..................................................................................................... 2
TABLE 2. TREND SLOPES CALCULATED FOR THE ANNUAL VALUES IN DAYS/DECADE............................................................................ 63
TABLE 3. MONTHLY AVERAGE NUMBER OF MISTY DAYS AT THE WEATHER STATIONS IN CLUJ COUNTY (1969-2014).............................. 64
TABLE 4. MONTHLY MAXIMUM NUMBER OF MISTY DAYS AT THE WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ........................... 64
TABLE 5. MONTHLY AVERAGE NUMBER OF FOG DAYS AT THE WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ................................ 65
TABLE 6. MONTHLY MAXIMUM NUMBER OF FOG DAYS AT THE WEATHER STATIONS IN CLUJ COUNTY (1969-2014) .............................. 65
TABLE 7. MONTHLY AVERAGE NUMBER OF SQUALL DAYS AT THE WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ........................... 66
TABLE 8. MONTHLY MAXIMUM NUMBER OF SQUALL DAYS AT THE WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ......................... 67
TABLE 9. MONTHLY AVERAGE NUMBER OF THUNDERSTORM DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ..................... 67
TABLE 10. MONTHLY MAXIMUM NUMBER OF THUNDERSTORM DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ................. 68
TABLE 11. MONTHLY AVERAGE NUMBER OF HAIL DAYS AT WEATHER STATION IN CLUJ COUNTY (1969-2014) ..................................... 69
TABLE 12. MONTHLY MAXIMUM NUMBER OF HAIL DAYS AT WEATHER STATION IN CLUJ COUNTY (1969-2014) ................................... 69
TABLE 13. MONTHLY AVERAGE NUMBER OF HOAR-FROST DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014). ....................... 70
TABLE 14. MONTHLY MAXIMUM NUMBER OF HOAR-FROST DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014)....................... 70
TABLE 15. MONTHLY AVERAGE NUMBER OF BLIZZARD DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014).............................. 72
TABLE 16. MONTHLY MAXIMUM NUMBER OF BLIZZARD DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ........................... 72
TABLE 17. MONTHLY AVERAGE NUMBER OF FREEZING RAIN DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ...................... 73
TABLE 18. MONTHLY MAXIMUM NUMBER OF FREEZING RAIN DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) .................... 73
TABLE 19. MONTHLY AVERAGE NUMBER OF RIME DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ................................... 74
TABLE 20. MONTHLY MAXIMUM NUMBER OF RIME DAYS AT WEATHER STATIONS IN CLUJ COUNTY (1969-2014) ................................. 74
TABLE 21. OVERVIEW OF CLIMATE CHANGES IN EXTREME TEMPERATURE AND PRECIPITATION EVENTS IN CLUJ COUNTY (1961-2013) ...... 76
TABLE 22. HISTORIC FLOODS IN CLUJ COUNTY .......................................................................................................................... 81
TABLE 23. NUMBER OF I.S.U. INTERVENTIONS ON ADMINISTRATIVE SUBDIVISIONS IN CASE OF FLOODS (2007-2018) ........................... 93
TABLE 24. LARGE STREAM DEVIATIONS IN THE HYDROGRAPHIC NETWORK OF CLUJ COUNTY .............................................................. 97
TABLE 25. CADASTRAL LEVEES IN THE HYDROGRAPHIC NETWORK OF CLUJ COUNTY .......................................................................... 98
TABLE 26. DAMS WHICH CREATE NON-PERMANENT RETENTION PONDS IN CLUJ COUNTY .................................................................. 99
TABLE 27. DAMS WHICH CREATE PERMANENT RETENTION PONDS IN CLUJ COUNTY ......................................................................... 99
TABLE 28. EMERGENCY LEVELS AND DISCHARGES AT HYDROMETRIC STATIONS IN CLUJ COUNTY ........................................................ 103
TABLE 29. OVERVIEW ON LOCALITIES AFFECTED BY FLOODS IN CLUJ COUNTY ................................................................................ 104
TABLE 30. TYPES OF WATER DEPLETION ON RIVERS AFFECTED BY DROUGHT .................................................................................. 117
TABLE 31. NUMBER OF MONTHLY CASES WITH DEPLETION PHENOMENA ON STREAMS .................................................................... 117
TABLE 32. DISTRIBUTION OF LANDSLIDES ACROSS THE TERRITORIAL-ADMINISTRATIVE UNITS OF CLUJ COUNTY .................................... 120
TABLE 33. CLASSES OF LANDSLIDE PROBABILITY FOR THE ADMINISTRATIVE AND TERRITORIAL UNITS OF CLUJ COUNTY ........................... 138
TABLE 34. INVENTORY OF BUILDINGS IN CLUJ COUNTY ASSESSED AND CLASSIFIED FOR SEISMIC RISK CLASSES 1, 2 AND 3 ....................... 145
TABLE 35. CLASSES OF EROSION RISK CLASSES FOR TAUS OF CLUJ COUNTY .................................................................................. 148
TABLE 36. DISTRIBUTION OF FOREST FIRES DURING 2009-2018, ON YEARS, MONTHS AND 10-DAY PERIODS ..................................... 153
TABLE 37. CLASSIFICATION OF ADMINISTRATIVE UNITS WITH REGARD TO CIVIL PROTECTION, ACCORDING TO SPECIFIC RISKS ................... 158
TABLE 38. SET-UP, EQUIPMENT AND PERSONNEL STRUCTURE OF VOLUNTARY EMERGENCY SERVICES ................................................. 163
TABLE 39. PERSONNEL STRUCTURE OF VOLUNTARY EMERGENCY SERVICES ................................................................................... 164
TABLE 40. SET-UP, EMERGENCY INTERVENTION EQUIPMENT, OWN SERVICES ................................................................................ 164
TABLE 41. PERSONNEL STRUCTURE OF PRIVATE EMERGENCY SITUATIONS SERVICES SET-UP AS OWN SERVICES ..................................... 165
TABLE 42. SET-UP AND EMERGENCY INTERVENTION EQUIPMENT, SERVICE PROVIDERS .................................................................... 165
TABLE 43. PERSONNEL STRUCTURE OF PRIVATE EMERGENCY INTERVENTION SERVICES SET-UP AS SERVICE PROVIDERS ........................... 166
TABLE 44. SITUATION OF VOLUNTEER OPERATIVE SERVICES FOR EMERGENCY SITUATIONS VALID FOR 01.07.2019 ............................. 168
TABLE 45. PRIORITY ACTIONS APPLICABLE AT THE LEVEL OF A.P.S.F.R. FOR CLUJ COUNTY .............................................................. 176
TABLE 46. OCCURRENCE OF LANDSLIDES REQUIRING ISU INTERVENTIONS .................................................................................... 192
TABLE 47. RETURN PERIOD OF ANNUAL RAINFALL AS RECORDED BY THE CLUJ NAPOCA WEATHER STATION ......................................... 193
1. DEMARCATION OF THE REVIEWED OBJECTIVE
This chapter deals with the analysis of the extreme natural events affecting Cluj County. Specifically, the
analysis refers to weather phenomena, with a particular focus on the events generated by extreme
(maximum and minimum) temperatures as well as by extreme rainfall. The trends in the evolution of these
phenomena were also determined based on the available datasets. Moreover, detailed studies were
carried out with regard to extreme hydrological events and landslides. Part two comprises a synthetic
analysis of extreme hydric events, paying particular attention to the formation of floods and the
identification of water scarce areas. Thus, the analysis covered the issue of flood levels exceeding the area
of the administrative-territorial units under various scenarios on the occurrence of extreme events.
In this chapter, the landslides were analyzed and the hazard maps for landslides were obtained according
to the government decision 447/2003. Taking into account the spatial distribution of the landslides and the
ISU interventions in the territory, scenarios of spatial probability of occurrence of the landslides have also
been elaborated and the elements exposed to the risk induced in the territory by landslide were identified.
2. CRITICAL ANALYSIS OF THE CURRENT SITUATION
2.1. Climate risks and their associated changes
2.1.1. General information, data sources and methods used
In this report, 26 temperature indices and 9 extreme rainfall indices were used, for which the multiannual
averages were calculated and trends were determined.
For the purposes of this report, the grid data in the ROCADA database (made available by the National
Meteorological Administration) were employed (Bîrsan and Dumitrescu, 2015). Data are available at a one-
day time step for the 1961-2013 period and the spatial resolution is 0.1°latitude/longitude (11 km x 11 km).
Cluj County extends over 113 grids. Therefore, in the following chapters, the multiannual regime will be
analyzed and the spread will be presented for the entire examined area.
For the purposes of this report, the average and extreme monthly and annual values were calculated for
each grid point in the analyzed area for the 53-year period available and they were subsequently averaged
for the entire region.
The indicators were chosen from among those internationally established by the World Meteorological
Organization’s Commission for Climatology (CCI) and by the Experts Team on Sector-Specific Climate
Indices, ET-SCI (Alexander and Harold, 2016). The data were processed with the ClimPACT2 application
(Alexander and Harold, 2016). The list of calculated indicators, their acronyms, definitions and
measurement units are presented in Table 1.
1
Table 1. Extreme temperature and precipitation indices
Unit of
No. Abbreviation Name Definition Scope*
measurement
Temperature indices
1. CWN Cold wave Cold wave number in the cold season No. of cases H, AFS,
number (October-April). The cold wave is WRS, T
defined as an event of at least 3
consecutive days when the excess
cold factor-ECF is negative.
The percentiles are calculated for the
1961-1990 period.
2. CWF Cumulative Number of days included (cumulative days H, AFS,
duration of cold duration in a year) in the cold waves WRS, T
waves defined by CWN
3. CWD Cold wave Maximum duration of a cold wave days H, AFS,
duration WRS, T
4. CWA Maximum Daily minimum temperature in the °C H, AFS,
intensity coldest cold wave WRS, T
(amplitude) of a
cold wave
5. DTR Diurnal Mean annual difference between the °C A, AFS,
temperature daily maximum and minimum DTR
range temperatures
6. FD0 Frost days Days with minimum temperature days H, AFS
below 0°C
7. GDDgrow10 Growing degree Annual sum of the temperatures in °C H, AFS,
days the growing season cumulated in T
days with temperature of 10°C or
more, with a base temperature of
10°C
8. GSL Growing season Annual number of days between the days AFS
length first period of at least 6 consecutive
days with average temperatures >
5°C and the first period of at least 6
consecutive days with average
temperatures < 5°C
9. HWN Heat wave Number of heat waves in the warm No. of cases H, AFS,
number season (May-September). The heat WRS, T
wave is defined as an event of at least
3 consecutive days when the excess
heat factor (EHF) is positive. The
percentiles are calculated for the
1961-1990 period.
10. HWD Heat wave Maximum duration of a heat wave days H, AFS,
duration identified by HWN** WRS, T
11. HWF Cumulative Cumulative duration (frequency) of days H, AFS,
duration heat waves is the number of days WRS, T
(frequency) of included in the heat waves defined by
heat waves HWN*
2
Unit of
No. Abbreviation Name Definition Scope*
measurement
12. HWA Maximum Daily maximum temperature in the °C H, AFS,
intensity hottest heat wave (defined by highest WRS, T
(amplitude) of a HWM)*
heat wave
13. ID Very cold days Annual number of days with days H, AFS,
maximum temperature below 0°C T
14. SU25 Summer days Annual number of days with days H
maximum temperature above 25°C
15. TXGE30 Tropical days Annual number of days with days H, AFS
maximum temperature above 30°C
16. TXGE35 Hot days Annual number of days with days H, AFS,
maximum temperature above 35°C T
17. TMm Mean daily Arithmetic mean of average daily °C
temperature temperatures
18. TNm Mean daily Arithmetic mean of daily minimum °C H, AFS,
minimum temperatures T
temperature
19. TNn The lowest daily The lowest daily minimum °C AFS, T
minimum temperature (hystorical minimum
temperature temperature)
20. TN10p Share of cold Percentage of days in a year when % H, AFS,
nights the daily minimum temperature is T
below the 10th percentile (the 10%
coldest nights)
21. TN90p Share of warm Percentage of days in a year when % H, AFS
nights the daily minimum temperature is
above the 90th percentile (the 10%
warmest nights)
22. TR Tropical nights Annual number of days with days H, AFS
minimum temperatures above 20°C
23. TX10p Share of cool Percentage of days in a year when % H, AFS
days the daily maximum temperature is
below the 10th percentile (the 10%
coldest days in the 1961-2013 period)
24. TX90p Share of very Percentage of days in a year when % H, AFS,
hot days the daily maximum temperature is T
above the 90th percentile (the 10%
hottest days in the 1961-2013 period)
25. TXm Mean daily Arithmetic mean of daily maximum °C H, AFS,
maximum temperatures T, TR
temperature
26. TXx The highest The highest daily maximum °C AFS, T,
values of daily temperature (hystorical maximum TR
maximum temperature)
temperature
Precipitation indices
1. CDD Consecutive dry The maximum number of consecutive days H, AFS,
days days in a year with precipitation WRS
below or equal to 1.0 mm (l/m2)
3
Unit of
No. Abbreviation Name Definition Scope*
measurement
2. CWDp Consecutive wet The maximum number of consecutive days AFS,
days days in a year with precipitation WRS, T,
above 1.0 mm (l/m2) TR
3. PRCPTOT Total Total annual precipitation amount in mm AFS,
precipitation wet days (the daily amount is above WRS
amount in wet 1.0 mm (l/m2)
days
4. R10 Heavy Annual number of days when the days AFS,
precipitation daily precipitation amount is above WRS, T,
days 10 mm (l/m2) TR
5. R20 Very heavy Annual number of days when the days AFS,
precipitation daily precipitation amount is above WRS, T,
days 20 mm (l/m2) TR
6. R95p Very wet days Total annual precipitation amount in mm AFS,
the wettest 5% days of the year WRS, TR
7. R99p Extremely wet Total annual precipitation amount in mm AFS,
days the wettest 1 % days of the year WRS, TR
8. Rx1day Maximum daily The highest amount of precipitation mm H, AFS,
amount of in a day WRS, T,
precipitation TR
9. Rx3days The 3-day The highest amount of precipitation mm H, AFS,
maximum in 3 consecutive days WRS, T,
precipitation TR
*H – health; AFS – agriculture and food security; WRS – water resources; T – tourism, TR - transport.
Source: Alexander an Harold, 2016.
For weather events other than thermal and pluviometric events (such as mist, fog, thunderstorms, hail,
hoar frost, blizzard, storms, and freezing rain), as well as for the snow cover thickness, the data recorded
in the 1969-2014 period at six weather stations in Cluj County (Băișoara, Cluj-Napoca, Dej, Huedin, Turda
and Vlădeasa-1800) and provided by the National Meteorological Administration were used.
4
2.1.2. Indices on the average air temperature and extreme air temperatures
2.1.2.1. Average air temperature
In a multiannual regime, the average monthly temperature recorded in Cluj County has significantly varied
as a result of the altitude difference in the region, as well as of the shading effect on the northern
mountainsides due to the exposition. Thus, the comparison of annual values to a regional average of 8.5°C
shows that temperatures lower by more than 4°C were recorded in the highest mountain areas in the
western and south-western parts of the county, in Măguri-Răcătău, Valea Ierii and Săcuieu communes (4.5-
5.4°C), while higher temperatures were recorded in the lower, eastern half of the county (up to 9.9°C)
(Figure 1). The highest temperatures are characteristic to urban areas and surrounding areas.
Figure 1 – Mean annual air temperature (Tmed) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
The changes occurred throughout the historical period under review point to a trend of statistically
significant increase county-wide (Figure 2).
5
Figure 2 – Trend in mean annual air temperature (Tmed) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
2.1.2.2. Maximum air temperature
The maximum air temperature is of interest from a twofold perspective, i.e. the annual averages of the
daily maximum temperatures (TXm) and the absolute maximum values (the highest values recorded in the
reviewed historical period) in the 53-year period under consideration (TXx).
Thus, the multiannual averages of the daily maximum temperatures (calculated as the arithmetic mean of
maximum temperatures during a year) exceed 10°C in most of the county. During the year, the averages
recorded county-wide vary in a range of approximately 6.5°C (7.6-15.1°C), with a county average of 13.4°C.
The highest values are generally 1.5°C higher than the county average and were recorded in the low county
areas. The lowest averages of maximum temperatures were registered in the western and south-western
parts of the county, featuring also the highest altitudes (7.6-9.0°C) (Figure 3).
The hystorical maximum temperatures (TXx) recorded in Cluj County in the period under review generally
exceeded 36.0°C. The highest temperatures registered in the mountain areas in the western and south-
western parts of the county ranged from 28.0 to 33.0°C, depending on the altitude and the slope aspect
(Figure 4).
6
The global warming process is visible across the county, as confirmed by a trend of statistically significant
increase in the two indicators (Figures 5 and 6).
Figure 3 – Mean maximum temperature (TXm) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
7
Figure 4– The highest temperature (TXx) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 5 – Trend in mean maximum temperature (TXm) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
8
Figure 6 – Trend in the highest annual temperature (TXx) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
2.1.2.3. Minimum air temperature
Similarly to the maximum air temperature, the minimum air temperature is of interest from a twofold
perspective, i.e. the annual averages of the daily minimum temperatures (TNm) and the hystorical
minimum values (the lowest values recorded) in the 53-year period under consideration (TNn).
The multiannual averages of the daily minimum temperatures (calculated as the arithmetic mean of
minimum temperatures during a year and then weighted for the entire 53-year period) exceed 1.0°C in any
part of the county, with a county-wide average of 3.7°C. The highest mean daily minimum temperatures
exeed 4.0°C and were recorded in the lowlands of the county, and the lowest averages of daily minimum
temperatures are characteristic to the mountain area (1.0-2.0°C) (Figure 7).
The lowest temperatures recorded in Cluj County are specific to the winter season and varied between
-24.9 and -33.1°C at county level. Due to frequent temperature inversions in the analyzed area, the lowest
temperatures (below -31.0°C) were registered in the lowlands of the eastern and south-eastern areas of
the county, wheareas the highest absolute minimum temperatures (-24.0-25.0°C) were recorded in the
mountain area of the county (Figure 8).
The global warming process is visible across the county, as confirmed by a trend of statistically significant
increase in the mean values of the minimum temperatures (Figure 9), while it is less visible in most of the
county in terms of hystorical minimum temperatures, although they decrease, yet not statistically
significant. In a few county areas, statistically significant increasing trends were observed (Figure 10). Under
9
these circumstances, it should be mentioned that, despite the overall warming affecting Cluj County, there
are further records of extremely low temperatures, and if this trend persists over the coming decades, they
will continue to occur, even though they will not equal the lowest historical values posted in recent
decades.
Figure 7 – Mean minimum temperature (TNm) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
10
Figure 8 – The lowest temperature (TNn) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 9 – Trend in mean minimum temperature (TNm) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
11
Figure 10 – Trend in the lowest annual temperature (TNn) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
2.1.2.4. Heat waves
At global scale, the heat waves are the weather events causing the most deaths and illnesses among the
population. This is due, on the one hand, to its non-violent nature (it occurs slowly) and, on the other hand,
to the poor information of population on the effects of such events. A number of studies have been recently
carried out for Cluj-Napoca, pointing to a 14% increase in the general population mortality during heat
waves (Croitoru et al., 2018), while the potential economic losses estimated as a heat wave-induced
decrease in labour productivity, also at the level of Cluj-Napoca City, during solely 3 heat waves in the
summer of 2015, amounted to approximately EUR 34 million (Herbel et al., 2017).
Under these circumstances, we deemed it necessary to incorporate in this study a sub-chapter dedicated
to heat waves. In addition, the results of the analysis of four indicators characterizing the heat waves in Cluj
County will be presented as follows: the number of heat waves (HWN), the average duration of a heat wave
event (HWD), the average cumulative duration of heat waves in a year (total annual number of days
associated with heat waves) (HWF), and the intensity of the event as shown by the highest temperature
recorded during a heat wave (HWM). Heat waves were identified on the basis of the excess heat factor
(EHF), which is the newest method used to identify such events. The EHF is a factor that takes into account
both the climate of the analyzed area (historical temperature values) and the possibility for human body
acclimatization to a specific event (the temperature values in the 3 days preceding the occurrence of the
12
heat wave). For this study, consideration was given to events which occurred from May to September and
lasted for at least 3 consecutive days.
Over the period 1961-2013, the average number of heat waves at the county level ranged between 2.9 and
3.3 events per year. From the point of view of spatial distribution, the areas with the lowest number of
heat waves are mountain areas and the north-eastern parts of the county (Figure 11). However, it should
be taken into account that, in the 53-year period under review, there were also years when 9-11 heat waves
occurred in a year and years with no occurrence of such events.
Figure 11 – Mean annual number of heat waves (HWN) in Cluj County (1961-2013) (events/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
The average duration of a heat wave ranged between 6.5 and 7.3 days, the peaks being recorded in the
southern and south-eastern parts of the county and the lows in the northern part (Figure 12). The longest
heat waves lasted between 13 and 20 days.
Heat waves in a year last for approximately 15 to 17 days, the most exposed areas being the southern and
eastern extremes of the county (Figure 13).
In terms of intensity, the average temperature during heat waves exceeds by about 5-9°C the reference
values of the periods when they occur and it may be noticed that the highest exceedance probabilities are
characteristic to the higher areas in the western third of the county (Figure 14).
13
As for the changes in the heat waves features over the past decades, it is noticeable that both the number
and duration of heat waves (considered for individual events or cumulatively for all the events in a year)
increased statistically significant (Figures 15-17). This means that, over the period under review, both the
number and duration of heat waves increased at the county level. In terms of intensity, the increase
although broad-based county-wide, is statistically significant only in the western extreme of the county
(Figure 18).
Figure 12 – Mean duration of a heat wave event (HWD) in Cluj County (1961-2013) (days/event)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
14
Figure 13 – Mean annual frequency (cumulative duration) of heat waves (HWF) in Cluj County (1961-2013)
(days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 14 – Mean annual intensity of heat waves (HWA) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
15
Figure 15 - Trend in mean number of heat waves (HWN) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 16 - Trend in mean annual duration of a heat wave event (HWD) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
16
Figure 17 - Trend in mean annual frequency (cumulative duration) of heat waves (HWF) in Cluj County (1961-
2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 18 - Trend in heat wave intensity (HWA) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
17
2.1.2.5. Cold waves
Cold waves are weather events that have a negative impact particularly on agriculture and population
health. Due to global warming, cold waves are expected to decline in Romania, although they will continue
to be manifest in the coming period (Croitoru et al., 2018).
Under these circumstances, we deemed it necessary to incorporate in this study a sub-chapter dedicated
to cold waves. In addition, similarly to heat waves, the results of the analysis of four indicators of cold waves
in Cluj County will be presented as follows: the number of cold waves (CWN), the average duration of a
cold wave event (CWD), the average cumulative duration of cold waves in a year (total annual number of
days associated with cold waves) (CWF), and the intensity of the event as shown by the lowest temperature
recorded during a cold wave (CWM). Cold waves were identified based on the excess cold factor (ECF),
which is the newest method used to identify such events. ECF is a factor that takes into account both the
climate of the analyzed area (historical temperatures) and the possibility for human body acclimatization
to a specific event (the temperature values in the 3 days preceding the occurrence of the cold wave). For
this study, consideration was given to events which occurred from October to March and lasted for at least
3 consecutive days.
Over the period 1961-2013, the average number of cold waves at the county level ranged between 2.1 and
2.6 events/year. From the point of view of spatial distribution, the areas with the lowest number of cold
waves are those in the northern half of the county (Figure 19). In the 53-year period under review, there
were also years when 3-5 cold waves occurred in a year (depending on the county area) and years with no
occurrence of such events.
Figure 19 - Mean annual number of cold waves (CWN) in Cluj County (1961-2013) (events/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
18
The average duration of a cold wave ranged between 6 and 10 days, the longest cold waves of over 8 days,
on average, being recorded in the eastern half of the county and the shortest in the mountain area (Figure
20). The longest cold waves recorded in the 53-year period under review lasted between 20 and 23 days.
During a year, cold waves are less long than heat waves, i.e. between 12.5 and 15.5 days, the most exposed
being the south-eastern parts of the county (Figure 21).
The highest intesity of cold waves reflected in temperatures of -16…-28°C. The lowest temperatures are
characteristic to the areas in the eastern end of the county (Figure 22).
As for the changes in the cold waves features over the past decades, it is noticeable that both the number
and duration of events did not change significantly. Specifically, the number of cold waves generally
decreased in the eastern half of the county and increased in the western half (Figure 23); the duration of
cold waves (considered for individual events or cumulatively for all the events in a year) declined slightly
county-wide (Figures 24 and 25). However, the magnitude of cold waves dropped considerably in most of
the county, except for some areas in the southern half, where it was somewhat lower (Figure 26).
As a result, due to the global warming also affecting Cluj County, the number and duration of cold waves
were not significantly lower, but their magnitude decreased significantly (the lowest temperatures do not
fall that much during the events in recent decades as compared with the beginning of the period under
review). Therefore, these events are still possible and action is needed to reduce their negative impact.
Figure 20 - Mean duration of a cold wave event (CWD) in Cluj County (1961-2013) (days/event)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
19
Figure 21 – Mean annual frequency (cumulative duration) of cold waves (CWF) in Cluj County (1961-2013)
(days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 22 – Mean annual cold wave intensity (CWA) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
20
Figure 23 – Trend in mean annual number of cold waves (CWN) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 24 – Trend in mean annual duration of a cold wave event (CWD) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
21
Figure 25 - Trend in mean annual frequency (cumulative duration) of cold waves (CWF) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 26 - Trend in cold wave intensity (CWA) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
22
2.1.2.6. Days with characteristic temperatures specific to the warm half-year
a. Summer days (SU25)
Defined as the days when the maximum air temperature is above 25.0°C, summer days can occur from May
to September in the low parts of Cluj County and in the summer months, in particular, in high mountain
areas. Their number is very much different depending on the altitude. Specifically, in most of the eastern
half of the county, the number of summer days ranges between 45 and 75 days/year, while in the mountain
areas it goes down to values ranging from 1 to 15 days/year (Figure 27). In some years and especially in
recent decades, the number of summer days grew significantly, the highest values recorded in the county
being in the range from 11 to 123 days/year. In fact, the evolution trend of this indicator shows that the
number of summer days increased considerably county-wide during the period under consideration, i.e. in
the range between 1 and 6 days/decade (Figure 28).
b. Tropical days (TXGE30)
Tropical days are those days when the maximum temperature goes above 30.0°C. Alongside summer days,
this indicator is very much used in tourism and recreational activities.
In Cluj County, the annual number of tropical days is much lower than that of summer days and does not
exceed 20 days in a year, on average. In the highest county areas, no tropical days were recorded in the
analyzed historical period, while in the most developed urban areas (Cluj-Napoca and Turda) and their
surroundings, their number is higher than 12 days/year, reaching up to 18.6 days/year as a multiannual
average (Figure 29). Compared to the above-mentioned averages, most of the tropical days recorded in a
year can reach the 66-day threshold in the low county areas.
The trend over the 53 years of the analysis shows that the number of tropical days saw a statistically
significant increase in most of the county, generally by 1-3 days/decade. The number of tropical days in the
highest south-western and western parts of the county point to a smaller, statistically non-significant
increase, or even stationary trend (Figure 30).
23
Figure 27 - Mean annual number of summer days (SU25) in Cluj County (1961-2013) (days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 28 – Trend in mean annual number of summer days (SU25) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
24
Figure 29 - Mean annual number of tropical days (TXGE30) in Cluj County (1961-2013) (days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 30 - Trend in mean annual number of tropical days (TXGE30) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
25
c. Hot days (TXGE35)
Hot days, defined as the days when the maximum daytime temperature is above 35.0°C, are however
extremely rare in Cluj County. Overall, the multiannual average is no higher than 1 day/year in the county
(Figure 31). In low areas, such days are the rarest instances, occurring once in 5 years, on average.
Nevertheless, there were also years when up to 19 hot days/year were recorded in low areas.
Figure 31 – Mean annual number of hot days (TXGE35) in Cluj County (1961-2013) (days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Due to the current climate changes, this indicator is also seen posting a statistically significant increase in
the low eastern county areas, while the trends prevailing in the western and south-western parts of the
county indicate a stationary evolution or a statistically non-significant slight growth (Figure 32).
It should be noted that the number of hot days would be considerably higher in the cities if calculated for
the central urban area, where the temperature is considerably higher, i.e. by 1 to 3 °C, than those recorded
at the weather stations (see the chapter on urban heat islands in SF 2.1.1.).
d. Tropical nights (TR)
Tropical nights are those nights when the minimum temperature does not fall below 20.0°C. Practically,
this indicator is very important for the thermal comfort, as the thermal stress is significantly higher for the
population when the daily minimum temperatures exceed the 20.0°C threshold, especially if these nights
26
come after tropical/hot days. The sleep quality is lower and, as a result, the next day’s intellectual and
physical productivity is also greatly diminished. The repercussions are thus felt at the social and economic
level (Herbel et al., 2018).
In Cluj County, the values of this indicator are extremely low, the average annual number of cases generally
ranging from 0.0 to 0.2 days/year in the most part of the analyzed area and only incidentally reaching 0.4
days/year in very small areas. The highest values recorded during a year varied between 0 and 7 tropical
nights (Figure 33).
Figure 32 – Trend in mean annual number of hot days (TXGE35) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
27
Figure 33 – Maximum annual number of tropical nights (TR) in Cluj County (1961-2013) (days)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
The trend of this indicator points to an increase, either statistically significant or non-significant, in the
eastern half of the county, while in the western part, the trend was dominantly stationary (Figure 34).
28
Figure 34 – Trend in mean annual number of tropical nights (TR) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
e. Warm nights (TN90p)
This indicator includes the share of those days when the minimum daily temperature (normally recorded
during the night) is higher than that of the 90th percentile, i.e. the threshold above which the 10% warmest
nights were recorded in the 1961-1990 reference period. Thus, due to the upward trend of the minimum
temperature (which is documented above for TNm and TNn), this threshold has been increasingly
exceeded, especially in the recent decades. Specifically, the share of warm nights goes above 10% county-
wide, posting most frequently values in the 13-14% range (Figure 35).
The evolution trend is indicative of a statistically significant increase across the county (Figure 36), generally
at a rate of approximately 2%/decade.
f. Very warm days (TX90p)
Similarly to warm nights, very warm days are those days when the daily maximum temperature exceeds
the 90th percentile for the maximum temperatures recorded in the 1961-1990 reference period. Their
share, as a multiannual average, exceeds 12.8% throughout the county, these days occurring most
frequently in the south-western third of the county (13.3...13.5%) (Figure 37). As also shown by the
29
multiannual averages above 10%, the trend is indicative of a significant overall growth county-wide (Figure
38), at a rate generally ranging from 1.7 to 2.5%/decade.
Figure 35 - Share of mean annual number of warm nights (TN90p) in Cluj County (1961-2013) (%)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 36 - Trend in share of mean annual number of warm nights (TN90p) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
30
Figure 37 – Share of mean annual number of very warm days (TX90p) in Cluj County (1961-2013) (%)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 38 – Trend in share of mean annual number of very warm days (TX90p) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
31
In this context, in the case of large urban areas, it is necessary to install systems to monitor urban
weather/climate in order to calculate the level of thermal comfort/discomfort within the cities, due to the
temperature increase above the values measured at the standard weather stations, as a result of urban
heat islands, and develop an urban forecast model.
2.1.2.7. Days with characteristic temperatures specific to the cold season of the year
These indicators have a particular significance for both human health and agriculture.
a. Very cold days (ID)
Defined as those days when the air temperature remains negative throughout the day (maximum
temperature below 0.0°C), the number of very cold days varies, as a multiannual average, in Cluj County,
from more than 26 days/year in lowlands areas to 75 days/year in mountain region (Figure 39). As for their
evolution during the historical period, there is a statistically insignificant decrease in the number of very
cold days across the county (Figure 40).
b. Frost days (FD)
Frost days are those days when the minimum temperature falls below the freezing threshold (0.0°C). For
agriculture, the risk is considerably higher when frost days occur closer to the growing season (April-
October), but it is also present during the winter when the snow cover is absent, thus affecting fall crops.
At the level of Cluj County, the average number of frost days is in the range of 104-130 days/year in the
lowlands and up to 146-156 days/year in the mountain region (Figure 41). In the colder years, the number
of frost days stood by approximately 30 days higher than the multiannual averages, i.e. between 129 and
181 days/year.
Figure 39 – Mean annual number of ice days (ID) in Cluj County (1961-2013) (days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
32
Figure 40 – Trend in mean annual number of ice days (ID) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 41 – Mean annual number of frost days (FD) in Cluj County (1961-2013) (days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
33
Due to global warming, this indicator also recorded a decrease at the level of Cluj County over the 53 years
under review, which was more accelerated in the western and south-western parts, where statistically
significant trends were identified, and slower in the low county areas, where the overall downward trend
is statistically non-significant (Figure 42).
Figure 42 – Trend in mean annual number of frost days (FD) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
c. Cold nights (TN10p)
Similarly to warm nights, which occur mainly in the warm half-year, cold nights are an indicator
representing the share of those days when the minimum daily temperature (normally recorded during the
night) fell below that of the 10th percentile, i.e. the threshold below which the 10% coldest nights were
recorded in the 1961-1990 reference period. Thus, due to the increasing trend of the minimum
temperature (which is documented above based on TNm and TNn), the number of days with minimum
temperatures below this threshold is slowly increasing, especially over the recent decades. Specifically, the
frequency of cold nights county-wide goes below 10% (9.26...9.72%) county-wide. The smallest number of
cold nights is recorded in the central and north-eastern parts of the county (Figure 43).
The calculated trends, estimated based on the average ratio, show a statistically significant decrease in the
frequency of cold nights in the period close to the end of the period under review (Figure 44).
34
d. Cool days (TX10p)
Similarly to very warm days, cool days are those days when the maximum daily temperature falls below
the 10th percentile (a threshold below which are the lowest 10% of the maximum temperature values),
calculated for the maximum temperatures recorded during the 1961-1990 reference period. Their share,
as a multiannual average, is less than 10% in the county as a whole, these days occurring most frequently
in the northern and south-eastern ends of the county (9.61...9.72%). In most of the county, the frequency
of these days decreased in a range between 9.41 and 9.60% (Figure 45). As also shown by the multiannual
averages below 10%, the trend is indicative of an overall decline county-wide statistically significant (Figure
46), at a rate generally ranging from 0.5 to 0.8%/decade.
Figure 43 – Share of mean annual number of cold nights (TN10p) in Cluj County (1961-2013) (%)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
35
Figure 44 – Trend in mean annual number of cold nights (TN10p) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 45 – Share of mean annual number of cool nights (TX10p) in Cluj County (1961-2013) (%)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
36
Figure 46 - Trend in mean annual number of cool nights (TX10p) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
e. Average daily temperature range (DTR)
The daily temperature amplitude (DTR) is calculated as the daily average of the differences between the
highest and lowest temperatures recorded throughout the same day. The larger the difference, the higher
the plant and human stress is. The limit of tolerance to this daily temperature difference is an important
feature of crops and ornamental plants.
In general, the daily temperature amplitude is inversely proportional to the altitude of the analyzed area.
Thus, in Cluj County, the largest daily temperature differences (generally, from day to night) are in the hilly
and highland areas, where the mean value varies between 9 and 11°C, on average, while in the mountain
areas, they fall to 6-8°C (Figure 47). Conversely, the extreme values ranged between 5 and 12°C, at the
county level.
From the point of view of changes in the evolution of this parameter over the period considered, there is a
slight increase in the average daily amplitude for most of the county (Figure 48). This shows that although
an increasing trend was detected also for the maximum and minimum temperatures, the rate of increase
is not the same for both parameters, i.e. the maximum temperatures rose at a faster pace than the
minimum temperatures. In the south-western end of the county, there is a slight downtrend or even
stationary trend in small areas.
37
Figure 47 – Mean diurnal temperature range (DTR) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 48 – Trend in mean diurnal temperature range (DTR) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
38
2.1.2.8. Agriculture-specific indices
This category of indicators is used exclusively in the field of agriculture and food safety and is part of the
indicators characterising the environmental conditions for the different types and varieties of agricultural
crops. Against the background of current climate changes, identifying the changes that occur can result in
the re-zoning of agricultural crops across the county.
a. Growing season length (GSL)
Growing season length, calculated as the number of days when the temperature allows the growing process
(average temperatures above 5°C), is one of the most important ecological parameters of crops
(agricultural or ornamental).
In most of the county, the growing season lasts for over 210 days/year, on average, from a thermal
perspective. Only the highest areas in the western and south-western parts of the county make an
exception, as the growing season length ranges between 180 and 210 days/year in their case. The areas
with the longest growing season are located in the central and northern parts of the county, where the
multiannual average is higher than 240 days (Figure 49).
As for the evolution over time, no major changes were detected, meaning that the largest part of the county
(over 80% of the total area) was affected by a slight increase, while the areas with the longest growing
season indicated a statistically non-significant decrease in the 53 years under review (Figure 50). In general,
the changes do not exceed ± 4 days/decade.
Figure 49 – Mean annual growing season length (GSL) in Cluj County (1961-2013) (days)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
39
Figure 50 – Trend in mean annual growing season length (GSL) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
b. Growing degree days (GDDgrow10)
The second most important parameter for crops is the growing degree days, i.e. the sum of cumulative
temperature values in the growing season that exceed the 10°C threshold.
Cluj County reports multiannual averages ranging from approximately 400 (in the highest areas) to 1,400°C
(in the low areas). The temperatures are higher than 1,000°C in more than 75% of the county area, while
the annual cumulative temperature exceeds 1,200°C in most of the eastern half of the county (Figure 51).
As a result, most of the county may be integrated in various crop favourable areas (e.g. the autumn wheat
2nd crop area, maize 1st crop area). As compared to these averages, the sum of temperatures in the
growing degree days in the warmest years varied between 800°C in the mountain areas and 1,900°C in the
hilly and highland areas. From the perspective of climate changes, this parameter has witnessed a
statistically significant increase county-wide in recent decades (Figure 52).
When interpreted together with the previous indicator, it can be concluded that, by increasing the
magnitude (the sum of temperatures in growing degree days), the plants need a shorter period to reach
maturity and more productive hybrids (with higher thermal needs) may be used for agricultural crops,
although the length of growing season is not longer.
40
Figure 51 – Mean annual growing degree days (GDDgrow10) in Cluj County (1961-2013) (°C)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
Figure 52 – Trend in mean annual growing degree days (GDDgrow10) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
41
2.1.3. Extreme rainfall indices
Nine extreme rainfall indicators were analyzed in this sub-chapter. Based on their calculation method, they
are frequency indicators (CDD, CWD, R10 and R20) and intensity indicators (PRCPTOT, R95pTOT, R99pTOT,
Rx1day and Rx3days).
In order to calculate certain precipitation indicators (CDD, CWD, PRCPTOT), the 1.0 mm required threshold
was set for practical reasons. Specifically, precipitation amounts lower than 1.0 mm are not sufficient for
the plant (usually, such small amounts of water do not even reach the plant roots) and are not efficient in
terms of water resources (they do not have a significant impact on the flow or level of rivers, on the water
level in reservoirs, etc.).
2.1.3.1. Frequency indicators
a. Consecutive dry days (CDD)
This indicator shows the maximum annual number of consecutive days when the daily precipitation amount
is below or equal to 1.0 mm (l/m2). It is an indicator of water scarcity, also pointing to the length of the
critical period.
In Cluj County, the average annual length of these periods increases starting from the mountain areas and
the eastern part of the county, where there are 20-24 consecutive dry days, to the central area, where the
length is longer, up to 24-28 consecutive dry days. In general, the longest dry spells (26-28 days) are specific
to the proximity of the largest urban centres (Cluj-Napoca and Turda) (Figure 53, up). Compared to these
averages, the longest periods recorded in the analysis period lasted between 43 and 71 days. The maximum
duration was also registered in the central area of the county, but it extended to the western part of the
county (Figure 53, down). As for the trend over the historical period, it may be noted that the CDD posted
a statistically non-significant decrease in most of the county; other trends (slight increase, stationary
increase or significant decrease) were manifest only in isolated cases, in the county extremities (Figure 54).
42
Figure 53 – Mean (up) and maximum (down) annual number of consecutive dry days (CDD) in Cluj County (1961-
2013) (days)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
43
Figure 54 – Trend in mean annual number of consecutive dry days (CDD) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
b. Consecutive wet days (CWDp)
At the county level, the multiannual averages of the maximum annual number of consecutive days when
the daily amount of precipitation is higher than 1.0 mm (l/m2) are between 6 and 12 days, whereas the
longest periods ever recorded lasted from 10 to 26 days (Figure 55). In both cases, the peaks of both the
mean and maximum values were recorded in the mountain region.
44
Figure 55 – Mean (up) and maximum (down) annual number of consecutive wet days (CWDp) in Cluj County
(1961-2013) (days)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
45
The identified trends show an uneven spatial distribution, i.e. an insignificant decrease of CWDp in the
western part and in the extreme east of the county and a slight, insignificant increase in the rest of the
county. In isolated cases (less than 10% of the county area), there statistically significant trends were
recorded, particularly upward trends in the central and northern parts of the county (Figure 56).
Figure 56 – Trend in mean annual number of consecutive wet days (CWDp) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
c. Days with heavy precipitation (R10)
Days with heavy precipitation, defined as those days when the daily amount of precipitation is higher than
10.0 mm (l/m2), occur most often in the mountain areas where the multiannual average ranges from 20 to
35 days/year. The smallest number of days with heavy precipitation was mostly recorded in low areas (10-
15 days/year). In the north of the county, precipitation amounts higher than 10 mm (l/m2) are recorded,
on average, in 15-20 days a year (Figure 57, up). In the analyzed observation period (1961-2013), the highest
number of days with rainfall exceeding the 10 mm threshold stood well above the multiannual average.
Specifically, in the central and southern parts of the county, the number of days with heavy precipitation
was in the range of 18-26 days, while in the northern and western parts, the number ranged between 27
and 35 days. In the south-western part of the county and in the highest areas, the number of these days
rose considerably, up to 55 days (Figure 57, down).
46
The average number of these days is on a slight increase in most of the county, except for the north-eastern
part, where a trend of marginal, insignificant decrease is prevalent. In isolated cases, significant increases
in the average number of days with heavy precipitation were reported in the western half and southern
part of the county (Figure 58).
Figure 57 – Mean (up) and maximum (down) annual number of heavy precipitation days (R10) in Cluj County
(1961-2013) (days/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
47
Figure 58 – Trend in mean annual number of heavy precipitation days (R10) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
d. Days with very heavy precipitation (R20)
This indicator refers to the days when the daily amount of precipitation exceeds 20 mm (l/m2). Unlike the
previous indicator, the number of these days is much smaller, ranging from 1.5 to 8.5 days/year, on
average, at the county level. A maximum number of 3 days is recorded in the largest part of the county.
This value is exceeded only in the higher western and south-western areas, as well as in the north-eastern
end of the county (Figure 59, up). The maximum annual number of days with very heavy precipitation is of
8-11 days in most of the county. The highest annual number was 4-7 days/year in the eastern and southern
parts and 15-21 days/year in the south-western end of the county (Figure 59, down).
There are no major changes that occurred in the analyzed period: the average annual number of days with
very heavy precipitation increased marginally in the western and eastern parts of the county, while a slight,
statistically non-significant decline prevailed in the central part of the county. In isolated cases, particularly
in the eastern half of the county, there were also records of a stationary trend (Figure 60).
48
Figure 59 – Mean (up) and maximum (down) annual number of very heavy precipitation days (R20) in Cluj County
(1961-2013) (days)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
49
Figure 60 – Trend in mean annual number of very heavy precipitation days (R20) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
2.1.3.2. Indicators of intensity
a. Total amount of precipitation in wet days (PRCPTOT)
PRCPTOT is the total annual amount of precipitation in wet days (days when the daily precipitation amount
exceeds 1.0 mm).
In Cluj County, the highest annual rainfall has a similar spatial distribution in terms of both average (Figure
61, up) and maximum values (Figure 61, down). Thus, the highest values are specific to mountain areas in
the south-western parts of the county, followed by the northern and north-eastern parts and the central
and southern parts. The averages range from 505 to 1,106 mm/year and from 680 to 1,546 mm/year in the
wettest years.
During the period under review, these amounts were slightly higher in most of the county. The increase
was more substantial and statistically significant in the southern part, while in the eastern part of the
county, there were also slight downward trends (Figure 62).
50
Figure 61 – Mean (up) and maximum (down) annual amount of precipitation fallen in wet days (PRCPTOT) in Cluj
County (1961-2013) (mm/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
51
Figure 62 – Trend in total annual amount of precipitation fallen in wet days (PRCPTOT) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
b. Maximum daily amount of precipitation (Rx1day)
This indicator shows the highest amount of precipitation in a single day in a year. The maximum daily
precipitation amounts are extremely important from a practical point of view, as they can cause flash-
floods both in river valleys and urban areas with large impervious surfaces when recording the above-
mentioned values. Therefore, when designing the street drainage system in the new districts it is necessary
to consider these values for the likelihood of such events occurring and to also take into account the trend
of this parameter.
The multiannual average at the level of Cluj County varied from around 25 mm in the eastern half to more
than 40 mm in the mountain area (Figure 63, up). The highest values of this indicator recorded in the 53-
year period were almost double compared to the average values, ranging from 41.4 to 80.1 mm (Figure 63,
down).
52
Figure 63 – Mean (up) and maximum (down) annual total amount of daily precipitation (Rx1day) in Cluj County
(1961-2013) (mm/day)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
53
The changes that occurred in recent decades point to a slight, statistically non-significant increase in
these amounts in most of the county, while the prevailing trend in the north-eastern and north-western
parts was a marginal, statistically non-significant decline (Figure 64).
Figure 64 – Trend in maximum total amount of daily precipitation (Rx1day) in Cluj County (1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
c. The 3-day maximum precipitation (Rx3days)
The highest annual precipitation amount recorded in 3 consecutive days is also calculated the same as the
previous indicator. These amounts are usually associated with atmospheric fronts.
The multiannual averages are indicative of precipitation amounts between 40 and 50 mm in 3 days in most
of Cluj County, while they exceeded 60 mm in the mountain areas in the south-western part of county
(Figure 65, up). In the period under consideration, the total maximum historical values of 72-hour
precipitation amounts in the hilly and highland areas of the county ranged between 65 and 105 mm and
increased well above this limit in mountain areas, exceeding 152 mm (Figure 65, down).
The spatial distribution of climate changes is similar to that of the maximum 24-hour precipitation amounts,
but mention should be made that the statistically non-significant growth trends are prevalent in most of
the county, including the western region, and the slight downward trends, compared to the previous
54
indicator, are restricted to the eastern part of the county and occur only seldom in the western and
southern parts of the county (Figure 66).
Figure 65 – Mean (up) and maximum (down) annual total amount of precipitation fallen in 3 consecutive days
(Rx3days) in Cluj County (1961-2013) (mm)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
55
As for their effects, the high total amounts of 3-day precipitation may cause floods that can spread
regionally through the rivers of the county. Therefore, these values should be taken into account when
designing the flood defence infrastructure. The evolution trend of this parameter should also be
considered.
Figure 66 – Trend in maximum total amount of precipitation fallen in 3 consecutive days (Rx3days) in Cluj County
(1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
d. Very wet days (R95p)
R95p is the sum of the total annual precipitation amount in the wettest 5% days in a year. This indicator is
useful to identify the concentration of precipitation, identifying high amounts fallen in a low number of
days. These are the events that often generate floods and snow blockages. In Cluj County, the total
multiannual average amount of precipitation in very wet days is in the range of approximately 100-130
mm/year in most of the county to 220-244 mm/year in the south-western end of the county (Figure 67,
up).
The peaks recorded in the analyzed period have a similar spatial distribution, but the precipitation amounts
are at least double compared to multiannual averages, i.e. 200-300 mm in the hilly and highland areas up
to 500-600 mm in the south-western end (Figure 67, down). In the case of multiannual averages, their share
is of 18-23% of the total annual precipitation amount (Figure 68, up), while the total maximum precipitation
56
amounts in 5% of very wet days is in the range of 25% and 45% of the needed annual rainfall (Figure 68,
down).
Figure 67 – Mean (up) and maximum (down) annual total amount of precipitation fallen in very wet days (RP95)
in Cluj County (1961-2013) (mm/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
57
Figure 68 – Share of mean (up) and maximum (down) annual total amount of precipitation fallen in very wet days
in Cluj County (1961-2013) (%)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
58
As for the evolution over time, the total precipitation amounts in the wettest 5% days in a year saw a
decrease in the central-north-eastern part of the county, while they were slightly higher in the rest of the
county. In isolated cases, the increase was statistically significant in the south-western part of the county
(Beliș and Mărgău communes) (Figure 69).
Figure 69 – Trend in mean annual total amount of precipitation fallen in very wet days (R95p) in Cluj County
(1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
d. Extremely wet days (R99p)
This indicator is the total annual amount of precipitation in the wettest 1% days in a year (“wet days” is a
general name that includes all forms of liquid and solid precipitation). The same as in the previous case, it
is useful to identify the concentration of precipitation in few days with high amounts, these events resulting
mainly in floods and/or snow blockages.
The spatial distribution of this indicator is similar to the total amount of precipitation in very wet days.
Specifically, in the largest part of Cluj County the total multiannual average amount in extremely wet days
varies in the range of 28-40 mm/year to 70-75 mm/year in the mountain area in the south-western end of
the county (Figure 70, up). The maximum amounts of precipitation recorded in the analyzed period are at
least three times higher than the multiannual averages, i.e. 92-132 mm/year in the southern part of the
county, 172-212 mm/year in the northern areas and up to 252-292 mm/year in the western end (Figure 70,
59
down). The share of multiannual averages is of 5%-8% (Figure 71, up) of the total annual amounts of
precipitation, while the total maximum precipitation amounts in 1% of wet days ranges between 12% and
26% of the needed annual rainfall (Figure 71, down). As for the evolution over time, the total precipitation
amounts in extremely wet days saw a decrease in the north-eastern part, the western end and, in isolated
cases, in the centre of the county, while they were slightly higher in the rest of the county (Figure 72).
Figure 70 – Mean (up) and maximum (down) annual total amount of precipitation fallen in extremely wet days
(R99p) in Cluj County (1961-2013) (mm/year)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
60
Figure 71 – Share of mean (up) and maximum (down) annual total amount of precipitation fallen in extremely
wet days in Cluj County (1961-2013) (%)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
61
Figure 72 – Trend in mean annual total precipitation amount fallen in extremely wet days (R99p) in Cluj County
(1961-2013)
Source: data processed based on ROCADA (Dumitrescu and Bîrsan, 2015)
2.1.3.3. Snow cover thickness
In general, the snow cover is present in the period from October to April and is thicker during the winter,
i.e. 4-8 cm at the lowland weather stations and between 10 and 30 cm in mountain weather stations,
Băișoara and Vlădeasa-1800 (Figure 73, left). In the May-June period, the snow cover is found only at
Vlădeasa-1800 weather station, but it is very thin. The maximum values have the same characteristics, but
they are much higher than multiannual averages: 10-40 cm in winter months at low weather stations and
30-75 cm at the mountain weather stations (Figure 73, right). The explanation for the thicker snow cover
at Băișoara weather station as compared to Vlădeasa-1800 lies with the location of the two stations:
Vlădeasa-1800 is located on the peak, where snow is frequently blown, while Băișoara is on a mountain
side, being protected against snow blowing wind.
In terms of trends detection, no major changes were recorded at each of the two stations in the period
under review. Thus, the changes identified were in the range of -1.41 cm/decade to 1.20 cm/decade (Table
2).
62
Figure 73 – Monthly average thickness (left) and maximum thickness (right) of the snow cover at the weather
stations in Cluj County (1969-2014) (cm)
Source: data processed based on the NMA data
Table 2. Trend slopes calculated for the annual values in days/decade.
Weather station SCT1 Mist Fog Thunderstorms Hail Hoar frost Ice
Băișoara -1.41 -30.31 5.50 0.40 -0.53 0.00 0.00
Cluj-Napoca -0.06 -10.91 -0.83 -0.86 0.00 0.23 -0.59
Dej -0.18 -28.70 -5.71 -2.00 0.00 -3.42 -0.42
Huedin 1.20 -26.43 -2.50 -3.33 0.00 0.00 -0.38
Turda 0.21 -40.63 -4.44 -4.77 0.00 -1.00 0.38
Vlădeasa-1800 0.02 -3.04 -8.24 1.00 0.00 3.61 -1.92
Source: data processed based on the NMA data
1 – Snow cover thickness (cm/decade); * - bold figures are statistically significant at a confidence level of 0.05.
2.1.4. Year-round weather events
The most frequent year-round weather events are mist, fog and squalls.
2.1.4.1. Mist
Mist is defined as the event that causes a decline in horizontal visibility within a distance from 1 to 10 km.
The annual number of misty days posts a simple variation, recording a minimum in April-May and a
maximum in winter months at all the weather stations except for Vlădeasa. In annual terms, this event
occurred, on average, in 12 days/year at Vlădeasa, while it exceeded 117 days/year at all other weather
stations. The average number of misty days recorded in Dej was 278 days/year (Table 3). The maximum
values ranged from 77 to 337 days/year (Table 4).
The highest average number of misty days was recorded at Dej weather station, due most likely to the
proximity to the large water areas in the immediate vicinity of Dej (confluence of two rivers: Someșul mare
and Someșul Mic). This is followed by Băișoara weather station in the warm season months and Cluj-
Napoca in the cold season months. On average, there are between 5 and 15 misty days/month in the warm
period of the year and 10 to 25 misty days/month in the cold period. The exception is the Vlădeasa-1800
weather station where the monthly number of misty days was no higher than 2 days/month (Figure 74,
left). The maximum values are much higher, as mist can occur very often in winter months at the medium-
63
and low-altitude weather stations, while the frequency of this event at Vlădeasa was of 4-13 days/month
in the cold period of the year and 6-14 days/month in the warm period (Figure 74, right).
The small number of misty days at the Vlădeasa-1800 weather station is offset by the high number of fog
days.
Table 3. Monthly average number of misty days at the weather stations in Cluj County (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Annual
Băișoara 17.3 17.5 18.6 15.7 13.4 13.0 12.9 13.0 13.9 14.1 14.8 17.4 181.4
Cluj-Napoca 25.2 20.8 15.8 10.3 7.5 8.6 8.7 11.8 13.7 18.6 22.3 25.2 188.5
Dej 28.6 24.5 21.6 18.0 18.4 19.7 21.8 23.9 23.1 24.0 26.3 28.1 278.0
Huedin 18.0 13.6 10.8 7.2 5.0 4.5 4.6 5.3 7.3 9.5 14.5 17.0 117.5
Turda 19.2 16.6 12.7 8.1 7.1 7.4 7.2 8.7 10.4 14.5 16.2 18.8 146.9
Vlădeasa-1800 0.4 0.7 1.2 1.2 1.4 1.3 1.5 1.7 1.3 0.7 0.4 0.7 12.6
Source: data processed based on the NMA data
Figure 74 – Monthly average number (left) and maximum number (right) of misty days at the weather stations in
Cluj County (1969-2014) (days)
Source: data processed based on the NMA data
Table 4. Monthly maximum number of misty days at the weather stations in Cluj County (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Annual
Băișoara 28 28 31 28 31 28 29 28 29 29 29 31 284
Cluj-Napoca 30 28 24 19 17 20 19 20 29 25 29 30 243
Dej 31 29 29 28 29 28 31 31 30 30 30 31 337
Huedin 28 28 24 18 16 15 20 13 24 21 28 31 233
Turda 31 28 27 22 22 20 23 22 24 28 27 31 266
Vlădeasa-1800 4 6 10 6 9 14 14 14 10 5 5 13 77
Source: data processed based on the NMA data
64
The changes that occurred during the analyzed historical period show there is a sharp decline in the number
of misty days, generally between 25 and 40 days/decade (Table 2). This could be attributed to the decrease
in condensation nuclei resulting from industrial activities, along with its significant fall after 1989.
2.1.4.2. Fog
Fog is the reduction in horizontal visibility to less than 1 km. This event occurs most frequently (both
average and maximum values) all year round at the Vlădeasa-1800 weather station, due to the high altitude
of the station. It is followed by the Băișoara and Dej weather stations. The event is specific to low-altitude
weather stations, particularly in the cold season, but it is not an exception in any month. In the period from
November to February, there are more than 5 fog days and the maximum value is higher than 15 fog days
across the county (Figure 75, Tables 5 and 6).
Figure 75 – Monthly average number (left) and maximum number (right) of fog days at the weather stations in
Cluj County (1969-2014) (days)
Source: data processed based on the NMA data
Table 5. Monthly average number of fog days at the weather stations in Cluj County (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Annual
Băișoara 8.1 8.6 7.6 6.9 5.2 3.9 3.7 3.4 5.5 6.7 7.4 9.2 76.2
Cluj-Napoca 8.8 4.9 1.4 0.8 0.4 0.7 0.6 0.8 1.7 4.2 7.6 8.4 40.2
Dej 7.8 6.6 2.7 1.9 3.6 4.2 5.2 7.8 9.5 10.2 9.0 9.0 77.6
Huedin 6.8 3.8 2.6 1.2 0.4 0.3 0.3 0.4 1.3 2.5 6.0 7.2 32.8
Turda 7.6 3.9 1.4 0.5 0.3 0.4 0.4 0.3 0.9 3.4 6.4 8.2 33.9
Vlădeasa-1800 22.2 20.0 23.0 18.7 19.7 18.8 18.6 16.6 20.1 20.0 20.8 22.6 241.2
Source: data processed based on the NMA data
Table 6. Monthly maximum number of fog days at the weather stations in Cluj County (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Annual
Băișoara 21 18 15 18 12 11 11 8 14 16 17 20 137
Cluj-Napoca 22 13 5 4 2 4 4 4 13 16 16 15 83
Dej 16 18 9 7 10 9 12 14 22 20 20 16 119
Huedin 21 12 10 5 2 2 2 2 5 10 23 16 60
Turda 20 12 8 3 3 2 6 2 4 14 20 16 74
Vlădeasa-1800 31 28 30 25 30 27 28 29 30 30 29 31 299
Source: data processed based on the NMA data
65
At the county level, the evolution trend is generally indicative of a decline, albeit statistically non-
significant. A significant drop of more than 8 days/decade was registered only at the Vlădeasa-1800
weather station (Table 2).
2.1.4.3. Squalls
Squalls are defined as those events characterised by a sudden change in wind velocity (direction and speed)
as well as by a relatively sudden start and end, caused by a sharp decrease in air pressure. They are generally
associated with the storm (convective) clouds that may occur either isolated, during the warm season of
the year, or associated to the squall line along a cold front throughout the year. This event can no longer
be detected after the installation of automatic weather stations and staff reduction. As a result, the
frequency of this event fell sharply after 2000. Therefore, the values presented in this sub-chapter need to
be analysed bearing this aspect in mind. The highest values, both average and maximum values, are specific
to the Cluj-Napoca and Dej weather stations, the two of them being the only ones with a full program of
observations among low-altitude stations. In fact, at the Băișoara weather station, there is only one
instance of a squall in the observation period (Figure 76).
Figure 76 – Monthly average number (left) and maximum number (right) of squalls days at the weather stations in
Cluj County (1969-2014) (days)
Source: data processed based on the NMA data
Monthly average values are less than 1 case/month, totalling between 0.2 and 2.6 days/year in annual
terms. The maximum values increase to 11 cases/year, the event occurring predominantly in the summer
months (Tables 7 and 8).
Table 7. Monthly average number of squall days at the weather stations in Cluj County (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Annual
Băișoara 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Cluj-Napoca 0.0 0.0 0.1 0.1 0.3 0.7 0.5 0.2 0.0 0.1 0.1 0.0 2.2
Dej 0.0 0.0 0.1 0.2 0.4 0.7 0.6 0.4 0.1 0.0 0.0 0.0 2.6
Huedin 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.2
Turda 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.2
Vlădeasa-1800 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.0 0.0 0.1 0.1 0.5
Source: data processed based on the NMA data
66
Table 8. Monthly maximum number of squall days at the weather stations in Cluj County (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Annual
Băișoara 0 0 0 0 1 0 0 0 0 0 0 0 1
Cluj-Napoca 0 1 2 2 3 7 4 2 1 1 1 1 8
Dej 1 1 2 2 2 5 3 3 2 1 0 0 11
Huedin 0 0 1 1 1 1 1 1 0 1 0 0 3
Turda 0 0 1 1 1 2 0 0 0 0 0 0 5
Vlădeasa-1800 0 1 1 0 2 2 1 1 0 0 1 1 3
Source: data processed based on the NMA data
Due to the very small number of cases and the anthropic changes in the observation and recording of this
event, the trend was not calculated.
2.1.5. Meteorological phenomena typical for the warm period of the year
The most hazardous among phenomena typical for the warm period of the year are the thunderstorms
and hail.
2.1.5.1. Thunderstorms
A phenomenon typical for the warm period of the year due to their association with the Cumulonimbus
clouds, thunderstorms usually occur between April and October, however, they can also accidentally occur
in the cold months of the year. The highest frequency is specific to high altitude stations (Băișoara and
Vlădeasa-1800). In the summer months there can be an average of 6-12 thunderstorms in a month, whilst
under strong instability conditions there can be 15-20 thunderstorm days in a month. The least exposed
areas are low altitude stations, located in depressions or river valleys (Huedin and Dej). The annual average
is between 30 and 46 days/year, whereas the highest values recorded were between 60 and 80 days/year
(Figure 77, Tables 9 and 10).
Table 9. Monthly average number of thunderstorm days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0.0 0.0 0.3 2.3 8.5 11.8 11.2 8.9 2.2 0.7 0.2 0.0 46.0
Cluj-Napoca 0.0 0.0 0.3 3.0 8.9 11.0 10.4 8.2 2.3 0.7 0.1 0.0 45.0
Dej 0.0 0.1 0.4 2.6 7.5 9.2 8.3 6.2 1.9 0.5 0.1 0.0 36.8
Huedin 0.0 0.0 0.2 1.8 6.2 7.8 6.8 5.9 1.7 0.3 0.0 0.0 30.8
Turda 0.0 0.0 0.2 1.8 5.6 7.8 7.1 6.0 1.7 0.5 0.1 0.0 30.8
Vladeasa-1800 0.1 0.1 0.5 2.1 9.0 10.9 10.5 8.8 2.4 0.6 0.1 0.0 45.2
Source: data processed based on the NMA data
67
Figure 77 – Monthly average number (left) and monthly maximum number (right) of thunderstorm days at
weather stations in Cluj county (1969-2014) (days)
Source: data processed based on the NMA data
Table 10. Monthly maximum number of thunderstorm days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 1 1 3 8 17 21 23 18 6 3 2 1 71
Cluj-Napoca 1 1 2 8 19 18 22 15 7 3 1 1 76
Dej 1 1 3 9 15 17 19 12 7 4 1 1 64
Huedin 0 1 3 7 17 15 14 13 7 2 1 2 60
Turda 0 1 2 11 14 17 18 14 8 4 2 0 71
Vladeasa-1800 2 1 3 8 18 20 19 19 7 4 1 1 80
Source: data processed based on the NMA data
As concerns changes as to the frequency of this phenomenon, there has been a decrease of this
phenomenon in low areas, significant from a statistical point of view in Turda and Huedin, and an increasing
trend, yet not too strong, in high mountain areas (Băișoara and Vlădeasa-1800) (Table 2).
2.1.5.2. Hail
From a genetic point of view, hail is formed from the same clouds thunderstorms. Nevertheless, for the
formation of hail the vertical development of the Cumulonimbus clouds has to be much bigger, for which
reason the circumstances under which hail is formed are by far less numerous as compared with those
under which thunderstorms occur. So it follows that in the reference area hail is formed mainly between
April and August, however, generally, with an average frequency of less than one day/month every year.
An exception is only Vlădeasa station-1800, where from May to August hail can be formed every year (1-3
days/month). The monthly maximum values do not exceed 4 days/month for low altitude stations, whereas
they increase up to 8-11 days per month at Vlădeasa in summer months. Exceptionally, hail was also
recorded in winter months (Figure 78).
Yearly, there is an average between 0.7 and 9.7 days, whereas in years with high instability there were 2-4
cases (in depressions) and 31 cases at Vlădeasa-1800 (Tables 11 and 12).
68
Table 11. Monthly average number of hail days at weather station in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0.0 0.0 0.0 0.4 0.9 0.8 0.3 0.4 0.2 0.0 0.1 0.0 3.1
Cluj-Napoca 0.0 0.0 0.1 0.4 0.6 0.6 0.4 0.3 0.0 0.0 0.0 0.0 2.3
Dej 0.1 0.0 0.0 0.2 0.4 0.4 0.2 0.2 0.0 0.0 0.1 0.0 1.7
Huedin 0.0 0.0 0.0 0.1 0.3 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.7
Turda 0.0 0.0 0.0 0.2 0.1 0.3 0.2 0.1 0.1 0.0 0.0 0.0 1.0
Vladeasa-1800 0.0 0.0 0.0 0.3 2.4 2.7 2.2 1.4 0.4 0.1 0.0 0.0 9.7
Source: data processed based on the NMA data
Table 12. Monthly maximum number of hail days at weather station in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0 0 0 3 4 4 2 2 2 1 2 0 8
Cluj-Napoca 0 0 1 4 4 3 2 3 0 1 0 0 7
Dej 2 0 1 2 2 2 2 2 1 0 2 0 5
Huedin 0 0 0 2 2 2 1 1 0 0 1 0 2
Turda 0 0 0 2 1 2 2 1 1 0 0 0 4
Vladeasa-1800 0 0 1 4 8 11 9 9 5 2 2 0 31
Source: data processed based on the NMA data
Figure 78 – Monthly average number (left) and monthly maximum number (right) of hail days at weather stations
in Cluj county (1969-2014) (days)
Source: data processed based on the NMA data
In the case of hail no changes were detected, except for Baisoara Weather Station, where a downward
trend of 0.53 days/decade was found.
69
2.1.6. Meteorological phenomena typical for the cold period of the year
The most frequent phenomena which can cause a negative impact on the population in the cold period of
the year are the hoar-frost, the blizzard, the freezing rain and the rime.
2.1.6.1. Hoar-frost
Hoar-frost is a phenomenon which is typical for the cold period of the year, however, it can form in any of
the transition season months at most of the weather stations in Cluj county. As a matter of fact, this
phenomenon causes the biggest damages in the transition seasons, especially in the agricultural field. Late
spring hoar-frost or early fall frost can cause significant damages to plants which have just begun the
vegetation cycle (spring) or to plants not yet harvested (fall), being able to compromise a whole year’s
crop. The first notable fall hoar-frost occurs in September (0.2…2.3 days/month as an average, 3-8
days/month in the coldest fall seasons, respectively) and continues to April when there can be up to 7-13
days of frost/month and May (1-6 days/month). Accidentally, hoar-frost can also form in the summer
months at some stations (Figure 79, tables 13-14).
Table 13. Monthly average number of hoar-frost days at weather stations in Cluj county (1969-2014).
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0.3 0.2 0.8 2.9 1.2 0.2 0.0 0.0 1.8 7.4 6.8 1.7 23.5
Cluj-Napoca 8.7 8.5 9.4 3.1 0.3 0.0 0.0 0.0 0.3 6.5 10.4 8.1 55.4
Dej 18.9 16.8 16.2 5.8 0.6 0.0 0.0 0.0 0.8 7.1 13.4 17.1 96.7
Huedin 3.8 4.8 5.6 2.5 0.2 0.0 0.0 0.0 0.5 6.6 8.2 4.2 36.5
Turda 2.8 2.8 4.2 2.1 0.2 0.0 0.0 0.0 0.2 4.9 7.1 3.5 27.7
Vladeasa-1800 1.3 0.9 1.8 1.9 1.6 0.4 0.0 0.1 2.3 4.4 4.3 2.0 21.0
Source: data processed based on the NMA data
Table 14. Monthly maximum number of hoar-frost days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 6 4 8 11 5 3 0 1 7 15 19 14 45
Cluj-Napoca 21 18 20 9 2 0 0 0 3 13 20 24 88
Dej 26 28 27 13 3 1 0 0 4 17 24 27 120
Huedin 18 13 15 7 2 0 0 0 5 16 19 16 72
Turda 24 19 12 7 1 0 0 0 3 13 16 16 92
Vladeasa-1800 7 6 9 12 6 3 1 2 8 13 14 13 61
Source: data processed based on the NMA data
70
Figure 79 – Monthly average number (left) and monthly maximum number (right) of hoar-frost days at weather
stations in Cluj county (1969-2014) (days)
Source: data processed based on the NMA data
As concerns the trends, except for Vlădeasa-1800 station where a statistical increase by 3.61 days/decade
was recorded, the curves recorded at the other stations are statistically not significant (Table 2).
2.1.6.2. Blizzard
Blizzard is one of the winter phenomena which causes the most damages in various fields of activity, despite
the fact that its frequency is relatively low. Even though normally the Transylvanian Plateau is considered
as compared to other areas in Romania to be one of the regions protected from this phenomenon, its
occurrence is not excluded. Generally, mountain weather station (Vlădeasa-1800 and Băișoara) are the
most exposed whereas depression areas are the least affected. The period of occurrence is between
September and May. As an average, in low altitude regions winter storm occurs with a very low frequency,
less than 0.1 and 1.0/year, whilst at mountain stations the overall frequency is 2.8 (Băișoara) and 11.6
days/year (Vlădeasa-1800), respectively. The maximum values recorded in one year increase to 1-7
days/year for the winter time in the lowlands and to 12 - 34 days/year, respectively, in the mountain region
(Figure 80, Tables 15 and 16).
Figure 80 – Monthly average number (left) and monthly maximum number (right) of blizzard days at weather
stations in Cluj county (1969-2014) (days)
Source: data processed based on the NMA data
71
Table 15. Monthly average number of blizzard days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0.8 0.6 0.3 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.7 2.8
Cluj-Napoca 0.3 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.2 1.0
Dej 0.2 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5
Huedin 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1
Turda 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
Vladeasa-1800 2.7 2.2 1.9 0.5 0.0 0.0 0.0 0.0 0.1 0.2 1.1 3.0 11.6
Source: data processed based on the NMA data
Table 16. Monthly maximum number of blizzard days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 7 4 3 2 2 0 0 0 0 1 3 4 12
Cluj-Napoca 3 4 2 1 1 0 0 0 0 2 2 2 7
Dej 4 2 3 0 0 0 0 0 0 0 0 1 5
Huedin 1 0 1 0 0 0 0 0 0 0 1 0 1
Turda 2 1 0 0 0 0 0 0 0 0 1 1 2
Vlădeasa-1800 12 14 9 6 1 0 0 0 5 3 8 14 34
Source: data processed based on the NMA data
Given the extremely small number of cases recorded, no trend in the development of this phenomenon
could be calculated.
2.1.6.3. Freezing rain
An occurrence rather less frequent, freezing rain forms generally with an average frequency less than one
day/month in lower areas in Cluj county between October and April. In the mountains (Weather Station
Vlădeasa-1800), the average frequency increases, but it does not exceed, however, 2 days/month. As
concerns the maximum values recorded in the historic period, in winter months there were 1-7 days of
freezing rain/month, whilst yearly there were 5-10 days at each station, except Weather Station Vlădeasa-
1800, where the maximum value recorded was 55 days in one year (Figure 81, tables 17 and 18).
72
Figure 81 – Monthly average number (left) and monthly maximum number (right) of freezing rain days at weather
stations in Cluj county (1969-2014) (days)
Source: data processed based on the NMA data
Table 17. Monthly average number of freezing rain days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.3
Cluj-Napoca 1.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.2 3.1
Dej 1.8 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.5 4.1
Huedin 0.3 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.4 1.0
Turda 0.4 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.5 1.1
Vladeasa-1800 1.6 1.0 0.8 0.4 0.5 0.1 0.0 0.0 0.2 0.8 1.7 1.6 8.6
Source: data processed based on the NMA data
Table 18. Monthly maximum number of freezing rain days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 1 2 0 0 0 0 0 0 0 1 3 1 5
Cluj-Napoca 6 4 1 0 0 0 0 0 0 1 2 6 10
Dej 6 4 1 0 0 0 0 0 0 0 2 7 9
Huedin 5 2 1 1 0 0 0 0 0 1 1 3 5
Turda 4 2 0 0 0 0 0 0 0 0 2 4 5
Vlădeasa-1800 29 26 22 4 4 2 1 1 3 7 20 19 55
Source: data processed based on the NMA data
Given the small number of cases recorded each year and given the fact that freezing rain din not occur
every year, no trend in the development of this phenomenon could be determined.
2.1.6.4. Rime
Rime is a winter occurrence which can be detrimental to socio-economic activities by massive accumulation
and high persistence. The most affected are cable transport modes given that the load accumulated on
them as a result of rime deposits requires remedial rime removal action in order to avoid cables break off.
73
In Cluj county, in low altitude areas, there is in the winter months an average number of days of rime,
ranging between 0.2 and 4.6, whilst the yearly average is between 1 and 12 days. The maximum values
increase up to 13 days/month and the yearly values vary between 12 and 31 days. In high mountainous
areas rime can occur in any month of the year and its frequency is in general much higher than in low areas,
covering in winter time all the days (Figure 82, tables 19 and 20).
Figure 82 – Monthly average number (left) and monthly maximum number (right) of rime days at weather
stations in Cluj county (1969-2014) (days)
Source: data processed based on the NMA data
Table 19. Monthly average number of rime days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 0.3 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.6 1.5
Cluj-Napoca 4.6 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 3.6 10.5
Dej 3.8 2.6 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0 3.6 11.3
Huedin 2.8 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 2.6 6.9
Turda 2.8 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 2.6 6.8
Vladeasa-1800 24.0 21.4 19.8 10.9 3.1 0.5 0.0 0.0 2.0 7.7 15.5 23.1 128.0
Source: data processed based on the NMA data
Table 20. Monthly maximum number of rime days at weather stations in Cluj county (1969-2014)
Weather station 1 2 3 4 5 6 7 8 9 10 11 12 Yearly
Baisoara 2 3 4 0 0 0 0 0 0 2 1 5 12
Cluj-Napoca 13 6 1 0 0 0 0 0 0 0 9 11 27
Dej 10 14 3 0 0 0 0 0 0 1 11 13 31
Huedin 13 3 1 0 0 0 0 0 0 1 12 11 18
Turda 13 4 0 0 0 0 0 0 0 1 13 12 19
Vlădeasa-1800 31 29 31 22 10 3 1 1 11 18 26 31 162
Source: data processed based on the NMA data
74
2.1.7. Conclusions
As concerns climate changes in Cluj county in the historic period looked at, there is a fast temperature
increase indicated by statistically significant changes in most of extreme temperature parameters, whilst
changes are neglecting in terms of precipitation over the second half of the XXth century and at the
beginning of the XXIst century. In general, in what precipitation is concerned, there is a dominant
statistically insignificant increasing trend, whereas considerable changes have been reported for intensity
indicators, more exactly an accumulation of more precipitation water in less days.
Against the background of the fast temperature increase and of the moderate or non-existent changes in
precipitation, the conclusion which can be draw is that over the past half-century the area of Cluj county
has become from a climatic point of view dryer, without excluding excessive precipitation events which
became manifest in the increase of the heavy precipitation days (e.g. exceeding 10 l/m2/day).
As concerns indicators relevant for the agricultural sector, it has been noted that, despite the fact that the
growing season length (GSL) does not increase, more heat is available so that the plants (GDDgrow10) can
use it over the interval above mentioned, which means that high-yielding hybrids can be used for crops.
Furthermore, in urban areas these more favorable thermal conditions allow for the selection of the most
efficient plants in terms of their cooling effect to be grown in urban green areas aiming to reduce the urban
heat island effects.
As concerns other elements and meteorological phenomena (thickness of snow layer, mist, fog, squalls,
hail, hoar-frost, rime, thunderstorms, blizzard, freezing rain), except for the mist which decreased
significantly in the period looked at, in general no significant changes were reported in the whole county,
apart from some isolated cases (fog: decrease at Vlădeasa; squalls: decrease at Huedin and Turda, hail:
decrease at Băișoara; hoar-frost: increase at Vlădeasa-1800). In certain instances, given the small yearly
number of cases no trend in their development could be noted. Similarly, it is not possible to highlight a
vulnerability at the TAU level for these phenomena, due to the lack of grid data and highly variable local
conditions which have a major influence on the spatial distribution and the intensity of extreme weather
events.
Therefore, in the context of the current climate changes, a differentiated impact can be seen, depending
on the field of activity. Specifically, the increase in air temperature in the case of agriculture leads to an
improvement in the agro-climatic potential across the county, especially in its low areas where it can be
extremely useful for both farmers and seed suppliers. As for health, the situation needs to be addressed in
even greater detail: the rise in temperature during the summer period causes a higher heat stress, while
the increase in the minimum or winter temperature leads to a decline in the cold stress, resulting in a higher
level of thermal comfort. At the same time, it may be recommended in this case to have air-conditioning
systems installed in residential buildings during the summer. Moreover, a lower energy consumption for
the heating of buildings, as well as a decrease in heating costs in the cold season can be expected.
An overview of trends in all extreme temperature and precipitation indicators broken down by TAU is
presented in Table 21.
75
Table 21. Overview of climate changes in extreme temperature and precipitation events in Cluj County (1961-
2013)
Abbrevi
No. Name Trend features
ation
Uneven, statistically non-significant trends at county level, which are
1. CWN Cold wave number on a decline in the eastern half and on a rise or stationary in the
western half.
Cumulative duration
2. CWF SNS decrease at county level
of cold waves
3. CWD Cold wave duration SNS decrease at county level
Mainly SS decrease at county level, but SNS decrease in the following
Maximum intensity TAUs: Huedin, Poieni, Sancraiu, Sacuieni, Margau, MArisel, Belis,
4. CWA (amplitude) of a cold Capusu Mare, Gilău, Băișoara, Ciurila, Moldovenești, Mihai Viteazu,
wave Turda, Campia Turzii, Calarasi, Tureni, Aiton, Luna, Viisoara, Ceanu
Mare, Frata, Tritenii de Jos, Ploscoș
SNS increase – county-wide, except for a part of Măguri-Răcătău
5. DTR Diurnal thermal range
(stationary) and Valea Ierii (SNS decrease)
SS decrease mainly in the western and south-western parts of the
county (Ciucea, Negreni, Poieni, Săcuieu, Mărgău, Beliș, Măguri-
6. FD0 Frost days Răcătău, Valea Ierii, Băișoara, Mărișel, Rîșca, Călățele, Mănăstireni,
Căpușu Mare, Ciurila, Tureni), as well as in Cornești, Chiuiești;
SNS decrease in the remaining TAUs
GDDgro
7. Growing degree days SS increase – county-wide
w10
SNS increase – west of the border of the following localities (including)
Aghireșu, Gârbău, Baciu, Florești, Ciurila, Tureni, Moldovenești, Mihai
Growing season Viteazu, Călărași;
8. GSL
length
SNS increase or decrease in the rest of the county, without a grouped
distribution
HWN
9. Heat waves number SS increase – county-wide
HWD
10. Heat waves duration SS increase – county-wide
HWF Cumulative duration
11. (frequency) of heat SS increase – county-wide
waves
Maximum intensity SNS increase –most of the county;
12. HWA (amplitude) of a heat Partial SS increase in the following TAUs: Ciucea, Poiei, Sancraiu,
wave Sacuieni, Margau, Huedin
13. ID Very cold days SNS decrease – county-wide
14. SU25 Summer days SS increase – county-wide
SS increase – county-wide, partially excepting Măguri Racatau, Valea
15. TXGE30 Tropical days
Ierii, Sacuieu, Mărgău (stationary or SNS increase).
76
Abbrevi
No. Name Trend features
ation
SS increase –county-wide, except for Ciucea, Poieni, Sancraiu, Mărgău,
16. TXGE35 Hot days Belis, Măguri Racatau, Valea Ierii, Sacuieu, Mărgău (stationary) and
Negreni, Calatele, Manastireni, Rîșca, Ciurila, Chiuiești (SNS increase).
Mean daily
17. TMm SS increase – county-wide
temperature
Mean daily minimum
18. TNm SS increase – county-wide
temperature
The lowest daily SS increase – most of the county;
19. TNn minimum SS increase in the following TAUs: Maguri Racatau, Valea Ierii,
temperature Chiuiesti, Mintiu Gherlii, Sanmartin, Unguras
20. TN10p Share of cold nights SS decrease – county-wide
21. TN90p Share of warm nights SS decrease – county-wide
Mainly stationary in the western part of the county, predominantly SS
22. TR Tropical nights
or SNS rise in the eastern part of the county
23. TX10p Share of cool days SS increase – county-wide
24. TX90p Share of very hot days SS increase – county-wide
Mean daily maximum
25. TXm SS increase – county-wide
temperature
The highest daily
26. TXx maximum SS increase – county-wide
temperature
Predominantly SNS decline, except for Unguraș, Sanmartin, Țaga,
Petreștii de Jos, Săndulești, Mihai Viteazu, Călărași, Viișoara (SNS
1. CDD Consecutive dry days
increase), or a partial decline in Săvădisla, Băișoara, Valea Ierii, Măguri-
Răcătău and Gilău (SS decrease)
SN decrease in the western and eastern ends of the county (Ciucea,
Negreni, Poieni, Săcuieu, Mărgău, Beliș, Măguri-Răcătău, Călățele,
Cuzdrioara, Mica, Mintiu Gherlii, Unguraș, Sanmartin, Țaga, Buza,
2. CWDp Consecutive wet days Geaca, Cătina, Cămărașu) – predominantly an SNS decline and in
isolated cases (Țaga) SS;
Predominantly at county level - SNS increase and, in isolated cases, SS
increase
SNS increase predominantly at county level, SS rise in Mărișel,
Băișoara, Ciurila, Săvădisla and partially in Gilău, Măguri-Răcătău and
PRCPTO Total precipitation Valea Ierii;
3.
T amount in wet days SNS decrease in the north-eastern part of the county: Jichișu de Jos,
Bobâlna, Dej, Cuzdrioara, Mica, Mintiu Gherlii, Fizeșu Gherlii, Unguraș,
Sânmărtin, Țaga, Buza
Predominantly SNS increase at county level, SS increase in isolated
Heavy precipitation
4. R10 cases (…), and SNS decrease in the north-eastern part of the county
days
(Cășeiu, Câțcău, Cuzdrioara, Jichișu de Jos, Mica, Unguraș, Sânmărtin)
SNS trends not clearly defined at county level: predominantly on a rise
Very heavy
5. R20 in the western and eastern parts of the county, on a decline in the
precipitation days
central part and stationary trends in isolated cases
77
Abbrevi
No. Name Trend features
ation
SNS changes county-wide, predominantly on a rise, south of the
respective alignment – Sânpaul, Chinteni, Palatca, Geaca, Buza (except
6. R95p Very wet days
for Jucu, Apahida, Cojocna, Aiton, Feleacu, Iara – SNS decline), and on
a decrease north of this alignment.
SNS changes county-wide: the western, north-eastern and, in isolated
cases, the central parts of the county are affected by a decline
(Negreni, Poieni, Săcuieu, Mărgîu, Călățele, Sâncraiu, Panticeu, Recea-
7. R99p Extremely wet days Cristur, Bobâlna, Vad, Câțcâu, Chiuiești, Cășeiu, Cuzdrioara, Mica, Dej,
Mintiu Gherlii, Jichișu de Jos, Aluniș, Iclod, Gherla, Sic, Fizeșu Gherlii,
Sânmartin, Unguraș, Mica, partially Cluj-Napoca, Apahida, Feleacu,
Cojocna), whereas a slight increase was seen for the remaining TAUs.
Overall SNS decrease in the north-western and north-eastern parts of
the county and in isolated cases in the central part of the county
(Negreni, Poieni, Săcuieu, Mărgău, Călățele, Sâncraiu, Huedin, Izvoru
Maximum daily Crișului, Panticeu, Recea-Cristur, Bobâlna, Vad, Câțcău, Chiuiești,
8. Rx1day amount of Cășeiu, Cuzdrioara, Mica, Jichișu de Sus, Dej, Aluniș, Iclod, Dăbâca,
precipitation Bonțida, Mintiu Gherlii, Jichișu de Jos, Aluniș, Iclod, Gherla, Sic, Fizeșu
Gherlii, Sânmartin, Unguraș, Mica, parțial Cluj-Napoca, Florești, Gilău,
Băișoara, Săvădisla, Valea Ierii) and SNS increase in the rest of the
county
Predominantly SNS increase at county level, except for the north-
eastern parts and other small, isolated areas, where the SNS
The 3-day maximum downward trends prevail (Recea-Cristur, Bobâlna, Vad, Jichișu de Jos,
9. Rx3days
precipitation Cuzdrioara, Mica, Unguraș, Mintiu Gherlii, Fizeșu Gherlii, Iclod, Sic,
Țaga, Geaca, Cătina, Buza, Sânmartin, Unguraș, Tritenii de Jos,
Viișoara, Luna, Moldovenești, Săcuieu)
SS – statistically significant; SNS – statistically non-significant.
Climate hazards have not been listed as important natural hazards in Romania and therefore there are no
vulnerability or risk maps available for these events. The exception is the atmospheric drought, which has
been approached in the RO-RISK project (www.ro-risk.ro) and for which vulnerability, exposure and risk
maps have been made, each TAU being classified in a value category. However, these maps cannot be used,
as their black and white layout creates confusion among the value categories.
Furthermore, the mandatory home insurance (PAD) covers no category of climate risk, only earthquakes,
floods and landslides.
Although the survey carried out shows that more than 74% of respondents consider that climate changes
are dangerous and interventions are required to mitigate such changes, there are no climate change
adaptation strategies in Cluj County. According to the same survey, between 8 and 15% of the population
were affected directly (in person), to a large or very large extent, by extreme weather events, such as
squalls, heat waves, frost and drought over the past 5 years, while more than 32% of the population stated
they incurred damages (at family level) caused by storms, wind storms and drought. However, more than
60% of respondents do not know how to react during such events. In these circumstances, it is mandatory
to inform the county inhabitans on how to react and what measures to take should such events occur, in
order to mitigate/avoid the negative impact.
78
As for the health condition, most respondents (40.6%) consider that, of all weather conditions, their health
is mostly affected by sudden changes in temperature. These are followed by cold waves (frost) (17.5%), the
sudden change in pressure (14.1%) and heat waves (7.6%). It would be beneficial to have specific weather
forecasts to warn.especially, about these events.
It is also important to notice that, in case of a negative impact of natural disasters, most respondents expect
to receive State aid from central and local authorities (74.8%) or local communities (8.9%). Surprisingly, the
respondents expressed far less confidence in the church and family (7.9 and 2.6% respectively).
At the same time, it is worth noting the high volunteering availability in the event of natural disasters, i.e.
47.1% of respondents are willing to take part in volunteering actions in any circumstance and 19.6% only
when a known person (friend, neighbour, colleague) is affected. In these circumstances, the competent
institutions and organisations should organize courses/training sessions for the persons willing to get
involved.
In the context of recent scientific research studies that showed the economic and health impact of other
extreme weather events (heat waves) at the level of Cluj-Napoca City (Croitoru et al., 2018, Herbel et al.,
2018), we believe that a reassessment of the list of natural disasters with an impact on the environment
and the society, including climate hazards, would be required for the national regulations. Moreover,these
events should be included in the mandatory home insurance (PAD) policies, while the development of local
climate change adaptation strategies (to extreme weather events as well) should become a priority.
2.2. Water risks
Hazard is a ”phenomenological category which refers to objects and conditions (air masses, water masses,
lithomass, biomass, populations, epidemics, avalanches, etc.), to their actions (floods, landslides, diseases,
etc.), as well as their features” (Mac, Petrea, 2003). What is not known with a hazard is the time and place
of occurrence, the intensity of the occurrence, its amplitude, as well as the effects which it will have. Risk
is a consequence of lapping natural hazard over the “interests” of human communities.
Risk can be defined as the probability of exposure of humans and property created by humans to the action
of one specific hazard of a certain size. The risk is the presumable level of loss of human life, number of
injured persons, damages caused to properties and economic activity by a certain natural phenomenon or
group of phenomena at a certain place and in a certain period. Elements at risk are the population,
properties, ways of communication, economic activities, etc., exposed to the risk in a specific area.
Water phenomena are external forms of water manifestation (in various states of matter) perceived by a
conscious individual. Among risk water phenomena we can have high water, floods, frost occurrences, etc.
For the classification of risk water phenomena various categories of delineation are used; the most
frequently used among them refer to their origin, mode of manifestation and affected area. The most usual
criterion (origin/genesis) requires the identification of two large categories: natural and anthropic. The
category of natural risk water phenomenon includes high water, floods, droughts, river bed, slope and coast
line erosion processes, frost occurrences, tsunami, big tidal waves, flash floods, excessive humidity. From
the category of anthropic risk water phenomena we would like to mention: physical, chemical, biological
and radioactive pollution, floods resulted from collapsing dams or levees, accidents involving spillage,
discharge or accelerated transit of large quantities of water, etc.
79
In Cluj county water risks are represented by high water, floods, droughts, river bed and slope erosion
processes, pollution of anthropic origin.
The geographic position, the heterogeneous character of the relief, the climate conditions, the edaphic and
vegetal variety determined a certain distribution in space of risks on the area of Cluj county.
Pursuant to Law No. 575 on the approval of the National Spatial Plan –Section V – Natural risk areas, floods,
earthquakes and landslides are considered to be potentially destructive natural phenomena which may
affect the population, the human activities, the natural and built environment, and may cause damage and
human casualties. Thus, the analysis of the two flood maps shows that, at Cluj County level, the maximum
amount of rainfall in 24 hours falls within the limits of the first range and 100 mm in most administrative-
territorial units. The mountainous area in the western part of the county, which includes the localities of
Negreni, Ciucea, Poieni, Săcuieu, Mărgău, Beliș, Călățele and Sâncraiu, a few municipalities in the south-
eastern part of the county, in the lower area of Arieș (Turda, Câmpia Turzii, Viișoara, Călărași and Luna) and
the northern end of the county (Chiuiești) are the areas with maximum rainfall amounts between 100 and
150 mm in 24 hours. In fact, after the processing of the flood strips, the increased likelihood of floods is
confirmed for most of these areas, with possible negative effects on the human communities and territorial
infrastructure. The second map, mentioned in the legislative act, shows the administrative-territorial units
that were affected by floods caused by watercourses and torrents. Compared to 2001, the situation was
significantly different, as the hazardous events extended further to other localities which were initially
beyond the potentially floodable areas. Thus, according to the data recorded in the legislative act issued in
2001, three urban centres (Cluj-Napoca, Dej and Gherla) and 19 communes (Beliș, Bonțida, Călățele, Cășeiu,
Cătina, Chiuiești, Ciucea, Fizeșu Gherlii, Geaca, Gilău, Iara, Măguri-Răcătău, Mănăstireni, Mintiu Gherlii,
Petreștii de Jos, Poieni, Sâncraiu, Țaga and Vad) were affected by floods from watercourses. As regards the
torrent flooding, it affected a single city (Gherla) and 17 communes (Beliș, Căpușu Mare, Cășeiu, Câțcău,
Cornești, Dăbâca, Fizeșu Gherlii, Gilău, Iara, Măguri-Răcătău, Mărgău, Mărișel, Mica, Recea Cristur, Săcuieu,
Vad and Valea Ierii).
The current state of affairs, backed by the studies carried out by the Water Basin Administration of Someș,
Crișuri andi Mureș, confirms that water risks in the category of flooding (from rivers overflow and run-off
water from coming down the slopes) may be extended to cover all administrative-territorial units. A
detailed presentation, including a summary of the current situation, is shown in Table 29.
2.2.1. High water. Floods.
High water is a hydrodynamic phenomenon characterized by a fast growing of the level and discharge up
to a peak value, followed by a drop which is slower than the increase stage. Their formation depends on
the origin of the water surplus in the drainage basin, whereas most frequently it is rain water.
The historic analysis of these phenomena led to the delineation of some high vulnerability areas, generally
located in lower regions, at low altitudes. The most exposed areas are the sectors of Someșul Mic river
(downstream of Cluj-Napoca, up to Dej), lower courses of the tributaries of Someșul Mic river (downstream
of Apahida), lower sector of Someșul Mare (Mica - Dej), Someșul river sector (Dej – county boundary), lower
course of Arieș river and limited sectors on the upper course of Crișul Repede river.
The direct consequence of these high waters manifested territorially by significant floods. According with
the Flood Management Plan elaborated by the territorial structures of the National Administration ”Apele
Române” (A.N.A.R.), the biggest floods occurred in the following years: 1970, 1974, 1975, 1980, 1981, 1989,
80
1995, 1999, 2000 and 2001 (Table 22). The significant high waters and floods in 1970 and 1974 required
the prioritization of the measures concerning the fighting of adverse consequences of floods by the
construction of dams and levees meant to limit the effects of floods on land and settlements. Against this
background large scale constructions are erected in the mountain area of the county, constructions which
later also became important milestones of the Romanian energetic sector: dams Gilău, Someșu Cald,
Tarnița, Beliș-Fântânele, Someșul Rece, Drăgan and Leșu. The man-made water reservoirs behind them
make it possible to break high water waves downstream, hence the operative management of critical
situations with excessive rainfalls which could cause water hazards.
Table 22. Historic floods in Cluj county
Duration
Water basin administration River name / month of occurrence Day of occurrence
(days)
Someș-Tisa Someș May 1970 11.05.1970 9
Someș June 1974 12.06.1974 6
Someș March 1981 11.03.1981 3
Someș December 1995 23.12.1995 1
Someș January 1999 08.01.1999 2
Someș March 2001 05.03.2001 3
Someș Mare March 2001 03.03.2001 4
Someș Mic June 2001 18.06.2001 3
Someș Mic September 2001 26.09.2001 2
Crișuri Crișul Repede June 1970 09.06.1970 8
Crișul Repede June 1974 12.06.1974 5
Crișul Repede July 1980 21.07.1980 22
Crișul Repede May 1989 07.05.1989 6
Crișul Repede December 1995 23.12.1995 10
Crisul Repede February 1999 17.02.1999 17
Crisul Repede April 2000 01.04.2000 11
Mureș Arieș July 1975 02.07.1975 12
Arieș March 1981 12.03.1981 13
Arieș December 1995 27.12.1995 8
Arieș April 2000 06.04.2000 8
Source: according with the Flood Risk Management Plan of A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
The Directive of the European Parliament and of the Council on the assessment and management of flood
risk (2007/60/EC), known as the Floods Directive, called for the member states to undertake a preliminary
flood risk assessment, to prepare flood hazard maps and flood risk maps and flood risk management plans.
Between 2010-2012 the first stage of the Floods Directive was implemented, more exactly the preliminary
flood risk assessment which involves two under-stages: selection of significant historic floods (including
their localizing in space) and identification of areas of potentially significant flood risk (ASPFR).
Based on the information provided by the A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri the respective
areas were highlighted and the map in Figure 83 was elaborated. The assessment indicated that on the
Someșul Mic, Someșul Mare and Someșul unit rivers a total area of 46.43 km2 is exposed, stretching from
Florești to the Northern boundary of the county, including 14 administrative subdivisions: Florești, Cluj-
Napoca, Apahida, Jucu, Bonțida, Iclod, Gherla, Mintiu Gherlii, Mica, Dej, Cuzdrioara, Cășeiu, Vad and
Câțcău. The second area of potentially significant flood risk is the lower sector of Arieș river, with an area
of 27.26 km2. It stretches on the administrative territory of 7 localities as follows: Iara, Moldovenești, Mihai
81
Viteazu, Turda, Câmpia Turzii, Viișoara and Luna. The third area belongs to the upper sector of Crișul Repede
river, covering an area of 12.09 km2 which belongs to the administrative subdivisions: Izvoru Crișului,
Huedin, Sâncraiu, Poieni, Ciucea and Negreni.
82
Figure 83 – Map of potentially significant flood risk areas
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
The lapping of the respective areas over the administrative subdivisions indicated absolute values and
variable shares at the level of the administrative subdivisions exposed to potentially significant flood risk.
As also emerging from the table attached to Figure 83, Luna commune has the largest area potentially
exposed to floods (12.50 km2), followed by Dej City (9.54 km2) and Mica locality (5.33 km2).
The second stage of Directive 2007/60/EC, rolled off between 2013-2014, was represented by the
elaboration of flood hazard maps and flood risk maps for areas designated as having a potentially significant
flood risk (APSFR). Flood hazard maps offer information concerning the extension of flooded areas, water
depth and, as case may be, water velocity, for high waters which can occur within a certain period of time.
Against this background, three distinct scenarios were simulated: floods with low exceedance probability
or in extreme cases for which the exceedance period of once in 1,000 years (discharge Q0,1%) was adopted;
floods with average probability, more exactly once in 100 years (discharge Q1%) and floods with high
probability, whose maximum discharge is exceeded once in 10 years (discharge Q10%).
The catenation of the data provided by the water basin administrations allowed for the generation of 3
distinct maps associated to the three scenarios above mentioned (Figures 84-86).
83
Figure 84 – Flood hazard map associated with the low probability scenario - Q 0,1%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
In what the discharge Q0,1% is concerned, the flooding zone amounts at county level to a total area of 163.62
km2, which closely follows the course of Someșul Mic river (downstream of Florești), Someșul Mare river,
Someșul unit river (downstream of Dej – county boundary), the lower sector of Arieș river (downstream of
Iara commune up to where it flows into Mureș river) and the Crișul Repede sector (downstream of Poieni
– county boundary).
On the Someș river sector plus tributaries the flooding zone overlaps 14 administrative subdivisions:
Florești, Cluj-Napoca, Apahida, Jucu, Bonțida, Iclod, Gherla, Mintiu Gherlii, Mica, Dej, Cuzdrioara, Cășeiu,
Vad and Câțcău, covering a total area of 105.23 km2 (Figure 84). In the lower sector of Arieș river and its
tributaries the flooding zone overlaps 7 administrative subdivisions as follows: Iara, Moldovenești, Mihai
Viteazu, Turda, Câmpia Turzii, Viișoara and Luna. The area occupied, associated to this discharge value, is
50.97 km2. In the Western part of the county, on the upper course of Crișul Repede river, the flooding zone
stretches over 3 administrative subdivisions: Poieni, Ciucea and Negreni, covering an area of 6.43 km2. The
territorial analysis indicated that the most vulnerable locality associated to this scenario in terms of the
area occupied is the City of Dej with 19.04 km2, followed by Luna commune with 15.75 km2 and the City of
Câmpia Turzii with 10.79 km2.
84
Figure 85 – Flood hazard map associated with the average probability scenario - Q 1%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
As concerns the Q1% scenario, the flooding zone amounts to a total area of 128.99 km2, which closely
overlaps the course of Someșul Mic river (downstream of Florești), Someșul Mare river, and Someșul unit
(downstream of Dej – county boundary), the lower sector of Arieș river (downstream of Valea Ierii
commune up to where it flows into Mureș river) and the sector of Crișul Repede sector (downstream of
Poieni – county boundary).
On the Someș river sector plus tributaries the flooding zone overlaps 15 administrative subdivisions:
Florești, Cluj-Napoca, Apahida, Jucu, Bonțida, Iclod, Gherla, Mintiu Gherlii, Mica, Dej, Jichișu de Jos,
Cuzdrioara, Cășeiu, Vad and Câțcău, covering a total area of 87.24 km2 (Figure 85). In the lower sector of
Arieș river and its tributaries the flooding zone overlaps 12 administrative subdivisions as follows: Valea
Ierii, Băișoara, Săvădisla, Iara, Moldovenești, Mihai Viteazu, Turda, Câmpia Turzii, Viișoara, Tritenii de Jos,
Ceanu Mare and Luna. The area occupied, associated to this discharge value, is 36.78 km2. On the upper
course of Crișul Repede river, the flooding zone stretches over 3 administrative subdivisions: Poieni, Ciucea
and Negreni, covering an area of 4.97 km2. The territorial analysis indicated that the most vulnerable
locality associated to this scenario in terms of the area occupied is the City of Dej with 17.81 km2, followed
by Mintiu Gherlii commune with 9.36 km2 and Vad commune with 8.11 km2.
85
Figure 86 – Flood hazard map associated with the high probability scenario - Q 10%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș și A.B.A. Crișuri
As concerns the Q10% scenario, the flooding zone amounts to a total area of 67.91 km2, which closely overlaps
the course of Someșul Mic river (downstream of Florești), Someșul Mare river, and Someșul unit (downstream
of Dej – county boundary), the lower sector of Arieș river (downstream of Iara commune up to where it flows
into Mureș river) and the sector of Crișul Repede sector (downstream of Poieni county).
On the Someș river sector plus tributaries the flooding zone overlaps 14 administrative subdivisions:
Florești, Cluj-Napoca, Apahida, Jucu, Bonțida, Iclod, Gherla, Mintiu Gherlii, Mica, Dej, Cuzdrioara, Cășeiu,
Vad and Câțcău, covering a total area of 55.98 km2 (Figure 86). In the lower sector of Arieș river and its
tributaries the flooding zone overlaps 7 administrative subdivisions as follows: Iara, Moldovenești, Mihai
Viteazu, Turda, Câmpia Turzii, Viișoara and Luna. The area occupied, associated to this discharge value, is
7.31 km2. On the upper course of Crișul Repede river, the flooding zone stretches over 3 administrative
subdivisions: Poieni, Ciucea and Negreni, covering an area of 2.84 km2. The territorial analysis indicated
that the most vulnerable locality associated to this scenario in terms of the area occupied is the City of Dej
with 11.45 km2, followed by Vad commune with 7.23 km2 and Mintiu Gherlii commune with 6.26 km2.
The flood risk maps were elaborated based on the flood hazard maps, looking at the data concerning the
elements exposed to hazard and their vulnerability. They indicate the potential adverse effects associated
to the flood scenarios depending on: population, economic activity, environment and cultural heritage
(Figures 87-89). The flood risk maps were elaborated for each probability of exceedance of the maximum
discharge of: 0,1%, 1% and 10%.
86
Figure 87 – Flood high risk map associated with the high probability scenario - Q 10%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
In Cluj county flood high risk areas cover a total area of 67.91 km2, of which on the course of Someșul river
and its tributaries 15 administrative subdivisions are affected as follows: Florești, Cluj-Napoca, Apahida, Jucu,
Bonțida, Iclod, Gherla, Mintiu Gherlii, Mica, Dej, Cuzdrioara, Cășeiu, Vad and Câțcău (Figure 87). They cover
an area of 55.98 km2. On the lower sector of Arieș river the flood high risk areas make up 8.52 km2, affecting
6 administrative subdivisions: Moldovenești, Mihai Viteazu, Turda, Câmpia Turzii, Viișoara and Luna. The
detailed analysis indicates that the largest area with flood high risk falls on the City of Dej with 11.45 km2,
followed by Vad commune with 7.23 km2 and Mintiu Gherlii commune with 6.26 km2.
Flood average risk areas cover regions in the sectors of Someșul Mic, Someșul Mare and Someșul ”unit”,
Crișul Repede and Arieș rivers, amounting to a total area of 129.41 km2. In Someșului basin a number of
14 administrative subdivisions are affected: Florești, Cluj-Napoca, Apahida, Jucu, Bonțida, Iclod, Gherla,
Mintiu Gherlii, Mica, Dej, Cuzdrioara, Cășeiu, Vad and Câțcău, which cover an area of 129.41 km2 (Figure
88). In Arieșului basin a number of 11 administrative subdivisions are affected like follows: Valea Ierii,
Băișoara, Iara, Moldovenești, Mihai Viteazu, Turda, Câmpia Turzii, Viișoara, Tritenii de Jos, Ceanu Mare and
Luna. Their aggregated area exposed to the average risk amounts to 35.01 km2. In Crișului Repede basin
flood average risk areas cover territories located in three administrative subdivisions as follows: Poieni,
Ciucea și Negreni, with a total vulnerable area of 4.97 km2. As concerns administrative subdivisions, the
87
most vulnerable is the City of Dej with an area of 17.81 km2, followed by Mintiu Gherlii commune with 9.36
km2 and Vad commune with 8,11 km2.
Figure 88 – Flood average risk map associated with the average probability scenario - Q 1%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
Flood low risk areas in Cluj county cover significant territories along the rivers Someșul Mic, Someșul Mare
and Someșul, Arieș and Crișul Repede. The amount to a total area of 162.63 km2. In the sector of Someșul
river areas on a number of 15 administrative subdivisions are affected: Florești, Cluj-Napoca, Apahida, Jucu,
Bonțida, Iclod, Gherla, Mintiu Gherlii, Mica, Dej, Cuzdrioara, Cășeiu, Vad, Jichișu de Jos and Câțcău, with a
total vulnerable area of 105.29 km2 (Figure 89). In Crișul Repede basin a number of 3 administrative
subdivisions are included: Poieni, Ciucea and Negreni, with a vulnerable area of 6.43 km2. The locality with
the most extensive area associated to low risk is the City of Dej with 19.04 km2, followed by Luna commune
with 15.75 km2 and the City of Câmpia Turzii with 10.79 km2.
The vulnerability highlights how much humans and property are exposed to various hazards and indicates
the amount of damages which a certain phenomenon can cause. The analysis of the impact of floods on
intra-urban areas identified some significantly large areas potentially compromised at the level of two low
altitude regions in the county: in the area of the water gathering in Dej and in the lower sector of Arieș
county.
88
Based on the typology associated to the three scenarios the Q0,1% discharge, Q1% discharge and Q10%
discharge, the vulnerable areas within intra-urban territories located in floodplains were identified, as well
as the number of the hazard exposed population, the percentage of this population from the total
population, the extent to which the population is affected (not significant, low, average, high).
Figure 89 – Flood low risk map associated with the low probability scenario - Q 0.1%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
Following the application of the first scenario, that of floods with high probability of occurrence, whose maxim
discharge is exceeded once in 10 years (Q10% discharge), a number of 45 intra-urban areas, belonging to 5
municipalities (Cluj-Napoca, Turda, Câmpia Turzii, Gherla and Dej), are vulnerable in the county, as well as 17
communes (Apahida, Bonțida, Cășeiu, Ciucea, Cuzdrioara, Florești, Iara, Iclod, Jichișu de Jos, Jucu, Luna, Mica,
Mintiu Gherlii, Negreni, Poieni, Vad, Viișoara). The distribution in space of intra-urban areas is presented in
Figure 90. The total area exposed in this scenario, located in the intra-urban areas mentioned, is around 2.57
km2, whilst the number of potentially exposed population with residence in the floodplains is 6,512
inhabitants.
Based on the second scenario, associated to the average probability of occurrence, whose maximum
discharge is exceeded once in 100 years (Q1% discharge), a number of 61 intra-urban areas, belonging to 5
municipalities (Cluj-Napoca, Turda, Câmpia Turzii, Gherla și Dej), are vulnerable in the county, as well as 21
communes (Apahida, Băișoara, Bonțida, Cășeiu, Ceanu Mare, Ciucea, Cuzdrioara, Florești, Iara, Iclod, Jichișu
de Jos, Jucu, Luna, Mica, Mihai Viteazu, Mintiu Gherlii, Negreni, Poieni, Vad, Valea Ierii, Viișoara. The
89
distribution in space of intra-urban areas is presented in Figure 91. The total area exposed in this scenario,
located in the intra-urban areas mentioned, is around 13.55 km2, whilst the number of potentially exposed
population with residence in the floodplains is 41,083 inhabitants.
Figure 90 – Flood vulnerability of intra-urban areas associated with the high probability scenario - Q 10%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
90
Figure 91 – Flood vulnerability of intra-urban areas associated with the average probability scenario - Q 1%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
Based on the third scenario, associated to the low probability of occurrence, whose maximum discharge is
exceeded once in 1,000 years (Q 0,1%, discharge), a number of 54 intra-urban areas, belonging to 5
municipalities (Cluj-Napoca, Turda, Câmpia Turzii, Gherla and Dej), are vulnerable in the county, as well as 18
communes (Apahida, Bonțida, Cășeiu, Câțcău, Cuzdrioara, Florești, Iara, Iclod, Jichișu de Jos, Jucu, Luna,
Mica, Mihai Viteazu, Mintiu Gherlii, Negreni, Poieni, Vad, Viișoara). The distribution in space of intra-urban
areas is presented in Figure 92. The total area exposed in this scenario, located in the intra-urban areas
mentioned, is around 112.99 km2, whilst the number of potentially exposed population with residence in the
floodplains is 66788 inhabitants.
In Cluj county the occurrence of floods following heavy rains, highly accelerated snowmelt, early thaw is
possible on most of the streams belonging to the three drainage basins: Someș, Crișul Repede and Arieș.
The most favorable areas for the occurrence of floods during abundant rainfalls, snow melting periods, also
indicated through the high frequency of the previous events, are as follows: Someșul Rece river in area
Măguri-Răcătău which includes Măguri-Răcătău commune and Someșul Rece village; Nadăș creek in area
Baciu commune – North-Western part of Cluj-Napoca City; Valea Ierii in area Iara commune; Ocnei valley
in Ocna Dej district; Olpret river in the area of Viile Dejului district.
A distinct category of floods are those caused by the occurrence of accidents at the dams of man-made
water reservoirs existing in the county. The most severe failures refer to the collapse of dams followed by
91
the outflow of the initial volumes existing in the basins of water reservoirs. These major failures are
associated to the most severe flood scenarios elaborated for localities in Cluj county, with the probability
of causing significant loss of human life and huge material damages.
Figure 92 – Flood vulnerability of intra-urban areas associated with the low probability scenario - Q 0,1%
Source: data processed after A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
At county level, the Inspectorate for Emergency Situations (ISU) also has among other tasks the
competence to act in case of floods. The data provided by the above mentioned institution for the period
2007-2018 concerning flood associated events are summarized in Table no. 23. The detailed analysis
encompasses all interventions defined as ”floods”, also including situations occurred in urban areas caused
by accidents at supply units and/or sewage systems of the urban water infrastructure system (burst pipes,
various failures). The analysis of the data in the table allows for drawing some conclusions concerning the
variability in terms of time and space of the events of the flood type where the Inspectorate for Emergency
Situations came into action.
92
Table 23. Number of I.S.U. interventions on administrative subdivisions in case of floods (2007-2018)
NAME OF ADMINISTRATIVE
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total
SUBDIVISION
AGHIREȘU 0 2 3 1 0 3 1 4 2 0 1 1 18
AITON 0 0 0 0 0 0 0 0 0 0 0 0 0
ALUNIȘ 0 0 0 0 0 0 0 0 0 0 0 0 0
APAHIDA 1 1 1 11 0 0 0 0 0 5 0 0 19
AȘCHILEU 0 1 0 0 0 0 0 0 0 0 0 0 1
BACIU 2 0 0 1 0 0 0 0 0 1 0 0 4
BĂIȘOARA 0 0 0 2 0 0 1 0 0 1 0 0 4
BELIȘ 0 0 0 1 0 0 0 0 0 0 0 0 1
BOBÂLNA 0 0 0 0 0 0 0 0 0 1 0 0 1
BONȚIDA 0 0 0 0 0 0 0 0 0 0 0 0 0
BORSA 1 1 0 11 0 0 0 0 0 0 0 0 13
BUZA 0 0 0 0 0 0 0 0 0 0 0 0 0
CÂMPIA TURZII 3 0 0 1 0 0 0 0 0 0 0 0 4
CÂȚCĂU 0 2 0 0 0 0 0 0 0 0 0 0 2
CĂIANU 0 0 0 5 0 0 0 0 0 0 0 0 5
CĂLĂRAȘI 1 0 0 0 0 0 0 0 0 1 0 0 2
CĂLĂȚELE 0 0 1 11 0 0 0 0 0 0 0 0 12
CĂMĂRAȘU 0 0 0 0 0 0 0 0 0 0 1 0 1
CĂPUȘU MARE 0 0 0 0 0 0 0 0 1 2 0 0 3
CĂȘEIU 0 3 1 0 0 0 0 0 0 32 1 0 37
CĂTINA 0 0 0 0 0 0 0 0 0 0 0 0 0
CEANU MARE 0 0 2 2 5 0 1 0 0 0 0 1 11
CHINTENI 0 0 0 6 0 0 0 0 0 1 0 0 7
CHIUIEȘTI 0 0 0 2 0 0 0 0 1 0 0 0 3
CIUCEA 0 0 0 1 1 0 0 0 0 29 0 0 31
CIURILA 0 0 0 0 0 0 0 0 0 0 7 0 7
CLUJ-NAPOCA 12 18 16 62 15 5 4 10 7 103 0 23 275
COJOCNA 0 0 0 3 0 0 0 0 0 0 0 0 3
CORNEȘTI 0 0 0 1 0 0 0 0 0 1 0 0 2
CUZDRIOARA 0 3 0 10 0 0 2 0 1 0 1 0 17
DĂBÂCA 0 0 0 0 0 0 0 0 0 0 0 0 0
DEJ 5 5 2 13 2 28 11 1 8 88 7 8 178
FELEACU 0 0 0 0 0 0 0 0 0 0 0 1 1
FIZEȘU GHERLII 0 0 0 0 0 0 0 0 1 0 0 0 1
FLOREȘTI 1 0 4 6 1 2 8 10 12 6 6 12 68
FRATA 0 0 0 0 0 0 0 0 0 0 0 0 0
GÂRBĂU 0 0 0 0 0 0 0 0 0 0 1 1 2
GEACA 0 0 0 5 0 0 0 0 0 0 0 0 5
GHERLA 0 0 1 1 0 0 0 0 0 2 0 5 9
GILĂU 0 0 1 2 2 0 0 0 4 3 2 0 14
HUEDIN 4 5 13 20 1 2 2 2 2 2 7 48 108
IARA 0 0 1 1 0 0 0 0 0 0 0 0 2
ICLOD 0 3 2 4 0 0 0 0 0 0 0 0 9
IZVORU CRIȘULUI 0 0 0 0 0 0 0 0 0 0 0 0 0
JICHIȘU DE JOS 0 0 0 0 0 0 0 0 0 64 0 0 64
JUCU 0 5 3 25 0 0 0 0 0 0 0 0 33
LUNA 0 0 0 0 0 0 0 1 0 2 0 0 3
93
NAME OF ADMINISTRATIVE
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total
SUBDIVISION
MĂGURI-RĂCĂTĂU 0 0 0 0 0 0 0 2 0 1 0 0 3
MĂNĂSTIRENI 0 0 0 0 0 0 0 0 0 0 0 0 0
MĂRGĂU 0 0 0 2 0 0 0 4 3 0 0 10 19
MĂRIȘEL 0 0 0 2 0 0 0 0 0 0 0 0 2
MICA 0 4 0 2 0 0 0 0 8 0 5 0 19
MIHAI VITEAZU 0 1 0 4 1 2 1 0 0 0 0 1 10
MINTIU GHERLII 0 3 0 7 0 0 0 0 0 0 0 2 12
MOCIU 0 0 0 0 0 0 0 1 0 0 0 0 1
MOLDOVENEȘTI 0 0 0 0 0 0 0 0 0 0 0 1 1
NEGRENI 0 0 0 2 0 1 0 0 2 0 1 1 7
PALATCA 0 3 0 3 0 0 0 0 0 0 0 0 6
PANTICEU 0 0 0 0 0 0 0 0 0 0 0 0 0
PETREȘTII DE JOS 0 0 0 1 0 0 0 0 0 0 0 0 1
PLOSCOȘ 0 0 0 0 0 0 0 0 0 0 0 0 0
POIENI 1 0 0 2 0 0 0 1 0 67 20 1 92
RECEA-CRISTUR 0 0 0 3 0 0 0 0 0 0 0 0 3
RÂȘCA 0 0 0 6 0 0 0 0 0 0 0 0 6
SÂNCRAIU 0 0 0 2 0 0 0 0 0 0 0 0 2
SÂNMARTIN 0 0 0 0 0 0 0 0 0 0 0 0 0
SÂNPAUL 0 1 0 1 0 0 0 0 0 2 0 0 4
SĂCUIEU 0 1 0 3 0 0 2 3 0 0 0 1 10
SĂNDULEȘTI 0 0 0 4 0 0 0 0 0 4 0 0 8
SĂVĂDISLA 0 0 0 3 0 0 0 0 1 0 0 0 4
SIC 0 0 0 1 0 0 0 0 0 0 0 0 1
SUATU 0 0 3 10 2 0 0 0 0 1 0 0 16
ȚAGA 0 0 0 11 1 2 2 0 0 0 0 0 16
TRITENII DE JOS 0 0 0 0 0 0 0 0 0 0 0 0 0
TURDA 1 7 3 27 11 5 4 7 8 31 1 8 113
TURENI 1 0 0 2 2 0 3 0 0 0 0 1 9
UNGURAȘ 0 2 0 1 0 0 3 0 0 3 5 0 14
VAD 0 4 0 0 0 9 0 0 0 0 0 0 13
VALEA IERII 0 0 0 0 0 0 0 0 0 0 0 0 0
VIIȘOARA 0 0 0 3 2 0 0 0 0 0 1 2 8
VULTURENI 0 0 0 0 0 0 0 0 0 0 0 0 0
TOTAL PER COUNTY 33 75 57 310 46 59 45 46 61 453 67 128 1380
Source: I.S.U. ”Avram Iancu” Cluj
Against this background, in what the various years are concerned, minimum values were recorded in 2007,
2011, 2013 and 2014, with a yearly number of interventions below 50. The highest values are associated
to the years 2016, 2010 and 2018. Looking at the distribution in space, there is the striking aggregated value
attached to the county capital city Cluj-Napoca, where they had a number of 275 interventions (most of
them associated with critical situations connected to household or public areas water transportation
systems). The City of Dej ranges second with a number of 178 interventions, the City of Turda with 113
interventions and Huedin city with 108 interventions. As expected, urban areas were more vulnerable than
rural areas in this respect, which is also justified by the extent of distribution systems (length, diameters,
degree of wear and tear) existing at this level. As a matter of fact the total number of interventions in urban
areas (678) makes up almost half of the total number of interventions at county level (1380). In rural areas,
94
most interventions took place in the communes of Poieni (92), followed by Florești (68) and Jichișu de Jos
(64). A detailed overview of the interventions is available in Figure 93.
Figure 93 – Number of ISU interventions on administrative subdivisions in case of floods (2007-2018)
Source: I.S.U. ”Avram Iancu” Cluj
Interestingly enough, despite the relatively extensive period of 12 years, there are at county level 14
communes with no intervention associated to this type of events: Aiton, Aluniș, Bonțida, Buza, Cătina,
Dăbâca, Frata, Izvoru Crișului, Mănăstireni, Ploscoș, Sânmartin, Tritenii de Jos, Valea Ierii and Vultureni.
2.2.2. Flood control measures
Various flood control measures have been implemented in Cluj county meant to mitigate and reduce the
adverse effects of floods, most of them referring to river beds. The infrastructure associated to the cadaster
of waters includes a complex system of hydrotechnical works with a role in the quantitative management
of water resources, made up of several types of structures, such as levees, permanent and non-permanent
water retention ponds, derivations turning water from one stream into the other, etc.
As concerns water derivations, their major role is the transfer of some water volumes between neighboring
drainage basins, put into practice by discharge supplementation in man-made water reservoirs located in
the mountainous area of the county. Derivations in the drainage basin of Someșul Mic ensures the
supplementation of discharges accumulated in lakes Fântânele and Someșul Rece, meant to increase the
production of electricity in associated hydroelectric power plants. Derivations in the drainage basins of
95
Arieș river, more exactly in the upper drainage basin of Iara river, are in their turn included in the complex
system of transfer of discharges to the drainage basin of Someșul Cald river (Table 24).
In the drainage basin of Someșul Mic river, the aggregated length of water derivations is 19.26 km, with an
installed capacity of 35.4 m3/s. In the drainage basin of Arieș river, the aggregated length of water
derivations is 13.192 km with an installed capacity of 3.97 m3/s. In the drainage basin of Crișul Repede river,
the aggregated length of water derivations is 27.9 km with an installed capacity of 45.64 m3/s.
Levees are longitudinal works which protect river banks, but at the same time also to control river beds and
guide the water flow on a certain route. The analysis of the distribution of levees in Cluj county indicates a
heterogenous situation with 23 structures located in the drainage basin of Someșul river, 14 structure in
the drainage basin of Arieș river and one single levee in the drainage basin of Crișul Repede river.
In the drainage basin of Someșul river, longitudinal flood relief works are located along the Someșul Mic
river, downstream of Cluj-Napoca, within the localities Bonțida, Răscruci, Hășdate, Livada, Nima, Mintiu
Gherlii, Gherla and Dej. Along the Someșul ”unit” levees can be found within the localities Dej, Cuzdrioara,
Mica, Cetan and Vad.
96
Table 24. Large stream deviations in the hydrographic network of Cluj County
Installed
Crt. Commune / Stream Length
Derivation name Stream derived Cadaster code capacity
no. locality derived into (m)
(m3/s)
1 Someșul Rece I Măguri Răcătău Someșul Rece II-1.31.9 Someșul Cald (Ac. Fântânele) 7206 17,8
2 Negruța Măguri Răcătău Pârâul Negru II-1.31.9.3 Someșul Rece (Ac. S.R. I) 4018 1
3 Dumitreasa Măguri Răcătău Dumitreasa II-1.31.9.2 Someșul Rece 1060 1,6
4 Răcătău Măguri Răcătău Răcătău II-1.31.9.4 Someșul
(Ac. S.R.I)Cald 3637 5
5 Someșul Rece II Măguri Răcătău Someșul Rece II-1.31.9 Someșul Cald
(Ac. Fântânele) 3339 10
Total drainage basin Someșul Mic (Ac. Tarnița) 19260 35.4
6 Iara (Bondureasa) Valea Ierii/Caps Iara IV-1.81.28 Someșul Cald 3970 0,97
7 Șoimu Valea Ierii / Măguri Răcătău Șoimu IV-1.81.28.2 Someșul Cald 5079 1,75
8 Calu Valea Ierii / Caps Valea Calului IV-1.81.28.3 Someșul Cald 3259 0,22
9 Lindru Valea Ierii / Caps Lindru Not codified Someșul Cald 884 1,03
Total drainage basin Arieș 13192 3.97
10 Derivation Aluniș Săcuieu Aluniș III-1.44.3.4 Iad 700 0.05
11 Penstock Dara Săcuieu Dara III-1.44.5.4 Iad 200 0.19
12 Drăgan – Remeți Lunca Vișagului Drăgan III-1.44.5 Iad 4300 40
13 Mona (Anișel – Valea cu Pești) Lunca Vișagului Drăgan III-1.44.5 Iad 3000 0.1
14 Răcad – Drăgan Săcuieu Răcad III-1.44.4.4 Iad 1000 0.27
15 Săcuieu – Drăgan Săcuieu Săcuieu (Henţ) III-1.44.4 Iad 16600 4.76
16 Valea lui Șerp Săcuieu Săcuieu (Henţ) III-1.44.4 Iad 500 0.07
17 Rujet Săcuieu Săcuieu (Henţ) III-1.44.4 Iad 600 0.04
18 Penstock Bănișor Săcuieu Vișag III-1.44.4.5 Iad 500 0.05
19 Penstock Zărnișoara Săcuieu Zârna III-1.44.5.2 Iad 500 0.11
Total drainage basin Crișul Repede 27900 45.64
TOTAL CLUJ COUNTY 60352 85.01
Source data: A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
97
Table 25. Cadastral levees in the hydrographic network of Cluj County
Position
Crt. Cadaster Length Height,
Works name Stream (left/righ river Locality Commissined
no. code (m)* average (m)*
bank
1 Levee Borșa in Răscruci Borșa II-1.31.22 MS Răscruci 1800 1 13.03.1928
2 Levee Borșa in Răscruci Borșa II-1.31.22 MD Răscruci 1800 1 13.03.1942
3 Levee Feiurdeni in Jucu de Mijloc Feiurdeni II-1.31.20 MS Jucu de Mijloc 900 2 13.03.1971
4 Levee Feiurdeni in Jucu de Mijloc Feiurdeni II-1.31.20 MD Apahida 1900 1.5 13.03.1971
5 Levee Fizeș in Mintiu Gherlii Fizeș II-1.31.28 MD Mintiu Gherlii 1650 1.5 13.03.2001
6 Levee Fizeș in Gherla Fizeș II-1.31.28 MS Gherla 2400 13.03.1980
7 Levee Pârâul Ocnei in Dej Pârâul Ocnei II-1.31.32 MS Dej 1700 2 13.03.1983
8 Levee Someș in Dej Someș II-1 MS Dej 1700 3 13.03.1981
9 Levee Someș in Cuzdrioara Someș II-1 MD Cuzdrioara 2100 3 13.03.1964
10 Levee Someș in Mica Someș II-1 MS Mica 1600 2 13.09.1964
11 Levee Someș in Cetan Someș II-1 MS Cetan 3800 0.7 13.11.2001
12 Levee Someș in Vad Someș II-1 MS Vad 700 1.5 13.08.2001
13 Levee Someș in Vad Someș II-1 MS Vad 1500 1.5 13.09.2001
14 Levee Someșul Mic in Gherla Someșul Mic II-1.31 MD Gherla 5800 3 13.10.1981
15 Levee Someșul Mic in Mintiu Gherlii Someșul Mic II-1.31 MD Mintiu Gherlii 1000 1.3 13.07.1982
16 Levee Someșul Mic near Cluj-Napoca airport Someșul Mic II-1.31 MD Cluj-Napoca 2400 2
17 Levee Someșul Mic in Hășdate Someșul Mic II-1.31 MD Hășdate 500 1.5 13.03.19
18 Levee Someșul Mic in Dej Someșul Mic II-1.31 MS Dej 300 13.09.1983
13.09.19
61
19 Levee Someșul Mic in Răscruci Someșul Mic II-1.31 MS Răscruci 1800 1.4 13.10.1960
61
20 Levee Someșul Mic in Bonțida Someșul Mic II-1.31 MS Bonțida 1640 2 01.01.2007
21 Levee Someșul Mic in Nima Someșul Mic II-1.31 MS Nima, Salatiu 5900 2.2 13.09.1965
22 Levee Someșul Mic in Mintiu Gherlii Someșul Mic II-1.31 MD Mintiu Gherlii 2400 1.5 13.10.1962
23 Levee Someșul Mic in Livada Someșul Mic II-1.31 MS Livada 1340 2 01.01.2007
TOTAL DRAINAGE BASIN SOMEȘUL MIC 46630
24 Development r. Arieș riverbank Section 8 - Viișoara Arieș IV-1.81 MD Viișoara 2100 2.5 31.12.1985
25 Development r. Arieș Section 6&7 - Poiana/Câmpia Turzii Arieș IV-1.81 MD Turda 5660 2 31.12.1987
26 Development r. Arieș Section 4 -Turda Arieș IV-1.81 MD Turda 480 2 31.12.1987
27 Development r. Arieș Section 5- Oprișani Arieș IV-1.81 MD Turda 1200 2 31.12.1987
28 Development r. Arieș Section 2 – Cement plant Arieș IV-1.81 MD Turda 720 2 28.04.1987
29 Backwater levee Câmpia Turzii Arieș IV-1.81.37a MS Câmpia Turzii 310 2 31.12.1987
30 Development r. Arieș Section 1 - Mihai Viteazu Arieș IV-1.81 MD Mihai Viteazu 5380 2.5 12.08.1988
Development r. Arieș Section 3 left riverbank - Arieș IV-1.81 MS Turda 590 2.5 31.12.1988
31 Electroceramica
32 Development r. Arieș .Section 3 right riverbank - Arieș IV-1.81 MD Turda 1000 2.5 21.04.1988
Electroceramica
98
Position
Crt. Cadaster Length Height,
Works name Stream (left/righ river Locality Commissined
no. code (m)* average (m)*
bank
Development r. Arieș. left riverbank Section 8 - Viișoara Arieș IV-1.81 MS Viișoara 810 1.5 31.12.1988
33
34 Backwater levee Câmpia Turzii Arieș IV-1.81 MD Câmpia Turzii 1250 2 31.12.1988
35 Closing levee Cheia Arieș IV-1.81 MD Mihai Viteazu 1550 2 14.07.1988
36 Retention Tureni Valea Racilor IV-1.81.34 MS Tureni 500 2 31.12.1981
LI 407 CJ - Câmpia Turzii – downstream of wastewater plant Arieș IV-1.81 MD Câmpia Turzii 1970 0 17.06.2008
37
TOTAL DRAINAGE BASIN ARIEȘ 23520
38 Bucea left riverbank Crișul Repede III-1.44 MS Bucea 200 1 22.02.1970
TOTAL DRAINAGE BASIN CRIȘUL REPEDE 200
TOTAL CLUJ COUNTY 70350
Source data: A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
Table 26. Dams which create non-permanent retention ponds in Cluj County
Crt. Dam Regulation volume
Name of dam/retention River Cadaster code Dam height (m) Owner
no. type* (mil.m3)
1 Baraj Tăul Ceanului Valea Caldă Mare IV-1.81.34.2.1 8,5 PO 4,45 A.B.A. Mureș
Source data A.B.A. Mureș
Table 27. Dams which create permanent retention ponds in Cluj County
Dam NNR NME total Regulation
Crt. Name of Dam
River Cadaster code height volume volume volume Duty** Owner
no. Dam/retention type*
(m) (mil.m3) (mil.m3) (mil.m3)
1 Fântânele Someșul Cald II-1.31 92 AM 213 250.42 37.42( HVR Hidroelectrica S.A.
2 Tarnița Someșul Cald II-1.31 97 A 70.3 77.4 7.1 A,H,V,R Hidroelectrica S.A.
3 Someșul Cald Someșul Cald II-1.31 34 G 7,47 9,53 2.07 A,H,V,R Hidroelectrica S.A.
4 Someș Rece I Someșul Rece II-1.31.9 43.5 A 0.73 1.03 0.3 H Hidroelectrica S.A.
5 Florești II Someșul Mic II-1.31 13 G+AM 0.89 1.87 0.98 H Hidroelectrica S.A.
6 Gilău Someșul Mic II-1.31 23 G+AM 2.44 3525 1.085( A,H, A.B.A. Someș - Tisa
7 Mănăștur Someșul Mic II-1.31 6.42 SBB 0.01 0.01 0 H A.B.A. Someș - Tisa
99
Dam NNR NME total Regulation
Crt. Name of Dam
River Cadaster code height volume volume volume Duty** Owner
no. Dam/retention type*
(m) (mil.m3) (mil.m3) (mil.m3)
8 Aruncuta Suatu II-1.31.23.1 3.33 PM 0.110 0.241 0.131 P S.C. AQUA
9 Mica Someș Mare II-1 3 SBB 0.75 0.75 0 A S.C. M.H.P.P. Energy Someș
FISHProduction
10 Berchieșu Suatu II-1.31.23.1 3.21 PM 0.15 0.33 0.18 P S.C. AQUA
S.R.L. Brașov
11 Câmpenești Feiurdeni II-1.31.20 8.5 PM 1.65 3.2 1.55 P,A Prim ăria Apahida
FISHProduction
12 Cătina V. Fizeș II-1.31.28 4.00 PM 0.86 2.36 1.5 P S.C. GRPL -S.C.
S.R.L.
13 Chiejd I V. Chiejd II-1.31.32.1 3.00 PO 0.012 0.020 0.008 A S.C. Metalispas Dej
Piscicola
14 Chiejd II V. Chiejd II-1.31.32.1 2.3 PO 0.01 0.018 0.008 A,P Dragoș Ionel
15 Chiejd III V. Chiejd II-1.31.32.1 1.70 PO 0.004 0.006 0.002 A,P Ursu Ștefan Târnovan
16 Chinteni V. Chinteni II-1.31.15 2 PA 0.112 0.243 0.131 A,P Primăria Chinteni
17 Geaca I V. Fizeș II-1.31.28 2.25 PM 0.37 0.59 0.22 P S.C. GRPL Piscicola
18 Geaca II V. Fizeș II-1.31.28 3;2.30 PM 0.27 0.52 0.25 P S.C. GRPL Piscicola
19 Geaca III V. Fizeș II-1.31.28 3.90 PM 0.23 0.43 0.20 P S.C. CIM Service SPED S.R.L. Cluj
20 Roșieni V. Fizeș II-1.31.28 4,70 PM 0.24 0.53 0.29 P S.C. GEMATO Prod S.R.L.
21 Sfântu Florian V. Fizeș II-1.31.28 2.4 PO 0.45 0.45 0 P Fed. Nat. a Pompierilor
22 Năsal V. Suciuaș II-1.31.28.7 4.78 PM 0.315 0.546 0.231 P S.C. ACVA MC
23 Sântejude V. Sicu II-1.31.28.8 3.40 PM 0.48 1.31 0.83 P S.C.
NasalGRPL Piscicola
S.R.L.
24 Sântejude II Borzaș V. Sicu II-1.31.28.8 3.50 PM 0.49 1.59 1.1 P S.C. Dermatin Construct
25 Sucutard I V. Fizeș II-1.31.28 2.80 PM 0.44 0.96 0.52 P S.C. CIM Service SPED S.R.L. Cluj
S.R.L.
26 Sucutard II V. Fizeș II-1.31.28 2.50 PM 0.57 1.06 0.49 P S.C. GRPL Piscicola
27 Țaga Mare V. Fizeș II-1.31.28 3.90 PM 1.31 3.54 2.23 P S.C. CIM Service SPED S.R.L. Cluj
28 Țaga Mică V. Fizeș II-1.31.28 3.50 PM 0.2 0.33 0.13 P S.C. GRPL Piscicola
29 Tăul Popii V. Fizeș II-1.31.28 2.60 PM 0.57 1.03 0.46 P S.C. GRPL Piscicola
30 Mânăstirea Someș Mic II-1.31 4.5 SBB 0.5 0.5 0 H S.C. Three Pharm S.R.L.Tg.Mureș
TOTAL DRAINAGE BASIN SOMEȘUL MIC 297.463
31 Rediu Pr. Mărtinești IV-1.81.34.1 14.7 PO 0.2 2.45 2.25 V,P A.B.A. Mureș
32 Tureni Pr. Racilor IV-1.81.34 14.5 PO 0.27 10.5 9.78 V,P A.B.A. Mureș
33 Fâneața Vacilor Fâneața Vacilor IV-1.81.34.2 17 PO 0.45 8.32 7.87 V,P A.B.A. Mureș
34 Mărtinești pr. Mărtinești IV-1.81.34.1 5 PM 0.67 P ANPA
35 Fâneața Vacilor Fâneața Vacilor IV-1.81.34.2 6 PM 1.2 P ANPA
36 Beclean Valea Caldă IV-1.81.34.2.1 5 PM 0.61 P ANPA
37 Pădureni Pr.
MareHășdate IV-1.81.31 5 PM 0.507 P Piscicola Cluj
38 Șutu Pr. Hășdate IV-1.81.31 5 PM 0.2 P Piscicola Cluj
39 Filea Pr. Hășdate IV-1.81.31 5 PM 0.283 P Piscicola Cluj
40 Micești Pr. Micuş IV-1.81.31.4 8.5 PM 0.042 0.087 P,R Întrepr. Indiv. Bodea Valer
41 Stejăriș Pr. Unirea IV-1.85 2.15 PM 0.113 P,R C.L. Moldovenești
42 Bădeni Pr. Unirea IV-1.85 4.43 PM 0.096 P,R C.L. Moldovenești
43 Valea Grindului (aval) Pr. Grindu Necodificat 5.5 PM 0.042 0.085 P,R KSA Teocrista SRL Câmpia Turzii
100
Dam NNR NME total Regulation
Crt. Name of Dam
River Cadaster code height volume volume volume Duty** Owner
no. Dam/retention type*
(m) (mil.m3) (mil.m3) (mil.m3)
44 Valea Grindului II Pr. Grindu Necodificat 5.6 PM 0.05 0.076 P,R KSA Teocrista SRL Câmpia Turzii
45 Valea Grindului III Pr. Grindu Necodificat 5.4 PM 0.088 0.135 P,R KSA Teocrista SRL Câmpia Turzii
TOTAL DRAINAGE
(Amonte) BASIN ARIEȘ 4.821
46 Drăgan Drăgan III-1.44.5 120 A 112 127.05 15.1 H Hidroelectrica S.A.
47 Săcuieu Săcuieu III-1.44.4 20.5 PM-SS 0.600 0.910 0.31 H Hidroelectrica S.A.
TOTAL DRAINAGE BASIN CRIȘUL REPEDE 112.600
TOTAL CLUJ COUNTY 414.884
Source data: A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
Dam type*
A Arch concrete dam (or arch-gravity dam) ** Duty
AM Rockfill dam sealed with upstream mask V – flood control
G Concrete gravity dam P - fish farming
C Concrete abutment dam A – water supply
PO Homogenous earthen dam I - irigation
PA Earthen dam sealed with clay (fine earth) R - pleasure (recreation)
PM Earthen dam sealed with upstream mask or pitching H - hydropower
AA Rockfill dam sealed with clay X - other usages which do not fall under the above mentioned
SS Barrier with surface barriers types
SBB Barrier with concrete closing dam
SBML Barrier with closing dam or local material cut-out
101
Significant works can also be found along some tributaries of Someșul Mic river: Borșa (in Răscruci),
Feiurdeni valley (in Jucu de Mijloc) and Fizeșului valley (Mintiu Gherlii and Gherla). The total length of the
levees located in the drainage basin of Someșul river is 46.63 km (Table 25).
In the drainage basin of Arieș river, flood relief works are located almost exclusively along the main river
course, within the localities Mihai Viteazu, Turda, Câmpia Turzii and Viișoara. One exception is the course
of Văii Racilor which has a levee within Tureni commune. The total length of the levees located in the
drainage basin of Arieș river is 23,52 km.
In the drainage basin of Crișul Repede river pertaining to Cluj county there is one single levee section (200
m), built on the left river bank of the main river course, within Bucea locality.
In Cluj county, according with the data provided by the water basin administrations, there is only one non-
permanent water retention facility which is located on Valea Caldă Mare river, known as Tăul Ceanului
(Table 26).
Much better represented are permanent water retention facilities, 47 in number in Cluj county, which
have complex functions ranging between hydropower generation, flood control, fish farming, irrigation,
water supply (Table 27). The most numerous are located in the drainage basin of Someș river (30 retention
facilities), followed by the ones in the drainage basin of Arieș river (15 retention facilities) and the ones in
the drainage basin of Crișul Repede river (2 retention facilities). The detailed analysis of the ones which
also ensure the generation of hydropower shall be conducted in the chapter dedicated to hydrotechnical
developments. In Transilvaniei Plain, retention facilities located on Fizeșului valley and its tributaries are
mainly meant to serve for fish farming purposes, but additionally, they also ensure the protection of the
human settlements against floods. In the drainage basin of Arieș river, permanent retention facilities are
located on the left tributaries of the main stream: Valea Hășdate, Valea Racilor with Mărtinești creek,
Valea Caldă and Fâneața Vacilor. Furthermore, some retention facilities are located on two tributaries
which flow directly into the Mureș river: Unirea creek and Grindu creek. All retention facilities in this
drainage basin have fish farming function. In the drainage basin of Crișul Repede river there are only two
permanent retention facilities: Drăgan and Săcuieu, both with the main function the generation of
hydropower.
For the efficient management of critical situations caused by the occurrence of flash-floods and floods in
the drainage basins in Cluj county water basin administrations have the obligation to closely monitor the
development of the hydro-dynamic phenomena mentioned, with the purpose to issue warnings and
hydrologic alerts. In this respect, hydrometric stations determine the levels of flood emergency activity
which are associated with reaching some critical thresholds during hazardous occurrences on streams
(Table 28). The exceedance of these levels compels the competent authorities to pass on this information
to decision makers in the field of emergency response in order to take the measures which are necessary
for the protection of human life and property in potentially affected communities.
Based on the information provided by the water basin administrations Someș-Tisa, Mureș and Crișuri in
the County plan for protection against floods, ice, drought, accidents at hydrotechnical facilities and
accidental pollution please find further below the list of administrative subdivisions affected by floods in
Cluj county (Table 29).
102
Table 28. Emergency levels and discharges at hydrometric stations in Cluj county
Discharges Historic
Crt. Hydrometric Ground Levels (cm) Historic levels
Stream (m3/s) discharges
no. station "0" Mira
AL FL DL AL FL DL cm data m3/s
Someș drainage basin
1 Dej Someș 227,13 450 550 620 582 860 1126 808 13.V.1970 2300
Someșul
1002,29 254 11.II.1977 108
2 Smida Cald 100 150 200 51.8 108 212
3 Cluj-Napoca Someșul Mic 347,00 200 280 320 177 313 396 320 11.VI.1970 274
4 Apahida Someșul Mic 298,44 110 150 200 71.5 136 251 244 1.IV.1962 305
5 Salatiu Someșul Mic 1002,29 200 300 400 102 235 982 350 4.VII.1975 492
6 Poiana Horea Beliș 1008,62 80 120 150 19.7 40.6 62.2 113 27.XII.1995 36.1
Someșul
1005.94 150 180 220
7 Măguri Rece
Someșul
200 27.XII.1995 98.0
8 Someș Rece Sat Rece 429.12 130 180 200 51 84 97.5
9 Răcătău Răcătău 662,39 180 200 250 38 48 85.8 192 3.VII.1975 63.2
10 Căpușul Mare Căpuș 437,92 280 320 370 35.3 48 65.5 420 19.VI.1999 163
11 Aghireșu Nadăș 440,46 100 200 300 1.35 9.26 25 370 11.VI.1970 73.6
12 Rădaia Nadăș 367,52 370 420 480 602 11.VII.1999 130
13 Borșa Borșa 302.39 200 300 340 14.8 36 54 367 08.V.1989 138
14 Bonțida Gădălin 274,71 300 350 400 18.3 33.5 54 400 14.II.1978 34.2
15 Luna De Jos Lonea 283,84 270 320 370 20.5 33.5 58 408 10.III.2000 180
16 Fizeșu Gherlii Fizeș 260,84 350 400 450 57.6 82.6 165 413 19.VI.1998 110
17 Maia Olpret 260,19 350 450 500 17 39.2 55.4 580 28.VII.1980 131
18 Cășeiu Sălătruc 233,01 320 370 400 71.5 103 124 448 22.III.1964 149
Crișul Repede drainage basin
Crișul
19 Ciucea Repede 430.52 100 150 200 79.3 157 239 236
20 Călata Călata 602.62 200 240 300 63.5 96.1 154 338
21 Morlaca Carieră Călata 508.42 225 325 350 37 84.2 111 290
Săcuieu/Hen
22 Răchițele ț 796.98 100 150 200 13.1 29.6 52.5 150
23 Morlaca Henț Henț 548.58 125 175 225 41.5 72.5 125 226
Valea
24 Drăganului Drăgan 542.08 150 220 250 50 157 226 265
25 Vânători Poicu 463.66 125 200 225 14.5 38 61.3 220
Arieș drainage basin
26 Buru Arieș 363.85 250 350 450 288 485 764 465 12.III.1981 822
27 Turda Arieș 315.22 300 350 500 300 385 801 617 03.VII.1975 950
28 Valea Ierii Iara 756.31 120 200 250 10 44 87 86
29 Iara Iara 454.94 170 250 300 39.1 87 159 12.VII.2005 182
30 Petreștii de Jos Hășdate 460.27 200 250 300 17 45,7 134 64.8
31 Viișoara Valea Largă 295.66 225 275 330 14,5 38.5 144 16.V.1996 27.1
Source data: A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
AL – attention level; FL – flood level; DL – danger level.
103
Table 29. Overview on localities affected by floods in Cluj county
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
SOMEȘ DRAINAGE BASIN
Torrents, water running
R.Someșul Mare - II.1. Levee left river bank L= 1.5 km r.Someșul Mare
downhills
Torrents, water running
1 MICA R.Someșul Mic II.1.31
downhills
Torrents, water running
V. Bandău II.1.31.33
downhills
Torrents, water running
R.Someșul Mare II.1. Dam right river bank L=1.8 km r.Someșul Mare
downhills
2. CUZDRIOARA
Torrents, water running
V. Gârboul Dejului II.1.30
downhills
Torrents, water running
R.Someș II.1 Levee left river bank r.Someș L=1,745 m; B=14 m; b=4 m; h=3.5 m
downhills
Torrents, water running
P. Ocnei II.1.31.32 Levee left river bank L = 2,000 m, P. Ocnei
downhills
3. DEJ
Torrents, water running
P. Ocnei II.1.31.32 River bed development L=2,100 m r. Someșul Mare II.1
downhills
Torrents, water running Concrete supporting wall development on both river banks L = 2,200 m,
V. Salca II-I.32.
downhills V.Salca
Torrents, water running
R.Someș II.1 -
downhills
4. CĂȘEIU
Torrents, water running
V. Sălătruc II-1.34
downhills
Torrents, water running Dam left river bank r. Somes L = 5,900m Levee in Cetan L=3,800 m,
R.Someș II-1
downhills Levee in Vad L= 2,100 m
Torrents, water running
R Someș II-1 River bed development r.Somes L=1,030 m
downhills
5. VAD
Torrents, water running
R.Someș II-1 River bed development r. Somes L= 600 m
downhills
Torrents, water running
R.Vad II-1.35 River bank embankment Vad L = 1,860 m
downhills
104
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
R.Someș II-1 River bed development r. Somes L=1600 m
downhills
6. CÂŢCĂU
Torrents, water running
V.Muncelului II-1.35.a.
downhills
Torrents, water running
R.Someș Cald II-1.31. Retention Fântânele V = 220 mil.m³ Vat=37.5 mil m³ r. Somes Cald
downhills
7. BELIŞ
Torrents, water running
V.Beliş II-1.31.5
downhills
Torrents, water running
8. MĂRIŞEL R.Someșul Cald II-1.31 Retention Tarnita V = 70.3mil.m³ Vat=8mil m³ r. Somes Cald
downhills
Torrents, water running
R.Someșul Cald II-1.31. Retention Tarnita V = 70.3mil.m³ Vat=8mil m³ r. Somes Cald
downhills
9. RÂŞCA
Torrents, water running
V. Risca Mare II.1.31.9.5. River bed development, Rasca Mare L=900m
downhills
Torrents, water running
R.Someșul Mic II-1.31. Retention Tarnita V = 70.3mil.m³ Vat=8mil m³ r. Somes Cald
downhills
Torrents, water running
R.Someșul Mic II-1.31. Retention Gilău V = 4.2 mil. m³ r. Somes Mic
downhills
Torrents, water running
10. GILĂU V. Agârbiciu II-1.31.2
downhills
Torrents, water running River bed development L = 2,000 m River bank embankment L = 3,717
V. Căpuş II-1.31.10
downhills m r.Capus
Torrents, water running
R.Someș Rece II-1.31.9 River bank embankment L = 1,000 m r. Somes Rece
downhills
Torrents, water running
R.Someșul Mic II.1.31
downhills
Torrents, water running
V.Feneş II-1.31.11
downhills
Torrents, water running
11. FLOREŞTI V. Sărată II-1.31.NC
downhills
Torrents, water running
V.Pe Vale II.1.31.12 River bed development L=2,039m Pe Vale
downhills
Torrents, water running
V. Bosorului II-1.31.NC
downhills
105
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
V.Ciurgau River bed development L=2,039m V.Ciurgau
downhills
Torrents, water running
V.Sanaslau River bed development L=460m V. Sanaslau
downhills
River bed development L = 13.18 km River bank embankment L = 6.2
Torrents, water running
R.Someșul Mic II.1.31 km
downhills
r. Somes Rece
River bed development L = 7,200 m River bank embankment L = 8,800
Torrents, water running
Canalul Morii m
downhills
Canalul Morii
Torrents, water running
P. Ţiganilor I Development L = 1,400 m P.Tiganilor I
downhills
Torrents, water running
P. Ţiganilor II Development L = 1,744 m Tiganilor II
downhills
Torrents, water running
V. Calvaria II-1.31.NC River bed development, sloping and channeling L = 2,000 m
12. CLUJ-NAPOCA downhills
Torrents, water running River bed development L = 5,700 m River bank embankment L = 5,700
V. Nadăş II-1.31.14.
downhills m Nadas
Torrents, water running
V. Chintenilor II-1.31.15. River bed development L = 1 km V.Chintenilor
downhills
Torrents, water running
V. Becaş II-1.31.16. River bed development L = 2,500 m Becas
downhills
River bed development L = 1,600 m Supporting wall left river bank L =
Torrents, water running
V. Popeşti II.1.31.16.1. 60 m
downhills
River bed development L = 150 m Popesti
Torrents, water running
V. Zăpodie II.1.31.17. Development and sloping with concrete plates L = 7,000 m Zapodiei
downhills
Torrents, water running
R.Someș Mic II.1.31 River bed development L=2,350 m r. Somes Mic
downhills
Torrents, water running
V. Feiurdeni II-1.31.20. Dam right river bank L=1,900 m V.Feiurdeni
downhills
13. APAHIDA
Torrents, water running
V. Feiurdeni II-1.31.20. Fish farming Campenesti L=310m, H=8m. B=5m; V=1.3mil.m³
downhills
Torrents, water running
V. Mărăloiu II-1.31.19. River bed development L=6,700 m V.Maraloiu
downhills
106
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
V. Caldă II-1.31.18. River bed development L=6,700 m V.Calda
downhills
Torrents, water running
V. Feiurdeni II-1.31.20 River bed development L = 2,700 m V.Feiurdeni
downhills
Torrents, water running
R.Someș Mic II.1.31
downhills
Torrents, water running
V. Feiurdeni II-1.31.20. Dam left river bank L=900 m v. Feiurdeni
downhills
Torrents, water running
14. JUCU V. Prodaie II-1.31.21.
downhills
Torrents, water running
V. Gădălin II-1.31.23. River bed development L = 12.500 m V.Gadalin
downhills
Torrents, water running
V. Tocbeşti II.1.31.234.
downhills
Torrents, water running
R.Someș Mic II.1.31 Earthen levee left river bank L = 1,800 m, r. Somes Mic
downhills
Torrents, water running Grass covered earthen levee on both river banks L = 1,800 m; B = 10 m;
15. BONŢIDA V. Borşa II.1.31.22
downhills b = 2 m; h = 2 m, Borsa
Torrents, water running
V. Gădălin II-1.31.23 River bed development L = 3,000 m, V Gadalin
downhills
Torrents, water running
R.Someș Mic II.1.31 River bed development L = 1,700 m Somes Mic
downhills
Torrents, water running
V. Lonea II.1.31.24.
downhills
Torrents, water running
16. ICLOD V. Mărului II.1.31.26. River bed development L = 700 m V.Marului
downhills
Torrents, water running
V. Lujerdiu II-1.31.25. River bed development L =12,000 m V.Lujerdiu
downhills
Torrents, water running
V. Orman II-1.31.27.
downhills
Torrents, water running Complex flood control works City of Gherla. Levee right river bank L =
R.Someș Mic II.1.31
downhills 5,800 m; B = 12 m; b = 4 m; H = 2.5-3 m r.Somes Mic
17. GHERLA
Torrents, water running
V. Băiţa II.1.31.NC
downhills
107
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
V. Fizeş II.1.31.28 Grass covered earthen levee left river bank L = 2,400 m. v.Fizes
downhills
Torrents, water running
V. Fizeş II.1.31.28 Grass covered earthen levee right river bank L = 1,650 m, Fizes
downhills
Torrents, water running
V. Fizeş II.1.31.28 River bed development downstream confl Hosu L=3,040 m, Fizes
downhills
Torrents, water running Earthen levee left river bank L = 6,000 m, B = 22 m, b = 3 m Levee right
R.Someș Mic II.1.31
downhills river bank area Salatiu – earthen levee L = 2,400 m r.Somes Mic
Torrents, water running
V. Fizeş II.1.31.28 Grass covered earthen levee right river bank L = 1,650 m, Fizes
downhills
Torrents, water running
18. MINTIU GHERLEI V. Buneşti II.1.31.29. Compartment levee Bunesti L=1,200m
downhills
Torrents, water running
V. Nima II.1.31.30. Compartment levee Nima L=900m
downhills
Torrents, water running
V. Mintiului
downhills
Torrents, water running River bed reshaping L = 1,500 m Gabion embankment, left river bank L =
R.Someșul Rece II-1.31.9.
downhills 650 m Supporting wall embankment L = 800 m, r. Somes Rece
19. MĂGURI RĂCĂTĂU
Torrents, water running Embankment L = 50 m S.G.A. gabion type Gabion embankment L = 500
V.Răcătău II.1.31.10.
downhills m, Racatau
Torrents, water running
20. MĂNĂSTIRENI V. Căpuş II.1.31.10.
downhills
Torrents, water running River bed reshaping in Capusu Mare L=1,400 m, River bed development
21. CĂPUŞU MARE V. Căpuş II.1.31.10.
downhills L=2,475 m, gabion embankments L=1,250 m, r.Capus
Torrents, water running
V. Feneş II-1.31.11 River bed reshaping L=1,000 m, Fenes
downhills
Torrents, water running
P. Racoş II-1.31.11.1.
downhills
22. SĂVĂDISLA
Torrents, water running
P. Stolna II-1.31.11.2. River bed reshaping L=150 m, Stolna
downhills
Torrents, water running
P. Hăjdate II-1.31.NC
downhills
Torrents, water running
23. AGHIREŞU P. Nadăş II-1.31.14. River bed development la Aghires L=4,215 m, Nadas
downhills
108
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
P. Leghia II-1.31.141. River bed development L=5,000 m, Leghia
downhills
Torrents, water running
P. Inuc II-1.31.142. River bed development L=5,000 m, Inuc
downhills
Torrents, water running
V. Măcău II-1.31.143. River bed development L=2,000 m, Macau
downhills
Torrents, water running
P. Nadăş II-1.31.14.
downhills
Torrents, water running
Valea Mare II-1.31.14.5
downhills
24. GÂRBĂU
Torrents, water running
P. Şomtelec II-1.31.14.4. River bed development L=7,000 m, Somtelec
downhills
Torrents, water running
V. Gârbăului II-1.31.14.NC
downhills
Torrents, water running
P.Valea Mare II-1.31.14.5 River bed development L = 12,328 m, Valea mare
downhills
Torrents, water running
25. SÂNPAUL V.Topa Mică II-1.31.14.5.1. River bed development L =3,072 m, Topa Mica
downhills
Torrents, water running
V.Sălişte II-1.31.14.9.2.
downhills
Torrents, water running
V.Nadăş II-1.31.14. Development - River bed reshaping L = 1,500 m, r. Nadas
downhills
Torrents, water running
26. BACIU V.Popeşti II-1.31.14.6.
downhills
Torrents, water running
V. Suceag II-1.31.14.5b
downhills
Torrents, water running
27. CHINTENI P. Chintenilor II-1.31.15. Creation of fish ponds and recreational ponds p.Chintenilor
downhills
Torrents, water running
V.Borşa II-1.31.22. River bed development L=13,230 m, r. Borsa
downhills
Torrents, water running
28. AŞCHILEU MARE V.Cristorel II-1.31.22.1. River bed development L=6,930 m, v.Cristorel
downhills
Torrents, water running
V. Dorna II-1.31.NC
downhills
109
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
V. Fodora II-1.31.NC
downhills
Torrents, water running
29. VULTURENI V. Borşa II-1.31.22. River bed development L=5,960 m, Borsa
downhills
Torrents, water running
30. BORŞA V. Borşa II-1.31.22.
downhills
Torrents, water running
V. Gădălin II-1.31.32. River bed reshaping L = 5,500 m, r.Gadalin
downhills
31. COJOCNA
Torrents, water running
V. Cojocna II-1.31.23.2 River bed reshaping L = 7,700 m, v. Cojocna
downhills
Torrents, water running
32. SUATU V. Suatu II-1.31.23.1. Fish farms, Suatu
downhills
Torrents, water running
33. CĂIANU V. Gădălin II-1.31.23. River bed reshaping L = 8,000 m, v.Gadalin
downhills
Torrents, water running
34. RECEA CRISTUR V. Lonea II-1.31.24.
downhills
V. Gomboşoaiei II- Torrents, water running
35. PANTICEU
1.31.24.1. downhills
Torrents, water running
36. DĂBÂCA V. Lonea II-1.31.24. River bed development L=12,000 m, v.Lonea
downhills
Torrents, water running
37. CORNEŞTI V. Lujerdiu II-1.31.25. River bed development L=14,030 m, Lujerdiu
downhills
Torrents, water running
V. Mărului II-1.31.26. River bed development L=12,520 m v.Marului
downhills
38. ALUNIŞ
Torrents, water running
P. Ghirolt II-1.31.26.2
downhills
Torrents, water running
39. MOCIU P. Mociu II-1.31.28.4. River bed development L=783 m, v.Mociu
downhills
Torrents, water running
40. CĂMĂRAŞU V. Fizeş II-1.31.28.
downhills
Torrents, water running
V. Fizeş II-1.31.28. Creation of fish ponds, r.Fizes
downhills
41. CĂTINA
Torrents, water running
V. Cătina II-1.31.28.3.
downhills
110
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
42. PALATCA P. Chiriş II-1.31.28.5.
downhills
Torrents, water running
43. GEACA V. Fizeş II-1.31.28. Creation of fish ponds Fizes
downhills
Torrents, water running
V. Fizeş II-1.31.28. Fish farms Fizes
downhills
Torrents, water running
V. Sicu II-1.31.28.8. River bed development L=5,400 m, Sicu
downhills
44. ȚAGA
Torrents, water running
V. Sicu II-1.31.28.8. Fish farms, Sicu
downhills
Torrents, water running
V. Sântejude II-1.31.28.8.1. Fish farms, Santejude
downhills
Torrents, water running
V. Suciuaş II-1.31.28.9.
downhills
45. BUZA
Torrents, water running
V. Morii II-1.25.1.1.
downhills
V. Diviciorii Mari II- Torrents, water running
River bed development L=3,180 m Diviciorii Mari
1.31.28.10. downhills
46. SÂNTMĂRTIN
V. Sântmărtin II- Torrents, water running
1.31.28.10.1. downhills
Torrents, water running
47. SIC
downhills
Torrents, water running
V. Hosu II-1.31.28.11. River bed development L=6,800 m, Hosu
downhills
48. FIZEŞU GHERLII
Torrents, water running
V. Fizeş II-1.31.28.
downhills
Torrents, water running
49. UNGURAŞ V. Bandău II-1.31.33.
downhills
Torrents, water running
V. Codor II-1.32.
downhills
50. JICHIŞU DE JOS
Torrents, water running
V. Jichiş II.1.32.1 River bed development L=150 m, Jichis
downhills
Torrents, water running
51. BOBÂLNA V. Olpret II-1.33. River bed development L=300 m, Olpret
downhills
111
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
V. Sălătruc II-1.34.
downhills
52. CHIUIEŞTI
Torrents, water running
V. Strâmbu II-1.34.1.
downhills
CRIȘUL REPEDE DRAINAGE BASIN
Torrents, water running
53. IZVORUL CRIŞULUI R.Crişu Repede III.1.44.
downhills
Torrents, water running
R. Crişul Repede III.1.44.
downhills
Development and relief works L = 5 km, Crisul Repede
Torrents, water running
54. HUEDIN V. Domoş III.1.44-2
downhills
Torrents, water running
V. Spitalului River bed reshaping L = 0,5 km, Spitalului
downhills
Torrents, water running
R. Crişu Repede III.1.44.
downhills
Torrents, water running
55. POIENI V. Călata III.1.44.3.
downhills
V. Drăganului Torrents, water running Retention Drăgan - Floroiu L=424m, H=12om. V=112 mil.m³
V. Draganului III.1.44.5 downhills Vat.=12mil.m³
Torrents, water running
R. Crişul Repede III.1.44.
downhills
56. NEGRENI
Torrents, water running
V. Negreni
downhills
Torrents, water running
R. Crişul Repede III.1.44.
downhills
V. Poicu
57. CIUCEA Torrents, water running
V. Făgădinului III.1.44.NC
downhills
Torrents, water running
V. Negrii
downhills
Torrents, water running
58. CĂLĂŢELE V. Călata III.1.44.3. Development V. Călata L = 1,460 m
downhills
Torrents, water running
59. SÂNCRAIU V. Călata III.1.44.3.
downhills
112
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
V. Aluniş III.1.44.NC
downhills
Torrents, water running
V. Domos III.1.44.2
downhills
Torrents, water running
V. Henţ III.1.44.4
downhills
60. MĂRGĂU
Torrents, water running
V. Mărgăuţa III.1.44.3.
downhills
Torrents, water running
61. SĂCUEU V. Henţ III.1.44.4
downhills
ARIEȘ DRAINAGE BASIN
Torrents, water running
R. Arieş IV.1.81.
downhills
Torrents, water running
62. IARA V. Iara IV.1.81.28
downhills
Torrents, water running
V. Ocolişel IV.1.81.27
downhills
Torrents, water running
R. Arieş IV.1.81.
downhills
Torrents, water running
V. Văleni IV.1.81.30
downhills
Torrents, water running
63. MOLDOVENEŞTI V. Plăieşti IV.1.81.33
downhills
Torrents, water running
V. Longes IV.1.85.
downhills
Torrents, water running
V. Stejeriş
downhills
Torrents, water running
R. Arieş IV.1.81. Levee right river bank
downhills
Torrents, water running
V. Hăjdate IV.1.81.31
downhills
64. MIHAI VITEAZU
Torrents, water running
V. Plăieşti IV.1.81.33
downhills
Torrents, water running
V. Bădeni IV.1.81.33.
downhills
113
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
R. Arieş IV.1.81. Earthen levee right river bank L= 6,600 m
downhills
Torrents, water running
65. TURDA V. Racilor IV.1.81.34 Development V. Racilor L = 2,420 m
downhills
V. Fâneaţa Vacilor Torrents, water running Retention Fâneaţa Vacilor earthen dam H=18.2m, Lc=346 m, lc=4 m,
IV.1.81.31.2.1. downhills Vmax=8,43 mil.m³, Vp=0,430 mil.m³
Torrents, water running
R. Arieş IV.1.81. - Grass covered earthen levee right river bank L = 5,780 m
66. CÂMPIA TURZII downhills
Earthen levee left river bank L = 320 m, r.Aries
R. Arieş IV.1.81. - Levee right river bank L=1,650 m; B=7 m; b=3,5 m; h=2 m
Torrents, water running
Grass covered levee left river bank L=580 m; B=7 m; b=2.5 m; h=2 m,
downhills
r.Aries
Torrents, water running
V. Largă IV.1.81.37
67. VIIŞOARA downhills
Torrents, water running
p. Tritu IV.1.81.27.1
downhills
Torrents, water running
V. Lată IV.1.81.37.2
downhills
Torrents, water running
68. LUNA R. Arieş IV.1.81.
downhills
Torrents, water running
69. CĂLĂRAŞI P. Crindu
downhills
Torrents, water running
V. Iara IV.1.81.28 River bed development L = 2.5 km, Iara (including gabions L - 600 m)
downhills
Torrents, water running
70. VALEA IERII P. Şoimu IV.1.31.28.2.
downhills
Torrents, water running
V. Calului IV.1.81.28.3.
downhills
Torrents, water running
V. Iara IV.1.81.28
downhills
71. BĂIŞOARA
Torrents, water running
p. Ierţa IV.1.81.27.7.
downhills
P. Hăjdate IV.1.81.31 Torrents, water running Fish farm Ciurila (ponds: Filea, Sutu, Padureni) with Vtot=1.4 thousand
72. CIURILA
downhills m³
114
Location and causes
Crt. no. Locality affected by floods Name of relief works
River name and code Flooding causes
Torrents, water running
P. Hăjdate IV.1.81.31
downhills
Torrents, water running
73. PETREŞTII DE JOS P. Micuş IV.1.81.31.4.
downhills
Torrents, water running
V. Negoteasă IV.1.81.35.
downhills
Torrents, water running
74. FELEACU P. Racilor IV.1.81.34.
downhills
V. Racilor IV.1.81.34. Non-perm. retention Tureni Earthen gravity dam;
Torrents, water running
H=17 m; Va=8.77 mil. m³; Lc=440 m; lc=5 m, Vmax=10.4 mil.m³,
downhills
Vp=0.175 mil.m³, V.Racilor
75. TURENI Fish farm Fermele Martinesti, Finata Vacilor, Beclean with Vtot= 2,204
thousand m³
Torrents, water running Non-perm. retention Rediu. Earthen gravity dam H=15.5m, Lc=249m,
V. Mărtineşti IV.1.81.34.1.
downhills lc=5 m, Vmax=2.52 mil.m³, Vp=0.250 mil.m³, Martinesti
Torrents, water running
76. SĂNDULEŞTI P. Racilor IV.1.81.34.
downhills
Torrents, water running
77. AITON V. Caldă Mare IV.1.81.34.2.
downhills
Torrents, water running
78. PLOSCOS P. Florilor IV.1.81.36.
downhills
Torrents, water running
V. Morii IV.1.78.4.
downhills
79. FRATA
Torrents, water running
V. Largă IV. 1.81.37.
downhills
Torrents, water running
80. CEANU MARE V. Largă IV. 1.81.37.
downhills
Torrents, water running
81. TRITENII DE JOS V. Tritu IV.1.81.37.1.
downhills
Source data: A.B.A. Someș-Tisa, A.B.A. Mureș and A.B.A. Crișuri
115
2.2.3. Winter occurrences on streams
Winter occurrences on streams are caused by a series of factors associated to the water environment,
but also by negative air temperatures during a longer period of time.
Given the complex way in which ice forms occur, certain factors have a larger contribution to the
formation and development of ice on rivers. The following factors have to be mentioned in this
respect: air temperature, water flow velocity, liquid discharge, degree of mineralization, extent of
underground supply, discharge of water resulted from various usages and its characteristics. The
analysis of these occurrences refers mainly to the determination of the frequency of formation of ice
at river banks, as well as of ice floes, date of appearance and disappearance of these ice forms,
chronological assessments of ice bridges. In Cluj county the appearance and development of winter
occurrences on streams correlates with the development of the thermal regime of air in the cold
season. Given their relatively small size, most of the streams in the county are characterized in winter
time by the presence of ice forms, with periods which differ in terms of interval and altitude.
The higher frequency of these occurrences is reported in the mountainous areas of the county,
especially on smaller streams, with small depths and low discharges. At the same time, such
phenomena can also occur on sectors of major streams, especially on sectors with mild slopes which
allow for fast formation of ice (Arieș river when it leaves the mountainous area behind). The
phenomena also affect lakes which takes the form of thick, compact ice blocks which can be used as
natural skating rinks (Lake Chios – Cluj-Napoca).
2.2.4. Excessive humidity
Excessive humidity is a water hazard looked at in the chapter dedicated to land development, also
referring to draining, irrigation, land erosion control.
2.2.5. River depletion
River depletion takes the shape of the total disappearance of water from the stream bed, following
small water levels and drought. The spatial-geographical extension of river depletion is analyzed based
on various elements: number and drainage basin distribution of rivers affected by this phenomenon;
elevation levels and development of the phenomenon on the respective rivers; units and sub-units
affected and forms of flow cessation.
From a hydrologic point of view any situation in which the discharge of a river become null is relevant
as it manifests itself as situations in which the river ceases to flow. There are three individual cases of
manifestation in this respect: ponding of water in the river bed (the transition to full depletion),
depletion caused by high temperatures, lack of precipitations and groundwater depletion, full frost
(up to the bottom of the river bed), caused by negative temperatures.
In-depth knowledge of depletion has a special importance for the management of water resources in
scarce water times, as well as for the forecast of the volumes available to various users. It can be
obtained in two ways: hydrologic inquiries on-site and systematic hydrometric monitoring.
In Cluj county, the most vulnerable area in terms of river depletion is associated to the drainage basin
of lower Arieș river. Here the most depletion vulnerable rivers are the rivers in the plains: Valea Largă,
Valea Lată, Tritul, Racoşa, Valea Florilor, Cheița, Livada, but also those in the terrace area, where Arieș
river leaves the mountainous area behind: Plăieşti, Bădeni. The phenomenon does not occur in a
uniform manner, there are differences in terms of frequency, intensity and distribution in space. Small
116
rivers (10 – 50 km2) have the biggest share, followed by rivers with medium and large surfaces (Table
30).
Table 30. Types of water depletion on rivers affected by drought
Crt. Types of flow
River name Basin area (km2)
no. Permanent Each year Once in 2 - 5 years
1 Livada 10 - * -
2 Plăieşti 29 - - *
3 Bădeni 14 * - -
4 Cheița 10 - * -
5 Pârâul Florilor 64 - - *
6 Tritul 56 - - *
7 Valea Lată 52 - * -
8 Racoşa 27 - - *
9 Valea Largă 193 - - *
10 TOTAL 455 1 3 5
Source data: A.B.A. Mureș
Alongside the frequency, intensity and territorial distribution of the depletion phenomenon, a big
relevance in terms of water management and eco-system balance rests with the temporal distribution
and duration of the phenomenon. Against this background, the table below (Table 31) is indicative of
the fact that the maximum monthly frequency of the minimum flow is in: July, August and September
when most of the rivers have minimum discharges, caused by the lack of precipitations and strong
evaporation. There is also a period of small winter flow on the rivers in Câmpia Transilvaniei.
Table 31. Number of monthly cases with depletion phenomena on streams
Crt. Month of the year
Stream
no. I II III IV V VI VII VIII IX X XI XII
1 Livada * *
2 Plăieşti * * *
3 Bădeni * * *
4 Cheița * *
5 Pârâul Florilor * * * * *
6 Tritul * * * *
7 Valea Lată * * * * * * *
8 Racoşa * *
9 Valea Largă * * *
10 TOTAL 2 3 2 7 9 6 2
Source data: A.B.A. Mureș
2.2.6. Conclusions
In Cluj county water hazards and associated risks indicate for the period analyzed fluctuating
intensities generated by genetic factors, altitude and the way in which the land is used. The
vulnerability to flash-floods and floods of the areas close to river beds has been reduced as a result of
the investments conducted/being conducted by the water basin administrations. The frequency of
frost phenomena with generation of winter phenomena on streams or lakes is caused by the climate
variables specific to the cold season (negative temperatures during rather longer periods of time,
wind manifestations, solid precipitations – snow). The intensity and duration of hydrologic drought
117
phenomena, more concretely river depletion, are also influenced by climate factors specific to the
warm season.
The survey on water risks, particularly floods, among respondents in Cluj County gave rise to
interesting responses in terms of how the population is affected, the preparedness to deal with critical
situations arising from hazardous phenomena, as well as the possible engagement to help those in
need. Thus, the high exposure to these phenomena in the past 5 years affected only 5.6% of the survey
respondents, but the hazard-related losses/damages affected almost 10.3% of individuals.
Unfortunately, the lack of education in getting the population ready to cope with water hazards as
effectively as possible is confirmed by the high percentage of respondents (67.3%) who cannot react
effectively in such a situation. However, it appears that volunteering actions that may involve rescuing
the people in danger, the construction of dams, the restoration of access routes have broad support
from the participants in the survey (more than 47%).
2.3. Landslide risks
Landslide risks and erosion-induced processes are one of the main factors leading to removal of large
areas of land from agricultural production, with major implications on the management of farming
land and the economic development of the affected areas.
This study is aimed at identifying the potential degraded land subject to landslides based on the
established methodology that is currently applicable in Romania (G.D. 447/2003), and at the
identification of hot spots, which will become very useful in order to prioritise measures to prevent
the emergence of these natural landsliding processes and mitigate their medium and long-term
effects.
2.3.1. Critical analysis of the current situation
The geomorphological risk in Cluj County is caused by landslides that are active or likely to re-occur,
due to the morphological traits and changes that occur on medium and high inclination slopes
following building activities, vibrations caused by transportation means and the out-of-balance slopes
caused by the presence of clays and marl following the accumulation of large amounts of rainfall water
or the breakage of underground water supply pipelines.
The landslide risks and proposals to address such risks are found in Section V of Law No 575 of 22
October 2001, while the geomorphological risks classification is regulated by the Government Decision
No 447/2003 - Implementation rules for the drafting and content of landslide natural risk maps, which
set landslide risk classes based on influence scores of landslide contributing and triggering factors that
relate to geology, geomorphological characteristics (slope and attitude), morphostructural,
hydroclimatic, hydrogeological, seismic, forestry and man-made factors.
The damage caused by landslides in Cluj County consist in loss of property and human lives directly
linked to landslides, where the risk is defined as the product of potential landslides (expressed as the
average hazard ratio (Km)) and the value of lost property (expressed as the total elements subject to
landslide hazard).
The elements exposed to landslide events consist in homes, roads, bridges, utilities (gas, water,
sewerage, power, phone network), farming land, forests and built-up areas.
The geomorphological processes which cause loss of property and of agricultural crops in Cluj County
include the soil erosion leading to loss of farming land productivity and the removal of eroded farming
land from agricultural production. To classify the Cluj County's territory into soil erosion classes, this
118
study emplyed the RUSLE (Revised Universal Soil Equation Erosion) model, which allowed the
classification of each component TAU of the county into erosion classes.
2.3.2. Risks of landslides (areas affected and/or at risk, existing facilities to tackle
landslides, condition, investments etc.)
Various studies have been performed over time on territories from Cluj County, aimed at the
classification of the entire county into landslide risk classes or the identification of the risk arising from
these geomorphological processes.
An overview of the methodological initiatives aimed at the prediction of natural hazards, and implicitly
of landslides, clearly show they are predominantly based on a spatial analysis of the underlying
phenomena and factors, while the temporal conditions which influence them usually become
secondary, or go entirely ignored in the absence of the required data. For this reason, specialized
studies stress the determination of landslide hazards by taking into account, to the greatest extent
possible, the need to capture the spatial-temporal divide of this type of phenomena.
The differences originate both in the various analysis methods and the accuracy of research, which is
increasingly thorough. Another major aspect is the fact that over the past decade, human
development increasingly spread towards areas with landslide risks, and man-made interventions on
the slopes is one of the contributing factors of recent landslides in built-up areas.
Choosing the level of detail in such situations is a defining condition for the team, but the identification
of hotspots at the level of each territory and the possibility to identify, at pixel level, all the factors
included in the analysis and the probability of landslides to occur become an objective we pursue in
the current research study, according to the regulations specific to the classification methodology of
the territory by landslide type.
The identification and mapping of factors that threaten the stability of slopes are main goals in the a
priori determination of landslide causes (Guzzetti et al., 1999). In general, the studies that are mainly
aimed at identifying the landslide hazards assume that a combination of factors that caused landslides
in the past will have the same effect in the future (Varnes, 1984, Carrara et al., 1991, 1995, Chung and
Fabbri, 1999, Petrea e al., 2014, Bilașco et al., 2019).
The study carried out by Petrea et al., 2014, in order to identify the spatial likelihood of landslides in
the Transylvanian Plain, taking into account the morphometric and morphographic parameters of
elevation reveals the large extent of the medium likelihood class for the slopes in the hilly part of Cluj
County, thus classifying narrow parts the mountain area into the medium-large likelihood class.
Most hazard studies employ GIS technologies to identify the prospects of landslides based on a
classification of the contributing and triggering factors, depending on the presence or absence of the
phenomenon. The studies which address the hazard from a spatial perspective include the pragmatic
models (van Westen et al., 2006), where the spatial likelihood of landslides is determined with GIS
tools (Carrarra et al., 1995, 1999), which facilitate the use of statistical methods: bi-varied statistical
analysis (such as weight of evidence-based method), multi-varied analysis, logistic regression, and
ANN (artificial neural network), the fuzzy method, etc.
In Romania, recent studies on the spatial susceptibility of landslides at national, regional and local
level have been carried out by Bălteanu et al., 2010, Armaş, 2011, Bilaşco et al., 2011, Manea, 2012,
Arghiuş, 2013, Petrea et al., 2014, Roșca et al., 2015, Roșca et al., 2016, Sestraș et al., 2019 etc.
119
The hazard identification implies both the assessment of spatial likelihood (susceptibility maps) and
the temporal likelihood, based on an analysis of triggering factors (Aleotti, 2004, van Westen et al.,
2006, Rădoane et al., 2007, Brunetti et al., 2010, Roșca et al., 2015). Frequently, when limiting
conditions apply, the return periods of the contributing and triggering factors of landslides are used
(mainly of atmospheric nature - i.e. intensity of rainfall, and geodynamic factors - i.e. earthquakes).
Varnes (1984) defines hazard in terms of probability, as a phenomenon likely to occur in a certain
place and time. The switchover to hazard maps requires to identify the temporal occurrence of
landslides (IAEG, 1984, Multihazard Mitigation Council, 2002, EEA, 2005).
This is made difficult by the limited availability of data on the triggering of landslides over long periods
of time (Dihau and Schritt, 1999, Magiulio, P. et al., 2007), the direct estimation of landslide frequency
thus becoming impractical (Guzzetti et al., 2005). An essential role is that of identifying the frequency,
magnitude and periods of renewed rainfall which cause the emergence and worsening of landslides,
as their knowledge later enables a spatial-temporal prediction of landslides (Varnes, 2003, Guzzetti et
al., 2006, Dragotă et al., 2008).
Building on the data supplied by the Emergency Situations Inspectorate and the direct land mapping
and use of satellite images, a landslide database was built to include landslides that have occurred in
Cluj County, made of 3836 landslide bodies (Table 32).
Table 32. Distribution of landslides across the territorial-administrative units of Cluj County
Total areas Total areas
% of total
No TAU Total TAU area unaffected by affected by
(landslides)
landslides landslides
1 AGHIRESU 10578.261 10524.100 54.161 0.51%
2 AITON 4525.764 4406.980 118.784 2.62%
3 ALUNIS 5652.210 5609.540 42.670 0.75%
4 APAHIDA 10612.753 10397.700 215.053 2.03%
5 ASCHILEU 6451.818 6415.510 36.308 0.56%
6 BACIU 8752.659 8671.590 81.069 0.93%
7 BAISOARA 11074.470 11070.800 3.670 0.03%
8 BELIS 20543.700 20543.700 0.000 0.00%
9 BOBÂLNA 9747.293 9662.960 84.333 0.87%
10 BONTIDA 8136.910 8030.290 106.620 1.31%
11 BORSA 6158.626 6073.680 84.946 1.38%
12 BUZA 2974.453 2955.060 19.393 0.65%
13 CAIANU 5488.148 5413.880 74.268 1.35%
14 CALARASI 3782.206 3771.630 10.576 0.28%
15 CALATELE 7505.185 7501.510 3.675 0.05%
16 CAMARASU 4817.952 4759.320 58.632 1.22%
17 CÂMPIA TURZII 2376.840 2375.750 1.090 0.05%
18 CAPUSU MARE 13439.679 13393.600 46.079 0.34%
19 CASEIU 8326.491 8288.880 37.611 0.45%
20 CÂTCAU 3756.711 3749.300 7.411 0.20%
21 CATINA 5308.602 5209.760 98.842 1.86%
22 CEANU MARE 9503.354 9286.150 217.204 2.29%
23 CHINTENI 9648.711 9484.240 164.471 1.70%
24 CHIUIESTI 11235.907 11211.500 24.407 0.22%
25 CIUCEA 4863.935 4850.930 13.005 0.27%
26 CIURILA 7233.131 7171.100 62.031 0.86%
27 CLUJ-NAPOCA 17482.825 16963.400 519.425 2.97%
120
Total areas Total areas
% of total
No TAU Total TAU area unaffected by affected by
(landslides)
landslides landslides
28 COJOCNA 13859.008 13651.800 207.208 1.50%
29 CORNESTI 8295.627 8192.950 102.677 1.24%
30 CUZDRIOARA 2387.714 2376.620 11.094 0.46%
31 DABÂCA 5022.962 5005.510 17.452 0.35%
32 DEJ 10888.888 10696.000 192.888 1.77%
33 FELEACU 6622.496 6469.050 153.446 2.32%
34 FIZESU GHERLII 6721.696 6697.020 24.676 0.37%
35 FLORESTI 6086.001 6053.330 32.671 0.54%
36 FRATA 7287.334 7239.760 47.574 0.65%
37 GÂRBAU 7210.267 7172.610 37.657 0.52%
38 GEACA 6819.543 6738.160 81.383 1.19%
39 GHERLA 3627.063 3602.500 24.563 0.68%
40 GILAU 11721.899 11719.500 2.399 0.02%
41 HUEDIN 6122.943 6093.770 29.173 0.48%
42 IARA 14373.518 14356.100 17.418 0.12%
43 ICLOD 6789.224 6695.060 94.164 1.39%
44 IZVORU CRISULUI 4139.292 4128.930 10.362 0.25%
45 JICHISU DE JOS 4343.368 4264.510 78.858 1.82%
46 JUCU 8413.389 8312.910 100.479 1.19%
47 LUNA 5318.012 5317.470 0.542 0.01%
48 MAGURI-RACATAU 26832.900 26832.900 0.000 0.00%
49 MANASTIRENI 6269.405 6262.390 7.015 0.11%
50 MARGAU 21192.643 21188.400 4.243 0.02%
51 MARISEL 8566.850 8566.850 0.000 0.00%
52 MICA 6482.303 6426.860 55.443 0.86%
53 MIHAI VITEAZU 4762.376 4712.650 49.726 1.04%
54 MINTIU GHERLII 7850.521 7791.620 58.901 0.75%
55 MOCIU 7251.405 7157.350 94.055 1.30%
56 MOLDOVENESTI 13901.762 13862.800 38.962 0.28%
57 NEGRENI 6549.060 6549.060 0.000 0.00%
58 PALATCA 5012.963 4973.860 39.103 0.78%
59 PANTICEU 9044.126 8993.880 50.246 0.56%
60 PETRESTII DE JOS 7269.547 7253.110 16.437 0.23%
61 PLOSCOS 4164.907 4061.710 103.197 2.48%
62 POIENI 18638.737 18632.100 6.637 0.04%
63 RECEA-CRISTUR 7614.911 7511.600 103.311 1.36%
64 RISCA 6578.330 6576.700 1.630 0.02%
65 SACUIEU 12096.500 12096.500 0.000 0.00%
66 SÂNCRAIU 5713.948 5688.500 25.448 0.45%
67 SANDULESTI 2232.248 2195.740 36.508 1.64%
68 SÂNMARTIN 7170.155 7123.070 47.085 0.66%
69 SÂNPAUL 9273.457 9195.020 78.437 0.85%
70 SAVADISLA 11004.893 10989.200 15.693 0.14%
71 SCI 5624.515 5535.360 89.155 1.59%
72 SUATU 5307.122 5278.490 28.632 0.54%
73 TAGA 9997.794 9938.520 59.274 0.59%
74 TRITENII DE JOS 5943.923 5853.100 90.823 1.53%
75 TURDA 9152.275 9009.970 142.305 1.55%
76 TURENI 7396.660 7326.990, 69.670 0.94%
77 UNGURAS 6362.086 6323.400 38.686 0.61%
78 VAD 7721.829 7679.620 42.209 0.55%
121
Total areas Total areas
% of total
No TAU Total TAU area unaffected by affected by
(landslides)
landslides landslides
79 VALEA IERII 14844.500 14844.500 0.000 0.00%
80 VIISOARA 6152.613 6066.530 86.083 1.40%
81 VULTURENI 7157.287 7140.350 16.937 0.24%
County total 667163.389 662215.120 4948.269 0.74%
Certain territorial administrative units stand out, such as Cluj Napoca, with 2.97% of the total area
affected by landslides in the county, TAU Aiton (2.62%), Ploscos (2.48), Feleacu (2.32), Ceanu Mare
(2.29), Apahida (2.03), and others. These landslides take areas between 0.54 hectares, such as the
Luna administrative territorial unit, to 519 hectares for TAU Cluj Napoca.
Thus, the area affected by landslides totals 4948.26 hectares, i.e. 0.74% of the entire county area, but
the loss of property and the risks the population is subject to due to loss of property, more difficult
travelling conditions, water and power cuts requires an increased attention to the likelihood of new
landslides and reactivation of existing ones.
At the other end of the spectrum, there just six territorial administrative units without any observed
active or stabilized landslides: Beliș, Măguri-Răcătău, Mărișel, Negreni, Săcuieu and Valea Ierii, all
these administrative territorial units being found in the mountain area of the Vlădeasa and Gilău
Massif (Fig. 94).
Figure 94 - Map of landslides in Cluj CountySource data: data processed after ISU Cluj
122
Figure 95 - Map of risks linked to the geomorphological processes occurring nearby the transport network
Figure 96 - Map of current geomorphological risks affecting the transport network
123
The field campaigns identified current geomorphological processes such as landslides, collapses and
sectors with side erosion (Fig. 95), which enabled the classification of the Cluj County transport
network into related risk classes (Fig.96). This first aspect is highly important during the initial pre-
modelling of the risks arising from landslides, since it provides an up-to-date picture of the negative
effects such geomorphological processes are causing in a territory.
At county level, among its many tasks, the Emergency Situations Inspectorate is also tasked with
interventions in cases of landslides. The Cluj County data provided by the above mentioned institution
covers the 1972-current period, with landsliding events summarized in Annex 1.
Figure 97 – ISU interventions per territorial administrative units in cases of landslides
Data source: ISU ”Avram Iancu” Cluj
An assessment of the summarized data leads to conclusions related to the temporal and spatial
variability of landsliding events that were subject to interventions by the Emergency Situations
Inspectorate (Fig. 97). Thus, most of the interventions occurred in TAU Cluj Napoca (28), Ceanu Mare
(14), Căpușu Mare (13), Cășeiu (9), etc., these events being mainly caused by the a change of terrain
tension due to overloading of the upper parts of embankments with earth and construction and
demolition waste, and by heavy rainfall within built-up areas and the reactivation of old sliding bodies
caused by anthropic pressure and leading to displacements of earth masses due to very inclined slopes
and the enabling geology, but also by the erosion of the slope base by water streams from heavy water
flows.
The data assessment reveals 22 territorial administrative units with no intervention against such types
of events: Săcuieu, Beliș, Mârgău, Iara, Ciucea, Mănăstireni, etc, a fact possibly explained by the
occurrence of landslides mainly on farming land, with interventions by the Emergency Situations
124
Inspectorate being therefore less needed, and only required when the landsliding event caused the
obstruction of roads, railways, utility networks, etc.
2.3.3. General information, data sources and methods used
The landslides are geomorphological processes occurring on slopes, frequently caused and facilitated
by the increase of gravitational loads (constructions, earth or material storage) or by unauthorized
interventions on the system's geometry (local excavations at the base, ditches, increased inclination
angles of embankments).
The landslide risk map for Cluj County was prepared according to Government Decision 447/2003 -
Implementation rules regarding the preparation and content of landslide natural risk maps, which
describe both the general sequence of preparing landslide natural risk maps and their content.
According to art. 2 din G.D. 447/2003, the landslide natural risk map is: “a data summary forecasting
the balance of slopes, the loss of property and lives possibly caused by landslides in a certain area and
period of time”, being a part of the county spatial planning documentation, and which will be included
into the general urban plans and local urban planning regulations of localities in every county (Art. 3,
G.D. 447/2003).
This map consists in a supporting study for the County Spatial Plans (CSP) and the General Urban Plans
(PUG) of the county's territorial administrative units, in order to facilitate specific measures to mitigate
and prevent the negative effects of landslides against buildings, and to ensure a sound use of land by
performing specific works and taking structural and non-structural measures to limit economic
damage and protect future investments.
The mitigation of negative effects in the territory requires studies on its vulnerability and the
identification of risks arising from active geomorphological processes which allow the identification of
spatial probabilities of future occurrences and also provide forecasts of future evolutions.
The land model data used for the preparation of the project was extracted from the EU-DEM (Digital
Elevation Model-European Space Agency) database, with freely available satellite imagery data being
used to re-update the infrastructure data, the hydrograhic network, etc.
The lithological layers related to the analysed area were extracted from Romania's Geologic Map
1:00000, 1960.
The hydrographic network distribution layers were digitized based on the maps available in the
Romanian Water Cadaster, 1991. Moreover, the Topographical Map was used to support the database
of enabling and triggering factors of landslides.
According to the methodology for the preparation of the landslide hazard map, a database that
includes the following 8 factors was used:
Ka – Lithological coefficient,
Kb – Geomorphological coefficient,
Kc – Structural coefficient,
Kd – Hydrological and climatic coefficient,
Ke – Hydrogeological coefficient,
Kf – Seismic coefficient,
Kg – Forestry coefficient,
Kh – Anthropic coefficient.
125
Figure 98 – Methodological diagram of the applied model
Source: Roșca, 2015
The entire analysis was performed under an ArcGIS project where layers were handled for each
coefficient, therefore securing the related rasters and the spatial database of potential landslide
hazards within Cluj County.
The average hazard coefficient (Km) was obtained by employing formula (1):
Where: Ka – Lithological coefficient, Kb – Geomorphological coefficient, Kc – Structural coefficient, Kd
– Hydrological and climatic coefficient, Ke – Hydrogeological coefficient, Kf – Seismic coefficient, Kg –
Forestry coefficient, Kh – Anthropic coefficient, Km – Average hazard coefficient.
126
2.3.4. Lithological coefficient (Ka)
The friable geological sublayer consisting in sedimentary rocks on the Transylvanian Basin, which were
laid during a marine and lacustrine period, features a high structural diversity. Thus, the geological
and lithological structure favours the emergence of mass displacement processes when the remaining
enabling factors are met. A statistical analysis of the geological distribution of landslides reveals they
are highly prevalent on formations such as: marls, marl clays, marl argile marnoase, marl shale with
intertwined volcanic tuffs (Fig. 99).
The presence of domes and newly emerging formations dominated by diapiric wrinkling, in
combination with volcanic tuffs, marls and clays further add to the causes of landslides. Superficial
landslides, predominantly translational, are well represented in the Transylvanian Plain, where the
slopes geology predominantly features marls and clays (Morariu et al., 1964). Deep landslides have a
specific distribution, being generally related to successive occurrences due to enabling rainfall
conditions, but also to the neotectonics of salt (Irimuș, 1998, Sadu, 1998, Surdeanu et al., 2016,
Gârbacea, 2013, Bilașco et al., 2018).
Figure 99 – Distribution per geological classes of landslides in Cluj County
Source data: data processed by Geologycal Map of Romania
An analysis of the landslides per geological classes reveals a 28.75% percentage of marls and tuffs, as
well as marl clays, and sands and tuffs on which 22.5% of the current landslides occur on the slopes of
the hilly sections in Cluj County. The clays, carbon sandstones, marl, marl shales and tuffs account for
14% of landslides, while 9.65% of landslides are found on conglomerates, sandstones, marl clays (Hida
layers) (Fig. 100). The landslides affecting the terraced floodplains of the Someșul Mic and Someșul
Mare river courses develop on sands and holocene gravel. The remaining landslides (6.06%) are found
on other geological formations.
127
Figure 100 – Lithological coefficient map
Source data: data processed by Geologycal Map of Romania
2.3.5. Geomorphological coefficient (Kb)
The analysis of the geomorphological coefficient for the modelling of landslide probability in Cluj
County, based on the digital elevation model and the terrain inclination according to the intervals set
under Government Decision 447/2003, reveals that the relief characterized by low inclinations (0-20)
and affected by insignificant erosion processes crossed by valleys in advanced stages of maturity is
characterized by a low likelihood of landslides (0.1). Just 1% of all active landslides are now found on
these territories.
The medium-high likelihood (0.3-0.5) of landslides is found on hilly relief with medium and high
inclinations, fragmented by valleys in an advanced stage of maturity. These territories account for 80
% of active landslides, with the remaining 19% occurring on hilly areas with inclinations in excess of
150, where they give rise to a very high probability of hazards (Fig. 101).
128
Figure 101 – Geomorphological coefficient map
2.3.6. Structural coefficient (Kc)
Figure 102 – Structural coefficient map
129
2.3.7. Hydrological and climatic coefficient (Kd)
The annual average rainfall modelled for Cluj County supports the identifying the probability of
landslides according to the hydrological and climatic coefficient. Therefore, the territories with
average rainfall and the mountain and hill hydrographical basins which are generally controlled by the
rainfall from such areas show an medium likelihood of landslides (0.1-0.3). The territories with
moderate rainfall and mature valleys, but with tributaries affected by flash floods and with sectors
subject to active vertical erosion, have an medium to high likelihood of landslides (0.3-0.5), while
territories with rainfall in excess of 850 mm/year have a higher likelihood of landslides (0.5-0.8) (Fig.
103).
Figure 103 – Hydrological and climatic coefficient map
2.3.8. Hydrogeological coefficient (Ke)
The influence of hydrogeology with regard to the occurrence of landslides was determined based on
the hydrogeological map of Europe. Thus, the areas characterized by water table levels at depths
lower than 5 m have a low likelihood of landslides, while areas where water tables flow under large
gradients, at the bases of slopes where springs occasionally emerge, have an medium likelihood of
landslides, and the areas with permeable layers on upper parts have medium-high and high likelihoods
of landslides (Fig. 104).
130
Figure 104 – Hydrogeological coefficient map
2.3.9. Seismic coefficient (Kf)
The seismic zoning studies on the national territory include the analysed sector in the seismic category
VI on the MSK seismic scale (according to STAS 11100/1993 and the seismic zoning parameters
according to the P100/1992 norms for earthquakes above 6 degrees on the Richter scale). Therefore,
the likelihood of landslides caused by earthquakes covers approximately 80% of the Cluj County
territory, and is thus average (Fig. 105).
131
Figure 105 – Romanian territory zoning in terms of peak terrain acceleration values for ag designing for
earthquakes with an average IMR recurrence interval = 100 years. Design code P100-1/2006
Figure 106 – Seismic zoning of the Romanian territory – MSK intensity categories, according to SR 11100–
1:93 Seismic zoning. Macrozoning of the Romanian territory
Source data: SR 11100-1:93 Seismic zoning. Macrozoning of the Romanian territory
132
For the south-eastern part of the county's territory, where it falls within the seismic category VII on
the MSK seismic scale (according to STAS 11100/1993 and the seismic zoning parameters according
to the P100/1992 norms for earthquakes above 7 degrees on the Richter scale), the likelihood of
landslides caused by the seismic factor is medium-high. This category also includes territorial
administrative units such as: Cămărașu, Cătina, Frata, Ceanu Mare, Călărași, etc (Fig. 107).
Figure 107 – Seismic coefficient map
Source data: data processed by SR 11100-1:93 Seismic zoning. Macrozoning of the Romanian territory
2.3.10. Forestry coefficient (Kg)
The stabilizing role of forest vegetation on slopes is referred to in various studies aimed at the stability
of slopes (Rădoane et al., 2005, Petrea et al., 2014). According to Copernicus Land Monitoring Service
data, the territories with forest coverage higher than 80% and characterized by a low landslide
probability (0-0.1) are mainly found in mountain areas, where they take 55% of the land. The
territories with forests on 50 to 80% of their area have an medium likelihood of landslides (0.1- 0.3),
while farming land or transition land (deforested) is characterized by a high and very high likelihood
of landslides (0,3-0,5) (Fig. 108).
133
Figure 108 – Forest coefficient map
Data source: data processed by Copernicus Land Monitoring Service, EEA
134
2.3.11. Anthropic coefficient (Kh)
The territories that are not built on are characterized by a low likelihood of landslides (0.1), but slope
sectors with dense constructions and roads are characterized by a high likelihood of landslides (0.9)
due to their overloading (Fig. 109).
Figure 109 – Anthropic coefficient map
Data source: Data processed by Corine Land Cover 2018, v.20, European Environment Agency (EEA) -
Copernicus Land Monitoring Service
2.3.12. Spatial likelihood of landslides
The landslide probability map of Cluj County and the related risk coefficient (Km) obtained according
to Government Decision 447/2003 reveals the territories falling under the various landslide probability
classes at county level and within the component territorial administrative units.
The high landslide probability class with a risk coefficient (Km) between 0.5-0.8 covers 92.3 km2, which
accounts for 1.% of the Cluj County territory (Fig. 110).
Most of the county area (3007.7 km2), which accounts for 45.1% of the entire territory, shows an
medium likelihood of landslides, as they are mainly located in hilly sectors geologically dominated by
clays, marls and intertwined tuffs. The following TAUs stand out: Câmpia Turzii, Negreni, Călărași,
whose territories are characterized by an medium likelihood of landslides for approximately 90% of
its administrative territory.
135
The medium/high probability class characterizes 1.4% of Cluj County. The TAUs to standout are Beliș,
Valea Ierii, Margău, Mărișel and Băișoara (Fig. 111), with more than 5% of the administrative territory
falling under the medium-high probability class. The territorial administrative units are included under
this probability class in iew of the morphometrical properties related to the terrain inclinations, the
heavier rainfall, the higher density of the primary hydrographic network, the enabling and triggering
factors of landslides.
Figure 110 – Likelihood of landslides and the related risk coefficient (km)
Figure 111 – Relative distribution of territorial administrative units with large areas falling under the
medium landslide probability class
136
Figure 112 – Relative distribution of territorial administrative units with large areas falling under the
medium-high landslide probability class
At the other end of the spectrum, the territorial administrative units with low landslide probability are
Săcuieu, Măguri Răcătău, Mărișel, etc., with 9.5% of the county territory falling under this probability
class (Figure 113, Table 33).
Figure 113 – Relative distribution of territorial administrative units with large areas falling under the low
landslide probability class
137
Table 33. Classes of landslide probability for the administrative and territorial units of Cluj County
Landslide probability
No TAU Low Medium Medium-high High
(Km <0.10) (0.10<Km>0.30) (0.30<km>0.50) (0.50<Km>0.80)
km2 % km2 % km2 % km2 %
1 AGHIRESU 0.3 0.2 67.1 63.5 38.3 36.2 0.0 0.0
2 AITON 0.0 0.0 36.2 80.0 9.0 20.0 0.0 0.0
3 ALUNIS 0.0 0.0 16.3 28.8 40.1 70.9 0.2 0.3
4 APAHIDA 0.6 0.5 41.2 38.9 64.2 60.5 0.1 0.1
5 ASCHILEU 0.0 0.0 25.2 39.0 39.3 61.0 0.0 0.0
6 BACIU 0.1 0.1 52.7 60.3 34.6 39.5 0.1 0.1
7 BAISOARA 24.3 21.9 39.0 35.2 41.4 37.4 6.0 5.4
8 BELIS 8.2 4.0 34.6 16.9 129.0 62.8 33.4 16.3
9 BOBÂLNA 0.0 0.0 29.4 30.2 68.0 69.7 0.1 0.1
10 BONTIDA 0.3 0.4 32.8 40.3 48.2 59.2 0.1 0.1
11 BORSA 0.1 0.2 24.3 39.4 37.2 60.4 0.0 0.0
12 BUZA 0.0 0.0 2.3 7.6 27.4 92.1 0.1 0.2
13 CAIANU 0.0 0.0 16.6 30.2 38.3 69.7 0.0 0.1
14 CALARASI 0.0 0.0 35.3 93.6 2.4 6.4 0.0 0.0
15 CALATELE 10.5 14.0 40.3 53.7 24.1 32.1 0.1 0.2
16 CAMARASU 0.0 0.0 7.6 15.8 40.5 84.2 0.0 0.0
17 CÂMPIA TURZII 33.0 24.5 61.6 45.8 39.8 29.6 0.0 0.0
18 CAPUSU MARE 0.0 0.0 42.3 50.8 40.4 48.5 0.5 0.6
19 CASEIU 0.0 0.0 4.5 8.5 48.6 91.5 0.0 0.0
20 CÂTCAU 0.0 0.0 22.6 95.2 1.0 4.3 0.1 0.5
21 CATINA 0.0 0.0 20.9 55.8 16.5 44.1 0.0 0.0
22 CEANU MARE 0.0 0.0 14.3 15.0 80.7 84.9 0.0 0.0
23 CHINTENI 0.1 0.2 35.7 37.0 60.6 62.8 0.0 0.0
24 CHIUIESTI 0.0 0.0 43.9 39.1 67.9 60.5 0.4 0.3
25 CIUCEA 5.3 10.9 39.3 80.9 4.0 8.1 0.0 0.0
26 CIURILA 0.0 0.0 62.8 86.8 9.5 13.1 0.1 0.1
27 CLUJ-NAPOCA 1.0 0.6 93.8 53.6 79.8 45.6 0.3 0.2
28 COJOCNA 0.0 0.0 34.9 25.1 103.6 74.7 0.1 0.1
29 CORNESTI 0.0 0.0 30.6 36.9 52.2 62.9 0.1 0.1
30 CUZDRIOARA 0.0 0.0 18.4 76.9 5.5 23.0 0.0 0.1
31 DABÂCA 0.4 0.9 22.8 45.5 26.9 53.6 0.0 0.0
32 DEJ 0.0 0.0 52.8 48.5 55.6 51.1 0.4 0.4
33 FELEACU 0.1 0.2 55.1 83.2 11.0 16.7 0.0 0.0
34 FIZESU GHERLII 0.0 0.1 28.3 42.1 37.9 56.3 1.0 1.5
35 FLORESTI 0.4 0.7 35.9 59.0 24.4 40.2 0.1 0.2
36 FRATA 0.0 0.0 11.5 15.8 61.3 84.2 0.0 0.0
37 GÂRBAU 0.1 0.2 55.1 76.4 16.9 23.4 0.0 0.0
38 GEACA 0.0 0.0 17.2 25.2 50.9 74.7 0.1 0.1
39 GHERLA 0.0 0.0 15.5 42.8 20.7 57.0 0.1 0.2
40 GILAU 18.8 16.1 76.7 65.4 21.4 18.2 0.3 0.3
138
Landslide probability
No TAU Low Medium Medium-high High
(Km <0.10) (0.10<Km>0.30) (0.30<km>0.50) (0.50<Km>0.80)
km2 % km2 % km2 % km2 %
41 HUEDIN 0.4 0.6 53.3 87.2 7.4 12.1 0.0 0.0
42 IARA 13.0 9.0 102.0 71.1 27.6 19.3 0.9 0.6
43 ICLOD 0.4 0.6 29.7 43.7 37.7 55.6 0.1 0.1
44 IZVORU CRISULUI 0.1 0.2 34.1 82.4 7.2 17.4 0.0 0.0
45 JICHISU DE JOS 0.0 0.0 13.2 30.5 30.0 69.0 0.2 0.5
46 JUCU 2.8 3.4 38.5 45.8 42.7 50.8 0.1 0.1
47 LUNA 0.1 0.3 46.4 87.3 6.3 11.8 0.3 0.6
48 MAGURI-RACATAU 143.7 53.6 51.1 19.0 65.1 24.3 8.4 3.1
49 MANASTIRENI 2.8 4.5 28.9 46.1 30.8 49.1 0.2 0.3
50 MARGAU 48.1 22.7 76.3 36.0 71.3 33.6 16.2 7.7
51 MARISEL 38.4 44.8 26.9 31.4 18.9 22.1 1.5 1.8
52 MICA 0.0 0.0 31.7 49.0 32.7 50.4 0.4 0.6
53 MIHAI VITEAZU 8.8 18.6 35.2 73.8 3.6 7.5 0.1 0.1
54 MINTIU GHERLII 0.0 0.0 31.6 40.3 46.3 58.9 0.6 0.8
55 MOCIU 0.0 0.0 11.5 15.9 60.9 84.0 0.1 0.1
56 MOLDOVENESTI 40.1 28.9 79.6 57.3 18.8 13.5 0.4 0.3
57 NEGRENI 4.1 6.3 61.3 93.6 0.1 0.1 0.0 0.0
58 PALATCA 0.1 0.1 15.5 31.0 34.5 68.8 0.1 0.2
59 PANTICEU 0.0 0.0 45.4 50.2 45.0 49.8 0.0 0.0
60 PETRESTII DE JOS 11.2 15.4 55.7 76.7 5.5 7.5 0.3 0.5
61 PLOSCOS 0.0 0.0 16.5 39.6 25.2 60.4 0.0 0.0
62 POIENI 75.8 40.7 97.6 52.4 12.2 6.5 0.6 0.3
63 RECEA-CRISTUR 0.0 0.0 22.2 29.1 53.9 70.8 0.0 0.1
64 RISCA 21.5 32.7 36.5 55.5 7.6 11.6 0.1 0.2
65 SACUIEU 75.8 62.7 34.8 28.8 9.2 7.6 1.2 1.0
66 SÂNCRAIU 4.2 18.9 10.0 45.0 7.8 35.0 0.2 1.1
67 SANDULESTI 11.1 10.0 59.7 54.3 39.1 35.5 0.1 0.1
68 SÂNMARTIN 2.7 4.8 48.9 85.5 5.5 9.7 0.0 0.0
69 SÂNPAUL 0.0 0.0 21.1 29.5 50.0 69.8 0.5 0.7
70 SAVADISLA 0.0 0.0 81.1 87.5 11.6 12.5 0.0 0.0
71 SCI 0.0 0.0 15.6 27.8 40.5 72.0 0.1 0.2
72 SUATU 0.0 0.0 10.5 19.7 42.6 80.2 0.0 0.1
73 TAGA 0.1 0.1 37.6 37.6 61.7 61.8 0.6 0.6
74 TRITENII DE JOS 0.0 0.0 5.0 8.5 54.3 91.5 0.0 0.1
75 TURDA 0.0 0.0 48.7 53.2 42.7 46.6 0.2 0.2
76 TURENI 0.5 0.6 60.1 81.2 13.4 18.1 0.1 0.1
77 UNGURAS 0.0 0.0 10.6 16.6 52.1 81.8 1.0 1.5
78 VAD 0.0 0.0 30.0 39.0 47.0 60.9 0.1 0.1
79 VALEA IERII 21.0 14.2 30.0 20.2 85.1 57.3 12.3 8.3
80 VIISOARA 0.0 0.0 21.6 35.2 39.0 63.4 0.9 1.4
81 VULTURENI 0.1 0.1 44.5 62.1 26.9 37.6 0.1 0.1
139
Landslide probability
No TAU Low Medium Medium-high High
(Km <0.10) (0.10<Km>0.30) (0.30<km>0.50) (0.50<Km>0.80)
km2 % km2 % km2 % km2 %
TOTAL 630.8 9.5 3007.7 45.1 2938.5 44.1 92.3 1.4
An inventory of landslides based on satellite images freely available on the Google platform revealed
there are currently 3836 landslides which could potentially become reactivated in case of heavy
rainfall or earthquakes that cause instability of the slopes.
Figure 114 – Drainage sites in Cluj County
Source data: data processed by ANIF Cluj
According to the National Land Development Agency data, there are 21 sites in Cluj County where
drainage works have been performed: Valea Crișului (TAUs Huedin, Sâncraiu and Podeni), Bedeciu
(TAU Mănăstireni), Valea Mare (TAUs Gârgău and Sânpaul), SDE Cluj (TAU Florești), Borșa (TAUs Borșa
and Bonțida), Maraloiu (TAU), Zăpodie (TAUs Apahida and Cluj Napoca), Hășdate (TAUs Ciurila,
Săvădisla, Petreștii de Jos and Tureni), Fâneața Vacilor (TAUs Tureni, Turda, Aiton and Săndulești),
Soporului (TAUs Ceanu Mare, Frata, Viișoara and Tritenii de Jos), Căian (TAUs Căianu and Cojocna),
Cojocnei (TAUs Cojocna and Căianu) (Fig. 114).
Among other measures to prevent and mitigate the negative effects of landslides that are required
within territorial administrative units in Cluj County, we recommend the following:
• The forestation of landslide-prone slopes with fast-growing and easily-adaptable hydrophilic
forest vegetation (black locust, pine, etc.);
140
• Terracing of slopes and planting of grapevine or fruit trees, which are very well adapted to the
weather and soil conditions of the territory;
141
• Digging of drains and ditches for the drainage of surface waters, in order to mitigate soil
erosion and depth erosion and reduce water infiltrations into the soil.
Besides such environment redevelopment measures, other very significant measures consist in
educating the public how to maintain the balance of the slopes, preparing geotechnical studies prior
to construction works within areas with medium and medium-high probability of landslides, in order
to identify needs for slope redevelopment prior to performing construction works in such areas.
To avoid future problems, it is recommended to better disseminate the risk studies so the population
become better informed of the situation on the ground, the risk they are exposed to, and the
classification for landslide purposes of the properties, households, farming land, forests and land
owned or to be purchased.
It is also necessary to have a program to inform the population about the risk they are exposed to and
about the effects of deforestation, the importance of maintaining the balance of slopes as well as the
importance of planting forest species to stabilize the territories where landslides are highly likely to
occur.
Moreover, the local persons in charge should be informed of any new landsliding events and direct
and indirect consequences such as: cracks in soil or walls, horizontal or vertical displacements of
buildings, trees, poles, emergence of new water streams on slopes, changes in the position of older
streams, turbidity of well waters for no apparent reason, rutting, holes, ditches), so that local
authorities be able to take mitigation measures.
Informing the public should be aimed not only at being aware of the classification of landslide hazard
classes of the territory where they work and live, but also at knowing the emergency phone numbers
of the nearest locations where they can receive medical attention and social assistance in cases of
extreme events.
Not least, property and homes should be insured for disaster situations, based on specialized studies
that would later enable a fair assessment of insurance premiums and damage amounts.
Local decision makers also have several obligations relating to informing the population of the
situation on the ground and the risks it is exposed to, the introduction of Cluj County hazard and risk
information into the General urban plan, and the obligation to prepare geotechnical stability studies
(by specialized companies) prior to erecting new buildings or rehabilitating existing ones, and
enforcing financial penalties in cases of failure to comply with such obligations.
It is also necessary to prepare public educational programmes to raise awareness of the risks the
population is exposed to and on measures to reduce deforestation, and the importance of slope
development works and of planting tree species that stabilize the areas affected by or at risk of
landslides.
An absolutely necessary measure is to ensure the permanent monitoring of unstable land within built-
up areas, alert the Emergency Situations Inspectorate when recent unstable areas have been found,
and not least attract the necessary funding to carry out forestation campaigns and withhold building
permits in areas with high and medium probability of landslides. This monitoring should be made with
the support of public institutions, with the help of volunteers as well as with the members of affected
communities trained for this purpose.
142
2.4. Seismic risk
Earthquakes that occur in Romania are of tectonic origin. Romania's seismicity results from the energy
released by crustal earthquakes (also called shallow earthquakes), whose depth does not exceed 60
km, and intermediate-depth earthquakes.
The seismic zoning maps (according to Section V in the National Spatial Plan) indicate that most of Cluj
County's territory, except the south-eastern part, is subject to degree 6 on the MSK scale (according
to STAS SR 11100/1993), i.e. zone F (seismic coefficient kS=0.10 and control period TC=0.7 – according
to Earthquake prevention rules P100-1/2013).
Figure 115 – Mapping the territory of Cluj County by class of seismic risk
Source data: data processed by SR 11100-1:93 Seismic zoning. Macrozoning of the Romanian territory
According to the Risk coverage and assessment plan of Cluj County, the effects of earthquakes can
amount to damages and/or collapses of vulnerable buildings1, the damaging and/or decommissioning
of infrastructure (utility) networks, the failure of certain land features (slopes) and/or of certain
engineering works (dams) within that territory or upstream; serious damaging of high-risk industrial
facilities, etc., and any other negative chains of events (fire, explosions).
1
A number of buildings falling under the II and III risk classes have been identified on the county's territory (the
Risk coverage and assessment plan of Cluj County, p. 102),
143
The earthquakes can trigger landslides, compaction and liquefaction of land with specific properties,
while weather conditions can enable or hasten the occurrence of such events. Where a seismic event
occurs that could lead to a chemical or nuclear incident or fire, appropriate measures will be taken
according to protection and intervention plans in case of large underground or srface explosions,
chemical accidents and very serious damage to main and urban pipelines, as provided under the
protection and intervention plans in cases of nuclear accidents and meteorite falls, and in protection
and intervention plans aimed at mass fires.
Figure 116 – Romanian territory zoning in terms of peak terrain acceleration values
Figure 117 – Seismic zoning of Cluj County
Romanian territory zoning in terms of peak terrain acceleration values for ag designing for earthquakes with an
average IMR recurrence interval = 100 years, according to the P100-1/2013 set of rules.
Source data: data processed by SR 11100-1:93 Seismic zoning. Macrozoning of the Romanian territory
144
Romania's seismicity results from the energy released by crustal earthquakes (also called shallow
earthquakes), whose depth does not exceed 60 km, and intermediate-depth earthquakes.
The intervention and defence measures consist in: protection of buildings and facilities that include
sources of high-risk for human communities; protection of constructive and functional capacities;
protection of the urban infrastructure, with emphasis on support systems required for current services
of community interest (the healthcare system network, the protection system infrastructure against
fires and other accidents, fire departments, the management and administration system
infrastructure, and the IT system infrastructure); restoration of affected utility networks, functional
and operational capacities, and of supply capacities, for the return to normal of social and economic
life; alerting and evacuating the population from damaged buildings.
Table 34. Inventory of buildings in Cluj County assessed and classified for seismic risk classes 1, 2 and 3
Construction Risk
No Locality Address Building type Building use
year class
Clinicilor street,
1. Cluj-Napoca adjacent to building Ground floor Residential 1840 II
from 103 Moţilor street
3 Matei Corvin street Basement +
2. Cluj-Napoca Residential 1942 II
ground floor
Sub-
Roman Catholic
3. Cluj-Napoca 2-6 I. Maniu street basement+ground 1926 II
Diocese buildings
floor+1
Sub-
4. Cluj-Napoca 3 Eroilor Blvd basement+ground Residential 1933 II
floor+1
Block of flats
7B Calea Victoriei, bldg.
5. Turda Basement + Residential 1955 II
L140
ground floor+4
Sânpaul Sumurduc village Basement +
6. Residential 1954 II
commune No. 17. ground floor
Sub-
Sânmărtin
7. Măhal village, No 57 basement+ground Residential 1962 II
commune
floor
Basement +
8. Cluj-Napoca 9 D. Francisc street Residential 1930 III
ground floor+floor
Basement +
9. Cluj-Napoca 18 I.C. Brătianu street Building 1952 III
ground floor
Bonţida Banffy Castle,
10. Bonţida village, n.n. Residential 1895 III
commune wing III
Source data: H. 642/29.06.2005According to the classification of territorial administrative units per the
specific types of risks as provided under Decision No 642 of 29 June 2005 approving Criteria for the
classification of territorial administrative units, public institutions and economic operators for purposes of
civil protection per specific risks, all territorial administrative units of the county are included into the
secondary risk class for earthquakes.
145
2.5. Soil erosion
Following the technological and social-economic development and changing of administrative
measures, land use underwent major changes on the territory of Romania and implicitly in Cluj County,
which are still developing.
The modelling of the spatial likelihood of soil erosion and landslides is of significant importance in the
identification of the most useful intervention and mitigation measures aimed at the adverse impacts
on the human and anthropic environment.
The soil losses in Romania are assessed based on the ROMSEM model (Romanian Soil Erosion Model).
This surface soil erosion assessment model is based on an empiric model (determined on statistical
basis) and an equation designed by the academician Moţoc, M. et al. (1963, reviewed in 1979,
reconfirmed in 2002) for the territory of Romania, which was based on the universal equation used by
the U.S. Soil Conservation Service. (Revised Universal Soil Loss Equation), taking Romanian climate
conditions into account. Since that equation has a general form, a quantification as objective as
possible is needed for each factor that was considered in the assessment of a specific territory (Roșca,
2015, Bilașco et al., 2018).
Figure 118 – Stages of the soil erosion determination model
Source data: Roșca, 2014
146
Where:
E – Annual average erosion (t/ha/year),
K – Soil erodibility as established based on the climate aggressiveness,
S – Soil erodibility correction coefficient,
C – Cropping management correction coefficient,
Cs – Anti-erosion works correction coefficient,
Lm – Length of slopes (m),
In – Terrain slope (%).
Based on the data supplied following the studies carried out by the Terrestrial Resources Management
Unit (the Environment and Sustainability Institute, JRC Ispra), a soil loss map of Europe was prepared
(Panagos et al., 2014, 2015a, 2015b).
The scientific literature addresses the term of "admissible"/tolerated soil erosion, which describes the
existing erosion caused by farming that does not impact future agricultural development. For this to
happen, a set of general rules is needed: the soil layer has to be sufficiently deep to ensure agricultural
and forestry production for longer periods of time, therefore the erosion effects have to be considered
for each soil class and type (Băldoi, V., Ionescu, V., 1986). The category of large territorial
administrative units likely to be affected by soil erosion includes: TAUs Viișoara, Apahida, Luna, Baciu,
Gârbău, Aghireșu, Călățele etc.
Note should also be taken of the 27 TAUs (Mârgău, Măguri Răcătău, Mănăstireni, Aghireșu) that were
included into the medium risk class for areas in excess of 100 hectares.
Figure 119 – Soil erosion risk map for Cluj County
Data souces: data processed according to IPCA/Order no. 223 of 28/05/2002 approving the Methodology for
the preparation of pedologic and agrochemical studies of the national and county soil-terrain monitoring
system for farming purposes
147
Table 35. Classes of erosion risk classes for TAUs of Cluj County
Likelihood of landslides
Medium-
N Very low Low Medium High Very high
TAUU high
o.
ha % ha % ha % ha % ha % ha %
1 AGHIRESU 760.0 72.6 174. 16.7 104.5 10.0 6.5 0.6 0.7 0. 0. 760.0
8 1 0
2 AITON 371.9 82.2 59.6 13.2 20.0 4.4 1.1 0.2 0.0 0. 0. 371.9
0 0
3 ALUNIS 395.8 70.0 123. 21.9 45.2 8.0 0.3 0.0 0.0 0. 0. 395.8
9 0 0
4 APAHIDA 795.7 75.0 170. 16.0 86.6 8.2 7.4 0.7 1.2 0. 0. 795.7
1 1 1
5 ASCHILEU 586.9 91.7 40.7 6.4 12.0 1.9 0.1 0.0 0.0 0. 0. 586.9
0 0
6 BACIU 619.8 70.8 167. 19.1 82.6 9.4 4.7 0.5 1.0 0. 0. 619.8
2 1 0
7 BAISOARA 944.4 85.4 125. 11.3 36.8 3.3 0.2 0.0 0.0 0. 0. 944.4
0 0 0
8 BELIS 1852.7 90.8 138. 6.8 47.7 2.3 1.0 0.1 0.0 0. 0. 1852.7
7 0 0
9 BOBÂLNA 754.4 77.6 164. 16.9 53.8 5.5 0.0 0.0 0.0 0. 0. 754.4
6 0 0
10 BONTIDA 635.5 78.1 132. 16.3 44.1 5.4 1.6 0.2 0.2 0. 0. 635.5
4 0 0
11 BORSA 536.4 87.1 70.3 11.4 9.2 1.5 0.1 0.0 0.0 0. 0. 536.4
0 0
12 BUZA 219.8 74.6 60.0 20.4 15.0 5.1 0.0 0.0 0.0 0. 0. 219.8
0 0
13 CAIANU 469.2 85.5 65.0 11.8 14.6 2.7 0.0 0.0 0.0 0. 0. 469.2
0 0
14 CALARASI 329.4 87.6 40.8 10.9 5.7 1.5 0.0 0.0 0.0 0. 0. 329.4
0 0
15 CALATELE 521.2 69.4 144. 19.2 80.5 10.7 4.0 0.5 0.5 0. 0. 521.2
4 1 0
16 CAMARASU 438.1 93.1 25.4 5.4 6.9 1.5 0.2 0.0 0.0 0. 0. 438.1
0 0
17 CÂMPIA TURZII 1079.5 80.3 210. 15.6 54.3 4.0 0.2 0.0 0.0 0. 0. 1079.5
2 0 0
18 CAPUSU MARE 635.8 77.7 103. 12.7 77.0 9.4 2.1 0.3 0.0 0. 0. 635.8
8 0 0
19 CASEIU 435.2 82.6 76.0 14.4 16.0 3.0 0.0 0.0 0.0 0. 0. 435.2
0 0
20 CÂTCAU 217.0 91.3 17.5 7.3 2.9 1.2 0.3 0.1 0.1 0. 0. 217.0
0 0
21 CATINA 341.6 91.9 25.4 6.8 4.7 1.3 0.0 0.0 0.0 0. 0. 341.6
0 0
22 CEANU MARE 806.6 85.2 106. 11.3 31.6 3.3 1.4 0.1 0.0 0. 0. 806.6
6 0 0
23 CHINTENI 861.1 89.2 84.0 8.7 19.3 2.0 0.5 0.0 0.0 0. 0. 861.1
0 0
24 CHIUIESTI 1008.4 91.2 77.2 7.0 19.7 1.8 0.3 0.0 0.0 0. 0. 1008.4
0 0
148
Likelihood of landslides
Medium-
N Very low Low Medium High Very high
TAUU high
o.
ha % ha % ha % ha % ha % ha %
25 CIUCEA 468.2 97.3 11.4 2.4 1.8 0.4 0.0 0.0 0.0 0. 0. 468.2
0 0
26 CIURILA 655.0 90.5 44.7 6.2 22.6 3.1 1.3 0.2 0.0 0. 0. 655.0
0 0
27 CLUJ-NAPOCA 1487.9 85.1 194. 11.1 63.0 3.6 2.7 0.2 0.5 0. 0. 1487.9
3 0 0
28 COJOCNA 1118.1 80.7 210. 15.2 54.9 4.0 2.3 0.2 0.2 0. 0. 1118.1
6 0 0
29 CORNESTI 639.8 77.1 152. 18.4 37.2 4.5 0.2 0.0 0.0 0. 0. 639.8
4 0 0
30 CUZDRIOARA 208.8 89.7 18.3 7.9 5.5 2.4 0.3 0.1 0.0 0. 0. 208.8
0 0
31 DABÂCA 415.6 82.7 63.5 12.6 21.9 4.4 1.2 0.2 0.2 0. 0. 415.6
0 0
32 DEJ 869.1 79.8 163. 15.0 55.8 5.1 0.8 0.1 0.0 0. 0. 869.1
4 0 0
33 FELEACU 547.7 82.7 85.8 12.9 28.5 4.3 0.3 0.1 0.2 0. 0. 547.7
0 0
34 FIZESU GHERLII 526.7 78.4 85.7 12.8 56.0 8.3 3.6 0.5 0.2 0. 0. 526.7
0 0
35 FLORESTI 417.2 68.5 108. 17.8 76.1 12.5 6.5 1.1 0.4 0. 0. 417.2
5 1 0
36 FRATA 642.2 88.8 71.3 9.9 9.4 1.3 0.1 0.0 0.0 0. 0. 642.2
0 0
37 GÂRBAU 528.8 73.5 113. 15.8 68.0 9.4 8.3 1.1 0.7 0. 0. 528.8
8 1 0
38 GEACA 579.1 84.9 83.0 12.2 19.6 2.9 0.2 0.0 0.0 0. 0. 579.1
0 0
39 GHERLA 227.1 62.6 81.1 22.4 52.9 14.6 1.6 0.4 0.0 0. 0. 227.1
0 0
40 GILAU 1013.2 86.4 118. 10.1 39.2 3.3 1.2 0.1 0.0 0. 0. 1013.2
7 0 0
41 HUEDIN 440.5 73.4 117. 19.6 40.5 6.7 1.4 0.2 0.0 0. 0. 440.5
6 0 0
42 IARA 1303.5 91.7 88.2 6.2 29.6 2.1 0.3 0.0 0.0 0. 0. 1303.5
0 0
43 ICLOD 487.9 71.8 130. 19.2 59.0 8.7 1.5 0.2 0.1 0. 0. 487.9
7 0 0
44 IZVORU CRISULUI 299.4 73.0 71.2 17.4 37.8 9.2 1.8 0.4 0.1 0. 0. 299.4
0 0
45 JICHISU DE JOS 322.3 74.2 81.0 18.6 31.1 7.2 0.1 0.0 0.0 0. 0. 322.3
0 0
46 JUCU 672.6 79.9 125. 14.9 42.5 5.1 0.8 0.1 0.0 0. 0. 672.6
6 0 0
47 LUNA 486.4 93.5 20.6 4.0 9.4 1.8 3.0 0.6 1.0 0. 0. 486.4
2 1
48 MAGURI- 2178.8 81.8 362. 13.6 121.4 4.6 0.0 0.0 0.0 0. 0. 2178.8
RACATAU 0 0 0
49 MANASTIRENI 354.9 56.6 148. 23.7 118.3 18.9 5.1 0.8 0.1 0. 0. 354.9
4 0 0
149
Likelihood of landslides
Medium-
N Very low Low Medium High Very high
TAUU high
o.
ha % ha % ha % ha % ha % ha %
50 MARGAU 1636.0 77.5 340. 16.1 134.5 6.4 0.7 0.0 0.0 0. 0. 1636.0
8 0 0
51 MARISEL 731.5 85.4 107. 12.5 17.9 2.1 0.0 0.0 0.0 0. 0. 731.5
2 0 0
52 MICA 477.9 74.1 94.9 14.7 69.8 10.8 1.9 0.3 0.0 0. 0. 477.9
0 0
53 MIHAI VITEAZU 389.4 81.8 56.7 11.9 29.2 6.1 1.0 0.2 0.0 0. 0. 389.4
0 0
54 MINTIU GHERLII 568.1 72.4 122. 15.6 90.0 11.5 4.1 0.5 0.1 0. 0. 568.1
8 0 0
55 MOCIU 618.9 85.3 85.4 11.8 20.7 2.9 0.3 0.0 0.0 0. 0. 618.9
0 0
56 MOLDOVENESTI 1091.1 79.6 190. 13.9 85.8 6.3 2.1 0.1 0.3 0. 0. 1091.1
6 0 0
57 NEGRENI 610.1 95.8 25.3 4.0 1.4 0.2 0.0 0.0 0.0 0. 0. 610.1
0 0
58 PALATCA 435.1 86.8 59.5 11.9 6.8 1.3 0.0 0.0 0.0 0. 0. 435.1
0 0
59 PANTICEU 787.2 87.7 86.2 9.6 23.1 2.6 0.8 0.1 0.1 0. 0. 787.2
0 0
60 PETRESTII DE JOS 633.9 87.2 65.9 9.1 25.9 3.6 0.9 0.1 0.3 0. 0. 633.9
0 0
61 PLOSCOS 326.1 78.3 63.5 15.2 25.6 6.1 1.5 0.4 0.0 0. 0. 326.1
0 0
62 POIENI 1708.3 93.1 105. 5.8 20.1 1.1 0.5 0.0 0.0 0. 0. 1708.3
7 0 0
63 RECEA-CRISTUR 620.7 83.2 100. 13.4 24.7 3.3 0.4 0.0 0.0 0. 0. 620.7
2 0 0
64 RISCA 462.6 70.3 111. 17.0 82.1 12.5 1.4 0.2 0.0 0. 0. 462.6
8 0 0
65 SACUIEU 1026.0 85.4 125. 10.5 46.8 3.9 2.5 0.2 0.0 0. 0. 1026.0
5 0 0
66 SÂNCRAIU 154.4 69.2 56.6 25.4 12.3 5.5 0.0 0.0 0.0 0. 0. 154.4
0 0
67 SANDULESTI 876.2 79.6 148. 13.5 72.9 6.6 3.3 0.3 0.0 0. 0. 876.2
1 0 0
68 SÂNMARTIN 516.3 90.4 43.8 7.7 11.1 2.0 0.1 0.0 0.0 0. 0. 516.3
0 0
69 SÂNPAUL 639.6 90.4 61.4 8.7 6.3 0.9 0.0 0.0 0.0 0. 0. 639.6
0 0
70 SAVADISLA 822.9 89.1 77.8 8.4 22.3 2.4 0.8 0.1 0.0 0. 0. 822.9
0 0
71 SCI 444.0 78.9 96.9 17.2 21.4 3.8 0.2 0.0 0.0 0. 0. 444.0
0 0
72 SUATU 471.6 88.9 35.2 6.6 22.9 4.3 1.1 0.2 0.0 0. 0. 471.6
0 0
73 TAGA 861.7 86.3 111. 11.2 24.9 2.5 0.0 0.0 0.0 0. 0. 861.7
3 0 0
74 TRITENII DE JOS 520.5 88.7 53.4 9.1 13.0 2.2 0.1 0.0 0.0 0. 0. 520.5
0 0
150
Likelihood of landslides
Medium-
N Very low Low Medium High Very high
TAUU high
o.
ha % ha % ha % ha % ha % ha %
75 TURDA 780.0 85.2 76.8 8.4 54.2 5.9 4.1 0.5 0.1 0. 0. 780.0
0 0
76 TURENI 558.6 75.5 121. 16.5 56.4 7.6 2.4 0.3 0.5 0. 0. 558.6
8 1 0
77 UNGURAS 541.0 85.8 59.0 9.4 30.4 4.8 0.0 0.0 0.0 0. 0. 541.0
0 0
78 VAD 662.1 86.9 72.2 9.5 27.5 3.6 0.1 0.0 0.0 0. 0. 662.1
0 0
79 VALEA IERII 1296.7 87.5 142. 9.6 43.3 2.9 0.0 0.0 0.0 0. 0. 1296.7
0 0 0
80 VIISOARA 435.4 71.5 116. 19.2 49.1 8.1 6.7 1.1 1.4 0. 0. 435.4
8 2 0
81 VULTURENI 654.8 91.5 49.6 6.9 11.3 1.6 0.1 0.0 0.0 0. 0. 654.8
0 0
TOTAL 54865.4 82.6 8220 12.4 3175 4.8 113. 0.2 10. 0 0. 0
5 3 2
The reduction of soil erosion requires measures such as: the need to carry out terrain works where
the soil is friable, i.e. from slightly dry to slightly wet, thus rendering it very grainy.
Considering the research results on the anti-erosion role of vegetation (Moțoc, 1975), and the
identification of areas highly exposed to soil erosion in Cluj County, several invasive methods could be
suggested to mitigate the negative effects of soil erosion, such as: crop rotation (replacement of
perennial crops and alfalfa, which provide improved soil protection thanks to high density and high
water consumption), earthworks along the hill-valley direction, earthworks around contour lines,
terracing, etc.
The works aimed at mitigating soil erosion are limited to the following territorial administrative units,
as per the data supplied by the National Land Development Agency: Aghireșu, Aiton, Apahida,
Așchileu, Baciu, Bobâlna, Borșa, Căianu, Călățele, Căpușu Mare, Câmpia Turzii, Chinteni, Ciurila, Cluj
Napoca, Cojocna, Dej, Feleacul, Florești, Gârbou, Jichișu de Jos, Jucu, Mănăstireni, Mintiu Gherlii,
Petreștii de Jos, Ploscoș, Recea Cristur, Rîșca, Săvădisla and Sânpaul.
151
Figure 120 – Works aimed mitigating soil erosion in Cluj County
2.6. Other natural risks (forest fires, snow avalanches) - affected areas,
existing infrastructure, investments etc.)
2.6.1. Forest fires
Forest fires2 are among the most serious threats to forest ecosystems. During the study period, i.e.
2009-2018, 136 forest fires occurred in Cluj County. The share of land affected by fires was very low
(below 0.1%) during the study period when compared to the total forest area of the county, with peak
values registered in 2012.
According to the Risk coverage and assessment plan of Cluj County, forest fire risk is a medium risk.
Forest fire interventions are carried out by each subunit, by means of emergency crews and vehicles
inside each forest district. The travelling times cannot be predicted, since a fire can break out in
anywhere, with those places being several hours away, even by foot in most situations. The
interventions must be attended by subunits of the Gendarmerie, Ministry of Defence, citizen groups
organized by local authorities, and the staff of forest directorates and districts.
2 A forest fire is classified as such when the fire covers at least 1 ha of forest vegetation and affects several vegetation
sublayers. (L. Holonec, 2007, Forest fires and their effects on forest ecosystems, Protecția Plantelor Magazine, no. 65, pp. 17-
21)
152
The number of fires changed significantly over time: from one fire in 2017 to 37 fires in 2012 (see the
table below).
According to the table below, the periods with most frequent fires were concentrated in spring: the
second 10-day period of March – first 10-day period of May-April (56%), with less frequent cases in
the second 10-day period of December – first 10-day period of March and end of May – June (Table
36).
The most prevalent cause is anthropic, namely negligence, combined with adverse weather
conditions, mainly wind: burning of pastures and uncontrolled fires (67.82%), or open fires left
unsupervised by tourists (32.17%). The cause could not be determined in just four cases, and was
assumed to be arson. Lightning, as a potential cause of forest fires, was not recorded during the
analysed period.
Table 36. Distribution of forest fires during 2009-2018, on years, months and 10-day periods
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total:
1 1 1
I 2 1 1
3
1
II 2
3
1
III 2 1 1 1 1 4
3 7 2 3 20 2 34
1 6 2 2 10
IV 2 1 3 6 2 12
3 4 1 1 1 7
1 2 1 5 1 9
V 2 1 1
3
1 1 2 3
VI 2
3
1 2 1 3
VII 2 2 1 3
3 1 1
1
VIII 2 5 5
3 1 1 1 1 4
1 1 3 4
IX 2 1 2 2 5
3
1 1 2 1 4
X 2 2 2
3 2 2 4
1 3 3 6
XI 2 1 4 1 6
3 1 1
1 3 1 4
XII 2 1 1
3
Total: 28 5 27 37 4 3 16 3 1 12 136
153
In terms of territory, no strong correlation was noted to exist between geographical units and the
distribution of forest fires (Fig. 121).
154
Most fires occurred on forest edges, a fact also explained by the most frequent cause of forest fires,
namely pasture 'clearing'. Most fires occurred on forest edges and particularly affected pine
plantations or secondary forests. Treeline fires were few, but they caused the greatest damage to both
property and ecosystems. No underground fires were recorded during the analysed period.
Figure 121 – Spatial distribution per years of forest fires in Cluj County, 2009-2018
Source data: data processed by ISU Cluj
If the fires occurring in plateau areas or the south-eastern part of the administrative territory of the
city of Turda, although in large numbers, did not cause significant damage due to their limited
spreading and the unimpeded interventions, the mountain areas saw however the greatest damage,
due to the large affected areas and the better quality of tree wood. In some cases, due to difficult
access, the area affected by fire exceeded 40 ha, with a loss of more than 700 cubic metres of wood.
The areas at risk of forest fires in Cluj County are depicted by dots on the map below. Also depicted
are areas travelled by tourists, who may start fires due to negligence.
155
Figure 122 – Number of forest fires in Cluj County per territorial administrative unit (2009-2018)
Source data: data processed by ISU Cluj
In hill and plateau areas, there is land covered with softwood forests, which varies from 10 to 100 ha.
In forest districts with greater softwood coverage (approx. 80%), the risk of fires is significantly
increased.
The influence of weather factors is also very high, which requires an annual assessment of fire risks
that also covers multiple year periods. The intervention effectiveness is influenced by: travelling
timenumber of intervening forces, access routes used, the location of the affected forest part, the
type of vegetation, weather factors (particularly the direction of wind).
156
Figure 123 – Map of intervention times of fire departments in Cluj County
The travelling times (Fig. 123) required to reach the fire location is relatively high, sometimes in excess
of two hours, mainly due to the poor state of access routes and large grades in some sections. Since
interventions require large numbers of people, the mayor's offices have a particularly important role
in this regard.
It is a promising sign that most fires, thanks to the timely intervention of firemen, forest workers and
the local population, were located and extinguished without significant damage and without loss of
human lives.
However, the risk that forest fires cause serious damage remains high, particularly against the
background of climate change, but few are aware of this.
157
Table 37. Classification of administrative units with regard to civil protection, according to specific risks
Failure
Earthquakes Landslides Floods Drought Avalanche Forest fires Chemical accidents Nuclear accidents Mass fires Traffic accidents Epidemics Epiz.
No Risk types and classes u. p.
Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Sc.
Locality C c At at Id id S s Av av Ip ip Ach ach An an Im im Atp atp Eup eup Ed ed ed
1 City of CÂMPIA TURZII - X - - X - X - - X - X X - - X - X X - X - - X X
2 City of CLUJ-NAPOCA - X X - X - - X - X - X X - - X - X X - X - - X X
3 City of DEJ - X X - X - - X - X - X X - - X - X X - X - - X X
4 City of GHERLA - X X - X - - X - X - X X - - X - X X - X - - X X
5 City of TURDA - X X - X - X - - X - X X - - X - X X - X - - X X
6 Town of HUEDIN - X X - X - - X - X - X X - - X - X X - X - - X X
7 Com. of AGHIREŞ - X X - X - - X - X - X - X - X - X - X X - - X X
8 Com. of AITON - X X - X - X - - X - X - X - X - X - X X - - X X
9 Com. of ALUNIŞ - X X - X - - X - X - X X - - X - X - X X - - X X
10 Com. of APAHIDA - X - X X - X - - X - X - X - X - X X - X - - X X
11 Com. of AŞCHILEU - X - X X - - X - X - X - X - X - X - X X - - X X
12 Com. of BACIU - X X - X - X - - X - X - X - X - X X - X - - X X
13 Com. of BĂIŞOARA - X - X X - - X X - X - - X - X - X - X X - - X X
14 Com. of BELIŞ - X - X X - - X X - X - - X - X - X - X X - - X X
15 Com. of BOBÎLNA - X X - X - - X - X - X - X - X - X - X X - - X X
16 Com. of BONŢIDA - X - X X - X - - X - X - X - X - X X - X - - X X
17 Com. of BORŞA - X X - X - X - - X - X - X - X - X - X X - - X X
18 Com. of BUZA - X - X X - X - - X - X - X - X - X - X X - - X X
19 Com. of CĂIANU - X X - X - X - - X - X - X - X - X - X X - - X X
20 Com. of CĂLĂRAŞI - X X - X - X - - X - X X - - X - X - X X - - X X
21 Com. of CĂLĂŢELE - X - X X - - X X - X - - X - X - X - X X - - X X
22 Com. of CĂMĂRAŞU - X - X X - X - - X - X - X - X - X X - X - - X X
23 Com. of CĂPUŞU MARE - X X - X - - X X - X - - X - X - X X - X - - X X
24 Com. of CĂŞEI - X X - X - - X - X - X X - - X - X X - X - - X X
25 Com. of CĂTINA - X - X X - X - - X - X - X - X - X - X X - - X X
26 Com. of CEANU MARE - X - X X - X - - X - X X - - X - X - X X - - X X
27 Com. of CHINTENI - X X - X - - X - X - X X - - X - X - X X - - X X
28 Com. of CHIUEŞTI - X X - X - - X - X - X - X - X - X - X X - - X X
29 Com. of CIUCEA - X - X X - - X X - X - - X - X - X X - X - - X X
30 Com. of CIURILA - X X - X - - X - X - X - X - X - X - X X - - X X
31 Com. of CÎŢCĂU - X X - X - - X - X - X X - - X - X X - X - - X X
32 Com. of COJOCNA - X X - X - X - - X - X - X - X - X - X X - - X X
33 Com. of CORNEŞTI - X - X X - X - - X - X - X - X - X - X X - - X X
158
Failure
Earthquakes Landslides Floods Drought Avalanche Forest fires Chemical accidents Nuclear accidents Mass fires Traffic accidents Epidemics Epiz.
No Risk types and classes u. p.
Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Sc.
Locality C c At at Id id S s Av av Ip ip Ach ach An an Im im Atp atp Eup eup Ed ed ed
34 Com. of CUZDRIOARA - X - X X - X - - X - X X - - X - X X - X - - X X
35 Com. of DĂBÎCA - X - X X - - X - X - X - X - X - X - X X - - X X
36 Com. of FELEACU - X X - X - - X - X - X - X - X - X X - X - - X X
37 Com. of FIZEŞUL HERLII - X X - X - X - - X - X - X - X - X - X X - - X X
38 Com. of FLOREŞTI - X X - X - X - - X - X X - - X - X X - X - - X X
39 Com. of FRATA - X - X X - X - - X - X - X - X - X - X X - - X X
40 Com. of GEACA - X - X X - X - - X - X - X - X - X - X X - - X X
41 Com. of GILĂU - X - X X - - X X - X - X - - X - X X - X - - X X
42 Com. of GÎRBĂU - X - X X - X - - X - X - X - X - X - X X - - X X
43 Com. of IARA - X - X X - - X X - X - - X - X - X - X X - - X X
44 Com. of ICLOD - X X - X - X - - X - X - X - X - X X - X - - X X
45 Com. of IZVORUL CRIŞULUI - X X - X - - X - X - X - X - X - X X - X - - X X
46 Com. of JICHIŞ - X X - X - X - - X - X X - - X - X - X X - - X X
47 Com. of JUCU DE SUS - X X - X - X - - X - X - X - X - X X - X - - X X
48 Com. of LUNA - X - X X - X - - X - X X - - X - X - X X - - X X
49 Com. of MĂGURI RĂCĂTĂU - X X - X - - X X - X - - X - X - X - X X - - X X
50 Com. of MĂNĂSTIRENI - X - X X - - X X - X - - X - X - X - X X - - X X
51 Com. of MĂRGĂU - X X - X - - X X - X - - X - X - X - X X - - X X
52 Com. of MĂRIŞEL - X - X X - - X X - X - - X - X - X - X X - - X X
53 Com. of MICA - X X - X - X - - X - X X - - X - X - X X - - X X
54 Com. of MIHAI VITEAZU - X X - X - X - X - - X X - - X - X X - X - - X X
55 Com. of MINTIUL GHERLII - X X - X - X - - X - X X - - X - X - X X - - X X
56 Com. of MOCIU - X X - X - X - - X - X - X - X - X X - X - - X X
57 Com. of MOLDOVENEŞTI - X - X X - X - X - X - X - - X - X - X X - - X X
58 Com. of NEGRENI - X - X X - - X X - X - - X - X - X X - X - - X X
59 Com. of PANTICEU - X - X X - - X - X - X - X - X - X - X X - - X X
60 Com. of PĂLATCA - X - X X - X - - X - X - X - X - X - X X - - X X
61 Com. of PETREŞTII DE JOS - X - X X - - X X - - X - X - X - X - X X - - X X
62 Com. of PLOSCOŞ - X X - X - X - - X - X - X - X - X - X X - - X X
63 Com. of POIENI - X - X X - - X X - X - - X - X - X X - X - - X X
64 Com. of RECEA CRISTUR - X X - X - - X - X - X - X - X - X - X X - - X X
65 Com. of RÎŞCA - X - X X - - X X - X - - X - X - X - X X - - X X
66 Com. of SÂNCRAI - X X - X - - X X - X - - X - X - X - X X - - X X
67 Com. of SÂNMĂRTIN - X X - X - X - - X - X - X - X - X - X X - - X X
159
Failure
Earthquakes Landslides Floods Drought Avalanche Forest fires Chemical accidents Nuclear accidents Mass fires Traffic accidents Epidemics Epiz.
No Risk types and classes u. p.
Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Pr. Sc. Sc.
Locality C c At at Id id S s Av av Ip ip Ach ach An an Im im Atp atp Eup eup Ed ed ed
68 Com. of SÂNPAUL - X X - X - X - - X - X - X - X - X X - X - - X X
69 Com. of SĂCUIEU - X - X X - - X X - X - - X - X - X - X X - - X X
70 Com. of SĂNDULEŞTI - X X - X - X - - X - X X - - X - X X - X - - X X
71 Com. of SĂVĂDISLA - X - X X - - X X - - X - X - X - X - X X - - X X
72 Com. of SIC - X X - X - - X - X - X - X - X - X - X X - - X X
73 Com. of SUATU - X - X X - X - - X - X - X - X - X X - X - - X X
74 Com. of TRITENII DE JOS - X X - X - X - - X - X - X - X - X - X X - - X X
75 Com. of TURENI - X - X X - X - - X - X X - - X - X - X X - - X X
76 Com. of ŢAGA - X X - X - X - - X - X - X - X - X X - X - - X X
77 Com. of UNGURAŞ - X - X X - X - - X - X X - - X - X - X X - - X X
78 Com. of VAD - X X - X - - X - X - X X - - X - X X - X - - X X
79 Com. of VALEA IERII - X - X X - - X X - X - - X - X - X - X X - - X X
80 Com. of VIIŞOARA - X - X X - X - - X - X X - - X - X - X X - - X X
81 Com. of VULTURENI - X X - X - - X - X - X - X - X - X - X X - - X X
Where: Ach – chemical accident main risk
C – earthquake main risk, ach – chemical accident secondary risk
c- earthquake secondary risk, An - nuclear accident main risk
At/Pt – Landslide main risk an - nuclear accident main risk
At/pt – Landslide secondary risk Im – Forest fire main risk
Id - flooding main risk im – Forest fire main risk
Id – flooding secondary risk Atp – Serious traffic accident main risk
S – drought main risk atp – Serious traffic accident main risk
s – drought secondary risk Eup – Utility failure main risk
Av – avalanche main risk eup – Utility failure main risk
av – avalanche secondary risk Ed – Utility failure main risk
Ip – forest fire main risk ed – Utility failure main risk
ip – forest fire secondary risk Ez - Epizootic main risk
Ach – chemical accident main risk ez - Epizootic main risk
ach – chemical accident secondary risk
Source: G.D. 642 of 29 June 2005, Official Gazette No 603 of 13 July 2005
160
2.6.2. Avalanches
Avalanches are unwanted phenomena consisting in the sliding or flowing on mountain slopes of snow
slabs, which sometimes carry rocks, boulders, bushes, etc. They are much feared due to their risk of
causing human casualties.
According to G.D. 642 of 29 June 2005 (Official Gazetee No 603 of 13 July 2005), 21 territorial
administrative units are located within the main avalanche risk area (Fig. 124). These represent TAUs
with inclined slopes in excess of 30° and very inclined slopes (in excess of 40°), where the accumulation
of snow can trigger avalanches.
In the mountain area of Cluj County, avalanche victims mostly consist in tourists, skiers and climbers.
Avalanches can however cause disasters by covering roads and obstructing access to places.
Figure 124 – Classification of territorial administrative units per classes of avalanche risk
Source data: G.D. 642 of 29 June 2005, Official Gazette No 603 of 13 July 2005
Prevention measures: the most accurate identification of dangerous areas; restricting building and
other activities in exposed areas; special protection measures aimed at communication routes and
buildings in high risk areas (temporary protection measures: regulations (access and travel
restrictions, evacuations in cases of imminent risks); artificial triggering of avalanches with specific
means, usually with explosives (artillery fire or special launchers, helicopter-dropped explosives);
permanent protection measures: passive defence, by: engineering works to deflect, delay or stop
avalanches; active defence: reinforcing of snow in the collapsible area by means of terrain changes:
draining, small barriers, banks, combined with ecological and effective forestation.
161
The preventive and intervention measures are ensured by the Mountain Rescue Service of
the Cluj County Council. No avalanches causing casualties or loss of property occurred during the last
decade.
2.7. Emergency situations infrastructure and management
According to the Risk coverage and assessment plan of Cluj County, the risk assessment and coverage
responsibilities are incumbent on all agencies that are tasked with the support functions aimed at
preventing and managing emergency situations at territorial level.
In Cluj County, the agencies tasked with the prevention and management of potential emergency-
inducing risks consist in professional emergency services (the "Avram Iancu" Emergency Situations
Inspectorate), voluntary emergency services (set up in the county's localities) and private emergency
services (set up by businesses and public institutions); medical emergency units– the Cluj County
Clinical Emergency Hospital, city and town hospitals, communal dispensaries and extrication services
– the "Avram Iancu" Emergency Situations Inspectorate; civil protection units: search and rescue
teams; NBRC defence teams; bomb defusing squad – all set up within the "Avram Iancu" Emergency
Situations Inspectorate.
Other rescue units are the Red Cross county branch, the County Public Service SALVAMONT,
professional divers; Cluj County Police – the police posts/offices from cities, towns and communes;
the Cluj Gendarmerie; the Community Polic – where such units exist; non-governmental organizations
specializing in rescue operations; healthcare and sanitary-veterinary units – the Sanitary Veterinary
and Food Safety Directorate.
There are eighty Sanitary Veterinary and Food Safety services (SVSU) in Cluj County, authorized
according to O.M.A.I. No 718/2005 : 57 category I SVSUs, 12 category II SVSUs, 1 categort IV SVSU
approved according to O.M.A.I. No 96/2016 (repealing O.M.A.I. No 718/2005 ): 10 category C1 SVSUs.
We also note that the Official Gazette No 533/28.06.2019 published O.M.A.I. No 75 approving
Performance criteria for the classification and endowment of SVSU/SPSU, which repeals O.M.A.I. No
96/2016 and provides that the approvals granted under the previous legislation become invalid within
6 months and require the revaluation and reapproval of all SVSU at TAU level during that grace period,
according to the terms of the new legislation.
The "Avram Iancu" Emergency Situations Inspectorate of Cluj County operates as a professional
emergency service and was set-up as public decentralized service under the management of the
General Emergency Situations Inspectorate and the coordination of the Cluj County Prefect.
The Cluj County Emergency Situations Inspectorate, the lcoal committees, the emergency situations
operative centres and the emergency cells are structures authorized to deal with emergency situations
and are part of the National Emergency Management System.
In order to fulfil their legal tasks, the specialists of the Cluj County Emergency Situations Committee
are organized into technical support groups coordinate by a committee member, with five technical
support groups operating at county level and organized according to risk groups:
162
1. The technical support group managing emergency situations caused by flooding, dangerous
weather conditions, accidents at water engineering works, and accidental pollution;
2. The technical support group managing emergency situations caused by snowing, blizzards and
ice;
3. The technical support group managing emergency situations caused by earthquakes and
landslides;
4. The temporary operative centre managing emergency situations caused by pest invasions and
contamination of crops with weed-killing products;
5. The county structure monitoring and managing risks caused by hail and severe drought.
The equipment and personnel structure of the voluntary emergency services as of 01.07.2019 is
presented under tables 38-43.
Table 38. Set-up, equipment and personnel structure of voluntary emergency services
SET-UP EQUIPMENT
water
firefighting water and
evacuation other fire
Legal basis categ. no. Total mechanical foam fire ambulances
mechanical engines
pump engine
pump
I 0 0 0 0 0 0
II 0 0 0 0 0 0
no approval 0
III 0 0 0 0 0 0
IV 0 0 0 0 0 0
I 57 7 151 0 7 0
approved acc. to II 0 0 0 0 0 0
70
OMAI 718, valid III 12 26 5 5 0 0
IV 1 5 1 2 1 0
approved acc. to C1 10 0 18 0 2 0
10
OMAI 96 C2 0 0 0 0 0 0
Total - 38 175 7 10 0
Source data: ISU Cluj
163
Table 39. Personnel structure of voluntary emergency services
PERSONNEL STRUCTURE
Employed staff Voluntary staff
out of
fire engine driver
fire engine driver
specialized team
specialized team
head of service
head of service
which
Total no.
out of which
prevention
prevention
Legal basis
machinery
machinery
specialist
specialist
operator
operator
qualified
firemen
firemen
Total
Total
volunteers
staff
staff
qualified
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
approval
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
no
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2246 43 30 2 5 0 22 102 41 14 105 3 86 39 1897 2144 61 993
d acc. to
approve
OMAI
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
valid
718,
763 10 15 19 0 5 0 49 9 2 46 18 11 29 608 714 28 114
29 1 1 6 0 4 0 12 12 0 0 0 0 17 0 17 12 17
258 10 19 0 0 0 2 31 15 0 60 4 0 2 161 227 15 227
acc
ed
ap
96
ov
pr
to
M
AI
O
.
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3296 64 65 27 5 9 24 194 77 16 211 25 97 87 2666 3102
Tot
al
Source data: ISU Cluj
Table 40. Set-up, emergency intervention equipment, own services
SET-UP EQUIPMENT
water
firefighting water and
evacuation other fire
Legal basis categ. no. Total mechanical foam fire ambulances
mechanical engines
pump engine
pump
no approval I 0 0 0 - - 0
II 0 0 0 - - 0
III 0 0 0 - - 0
IV 0 0 0 0 0 0
V 0 0 0 0 0 0
approved acc. to I 1 22 0 0 - - 0
OMAI 158, valid II 18 3 2 - - 0
III 1 1 0 - - 0
IV 1 0 0 1 0 0
V 1 0 0 3 0 1
approved acc. to C1 1 1 2 0 0 0 0
OMAI 96 C2 0 0 0 0 0 0
Total - 23 23 6 2 4 0 1
Source data: ISU Cluj
164
Table 41. Personnel structure of private emergency situations services set-up as own services
PERSONNEL STRUCTURE
Employed staff Staff employed on other positions
head of team
head of team
Total no.
out of which
out of which
prevention
prevention
specialized
specialized
fire engine
fire engine
machinery
machinery
team staff
team staff
specialist
specialist
operator
operator
qualified
qualified
fireman
fireman
head of
head of
service
service
driver
driver
Total
Total
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0
439 18 0 31 0 12 50 0 111 48 0 0 0 0 0 171 157 328 47
51 1 0 0 0 0 0 0 1 1 0 0 9 0 3 14 24 50 2
19 1 1 2 3 0 0 0 7 6 0 0 0 0 0 12 0 12 0
123 1 0 1 6 0 16 9 33 18 0 0 0 0 0 0 90 90 0
82 1 0 1 0 0 15 0 17 16 0 0 6 0 6 41 12 65 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
716 22 1 36 9 12 81 9 170 90 1 0 15 0 9 238 283 546 49
Source data: ISU Cluj
Table 42. Set-up and emergency intervention equipment, service providers
SET-UP EQUIPMENT
water
firefighting water and
evacuation other fire
Legal basis categ. no. Total mechanical foam fire ambulances
mechanical engines
pump engine
pump
approved acc. to IV 0 0 1 0 0
OMAI 158, valid V 0 0 0 0 0
approved acc. to C1 0 0 0 0 0 0
OMAI 96 C2 1 0 0 4 2 0
Total - 1 0 0 5 2 0
Source data: ISU Cluj
165
Table 43. Personnel structure of private emergency intervention services set-up as service providers
PERSONNEL STRUCTURE
Total head of head of prevention specialist fire engine machinery fireman specialized team staff
no. service team driver operator
23 0 1 0 3 3 6 10
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
82 1 3 3 10 3 20 42
105 1 4 3 13 6 26 52
Source data: ISU Cluj
Locations where inspectorate subunits operate:
1. Cluj-Napoca Firefighting Unit no. 1 - city of Cluj-Napoca.
2. Cluj-Napoca Firefighting Unit no. 2 - city of Cluj-Napoca.
3. Turda Firefighting Unit - city of Turda.
4. Dej Firefighting Unit no. 1 - city of Dej.
5. Huedin Firefighting Unit - town of Huedin.
6. Gilău Intervention Guard - Gilău village.
7. Aghireșu Intervention Guard - Aghireșu village.
8. Băișoara Intervention Guard - Băișoara village.
9. Mociu Unit - Mociu village.
10. Florești unit - Florești village.
166
Figure 125 – Areas of competence of the Inspectorate for Emergency Situations
Under the 2014 - 2020 Large Infrastructure Operational Program and the Effective Response Saves
Lives/ERSL I and II Project, the emergency situations inspectorate was endowed over the last two years
with new and complex intervention equipment, which only partly cover the intervention needs in the
respective areas of competence. However, new intervention equipments are still needed in all
subunits, including SMURD equipment, since there still are very old fire engines that became
technically and tactically obsolete.
The Salvamont Public County Service – Salvaspeo Cluj (SPJSS Cluj) operates on the administrative
territory of Cluj County. The 24/7 rescue facilitiesare the following: Cheile Turzii, Muntele Băișorii,
Beliș, Vlădeasa and Doda Pilii. During peak tourist seasons, the following areas are also patrolled:
Cascada Vălul Miresei, Tarnița, Tureni.
The service operates out of a central HQ in Cluj-Napoca and 6 rescue facilities in: Cheile Turzii,
Băișoara, Beliș, Râșca, Vlădeasa and Doda Pilii. Moreover, two first-aid facilities are located at the
Buscat ski slope. They are equipped with the following transportation means: 4 Mitsubishi L200, 1
Hyundai Tucson, 1 inflatable motor boat, 1 trailer, 1 boat trailer, 1 UTV, 1 snowmobile.
The personnel structure consists in 28 employees and 42 volunteers, supplemented by the members
of Salvamont Vlădeasa (outsourced service), i.e. 6 employees and 8 volunteers.
167
Table 44. Situation of volunteer operative services for emergency situations valid for 01.07.2019
Contracts or
cooperation Protocols with
No. of fire agreements other 24/7 shifts at facility locations
Approval
engines with other associations
Service category
(I-IV or C1-C2)
local in the field
SVSU authorities
No. County Remarks
name
non-functional
Total Employees/shift
functional
Area of
Establishment employ
competence Nominal Nominal
(no./date) ees in fire engine
(no./date) firemen
shifts driver
1 CLUJ CLUJ 29/15.03.2010 29/15.03.2010 I 0 0 0 0 0 ISU intervention guard
NAPOCA operating locally
2 CLUJ Câmpia 78/16.12.2014 78/16.12.2014 III 1 0 0 0 0 0
Turzii
3 CLUJ Turda 8/10.08.2017 7/10.08.2017 CI 0 0 0 0 0 ISU intervention guard
operating locally
4 CLUJ Gherla 40/14.05.2010 40/14.05.2010 IV 2 1 0 0 16 2 2
5 CLUJ HUEDIN 12/09.03.2009 12/26.02.2009 I 0 0 0 0 0 ISU intervention guard
operating locally
6 CLUJ Aghireşu 19/02.06.2009 19/02.06.2009 I 0 0 0 0 0 ISU intervention guard
operating locally
7 CLUJ AITON 38/29.04.2010 38/29.04.2010 I 0 0 0 0 0 Volunteer services are
difficult to implement
since young people do not
wish to become
volunteers, and many
localities have ageing
population, with most
residents exceeding 50
years of age;
8 CLUJ Alunis 55/23.03.2011 55/23.03.2011 I 0 0 0 0 0
9 CLUJ Apahida 69/26.11.2014 69/26.11.2014 I 0 0 0 0 0
10 CLUJ Aşchileu 09/14.11.2008 09/14.11.2008 I 0 0 0 0 0
168
Contracts or
cooperation Protocols with
No. of fire agreements other 24/7 shifts at facility locations
Approval
engines with other associations
Service category
(I-IV or C1-C2)
local in the field
SVSU authorities
No. County Remarks
name
non-functional
Total Employees/shift
functional
Area of
Establishment employ
competence Nominal Nominal
(no./date) ees in fire engine
(no./date) firemen
shifts driver
11 CLUJ Baciu 16/08.04.2009 16/08.04.2009 III 1 0 0 0 0 0 Does not fulfil the approval
criteria per OMAI 96/2016,
since it lacks the necessary
equipment - the fire
engine is non-operational
12 CLUJ Baisoara 71/26.11.2014 71/26.11.2014 I 0 0 0 0 0 ISU intervention guard
operating locally
13 CLUJ Belis 74/02.12.2014 74/02.12.2014 I 0 0 0 0 0
14 CLUJ Bobâlna 47/18.06.2010 47/18.06.2010 I 0 0 0 0 0
15 CLUJ Bonţida 9/22.08.2017 10/22.08.2017 CI 0 0 0 0 0
16 CLUJ Borsa 30/19.03.2010 30/19.03.2010 I 0 0 0 0 0
17 CLUJ Buza 43/14.05.2010 43/14.05.2010 I 0 0 0 0 0
18 CLUJ Caianu 17/10.10.2017 18/10.10.2017 CI 0 0 0 0 0
19 CLUJ Călăraşi 67/25.11.2014 67/25.11.2014 I 0 0 0 0 0
street
20 CLUJ Călaţele 46/18.06.2010 46/18.06.2010 III 1 0 0 0 0 0 Does not fulfil the approval
criteria per OMAI 96/2016,
since it lacks the necessary
equipment - the fire
engine is non-operational
21 CLUJ Cămaraşu 52/23.11.2010 52/23.11.2010 I 0 0 0 0 0
22 CLUJ Căpuşu 35/14.04.2010 35/14.04.2010 III 1 0 0 0 0 0 Does not fulfil the approval
Mare criteria per OMAI 96/2016,
since it lacks the necessary
equipment - the fire
engine is non-operational
169
Contracts or
cooperation Protocols with
No. of fire agreements other 24/7 shifts at facility locations
Approval
engines with other associations
Service category
(I-IV or C1-C2)
local in the field
SVSU authorities
No. County Remarks
name
non-functional
Total Employees/shift
functional
Area of
Establishment employ
competence Nominal Nominal
(no./date) ees in fire engine
(no./date) firemen
shifts driver
23 CLUJ Cătina 14/06.04.2009 14/06.04.2009 I 0 0 0 0 0
24 CLUJ Căşeiu 49/10.09.2010 49/10.09.2010 I 0 0 0 0 0
25 CLUJ Ceanu 05/01.08.2017 6/01.08.2017 CI 0 0 0 0 0
Mare
26 CLUJ Câţcau 62/22.04.2011 62/22.04.2011 I 0 0 0 0 0 Due to low local budgets,
27 CLUJ Chinteni 73/02.12.2014 73/02.12.2014 I 0 0 0 0 0 no sufficient financial
resources are allocated to
28 CLUJ Chiueşti 48/05.08.2010 48/05.08.2010 I 0 0 0 0 0
the SVSUs in terms of
29 CLUJ Ciucea 51/10.09.2010 51/10.09.2010 I 0 0 0 0 0 endowment and
30 CLUJ Ciurila 68/25.11.2014 68/25.11.2014 I 0 0 0 0 0 volunteers' rights;
31 CLUJ Cojocna 26/19.01.2010 26/19.01.2010 I 0 0 0 0 0
32 CLUJ Cornești 41/14.05.2010 41/14.05.2010 I 0 0 0 0 0
33 CLUJ Cuzdrioara 32/06.04.2010 32/06.04.2010 I 0 0 0 0 0
34 CLUJ Dăbâca 19/11.10.2017 20/11.10.2017 CI 0 0 0 0 0
35 CLUJ Feleacu 03/27.05.2008 03/27.05.2008 I 0 0 0 0 0
36 CLUJ Fizeşu 54/23.02.2011 54/23.02.2011 I 0 0 0 0 0
Gherlii
37 CLUJ Floreşti 01/11.02.2008 01/11.02.2008 III 0 0 0 0 0 ISU intervention guard
operating locally
38 CLUJ Frata 17/16.04.2009 17/16.04.2009 I 0 0 0 0 0
39 CLUJ Gârbău 50/10.09.2010 50/10.09.2010 I 0 0 0 0 0
40 CLUJ Geaca 23/30.11.2009 23/30.11.2009 I 0 0 0 0 0
41 CLUJ Gilau 45/15.06.2010 45/15.06.2010 I 0 0 0 0 0 ISU intervention guard
operating locally
170
Contracts or
cooperation Protocols with
No. of fire agreements other 24/7 shifts at facility locations
Approval
engines with other associations
Service category
(I-IV or C1-C2)
local in the field
SVSU authorities
No. County Remarks
name
non-functional
Total Employees/shift
functional
Area of
Establishment employ
competence Nominal Nominal
(no./date) ees in fire engine
(no./date) firemen
shifts driver
42 CLUJ Iara 33/09.04.2010 33/09.04.2010 III 1 0 0 0 0 0 Does not fulfil the approval
criteria per OMAI 96/2016,
since it lacks the necessary
equipment - the fire
engine is non-operational
43 CLUJ Iclod 3//25.07.2017 4//25.07.2017 CI 0 0 0 0 0
44 CLUJ Izvorul 22/14.10.2009 22/14.10.2009 III 1 0 0 0 0 0 Does not fulfil the approval
Crişului criteria per OMAI 96/2016,
since it lacks the necessary
equipment and sufficiently
qualified personnel
45 CLUJ Jichişu de 37/29.04.2010 37/29.04.2010 I 0 0 0 0 0
Jos
46 CLUJ Jucu 63/22.04.2011 63/22.04.2011 I 0 0 0 0 0
47 CLUJ Luna 15/27.09.2017 16/27.09.2017 CI 0 0 0 0 0
48 CLUJ Măguri 02/09.04.2008 02/09.04.2008 I 0 0 0 0 0
Racatau
49 CLUJ Mănăstire 05/04.07.2008 05/04.07.2008 III 1 0 0 0 0 0 Does not fulfil the approval
ni criteria per OMAI 96/2016,
50 CLUJ Mărgău 20/03.06.2009 20/03.06.2009 III 1 0 0 0 0 0 since it lacks the necessary
equipment and sufficiently
qualified personnel
51 CLUJ Mărișel 57/07.04.2007 57/07.04.2007 I 0 0 0 0 0
52 CLUJ Mica 36/23.04.2010 36/23.04.2010 I 0 0 0 0 0
53 CLUJ Mihai 1/29.06.2017 2/29.06.2017 CI 0 0 0 0 0
Viteazu
171
Contracts or
cooperation Protocols with
No. of fire agreements other 24/7 shifts at facility locations
Approval
engines with other associations
Service category
(I-IV or C1-C2)
local in the field
SVSU authorities
No. County Remarks
name
non-functional
Total Employees/shift
functional
Area of
Establishment employ
competence Nominal Nominal
(no./date) ees in fire engine
(no./date) firemen
shifts driver
54 CLUJ Mintiu 25/07.01.2010 25/07.01.2010 I 0 0 0 0 0
Gherlii
55 CLUJ Mociu 08/14.11.2008 08/14.11.2008 III 1 0 0 0 0 0 ISU intervention guard
operating locally
56 CLUJ Moldoven 80/16.12.2014 80/16.12.2014 III 1 0 0 0 0 0 Does not fulfil the approval
eşti criteria per OMAI 96/2016,
since it lacks the necessary
equipment - the fire
engine is non-operational
57 CLUJ Negreni 76/05.12.2014 76/05.12.2014 I 0 0 0 0 0
58 CLUJ Panticeu 58/18.04.2011 58/18.04.2011 III 1 0 0 0 0 0 Does not fulfil the approval
criteria per OMAI 96/2016,
since it lacks the necessary
equipment - the fire
engine is non-operational
59 CLUJ Palatca 24/14.12.2009 24/14.12.2009 I 0 0 0 0 0 Due to low local budgets,
60 CLUJ Petreştii 56/06.04.2011 56/06.04.2011 I 0 0 0 0 0 no sufficient financial
de Jos resources are allocated to
61 CLUJ Ploşcoş 61/22.04.2011 61/22.04.2011 I 0 0 0 0 0 the SVSUs in terms of
endowment and
62 CLUJ Poieni 42/14.05.2010 42/14.05.2010 I 0 0 0 0 0
volunteers' rights;
63 CLUJ Râşca 15/06.04.2009 15/06.04.2009 I 0 0 0 0 0
64 CLUJ Recea 39/04.05.2010 39/04.05.2010 I 0 0 0 0 0
Cristur
65 CLUJ Sâncraiu 11/03.03.2009 11/03.03.2009 I 0 0 0 0 0
172
Contracts or
cooperation Protocols with
No. of fire agreements other 24/7 shifts at facility locations
Approval
engines with other associations
Service category
(I-IV or C1-C2)
local in the field
SVSU authorities
No. County Remarks
name
non-functional
Total Employees/shift
functional
Area of
Establishment employ
competence Nominal Nominal
(no./date) ees in fire engine
(no./date) firemen
shifts driver
66 CLUJ Sânmartin 53/07.01.2011 53/07.01.2011 I 0 0 0 0 0
67 CLUJ Sânpaul 34/14.04.2010 34/14.04.2010 I 0 0 0 0 0
68 CLUJ Săcuieu 31/19.03.2010 31/19.03.2010 I 0 0 0 0 0
69 CLUJ Sănduleşti 13/18.09.2017 14/18.09.2017 CI 0 0 0 0 0
70 CLUJ Săvadisla 24/05.08.2009 24/05.08.2009 I 0 0 0 0 0
71 CLUJ Sic 64/04.02.2014 64/04.02.2014 I 0 0 0 0 0
72 CLUJ Suatu 11/05.09.2017 12/05.09.2017 CI 0 0 0 0 0
73 CLUJ Tritenii de 79/16.12.2014 79/16.12.2014 I 0 0 0 0 0 Due to low local budgets,
Jos no sufficient financial
74 CLUJ Tureni 60/22.04.2011 60/22.04.2011 I 0 0 0 0 0 resources are allocated to
the SVSUs in terms of
75 CLUJ Ţaga 75/05.12.2014 75/05.12.2014 I 0 0 0 0 0
endowment and
76 CLUJ Unguraş 27/19.01.2010 27/19.01.2010 I 0 0 0 0 0 volunteers' rights;
77 CLUJ Vad 59/21.04.2011 59/21.04.2011 I 0 0 0 0 0
78 CLUJ Valea Ierii 07/07.11.2008 07/07.11.2008 I 0 0 0 0 0
79 CLUJ Viişoara 70/26.11.2014 70/26.11.2014 I 0 0 0 0 0
80 CLUJ Vultureni 44/21.05.2010 44/21.05.2010 I 0 0 0 0 0
173
3. FLAWS AND INTERVENTION PRIORITIES
3.1. Flaws and intervention priorities identified concerning extreme
meteorological and climate phenomena and climate changes
The main flaws identified in the explanatory study concerning extreme weather events and their
associated changes, as well as the proposals for their suppression / reduction are presented below:
• Amplification of the urban heat island effect and the increase of the intensity of extreme heat
events in hot spots and the warning impossibility due to the absence of a monitoring systems
for the urban climate in the county cities;
• Increase in thermal stress in the periods with extreme temperatures;
• Low capacity of institutional and autonomous adaptation to climate change and ensuring an
adequate behaviour in case of extreme weather events, as a result of applying the
questionnaire;
• Absence of a set of measures that can be adopted individually by citizens or small communities
(tenants' associations, locality) to mitigate the impact of extreme weather events and climate
changes on a microscale ;
• Absence of agricultural re-zoning maps and of a catalogue of hybrids suitable for various
agricultural crops, as a result of changing agro-climatic conditions in the context of climate
changes over the last decades;
• Street flooding excessive;
• Lower profitability of winter sports tourism as a result of major estimated changes in the snow
layer thickness in the mountain area;
• Vulnerability of electricity distribution networks and telecommunications networks to
extreme weather events (squalls);
• Degradation of the asphalt in extreme temperature and snow conditions;
• Air pollution with particulate matter in building and demolition sites during windy
periods/wind storms;
• Traffic blockage in heavy snowfall conditions.
3.2. Flaws and priorities concerning extreme hydrologic phenomena
An overview of the main flaws identified in the explanatory study concerning extreme hydrologic
events and risks generated and the proposals for their suppression / reduction are presented below:
• High flood vulnerability on various scenarios of occurrence for a significant number of intra-
urban areas, located in the vicinity of streams;
• Silting of streams in areas with frequent flash floods leads to rising river beds;
• Increasing flood frequency on streams with low morphometry (small drainage basins);
• The high level of torrentiality of hydrographic basins makes the values of maximum discharges
higher than those recorded on other rivers similar in size in the country;
• Insufficiency of rainwater collection and drainage systems at the level of localities and/or their
under-calibration;
• Silting and improper maintenance of sewage and gutter systems existing in localities;
• Silting of the stream bed with development of abbundant vegetation which increased the flow
roughness in the stream bed, causing level increase in natural regime, as well as low height of
natural banks;
• Uncontrolled expansion of the construction of houses and vilas in floodplains;
• Lack of a proper education of the population in terms of conduct in floodplains.
174
In Cluj county the flood zone with the highest spatial expansion was associated to the river Someşul
Mic. The prioritization of actions of fighting the effects of extreme hydrologic phenomena shall be
focused on sectors with areas of potentially significant flood risk (A.P.S.F.R.): river Someșul Mic (sector
downhills of Florești – confluence Someșul Mare), creek Ocnei – uphills of Ocna Dejului and river
Olpret – downhills of Bobâlna.
175
Table 45. Priority actions applicable at the level of A.P.S.F.R. for Cluj county
Someșul Mic – Renaturation of the stream banks (vegetative protections) Vegetal cons. L= 7.8 km (Hm 1,000-1,780)
on the sector Improvement of forest management in floodplains Improvement of forest management in the floodplains of river Someșul Mic
downhills of – pertaining to the area of potentially significant flood risk (A.P.S.F.R.) S = 15.61
confluence ha
Someșul Mare Maintenance of forest areas in the catchment areas of A.P.S.F.R. Maintenance of forest areas in the drainage basin of river Someșul Mic
pertaining to the area of potentially significant flood risk (A.P.S.F.R.) S =
96,254.95 ha
Construction of new non-permanent small sized accumulations “ Harnessing of river Nadas and its tributaries Cluj county”, accumulation
Aghireș harnessing locality Aghireș V = 2.5 mil. m³ “Harnessing V. Borșa and
tributaries on Răscruci – Borșa sector”: acc. Ciumăfaia on r. Borșa, harnessing
locality Borșa Vat=5.1 mil. m³,Vut=0.2 mil. m³, Vt=5.3 mil. m³
Enhancing the level of security of existing hydrotechnical constructions “Securing Gilău accumulation”
(rehabilitation: modernization, measures to control infiltrations etc.)
River bank stabilization measures – recalibration of river banks, guard “Harnessing river Someșul Mic on Cluj-Dej sector phase II”, Total capacity: 42.3
walls, retaining walls, river bank reinforcement, stream bed stabilization km river bed improvement, 9.5 km length of protected levees, 3.2
superelevation of levees, 3,400 ha protected areas Still pending: stream bed
harnessing in Jucu, Bonțida, Răscruci, Fundătura, Iclod, Livada L= 32.96 km
“Harnessing V. Borșa and tributaries on Răscruci – Borșa sector”: stream bed
harnessing in Răscruci L=1.9km, in Borșa L=1.4 km and between Răscruci and
Borșa L=2.5 km
“Harnessing river Someșul Mic in Cluj”, Total capacity: 35.35 km river bed
improvement, 7.9 km airport levee, 17.94 km river bank reinforcement r.
Someș, 14.8 km river bank reinforcement tributaries; Still pending.: stream bed
harnessing L = 3.4525 km
“Harnessing V. Sic and tributaries”, Cluj county – stream bed harnessing V. Sic
in Sic, L=4 km
Protection measures along streams by the construction of local levees “Harnessing river Someșul Mic on Cluj-Dej sector phase II”, L = 0.3 km in Livada
(levee closure)
“Harnessing river Someșul Mic in Cluj”, L = 3.4 km levee
Measures for the reduction of water running downhills and silt / Control of torrents, prevention of landslides and floods in the drainage basin of
sediment retention Someșul Mic, V. Fizeșului – Palatca area, Cluj county – New capacity CES 4,963
ha
Maintenance of existing flood protection infrastructure Current maintenance of infrastructure
176
Maintenance of stream beds and removal of blocks and obstacles on Removal of obstacles in Jucu, Bonțida, Răscruci, Fundătura, Iclod, Livada, L= 10
streams km
Improvement of monitoring / forecast and warning /alarm systems Automation hydrometric station Rădaia, Aghireșu and Borșa, pluviometric
station Fizeșu Gherlii, establishment of automated pluviometric stations in
Chinteni and Săvădisla
Pârâul Ocnei – Renaturation of stream banks (vegetative protections) Vegetal cons. L= 7.8 km (Hm 1,000-1,780)
downhills of Maintenance of forest areas in the catchment areas of A.P.S.F.R. Improvement of forest management in the floodplains of river Someșul Mic
Ocna Dejului pertaining to the area of potentially significant flood risk (A.P.S.F.R.) S = 15.61
ha
Maintenance of existing flood protection infrastructure Stream bed maintenance L = 2 km
Maintenance of stream beds and removal of blocks and obstacles on Removal of obstacles L= 6.0 km (Hm 40-100)
streams
Râul Olpret – Renaturation of stream banks (vegetative protections) Vegetal cons. L= 7.8 km (Hm 1,000-1,780)
downhills of Improvement of forest management in floodplains Improvement of forest management in the floodplains of river Someșul Mic
Bobâlna pertaining to the area of potentially significant flood risk (A.P.S.F.R.) S = 15.61
ha
Maintenance of forest areas in the catchment areas of A.P.S.F.R. Maintenance of forest areas in the drainage basin of river Olpret pertaining to
the area of potentially significant flood risk (A.P.S.F.R.) S = 3,675.89 ha
Maintenance of existing flood protection infrastructure Stream bed maintenance L = 2.5 km
Maintenance of stream beds and removal of blocks and obstacles on Removal of obstacles L = 2.5 km (Hm 180-205)
streams
Improvement of monitoring / forecast and warning /alarm systems Automated station in Maia
Source: Flood risk management plan – Water Basin Administration Someș-Tisa
177
3.3. Flaws and priorities related to landslides
The main flaws and priority interventions to eliminate or at least mitigate the identified flaws with
regard to the problem of landslides are the following:
• Increased frequency of new landslides and the reactivation of existing landslides as the
conditions (both anthropogenic and natural) for their occurrence are met; the landslides
affect both infrastructure elements and agricultural land plots;
• Manifestation of the implied and potential negative effects, which are visible at the level of
landscape, building structure and road network, are generated by landslides and are
highlighted the magnitude of the damage caused;
• Instability of newly built structures and adjacent infrastructure affected by landslides;
• Lack of modern monitoring systems for active landslides, forecasting and warning/alerting
systems able to reduce the response times for intervention authorities.
Recommendations:
Considering the possible effects of landslides (whether the emergence of new events due to heavy
rainfall, seismic events, etc, or human interventions in the territory), effects could occur e.g.: total or
partial destruction of utility networks (gas, water, sewerage), obstruction of transportation routes
(roads, railways), the destruction of buildings, and landscape and environmental changes.
To mitigate such negative effects, certain prevention, protection and intervention measures are
recommended before and after landslide events.
Therefore, a very important task is to observe the enabling conditions of landslides, in order to allow
sufficient time to warn the exposed population in case of reactivation.
The measures required for the prevention and protection of population relate to the:
• timely performance of interventions required to establish the conditions in which landslides
appear and develop,
• application of adequate procedures for their keeping under control;
• the forecasting and timely preparation of adequate protection measures, by providing means
to drain water from the slope massif via a new drainage system;
• forestation and planting of grass on slopes (geotextile or geosynthetic nets may also be used).
• avoid the establishing of industrial sites or other buildings where the layer stability is
compromised or very costly;
• maintaining the population informed within the risk area;
• With the exception of particular cases, any intervention actions will be aimed at recovering
property and addressing damages. The survivors from collapsed buildings will be rescued
based on the same procedures applicable to earthquakes.
The prevention, protection and intervention activities in cases of landslides include three stages:
a) pre-disaster stage: with the following main activities:
• setting-up a disaster emergency committee and appropriate training of its staff;
• classification and supervision of potential landslide-inducing factors;
• establishing and ensuring the operation of the local emergency alarm system;
• preparing the population and intervention and rescue units according to the protection and
intervention plans;
• performing forestation and grass planting, or other similar activities, in potential risk areas.
178
b) disaster stage, with the following activities:
• alerting the population in the disaster area;
• organizing and leading the evacuation of the population and property from the disaster area;
• providing food, accommodation and medical assistance to disaster victims.
a) post-disaster stage: with the following activities:
• inventory and evaluation of effects and damages;
• continued provision of relief to disaster victims;
• informing the population on the current situation;
• planning and coordinating actions to rebuild the affected economic and social infrastructure.
The extent of each activity mainly depends on the speed of the landslides; if the speed is low, pre-
disaster stage measures will prevail; in case of high landslide speeds, the post-disaster activities will
prevail.
The territories with high and very high landslide probabilities will be included into the specific spatial
planning documents provided under Laws No 350/2001 and 244/2009.
These territories can be built on, but only with light traditional or modern constructions (made of
panels or wood, with dry brick foundations) (according to P100-1/2006), but only based on very
thorough geotechnical studies (acc. to NP 074-2007), that emphasize details on the stability of slopes
which will lead to stabilization measures to reduce the risks of current or potential landslides.
According to legal provisions, heavy constructions can only be built in these territories if they are of
national relevance and have a strategic and economic role, and only at the highest safety levels.
There are no building limitations for territories falling under the medium-high probability class (0.31-
0.50), but geotechnical studies according to NP 074-2007 and SR EN 1997-2/2008 are recommended
in order to identify needs for construction and drainage works.
The high probability class (characterized by an average hazard coefficient between 0.10-0.130) implies
the need to perform the same geotechnical studies as those in the medium-high probability class, but
selecting geotechnical category 2 in such a case.
3.4. Flaws and priorities regarding the emergency response infrastructure
and services
As for infrastructure and emergency management services, a number of flaws were identified
requiring immediate mitigation actions:
• Equipment with intervention techniques which do not entirely meet the intervention needs
covered by the ISU’s scope of competence;
• Insufficient financial resources necessary for the functioning of voluntary emergency services;
• low local budgets, insufficient financial resources are allocated to the functioning of the
voluntary emergency services, in accordance with the law, both in terms of their endowment
and the volunteer rights;
• cumbersome process to obtain the permits to establish and equip the SVSU by complying with
the annexes to MIA Order No.96/2016 due to the lack of specific funds and facilities.
179
4. PROPOSALS FOR SUPPRESSION/REDUCTION OF FLAWS
4.1. Proposals for suppression/reduction of flaws concerning extreme
meteorological and climate phenomena and climate changes
The priority intervention actions for the suppression / reduction of flaws identified in the present
explanatory study are:
Intervention priority Actions proposed
Stop the expansion and • Monitoring of urban hear islands by the implementation of competitive urban
reduction of the intensity of climate monitoring systems;
urban heat islands and • Adoption of sustainable urban textures in designing buildings and new housing
prevent the formation of new developments: inventory of urban textures of the type ”cool spot” and ”hot
heat islands within urban spot”; recommendation of the replication of ”cool spot” urban textures and
areas in the county which avoidance of the ”hot spot” ones;
increase the intensity of heat • Thermal and structural rehabilitation of public buildings and dwellings: for
waves in urban areas old/already existing buildings: via thermal rehabilitation programs, adoption of
”cool roof” solutions (for example those covered with river stones or other
natural materials, reflective roof coatings etc.);
• For buildings to be erected adoption of ”cool/green roof” solutions from the
design phase;
• Increase of permeable surfaces (green pavements, green areas) to reduce heat
accumulation by the choice of ”cool/green” materials for parking places and
pavements, where possible (honeycomb type, paved);
• Use of the most efficient plants in terms of the cooling effect (in consultation
with horticulturists) in the design of green areas within urban developments.
• Placement of bodies of water/water spray systems in ”hot spot” areas.
• Adoption and improvement of sustainable urban mobility for local adaptation
to climate changes: increase/establishment of electric car/hybrid car fleets;
encouraging the purchase of private hybrid/electric cars by fiscal measures at
local level; expansion of bicycle/scooter lane network, use of electric buses
mainly on routes/lines crossing the hottest spots, to avoid excessive heating
through exhaust emissions;
• Elaboration of guides / standards for the construction industry;
• Elaboration and implementation of appropriate urbanistic regulations.
Reduction of street flooding in • Construction of facilities (reservoirs) for the temporary collection of excessive
periods of excessive rain rain water before it reaches the drains (which could be subsequently used for the
falling irrigation of green areas);
• Extension/recalibration of storm drains;
• Appropriate maintenance and un-silting of the beds of local sectors of natural
emissaries;
• Ensuring an appropriate cleaning of streets and appropriate maintenance of
road side ditches.
Reduction of thermal stress in • Elaboration of maps of fix and mobile first aid points in case of excessive
periods of extreme heat/frost;
temperature • Increasing the number of health recreational facilities, including public
fountains and spring wells in case of heat waves.
Limited adjustments in the • Analysis of the appropriateness of agricultural areas for various crops and the
agricultural practices vis-a-vis agro-climatic re-zoning of the county;
the climate changes • Create a catalogue of the most suitable hybrids for agricultural crops in the
adaptation in recent decades current climate, based on the recommendations of the technical expert groups in
the Ministry of Agriculture, researchers, and farmers.
Reduce the level of • Ensuring the adaptation / resilience of the local transportation infrastructure to
degradation of asphaltic phenomena associated to climate changes and its appropriate maintenance.
coating under extreme
180
Intervention priority Actions proposed
temperature and snow
conditions
Reduction of air pollution • Improvement of the monitoring of environmental factors which impact human
through particulate matter health;
from construction and • Increasing the level of compliance with the regulations concerning the
demolition sites carried by construction site organization, watering of construction site vicinities and cleaning
winds/storms off dust and mud by companies (control).
Improvement of management • Improvement of the level of institutional cooperation in the health care sector;
capabilities in case of • Development of health care infrastructure and services (including the staff
emergency caused by natural needed).
disasters
Reduction of travel times • Optimization of the local public transportation sector and increasing its appeal to
within the passenger citizens;
transportation sector and • Encouragement of alternative transportation at the level of the City;
ensuring appropriate • Encouragement of the purchase of electric/hybrid cars (lower local taxes,
conditions of transportation in reduction of parking fees, spots in parking places in the most crowded areas
extreme heat periods reserved for hybrid or electric cars etc.)
Increasing the resilience of • Development of the metropolitan underground cable infrastructure
electric power distribution
and telecommunication
networks to extreme
meteorological phenomena
(wind storms)
Ensuring the traffic fluidity • Ensuring the easy access of snow clearance vehicles and of vehicles of the
under heavy snowfall Inspectorate for Emergency Situations ISU;
conditions • Use of snowmelt substances with low impact on the environment (avoidance, as
much as possible, of sodium chloride).
Enhancing the capacity of • Communication campaings adapted to the information and awareness needs of
institutional and autonomous the affected population categories, including through formal and non-formal
adaptation to climate changes education concerning the adaptation to climate changes and the urban heat
and ensuring a proper islands and their potential effects on local communities;
conduct in case of disasters • Proposal of optional school subjects at the level of the County School
Inspectorate for raising awareness within school age population;
• Other curricular or extra-curricular activities complementing the above-
mentioned topic, e.g. round tables/workshops on adaptation policies,
national/regional competitions, participatory funding to implement the
measures necessary for the local community to adapt to climate changes;
• Cooperate with civil society organisations to raise awareness on these themes
within specific target groups;
• Encourage the applied research in the field of adaptation to climate changes.
Absence of a set of measures • Identify measures to mitigate the impact in each locality and present them to the
that can be adopted citizens;
individually by citizens or • Create a catalogue of the plants that are the most efficient in lowering
small communities to mitigate temperatures.
the impact on a microscale
(tenants' associations, locality)
of extreme weather events
and climate changes
Major estimated changes in • The introduction of alternative tourism types in mountain areas where winter
the snow layer thickness in sports tourism will be affected;
the mountain area
The absence of a “calendar” of • Preparation of annual plans for the programming and organisation of outdoor
favourable weather conditions cultural, recreational and sports events considering the weather/climate
for the organisation of conditions
outdoor events
181
4.2. Proposals for suppression/reduction of flaws concerning extreme
hydrologic phenomena
The priority intervention actions for the suppression / reduction of flaws identified in the present
explanatory study are:
Intervention priority Actions proposed
Control of the frequency of flash-flood and • Improvement of forest management in floodplains
flood formation by cutting deforestation • Renaturation of stream banks (vegetative protections)
actions in mountainous areas, especially • Maintenance of stream beds and removal of blocks and
where higher slopes accelerate the formation obstacles on streams
of the superficial water running downhills
and determines the fast concentration of
water in stream beds
Possibility to mitigate flash-floods on small • Creation of small size facilities capable of reducing flash-flood
streams waves in drainage basins with torrential character
• Control of torrents, prevention of landslides and floods
(investments proposed in Palatca, Valea Fizeșului)
Verification of hydrotechnical stability in • Enhancing the level of security of existing hydrologic
drainage basins with a view to the efficient constructions (rehabilitation: modernization, measures to
management of highwater, flash-flood and control infiltrations etc.). Proposal for investment in securing
flood periods Gilău accumulation
• Verification of the stability of levees, of water accumulation
discharge systems (bottom collectors, power tunnels,
spillway)
Control of flash-flood generated effects by • Improvement of monitoring / forecast and warning / alarm
optimization of monitoring systems, systems by installation of automated stations to reduce the
especially in the most vulnerable areas response time of emergency responders. Applicable in the
(confluence area Someșul Mic with Someșul northern part of the county for the establishment of
Mare) automated stations in Mica and Vad
Limiting the transmission time for the • Effective warning systems;
essential information about possible • Rapid implementation of population evacuation plans where
dangerous phenomena to the population needed;
Decrease in the silting processes of river beds • De-silting works and actions to rearrange river beds;
Lower flood frequency on small watercourses • Maintenance works, damming of vulnerable areas associated
with the watercourses transiting built-up areas;
Reducing the level of torrentiality in • Calibration and rearrangement of torrents, constructions to
potentially dangerous hydrographic basins lower water energy, especially in the mountain areas wiht
steep slopes;
Making rainwater collection systems more • Upgrade rainwater collection systems; replace sections which
efficient in order to limit critical situations are inefficient or highly worn; increase the transportation
that lead to clogging and/or discharge them capacity of the rainwater drainage network or increase their
during heavy rainfall density where needed at local level;
• Regular cleaning and proper maintenance of drainage pipes,
gutters;
• Readjustment where possible, by increasing their hydraulic
capacity.
Remove the abundant algae vegetation • Regular maintenance works of riverbeds with a high potential
growing frequently either in the lacustrine for hydrophilic vegetation to grow in the river bed or in the
environment of energy interest (Gilău Lake) lake basin;
or in the urban areas of watercourses (e.g.
182
Intervention priority Actions proposed
Someșul Mic – on the section of Grigorescu
walkway – Garibaldi bridge in Cluj-Napoca)
Stop the uncontrolled expansion of • Comply with the building permits regime in what concerns the
construction in floodable areas prohibition to build any kind of construction in the flooding
area;
Improve the level of education of the • Simulation exercises of dangerous situations;
population in relation to measures to • Raising public awareness on how to behave in critical
prevent, act and combat dangerous water situations caused by extreme water events.
events
4.3. Proposals to eliminate/mitigate flaws related to landslides
The priority actions to eliminate/mitigate the flaws identified by this supporting study are the
following:
Intervention priority Proposed actions
Mitigating the negative impacts of • Studies on the vulnerability of the territory and the identification of
landslides by decreasing the frequency of risks arising from active geomorphological processes which allow
new landslides and the reactivation of the identification of spatial probabilities of future occurrences and
existing landslides. also provide forecasts of future evolutions.
• Preparing geotechnical studies prior to construction works within
areas with medium and medium-high landslide potential, in order
to identify needs for slope redevelopment prior to performing
construction works in such areas.
• Better disseminating the risk studies so the population become
better informed of the situation on the ground, the risk they are
exposed to, and the classification for landslide purposes of the
properties, households, farming land, forests and land owned or to
be purchased.
• Informing the local authorities on new landslide events and their
direct and indirect consequences, thus enabling them to take
appropriate mitigation action.
• Insuring property and homes against disasters.
• Obligation to prepare geotechnical stability studies (by specialized
companies) prior to erecting new buildings or rehabilitating existing
ones, and enforcing financial penalties in cases of failure to comply
with such obligations.
• Permanent monitoring of unstable land within built-up areas, and
alerting the Emergency Situations Inspectorate when recent
unstable areas are found
Awareness raising among the local • Obligation to prepare geotechnical stability studies (by specialized
population and authorities companies) prior to erecting new buildings or rehabilitating existing
ones, and enforcing financial penalties in cases of failure to comply
with such obligations.
• Preparing public educational programmes to raise awareness of the
risks the population is exposed to and on measures to reduce
deforestation, and the importance of slope development works and
of planting tree species that stabilize the areas affected by or at risk
of landslides.
183
4.4. Proposals to eliminate/mitigate flaws regarding the emergency response
infrastructure and services
Identified flaws Proposals to eliminate/mitigate flaws
Endowment with intervention • Endowment with new and complex intervention equipment, to cover
equipment. the intervention needs in the respective ISU's areas of competence.
Lack of clear legal provisions. • Abusive calls for Salvamont intervention (stuck cars, transport
to/from different locations, etc.).
• Unpreparedeness of mountain hikers (hikers leaving on trails with
inadequate equipment).
• Degradation and vandalizing of tourist trails.
Insufficient financial resources to fund • Volunteer services are understaffed since young people are not
the operation of volunteer emergency willing to serve as volunteers for emergency situations, and many
services. localities have ageing population, with little residents under 50 years
of age, as required by H.G.R. No 1579/2005.
• Due to low local budgets, no sufficient financial resources are
allocated to ensure the operation of volunteer emergency services as
legally required, both in terms of endowment and volunteers' rights.
• Most localities struggled to provide funding for SVSUs in order to
ensure compliance with the annexes of OMAI No 96/2016 with regard
to securing approvals and establishing the areas of competence.
• As far as the training providers of Cluj County are concerned, no
training courses were organized for “Head of specialized team”,
“Head of intervention team” and “Head of prevention unit” that were
required to secure the specific qualifications/skills and include such
positions into SVSUs at TAU level.
Conclusions on the operability of SVSU
It is imperative to earmark funds to endow these voluntary emergency response services with
intervention equipment where this is lacking, and to replace the obsolete equipment that is no longer
safe for use. Solutions have to be found to encourage volunteering in volunteer emergency response
services, that are attractive and particularly involve the middle-aged population.
Due to the low preparedness/training levels of the rural population, the prevention department of
SVSU finds very difficult to employ specialists, and their controls, if any, are inefficient and ineffective.
Moreover, preventive activities (dissemination of emergency situation-related information) at
community level are merely performed for appearance's sake and only at the insistence of our
inspectorate, or, unfortunately, after an emergency event already took place (such as multiple dry
vegetation fire, uncontrolled fires, etc.). In conclusion, this activity is not sufficiently consistent and
sustained to bring added value to TAUs.
184
5. PREDICTIVE RESEARCH, SCENARIOS OR DEVELOPMENT
ALTERNATIVES
5.1. Predictive research, scenarios or development alternatives concerning
meteorological and climate phenomena observed in their evolution
Numerical models which simulate the climate system behavior are used, alongside observation data,
to assess the features of climate changes medium and long term. Such assessments have been
conducted also for Romania – projections of climate changes in the future, valid in the context of
specific scenarios concerning the evolution of greenhouse gas concentrations in atmosphere. For the
assessment of future trends of climate at spatial scale in Romania numerical experiments were carried
out both with global climate models, available within the programs CMIP 3 and CMIP 5, and with
regional climate models, available within the program Euro CORDEX (Jacob et al., 2014, Bojariu et al.,
2015).
Against this background, the evolution assessed based on regional climate models indicates a
continuation of the warming process, both by increased maximum temperatures and minimum
temperatures, whilst the precipitation indicators will not be subject to any significant changes in the
following decades, except for the number of days with heavy and very heavy precipitation (Bojariu et
al., 2015, Croitoru et al., 2018, Harpa et al., 2019). This finding involves an increase of the heat stress
on the human body, with major impact on health and working capacity (Herbel et al., 2018).
For Cluj county, as for all regions, forecasts based on global and regional climate models were carried
out concerning the future climate development. In what concerns the county as a whole, according
with the worst case scenario of climate evolution (increase of greenhouse gas emissions),
temperatures are expected to increase by around 2°C for the interval 2021-2050 as compared to the
average of the reference period 1971-2000, with small variations which increase from the South of
the county to the North. A more accelerated increase is expected during the summer, of about 2.2 –
2.4°C, and a slower one during the winter, with values ranging between 1.4 and 1,5°C, in the county’s
Western part, and 1.5-1.6 °C in its Eastern half (Bojariu et al., 2015).
Heat waves are among the phenomena which will increase both in terms of their duration and
intensity in the decades to come (2021-2040), at annual and seasonal level. Against this background,
according with the moderate scenario of climate development there will be an estimated average
number of 4 extremely severe events/year in Central Romania, 6.1 severe intensity events/year and
more than moderate intensity events/year. At seasonal level, in winter the number of periods of
moderate intensity warming will increase by around 50%, whilst the number of severe and extremely
severe events will increase by 85-100% (Croitoru et al., 2018).
The cold waves, despite decreasing by 25-30% in number and by 30-40% as aggregated annual
duration, will not disappear as extreme events (of moderate, severe and extremely severe intensity)
in the upcoming two decades, which means that the measures for the prevention of their negative
effects have to be maintained (Croitoru et al., 2018).
As concerns precipitation, similarly to the findings referring to the historical period, the analysis of
scenarios indicates a less coherent image than in what temperature is concerned. According with the
moderate development scenario (RCP 4.5), it can be ascertained that, in general, during the upcoming
3 decades the precipitation quantity will not change significantly. For the summer there is the
estimation that they will range within the interval -5…0% and only in isolated cases between 0 and
+5% as compared to the historical period, whilst the worst case scenario (RCP 8.5) indicates an
increase by 0-10% as compared to the historical average (Bojariu et al., 2015). As concerns extreme
185
values of precipitations, for the interval 2021-2050 an increase of the number of days of abundant
and very abundant precipitation (R10 and R20) is expected as a continuation of the historical trend of
the two indicators (Harpa et al., 2019).
Increase of the frequency of events of extreme precipitation has to be taken into consideration in
terms of the management of hydric resources, especially in the context of a rough land as in the
Western part of the county where the coefficient of soil infiltration and the time of riverbed
concentration decrease significantly. Under these conditions there is an increased probability of the
formation of flash-floods with major effects on community and environment. The same type of risk
can occur also at the level of urban areas where the soil infiltration coefficient decreases to 0 as a
result of water repellent coatings applied on surfaces.
Furthermore, against the background of the temperature increase, a decrease of the average
thickness of the layer between 20 and 40% is expected according with the moderate scenario (RCP
4.5) and between 30 and 50% according with the worst case scenario (RCP 8.5). The most significant
drop is expected within the height interval 500 and 1,000 m altitude for the moderate scenario and
altitudes exceeding 1,000 m for the worst case scenario (Bojariu et al., 2015).
The measures which have to be adopted refer to two large categories: measures to reduce climate
changes and measures to adapt.
Given that the manifestation of climate changes exceeds the territory of a county, as concerns the
first category, the measures relate to the internationally well-known lines of action and aim, mainly,
at the reduction of greenhouse gas emissions. The most important quantities of such pollutants are
generated by transportation activities.
Against this background, the most significant measures have to be taken in urban areas where the
largest part of the county’s population lives, on the one hand, and where traffic is the most intense.
As a result, the quality of breathable air drops significantly from a chemical point of view and
temperatures increase by 2-4⁰C as compared to rural areas. For example, in terms of extreme
meteorological phenomena concerning maximum temperatures, there can be a major malfunction
following the lack of an urban climate monitoring system as it makes impossible to issue warnings in
case of heat waves, given that the warning threshold is not exceeded at the meteorological station
which is, usually, located at the outskirts of the town or outside the town, while the threshold
mentioned is exceeded in the town.
Following the analysis of the existing situation, there is the need for the elaboration of a strategy for
the reduction and adaptation to climate changes for the Cluj county which shall determine the
vulnerable sectors, as well as prioritize them based on the hazard (cause) – vulnerability – risk – effect
analysis.
Here are some recommendations for the most affected fields:
Urban development/life quality:
• Taking into consideration that the most part of the county’s population lives in urban
environments (more than 65%), there is the need for a detailed and long term monitoring of
the regional/urban climate with the help of automated systems; the implementation of such
systems would allow long term thanks to the measurement data obtained the materialization
of an urban forecast model, based on which bio-meteorological warnings can be issued
considering the real conditions in the town, not the conditions around the meteorological
station; furthermore, based on the same data, it would be possible to establish the most
186
appropriate locations for the organization of first aid points in case of dangerous meteorological
phenomena (heat waves, cold waves);
• Thermal insulation not only of walls, but also of roofs within the thermal rehabilitation
programs; among the solutions applied at international level, the construction of so called ”cool
roofs” (for example those covered with river stones or other natural materials, reflective roof
coatings etc.) should be preferable versus the ”green roof” solution (which involves the
construction of irrigation systems, as well as additional insulation for water collection), as both
the costs and the changes in terms of infrastructure are smaller for the first solution than for
the second;
• Choice of ”cool/green” materials for parking places and pavements, where possible (for
example honeycomb type) to increase the degree of soil infiltration of rainwater or appropriate
calibration of the sewage system in case of extreme phenomena which can cause urban
flooding, especially in lower areas of towns;
Transportation:
• Use of electric buses mainly on routes/lines which cross the hottest areas to avoid excessive
warming caused by the exhaust emissions of regular buses;
• Where bicycle lanes are extended it is recommended they do no follow the town’s main
transportation lines, but to be diverted on secondary routes (to avoid exposure of bike riders to
gas emissions;
• Stimulation of the population by the authorities to purchase passenger vehicles with low
greenhouse gas emissions, preferably hybrid and/or electric vehicles (fiscal facilities, traffic
restrictions in certain areas and/or times of the day for fossil fuel vehicles etc.).
Agriculture and green areas:
Depending on ecological conditions newly identified as concerns the county (duration of the growing
season, sum of effective temperatures and useful rainfall quantity) there is the possibility of choosing:
• The types of crops and hybrids with maximum efficiency for the agricultural field;
• The most effective plants in terms of their cooling effect for setting up green areas in towns to
reduce the urban heat island effect.
Tourism:
• In the mountain region, in the context of a dramatic decrease of the snow layer thickness, the
planning of touristic activities has to take into consideration, apart from the development of
winter sport infrastructure (ski), also the development of other forms of tourism;
• Identification of the periods which in terms of meteorological and climate conditions are
favorable for the organization of cultural, sports and recreational events outdoors.
Education:
• rising awareness among the population through mass and individual information measures
about recent and future climate changes and their impact through the organization of
information campaigns (based on the outcomes of the sociological inquiry);
• proposal of optional school subjects at the level of the County School Inspectorate in the field
of environmental education for the school age population.
187
The following scenarios for the climate evolution at the level of Cluj county are proposed:
• The passive scenario (”Do Nothing”) – is the one where the population and the authorities are not
aware or do not act to reduce the effects of climate changes, measures for reduction are not
adopted and the impact on various fields of activity will continuously increase.
• The reference scenario (”Do Minimum”) – only considers the application of minimum measures
for the reduction of the local impact in affected areas, adopted:
• at individual level: use of the most efficient plants in terms of their cooling effect for setting
up green areas in private households (in consultation with horticulturists); adoption of the
”green wall” measure by planting climbing plants meant to provide shade to walls all day
long, thus avoiding their overheating; adoption of the ”green balcony” measure which can
take the shape of decoration of balconies or terraces with lots of plants for a cooling and
shading effect, as well as for the shading of walls and rooms in houses; placement of water
sources (water spray systems, small bodies of water) in private households).
• at institutional level: in Cluj-Napoca and other cities the use of electric buses mainly on
routes/lines which cross the hottest areas to avoid excessive warming caused by exhaust
emissions; setting-up of (in consultation with horticulturists) and making available to the
community a list of the most efficient plants in terms of their cooling effect (bushes or fast
growing trees, with thick canopies etc.); choice of the most efficient plants in terms of their
cooling effect for the setting-up of green areas on the public domain in towns.
These measures will not have a major effect of reduction of the effect of extreme meteorological
phenomena, whilst hot spots in towns will stay close to their current values in terms of extension, but
with a slight drop in intensity.
• The major impact development/reduction scenario (”Do Something”) – is the one where the
local public authorities get involved primarily through regulatory measures and secondly through
implementation measures which include all the above mentioned recommendations.
The adoption of these measures could ensure a county development based on environmental
sustainability. This scenario includes complex measures, ranging from measures for the monitoring
and materialization of a local weather forecast model to active measures of public and private
investments in the insulation of buildings and adoption of cool urban textures for new buildings, to
the stimulation of the population to adopt sustainable decisions in this respect, investments in the
touristic and transportation infrastructure and long term education of the county’s population. This
scenario will allow a better climate change resilience of the local communities in Cluj county.
5.2. Predictive research, scenarios or development alternatives concerning
extreme hydrological phenomena
The increase of the number of extreme hydrological events in the county has to be looked at in terms
of the management of the available water resources. In the context of a mountainous area, with slopes
which favor the fast running of water downhills, the soil infiltration coefficient and the time of
concentration of rainfall water in riverbeds drop significantly. Under these circumstances the
probability for the formation of flash-floods with major effects on the community (residential areas,
transportation, energy distribution and communication infrastructure) and environment. The same
type of risk can also occur in urban areas where the soil infiltration coefficient drops to 0 as a result of
water repellent coatings applied on surfaces. The presence of major accumulations in the upper basin
of Someșul Mic river (especially on the Someșul Cald river) ensures the mitigation of flash-flood waves
formed in this basin, on the sector downhills of Gilău.
188
Following the analysis of the current situation and the critical situations which can be generated in
Cluj county as a result of extreme hydrological phenomena, there is the need for the elaboration of a
strategy for the reduction and mitigation of the consequences of such phenomena.
Here are some recommendations for the most vulnerable fields:
Urban development/life quality:
• The demographic growth associated especially to urban areas imperatively requires the setting-
up of efficient warning systems to limit the proportions of potential flash-floods which affect
areas of high demographic density. The harnessing projects which refer to Someșul Mic river at
the level of Cluj-Napoca City, concerning especially the sectors in the Western half of the town,
have to take into consideration potential level fluctuations, determined either by natural
causes, or by hydro-energetic measures needed for the optimal functioning of hydropower
stations. Furthermore, in the context of the setting-up of new green areas (for example the
Western sector of Canalul Morii in Cluj-Napoca) the level fluctuations of the main stream have
to be taken into consideration.
Agriculture:
• With vulnerable areas, crops should be oriented to those species which are capable to adapt
to an abundant hydric regime for short periods of time (more exactly until water withdraws)
• The protection of crops located in the vicinity of streams should be ensured through vegetative
protections meant to provide river bank stability.
Tourism:
• In the context of extreme events, this field of activity is indeed affected in most of the cases.
Counterintuitively, using the example of other countries, tourism can also benefit in a way from
the controlled discharge of significant volumes of water from retention ponds. We can mention
here esthetic, landscape and recreational effects which could increase the tourism circulation
in certain periods of the year (spring or at the week-end).
Education:
• Awareness raising actions among the populations are key for the limitation of the damages
caused, whereas these actions have to refer not only to the post-event reaction, but rather
actions to prevent critical situations. As a matter of fact, sociological studies on the perception
and behavior of the population in case of extreme natural phenomena often report a lack of
knowledge of the subject about the individual or collective management of such situations;
The following scenarios for extreme hydrological phenomena at the level of Cluj county are
proposed:
• The passive scenario (”Do Nothing”) – is the one where the population and the authorities are not
aware and do not act to reduce the effects of these phenomena, exposing themselves episodically,
to extreme situations which can generate major damages to the local economy, but also to life
quality. Against this background, individual and collective, but also institutional lack of interest can
generate a cutting up in the development of affected localities, if, prior to the occurrence of such
events, measures of reduction are not adopted, as well as subsequent measures of mitigation of
consequences.
189
• The reference scenario (”Do Minimum”) – it involves only the application of some minimum
measures of reduction of the local impact in affected areas, adopted:
• at individual level: cleaning of storms drains and street gutters, appropriate maintenance of
surfaces by removing various items, materials, etc. which could be taken over by meteoric
waters and carried to the hydrographic network, potentially creating obstructions in the
natural drainage. In rural areas it is very important to make sure that the river banks are not
occupied with various materials and items (usually wood), which, affected by flash-floods,
can block the stream beds and thus cause the submergence of the neighboring land.
• at institutional, administrative level: performance of protection works against flooding in
residential and agricultural areas. Ensuring the good functioning of the equipment used for
the mitigation of natural disasters; assuming the responsibility at the level of institutions and
work groups for actions which emerge from the management of emergency situations
caused by extreme hydrological phenomena; implementation of the management plans in
compliance with the Floods Directive 2007/60/EC which calls for the member states to take
into account, to the extent possible, the maintenance and/or restoration of floodplains, as
well as measures to prevent and reduce damage to human health, the environment, cultural
heritage and economic activity. The elements of flood risk management plans should be
periodically reviewed and if necessary updated, taking into account the likely impacts of
climate change on the occurrence of floods.
• The major impact development/reduction scenario (”Do Something”) – is the one where the
local and county public authorities actively and predominantly get involved in the
implementation of the recommendations presented in the “Do minimum” scenario.
Based on this scenario, all parties involved (population, public institutions, companies), prioritize the
management of extreme hydrological phenomena within their actions in terms of natural hazard
management, by encouraging investments which aim at the reduction of their adverse impact on the
community, implementation of systems for efficient warning/monitoring systems, tested periodically
through alarm and population training drills, application of educational policies for enhancing the
awareness among the population about the actions which have to be taken to reduce potential effects
of flash-floods and floods.
5.3. Forecasts, scenarios or alternate options regarding landslides
Landslides are geomorphological processes that may result in immediate loss of property due to
building collapsing, failure of supply systems, obstruction of roads or changes in landscape and loss of
extended farming land. These potential consequences call for specialized studies to determine the
spatial probability of such natural processes and to assess any related risks. The results thereof are
based on the current state of the art as reflected by the literature, the expert knowledge of study
authors, and by the database consisting in the total contributing and triggering factors of landslides,
and an inventory as detailed as possible of events prior to the study, prepared by local governmental
public authorities (RORISK, 2018).
The selection of the methods to mitigate the risks caused by landslides requires to asses the spatial
and temporal likelihood of potential landslides and of their related scale/magnitude. This type of
approach would require a hazard analysis (Roșca et al., 2015), which in turn would imply the
identification of an annual frequency of landsliding events, but in the absence of any knowledge as to
the rainfall that is a contributing factor of landslides. In Cluj County (where the contributing factors
are diverse, from the prevalent geologic factors on inclined slopes and high rainfall levels, as it is the
190
case of landslides outside built-up areas, to the overloading of slopes of erection of buildings sloped
terrain without prior geotechnical studies when building inside built-up areas), the assessments
remain limited to spatial probabilities (susceptibility) (Corominas et al, 2014).
The frequency assessment of landslides at county level, i.e. the number of processes per unit of time
(eg. one year) may be carried out based on the ISU records of events which required ISU field
interventions.
Figure 126 – Frequency of landslides requiring interventions by authorities during 1970-2018
Source data: ISU Cluj
Thus, a relevant period from that standpoint is 2005-2010, when there were more than 30 landsliding
events which required the intervention of authorities in 2006 (32 events) and 2010 (34 events).
The lack of a thorough inventory of landsliding events at county level makes a hazard analysis more
difficult to prepare, and restricts such an analysis to the total number of landslides in a specific area
and for a specific period, or to their density in terms of events per one area (landslides per km2/year)
or the density of TAUs affected in a specific region (Cascini 2008) etc. This type of approach is
employed in studies that require the assessment of landslide hazards per large areas, at small working
scales, when the assessment is focused on landslides deemed as regional phenomena (Crozier 2005).
In order to identify landslide hazards, the specialized studies use approaches such as the identification
of rainfall and/or earthquake thresholds which triggered landslides when exceeded, and assume the
return period of such geomorphological processes is the same as that of the determined triggering
threshold (Van Westen 2008, Roșca et al., 2015, Zezere et al., 2003).
It is known that shallow landslides are triggered by short and heavy rainfall, while landslides on low
permeability rock terrains are triggered by prior rainfall periods (Remaître şi Malet, 2012). The first
landslide hazard scenarios were performed on very limited areas, in studies by: Şandric (2008),
Jurchescu (2012), Roşca et al. (2015), and subsequently on larger areas (Buzău and Vrancea areas;
IGAR, 2014) (RORISK).
191
Thus, studies are prepared based on landslide scenarios according to the determining factor, namely
the amount of rainfall cumulated prior to landsliding events and earthquakes at county level.
In the case of modelling the amount of rainfall that triggers landslides, attention should be paid to
identifying their frequency based on the identification of a potential repetition of conditions that led
to the past events, building on the premise that the exceeding of a rainfall threshold that caused
landslides in the past will also have the same consequences in the future. Thus, rainfall return periods
of 10, 100 and 1000 years were analysed, according to the magnitude of the climate factor and the
magnitude of the expected landsliding event. These thresholds were set by the National Meteorology
Administration (Meteo-Ro) and the Geographic Institute of the Romanian Academy (IGAR) under the
ROORISK project, a study which assessed the enabling/triggering rainfall thresholds (amount,
duration) of significant landslide events.
Building on the premise that a cumulated amount of rainfall which caused landslide events in the past
will have the same effect in the future, and using the previously determined probability classes, a
landslide hazard scenario was prepared for the return period of landslide triggering rainfall with a
known occurrence date (event), for March 2013.
Following an analysis of months in terms of rainfall levels, based on ANM data, a preliminary picture
of landslide occurrence and reactivation periods emerged, outlining a high frequency of landsliding
events during the spring (March) and summer (May and June) seasons, when ISU indeed intervened
on the ground (Table 46).
Table 46. Occurrence of landslides requiring ISU interventions
Months of the year
Year
I II III IV V VI VII VIII IX X XI XII
2009 15.9 40.7 72 10 34.2 129.8 54.4 47 5 91.2 50.1 43.5
2010 45.9 39.2 25.4 51.8 139 166.8 97.8 58.2 73.2 28 26.1 60.3
2011 24.8 23.8 22.3 43.8 69.4 76.8 127.8 58.4 21.6 14.6 0.2 26.3
2012 45.5 28.6 13.3 57.2 82.3 72.6 64.5 38 32.6 30.4 22.7 41.8
2013 34.8 11.8 91.6 52 81 106.4 28.6 51.8 80.6 56.6 27 9.4
2014 46.4 20.3 37.5 30.4 78 78.8 120.8 46.2 25.2 71.2 48.9 77.4
2015 29.2 26 32.7 38.3 77.6 112.2 41.2 57.8 137.6 61.8 48.7 12.3
2016 33.3 31.9 56 47.8 84.2 113.6 113.4 115.2 29.7 77.6 43.7 16
2017 3.1 24.8 33 66.4 42.2 45.4 44.6 32.2 76.8 47.3 32.8 23.9
Where: Landslide event requiring an ISU intervention
To model landslide hazards (according to scenario 1) triggered by rainfall similar to that of March 2013
(91.6 mm), a month when warmer temperatures also release water from thawing snow, the return
period was determined to be 8.4 years. Thus, it may be concluded that once every 8 years there is a
likelihood of reaching a rainfall threshold that would trigger a similar number of landslide events as in
2013.
192
Figure 127 – Likelihood of landslides under scenario 1
In case of rainfall of approx. 166.8 mm (similar to June 2010, which also led to a high number of ISU
interventions against landslides), the return period was determined to be 23 years. Certainly, in case
of modelling landslide probability for conditions similar to those of June 2010, one also notes that the
previous spring months also had significant rainfall, with both April and May being rainy months,
hence the sublayer was soaked for long periods of time, which led to reactivation of already existing
landslides and emergence of new landsliding events.
Since the hazard class determination methodology according to G.D. recommends using the annual
rainfall for modelling the analysed territory, this study identified the return period of annual rainfall
as recorded by the Cluj Napoca station (Table 47).
Table 47. Return period of annual rainfall as recorded by the Cluj Napoca weather station
Return period Empiric PP Confidence
likelihood (mm) interval (95%)
10000 0.9999 1340 1140 - 1550
2000 0.9995 1230 1060 - 1410
1000 0.9990 1180 1020 - 1340
200 0.9950 1060 933 - 1190
100 0.9900 1010 891 - 1120
50 0.9800 949 847 - 1050
20 0.9500 866 782 - 950
10 0.9000 796 726 - 866
5 0.8000 717 660 - 773
193
Return period Empiric PP Confidence
likelihood (mm) interval (95%)
3 0.6667 649 601 - 696
2 0.5000 579 537 - 621
1.4 0.3000 503 462 - 544
1.2 0.2000 461 419 - 503
1.1 0.1000 406 361 - 450
1.05 0.0500 364 317 - 411
1.02 0.0200 320 271 - 370
1.01 0.0100 294 243 - 345
In case of rainfall with return periods of 10 years (796 mm) (scenario 2), the areas falling under the
medium-high and high likelihood classes become larger.
Figure 128 – Likelihood of landslides under scenario 2
Annual rainfall with a return period of 100 years are estimated at 1010 mm at the Cluj Napoca weather
station. If this amount of rainfall is reached and the remaining factors remain unchanged, the number
of territorial administrative units falling under the medium-high category increases particularly for the
units close to mountain areas.
194
Figure 129 – Likelihood of landslides under scenario 3
The following climate evolution scenarios for Cluj County are laid out below:
• The passive scenario (”Do Nothing”) – the population and authorities are not aware of or do
nothing against the negative impacts caused by landslides, no mitigation measures are taken, and
the impact on various fields of life is increasing.
• The reference scenario (”Do Minimum”) – only minimal action is taken to mitigate local impacts
in affected areas:
• by individuals: awareness raising and proactive behaviour by the population in order to
become aware of the risks they are exposed to and of measures to reduce deforestation,
and the importance of slope development works and of planting tree species that stabilize
the areas affected by or at risk of landslides.
• by institutions: obligation to prepare geotechnical stability studies (by specialized
companies) prior to erecting new buildings or rehabilitating existing ones, and enforcing
financial penalties in cases of failure to comply with such obligations, permanent
monitoring of unstable terrains within built-up areas.
Such measures will not significantly impact the negative effects of landslides, but will mitigate them,
the hot-spot areas within built-up areas will remain at almost the same levels due to urban sprawling
on inadequate terrains, which will lead to an overloading of slopes, but with a slight decrease in
intensity.
195
• The development/major impact mitigation scenario (”Do Something”) – the public authorities
become involved firstly by regulatory measures, secondly by implementation measures which
include all the previous recommendations.
5.4. Forecasts, scnearios or options regarding the emergency response
infrastructure and services
The following emergency response infrastructure and services scenarios are laid out below:
• The passive scenario (”Do Nothing”) – the population and authorities are not aware of or do
nothing to improve the emergency response infrastructure and services, with an increasing
negative impact on the population and various fields of life.
• The reference scenario (”Do Minimum”) – only minimal action is taken to mitigate the flaws
identified in the emergency response infrastructure and services:
• measures by individuals: better awareness raising among the population on the negative
implications of their behaviour in cases of emergence situations, the involvement of
volunteers in the emergency response infrastructure and services, with potential positive
impacts on their operation and access times in problem areas.
• measures by institutions: allocation of additional funds for the effective endowment of
emergency response infrastructure and services, improving legal provisions which currently
are ineffective.
• The development/major impact mitigation scenario (”Do Something”) – the public authorities
become involved firstly by regulatory measures, secondly by implementation measures which
include all the previous recommendations.
The measures under this scenario are complex, ranging from awareness raising and involvement of a
large number of volunteers specialized in various emergency situations, to active measures such as
public and private investments in he form of donations for better endowment of the emergency
response infrastructure and services.
196
ANNEX 1. LANDSLIDES EVENTS IN CLUJ COUNTY DURING 1972-2019
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
CITY OF CLUJ NAPOCA
1 221 Muncii Blvd. 2006 Exposed building foundation
Pomet street - west of
May
2 cemetery Dislodged utility poles
2010
215-229 Oaşului street
24 Tăietura Turcului June Damage to the building of 2 Vântului
3
street 2009 street
March-
241 Muncii Blvd. - The property at No 241 and one 400 Kv
4 May
Tineretului district utility pole were affected.
2013
5 221 Muncii Blvd. 2006 Exposed building foundation at no. 221
Uliului street, 150 Donath Buildings were damaged on 150 Donath
6 1975
street street
2006- Heavy damage on two buildings and
7 136-140 Donath street
2007 displacement of another building
8 169 Donath street 2005 500 sqm Damage to one orchard
2006-
9 258 Donath street Street surface damaged on one lane
2007
2005- Damaged foundation of a fence and a
10 55-57 Uliului street
2007 clogged sewer.
88-90 G-ral Dragalina Landfill accumulation at property from
11 2007
street no. 88
The pressure exerted of the building over
12 116 G-ral Dragalina street 2007
the land affected the street-side wall.
Calea Turzii – Obelisc
Damaged utility poles, and the street
13 area, along the Becaş 2010
surface cracked two times in a row.
valley
Damages to homes and farming land, the
32-34 E. Grigorescu 2005-
14 sewer and rainwater collection system,
street 2007
business areas.
34A to 36 E. Grigorescu Damaged buildings, faring land, water,
15
street – I.J.S.U. Cluj gas and power networks.
The landslide destroyed 2 buildings,
1995-
16 1-3 Valea Fânaţelor appurtenances, land on a length of
2001
approx. and farming land.
The left bank became loose under the
2006- buildings, as well as the left bank and
17 15-17 V. Seacă street
2008 upstream, and the bridge has an exposed
abutment on the right bank.
Uncontrolled land deposits and the
1-3 B Drumul Făgetului heavy traffic in the area affected 200 m
18 2005
street of street pavement and a building, and
the gutters became clogged.
Drumul Făgetului street – Damages to homes. appurtenances,
2006-
19 Colina area and farming land, roads and streets.
2008
Primăverii street Damages to the road pavement of
197
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
around 70% of 200 m of length, one
damaged home, loose land in the area on
a length of around 120 m.
Făget Road - 400 m The lack of a rainwater collecting gutter
20 downstream of the old 2005 led to a 60 m long collapse of the road
chalet pavement.
Displacement of a building by approx. 8-
10 m, collapse of a road section, cracking
and fracturing of the asphalt pavement
Făget Road - 50 m on a 50-70 m length. In April 2005,
21 downstream of the old 2007 compacting of the land and landslides
chalet occurred in the area due to the
depositing of earth resulting from
excavations and demolition waste, which
affected a wood chalet.
Damages to the sewer network, alley and
22 41 Zorilor street 2008
garages.
191-215 Maramureşului
23 Damages to street, farming land.
street
24 32 Axente Sever street Damages to street and house.
25 Vânătorului street, n.n. Damages to street, farming land.
Becaş Colony, n.n. – tree
26 Damages to street, farming land.
cultivation station
Dincolo de Becaş street,
27 Damages to street, farming land.
n.n.
28 26-30 Predeal street Damages to houses, land.
CITY OF TURDA
Collapse of the riverbank on a 2-2.5 m
width. Damages to building at No 22 – the
three appurtenances remained suspended
1 Sirenei street 2005
on the edge of the riverbank. A further
landslide would collapse the house at No
22 and affect neighbouring structures.
Collapse of the right slope of Valea Florilor.
Two landslide areas are present. Evolving
landslide.
2 Petrilaca district 2005 The property at No 25 was entirely
destroyed, residential house and
secondary uncompleted residential
building, and 2 ha of meadow.
Total destruction of the building at No 42,
65% destruction of the building at No 40,
Călăraşi, Vânători, damages to buildings from No 44 and 46.
1999-
3 Dorobanţi streets – Damages to the utility networks (breakage,
2013
landslide A1 cracks) – the water and gas supply pipes
and the overhead power cables, due to
collapsing poles and breaking cables.
The north-eastern slope
of the Almas hill, in the 1999- Changing of the primary vegetation layer.
4
former Carolina lake bed 2013 Destruction of DC 69 Turda-Ploscoş.
– landslide A2
198
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
Changing of the primary vegetation layer.
The north-eastern slope
The landslide advanced during the past
of the Almas hill, in the
1999- two years, and now stands at 4.5 m from
5 former Durgău lake bed,
2013 the three homes on Agriculturii street (Nos
landslide A2 Agriculturii
35, 37, 39). Destruction of DC 69 Turda-
street – landslide A3
Ploscoş. Damage to LEA 20 kV.
Erosion of the slope base by moving water.
Heavy rainfall. Land prone to erosion.
Changing of the tension in the land due to
DJ 107L Turda-Petreştii overloading of the upper riverbank with
de Jos, km 1 earth and construction and demolition
6 2011
+100 – km 1+300 – Valea waste.
Racilor right bank Any further development would obstruct
the Racilor Valley and flood the residential
areas in Turda Nouă district, on the left
bank of the stream.
CITY OF DEJ
1 Dej - Plevna street Damages to houses, street, farming land.
2 Dej - Dealul Viilor street Damages to houses, street, farming land.
Dej - Valea Codorului –
3 1987
A.N.I.F.
Damages to houses, street, farming land.
Dej - Valea Ocnei –
4 1987
A.N.I.F.
CITY OF GHERLA
Following heaby rainfall during April-May
Silivaş – outside of built- 2006, the landslide at the DC 37 Hăşdate-
up areas - DC 37 2005 Silivaş communal road section (km 4+450)
1 50 ml
Hăşdate-Silivaş (km -2006 extended to the Silivaş village stream,
4+450) damaging an appurtenance and 1 ha of
farming land.
Silivaş – No 6 (Oltean Collapsed house and appurtenances.
2 2010
Viorica) Damages to 9 tube culverts (clogged and
3 Silivaş 2010 damaged) which are no longer safe for use
4 Băiţa 2010 (5 in Silivaş and 4 in Băiţa)
5 Gherla – Fizeşului street Damages to street, farming land.
6 Gherla – Călăraşi street Damages to houses, street, farming land.
TOWN OF HUEDIN
1 Former garden 1990 10
Reactivation of old landslide bodies due to
2 Victoria farm 1996 5
man-made factors. The displacement of
3 CIS 1990 5
land masses is also enabled by the existing
4 Szeles Palak 1987 5
slope.
5 Galitan 1990 5
04-06.03.2006 – landslide which
endangered the water pump station in
6 Bicălatu 2006 Bologa village, Poieni commune, which
supplier drinking water for both the
Huedin town and the Poieni commune.
06-08.06.2006 – the DC 105 communal
7 Bicălatu – DC 105 2006 100 ml road connecting Huedin with Bicălatu
exhibited cracks and light compaction on a
199
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
100 ml length, that also included a culvert
that collected water from the nearby
slope. - 0.15 km.
AGHIREŞU COMMUNE
1 Arghişu – UTR 3.1, 3.2. 1972
1970- Reactivation of old landslide bodies due to
2 Dâncu – UTR 5.1, UTR 5.2
1974 man-made factors. The displacement of
1942 land masses is also enabled by the existing
3 Ticu – UTR 10.1 1970- slope.
1972
4 Ticu Colony – UTR 11.1 1976
The land underneath the DC 138 Dorolţu-
Inucu - DC 138 Dorolţu- Inucu communal road collapsed on approx.
5 2006 30 ml
Inucu 30 m, weakening the underlying structure
of the communal road.
AITON COMMUNE
April -
1 Aiton – Ciolt
Sept.
Podul cu acăţ – DJ 103 M April -
2
Aiton-Tureni Sept.
Reactivation of old landslide bodies due
to man-made factors. The displacement
3 DJ 71 – A.N.I.F. 1987
of land masses is also enabled by the
existing slope.
ALUNIŞ COMMUNE
The slope in the
1 neighbouring area of DC 2007 1.5 km Falling debris from slopes.
170 Aluniş-Pruneni
APAHIDA COMMUNE
Apahida – Borom, right
1 2010 8
slope
Câmpeneşti – p. lower
2 2010 20
Feiurd, right slope
Câmpeneşti – p. lower
3 2010 40
Feiurd, left slope
Dezmir – V. Zapodie,
4 2010 15
Rupturi, Cabaus
5 Sub Coastă 2005 8
6 Corpadea – Fata Mare 1998 8
Pata – Cluj-Pata road
7 2000 5
slopes
AŞCHILEU COMMUNE
1 Dorna 2000 5
Dorna - Valea Puturoasă
2 1980 10
– left and right slopes Reactivation of old landslide bodies due to
3 Dorna - Valea Urdigoaia 2000 3 man-made factors.
Valea Aşchileului – left
4 2000 20
and right slopes
Valea Cristorel – right Reactivation of old landslide bodies due to
5 2000 20
and left slopes man-made factors.
200
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
Valea Borşei, Fundături –
Dorna sub-ponds,
6 1998 40
Fundătura pond – left
and right slopes
BACIU COMMUNE
1 Baciu – No 584, 585 2013 Heavy rainfall. Cracked supporting wall.
Non-compliant constructions. Failure to
Baciu – A, B1, B2, C comply with the geologic study, which
2 2010
residential buildings provided for a supporting downstream wall
and performance of abutment works.
Baciu – DC 142 B – I.J.S.U.
3
Cluj
Popeşti – pump station –
4 2010
I.J.S.U. Cluj
Popeşti – DJ 142 – Reactivation of old landslide bodies due to
5 1985
A.N.I.F. man-made factors.
6 Coruşu – street The street.
Coruşu – Pe Deal – Reactivation of old landslide bodies due to
7 1987
A.N.I.F. man-made factors.
BĂIŞOARA COMMUNE
Băişoara – DJ Băşioara –
1 2005 10 km Heavy rainfall.
Muntele Băişorii
BOBÂLNA COMMUNE
Estate Valea Terca –
1 1980
A.N.I.F.
Estate Valea Budigut Reactivation of old landslide bodies due to
2 1988
1988– A.N.I.F. man-made factors.
Estate Valea Pruni –
3 1988
A.N.I.F.
Serious damage to the reinforced concrete
structure of the bridge overOlpret stream
Olpret stream – right 1970-
4 (1975). Reactivation in 1999, further
bank - bridge 2008
damaging the bridge structure, the stream
bed and the neighbouring farming land.
Total destruction of bottom thresholds of
the Olpret river, and total destruction and
clogging of the two tube culverts on the
Olpret stream – left bank 1960-
5 100 ml county road near the landslide area, failure
- DJ 108B 2008
to ensure an adequate drainage of waters
in the road gutters. Compaction by around
15-40 cm of the county road - DJ 108B.
BONŢIDA COMMUNE
Tăuşeni – la Fundătura –
1
A.N.I.F Reactivation of old landslide bodies due to
Tăuşeni – Pine forest – man-made factors.
2
A.N.I.F
BORŞA COMMUNE
Borşa – Holomburi area
1 Borşa V., left and right 1990 20 Coast streams. Heavy rainfall.
slopes (farming land)
201
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
Borşa – Pietrii-Dosu
2 Baronului area (farming 1985 30 Coast streams. Heavy rainfall.
land)
Giula – Uliţa de Sus area, Serious damage of the supporting
3 2010 1
No 68, 69, 70, 71, 82 structure of 10 homes and appurtenances.
Ciumăfaia – Fechetău
4 1993 25 Destruction of parts of farming land.
area
5 DJ 109 – I.J.S.U. Cluj
Borşa – Lower Borşa
6 1987
Valley – A.N.I.F.
Borşa – Pietrii area – Reactivation of old landslide bodies due to
7 1988
A.N.I.F. man-made factors.
Giula – Uliţa de sus area
8
– A.N.I.F
CĂLĂŢELE COMMUNE
1 Călăţele No 141, 144 Damages to houses, street, farming land.
CĂPUŞU MARE COMMUNE
1 Căpuşu Mare – Chendărăi 1980 5
2 Agârbiciu – Sub şatra 1985 2
3 Agârbiciu – Pe luncă 1985 3
4 Agârbiciu – Valea Micii 1985 5
5 Căpuşu Mic - Şatra 1985 20
6 Căpuşu Mic - Oşorhei 1985 3
Reactivation of old landslide bodies due to
7 Dumbrava - Hopa 1985 4
man-made factors.
Dumbrava – Mugii
8 1985 7
stream
9 Păniceni - Tufoi 1985 8
10 Straja – Goroni 1985 2
11 Straja - Luncă 1985 2
12 Dângău Mare – Pleşa 1985 4
13 Dângău Mic - Mireş 1985 4
CĂŞEIU COMMUNE
1 Guga – La Dumbravă
2 Guga – Tăietură
3 Leurda – La şesuri
4 Leurda – Pe picior
5 Leurda – Boceni
Gârbăul Dejului – Pe
6
luncă
Gârbăul Dejului – La
7
ogradă
8 Gârbăul Dejului – în Deal
Gârbăul Dejului – Pe
9
Mohile
CEANU MARE COMMUNE
Boian – Castanilor- 10 damaged households. 300 ml of
1 1970 40
Soponiţa coast destroyed village road.
Iacobeni – Coasta
2 35
Ticudenilor, Livada
3 Strucut – Coasta Viilor 30
202
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
4 Strucut – Coasta Suciului 20
Strucut – Coasta
5 16
Saraspatacului la Haiduc
6 Strucut – La Hădăreanu 8.5
Strucut – La Ioan
7 4.2
Mureşan
8 Strucut – Dosu Glodului 5
9 Dosu Napului – la Toma 2.8
Dosu Napului - la
10 5.6
Calmanu
11 Bolduţ – Fata Bolduţului 12
12 Boian – Dealu Crucii 7
13 Boian – Coasta Crişenilor 10
Fânaţe – Coasta
14 Fânaţelor – from DJ 150 – 32
Munteanu A.
CHINTENI COMMUNE
Chinteni – Pomi area,
1 2010
Beretcaia-Rocaiuc
2 Chinteni – La Râpă area Damaged farming land.
3 Satu Lung – Borişte area 2010
4 Măcicaşu – I.J.S.U. Cluj Damaged farming land.
Chinteni - Între Pomi –
5 1987
A.N.I.F.
Chinteni - Suseni –
6 1987 Reactivation of old landslide bodies due to
A.N.I.F.
man-made factors.
Chinteni – Rîtu Tistului –
7 1987
A.N.I.F.
Chinteni – Valea
8 1987
Feiurdului – A.N.I.F.
CHIUIEŞTI COMMUNE
Strâmbu – DC7 Strâmbu- April
1
Huta – bridge km 0+010 2013
Strâmbu – DC7 Strâmbu-
Huta – km 2 +350 (30
April
2 ml), km 3 90 ml
2013
+100 (10 ml), km 2+700
(15 ml), km 3+200 (35 ml)
April
3 Chiuieşti – DN 18B 300 ml
2013
CIURILA COMMUNE
1 Pruniş – I.J.S.U. Cluj Damaged farming land.
COJOCNA COMMUNE
Cojocna – 11 Republicii
1 2006 0.2 km Collapsed earthworks 15-20 years before
street
Cojocna – 26 Bărnuţiu
2 2006 100 ml
street
3 Cojocna – DJ 161A 2006
Cojocna – 9 Republicii 2000-
4
street 2012
203
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
1990-
5 Cojocna – Sărărie street
2013
6 Cojocna – DJ 161A 2010 Loose land. Vehicle traffic.
Cara – 11 Republicii
7 2010 Damages to house, street, farming land.
street
8 Pe Deal – A.N.I.F. 1987 Reactivation of old landslide bodies due
9 Ferma Largă – A.N.I.F. 1987 to man-made factors.
CORNEŞTI COMMUNE
DJ 109B – between
1 Fundătura and Lujerdiu –
I.J.S.U. Cluj
Lujerdiu – Jiman
2 2006 One damaged livestock pen.
Pantelimon household
Morău – Rus Andrei
3 2006
household
CUZDRIOARA COMMUNE
1 Mănăşturel
2 Valea Gârboului
DĂBÂCA COMMUNE
1 DJ 161- I.J.S.U. Cluj
FELEACU COMMUNE
Vâlcele – DC75 (Valea 2005 One collapsed section of DC75 in Valea
1 50 ml
Lupului) 2009 Lupului.
2 DN1 – km 469+900 Damages to DN1, farming land
Right side of DN1 –
3 1987
A.N.I.F.
Reactivation of old landslide bodies due to
Gheorgheni – ring road –
4 1987 man-made factors.
A.N.I.F.
Vâlcele – Sub pădure –
5 1987
A.N.I.F.
FIZEŞU GHERLII COMMUNE
The eastern slope of the Ghergheleu hill
(La stupină) is unstable. Damages to:
1 Nicula – Ghergheleu hill 2006
homes, appurtenances, farming land,
county roads, culverts, power networks.
FLOREŞTI COMMUNE
Floreşti – Teilor, Fagului,
Salcâmului, Stejarului,
1
Răzoare, Sub Cetate
street areas
Gârbăului Hill, Rotund
Hill, Cetatea Fetei Hill,
Spoială Hill, Coriu Hill,
2
Muncel Hill, Lunitie Hill,
Melcului Hill, La Înălţime
Hill
Răzoarele Hill - between
the premises of Polus
3
Center and the Building
Real Estate complex
204
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
GILĂU COMMUNE
1 Someşu Rece – DJ 107 2006 50 ml Displacement at km 4+200 and km 4+200.
Someşu Rece - upstream
of "Poeniţa" area (the
107N county road – left bank of Someşul
2 "Piatra Tăiată" 2006
Rece – and nearby houses.
landmark), right bank of
Someşul Rece
Landslide on right bank, upstream of dam,
3 Upstream of Gilău Dam 2006
by 120-150 ml (from south to north).
Landslide on right bank, upstream of dam,
4 Upstream of Gilău Dam 2007
by 500 ml (from south to north).
GÂRBĂU COMMUNE
Reactivation of old landslide bodies due to
1 Turea – A.N.I.F. 1972
man-made factors.
ICLOD COMMUNE
Livada – land at the
1 village limits towards 2005
Orman village
Orman Valley – left and
right bank (from Livada 2005
2
village limits to Orman 2010
village entrance)
Iclozel – the banks of
3 2010
Onaului Valley
IZVORU CRIŞULUI COMMUNE
Izvoru Crişului – Crişul
1 Repede river, left bank - 1995
S.G.A. Oradea
Izvoru Crişului – Crişul
Repede river, left bank,
2 1998
downstream of village -
S.G.A. Oradea
JICHIŞU DE JOS COMMUNE
Jichişu de Jos – Valea
1 1988
Codorului – A.N.I.F.
Reactivation of old landslide bodies due
Jichişu de Jos – Valea
2 1987 to man-made factors.
Jichişului – A.N.I.F.
3 Tărpiu – A.N.I.F. 1987
JUCU COMMUNE
Jucu de Sus – right bank Damages to housing and the Jucu de Sus-
1 2010
of Someşul Mic river Bonţida communal road.
Jucu de Sus - "La Borca"
2 1975 Raw land.
landmark
Jucu de Sus - the area at
The bed position relative to the hill was
the right of the Someş,
changed by 15 m, giving rise to even
3 from the houses at No 4- 1975
larger landslides and increased bank
85 up to the bridge
erosion, by approx. 40-80 cm/year.
crossing the Someş
Damages: minor damage on a building,
4 Gădălin 1995-2008
the Orthodox Church and parish house,
205
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
and minor damage on one side of the
school.
Jucu – Deasupra Morii, La
5 2010 Damages to houses, streets, farming
Criptă – I.J.S.U. Cluj
land, power networks.
6 Jucu Herghelie 2010
MĂGURI RĂCĂTĂU COMMUNE
Măguri – DJ 107T
21.03.
1 (downstream of basin
2013
dam)
Măguri Răcătău -
upstream ("La Pleazna" 107N county road – left bank of Someşul Rece –
2 2007
landmark), right bank of and nearby houses.
the Someşul Rece river
2005
3 Muntele Rece – DC 110 Collapsed road embankment.
2006
MICA COMMUNE
Nireş – Berghel,
1 Spring
Saralimba, Feti hills area
Mica – northern slope of
2 Spring
Pe coşuri Hill
Mănăstirea – Livada and
3 Spring
Vie Hills area
Sînmărghita – the Burzuc,
4 Spring
Meri, Sustirei Hills area
MIHAI VITEAZU COMMUNE
Damages to 58 households, 2 churches, 1
school. Out of the total 58 households, 7
exhibit foundation damage, with seriously
1 Cheia 1970
damaged structures,
33 exhibit heavily cracked walls and foudations,
and 18 exhibit small wall cracks.
MINTIU GHERLII COMMUNE
April
1 DC Mintiu-Pădureni .
2013
Reactivation of old landslide bodies due to
2 Nima – A.N.I.F.
man-made factors.
MOCIU COMMUNE
One damaged household - the family moved
1 Zorenii de Vale 2008
elsewhere.
MOLDOVENEŞTI COMMUNE
Right side of the road, km 437+650 - 437+900,
embankment area, affecting the parking
platform
Bădeni - DN 1 Aiud- 2006
1 and first and second traffic lane – longitudinal
Turda, eastern slope 2012
and transversal cracks, vertical landslides at
grades
of up to 2 m.
NEGRENI COMMUNE
Length of approx. 15m and a depth of
1 32 Bucea street 2017
5m
206
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
PANTICEU COMMUNE
Cubleşul Someşan – DC 2005
1 50 ml
155 2006
PĂLATCA COMMUNE
1 Pălatca – Bridge 2010
June
2 Pălatca – Bugles Hill
2013
3 Pălatca – Isec Hill 2008 No significant damage.
Pălatca No 171 –
4 Damages to house, street, farming land.
I.J.S.U.Cluj
PETREŞTII DE JOS COMMUNE
Petreştii de Jos – DJ 107L
1
– ISU CJ
POIENI COMMUNE
Poieni – road across the
1 bridge towards the Spring
Drăganul Valley
Poieni – road on the
2 Spring The slope collapsed and ended under the road.
Vărădeşti valley
Poieni – Punca Vişagului
3 Spring Collapsed slope.
– Dâlbă area, DJ 764 B
RECEA CRISTUR COMMUNE
Recea Cristur – Suseni
1 street, Osteaze,
Principală street
2 Căprioara – DJ 109A
3 Escu – Principală street May 2013 Rainfall.
4 Pustuta – DJ 108B
5 Osoi
Ciubanca – Principală
6
street
SĂNDULEŞTI COMMUNE
1 Copăceni – Contenit area
Sănduleşti Quarry – Reactivation of old landslide bodies due
2
A.N.I.F. to man-made factors.
SIC COMMUNE
Sic – 201, 202, 95, 88, 76,
62, 211, 205, 243, 63, 297 Damages to houses, streets, farming
1 2010
1st street and 104 2nd land, power networks.
street
SÂNCRAIU COMMUNE
Domoşu – DC 124
June
1 (between Huedin and
2010
Domoşu)
Horlacea – DC 124
June
2 (between Domoşu and
2010
Horlacea)
SÂNMĂRTIN COMMUNE
207
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
Cutca - in front of
2010 30
1 households at No 170-
2013 ml
173, parallel to the street
Cutca - in front of
2010 40
2 households at No 149-
2013 ml
150, parallel to the street
SÂNPAUL COMMUNE
1 Şardu 1970
SUATU COMMUNE
The cemetery, "La Ţigle",
1 2008
"Surduc" landmarks
TRITENII DE JOS COMMUNE
Triteni limits – western Damaged downstream farming land and
1
slope of Baia Hill DC 60.
Triteni limits – eastern
Damaged downstream farming land and
2 slope of Fînaţele
boundary roads.
Tritenilor Hill
Tritenii de Jos – western
3 Damaged downstream farming land.
slope of Derdelău Hill
Tritenii de Jos – northern
4 Damaged downstream pasture
slope of După Vii Hill
Pădureni – Popa Hill
5 Damaged downstream pasture.
slopes (pasture)
ŢAGA COMMUNE
Ţaga – DJ 172A (village 1970-
1
limits) 1975
1970-
2 Ţaga – V. Cistaşului
1975
Sîntioana – the SE slope 1970-
3
of Somos Hill 1975
Sântioana – DC 23, 1970-
4
downstream of cemetery 1975
VAD COMMUNE
1 Vad – DC 177 2013 60 ml
2 Bogata de Jos – DC177 2013 220 ml
3 Cetan - Uliţa Morii 2013 100 ml
4 Cetan - Uliţa de pe Deal 2013 100 ml
5 Bigata de Sus – DC177 2013 120 ml
6 Calna – DC177 100 ml
VALEA IERII COMMUNE
Valea Ierii – timber Damaged forest road, DJ 107J, the
1
factory area nearby gutter (clogging).
VIIŞOARA COMMUNE
1340 Silos Area – I.J.S.U.
1 Damaged street, farming land.
Cluj
Viişoara – după Vale – Reactivation of old landslide bodies due
2
A.N.I.F. to man-made factors.
Valea Ierii – DJ 107N La
3 2006 Damaged road and culverts
Moară
VULTURENI COMMUNE
208
occurrence
Period of
Year/
No
Area
(ha)
Location Causes and effects
1 Băbutiu Valley – A.N.I.F. 1980
Fundătura Valley area –
2 1988
A.N.I.F. Reactivation of old landslide bodies due
3 P. Vultureni – A.N.I.F. to man-made factors.
4 Valea Bădeşti – A.N.I.F.
5 Valea Făureni – A.N.I.F.
209
BIBLIOGRAPHY
1. Alexander L., Herold N. (2016) ClimPACT2 Indices and Software, The University of South
Wales, Sydney, Australia. Available at: https://github.com/ARCCSS-extremes/climpact2.
Accessed on 23 November 2018.
2. Aleotti, P., (2004): A warning system for rainfall-induced shallow failures, Eng. Geol., 73, 247–
265.
3. Arghiuș, Corina, Arghiuş, V.I., Ozunu, A., Munteanu, O.L., Mihăiescu, R., (2013), Landslide
susceptibility assessment in the Codrului Hills (North-Western part of Romania), Carpathian
Journal of Earth and Environmental Sciences, 8(3), 137-144.
4. Armas Iuliana, (2011), An analytic multicriteria hierarchical approach to assess landslide
vulnerability. Case study: Cornu Village/Romania, Zeitschrift fur Geomorphologie 55/2:209-
229.
5. Băldoi, V., Ionescu, V., (1986), Apărarea terenurilor agricole împotriva eroziunii, alunecărilor
şi Bălteanu, D., Micu, M., 2009, Landslide investigation: from morphodynamic mapping to
hazard assessment. A case-study in the Romanian Subcarpathians: Muscel Catchment, în
Landslide Process from Geomorphologic Mapping to Dynamic Modelling, editori Malet și
colab., 2009, Editura CERG, Strasburg, 235-241.
6. Bilaşco Ştefan, Roşca Sanda, Fodorean Ioan, Vescan Iuliu, Filip Sorin, Petrea Dănuţ, (2018),
Quantitative evaluation of the risk induced by dominant geomorphological processes on
different land uses, based on GIS spatial analysis models, Front. Earth Sci. 2018, 12(2): 311–
324 https://doi.org/10.1007/s11707-017-0679-3.
7. Bilasco Stefan, Roşca Sanda, Petrea Danut-Petru, Vescan Iuliu, Fodorean Ioan, Filip Sorin,
(2019), 3D Reconstruction of Landslides for the Acquisition of Digital Databases and
Monitoring Spatiotemporal Dynamics of Landslides Based on GIS Spatial Analysis and UAV
Techniques, Titlu volum: Spatial Modeling in GIS and R for Earth and Environmental Sciences,
ISBN volum: 978-0-12-815226-3, Editura: Elsevier, Editor: Hamid Reza Pourghasemi, Candan
Gokceoglu: 451-465.
8. Bilaşco, Șt., Horvath, Cs., Roşian,Gh., Filip S., Keller, I., E., (2011), Statistical model using gis
for the assessment of landslide susceptibility. Case-study: the Someş plateau, in Romanian
Journal of Geography, EdituraAcademieiRomâne, București, (2), 91-111.
9. Bojariu R., Bîrsan M.-V., Cică R., Velea, L., Burcea S., Dumitrescu, A., Dascălu, S.I., Gothard M.,
Dobrinescu A., Cărbunaru, F., Marin L. (2015), Schimbările climatice–de la bazele fizice la
riscuri și adaptare, EDITURA PRINTECHBUCUREȘTI 2015
10. Brunetti, M.,T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., Guzzetti, F., (2010), Rainfall
thresholds for the possible occurrence of landslides in Italy, Natural Hazards and Earth System
Sciences, no. 10, 447-458
11. Carrara A, Guzzetti F, Cardinali M, Reichenbach P (1999) Use of GIS technology in the
prediction and monitoring of landslide hazard. Nat Hazards 20(2– 3):117–135
12. CarraraA,CardinaliM,Guzzetti F, Reichenbach P (1995) GIS-based techniques for mapping
landslide hazard. In: Carrara A, Guzzetti F (eds) Geographical information systems in assessing
natural hazards. Kluwer Publications, Dordrecht, pp 135–176
13. Chung, C. F., and Fabbri, A. G., (1999), Probabilistic prediction models for landslide hazard
mapping. Photogrammetric Engineering and Remote Sensing, 65, 1389–1399.
210
14. Croitoru Adina-Eliza (Leader author:), Piticar Adrian, Sfîcă Lucian, Harpa Gabriela-Victoria,
Roșca Cristina-Florina, Tudose Traian, Horvath Csaba, Minea Ionuț, Ciupertea Flavius-Antoniu,
Scripcă Andreea-Sabina (Contributing authors:) (2018), Extreme temperature and
precipitation events in Romania, Editura Academiei Române, 359 p. ISBN 978-973-27-2833-8.
15. Dumitrescu, A., Bîrsan M.V. (2015), ROCADA: a gridded daily climatic dataset over Romania
(1961–2013) for nine meteorological variables. Natural Hazards 78(2):1045-1063. DOI:
10.1007/s11069-015-1757-z.
16. Dikau, R. &Schrott, L. (1999).The temporal stability and activity of landslides in Europe with
536 respect to climatic change (TESLEC): main objectives and results. Geomorphology, 30, 1–
12
17. Dragotă Carmen, Micu, M., Micu Dana, (2008), The relevance of pluvial regime for landslides
genesys and evolution. Case study Muscel Basin (Buzău Supcarpathians), Romania, in Present
Environment and Sustainablement no. 2, 242-257.
18. Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review
of current techniques and their application in a multiscale study, Central Italy. Geomorphology
31(1–4):181–216
19. Guzzetti, F., 2006, Landslide hazard and Risk Assessment, http://hss.ulb.uni-
bonn.de/2006/0817/0817.htm
20. Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M, Ardizzone, F. (2005) Probabilistic landslide
hazard assessment at the basin scale, Geomorphology 72, p. 272–299.Aleotti, P., (2004), A
warning system for rainfall-induced shallow failures, Engineering Geology 73, p. 247–265.
21. Harpa Gabriela‐Victoria, Croitoru Adina‐Eliza, Djurdjevic Vladimir, Horvath Csaba (2019),
Future changes in five extreme precipitation indices in the lowlands of Romania. International
Journal of Climatology. DOI: https://doi.org/10.1002/joc.6183.
22. Herbel I., Croitoru A-E, Rus A.V., Roșca C.F., Harpa G.V., Ciupertea A.F., Rus I. (2018) The impact
of heat waves on surface urban heat island and on local economy in Cluj-Napoca City,
Romania. Theoretical and Applied Climatology, Volume 133, Issue 3–4, pp 681–695. DOI:
10.1007/s00704-017-2196-4.
23. IRIMUŞ I. A(1998), Relieful pe domuri şi cute diapire în Depresiunea Transilvaniei,
Ed.Pres.Univ.Clujeană, ISBN 973-9354-55- 6, Cluj-Napoca, p.299
24. Magliulo, P., Di Lisio, A., Russo, F., Zelano, A., 2008, Geomorphology and landslide susceptibilit
assessment using GIS and bivariaate statistic: a case study in southern Italy, in Natural
Hazards, nr. 47, 411-435.
25. Manea, Ştefania, Surdeanu, V., 2012, Landslide Hazard Assessment in the Upper and Middle
Scetors of the Strei Valley, în Revista de Geomorfologie, Editura universităţii din Bucureşti, nr.
14, pg. 49-55.
26. Moţoc, M., Munteanu, S., Băloiu, V., Stănescu, P., Mihai, Gh. (1975), Eroziunea solului şi
metode de combatere, Editura Ceres, Bucureşti, p. 301.
27. Moţoc, M., Sevastel, M., (2002). Evaluarea factorilor care determină riscul eroziunii hidrice în
suprafaţă, Editura Bren, Bucureşti.
28. Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L.,
Alewell, .C., 2015b, The new assessment of soil loss by water erosion in Europe. Environmental
Science & Policy. 54: 438-447. DOI: 10.1016/j.envsci.2015.08.012
29. Panagos, P., Borrelli, P., Robinson, D.A., 2015a, Common Agricultural Policy: Tackling soil loss
across Europe. Nature 526, 195 doi:10.1038/526195d
211
30. Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., Alewell, C. (2014) Soil erodibility in
Europe: A high-resolution dataset based on LUCAS. Science of Total Environment, 479–480:
189-200
31. Petrea, D., Bilaşco, Şt., Roşca, Sanda, Vescan, I., Fodorean, I., 2014, The detremination of the
Landslide occurence probability by spatial analysis of the Land Morphometric characteristics
(case study: The Transylvanian Plateau), în Carpathian Journal of Earth and Environmental
Sciences., nr. 9,
32. Rădoane M, Rădoane N (2007) Geomorfologie aplicată, Edit. Universităţii din Suceava,
Suceava, 377p.
33. Radoane Maria , Dumitriu, D., Ichim, I. (2000), Geomorphology, vol. II, Editura Universitatii
"Stefan cel Mare"Suceava, 394 p.
34. Roşca Sanda, Bilaşco Ştefan, Petrea Dănuţ, Fodorean Ioan, Vescan Iuliu & Filip Sorin, 2015,
Application of landslide hazard scenarios at annual scale in the Niraj River basin (Transylvania
Depression, Romania), Natural Hazards, 77: 1573-1592, DOI 10.1007/s11069-015-1665-2
35. Roşca Sanda, Bilaşco Ştefan, Petrea Dănuţ, Vescan Iuliu, Fodorean Ioan, 2016, Comparative
assessment of landslide susceptibility. Case study: the Niraj river basin (Transylvania
depression, Romania), Geomatics Natural Hazards and Risk, 7 (3): 1043-1064, DOI
10.1080/19475705.2015.1030784
36. Sestras Paul, Bilasco Stefan, ROŞCA Sanda-Maria, Naș Sanda, Bondrea Mircea, Gâlgău Raluca,
Vereş Ionel, Salagean Tudor, Spalevic Velibor, Cimpeanu Sorin, 2019, Landslides Susceptibility
Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan
Area, Sustainability, 11 (5): 1 - 23, ISSN: 2071-1050.
37. Surdeanu, V. Goţiu, D., Rus, I., Creţu, A., (2006), Geomorfologie aplicată în zona urbană a
municipiului Cluj Napoca, Revista de Geomorfologie, vol. 8, p. 25-34
38. Van Westen, C.J., Van Asch, T.W.J., Soeters, R. (2006), Landslide hazard and risk zonation—
why is it still so difficult?, Bull. Eng. Geol. Env., 65, p. 167–184.
39. Varnes, D., J., with IAEG Commission on landslides and other Mass-Movements, 1984:
Landslide hazard zonation: a review of principles and practices. Paris,Unesco Press, 63 pg.
40. Varnes, D., J., with IAEG Commission on landslides and other Mass-Movements, 1984:
Landslide hazard zonation: a review of principles and practices. Paris,Unesco Press, 63 pg.
41. Varnes, D., J., with IAEG Commission on landslides and other Mass-Movements, 1984:
Landslide hazard zonation: a review of principles and practices. Paris,Unesco Press, 63 pg.
42. *** Law 575 approving the National Spatial Plan – Section V. Natural Risks, as published in
the Official Gazette no. 726, year XIII, 14 November 2001.
43. Mac I., Petrea D. (2003), Polisemia evenimentelor geografice extreme, Revista Riscuri și
catastrofe, vol. I, 2002, Editura Casa Cărții de Știință, Cluj-Napoca.
44. *** Flood Risk Management Plan, the Crișuri Water Basin Administration
45. *** Flood Risk Management Plan, the Mureș Water Basin Administration
46. *** Flood Risk Management Plan, the Someș-Tisa Water Basin Administration
47. *** Hidroelectrica S.A. presentation leaflet
48. *** County Defence Plan against floods, ice, drought, water engineering works accidents and
accidental pollution – Cluj County
49. *** Data supplied by the ”Avram Iancu” Emergency Situations County Inspectorate, Cluj.
50. (1995), Principii generale privind metodologia de zonare geotehnică a teritoriului
Romaniei. Indicativ P 136-95. Buletinul Construcțiilor 10, 67-97
51. (1998), Guide on the identification and monitoring of landslides and on intervention
solutions. Code GT006-97, (as approved by MLPAT Order No 18/N/1997), Buletinul
construcţiilor 10, 2-92
52. (1998), MLPAT Order No 62/N/1998 on the demarcation of natural risk areas. Off. Gaz.
354
212
53. (2000), Guidelines for the preparation of slope landslide hazards for building stability
purposes. Code GT006-97, (as approved by MLPAT Order No 80/N/1998), the Bucharest Land
Development Study and Project Institute, Buletinul construcţiilor 6, 117-165
54. (2001), Law no. 575 of 22 October 2001 approving the National Spatial Plan – Section V -
Natural risk areas, Off. Gaz. 726
55. (2003), Government Decision No 447/2003 approving Implementation rules for the
drafting and content of landslide natural hazard maps, Off. Gaz. 305 (1)
56. (2003), Ministry of Transport, Construction and Tourism Decision No 382/2003 approving
Implementation Rules on the minimum content requirements of spatial and urban planning
documentations for natural risk areas, Off. Gaz. 263 (1)
57. (2003), Hotărârea nr. 77/2003 privind instituirea unor măsuri pentru prevenirea
accidentelor montane și organizarea activității de salvare în munți, M. Of. nr. 91
58. (2013), Seismic Design Code – Part I – Design of buildings, Code P100-1/2013, the
Technical University of Civil Engineering of Bucharest
59. (2013), Government Decision No 663/2013 amending Government Decision No 447/2003
approving Implementation rules for the drafting and content of landslide natural hazard maps,
Off. Gaz. 565
60. (2017), Risk coverage and assessment plan of Cluj County, Cluj County "Avram Iancu"
Emergency Situations Inspectorate
61. (2017), Organization and Functioning Rules of the "Salvamont – Salvaspeo” Public County
Service, Cluj County. Annex to Decision No 188/2017, Cluj County Council
62. ***, IAEG– International Association for Enginnering Geology, 1984
63. ***EEA, 2004. Impacts of Europe’s changing climate -An indicator-based assessment.
European Environment Agency Report, 2.
213