The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed August 2021 https://worldview.earthdata.nasa.gov/. © 2021 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org All rights reserved This volume is a product of the staff of the World Bank Group and the International Center for Agricultural Research in the Dry Area (ICARDA). The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. 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The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed. © World Bank.” Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, the World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522- 2625; e-mail: pubrights@worldbank.org. Cover photo: Panorama view of the Aral Sea from the rim of the Ustyurt Plateau near Aktumsuk Cape, Karakalpakstan, Uzbekistan. . The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed i Contents Acknowledgments .......................................................................................................................................... vii Acronyms and Abbreviations......................................................................................................................... viii Currency Equivalents ......................................................................................................................................ix Executive Summary......................................................................................................................................... 1 Key Findings ............................................................................................................................................... 2 1 Introduction...................................................................................................................................... 4 Objectives of the Study ............................................................................................................................... 5 2 Review of Ecosystem Services in SDS Context............................................................................. 6 2.1. Impacts on Local Soil and Vegetation, and Dust Emission ............................................................... 6 2.2. Impacts on Regional Dust Emission and SDS Occurrence .............................................................. 8 2.3. Impacts on Air and Ecosystems: Dust Loads and Dust Deposition .................................................. 8 2.4. Dust Salinity ...................................................................................................................................... 9 2.5. Impacts of the Aralkum Dust on Human Health ................................................................................ 9 2.6. Impacts of the Aralkum SDS on Economic Activities ...................................................................... 10 2.7. Rehabilitation .................................................................................................................................. 11 3 Methods......................................................................................................................................... 12 Definition of suitable Aral Seabed rehabilitation approaches and scenarios ............................................. 12 (1) Wind erosion and dust assessment........................................................................................... 13 (2) Economic valuation of selected ecosystem services ................................................................. 13 4 Results and Discussion................................................................................................................. 15 4.1. Analysis of Land Use/Cover Change .............................................................................................. 15 4.2. Assessment of Ecosystem Services ............................................................................................... 20 4.2.1. On-Site Ecosystem Services.............................................................................................. 20 4.2.2. Off-Site Ecosystem Services, Vegetation Cover Change................................................... 21 4.2.3. PM2.5 Concentration and Impact on Health ........................................................................ 21 4.3. Costs of SDS Due to Inaction ......................................................................................................... 25 4.4. Benefits of Alternative Intervention Scenarios ................................................................................ 27 4.4.1. On-Site Benefits ................................................................................................................. 28 4.4.2. Off-Site Benefits ................................................................................................................. 28 4.5. Scenarios with the Highest Net Return ........................................................................................... 32 5 Conclusions and Recommendations ............................................................................................ 34 References..................................................................................................................................................... 37 Annex A: Assessment of the Aral Seabed Rehabilitation Approaches and Scenarios ............................... 40 Wind Erosion and Dust Assessment: Soil Detachment and Air Pollution.................................................. 41 On-Site Driving Force: Assessment of Wind and Erosion Occurrence ........................................... 41 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed ii Off-Site Effects: Transportation and Dispersion of Dust ................................................................. 43 Concentration of PM2.5 in the Study Area ....................................................................................... 44 Economic Valuation of Selected Ecosystem Services .............................................................................. 45 General Framework ........................................................................................................................ 45 Estimation of Economic Cost of SDS.............................................................................................. 45 Valuation of On-Site and Off-Site Costs and Benefits for Alternative Intervention Scenarios and Identification of the Scenario with the Highest Net Return .............................................................. 47 Annex B: Air Pollution: Data Source and Preparation.................................................................................. 49 Spatial Distribution of DUSMASS25 Data ................................................................................................. 50 Time Series Data ...................................................................................................................................... 52 Annex C: Extracting and Estimating PM2.5 Average per District for 2019 in Karakalpakstan ...................... 54 Annex D: On-Site Impacts: Quantification and Valuation of Ecosystem Services, Crop Yields, and Human Lives Lost Due to Inaction ................................................................................................ 67 Annex E: Off-Site Impacts ............................................................................................................................ 70 Annex F: Total Impacts ................................................................................................................................. 75 Annex G: Off-Site SDS Impact Assessment Using Regional Atmospheric Modeling System (RAMS): A Single Event Case Study ........................................................................................................... 77 Activity/Methodology ................................................................................................................................. 77 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed iii Contents List of Figures Figure 2.1 Desiccation and Land Cover Change of the Aral Sea During 1977–2015 .................. 7 Figure 2.2 SDS Occurrence in Central Asia During 1936–1980 (Left) and 1980–2000 (Right) ........................................................................................................................... 8 Figure 4.1 Major Land Cover Changes in the 100 km Impact Zone ........................................... 16 Figure 4.2 Land Cover Decline in the 100–300 km Impact Zone ............................................... 17 Figure 4.3 Land Cover Increase in the 100–300 km Impact Zone.............................................. 17 Figure 4.4 Land Cover Decline in the 300–500 km Impact Zone ............................................... 18 Figure 4.5 Land Cover Increase in the 300–500 km Impact Zone.............................................. 19 Figure 4.6 NDVI Trend Over Time (1990–2020) in Off-Site Irrigated Agriculture Areas (left) and the SPEI Anomalies (right).......................................................................... 21 Figure 4.7 Mortality (per Thousand ) in 2019 by District for Air Pollution-Related Diseases ..... 23 Figure 4.8 BCR for Different Scenarios Representing Lower Bound, Average and Upper Bound Values ............................................................................................................. 29 Figure 4.9 Annual Incremental Benefits of Restoration Compared to the Base Case (Scenario 1) ................................................................................................................ 32 Figure 4.10 BCR for the Best-Bet Intervention of Plantation with 100% Saxaul Trees ................ 33 Annex Figure A.1 Vegetation-Based Rehabilitation Effects: Obstacles to Wind Erosion ...................... 41 Annex Figure A.2 Erosion Threshold Analysis Using Flat and Uncovered Terrain Bare (-) Scenario For Different Soil Types Present in the Dry Aral Seabed........................... 42 Annex Figure A.3 Daily Event Distribution of Sediment Suspension Loads per SDS Class and Scenario ...................................................................................................................... 43 Annex Figure B.1 PM2.5 Dynamics in Uzbekistan, 1990-2017 ................................................................ 51 List of Tables Table 2.1: Impact on Ecosystem Services Associated with Different Intervention Scenarios, Categorized According to the NCA Approach. .......................................... 6 Table 2.2: Recorded Economic Impacts of the Aral Sea Disaster.............................................. 10 Table 3.1: Scenario Overview: Degraded Status vs. Rehabilitation Through Shrub and Tree Plantations. ........................................................................................................ 12 Table 3.2: SDS Impact Indicators under Four Different Scenarios............................................. 13 Table 3.3: Economic Valuation Methods Used to Estimate Costs of SDS in the Aralkum ........ 14 Table 4.1: Biomass and Carbon Stock for Rehabilitation Scenarios .......................................... 20 Table 4.2: Annual Average Concentration of PM2.5 in 2019 by Districts and SDS Impact Level ........................................................................................................................... 22 Table 4.3: Agroecological Classification of the Aralkum ............................................................. 26 Table 4.4: SLL Estimates and Monetary Value of Lives Lost in Karakalpakstan Due to SDS from the Aralkum ................................................................................................ 27 Table 4.6: Impacts of Implementing the Best Course of Action on the Total Value of Production Lost Due to SDS from the Aralkum ......................................................... 30 Table 4.7: Average Annual Values of On-Site and Off-Site Ecosystem Services Under Different Rehabilitation Scenarios .............................................................................. 31 Table 4.8: Number and Value of Lives Saved in Karakalpakstan by Using the Best Bet Practices of Planting Saxaul Trees ............................................................................ 33 Annex Table A.1 Scenario Overview: Degraded Status vs. Rehabilitation Through Shrub and Tree Plantations.......................................................................................................... 40 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed iv Annex Table A.2 Reported Success Rates of Tree Planting (Restoration) Depending on the Agro-ecologies (Based on the Literature Review) ..................................................... 47 Annex Table A.3 Reported Success Rates of Tree Planting (Restoration) Depending on the Agro-ecologies (Based on the Literature Review) ..................................................... 48 Annex Table B.1 Air Quality Guidelines and Standards ........................................................................ 50 Annex Table B.2 Area and Population Information of Focus Districts in Karakalpakstan and How They Match Observation Grid Cells with PM2.5 Data ................................................. 52 Annex Table B.3 Effects of Dust on Crop Production ............................................................................ 53 Annex Table D.1 Lower Bound: Carbon from Biomass, t/ha (Above Ground) with Assumed Total Success Rate of 49.5% (15.69% for the First Planting with 1.57% Natural Succession, 15.69% for the Second Replanting with 3.16% Natural Succession, and 15.69% for the Third Replanting with 4.78% Natural Succession) ................................................................................................................ 67 Annex Table D.2 Average: Carbon from Biomass, t/ha (Above Ground) with Assumed Total Success Rate of 72.3% (25.07% for the First Planting with 2.51% Natural Succession, 25.07% for the Second Replanting with 5.08% Natural Succession, and 25.07% for the Third Replanting with 7.71% Natural Succession) ................................................................................................................ 68 Annex Table D.3 Upper Bound: Carbon from Biomass, t/ha (Above Ground) with Assumed Total Success Rate of (43.3% for the First Planting with 4.33% Natural Succession and 43.3% for the Second Replanting with 8.85% Natural Succession with No Third Replanting but a High Natural Succession Rate of 9.23%%) .......................... 69 Annex Table E.1 Values of Total Production and Estimates of Specific Crop Values Lost Due to Inaction to Restore the Aralkum ................................................................................. 70 Annex Table E.2 Summary of Health Impacts of SDS from the Aralkum—Lower Bound ................... 72 Annex Table E.3 Summary of Health Impacts of SDS from the Aralkum—Average ............................ 73 Annex Table E.4 Summary of Health Impacts of SDS from the Aralkum—Upper Bound .................... 74 Annex Table F.1 Annual Values of On-Site and Off-Site Ecosystem Services Under Different Rehabilitation Scenarios—Lower Bound ................................................................... 75 Annex Table F.2 Annual Values of On-Site and Off-Site Ecosystem Services Under Different Rehabilitation Scenarios—Upper Bound ................................................................... 76 List of Maps Map 4.1 Literature-Based Estimated Areas of Off-Sites and SDS Impact in the 100 km (Severe Impact), 300 km (Medium Impact) and 500 km (Low Impact) Radius......... 15 Map 4.2 Land Cover Change Between 1992 (Left) and 2015 (Right) in the 100 km Impact Zone ................................................................................................................ 16 Map 4.3 Land Cover Change Between 1992 (Left) and 2015 (Right) in the 100–300 km Impact Zone ................................................................................................................ 18 Map 4.4 Land Cover Change Between 1992 (Left) and 2015 (Right) in 300–500 km Impact Zone ................................................................................................................ 19 Map 4.5 Land Cover of the Desiccating Aral Seabed Area in 2019 ........................................ 21 Map 4.6 Spatial Distribution of Annual Average (Left) and Coefficient of Variation (Right) PM2.5 in Karakalpakstan in 2019 ................................................................... 22 Map 4.7 Spatial Distribution of Air Pollution-Related Diseases in Karakalpakstan (2019), Estimated per Thousand............................................................................... 24 Map 4.8 Spatial Distribution of Population and District Level GDP in 2019 in Karakalpakstan ........................................................................................................... 25 Map 4.9 Distribution of Vegetated and Restorable Area of the Aral Seabed .......................... 25 Annex Map A.1 The Aral Sea Zoning (Left) and Satellite Image of an SDS Sourcing from the Aralkum (Right) ........................................................................................................... 44 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed v Annex Map B.1 Extent of the MERRA-2 Grid Data of Focus Area...................................................... 49 Annex Map B.2 Average Yearly Value of PM2.5 for 1980, 1990, 2000, 2010, 2019, and Jan–Apr 2020 ............................................................................................................................ 51 Annex Map B.3 Selected Districts for Daily Time Series Data Extraction ........................................... 52 Annex Map C.1 Amudaryo District and PM2.5 Cells.............................................................................. 54 Annex Map C.2 Beruniy District and PM2.5 Cells ................................................................................. 54 Annex Map C.3 Chimbay District and PM2.5 Cells ............................................................................... 55 Annex Map C.4 Elikkala District and PM2.5 Cells ................................................................................. 56 Annex Map C.5 Kanlykul District and PM2.5 Cells ................................................................................ 56 Annex Map C.6 Karauzyak District and PM2.5 Cells............................................................................. 57 Annex Map C.7 Kegeyli District and PM2.5 Cells .................................................................................. 57 Annex Map C.8 Turtkul District and PM2.5 Cells ................................................................................... 58 Annex Map C.9 Tahtakupir District and PM2.5 Cells............................................................................. 58 Annex Map C.10 Shumanay District and PM2.5 Cells ............................................................................ 59 Annex Map C.11 Nukus City District and PM2.5 Cells ............................................................................ 60 Annex Map C.12 Nukus District and PM2.5 Cells.................................................................................... 60 Annex Map C.13 Moynaq District and PM2.5 Cells ................................................................................. 61 Annex Map C.14 Kungrad District and PM2.5 Cells ................................................................................ 62 Annex Map C.15 Khujayli District and PM2.5 Cells ................................................................................. 63 Annex Map G.1 RAMS Simulation of Dust Concentration and Wind Field at 40 m Height During March 23, 2020 SDS Event ........................................................................................ 77 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed vi Acknowledgments This study was led by Paola Agostini (Lead Natural Resources Management Specialist, the World Bank), Elena Strukova Golub (Senior Environmental Economist, the World Bank) and Akmal Akramkhanov (Senior Scientist, International Center for Agricultural Research in the Dry Area (ICARDA), Uzbekistan). The team included Nodira Akhmedkhodjaeva (Environmental Specialist, the World Bank) and Camilla Sophie Erencin (Economist Consultant). The study was carried out by Stefan Strohmeier (ICARDA, Jordan), Yigezu A. Yigezu (ICARDA, Egypt), Mira Haddad (ICARDA, Jordan), Timon Smeets (Utrecht University, Netherlands), Geert Sterk (Utrecht University, Netherlands), Claudio Zucca (University of Sassari, Italy), and Abduvokhid Zakhadullaev (Uzbekistan State Committee on Forestry). This study benefitted from the valuable insights and comments of the following peer reviewers: Juan Pablo Castaneda Sanchez (Environmental Economist, the World Bank), Stephen Ling (Lead Environmental Specialist, the World Bank), and Feras Ziadat (Land Resources Officer, Food and Agriculture Organization). The team would like to thank Kseniya Lvovsky (Practice Manager, Environment, Natural Resources, and Blue Economy for Europe and Central Asia Region, the World Bank), Urvashi Narain (Lead Economist, the World Bank), and Raffaello Cervigni (Lead Environmental Economist, the World Bank) for their invaluable guidance. The World Bank team would like to express its sincere gratitude to government officials: Sobirjon Umarov and Abror Nozimov (Uzbekistan State Committee on Forestry), Tabassumkhon Ruzmetova (Karakalpakstan Ministry of Health), and other experts from Uzbekistan for their insights and generous cooperation during the study. Special thanks go to the participants of the January 29, 2020 Inception Workshop hosted by the Uzbekistan State Committee on Forestry; representatives of regional institutions, ministries, and committees engaged in forest management, tourism, environment, and public health administration; and to development partners for their valuable comments and recommendations. This study was supported by Wealth Accounting and the Valuation of Ecosystem Services (WAVES) and PROGREEN, the Global Partnership for Sustainable and Resilient Landscapes. WAVES is a World Bank-led global partnership that aims to promote sustainable development by ensuring that natural resources are mainstreamed in development planning and national economic accounts. PROGREEN is a World Bank partnership program that supports countries’ efforts to improve livelihoods while tackling declining biodiversity, loss of forests, deteriorating land fertility, and increasing risks such as uncontrolled forest fires, which are xacerbated by a changing climate.Support to the team provided by Grace Aguilar, Linh Van Nguyen, and Nigara Abate is gratefully acknowledged. All data of the Karakalpakstan Ministry of Health cited in this study was obtained with support from the Uzbekistan State Committee on Forestry. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed vii Acronyms and Abbreviations AAP Ambient Air Pollution AOD Aerosol Optical Density BCR Benefit to Cost Ratio COP Conference of the Parties COPD Chronic Obstructive Pulmonary Disease DALY Disability Adjusted Number of Life Years DISC Data and Information Services Center ESA European Space Agency GDP Gross Domestic Product GMAO Global Modeling and Assimilation Office ha hectare IASA Institute of Accelerating Systems and Applications ICARDA International Center for Agricultural Research in the Dry Areas IHD Ischemic Heart Disease ISRIC International Soil Reference and Information Centre LC Land Cover LC Lung Cancer LRI Lower Respiratory Illness MERRA-2 Modern-Era Retrospective Analysis for Research and Applications, Version 2 MODIS Moderate Resolution Imaging Spectroradiometer m/s Meter per Second NASA National Aeronautics and Space Administration NCA Natural Capital Accounting NDVI Normalized Difference Vegetation Index NPV Net Present Value PM2.5 Particulate Matter with a diameter of less than 2.5 μm PM10 Particulate Matter with a diameter of less than 10 μm PV Present Value Ppm Parts per million RAMS Regional Atmospheric Modeling System SDS Sand and Dust Storms SLL Statistical Lives Lost SPEI Standardized Precipitation Evapotranspiration Index (drought index) TEB Total Economic Benefits TEC Total Economic Cost T2D Diabetes Mellitus Type 2 UNCCD United Nations Convention to Combat Desertification US EPA The United States Environmental Protection Agency The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed viii VSL Value of Statistical Life WAVES Wealth Accounting and the Valuation of Ecosystem Services WB The World Bank μg/m3 Microgram per cubic meter Currency Equivalents Exchange rate for 2019 used in calculations Currency unit = Soum (UZS) UZS 1.00 = US$ 0.0001 US$ 1.00 = UZS 8,851 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed ix Executive Summary Central Asia experiences frequent sand and dust storms (SDS), which have been made worse by human activity. Formed from the dry Aral Seabed and with an estimated area of 60,000 km2, the Aralkum Desert with its high salt concentration has become an additional source of SDS. This has not only transformed the surrounding environment, triggering soil degradation and desertification processes, but also resulted in poor health and the loss of livelihoods. Immediate areas affected by the Aral Sea disaster are in Kazakhstan and Uzbekistan, with lasting impacts experienced by communities near the former seashore, including the Republic of Karakalpakstan and Khorezm region in Uzbekistan. The landscape’s assets, all within 500 km of the former seashore, consist of dry rangelands, irrigated agriculture areas, water bodies of various size, and human settlements. Rehabilitation of the land is crucial to reduce the negative effects of SDS. Without intervention, the exposed seabed experiences primary succession, with native vegetation growing on different sites, depending on the soil salinity, texture, and waterlogging. This change is slow, spontaneous, and contributes little to reducing erosion; however, the planting of adapted shrub and tree species to reduce the negative effects of SDS is a promising choice that the government supports. Such measures also address Uzbekistan’s pledge to the Bonn Challenge, a global initiative to restore degraded and deforested landscapes. The target—to restore 500,000 hectares (ha) by 2030—was actually met by 2020.1 Despite such success and plans to continue planting on a larger scale, restoration results will depend on the survival rate of planted species and the maintenance of the established afforested areas beyond 2030. The main objective of this study is to provide an economic analysis of the benefits of afforestation of the former Aral Seabed in Uzbekistan. To establish economic benefits, the best vegetation-based rehabilitation scenarios are defined. Then, the impact of wind erosion on ecosystem services is estimated by modeling sediment movement and dust production under each rehabilitation scenario. Finally, costs related to SDS in the Aral Seabed, and the potential benefits of vegetation-based rehabilitation scenarios, are estimated. Based on wind erosion modeling results, this study measures soil retention ecosystem services in the former Aral Seabed in Uzbekistan. The event-based biophysical modeling estimates wind erosion, associated sediment movement, and the resulting dust production. The benefit of rehabilitation is estimated by combining representative wind speed classes with various scenarios. Negative impacts on soil carbon (on-site impacts) and human health and crop production (off-site impacts) under current Aral Seabed desertification conditions are also estimated. Foregone benefits are evaluated, including carbon that could have been sequestered in vegetation above and below the ground, as well as forage and wood that could have been harvested under best-practice scenarios. Finally, the potential benefits of different intervention scenarios are evaluated over a period of 20 years. To understand the value of soil retention ecosystem services, several scenarios of landscape restoration are considered. Baseline scenarios represent current dry seabed conditions, while two rehabilitation options represent potential out-planting of native shrub and tree species. The scenarios facilitate the emergence of native vegetation (e.g., grasses) through shelter of primarily out-planted species or natural regeneration and succession. Simulation of SDS events with the scenarios demonstrates clear effects of shrub and tree vegetation—with the additional effect of grasses—on reducing erosion and sediment suspension. The impact of SDS on on-site and off-site ecosystem services is analyzed, including several factors as a function of the distance from the Aralkum Desert. The empirical model is used to estimate the Aralkum’s contribution to the concentration of particulate matter with a diameter of less than 2.5 μm (PM2.5) and to calculate economic impacts of SDS originating from the dry seabed. For each scenario, vegetation- 1 https://www.bonnchallenge.org/resources/spotlight-uzbekistan The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 1 specific information is identified, including plant height, quantity, breadth, and porosity. The soil organic carbon stock is estimated from global and published datasets. First, the on-site quantities and values of ecosystem services (i.e., soil carbon, carbon from above and below biomass, wood, and forage) being lost are determined. Second, the quantities and values of the specific ecosystem services that have been lost off-site are estimated, namely: 1) the number and economic value of statistical lives lost (SLL) and 2) the volumes and values of production of different crops lost due to associated SDS. Key Findings SDS from the Aralkum are causing Karakalpakstan to lose $44.2 million/year—equivalent to 2.1% of its Gross Domestic Production (GDP). Under existing conditions and assuming a planning period of 20 years, inaction will cost Karakalpakstan approximately $844 million. Of that total, 83% is on-site losses and forgone on-site benefits of ecosystem services and the remaining 17% is off-site losses. Continued current practices would result in the loss of on-site benefits averaging $32.6 million/year— equivalent to 1.54% of Karakalpakstan’s GDP. Simulation results show that 2.1 million tonnes of soil carbon valued at $207 million has been lost due to SDS from the restorable part of the Aralkum Desert in Uzbekistan. Also, a total of 2 and 2.7 million tonnes of carbon (valued at $108 million and $146 million, respectively) that could have been sequestered in the vegetation above and below the ground has been forgone. Finally, forage and wood that could have been harvested if the best course of action was taken represent a benefit loss of $111 million and $80 million, respectively. Therefore, over a period of 20 years, the total loss of on-site benefits is approximately $652 million. SDS generated by the former Aral Seabed lead to off-site effects due to wind erosion exposure, including health impacts and crop production losses averaging $11.6 million/year. Off-site production losses for all major crops grown in Karakalpakstan are estimated on average at $9.9 million/year, equivalent to approximately 0.45% of Karakalpakstan’s GDP. Dispersion modeling results show that the contribution of the Aralkum Desert to total ambient air pollution (AAP) reduces greatly over distance. The annual number of SLL attributable to SDS is estimated to be between 13 and 29 in this sparsely populated area. This leads to an annual welfare loss of approximately $1.7 million/year, equivalent to 0.08% of Karakalpakstan’s GDP. Therefore, over a period of 20 years, the total loss of off-site benefits is approximately $192 million. Landscape restoration interventions in the Aralkum can prevent ecosystem services losses and generate additional benefits of about $39 million/year—equivalent to 1.9% of Karakalpakstan's GDP. Interventions with planting of different vegetative covers at various levels of success rate in terms of the final percentage of total area covered by shrubs and/or trees were analyzed. The best course of action— simultaneous planting of trees and grass—would reduce the number of SLL attributable to SDS originating from the dry Aral Seabed by 12 on average; with a value of $1 million. This would represent a 58% reduction from the current scenario—equivalent to 0.05% of Karakalpakstan’s GDP. In addition, the simultaneous planting of trees and grasses would reduce on-site benefit losses. Landscape restoration would also prevent crop production losses of approximately $5.5 million—equivalent to 0.3% of the GDP. The estimated benefit- to-cost ratio (BCR) is, on average, 1.49 for the present values of benefits and costs of different interventions. Ecosystem service benefits from restoration projects in Uzbekistan provide far greater value than the economic and financial benefits of increased production if the appropriate restoration methods are applied. This study informs Uzbekistan’s resilient forest restoration program by estimating a value of major direct and indirect incremental benefits of afforestation projects. Overall, afforestation in Uzbekistan has proven to be economically viable. It is an important part of the green growth strategy that supports climate goals and economic development. The valuation of ecosystem services benefits, both local and global, contributes to dialogue and analysis of climate targets in a context of broader development (e.g., priority of economic recovery, jobs, etc.) that are supported by the Uzbekistan State Committee on Forestry and Ministry of Finance. The valuation of ecosystem services benefits also informs Uzbekistan’s ongoing legal The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 2 and regulatory reform (Environmental Code, Forest Strategy, regulation of greenhouse gases) by providing quantitative measurements and thresholds for financing the country’s afforestation activity. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 3 1 Introduction Globally, sand and dust storms (SDS) are mostly considered a natural phenomenon. The Global Assessment of SDS (UNEP, WMO, UNCCD, 2016) reviewed scientific estimates of the relative contribution of human activity to current levels of global dust emissions and indicated 25% as the most likely estimate. Desertification and land degradation are typical drivers of human-caused SDS (UN, 2001; UNESCAP, 2018). The Aralkum Desert, the seabed of the former Aral Sea, is a relatively new addition to global hotspot sources of SDS with high salt concentration. Although Central Asia has been historically characterized by a high frequency of SDS, due to the presence of the Kyzylkum and Karakum Deserts, human activity has exacerbated the frequency and intensity of SDS through unintentional creation of a vast area of land dominated by saline soils (Solonchaks) and bare areas. The rapid transformation of the Aral Sea into the Aralkum Desert in the course of a few decades is staggering. The Aralkum Desert’s area covers around 60,000 km2, of which 70% is salt desert (Breckle and Geldyeva, 2012). Consequently, the loss of sea not only has transformed the surrounding environment—triggering enhanced degradation and desertification—but also resulted in the loss of livelihoods, malnutrition, poor health, and migration issues. The resulting effect is thus not limited to environmental degradation, but also causes economic and social consequences. Considering the recent predictions of the temperature rise in Central Asia above global mean values (World Bank, 2014), ongoing desertification and socio-economic pressure on communities in the Aral Sea Basin might be worsened. Studies indicate that annual losses of agricultural production from soil salinization in Central Asia are estimated at $2 billion (World Bank, 1998), which could also translate into losses of soil carbon through reduced plant growth. The annual costs of rangeland degradation due to poor management are estimated at $4.6 billion between 2001 and 2009 (Mirzabaev et al., 2015). The Aralkum source for SDS covers 20,000–30,000 km2 while raised, suspended, and transported salt and dust particles reach the surrounding areas occupying over 500,000 km2 (Groll et al., 2013; Orlovsky and Orlovsky, 2001). The chemical composition of sand and dust originating from the Aralkum is dominated by higher salt concentrations compared to those deriving from the Kyzylkum Desert (Aslanov et al., 2013). Spatially, the distribution of salt and dust transfer occurs in the south and southwest directions (Groll et al., 2013; Orlovsky and Orlovsky, 2001), affecting ecosystems, irrigated and populated areas of Karakalpakstan and Khorezm province in Uzbekistan as well as Dasoguz in Turkmenistan. Since the Aral Sea is in a depression, receiving discharge from Amudarya and Syrdarya Rivers irrigating and draining vast areas, numerous reports suggest that accumulated sediments have high concentrations of toxic elements (Micklin, 1988; UNEP, WMO, UNCCD, 2016). For example, Thenardite—suspended salt residues in the air—are suspected to be one of the main causes of lung disease in the region (Letolle et al., 2005). However, few studies analyze the direct effect of salt and dust from the Aralkum on human health, despite the high rates of anemia, lung cancer, respiratory and diarrheal diseases, heart attack, hepatitis, birth defects and higher blood level of toxins among population in the adjacent region (Crighton et al., 2011). The inter- relationships between the environment and production, human health and rural livelihoods are complex to estimate and require proper attention and investigation. There are various rehabilitation options to reduce the negative effects of SDS. Tree plantations, indigenous or adapted, to the region offer improved ecosystem services via soil and water retention, preventing polluted dust from being transported. Several initiatives have been launched and tested in distinct parts of the former Aral Seabed under international projects and supported by the local government. In 2017, given the relevance of land degradation and desertification to SDS, the 13th Session of the United Nations Convention to Combat Desertification (UNCCD) Conference of the Parties (COP) adopted Decision The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 4 31/COP.13 2 on SDS and invited countries to use the UNCCD Policy Advocacy Framework to address the impact of SDS. Building on earlier successful experiences with restoration and rehabilitation, along with the need to mitigate SDS effects, Uzbekistan joined the Bonn Challenge,3 a global initiative to restore degraded and deforested landscapes. In 2018, it pledged to restore 500,000 ha by 2030 through the Astana Resolution.4 That target was achieved by 2020.5 Objectives of the Study The objective of this study is to estimate economic benefits attributed to afforestation of the former Aral Seabed in Uzbekistan. Proper estimation and categorization of economic benefits associated with each scenario of landscape restoration enables the Government of Uzbekistan and local authorities to allocate limited resources in an efficient way, supporting promising rehabilitation techniques and practices. 2 https://www.unccd.int/sites/default/files/sessions/documents/2019-08/31COP13_0.pdf 3 https://www.bonnchallenge.org/about 4 https://www.bonnchallenge.org/resources/ministerial-roundtable-forest-landscape-restoration-caucasus-and-central-asia-summary 5 https://www.bonnchallenge.org/resources/spotlight-uzbekistan The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 5 2 Review of Ecosystem Services in SDS Context The review and classification of literature focused on SDS generated by the Aral Seabed helped to categorize the impacts according to the Natural Capital Accounting (NCA) approach. 6 The SDS impacts were grouped according to two categories: impact on on-site and off-site ecosystem services (Table 2.1). These services present actual or potential annual flows of goods and services provided by ecosystems in the targeted area to people (via economic production or directly to individuals and society) 7. The annual flows are estimated both under the current scenario and alternative landscape restoration scenarios. Table 2.1: Impact on Ecosystem Services Associated with Different Intervention Scenarios, Categorized According to the NCA Approach. On-Site Off-Site Potential of timber, firewood, and Provisioning Services Crop production forage production Soil erosion: soil loss, degradation Regulating Services or pollution; and potential climate regulating Potential climate regulation services services (including soil carbon and biomass) Health Impact Disease and mortality costs Source: Based on Wealth Accounting and the Valuation of Ecosystem Services (WAVES) The SDS impacts affecting ecosystem services can be observed both on-site and off-site. The distinction between these effects cannot be rigid because of the continuity of spatial scales involved. Inside the source area (on-site), where both soil particle detachment and entrainment take place, all types of impacts can be observed, associated with erosion (soil loss, undermining of structures, etc.), transport (air quality, visibility, etc.), or deposition (sand encroachment) of particles. Moving away from the source area (off-site), depending on the distance and wind speed, as well as wind direction, various types of impacts can still be observed. For example, at regional-to-global scale, impacts caused by transport and deposition of very fine particles can be observed. 2.1. Impacts on Local Soil and Vegetation, and Dust Emission The desiccation of the Aral Sea exposed the seabed, forming large bare areas of saline soils (Figure 2.1) and creating a sand and salt desert ecosystems (the new “Aralkum” Desert). Their characteristics are influenced by the variable and complicated geological and geomorphological structure of the desiccated seafloor. 6 The World Bank Group leads the Wealth Accounting and the Valuation of Ecosystem Services (WAVES) partnership to advance NCA internationally. The NCA is based on the Millennium Ecosystem Assessment (Reid et al.,2005), a major assessment of the human impact on the environment which popularized the term ecosystem services. 7 Impacts could be positive or negative, for soil erosion cost and health/lost crop cost associated with SDS. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 6 Figure 2.1 Desiccation and Land Cover Change of the Aral Sea During 1977–2015 Source: Shen et al. (2019). According to Löw et al. (2013) the sandy surfaces and the salt-affected soils increased by more than 36% between 2000 and 2008. Indoitu et al. (2015) state that exposed heavy takyr, takyr-like and Solonchak surfaces have high potential to be a source of severe SDS: the most active emission site consisting of sands (75%), Solonchaks (17%) and takyrs/takyr-like soils (8%) which are heavy clay. Most of the SDS events in the Aral Sea basin originate from the north-eastern Aral Seabed area, usually under action of winds from the east (57%) and more generally from eastern areas (80%). Dust plumes often reach lengths of 150 km to more than 600 km. Semenov (2012) developed a physical model to evaluate the amounts of aerosols transported from the desiccated seabed of the Aral Sea. Evidence shows that the increase in size of the desiccated seafloor contributes to higher amounts of smaller size (less than 10 μm) particles, with higher salt content. The small size increases the distance over which particles are transported by wind. The salt-dust clouds can be up to 400 km long, while finer particles can travel up to 1,000 km away. Bare sediments start to be taken over by native vegetation species, with colonization patterns and rates depending on salinity and texture of the newly formed soils (Dimeyeva, 2007; Wucherer et al., 2012a). This has transformed the Aralkum into the largest area worldwide where primary succession is taking place (Wucherer et al., 2012a). Although the rate of spontaneous vegetation cover is not sufficient to reduce dust generation significantly, the observed process poses a great ecological interest and a learning opportunity for restoration scientists. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 7 2.2. Impacts on Regional Dust Emission and SDS Occurrence Dust generation activated by the exposed seabed has transformed the Aral Sea region into a regional SDS hotspot. Indoitu et al. (2012) analyzed all SDS events in Central Asia between 1936 and 2005. The northern Aral Sea region became the regional hotspot with more than 40 SDS days/year after the 1980s, while decreasing trends were most obvious for Karakum Desert, where the number of SDS declined from an average of 30 days/year to less than 20 (Figure 2.2). Other spatial changes occurred in the Northern Caspian Deserts, showing a shift of few hundred kilometers to the east. Orlovsky (2011) reports the Kyzylkum Desert and the south Balkhash Lake area underwent an important surface reduction of major source areas. This was largely due to the recovery of ecosystems instigated by reduced human activities in the region post- 1980s and partly by the decreasing trends of SDS frequencies registered worldwide. Accordingly, the Aralkum dynamics contrasted the regional ones. The diffusion of dust originating from the Aral Sea Basin showed strong increasing trends of aerosol indices after 2005 until 2013, associated with the continuous decrease in water level (Ge et al., 2016). Figure 2.2 SDS Occurrence in Central Asia During 1936–1980 (Left) and 1980–2000 (Right) Source: Indoitu et al. 2012. 2.3. Impacts on Air and Ecosystems: Dust Loads and Dust Deposition The transport and fall-out of dust particles generated from the Aral Sea region affects air quality of the downwind regions. Simulation modeling based on MODIS 8 and AERONET 9 data for the period of April 2008– July 2009 showed that dust was the largest component of particulate matter (both PM2.5, up to 2.5 µm size, and PM10, up to 10 µm size) mass in Central Asia in all seasons except winter, as well as the driver of seasonal PM and AOD (Aerosol Optical Density) cycles (Kulkarni et al., 2015). Based on all SDS events during May–October 2000 and monthly dust deposition at 16 sites in Karakalpakstan, Wiggs et al. (2003) observed extremely high monthly fine PM concentrations. Concentration levels were comparable to the United States Environmental Protection Agency (US EPA) quality standards of 150 µg/m3 in a 24-hour period and 50 µg/m3 as yearly average, with deposition rates as high as 2.5 tonnes/ha. High levels of dust deposition were observed throughout the country in the summer months, with particularly high rates of deposition in the north close to the shore of the former Aral Sea. For seven sites located in western and central Uzbekistan (Moynaq, Jaslyk, Takhiatash, Yangibozor, Beruniy, and Buzubay) Aslanov et al. (2013) found average dust deposition rates during the 2007–2010 period were five to six times higher than during 1982–1995. On the other hand, they also found a lower average deposition rate near the Aralkum than near the Kyzylkum (e.g., 450 kg/ha per month in Moynaq and 1,200 kg/ha per month in Buzubay). 8 Moderate Resolution Imaging Spectrometer (MODIS). https://modis.gsfc.nasa.gov/ 9 AErosol RObotic NETwork (AERONET) project. https://aeronet.gsfc.nasa.gov/ The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 8 2.4. Dust Salinity In the Aral Sea region, white or “salty” storms first appeared with the intensive irrigation development in the 1960s. Large areas of newly salinized lands and human-caused saline soils (Solonchaks) formed, with unusually strong SDS first recorded in 1975. Orlovsky and Orlovsky (2001) report that soils formed on sandy and sandy-loam exposed marine sediments have high sulfate-chloride and chloride-sulfate salts that may reach 8–10%, corresponding to about 2,200 tonnes/ha in the aeration zone of the soil. They also report that the total amount of deposited aerosols in the southern Aral Sea zone, studied between 1982 and 1991 at 43 sites, was 1.5–6.0 tonnes/ha and included about 170–800 kg/ha of soluble salts, with maximum values of up to 1,600 kg/ha in the dried seabed of the Aral Sea. This value was lower (150–300 kg/ha) in the irrigated lands of Karakalpakstan. The salt content in deposited aerosols in the Amudarya delta was estimated at 5–6%, and up to 20–30% in areas close to Solonchaks soils. Groll et al. (2019) analyzed dust deposition samples collected during 2003–2012 from 23 meteorological stations in four regions of the Aral Sea basin (Aralkum, Khorezm, Karakum, and Kyzylkum). They observed that the majority (86–98%) of the material deposited at 3 m height in the study area was part of the PM5 group (fine silt and clay particles; <0.0063 mm) and that the Aralkum dust samples were characterized by a much higher concentration of sulfites compared to the Karakum and Kyzylkum (2,365 parts per mile (ppm) vs. 232 ppm and 512 ppm). Khorezm also showed high sulfite content (1,681 ppm) and had the highest concentration of phosphorus pentoxide (1,857 ppm compared to 1,074 ppm in the Aralkum 866 ppm in the Karakum and 465 ppm in the Kyzylkum). The high concentrations of phosphorus in Khorezm and the Aralkum samples reflected the strong human impact of local agricultural dust sources (Khorezm) and the accumulation of agrochemicals in the Aral Sea sediments. 2.5. Impacts of the Aralkum Dust on Human Health Health hazards emanating from SDS receive limited attention despite their cumulative effects on society (Middleton et al., 2019). An increasing body of literature confirms that PM2.5 contributes to cardiovascular and respiratory diseases, and consequently can lead to premature death (Lelieveld et al., 2020). Regional evidence, however, linking SDS caused air pollution and health burden is lacking. Few studies covering the Aral Sea region acknowledge the health risk due to high dust concentrations (Opp et al., 2017). A literature review compiled by Crighton et al. (2011) on the effects of the Aral Sea disaster on children’s health mentioned 26 peer-reviewed articles and four major reports published between 1994 and 2008. Anemia, diarrheal diseases, and high body burdens of toxic contaminants were identified as significant health problems for children in the area. These health issues are associated either directly with the environmental disaster or indirectly via the deterioration of the region’s economy and social and health care services. Adult and children respiratory diseases studied in Turkmenistan (O’Hara et al., 2000) were a major cause of illness and death among all age groups but accounted for 50% of all reported illnesses in children. Conversely, in a study conducted in Karakalpakstan (Bennion et al., 2007; Wiggs et al., 2003), respiratory health surveys of children (aged 7–11) did not show a significant relationship between respiratory health problems and proximity to dust sources. Other studies showed that populations living near the former Aral seashore suffer from the worsening of several diseases (Kunii et al., 2003). A significant increase of cough and wheezing, lower forced vital capacity, and restrictive pulmonary function closer to the shore were prevalent in the area. Cancer cases (all cancer types, 2003–2014) around the north of the Aral Sea in Kazakhstan were 1.5 times more frequent compared to distal areas, due to higher nickel and cadmium levels (Mamyrbayev et al., 2016). Although clear evidence for the link between dust exposure and respiratory functions might be lacking, these studies unequivocally confirm the impact of the Aral Sea disaster on public health, underlining the knowledge gaps and the need for further specific research. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 9 2.6. Impacts of the Aralkum SDS on Economic Activities Economic impacts of SDS generated from the Aralkum Desert appear poorly explored in publications. INTAS and RFBR (2001) provide an overview of actual economic impacts with cost estimations referring to the entire Aral Sea disaster, which only partly can be associated with SDS. The assessment is based on a multi- sector approach, addressing both land and water resources. Identified impacts are summarized in Table 2.2. The costs for the entire Aral Sea region were estimated at $100 million/year ($59 million for agriculture and $41 million for manufacturing and service industries). Other papers address concurrent uses of water resources in the region and related impacts (e.g., availability of drinking water and irrigation water, crop yields, fishery, etc.). Orlovsky and Orlovsky (2001) report losses of cotton at 5–15% and rice yields at 3–6% resulting from dust. The salt content of rain is also reported to have increased to 100–150 mg/l compared to 30–100 mg/l in 1975. In the springtime, this rain creates salty crusts affecting seed germination and shortening the lifespan of supporting structures of high-voltage transmission lines. Additional finance to repair transmission lines in the Raushan-Beruniy of the Kungrad railway section in 1981–1990 increased to $15 million, and property damage as a result of power breaks raised expenditure to $9 million. As a result, total capital expenditures exceeded the budgeted investments 2.8 times for the same period. Table 2.2: Recorded Economic Impacts of the Aral Sea Disaster Sector Issues Costs, $ million Amount of fish catch in the Sea and adjacent lake systems 28.57 (fishery and fish breeding) Fishery reduced by 90% 9.0 fish industry) Muskrat habitats sharply diminished, resulting in lower 4.0 (hunting) Hunting muskrat numbers and decreased catch by hunting 18.0 (pelt processing) Cane Reduction of habitat for cane growing 12.6 (cane processing) From 1994–95, the used irrigated lands throughout the Aral 6.55 (crop production) Irrigated Sea zone have reduced by 25%, causing reduced crop Farming productions. The most affected crops were grain crops, rice, forage, maize, cotton, vegetables, and cucurbits Decreased river runoff into the Amudarya Delta and drying of 8.4 (cattle breeding) vast areas of former seabed resulted in an acute reduction of Rangeland natural highly productive rangelands and hay-mowing areas affecting cattle breeding and sheep and goat numbers, particularly in the Tahtakupyr district Production of wool and Astrakhan pelts dropped to a half, Wool driven by the drop in the number of sheep and goats and by N/A Production the deteriorated conditions of pasture and rangelands The number of tourists attracted by hunting and fishing Tourism 11.16 sharply decreased Cost of Rapid withdrawal of coastline hampering rehabilitation N/A Rehabilitation activities in the coastal zone Source: INTAS and RFBR (2001). In addition to the direct costs, INTAS and RFBR (2001) report $17 million of indirect losses and $29 million associated with social losses, annually. Thus, total direct and indirect losses caused by the environmental disaster in the Aral Sea region amounted to $146 million/year. It is worth noting that this was quantified in 2001 when the level of impact on land and water resources was much lower. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 10 2.7. Rehabilitation Desiccation of the Aral Sea exposes large areas of dry seabed. Without human intervention, natural succession of the spatial sequence of land cover around the shoreline of the former Aral Sea can be summarized as follows: “water”>“salt crust”>“salt soils”>“bare areas and desert soils” (Löw et al., 2013). The water recession results in a quick build-up of extensive salt crusts directly adjacent to the sea. Then, most of these salt crusts convert into a series of different Solonchaks and takyr types (classified as “salt soil”) and subsequently, in some parts, into "bare areas" reflecting a gradual landscape evolution under arid conditions, with the transformation of salt soils into desert soils prone to erosion. In parts of the Aralkum, under the leaching action of precipitations, natural desalinization of soils occurred within 4–8 years (Löw et al., 2013). While there is long-to-medium-term spontaneous recovery of vegetation (Dimeyeva, 2007; Wucherer et al., 2012b), the process is slow, and its success depends on many factors, including the intensity of wind erosion. Active restoration options should provide a faster and more effective establishment of the vegetation cover and a subsequent reduction of generated dust. Additional direct benefits would include the establishment of other pastureland, which would contribute to improved livelihoods. Practices already tested in the region include planting of various native species adapted to salinity and drought conditions. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 11 3 Methods To meet the objectives of the study and estimate economic benefits from landscape rehabilitation scenarios, the following approach was applied: first, best-applicable vegetation-based rehabilitation scenarios for the Aral Seabed were defined. Second, the impact of wind erosion on ecosystem services was estimated by modelling a sediment movement and dust production under each scenario. Third, costs related to SDS in the Aral Seabed and potential benefits of vegetation-based rehabilitation scenarios were estimated. For further details on the methodology refer to 0. Definition of suitable Aral Seabed rehabilitation approaches and scenarios For the valuation of soil retention ecosystem services, several scenarios of landscape restoration are formulated. Scenario 1.1 Bare (-) and Scenario 1.2 Bare (+) represent the actual dry seabed conditions. Scenario 2.1 Shrub (-), Scenario 2.2 Shrub (+), Scenario 3.1 Tree (-), and Scenario 3.2 Tree (+) represent potential out-planting of native shrub and tree species. The scenarios with (+) include the emergence of native vegetation (e.g., grasses) facilitated through shelter of primarily out-planted species or natural regeneration and succession. The actual on-site environmental conditions, as well as the selected rehabilitation scenarios are described in Table 3.1. Table 3.1: Scenario Overview: Degraded Status vs. Rehabilitation Through Shrub and Tree Plantations. Scenario Description Expected Model Output Environmental Condition Scenario (Vegetation Cover) (Notes) 1. Degraded Bare (-) Bare: dried-up areas with Present: most erosion susceptible (present condition) no vegetation cover scenario (worst case) (highest vulnerability) Bare (+) Bare, with marginal grass cover: Present: slightly reduced erosion older dried up areas with limited susceptibility (checking the upper natural vegetation emergence range of proneness to wind erosion) (few bunch grasses) 2. Rehabilitated – Shrubs Shrub (-) Shrub cover: rehabilitated Limited reduction of erosion: (shrub-based intervention) (Salsola or Atriplex) with minimum of erosion resistance out-planted shrubs without or through human intervention with marginal natural recruitment (shrubs); the lower boundary of (pure shrub out-planting effect) rehabilitation impact range Shrub (+) Shrub cover: rehabilitated Reduction of erosion: checking the (Salsola or Atriplex), incl. significance of human intervention recruitment and grasses; vs. natural recruitment represents: out-planted shrubs with specific extent recruitment and grass cover 3. Rehabilitated – Trees Tree (-) Tree cover: rehabilitated Limited reduction of erosion: (tree-based intervention) (Saxaul); represents: out-planted minimum erosion resistance trees with marginal or no natural through human intervention (trees); recruitment (pure tree out- the lower boundary of rehabilitation planting effect) impact range and check vs. shrub intervention 2. Tree (+) Tree cover: rehabilitated Highest reduction of erosion (Saxaul) incl. recruitment and through three different layers: grasses; represents: out-planted 1) trees, 2) shrubs, 3) grasses trees with specific extent (checking the significance of human recruitment, shrub and grass intervention vs. natural recruitment) cover (highest cover scenario) The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 12 (1) Wind erosion and dust assessment The methodology for wind erosion and dust assessment in the dry Aral Seabed included several steps reflecting an analysis of the wind erosion biophysics. It included modeling of two major processes: 1) surface sediment movement through creeping and saltation, and 2) sediment suspension. Particle creeping and saltation can move substantial amounts of sediment and cause severe erosion and sediment accumulation in the target/source on-site area, yet hardly affect the off-site environment, as the predominantly coarse particles settle and deposit in nearby locations, as described by Smeets (2020). On-site modeling: Large-scale modeling was undertaken to define the two on-site processes for different representative wind events—considering the thresholds of soil erosion (and consequential suspension), based on sensitivity and uncertainty analysis investigating the most common soil texture and initial soil conditions occurring in the dry Aral Seabed (Smeets, 2020). Wind speed analyses were performed based on three-hourly data from on-site meteorological stations, targeting daily wind events described through peak velocities (three hourly) and average wind speeds. Combined with fine resolution threshold analysis (i.e., threshold for substantial wind erosion) three different “storm classes” were defined according to their magnitude (erosivity) and occurrence probability (frequency). Simulation of the three classes, coupled with their statistical occurrence and per defined vegetation cover scenario (erodibility), enabled the estimation of on-site wind erosion dynamics and sediment balances. Off-site modeling: The primary threat to off-site ecosystem services and health come from the suspension processes, which are a result of the saltation of coarser soil fragments releasing fine particles, often then lifted to higher elevations and transported over large distances. The off-site effects through 1) fine particle concentration in the air and 2) cumulative sedimentation processes were estimated using an empirical radial dispersion approach. The empirical model was manually adjusted using measured sediment accumulation data, available from the literature (partial manual calibration) and verified through selected event dust- atmospheric simulations using RAMS (Cotton et al., 2003; Pielke et al., 1992). (2) Economic valuation of selected ecosystem services In a first step, on-site and off-site SDS impacts are defined. Related to on-site effects, the value of soil that is eroded by wind is considered, as well as the opportunity cost of forest that could have been planted on the vast land of the Aralkum Desert—serving as an important carbon sink. For off-site impacts, health and crop production impacts are considered. In the second step, four different rehabilitation scenarios are defined based on Table 3.1. Scenario 1 (which represents the current scenario) is a combination of Scenarios 1.1 and 1.2, i.e., 90% Bare (-) and 10% Bare (+). Scenario 2 is a combination of Scenarios 2.1 and 2.2, i.e., 50% Shrub (-) and 50% Shrub (+). Scenario 3 is a combination of Scenarios 3.1 and 3.2, i.e., 50% Tree (-) and 50% Tree (+). Finally, we define Scenario 4 as the best scenario, which assumes the full implementation of Scenario 3.2, i.e., 100% Tree (+). Table 3.2: SDS Impact Indicators under Four Different Scenarios Scenario 1 Scenario 2 Scenario 3 Location 90% Bare (-) and 50% Shrub (-) 50% Tree (-) and Scenario 4 of Impact Impact 10% Bare (+) and 50% Shrub (+) 50% Tree (+) 100% Tree (+) Soil Carbon, t/ha Carbon from biomass (above ground), t/ha On-Site Carbon from biomass (below ground), t/ha Firewood and forage The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 13 Scenario 1 Scenario 2 Scenario 3 Location 90% Bare (-) and 50% Shrub (-) 50% Tree (-) and Scenario 4 of Impact Impact 10% Bare (+) and 50% Shrub (+) 50% Tree (+) 100% Tree (+) Cropland (all crops) yield + quality Off-Site Health In a third step, costs of SDS due to inaction are calculated using 1) district data on population size, crop areas, yield, and regional GDP for each area delineated as highly, moderately, or lightly affected by SDS from the Aralkum, and 2) prices of major crops, international carbon, and the Value of Statistical Life (VSL).10 The total value of SDS impacts for defined on-site and off-site ecosystem services are the product of per- unit value of impacts and the population size/total area affected. Table 3.3: Economic Valuation Methods Used to Estimate Costs of SDS in the Aralkum Impact Valuation Method Soil Carbon Social cost of carbon Converting total biomass of forest into carbon equivalents Carbon from biomass (above ground) using standard conversion factors On-Site Converting total biomass of forest into carbon equivalents Carbon from biomass (below ground) using standard conversion factors Provisioning services Values of forage and firewood based on current prices Crops lost due to SDS from the Aralkum times Crop yield the average price of crops Off-Site Health Welfare loss of SDS induced PM2.5 pollution using VSL In a last step, benefits of alternative intervention scenarios are estimated as the difference in ecosystem services (annual flows) provided under rehabilitation scenarios and the base case scenario (Scenario 1) for upper bound, average, and higher bound outcomes of the rate of success for tree planting. The intervention with the highest net benefit is equal to the largest difference between the total economic benefit of the intervention and the total economic cost of implementing the intervention. 10 Narain and Sall, 2016. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 14 4 Results and Discussion 4.1. Analysis of Land Use/Cover Change Assessed landscapes within proximity (100, 300 and 500 km radius) to the dry Aral Seabed (Map 4.1) consist of 1) dry rangelands, 2) irrigated agriculture areas, and 3) human settlements. A remote sensing-based study over the past two decades (2000–2020) allowed the investigation of degradation trends—comparing the assets’ status to the areal extent of the changing Aral Seabed and other potential impacts, such as climate. This pre-study provided insights into the environmental (historical) context and was used as a basis and reference for SDS occurrence/pattern assessed through modeling. Map 4.1 Literature-Based Estimated Areas of Off-Sites and SDS Impact in the 100 km (Severe Impact), 300 km (Medium Impact) and 500 km (Low Impact) Radius Source: Map constructed by authors. The drying of the Aral Sea and forming of the Aralkum Desert has a direct impact on the ecosystem balance. The analysis of land cover change within suggested zones in Map 4.1 from 1992–2015 revealed the following: • In the 100 km impact zone, “water body” has decreased dramatically while “bare areas” and “unconsolidated bare areas” increased (Figure 4.1 and Map 4.2). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 15 Figure 4.1 Major Land Cover Changes in the 100 km Impact Zone Source: Authors’ estimates based on ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. Map 4.2 Land Cover Change Between 1992 (Left) and 2015 (Right) in the 100 km Impact Zone Source: ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. • The area between 100–300 km indicates several changes; for example, areas classified as “cropland” and “tree cover, needle-leaved, evergreen, closed to open (>15%)” declined over time while “urban areas” and “tree cover, broadleaved, deciduous, closed to open (>15%)” increased (Figure 4.2 and Figure 4.3). Also, “water bodies” decreased, and “bare areas” increased similarly to the first impact zone. The spatial representation of the land cover in both 1992 and 2015 is shown in Map 4.3. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 16 Figure 4.2 Land Cover Decline in the 100–300 km Impact Zone Cropland, rainfed 50 40 Tree cover, needleleaved, evergreen, closed to open (>15%) Area ha (0000) 30 Mosaic tree and shrub (>50%) / herbaceous cover (<50%) 20 Mosaic herbaceous cover (>50%) / tree and shrub (<50%) 10 Mosaic natural vegetation (tree, 0 shrub, herbaceous cover) (>50%) / cropland (<50%) 1992 1995 1998 2001 2004 Year 2007 2010 2013 Source: Authors’ estimates based on ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. Figure 4.3 Land Cover Increase in the 100–300 km Impact Zone 10 9 8 7 Tree cover, broadleaved, deciduous, closed to open (>15%) Area ha (0000) 6 5 Tree cover, needleleaved, 4 deciduous, closed to open (>15%) 3 2 Urban areas 1 0 1992 1995 1998 2001 2004 2007 2010 2013 Source: Authors’ estimates based on ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 17 Map 4.3 Land Cover Change Between 1992 (Left) and 2015 (Right) in the 100–300 km Impact Zone Source: ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. • For areas located in the 300–500 km impact zone, “cropland” declined dramatically together with “mosaic cropland (>50%)/natural vegetation (tree, shrub, herbaceous cover) (<50%)” and “mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%)/cropland (<50%)” with the absence of “tree cover, needle-leaved, evergreen, closed to open (>15%)” after 1998 (Figure 4.4 and Figure 4.5). The spatial representation of the land cover in this zone is shown Map 4.4Map 4.4. • Based on the analysis, land cover indicated as “cropland, irrigation or post-flooding” in the 100–300 km and 300–500 km impact zones remained unchanged from 1992–2015. Figure 4.4 Land Cover Decline in the 300–500 km Impact Zone 9 Cropland, rainfed 8 7 Mosaic cropland (>50%) / 6 natural vegetation (tree, shrub, Area ha (0000) 5 herbaceous cover) (<50%) 4 Mosaic natural vegetation (tree, 3 shrub, herbaceous cover) (>50%) / cropland (<50%) 2 1 Tree cover, needleleaved, evergreen, closed to open 0 (>15%) 1992 1996 2000 2004 2008 2012 Source: Authors’ estimates based on ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 18 Figure 4.5 Land Cover Increase in the 300–500 km Impact Zone 40 35 30 Area ha (0000) 25 20 Mosaic tree and shrub (>50%) / herbaceous cover (<50%) 15 10 Urban areas 5 0 1992 1996 2000 2004 2008 2012 Source: Authors’ estimates based on ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. Map 4.4 Land Cover Change Between 1992 (Left) and 2015 (Right) in 300–500 km Impact Zone Source: ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 19 4.2. Assessment of Ecosystem Services 4.2.1. On-Site Ecosystem Services 4.2.1.1 Carbon Stock and Biomass Estimation Above-ground biomass for different scenarios was estimated highest with tree (+) and shrub (+) options constituting 4.40 t/ha and 2.25 t/ha, respectively. For each scenario, a description and vegetation-specific information was set, including plant breadth, height, quantity (No/ha), breadth bark, and porosity. Due to COVID-19, a visit to the Aralkum was not possible. Therefore, with the help of the Uzbekistan State Committee on Forestry, the developed scenarios were validated. One essential ecological variable for vegetation cover is biomass. Values reported by Thevs et al. (2013) were used to estimate above-ground biomass. The carbon content was estimated using 48% of plants’ biomass, as suggested by Buras et al. (2012). The carbon content of litter was neglected based on a Thevs et al. (2013) recommendation of deadwood carbon storage in the saxaul vegetation, as its decay is affected by the arid climate. Above-ground biomass of Bare (+) was five to 10 times lower than Tree (+) and Shrub (+) scenarios. Estimated below- ground biomass in Tree (+) and Shrub (+) scenarios was 5.98 t/ha and 2.88 t/ha, respectively, proportionately five to 10 times higher than Bare (+) scenario. Carbon equivalent of above- and below-ground biomass is presented in Table 4.1. The soil organic carbon stock in the study area varies around 23–25 t/ha depending on land cover. Estimations were based on the International Soil Reference and Information Centre (ISRIC) datasets and on An et al. (2018). Published local soil sampling data (dry Aral Seabed) were merged with the spatial information provided by ISRIC soil grids datasets, generating homogenized soil carbon pools for each dry Aral Seabed zone. The biomass-related carbon values were estimated based on literature on e.g., monitoring of success of saxaul tree planting undertaken in Uzbekistan and Kazakhstan,11 and the conversion of dry biomass to carbon using a factor of 0.48. For low vegetation performing scenarios (-), a low percentile from various literature references was applied (25th percentile), while for well performing scenarios (+), the larger percentiles were selected (60th and 80th percentiles for shrub and tree scenarios, respectively). Estimated plant biomass and soil organic carbon values are summarized in Table 4.1. The land-cover map of the Aralkum (Map 4.5) demonstrates a large area of bare and sparsely vegetated cover that with Tree (+) and Shrub (+) scenarios can accumulate organic carbon. Organic carbon stored in the soil can contribute the most to the ecosystem services of the Aralkum. Table 4.1: Biomass and Carbon Stock for Rehabilitation Scenarios Biomass (t/ha) Carbon Stock (t/ha) Code Scenario Above Ground Below Ground Above Ground Below Ground Soil Carbon S1.1 Bare (-) 0.00 0.00 0.00 0.00 23.26 S1.2 Bare (+) 0.40 0.54 0.19 0.26 23.26 S2.1 Shrub (-) 1.50 2.00 0.72 0.96 23.26 S2.2 Shrub (+) 2.25 2.88 1.08 1.38 24.69 S3.1 Tree (-) 2.00 2.80 0.96 1.34 23.26 S3.2 Tree (+) 4.40 5.98 2.11 2.87 25.41 Source: Authors compilation. 11 A simple tree biomass growth model and a proper soil/sand transport model were used to estimate the annual growth of forest and forage biomass and depletion of soil. The amount of biomass at a given time using a simple tree growth model of the form Y=Y0*(1+r)t where the annual growth rate (r) was estimated from the average full size of a saxaul tree after 20 years. Then, in each of the 20 years, certain percentages of harvestable biomass which is proportional to the growth level of the tree is assumed and the value calculated accordingly. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 20 Map 4.5 Land Cover of the Desiccating Aral Seabed Area in 2019 Source: ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. 4.2.2. Off-Site Ecosystem Services, Vegetation Cover Change Negative trends and changes in crop vegetation greenness/health occurred after early 2000. Figure 4.6 shows remote sensing-based vegetation trend analysis (NDVI) and its relation/anomalies to drought index, e.g., using Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI compares anomalous dry and wet conditions with the long-term average conditions and has a negative value during droughts (red color in Figure 4.6). However, the SPEI anomalies do not allow conclusions on drought relation. Figure 4.6 NDVI Trend Over Time (1990–2020) in Off-Site Irrigated Agriculture Areas (Left) and the SPEI Anomalies (Right) Source: Authors’ estimates. 4.2.3. PM2.5 Concentration and Impact on Health As the Aralkum is not the single source of Ambient Air Pollution (AAP) in the region, the share of PM2.5 concentration is estimated. The PM2.5 data for the 2019 overlaying district and populated areas is presented in Map 4.6. While average dust concentration seems generally larger towards the south (e.g., desert areas The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 21 towards Turkmenistan), the variance (coefficient of variance) indicates a highly fluctuating dust pattern closer to the Aralkum. The considerable temporal variance might indicate an event-based dust occurrence and therefore identify the dry Aral Seabed and erosive wind events as the source and cause of high dust concentrations around the Aralkum. The weighted average was used to estimate PM2.5 concentration for each district. Additional details are provided in Annexes B and C. Determining the shared contribution of the Aralkum into district AAP is based on erosion and dispersion modeling. Assuming areas closer to the source have larger shares of AAP, Table 4.2 provides estimates of the share of the Aralkum in PM2.5 data for each district. The impact of SDS originating from the Aralkum is low in Amudaryo, Beruniy, Turtkul, and Elikkala, likely because of the distance from the source. Impact on Moynaq district is the highest as it is located closest to the Aralkum. Map 4.6 Spatial Distribution of Annual Average (Left) and Coefficient of Variation (Right) PM2.5 in Karakalpakstan in 2019 Source: Map constructed by authors. Table 4.2: Annual Average Concentration of PM2.5 in 2019 by Districts and SDS Impact Level Estimated Share of PM2.5 from the Aralkum (μg/m3) Distance from PM2.5 No. District Name Aralkum, km (μg/m3) Lower bound Average Upper bound Impact 1 Moynaq 100-200 41.27 35.08 37.76 40.44 High 2 Tahtakupir 200-300 65.58 8.77 9.44 10.11 Medium 3 Khujayli 200-300 59.97 8.77 9.44 10.11 Medium 4 Chimbay 200-300 53.93 8.77 9.44 10.11 Medium 5 Shumanay 200-300 50.77 8.77 9.44 10.11 Medium 6 Karauzyak 200-300 63.01 8.77 9.44 10.11 Medium 7 Kegeyli 200-300 51.56 8.77 9.44 10.11 Medium 8 Kungrad 200-300 54.34 8.77 9.44 10.11 Medium 9 Kanlykul 200-300 51.28 8.77 9.44 10.11 Medium 10 Nukus 200-300 56.81 8.77 9.44 10.11 Medium 11 Takhiatash 200-300 59.97 8.77 9.44 10.11 Medium 12 Nukus city 200-300 55.28 8.77 9.44 10.11 Medium 13 Amudaryo 300-500 63.77 1.64 1.77 1.90 Low 14 Beruniy 300-500 78.18 1.64 1.77 1.90 Low 15 Turtkul 300-500 79.18 1.64 1.77 1.90 Low 16 Elikkala 300-500 73.48 1.64 1.77 1.90 Low The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 22 Source: Authors’ estimates. Moynaq and Kegeyli districts, located closest to the Aralkum SDS source, have the highest morbidity as compared to other districts. The following figures provide a summary of health-related data obtained and estimated from local sources. Total number of disease cases was converted to represent district incidence per thousand (Figure 4.7 and Map 4.7) as the population in the districts varies, with more densely populated areas located to the south of Karakalpakstan. (Map 4.8). Figure 4.7 Mortality (per Thousand) in 2019 by District for Air Pollution-Related Diseases Source: Authors’ representation based on the Karakalpakstan Ministry of Health data obtained with support of the Uzbekistan State Committee on Forestry. Note: Ischemic Heart Disease (IHD), Stroke, Lung Cancer (LC), Chronic Obstructive Pulmonary Disease (COPD), Lower Respiratory Illness (LRI), Diabetes Mellitus Type 2 (T2D). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 23 Map 4.7 Spatial Distribution of Air Pollution-Related Diseases in Karakalpakstan (2019), Estimated per Thousand Source: Map constructed by authors based on the Karakalpakstan Ministry of Health data obtained with support of the Uzbekistan State Committee on Forestry. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 24 Map 4.8 Spatial Distribution of Population and District Level GDP in 2019 in Karakalpakstan Source: Map constructed by authors based on data from Uzbekistan Statistics Committee (www.stat.uz). 4.3. Costs of SDS Due to Inaction Desiccation of the Aral Sea has led to a substantial area of bare land subject to significant wind erosion. However, as of 2019, not all the exposed seabed surface is restorable due to either presence of water or shallow groundwater. Map 4.9 depicts the area that is suitable for rehabilitation in the Aral Seabed (see red striped area). While about 2.25 million ha of the Aralkum are located in Uzbekistan, about 58% of this area is restorable (Table 4.3). Map 4.9 Distribution of Vegetated and Restorable Area of the Aral Seabed Source: ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 25 Table 4.3: Agroecological Classification of the Aralkum Geographic Region Restorable Area, ha Non-restorable Area, ha Total Area, ha Total Area of Dried Aral Seabed 3,060,700 1,606,200 4,666,900 Area Located in Uzbekistan 1,322,100 926,400 2,248,500 Source: Authors’ estimates based on data from ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_CCI): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c. Considering a planning period of 20 years, inaction can cause Karakalpakstan to lose on-site benefits valued at $652 million. As there is a significant global debate on the appropriate carbon values as well as uncertainty on how carbon values are realized at the local level, a relatively conservative CO2 price of $10/tonne is used and converted to the carbon price following the World Bank report “State and Trends of Carbon Pricing 2019.” Simulation results show that, over 20 years, a total of 2.1 million tonnes of soil carbon valued at $207 million has been removed due to SDS from the restorable part of the Aralkum in Uzbekistan. Also, a total of 2.0 and 2.7 million tonnes of carbon (with values of $108 million and $146 million, respectively) that could have been sequestered in the vegetation above and below the ground, respectively, is lost due to SDS. In addition, if the best course of action is taken, rehabilitated landscapes could provide forage and wood.12 Therefore, the values of forage and wood-related benefits that were forgone were estimated at $111 million and $80 million, respectively. Inaction can cause Karakalpakstan to lose and forgo on-site benefits with an average of $32.6 million/year, equivalent to 1.54% of Karakalpakstan’s GDP (Annex Table D.1, Annex Table D.2, and Annex Table D.3). SDS generated from the dried Aral Seabed lead to off-site effects due to wind erosion exposure, including health impacts and crop production losses, valued on average at $11.6 million/year. The Aralkum is believed to be one of the main sources of SDS in Karakalpakstan. For example, in Moynaq, which is the closest district (100–200 km from the center of the Aral Seabed), between 86–98% of total AAP is attributed to SDS from the Aralkum (Groll et al., 2019). Results of the dispersion model show that the contribution of the Aralkum to total AAP decreases exponentially with distance. Based on these results, the annual values of production losses for all major crops grown in Karakalpakstan are estimated to be between $5–14 million with an average of about $9.9 million, which is equivalent to 0.45% of Karakalpakstan’s GDP (Annex Table E.1). The annual number of statistical lives lost (SLL) attributable to SDS from the Aralkum is estimated to be 13–29 in this sparsely populated area, with an average of approximately 21. The welfare loss of SLL/year is approximately $1.7 million/year, equivalent to 0.08% of Karakalpakstan’s GDP (Table 4.4 .). Economic costs of SDS-related health impacts from the Aralkum are minor compared to other costs given the relatively low population density of the near Aral Sea region. Therefore, over a period of 20 years, the total loss of off-site benefits is approximately $192 million. Under existing conditions and assuming a 20-year time horizon, inaction is causing Karakalpakstan to lose potential benefits of $782–986 million. The loss of on-site and off-site ecosystem services, as well as forgone benefits, including timber, firewood, and forage production are estimated at $44.2 million/year— equivalent to 2.1% of Karakalpakstan’s GDP. 12 During the growing period, the saxaul trees, can be pruned annually to enhance vegetative growth. The leafy parts of the harvested branches can be used as forage for animals while the harder parts can be used as fuel wood and construction material. Therefore, these benefits are estimated in addition to the value of sequestered carbon in the above-ground biomass of the fully matured trees. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 26 Table 4.4: SLL Estimates and Monetary Value of Lives Lost in Karakalpakstan Due to SDS from the Aralkum As % of Karakalpakstan’s Item Value GDP Total value of health damages in Karakalpakstan due to Ambient Air 7,859,798 0.37% Pollution (AAP) regardless of source ($) Total number of deaths due to AAP regardless of source 97 (persons/year) Total number of deaths regardless of cause 3,270 (persons/year) Lower bound for total value of health damages due to SDS from the 1,074,803 0.05% Aralkum ($/year) Upper bound for total value of health damages in Karakalpakstan due 2,354,896 0.11% to SDS from the Aralkum ($/year) Average total value of health damages due to SDS from the 1,714,850 0.08% Aralkum ($/year) Share of SDS from the Aralkum in the total value of health damage 21.82% due to total PM2.5 (%) Lower bound for the total number of deaths due to SDS from the 13.26 Aralkum (persons/year) Upper bound for the total number of deaths due to SDS from the 29.06 Aralkum (persons/year) Average of the total number of deaths due to SDS from the Aralkum 21.16 (persons) Share of SDS from the Aralkum in the total number of deaths due to 21.82 total PM2.5 (%) Share of AAP in the total number of deaths (%) 2.97 Share of SDS from the Aralkum in total deaths due to AAP (%) 21.82 Share of SDS from the Aralkum in total deaths (%) 0.65 Source: Authors’ estimates. 4.4. Benefits of Alternative Intervention Scenarios Three alternative outcomes—low, average, and high—are developed for each scenario to reflect potential success rates of planting and robustness of results. The literature shows varying levels of success/failure rates of tree planting (restoration) depending on the agro-ecologies where planting was done and the weather conditions of the year under consideration (see Annex Table A.3). The averages for the minimum and maximum reported success rate from the literature are utilized to establish the lower bound and upper bound outcomes. Hence, the following assumed cumulative total success rates are used: • Lower bound: 49.5% (15.69% for the first planting and 1.57% natural succession, 15.69% for the second replanting with 3.16% natural succession, and 15.69% for the third replanting with 4.78% natural succession). • Average: 72.3% (25.07% for the first planting and 2.51% natural succession, 25.07% for the second replanting with 5.08% natural succession, and 25.07% for the third replanting with 7.71% natural succession). • Upper bound: 79.4% (43.3% for the first planting and 4.33% natural succession and 43.3% for the second replanting with 8.85% natural succession with no third replanting but high natural succession rate of 9.23%). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 27 4.4.1. On-Site Benefits On-site benefits of landscape restoration, including the prevention of ecosystem service losses and regeneration of new ecosystem services, have a range of $146–699 million. Considering a planning period of 20 years, simulation results show that, by intervening with planting of different vegetative covers and distinct levels of success rates (in terms of percentage of total area covered by shrubs and/or trees), the value of ecosystem services prevented from being lost and regenerated can vary widely. Benefits of restoration are estimated as the difference in ecosystem services (annual flows) provided under rehabilitation scenarios and the base case scenario. Shrub-based intervention (Scenario 2) in the Aral Seabed can provide on-site benefits of $146–207 million over a 20-year time horizon (Annex Table D.1 and Annex Table D.3). In turn, the implementation of best-bet practices, including the planting of saxaul trees and grasses, can lead to additional on-site benefits of $488–699 million. The prevention of soil carbon loss and the regeneration of provisioning services, such as wood, increase the value of restorable land in the Aralkum by a range of $111–529/ha. These figures are lower compared to the estimates in other studies, e.g., $1,588/ha for temperate forests (de Groot et al., 2012) and $1,588/ha (Costanza et al., 2014), or the estimates of unrestored or unforested deserts, which are also relatively high, including $173.84/ha (Kroeger and Manalo, 2007) and $234/ha (Costanza et al., 2014). In comparison, the estimates made in this study can be considered extremely conservative. 4.4.2. Off-Site Benefits Estimates show that different interventions have varying effects on the magnitude of crop production loss due to SDS from the Aralkum. Estimates of different intervention scenarios on the total production of specific crops and the total monetary values by district are given in Table 4.5. While the interventions under Scenarios 2 and 3 have sizeable effects in the reduction of crop loss, the intervention under Scenario 4 (i.e., planting of 100% saxaul trees, allowing undergrowth of grass) provides the best outcome. The total value of crop production in Karakalpakstan lost due to SDS from the Aralkum under the current scenario is estimated at an average of $9.88 million/year, which is expected to decrease by 56% to $4.36 million/year if the best practices of planting 100% saxaul trees is implemented—saving the country $5.52 million/year. By implementing different intervention scenarios to reduce SDS from the Aralkum, it is possible to reduce negative health impacts to a range of $0.075–0.242 million (i.e., by 8–96%). The economic value of SLL/year is estimated at approximately $1.7 million, equivalent to 0.08% of Karakalpakstan’s GDP. Summaries of lower and upper bound estimates of AAP health impacts caused by SDS from the Aralkum are provided in Annex Table E.2, Annex Table E.3 and Annex Table E.4. Under vegetation-based rehabilitation, the burden on human health decreases due to the decreased share of PM2.5 concentration originating from the Aralkum. Under shrub-based intervention, the average benefit of rehabilitation is at $719,000 / year (i.e., 42% reduction of health costs compared to average Scenario 1). Under the best-bet scenario, the negative health impacts related to SDS from the Aralkum can be reduced on average by 58%, leading to an annual benefit of $1 million. The implementation of different intervention scenarios to restore the Aralkum can generate total annual benefits ranging between $10–44 million. Table 4.6 provides a summary of total annual values of on-site and off-site ecosystem services gained assuming different intervention scenarios. The annual Present Value (PV) based on different interventions per ha of land that is restored in the Aralkum Desert ranges between $7/ha/year (the lower bound estimate of Scenario 2) and $34/ha/year (the upper bound of Scenario 4). Estimated values of Benefit-to-Cost-Ratio (BCR) of the different interventions (Figure 4.8) range from 0.5 (the lower bound estimate of Scenario 2) to 1.6 (the upper bound of Scenario 4). Considering only the best scenario of 100% planting of saxaul trees, estimates of the BCR range between 1.38 and 1.60 with an average of 1.49. These ratios may appear rather low, given that: 1) estimates were based on extremely conservative assumptions, and 2) several other damages from SDS and benefits from the restoration of the Aralkum are ignored. Therefore, the results represent the lower bound costs of inaction and the lower bound The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 28 benefits that can be expected from action. The relatively modest estimate of economic return of landscape restoration in the Aral Seabed highlights the need for careful planning and use of appropriate restoration methods to ensure benefits are greater than the cost. Moreover, restoration projects in the Aralkum have the potential to deliver regional and global benefits in addition to the economic return calculated in this study. These benefits contribute to the Nationally Determined Contribution (NDC) targets, Land Degradation Neutrality goals, and the Bonn Challenge, among others. Figure 4.8 BCR for Different Scenarios Representing Lower Bound, Average and Upper Bound Values 1.8 1.6 1.49 1.4 benefit : cost ratio 1.2 1.12 1.0 0.8 0.6 0.62 Average 0.4 0.2 0.0 Scenario 2: Scenario 3: Scenario 4: 50% Shrub (-) and 50% Shrub (+) 50% Tree (-) and 50% Tree (+) (Best possible scenario includes trees, shrubs, grasses): 100% Tree (+) Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 29 Table 4.5: Impacts of Implementing the Best Course of Action on the Total Value of Production Lost Due to SDS from the Aralkum Value of Total Production Loss Due to SDS from the Aralkum The Value of Production ($, million) Loss that can be Prevented by Implementing Value of Scenario 1 Scenario 2 Scenario 3 Scenario 4 the Best Course of Action (Base-Case) (50%-50% Shrub (-) & Shrub (+)) (50%-50% Tree (-) & Tree (+)) (100% Tree (+)) (Scenario 4) Total Production Loss: Lower Loss: Loss: Upper Loss: Lower Loss: Loss: Upper Loss: Lower Loss: Loss: Upper Loss: Lower Loss: Loss: Upper Loss: Lower Loss: Loss: Upper Bound Average Bound Bound Average Bound Bound Average Bound Bound Average Bound Bound Average Bound District ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) ($, million) Nukus City 4.51 0.08 0.15 0.23 0.03 0.07 0.10 0.03 0.07 0.10 0.03 0.06 0.09 0.05 0.10 0.15 Amudaryo 121.83 0.33 0.65 0.98 0.15 0.29 0.44 0.14 0.29 0.43 0.11 0.22 0.33 0.22 0.43 0.65 Beruniy 59.27 0.16 0.32 0.48 0.07 0.14 0.21 0.07 0.14 0.21 0.05 0.11 0.16 0.11 0.21 0.32 Karauzyak 18.08 0.31 0.62 0.93 0.14 0.28 0.41 0.14 0.27 0.41 0.11 0.23 0.34 0.20 0.39 0.59 Kegeyli 22.34 0.38 0.77 1.15 0.17 0.34 0.51 0.17 0.33 0.50 0.14 0.28 0.42 0.24 0.48 0.73 Kungrad 25.72 0.44 0.88 1.32 0.20 0.39 0.59 0.19 0.39 0.58 0.16 0.32 0.49 0.28 0.56 0.84 Kanlykul 16.76 0.29 0.57 0.86 0.13 0.26 0.38 0.13 0.25 0.38 0.11 0.21 0.32 0.18 0.36 0.55 Moynaq 4.26 0.43 0.85 1.28 0.39 0.78 1.17 0.39 0.78 1.16 0.38 0.77 1.15 0.04 0.09 0.13 Nukus 31.31 0.78 0.61 0.44 0.71 0.56 0.40 0.71 0.56 0.40 0.70 0.55 0.40 0.08 0.06 0.04 Takhiatash 9.19 0.16 0.31 0.47 0.07 0.14 0.21 0.07 0.14 0.21 0.06 0.12 0.17 0.10 0.20 0.30 Tahtakupir 16.07 0.28 0.55 0.83 0.12 0.25 0.37 0.12 0.24 0.36 0.10 0.20 0.30 0.17 0.35 0.52 Turtkul 57.58 0.15 0.31 0.46 0.07 0.14 0.21 0.07 0.13 0.20 0.05 0.10 0.16 0.10 0.20 0.31 Khujayli 21.14 0.36 0.72 1.09 0.16 0.32 0.48 0.16 0.32 0.48 0.13 0.27 0.40 0.23 0.46 0.69 Chimbay 45.05 0.77 1.54 2.32 0.34 0.69 1.03 0.34 0.68 1.01 0.28 0.57 0.85 0.49 0.98 1.47 Shumanay 22.49 0.39 0.77 1.16 0.17 0.34 0.52 0.17 0.34 0.51 0.14 0.28 0.42 0.24 0.49 0.73 Elikkala 44.28 0.12 0.24 0.36 0.05 0.11 0.16 0.05 0.10 0.16 0.04 0.08 0.12 0.08 0.16 0.24 Total 519.88 5.42 9.88 14.35 2.98 5.09 7.19 2.94 5.01 7.09 2.61 4.36 6.11 2.81 5.53 8.24 % of GDP 24.61% 0.26% 0.47% 0.68% 0.14% 0.24% 0.34% 0.14% 0.24% 0.34% 0.12% 0.21% 0.29% 0.13% 0.26% 0.39% Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 30 Table 4.6: Average Annual Values of On-Site and Off-Site Ecosystem Services Under Different Rehabilitation Scenarios Annual Total Benefits of On-Site and Off-Site Ecosystem Services Compared to the Base Case Annual Losses 13 (Scenario 1) Scenarios On-Site ($, million) 14 Off-Site ($, million) 15 Value Gained Avg PV 16 Annual, BCR Above/ Below from Action of Action As % of Annual Cost Avg NPV (assuming a Ground Forage and Crop Total ($/year, ($/year/ha Karakalpaksta of Action of Action period of Soil Carbon Biomass Firewood Yields Health ($, million) million) ) n’s GDP ($, million) ($/ year/ ha) 20 years) Scenario 1 Min 10.3 12.7 9.6 5.4 1.1 39.1 (Current Scenario): Avg 10.3 12.7 9.6 9.9 1.7 44.2 90% Bare (-) and 10% Bare (+) Max 10.3 12.7 9.6 14.3 2.4 49.3 Min 6.9 7.5 7.5 3.0 0.7 25.6 13.5 10.21 0.64% 26.2 -9.63 0.51 Scenario 2: 50% Shrub (-) and Avg 6.9 7.5 7.5 5.1 1.0 28.0 16.2 10.97 0.77% 26.2 -8.87 0.62 50% Shrub (+) Max 6.9 7.5 7.5 7.2 1.3 30.3 18.9 14.33 0.90% 26.2 -5.51 0.72 Min 5.1 3.4 0.4 2.9 0.7 12.6 26.5 20.05 1.25% 26.2 0.21 1.01 Scenario 3: 50% Tree (-) and Avg 5.1 3.4 0.4 5.0 1.0 14.9 29.3 20.84 1.39% 26.2 1 1.12 50% Tree (+) Max 5.1 3.4 0.4 7.1 1.3 17.3 32.0 24.23 1.52% 26.2 4.38 1.22 Min - 2.6 0.2 2.8 36.3 27.47 1.72% 26.2 7.63 1.38 Scenario 4: (best possible scenario): Avg - 4.4 0.7 5.1 39.1 28.29 1.85% 26.2 8.45 1.49 100% Tree (+) Max - 6.1 1.3 7.4 41.9 31.71 1.98% 26.2 11.86 1.6 Source: Authors’ estimates. Note: In 2019, the GDP of Karakalpakstan was $2,112,874,907. The total cost of planting saxaul trees or other shrubs on the Uzbekistan side of the restorable part of the Aralkum (assuming arial planting of 85% of the area and manual planting for the remaining 15%) is estimated at $462,082,634.48. 13 Considering a 20-year planning period, the total values of soil carbon, above and below ground biomass, crop yields and human lives lost are estimated at $652 million (see Annex Table D.2). The annual impacts reported in Table 4.6 are generated by dividing total impacts reported in Annex Table D.2 by the number of years in the planning period (i.e., by 20). 14 For quantities and values of total on-site impacts of SDS from the Aralkum see Annex D. 15 For quantities and values of total off-site impacts of SDS from the Aralkum see Annex E. 16 A discount rate of 9% is used based on IndexMundi. 2020. Uzbekistan Central Bank discount rate. Available at: https://www.indexmundi.com/uzbekistan/central_bank_discount_rate.html (accessed on April 20, 2021). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 31 4.5. Scenarios with the Highest Net Return Scenario 4, comprising planting of saxaul trees and undergrowth of grass on 100% of the area provides the highest net return (Figure 4.9). Benefits from vegetative rehabilitation increase per scenario compared to the base case representing the actual Aral Seabed characterized by dried-up areas with no vegetation cover. While annual incremental benefits reach $16.2 million under shrubs-based intervention (Scenario 2), approximately $39.1 million/year can be gained under the best-bet practices of saxaul trees and undergrowth of grass compared to the base case (Scenario 1). The best scenario of 100% planting of saxaul tree leads to an estimated BCR range between 1.38 and 1.60 with an average of 1.49 (Figure 4.10). Approximately $23 million, or 59% of total benefits, represent soil carbon and biomass above and below the ground (Figure 4.9). Simulation of SDS events with rehabilitation scenarios indicate a clear effect that the vegetation has in minimizing soil carbon erosion by wind. In addition, planting increases the sequestered carbon in above and below ground biomass. During the growing period, the saxaul trees can be pruned annually to enhance vegetative growth. The leafy parts of the pruned tree branches can be used as forage for animals while the harder parts can be used as firewood and construction material. These annual benefits of $9.6 million under best-bet practice are estimated in addition to the value of sequestered carbon in the above ground biomass of the fully matured trees. The best-bet practice (Scenario 4) reduces the number of SLL as well as crop production losses attributable to SDS from the dry Aral Seabed significantly (Figure 4.9). The value of mortality reduction due to rehabilitation measures is equivalent to $1 million, representing a 58% reduction from the base case scenario—equivalent to 0.05% of Karakalpakstan’s GDP (Table 4.7). The planting of saxaul trees and grass undergrowth would prevent crop production losses in Karakalpakstan of approximately $5.5 million— equivalent to 0.26% of Karakalpakstan’s GDP. (Table 4.5). In the absence of restoration, it is assumed that limited and scattered native vegetation would grow over a 20-year period in the Aralkum. The exposed seabed might naturally develop a certain vegetation cover through primary succession with native vegetation species, mostly scattered grasses, occupying different preferential sites. The potential vegetation development is largely dependent on the available seed material and influx (e.g., through wind transportation) and the environmental conditions (e.g., soil physiochemical conditions, including available soil water, soil crust, and salinity). However, such land cover change is slow, spontaneous, and contributes little to erosion mitigation. Beyond the 20-year time horizon used in this analysis, uncertainties in predictions of vegetation cover increase. Shifts in water management might affect soil factors, while changes in climate, nature of the surface, and human activities can further influence future vegetation of the former Aral Seabed. Figure 4.9 Annual Incremental Benefits of Restoration Compared to the Base Case (Scenario 1) 40 35 30 in $, million 25 20 15 10 5 0 Scenario 2 Scenario 3 Scenario 4 Soil carbon Above and below ground biomass Forage and firewood Crop yields Health Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 32 Figure 4.10 BCR for the Best-Bet Intervention of Plantation with 100% Saxaul Trees 2.00 1.50 1.00 0.50 0.00 Scenario4-Minimum Scenario4-Average Scenario4-Maximum NPV-Lower-Bound NPV-Average NPV-Upper-Bound Source: Authors’ estimates. Table 4.7: Number and Value of Lives Saved in Karakalpakstan by Using the Best Bet Practices of Planting Saxaul Trees Best-Bet Practice (saxaul Trees on 100% Saving from Using Item Base Case of the Area) Best-Bet Practice Average of upper and lower bounds for the total value of health damages due to SDS 1.7 0.7 1.0 from the Aralkum ($, million) Share of SDS from the Aralkum in the total value of health damage due to total PM2.5 21.8 9.1 13.0 (%) Average of upper and lower bounds for the total number of deaths due to SDS from 21.2 8.8 12.4 the Aralkum (persons) Share of SDS from the Aralkum in the total 21.8 9.1 12.7 number of deaths due to total PM2.5 (%) Share of Ambient Air pollution (AAP) in the 3.0 3.0 0.0 total number of deaths (%) Share of SDS from the Aralkum in total 0.65 0.27 0.38 deaths (%) Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 33 5 Conclusions and Recommendations The combination of the Aralkum’s high wind speed occurrence pattern and its exposed and dry seabed surface trigger significant wind erosion. Various processes, including surface sediment “creeping,” “saltation,” and “suspension” contribute to soil erosion. The vast areas near the Aral Sea region have little industry and traffic pollution. Wind erosion and suspension of small sediment particles (e.g., salt, clay, and silt) are exclusive contributors to dust formation and consequently dust-sediment transport from the source areas to off-site areas beyond the dry Aral Seabed. Event-based wind erosion modeling indicated the erosive wind speed threshold ranges between 10–15 m/s depending on the soil type. Wind speeds exceeding 10–15 m/s are expected to cause substantial erosion— noting that lower wind speeds can also cause (minor) erosion depending on the environmental conditions (e.g., soil surface structure/compound (crusting), soil moisture, and temperature). Time-series analysis using local meteorological observation datasets (spanning over the last 20 years of three-hourly wind speed data) showed that the critical wind speed criterion was reached on 16 days/year. At the same time, the biophysical wind erosion modeling shows a vast increase of sediment movement and consequential dust production with increasing wind speed. Three erosive wind classes were defined based on the local wind speed occurrence and frequency obtained from local meteorological stations (wind speed data). Due to the exponential behavior of increased wind erosion (and suspension) with increased wind speed, the prediction uncertainty of storm class 3 (highest) was accordingly large; however, because of the low occurrence probability, the overall contribution of storm class 3 events was around the magnitude of the more frequently occurring storm class 2 events. Therefore, the potential error generated through the extreme storm class 3 uncertainties may be a factor. Combining representative wind speed classes with various rehabilitation scenarios demonstrated that the on- site “suspension” fraction of total erosion may be reduced on average by approximately 75% through vegetating the bare, dry Aral Seabed with shrubs and trees. The study considered certain landscapes that cannot be rehabilitated due to salt-crusts formed on recently desiccated and dried former seabed areas, which would reduce the overall effects from restoration efforts through planting of suitable shrub and tree species. The dry Aral Seabed continuously changes due to further shrinking of the waterbody, decrease of groundwater levels, and salt crust dynamics. The defined zoning (e.g., restorable area) reflects the most representative landscape pattern over the past 20 years (simulation period); however, ongoing change certainly impacts the zoning over time, which adds uncertainty. Similarly, potential rehabilitation efficiency by conducting one planting and sowing event was considered—as the success rate is often less than 100%. Depending on germination rate, planting method, and survival success of planted species, the replication of field replanting operations might be necessary to increase surface cover with vegetation. Wind erosion simulations suggest that large “single object” vegetation, such as trees, should be planted in a well-designed rehabilitation approach. The planting of too few trees at large distances could exacerbate erosion by increasing local wind turbulences. Therefore, only a uniform combination of vegetation cover can achieve the desired results towards protecting the erosion prone surface, while inappropriate rehabilitation design, or a poor or scattered survival rate, can lead to adverse effects and increased erosion. Literature on dry Aral seabed rehabilitation projects (both shrub and tree-based) and related development and survival monitoring shows varied success rates; however, vegetation survival often follows a certain landscape pattern (healthy vegetation patches interrupted by larger unsuccessful planting areas), which would decrease the overall soil cover and protection functions, but would likely not generate such uniform far-distance single tree landscapes that exacerbate the erosion problem. Quasi-homogeneous patches of successful vs. unsuccessful rehabilitation areas were considered by combining different scenarios with certain percentage composition (i.e., 50% Tree (-) and 50% Tree (+)) to evaluate the resulting impacts and uncertainties through likely rehabilitation failure. The identified issues of vegetation patches development and single shrub/tree survival should be addressed in additional targeted field studies to holistically determine optimum rehabilitation approaches, considering various ecosystem synergies and economic gains, but conditioned by The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 34 the desired wind-biophysical effects. The establishment of pilot sites could test the success rates of various rehabilitation approaches, from biophysical and socio-economic aspects, allowing for wind erosion occurrence and impact monitoring before advancing to large scale implementation. The biophysical modeling study revealed potentially huge effects that annual and perennial grasses have on reducing wind erosion. Rehabilitation measures with and without grasses need to be carefully considered— outcomes of any intervention can be improved with the promotion of grass cover. As local model calibration and validation were not performed, simulation results of this study must be considered with caution. Further assessment, through e.g., field validation of grass impacts as well as investigation of the linkages between ecosystem recovery and native grass seed emergence, is highly recommended. Model-inherent uncertainties and limitations, as well as the rather unknown representation of the available model input data, need to be considered when interpreting study results. The radial assessment model estimated the reduction of dust concentration and deposition with increasing distance to the Aralkum. The subsequent merger of modeled on-site erosion occurrence with a simple radial dispersion approach allowed the estimation of off-site dust concentration and deposition ratios. The simple radial dispersion model facilitated the on-site dust simulations and considered information on local dust deposition (including monitoring data and a remote sensing PM2.5 spatial analysis. Both remote sensing and modeling-based approaches indicated a significant drop of dust concentrations at a distance of 100–200 km from the Aralkum (former Aral Seashore) and identified only minor Aralkum-borne dust effects at a distance of 300–500 km to the center of the dry Aral Seabed. This phenomenon is consistent with the findings in other literature (e.g., Aslanov et al., 2013). For a planning period of 20 years, estimates show that SDS from the Aralkum cause losses to Karakalpakstan of $884 million. This study estimates on-site and off-site costs of ecosystem service loss under current conditions of approximately $44.2 million/year (equivalent to 2.1% of Karakalpakstan’s GDP). The cost estimate includes: 1) on-site loss of 2.1 million tonnes of soil carbon with an estimated value of $10.3 million/year; 2) off-site impacts in terms of number of SLL equal to 21, which are valued at $1.7 million/year, as well as loss of agricultural production worth $9.9 million/year; and 3) value of forgone on-site benefits of $22.3 million/year, including carbon from above- ($5.4 million/year) and below-ground biomass ($7.3 million/year), as well as forage ($5.5 million/year) and wood ($4 million/year) that could have been harvested under the best course of action. Planting of saxaul trees and undergrowth of grass on 100% of the area provides the highest economic return. The implementation of different intervention scenarios to restore the Aralkum can generate total annual benefits ranging between $28–44 million. About $39 million/year can be gained under the best-bet practices of planting saxaul trees and undergrowth of grass compared to the base case—equivalent to 1.9% of Karakalpakstan’s GDP. The related average BCR is estimated at 1.49 for the present value of benefits and costs of the best-bet practices. This ratio may appear rather low, given that: 1) estimates were based on extremely conservative assumptions, and 2) several other damages from SDS and benefits from the restoration of the Aralkum are ignored. Therefore, the study results represent the lower-bound costs of inaction and the lower-bound benefits that can be expected from action. The results underscore the importance of careful estimation of all benefits while planning future restoration programs. This study informed a resilient forest restoration program in Uzbekistan by proving that afforestation in Uzbekistan is economically viable, yet requires careful planning and use of appropriate restoration methods to ensure that benefits are greater than the cost. Restoration projects in Uzbekistan provide values far beyond their direct economic and financial benefits. If appropriately implemented, restoration projects in the Aralkum have the potential to deliver regional and global benefits, in addition to the economic return calculated in this study. These benefits contribute towards achievement of the NDC targets, Land Degradation Neutrality goals and the Bonn Challenge, among others. In addition, afforestation is an important part of the green-growth strategy that mainstreams climate goals and economic development. 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Washington DC: World Bank Group. https://documents.worldbank.org/en/publication/documents- reports/documentdetail/789791609753275902/costs-of-environmental-degradation-in-the-mountains-of-the-republic-of-tajikistan Zia-Khan, S., Spreer, W., Pengnian, Y., Zhao, X., Othmanli, H., He, X., and Muller, J. 2015. “Effect of Dust Deposition on Stomatal Conductance and Leaf Temperature of Cotton in Northwest China.” Water (7): 116–131. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 39 Annex A: Assessment of the Aral Seabed Rehabilitation Approaches and Scenarios The effects of vegetation-based rehabilitation of the dry Aral Seabed on protecting the erodible sediments from the wind-induced movement are controlled by the on-site environmental conditions (actual soil erodibility) and the selected rehabilitation measures (out-planted and successively developed vegetation cover). During this study, an expert team consisting of national and international scientists discussed and agreed on a set of target rehabilitation options to investigate further. The actual on-site environmental conditions, as well as the selected rehabilitation scenarios are described in Table 3.1 based on local knowledge and data obtained from reference pilot sites established in the Aralkum. Biophysical simulation models were used and set up, reflecting defined and locally proven rehabilitation measures and development stages. The simulations tackle impacts of implementation at scale and trade-offs between on-site and off- site areas to the south of the former Aral Sea. Compromising between explicit results and covering a wider range of impacts of potential rehabilitation measures and stages, the work focused on three scenarios with upper and lower ranges, representing a total of six scenarios simulated and spatially combined at a later stage (Annex Table A.1). Annex Table A.1 Scenario Overview: Degraded Status vs. Rehabilitation Through Shrub and Tree Plantations Environmental Scenario Description Expected Model Output Condition Scenario (vegetation cover) (notes) Bare Represents: recently dried-up areas Present: most erosion susceptive Bare (-) with no vegetation cover (highest scenario (worst case) Degraded vulnerability) (present Bare, with marginal grass cover condition) Represents: older dried up areas Present: slightly reduced erosion Bare (+) with a limited degree of natural susceptivity (checking the upper range vegetation emergence (few bunch of proneness to wind erosion) grasses) Represents: out-planted shrubs Limited reduction of erosion (minimum with marginal or absent natural of erosion resistance through human Shrub (-) recruitment (pure shrub out-planting interventions, e.g., shrubs; the lower Rehabilitated effect) boundary of rehabilitation impact range) Shrubs Shrub cover: rehabilitated (Salsola (shrub-based or Atriplex) incl. recruitment and Reduction of erosion (checking the intervention) grasses 2. Shrub (+) significance of human intervention vs. Represents: Out-planted shrubs natural recruitment) with specific extent recruitment and grass cover Tree cover: rehabilitated (Saxaul) Limited reduction of erosion (minimum Represents: out-planted trees with of erosion resistance through human Tree (-) marginal or absent natural interventions (trees); the lower Rehabilitated recruitment (pure tree out-planting boundary of rehabilitation impact range Trees effect) and check vs. shrub intervention) (tree-based Highest reduction of erosion through intervention) Represents: out-planted trees with three different layers: 1) trees, 2) specific extent recruitment, shrub, Tree (+) shrubs, and 3) grasses (checking the and grass cover (highest cover significance of human intervention vs. scenario) natural recruitment) Scenario 1.1 Bare (-) and Scenario 1.2 Bare (+) represent actual dry seabed conditions, while rehabilitation Scenario 2.1 Shrub (-), Scenario 2.2 Shrub (+), Scenario 3.1 Tree (-), and Scenario 3.2 Tree (+), represent potential out-planting of native shrub and tree species. For on-site wind erosion modeling, the selected The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 40 vegetation options represent obstacles to the driving force wind, which create friction—e.g., energy dissipation through turbulence—(Annex Figure A.1). The wind obstacles depend on the vegetation size, density/extent, and spatial distribution. The scenarios with (+) include the emergence of native vegetation (e.g., grasses) facilitated through the shelter of primarily out-planted species or natural regeneration and succession. All scenarios are based on field observations and expert knowledge and reflect realistic stages achievable with the Aralkum. Annex Figure A.1 Vegetation-Based Rehabilitation Effects: Obstacles to Wind Erosion Source: http://livingasia.online/aralsea/uz_kaz (left); Wolfe and Nickling (1993) (right). Wind Erosion and Dust Assessment: Soil Detachment and Air Pollution Wind speed is the driving force of top-soil movement, erosion, and, consequently, the detachment and uptake of small sediment particles into suspension, as well as their dispersion and transportation to off-site areas. To analyze erosion and dispersion processes, the assessment of the on-site (Aralkum) wind speed occurrence and frequency pattern is key, as it helps to narrow down the most important conditions and consequentially to define critical events without simulation of long-period sub-daily processes that are computer-intensive. On-Site Driving Force: Assessment of Wind and Erosion Occurrence Time-series analysis of wind parameters (speed, direction) reveals the occurrence and frequency pattern of wind as a driving force, while threshold analysis identifies potential erosive events. Wind speed data is coupled with biophysical wind erosion simulations to investigate the most critical wind speed for the initiation of sediment movement. For the wind speed threshold analysis, Scenario Bare (-) represents the most vulnerable surface conditions, independent of the existence of less erosion-prone environmental patches within the Aral Seabed. The Scenario Bare (-) prevalence is likely to cause erosion when local wind speeds exceed the critical threshold. The biophysical model-based investigation of Bare (-) was pursued under local surface soil heterogeneity to define upper and lower erodibility conditions. The soils’ conditions were obtained from published reports. Annex Figure A.2 shows the erosion threshold analysis considering different soil types (soil texture). The pre-analysis of the dry Aral Seabed erodibility resulted in the following findings: 1. Various soil textural conditions present in the dry Aral Seabed have different susceptibility to erosion— especially in terms of sediment movement magnitude with increasing wind speed. 2. Various soil compaction stages (bulk density) have minor effects. However, surface crusting effects need to be addressed through expert opinion as the field visit could not be undertaken due to COVID- 19. 3. Various soil conditions present in the dry Aral Seabed (both texture and density) have a similar overall threshold of erosion initiation—estimated at 10–15 m/s wind speed (Annex Figure A.2). In this study, the wind speed threshold was set to 15 m/s (based on Annex Figure A.2 and literature analyses). This threshold will be higher in areas with soil crust (e.g., salt crust in proximity to Aral water bodies) and/or the presence of surface obstacles such as vegetation cover. The 15 m/s critical wind speed is related to overall low–medium sediment movement and dust production. Potential erosion occurring at The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 41 lower wind speeds (e.g., 5–15 m/s) is minor in this study and its effect restricted to immediate surroundings only. Annex Figure A.2 demonstrates that soil erosion greatly increases with higher wind speeds and, therefore, higher wind speeds might dominate sediment movement processes. However, time-series analysis was required to disclose the occurrence, frequency, and magnitude of wind speeds that are larger than the defined threshold (Annex Figure A.2, right). Annex Figure A.2 shows the wind speed occurrence distribution as an exceedance graph, which indicates that less than 10–20% of the three-hourly wind speeds exceed the critical threshold values (10–15 m/s). Annex Figure A.2 Erosion Threshold Analysis Using Flat and Uncovered Terrain Bare (-) Scenario for Different Soil Types Present in the Dry Aral Seabed Source: Authors’ estimates. Note: Sediment movement under dry initial soil conditions starts between 10–15 m/s wind speed (left). Three-hour wind speed occurrence in the dry Aral Seabed based on observations from Kungrad meteorological station, located near the southwest border of the Aralkum (right). Frequency analysis of potentially erosive events was used to define three storm severity classes. The storm severity classification relates to the maximum three-hourly wind speed recorded at a specific day, classified as: Storm class 1: maximum three-hourly wind speed > 15 m/s (15–20 m/s); Storm class 2: maximum three-hourly wind speed > 20 m/s (20–25 m/s); and Storm class 3: maximum three-hourly wind speed > 25 m/s. The three storm severity classes were analyzed according to: i) their overall occurrence across the Aralkum (potentially erosive events); ii) their occurrence during highly erodible soil states (based on pre-event meteorological conditions such as rainfall and temperature); and iii) storm occurrence during highly erodible soil states with target wind direction (towards southern target area). Potentially erosive wind speeds occurring during wet or frozen soil conditions—related to surface soil protection and/or low erodibility effects during such conditions—were removed. For subsequent SDS simulation, Class 3 storm events were particularly important. Data from meteorological stations suggests that in the last 20 years (spring 2000–summer 2020) on average, the wind speed threshold was exceeded 16 days/year. However, only 9.4 erosive event days/year occurred due to favorable conditions such as dry soil, predominant wind directions from east, northeast, west, and northwest. The data matches with aerosol observations reported by Spivak et al. (2012), recorded on average 13 SDS event days/year between 2000 and 2009. Simulation of storm classes 1–3 for various vegetation scenarios using the wind erosion simulation model (Smeets, 2020) indicated specific sediment suspension distribution (Annex Figure A.3) with a fraction of fine particles (mostly silt and clay) suspended above the ground. Coarser sediment particles (e.g., sand) might move through “saltation” processes only and are not dispersed into the higher above-ground layers for transport to off-site areas. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 42 Annex Figure A.3 Daily Event Distribution of Sediment Suspension Loads per SDS Class and Scenario Source: Authors’ estimates. Annex Figure A.3 indicates clear effects of the out-planted vegetation (scenarios Shrub and Tree) on reducing sediment suspension. Differences between Bare (-) and Bare (+) scenarios are based on grass cover, which, according to local experts, is present in some locations of the Aralkum. For subsequent dispersion modeling and transport to off-site areas, diverse options of current landscape pattern, e.g., a combination of Bare (-) and Bare (+) mosaic), as well as potential landscape with rehabilitation options consisting of both low and well-performing areas, e.g., a combination of Shrub (-) and Shrub (+), and Tree (- ) and Tree (+) mosaics, were considered. The team defined most scenario combinations using a 50% spatial distribution of both (-) and (+) scenarios. Off-Site Effects: Transportation and Dispersion of Dust Off-site transportation of dust (suspension fraction of the on-site model) is a two-step process. First, a simple empiric model was set up considering wind speed and direction to model all class 1–3 storms that occurred in the last 20 years (observation period: spring 2000–summer 2020). The empiric model considers various zones of the Aralkum (Annex Map A.1) covering the target area (Annex Map A.1) from the center of the Aralkum in circular buffer zones (100–200, 200–300, and 300–500 km) to the south. The model considers various dispersion factors and estimates the radial suspension movement as a function of the distance from the source area (Aralkum) and the settling fraction of sediments. The model provides radial (spatial) information on average and peak dust concentrations (PM2.5) as well as on seasonal and long-term dust cumulation. Both parameters are important; dust concentration (in the air) was used for consecutive human health related assessment, while dust cumulation (on the ground), over time, was defined as the dominant cause for agricultural production decline. However, the model does not consider wind trajectory changes along the SDS pathways, as shown in the satellite image (Annex Map A.1)), adding a significant source of uncertainty. Although the model is simplistic, it was preliminarily adjusted (hand-calibrated) to various sediment accumulation datasets (observations) obtained from the literature. Actual stage off-site effect assessment is based on the first-step simplistic model solely at this stage. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 43 Annex Map A.1 The Aral Sea Zoning (Left) and Satellite Image of an SDS Sourcing from the Aralkum (Right) Source: Map constructed by authors (left); https://worldview.earthdata.nasa.gov/ (right). Note: The water body (left) reflects the median area during the observation period 2000–2020 to represent average on-site conditions over the entire target simulation period. In the second step, the Regional Atmospheric Modeling System (RAMS) (Cotton et al., 2003; Pielke et al., 1992) was used to simulate selected events from storm classes 1–3 to generate physically sound SDS trajectory simulations. Despite more accurate outputs provided by the RAMS model with the use of atmospheric-physical laws, the limited computation power and resources available restricted an in-depth analysis of the entire 2000–2020 period atmospheric dynamics. Because the RAMS model requires a broad spatial array (several thousand km) of explicit knowledge of its environment, its use was limited to specifically selected event simulation only. Outputs of the RAMS model feed into the simplistic empirical model assessment towards a blended product for spatial SDS impact assessment within the Aralkum. Results of RAMS analyses are expected to be integrated in follow up studies. Concentration of PM2.5 in the Study Area National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA GMAO) provides global coverage of air pollution data that includes variables to calculate PM2.5. The latest atmospheric reanalysis produced by the Modern-Era Retrospective Analysis for Research and Applications, Version 2, (MERRA-2) provides long-term (1980–present) record of global atmospheric analyses, with a detailed list of variables described by He et al. (2019). Air pollution dataset for any location is accessible via Data and Information Services Center (DISC). 17 Based on variables estimated by MERRA-2, PM2.5 is calculated by the following equation: 2.5 = 1.375 × 4 + 1.6 × + + 2.5 + 2.5 Where, SO4, OC, BC, Dust2.5, and SS2.5 represent sulfate, organic carbon, black carbon, dust, and sea-salt particulate matter with a diameter of less than 2.5 µm respectively. More details and description is provided in Annex Map A.1. 17 https://disc.gsfc.nasa.gov/ The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 44 Economic Valuation of Selected Ecosystem Services General Framework Knowing the on-site erosion and dust mobilization and transport to off-site affected areas, and simulation of on- and off-site ecosystem services inform decision making on the suitability of land rehabilitation in the dry Aral Seabed. Reduction of sediment load transported from the dry Aral Seabed as a result of the established vegetation cover is one ecosystem service benefit. At the same time, the vegetation-based rehabilitation of marginal land increases the variety of ecosystem services—with different assessment complexity and pre- estimated value. Specific ranking, based on local and international expert knowledge, was undertaken to select the most suitable on- and off-site ecosystem services to be considered in this study. Most economic valuations of land degradation are made in comparison to a scenario with no-land degradation. This implicitly assumes that after the interventions, land will be restored to 100% of its potential (Quillérou et al., 2016). We argue that these assumptions are not realistic as it is difficult, if not impossible, to know the land attributes if degradation would not have taken place (the counterfactual). As argued by Quillérou et al. (2016), interventions may not restore land to its original state and, therefore, the benefits of action and the costs of inaction may be overestimated. Following Yigezu et al. (2019), we apply the concept of yield gap analysis (van Ittersum and Cassman, 2013) for the quantitative estimation of land use-values, natural and environmental resources, and other assets. In this study, the loss of land value and other assets affected by SDS is estimated as the difference between the value of ecosystem services of land in other areas with similar social, economic, biophysical, and other conditions, but not affected by SDS, and the value of the ecosystem services in the affected areas (van Ittersum and Cassman, 2013) for the quantitative estimation of land use-values, natural and environmental resources, and other assets. In this study, the loss of land value and other assets affected by SDS is estimated as the difference between the value of ecosystem services of land in other areas with similar social, economic, biophysical, and other conditions, but not affected by SDS, and the value of the ecosystem services in the affected areas. To determine the economic cost of SDS and the economic benefits of the proposed mitigation measures, it is essential to identify natural and environmental resources as well as other assets that are affected by SDS and the associated losses/gains in social, economic, and environmental benefits. Different components of the total economic value of land can be estimated using a variety of valuation methods, which can be classified as market and non-market demand-based economic valuation methods. In this study, we use a combination of the market and non-market valuation methods to attach monetary values to different natural and environmental resources, as well as other assets that are adversely affected by SDS. Where markets for the resource or its services exist, assessment is straightforward. An example would be a local real estate market. Observations on the number and value of transactions provide information about the people's willingness to pay for land and the quantity of land changing hands. These market data provide means through which to deduce the market demand curve and the actual payments made during a given period. Market demand-based methods include the revealed and stated preference methods. In the revealed preference method, the value of an ecosystem service is measured in terms of the market price for that service in the market, or indirectly by examining the purchase of a related service (complementary or substitute service) in the private marketplace (Garrod and Willis, 1999). Tilahun et al. (2018) provide a detailed description of the different methods available for the valuation of natural resources and environmental assets. Estimation of Economic Cost of SDS This study provides a first attempt to assess impacts of SDS from the Aralkum. SDS can have adverse effects on public health, crop and woody biomass productivity, infrastructure, and land and air transport. Therefore, the quantification and valuation of impacts are based on broad categories, including nature, scale, spatial The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 45 and temporal coverage, frequency and intensity of effects identified in the previous section. Estimation of SDS effects and the monetary value of these impacts were generated based on the following steps: 1. Delineate the affected area. 2. Using PM2.5 values to classify the ambient air pollution (AAP) level in each district. 3. Use results of the dispersion model to determine the level of dust deposited by SDS originating from the dry Aral Seabed. 4. Classify districts based on their susceptibility to impacts of SDS. As a result, districts within 100–200 km from the center of the Aral Seabed are classified as high impact, while those between 200–300 km and 300–500 km as medium, and low impact, respectively. 5. Identify affected biophysical, socio-economic, and ecosystem services. Develop a priority list to identify services that are significant and relevant with respect on-site and off-site effects. From on-site effects, we consider the value of soil eroded by wind and the opportunity cost of forest that could have been planted on the vast land of the Aralkum Desert, which could serve as an important carbon sink. For off- site impacts, we consider health and crop production impacts. 6. Develop a list of relevant impact indicators for each of the prioritized ecosystem services, crop production and health impacts. 7. Determine the per-unit or percentage effects (e.g., per person, per hectare, per district, etc.) of SDS on the different biophysical and socio-economic indicators (i.e., yield, health, and soil erosion) across distinct levels of effects, taking the difference between the condition in the least affected districts and the corresponding districts in the highly and moderately affected areas. For the on-site effects, we first defined four different scenarios based on Table 3.1. Scenario 1 (which represents the current scenario) is defined as a combination of scenarios 1.1 and 1.2, e.g., 90% Bare (-) and 10% Bare (+). Scenario 2 is defined as a combination of scenarios 2.1 and 2.2, e.g., 50% Shrub (-) and 50% Shrub (+). Scenario 3 is defined as a combination of scenarios 3.1 and 3.2, e.g., 50% Tree (-) and 50% Tree (+). Finally, we define Scenario 4 as the best scenario which assumes full implementation of Scenario 3.2. e.g., 100% Tree (+). Subsequently, a comparison was made based on simulation results between the best- practice (Scenario 4) and the remaining scenarios, including the base case (i.e., Scenario 1 representing the current condition). For health impacts, we followed Golub (2018) to estimate the number of statistical lives lost (SLL) due to SDS from the Aralkum using relative risk figures obtained from the global burden of diseases study (GBD 2017 Risk Factor Collaborators). The relative risk function is defined as the ratio of the probability of a health outcome occurring in an exposed group to the probability of it occurring in a non-exposed group. GBD 2017 Risk Factor Collaborates presents relative risks within uncertainty intervals. District level 2019 data of mortality was collected for six diseases (ischemic heart disease, stroke, lung cancer, chronic obstructive pulmonary disease, lower respiratory illness, diabetes mellites type 2) and the value of morbidity was assumed to be 10% of the value of mortality. 8. Obtain district-wise data on population size, crop areas, yield, and regional GDP for each area classified as high, moderate or low impact. 9. Obtain prices of major crops, international carbon price, and Value of Statistical Life (VSL). 10. Generate the aggregated value of SDS impacts for each class of affected areas as the product of per- unit value of impacts and the population size/total area affected in each class. 11. Sum the value of impacts across all three classes of affected areas to obtain the total provincial cost of SDS originating from the Aralkum. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 46 Valuation of On-Site and Off-Site Costs and Benefits for Alternative Intervention Scenarios and Identification of the Scenario with the Highest Net Return 1. The following is required to determine the values of benefits derived from the proposed alternative interventions: 2. Identification of the land, natural and environmental resources, other assets, and socio-economic parameters (Annex Table A.2), and their qualities, quantities or values which are affected by alternative interventions; 3. Estimation of annual economic benefits of land, natural and environmental resources, and other assets, and socio-economic parameters under current conditions, e.g., Scenario Bare (-) to serve as the counterfactual; Estimation of annual economic benefits of land, natural and environmental resources, and other assets, and socio-economic parameters under alternative intervention scenarios. These values are generated for each of the three categories of effects (i.e., on-site effects on soil carbon, off-site effects on crops, and off-site effects on health). The economic benefits of each alternative intervention are estimated as the difference between step (3) and (2) above. The intervention with the highest net benefit is equal to the largest difference between the economic benefits of the intervention (3) and the economic cost of implementing the intervention. Annex Table A.2 Reported Success Rates of Tree Planting (Restoration) Depending on the Agro-ecologies (Based on the Literature Review) Location 90% Bare (-) and 50% Shrub (-) and 50% Tree (-) and Scenario 4 of Impact Increase Per Year 10% Bare (+) 50% Shrub (+) 50% Tree (+) 100% Tree (+) Soil Carbon, t/ha Carbon from biomass On-site (above ground), t/ha Carbon from biomass (below ground), t/ha Cropland (all crops) yield + quality Off-site Health Cost Avoided Robustness of the Analysis Robustness of the analysis is ensured using lower-bound, average, and upper-bound estimates for each scenario that reflect success rates for tree planting. These bounds are estimated using the literature review reflected in Annex Table A.3. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 47 Annex Table A.3 Reported Success Rates of Tree Planting (Restoration) Depending on the Agro-ecologies (Based on the Literature Review) Deviation from the Min Av Max Mean (%) Seed emergence rate in soils, can Dimeeva and be improved to reach 28% with Permitina, 2006 5.00 8.50 12.00 41% proper agrotechnology. New collected seeds have laboratory emergence rate 92–94% Seashore sandy soils, Dimeeva 2011; 76.00 87.00 98.00 13% saplings/seedlings Dimeeva and Permitina, 2006 Near seashore slightly saline soils Dimeeva, 2011; 17.63 37.00 56.37 with sand layer brought by wind, Dimeeva and saplings Permitina, 2006 Near seashore light and highly Dimeeva, 2011; saline loamy soils, seedlings. Dimeeva and 12.00 13.00 14.00 8% Improved methods improved Permitina, 2006 survival rate by 26 times Saplings, experimental plots in Dimeeva, 2011 8.10 17.00 25.90 north seabed, sandy soils Saplings, experimental plots in Dimeeva, 2011 10.00 50.00 90.00 80% north seabed, sandy soils 1.00 7.50 14.00 87% Dry year 54mm, saplings Dimeeva, 2011 13.00 22.50 32.00 42% Wet year 260mm, saplings Dimeeva, 2011 Areas with heavy lithology, Dimeeva and 0.00 32.00 64.00 100% seedlings and seeds, good on Permitina, 2006 sandy and loamy sand areas Heavy soils, spring planting Dimeeva and 46.00 47.50 49.00 3% Permitina, 2006 Saline loamy soils, planted Dimeeva and 8.00 16.00 24.00 50% seedlings in sand accumulating Permitina, 2006 ditches Saline soils, seedling planted Dimeeva and 0.44 0.92 1.40 manually in pits with add sand Permitina, 2006 layer Saline soils, planting in deep Dimeeva and 9.53 20.00 30.47 ditches, saplings Permitina, 2006 GIZ project, seedlings, Navratil and Wilps, summarized average survival rate 2009 28.59 60.00 91.41 for saxaul and selin (Aristida karelini) Ditches, data from control plots Kabanov et al., 0.00 23.50 47.00 100% without soil amendments 2017 Average 15.69 25.07 43.30 52% Note: 85% of the actual average is utilized in the study for a conservative estimation of the impacts of interventions. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 48 Annex B: Air Pollution: Data Source and Preparation The latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO) is the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). MERRA-2 is a long-term record of global atmospheric analyses that provides the newly-released product values of daily Particulate Matter with diameter ≤ 2.5 µm (PM2.5) and other data from 1980 and thereafter. The list of variables can be explored in He et al. (2019). The data can be accessed from the Modeling and Assimilation Data and Information Services Center (MDISC) available on the Goddard Earth Sciences (GES)—Data and Information Services Center (DISC) website: https://disc.gsfc.nasa.gov/. The PM2.5 data are calculated by the following equation: 2.5 = 1.375 × 4 + 1.6 × + + 2.5 + 2.5 Where SO4, OC, BC, Dust2.5, and SS2.5 represent sulfate, organic carbon, black carbon, dust, and sea-salt particulate matter with a diameter of less than 2.5 µm, respectively. The MERRA-2 reanalysis does not include nitrate particulate matter, which is primarily emitted by vehicle exhaust and industrial production, leading to biases compared with ground measurements (He et al., 2019). A buffer zone of a 500-km radius from the center of the Aral Sea was selected to clip and download the dust surface mass concentrations—PM2.5 (DUSMASS25) in kg/m3 for the period from Jan 1, 1980 to April 29, 2020. A total of (14,729) files were downloaded in netCDF-4 format and combined into one NetCDF file using “NetCDF Operators” (NCO) and “Climate Data Operators” (CDO). The time-series data was extracted from the NetCDF file using “R.” The DUSMASS25 spatial distribution in the 500-km radius area was represented in 357 cells (Annex Map B.1). Annex Map B.1 Extent of the MERRA-2 Grid Data of Focus Area Source: Map constructed by authors. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 49 Spatial Distribution of DUSMASS25 Data The data can be presented as a daily distribution or yearly average distribution. The daily data series was extracted and entered in an Excel spreadsheet to calculate the yearly average for each cell. The PM2.5 in kg/m3 was converted to µg/m3 to compare yearly data to international standards. The World Health Organization (WHO) guideline stipulates that PM2.5 does not exceed 10 μg/m³ annual mean and 25 μg/m³ 24-hour mean (WHO, 2006). The Air Quality Guideline (AQG) of 10 μg/m³ is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed. The WHO introduced three Interim Targets (IT): IT-1, IT-2, and IT-3 indicating the portion of a country’s population living in the aeras where mean annual concentrations of PM2.5 are greater than 35 μg/m³, 25 μg/m³, and 15 μg/m³ respectively. Following the WHO guidelines, Uzbekistan's air quality is considered unsafe (Annex Figure B.1). Uzbekistan sanitary guidelines, rules, and standards define maximum allowable concentrations (MAC) of suspended particles in ambient air in accordance with SanPiN №0293-11, dated May 16,2011 (SanPiN, 2011). The MAC list includes single maximum, daily, monthly, and annual levels allowed for different types of dust (Annex Table B.1). Annex Table B.1 Air Quality Guidelines and Standards Maximum Allowable Concentration, μg/m3 Substance Event Day Month Year Uzbekistan (SanPiN, 2011) Particulate matter ≤10 μm (PM10) 500 300 100 50 Suspended particulates/aerosol 500 350 200 150 Salt dust from Aral 500 300 200 150 Russia (Rospotrebnadzor, 2018) Suspended particulates/aerosol 500 150 Particulate matter ≤10 μm (PM10) 300 60 40 Particulate matter ≤2.5 μm (PM2.5) 160 35 25 Global (WHO, 2006) Particulate matter ≤2.5 μm (PM2.5) 25 10 PM2.5 Interim target-1 (IT-1) 37.5 15 PM2.5 Interim target-2 (IT-2) 50 25 PM2.5 Interim target-3 (IT-3) 75 35 Particulate matter ≤10 μm (PM10) 50 20 PM10 Interim target-1 (IT-1) 75 30 PM10 Interim target-2 (IT-2) 100 50 PM10 Interim target-3 (IT-3) 150 70 Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 50 Annex Figure B.1 PM2.5 Dynamics in Uzbekistan, 1990-2017 PM2.5 pollution 100 40 90 35 (micrograms per cubic meter) 80 30 70 25 (% of total) 60 50 20 40 15 30 10 20 10 5 0 0 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 2017 Population exposed to levels exceeding WHO Interim Target-1 value (% of total) Population exposed to levels exceeding WHO Interim Target-2 value (% of total) Population exposed to levels exceeding WHO Interim Target-3 value (% of total) Mean annual exposure (micrograms per cubic meter) Source: World Bank, World Bank Development Indicators, 2020. Contributors to poor air quality in Uzbekistan include SDS, waste burning; the mining, oil, and gas industries; and vehicle emissions (IAMAT, 2020). The average yearly spatial distribution of PM2.5 in Uzbekistan can vary yearly. To present the yearly data, a 50-km buffer zone was created around Uzbekistan to extract the country’s average yearly data. A total of 120 cell data are presented below for: 1980, 1990, 2000, 2010, 2019, and Jan–Apr 2020 (Annex Map B.2). Annex Map B.2 Average Yearly Value of PM2.5 for 1980, 1990, 2000, 2010, 2019, and Jan–Apr 2020 Source: Authors’ estimates based on https://disc.gsfc.nasa.gov/. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 51 Time Series Data The daily health data from the Uzbekistan Health Ministry is available for four districts highlighted in blue in Annex Map B.3 in Karakalpakstan. The cells' data of DUSMASS25 is extracted for most populated areas within the districts (Annex Table B.2). The closest population location is Qubla Usturt (Ka Sjem), located in Qo'ng'irot district located south of the Aral Sea’s west shoreline. The largest town is Moynaq, with a population of 13,524 located in the south of the Aral Sea. Annex Map B.3 Selected Districts for Daily Time Series Data Extraction Source: Map constructed by authors. Annex Table B.2 Area and Population Information of Focus Districts in Karakalpakstan and How They Match Observation Grid Cells with PM2.5 Data Cell Code District Area (ha) Population Information with PM2.5 Data Amudaryo 142,508.60 In total, 81 population settlements were identified. Data is V81 available for five locations with a total population of 61,072. V97 The main population areas are: V98 Qipshaq 3,007 Qarataw 3,021 Mang'it 9,200 Qilichboy 5,208 Ayaqchi 636 Located in 300–500 km buffer zone from the Aral Sea center The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 52 Cell Code District Area (ha) Population Information with PM2.5 Data Moynaq 2461540.80 In total, 11 population settlements were identified. Data is V31 available for six locations with a total population of 18,648. V46 The main population areas are: V47 Shag'irliq 1,289 Moynaq 13,524 Shege 1,772 Tiko'zek 400 Uchsay 732 Qipshaqdaryo 931 Located in the 100–300 km buffer zone from the Aral Sea center Shomanay 62447.44 In total, 31 population gatherings were identified. Data is available V63 for one location with a total population of 22400. The main V79 population area is: V80 Shomanay 22,400 Located in the 300–500 km buffer zone from the Aral Sea center Xo'jayli 133411.35 In total, 46 population gatherings were identified. Data is available V79* for four locations with a total population of (128,507). V80* The main population areas are: Nayman 2,229 Taqıyatas 49,475 Vodnik 5,976 Xo’jayli 70,827 Located in the 300–500 km buffer zone from the Aral Sea center Source: Authors compilation. Note: Data represent two districts. Review of reports and experiments with documented effect of dust on crop production was analyzed. The following Annex Table B.3 provides a summary of yield penalties on different crops. Annex Table B.3 Effects of Dust on Crop Production Impact on Crop Yield, % Crop Country Reference -5-30 Rice, Winter Wheat China (Chameides, et al., 1999) -10 Maize India, USA (Greenwald, et al., 2006) ±5 Wheat India, USA ±10 Rice India, Thailand, USA -10 (not significant) Winter wheat China (Liu, et al., 2016) -28% Cotton China (Zia-Khan, et al., 2015) -30% Alfalfa Iran (Naseri & Ahmady-Birgani, 2019) Source: Authors’ compilation. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 53 Annex C: Extracting and Estimating PM2.5 Average per District for 2019 in Karakalpakstan Average PM2.5 values for 2019 were calculated based on available daily values and are presented with spatial distribution of PM2.5 cells over each district in Annex Maps C.1-15. Populated places (black dots) and population gatherings (polygons) were examined to verify the cell/s representing the district. Inside each cell, the district area percentages were calculated and presented in tables that follow the district spatial map. Annex Map C.1 Amudaryo District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 304 33.23 60.34 305 11.98 67.21 283 3.73 64.66 284 0.03 74.83 Annex Map C.2 Beruniy District and PM2.5 Cells Source: Map constructed by authors. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 54 Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 285 100.00 85.00 264 75.69 80.12 305 50.07 67.21 306 36.90 80.18 284 28.21 74.83 327 12.40 80.71 263 11.80 69.11 326 9.65 69.14 286 9.62 71.87 265 3.99 71.61 307 0.83 75.04 Annex Map C.3 Chimbay District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 262 30.09 56.71 261 14.65 49.37 241 3.29 49.44 240 0.22 45.67 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 55 Annex Map C.4 Elikkala District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 286 64.42 71.87 265 56.75 71.61 306 31.63 80.18 307 24.56 75.04 266 2.23 53.42 327 1.35 80.71 305 1.21 67.21 Annex Map C.5 Kanlykul District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 282 11.91 55.28 261 9.44 49.37 260 6.05 46.83 281 2.64 50.21 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 56 Annex Map C.6 Karauzyak District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 283 56.42 64.66 262 48.83 56.71 284 36.56 74.83 241 22.07 49.44 305 14.72 67.21 263 2.28 69.11 Annex Map C.7 Kegeyli District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 261 61.98 49.37 240 15.67 45.67 283 13.70 64.66 262 5.83 56.71 282 4.79 55.28 241 0.03 49.44 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 57 Annex Map C.8 Turtkul District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 328 80.28 80.39 327 61.02 80.71 307 35.34 75.04 306 31.46 80.18 350 15.63 74.37 349 12.19 81.83 329 5.86 72.90 348 0.93 83.59 Annex Map C.9 Tahtakupir District and PM2.5 Cells Source: Map constructed by authors Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 243 98.80 68.45 263 85.92 69.11 244 72.85 68.88 242 56.39 59.27 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 58 Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 222 43.51 52.90 265 38.74 71.61 284 35.19 74.83 264 24.31 80.12 266 24.20 53.42 245 23.69 54.72 262 15.25 56.71 241 7.51 49.44 223 3.18 55.34 283 1.33 64.66 Annex Map C.10 Shumanay District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 281 15.33 50.21 282 3.12 55.28 260 3.10 46.83 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 59 Annex Map C.11 Nukus City District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 282 3.08 55.28 Annex Map C.12 Nukus District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 282 23.50 55.28 283 7.88 64.66 261 3.46 49.37 282 0.00 55.28 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 60 Annex Map C.13 Moynaq District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 218 100.00 37.84 221 99.36 44.35 239 92.32 43.52 240 84.11 45.67 220 75.00 40.17 219 71.63 38.04 241 67.10 49.44 197 49.75 31.56 176 47.49 27.06 242 43.61 59.27 177 39.58 27.19 222 22.83 52.90 200 21.20 33.84 155 15.10 23.57 217 12.86 44.89 238 12.63 52.40 198 6.65 32.27 196 5.51 35.71 199 1.45 32.99 156 1.30 23.31 261 1.28 49.37 243 1.20 68.45 260 0.12 46.83 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 61 Annex Map C.14 Kungrad District and PM2.5 Cells Source: Map constructed by authors. Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 174 100.00 30.24 194 100.00 44.52 195 100.00 41.71 215 100.00 57.89 216 100.00 53.98 236 100.00 65.34 237 100.00 60.54 257 100.00 63.56 258 100.00 59.36 259 100.00 50.58 278 100.00 72.03 279 100.00 59.79 173 90.42 34.28 260 90.29 46.83 193 90.27 47.14 214 90.21 58.79 235 90.16 64.24 256 90.10 68.02 277 90.04 74.45 298 89.95 72.64 299 87.62 72.20 238 87.37 52.40 217 87.14 44.89 319 79.84 65.60 175 74.36 27.81 320 70.94 64.06 196 69.24 35.71 172 57.12 38.81 280 53.54 51.41 154 44.93 24.10 300 19.61 65.74 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 62 Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 153 18.78 25.83 261 9.18 49.37 239 7.68 43.52 281 6.38 50.21 155 6.22 23.57 301 1.03 58.69 152 0.46 28.23 240 0.00 45.67 Annex Map C.15 Khujayli District and PM2.5 Cells Source: Percentage Occupied by District PM2.5 Average in 2019 PM2.5—Cell Number (%) (μg/m3) 282 29.92 55.28 283 14.54 64.66 304 0.20 60.34 282 0.00 55.28 District areas that present/occupy 10% of the PM2.5 cell area were considered, especially for large area districts. Only in the following cases, the cell value was considered as many population points exist and/or population gathering (polygons). (i) For Beruniy district the value of cell 362. (ii) For Kanlykul district, the values of cells 282, 261, and 260. (iii) For Kungrad district, the value of cell 261. (iv) For Nukus district, the value of cells 283 and 261. (v) For Nukus city the value of cell 282 due to the small extent of the city location and large population. (vi) For Shumanay district, the values of cells 282 and 260. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 63 Annex C: Table 1 Average PM2.5 Values by District Percentage Occupied by PM2.5 Average in 2019 District PM2.5—Cell Number District (%) (μg/m3) Amudaryo 304 33.23 60.34 Amudaryo 305 11.98 67.21 Amudaryo 283 3.73 64.66 Amudaryo 284 0.03 74.83 Beruniy 285 100.00 85.00 Beruniy 264 75.69 80.12 Beruniy 305 50.07 67.21 Beruniy 306 36.90 80.18 Beruniy 284 28.21 74.83 Beruniy 327 12.40 80.71 Beruniy 263 11.80 69.11 Beruniy 326 9.65 69.14 Beruniy 286 9.62 71.87 Beruniy 265 3.99 71.61 Beruniy 307 0.83 75.04 Chimbay 262 30.09 56.71 Chimbay 261 14.65 49.37 Chimbay 241 3.29 49.44 Chimbay 240 0.22 45.67 Elikkala 286 64.42 71.87 Elikkala 265 56.75 71.61 Elikkala 306 31.63 80.18 Elikkala 307 24.56 75.04 Elikkala 266 2.23 53.42 Elikkala 327 1.35 80.71 Elikkala 305 1.21 67.21 Kanlykul 282 11.91 55.28 Kanlykul 261 9.44 49.37 Kanlykul 260 6.05 46.83 Kanlykul 281 2.64 50.21 Karauzyak 283 56.42 64.66 Karauzyak 262 48.83 56.71 Karauzyak 284 36.56 74.83 Karauzyak 241 22.07 49.44 Karauzyak 305 14.72 67.21 Karauzyak 263 2.28 69.11 Kegeyli 261 61.98 49.37 Kegeyli 240 15.67 45.67 Kegeyli 283 13.70 64.66 Kegeyli 262 5.83 56.71 Kegeyli 282 4.79 55.28 Kegeyli 241 0.03 49.44 Khujayli 282 29.92 55.28 Khujayli 283 14.54 64.66 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 64 Percentage Occupied by PM2.5 Average in 2019 District PM2.5—Cell Number District (%) (μg/m3) Khujayli 304 0.20 60.34 Khujayli 282 0.00 55.28 Kungrad 174 100.00 30.24 Kungrad 194 100.00 44.52 Kungrad 195 100.00 41.71 Kungrad 215 100.00 57.89 Kungrad 216 100.00 53.98 Kungrad 236 100.00 65.34 Kungrad 237 100.00 60.54 Kungrad 257 100.00 63.56 Kungrad 258 100.00 59.36 Kungrad 259 100.00 50.58 Kungrad 278 100.00 72.03 Kungrad 279 100.00 59.79 Kungrad 173 90.42 34.28 Kungrad 260 90.29 46.83 Kungrad 193 90.27 47.14 Kungrad 214 90.21 58.79 Kungrad 235 90.16 64.24 Kungrad 256 90.10 68.02 Kungrad 277 90.04 74.45 Kungrad 298 89.95 72.64 Kungrad 299 87.62 72.20 Kungrad 238 87.37 52.40 Kungrad 217 87.14 44.89 Kungrad 319 79.84 65.60 Kungrad 175 74.36 27.81 Kungrad 320 70.94 64.06 Kungrad 196 69.24 35.71 Kungrad 172 57.12 38.81 Kungrad 280 53.54 51.41 Kungrad 154 44.93 24.10 Kungrad 300 19.61 65.74 Kungrad 153 18.78 25.83 Kungrad 261 9.18 49.37 Kungrad 239 7.68 43.52 Kungrad 281 6.38 50.21 Kungrad 155 6.22 23.57 Kungrad 301 1.03 58.69 Kungrad 152 0.46 28.23 Kungrad 240 0.00 45.67 Moynaq 218 100.00 37.84 Moynaq 221 99.36 44.35 Moynaq 239 92.32 43.52 Moynaq 240 84.11 45.67 Moynaq 220 75.00 40.17 Moynaq 219 71.63 38.04 Moynaq 241 67.10 49.44 Moynaq 197 49.75 31.56 Moynaq 176 47.49 27.06 Moynaq 242 43.61 59.27 Moynaq 177 39.58 27.19 The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 65 Percentage Occupied by PM2.5 Average in 2019 District PM2.5—Cell Number District (%) (μg/m3) Moynaq 222 22.83 52.90 Moynaq 200 21.20 33.84 Moynaq 155 15.10 23.57 Moynaq 217 12.86 44.89 Moynaq 238 12.63 52.40 Moynaq 198 6.65 32.27 Moynaq 196 5.51 35.71 Moynaq 199 1.45 32.99 Moynaq 156 1.30 23.31 Moynaq 261 1.28 49.37 Moynaq 243 1.20 68.45 Moynaq 260 0.12 46.83 Nukus 282 23.50 55.28 Nukus 283 7.88 64.66 Nukus 261 3.46 49.37 Nukus 282 0.00 55.28 Nukus city 282 3.08 55.28 Shumanay 281 15.33 50.21 Shumanay 282 3.12 55.28 Shumanay 260 3.10 46.83 Tahtakupir 243 98.80 68.45 Tahtakupir 263 85.92 69.11 Tahtakupir 244 72.85 68.88 Tahtakupir 242 56.39 59.27 Tahtakupir 222 43.51 52.90 Tahtakupir 265 38.74 71.61 Tahtakupir 284 35.19 74.83 Tahtakupir 264 24.31 80.12 Tahtakupir 266 24.20 53.42 Tahtakupir 245 23.69 54.72 Tahtakupir 262 15.25 56.71 Tahtakupir 241 7.51 49.44 Tahtakupir 223 3.18 55.34 Tahtakupir 283 1.33 64.66 Turtkul 328 80.28 80.39 Turtkul 327 61.02 80.71 Turtkul 307 35.34 75.04 Turtkul 306 31.46 80.18 Turtkul 350 15.63 74.37 Turtkul 349 12.19 81.83 Turtkul 329 5.86 72.90 Turtkul 348 0.93 83.59 Source: Authors’ compilation. Note: Values in light grey are the ones excluded from the average. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 66 Annex D: On-Site Impacts: Quantification and Valuation of Ecosystem Services, Crop Yields, and Human Lives Lost Due to Inaction Annex Table D.1 Lower Bound: Carbon from Biomass, t/ha (Above Ground) with Assumed Total Success Rate of 49.5% (15.69% for the First Planting with 1.57% Natural Succession, 15.69% for the Second Replanting with 3.16% Natural Succession, and 15.69% for the Third Replanting with 4.78% Natural Succession) Quantities and Values of the Ecosystem Services Lost Estimates of Quantities of Due to Inaction to Fully Restore the Aralkum Desert Ecosystem Services with Trees and Grass Cover (Scenario 4) Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 1 Scenario 2 Scenario 3 (Current Scenario) 50% Shrub (-) and 50% Tree (-) and (Best Possible (Current Scenario) 50% Shrub (-) and 50% Tree (-) and 90% Bare (-) and 50% Shrub (+) 50% Tree (+) Scenario) 90% Bare (-) and 10% 50% Shrub (+) 50% Tree (+) Item Increase Per Year 10% Bare (+) 100% Tree (+) Bare (+) Parameter values per Carbon from biomass, t/ha (above ground) with assumed 0.01 0.45 0.76 1.04 (1.04) (0.60) (0.28) unit area total success rate of 89% (30% with first planting, 50% with second replanting and 70% with third replanting) Carbon from biomass, t/ha (below ground) with assumed 0.01 0.58 1.04 1.42 (1.41) (0.84) (0.38) total success rate of 89% (30% with first planting, 50% with second replanting and 70% with third replanting) Soil carbon, t/ha 23.26 23.61 23.79 24.32 (1.06) (0.71) (0.53) Biodiversity, index Quantities of Carbon from above ground biomass (tonnes) 12,554 588,488 1,004,353 1,380,985 (1,368,430.85) (792,497.22) (376,632.34) ecosystem services Carbon from below ground biomass (tonnes) 16,792 765,819 1,377,062 1,875,315 (1,858,523.68) (1,109,496.11) (498,253.20) Soil carbon (tonnes) 30,752,046 31,219,567 31,454,962 32,157,879 (1,405,832.53) (938,311.48) (702,916.27) Values of ecosystem Total value of carbon from above-ground biomass ($) 460,328 31,456,232 53,837,471 74,107,272 (73,646,943.69) (42,651,039.64) (20,269,801.02) services under Total value of carbon from below-ground biomass ($) 615,689 40,927,256 73,823,454 100,638,712 (100,023,022.26) (59,711,455.49) (26,815,257.59) different scenarios 18 Total value of soil carbon ($) 1,127,575,020 1,175,149,595 1,199,904,844 1,269,021,181 (141,446,161.25) (93,871,585.90) (69,116,337.74) Total value of grazing or forage that can be harvested ($) 27,605,380 54,790,219 75,879,073 (75,879,073.41) (48,273,693.58) (21,088,854.61) Value of firewood that can be harvested ($) - 39,768,027 96,686,118 (96,686,117.83) (96,686,117.83) (56,918,091.19) Total values of ecosystem services after the 20th year 1,128,651,038 1,275,138,464 1,422,124,014 1,616,332,356 (487,681,318) (341,193,892) (194,208,342) Average annual value of ecosystem services lost due to inaction to fully restore (24,384,066) (17,059,695) (9,710,417) the Aralkum Desert with trees and grass cover ($) (assuming 20 years of loss) % gain (+) or loss (-) lost due to inaction -30.17% -21.11% -12.02% 0.00% Gain (+) or loss (-) as equivalent to % of Karakalpakstan’s GDP -1.15% -0.81% -0.46% 0.00% Cost of action: First planting + first replanting + second replanting: assuming NA 462,082,634 462,082,634 462,082,634 50% and 25% of original cost for the first and second replanting, respectively ($) Annualized benefit: Cost ratio (assuming 20-year planning period) 0.32 0.64 1.06 Source: Authors’ estimates. 18 A CO2 price of $10/tonne is used and converted to carbon price as follows: (44/12)*$10 = $36.67/tonne (World Bank, 2019). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 67 Annex Table D.2 Average: Carbon from Biomass, t/ha (Above Ground) with Assumed Total Success Rate of 72.3% (25.07% for the First Planting with 2.51% Natural Succession, 25.07% for the Second Replanting with 5.08% Natural Succession, and 25.07% for the Third Replanting with 7.71% Natural Succession) Quantities and Values of the Ecosystem Services Lost Estimates of Quantities of Due to Inaction to Fully Restore the Aralkum Desert Ecosystem Services with Trees and Grass Cover (Scenario 4) Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 1 Scenario 2 Scenario 3 (Current Scenario) 50% Shrub (-) and 50% Tree (-) and (Best Possible (Current Scenario) 50% Shrub (-) and 50% Tree (-) and 90% Bare (-) and 50% Shrub (+) 50% Tree (+) Scenario) 90% Bare (-) and 50% Shrub (+) 50% Tree (+) Item Increase Per Year 10% Bare (+) 100% Tree (+) 10% Bare (+) Parameter values Carbon from biomass, t/ha (above ground) with assumed 0.01 0.65 1.11 1.53 (1.51) (0.88) (0.42) per unit area total success rate of 89% (30% with first planting, 50% with second replanting and 70% with third replanting) Carbon from biomass, t/ha (below ground) with assumed 0.02 0.85 1.52 2.07 (2.06) (1.23) (0.55) total success rate of 89% (30% with first planting, 50% with second replanting and 70% with third replanting) Soil carbon, t/ha 23.26 23.78 24.04 24.81 (1.55) (1.04) (0.78) Biodiversity, index Quantities of Carbon from above ground biomass (tonnes) 18,355 860,408 1,468,430 2,019,092 (2,000,736.40) (1,158,683.35) (550,661.40) ecosystem Carbon from below ground biomass (tonnes) 24,550 1,119,678 2,013,356 2,741,835 (2,717,284.54) (1,622,156.69) (728,479.14) services Soil carbon (tonnes) 30,752,046 31,435,593 31,779,756 32,807,466 (2,055,420.14) (1,371,873.44) (1,027,710.07) Values of Total value of carbon from above-ground biomass ($) 673,031 45,991,092 78,713,943 108,349,733 (107,676,702.43) (62,358,640.74) (29,635,789.66) ecosystem Total value of carbon from below-ground biomass ($) 900,178 59,838,355 107,934,772 147,140,452 (146,240,273.72) (87,302,097.04) (39,205,680.07) services under different Total value of soil carbon ($) 1,127,575,020 1,197,132,196 1,233,326,006 1,334,378,663 (206,803,642.52) (137,246,466.93) (101,052,656.90) scenarios 19 Total value of grazing or forage that can be harvested ($) 40,360,891 80,406,217 110,940,224 (110,940,223.72) (70,579,332.67) (30,534,006.53) Value of firewood that can be harvested ($) - 102,454,664 80,223,290 (80,223,289.51) (80,223,289.51) 22,231,374.72 Total values of ecosystem services after the 20th year 1,129,148,229 1,343,322,534 1,602,835,602 1,781,032,361 (651,884,132) (437,709,827) (178,196,758) Average annual value of ecosystem services lost due to inaction to fully restore (32,594,207) (21,885,491) (8,909,838) - TRUE TRUE TRUE the Aralkum Desert with trees and grass cover ($) (assuming 20 years of loss) % gain (+) or loss (-) lost due to inaction -36.60% -24.58% -10.01% 0.00% Gain (+) or loss (-) as equivalent to % of Karakalpakstan’s GDP -1.54% -1.04% -0.42% 0.00% Cost of action: First planting + first replanting + second replanting: assuming NA 524,744,105 524,744,105 524,744,105 50% and 25% of original cost for the first and second replanting, respectively ($) Annualized benefit: Cost ratio (assuming 20-year planning period) 0.41 0.90 1.24 Source: Authors’ estimates. 19 A CO2 price of $10/tonne is used and converted to a carbon price as follows: (44/12)*$10 = $36.67/tonne (World Bank, 2019). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 68 Annex Table D.3 Upper Bound: Carbon from Biomass, t/ha (Above Ground) with Assumed Total Success Rate of (43.3% for the First Planting with 4.33% Natural Succession and 43.3% for the Second Replanting with 8.85% Natural Succession with No Third Replanting but a High Natural Succession Rate of 9.23%) Quantities and Values of the Ecosystem Services Estimates of Quantities of Lost Due to Inaction to Fully Restore the Aralkum Ecosystem Services Desert with Trees and Grass Cover (Scenario 4) Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 1 Scenario 2 Scenario 3 (Current Scenario) 50% Shrub (-) and 50% Tree (-) and (Best Possible (Current Scenario) 50% Shrub (-) and 50% Tree (-) and 90% Bare (-) and 50% Shrub (+) 50% Tree (+) Scenario) 90% Bare (-) and 50% Shrub (+) 50% Tree (+) Item Increase Per Year 10% Bare (+) 100% Tree (+) 10% Bare (+) Parameter values Carbon from biomass, t/ha (above ground) with assumed 0.02 0.71 1.22 1.68 (1.66) (0.96) (0.46) per unit area total success rate of 89% (30% with first planting, 50% with second replanting and 70% with third replanting) Carbon from biomass, t/ha (below ground) with assumed total 0.03 0.93 1.67 2.28 (2.25) (1.35) (0.60) success rate of 89% (30% with first planting, 50% with second replanting and 70% with third replanting) Soil carbon, t/ha 23.26 23.83 24.11 24.97 (1.71) (1.14) (0.85) Biodiversity, index Quantities of Carbon from above ground biomass (tonnes) 25,384 944,311 1,611,625 2,215,984 (2,190,599.47) (1,271,672.51) (604,359.21) ecosystem Carbon from below ground biomass (tonnes) 33,952 1,228,864 2,209,688 3,009,205 (2,975,253.73) (1,780,341.52) (799,516.88) services Soil carbon (tonnes) 30,752,046 31,502,249 31,879,973 33,007,901 (2,255,854.71) (1,505,651.86) (1,127,927.35) Values of Total value of carbon from above-ground biomass ($) 930,758 50,386,061 86,299,885 118,825,613 (117,894,854.50) (68,439,551.95) (32,525,727.66) ecosystem Total value of carbon from below-ground biomass ($) 1,244,889 65,553,314 118,339,860 161,368,687 (160,123,797.47) (95,815,372.73) (43,028,827.22) services under different Total value of soil carbon ($) 1,127,575,020 1,176,199,466 1,201,657,428 1,272,675,085 (145,100,065.09) (96,475,618.69) (71,017,656.60) scenarios 20 Total value of grazing or forage that can be harvested ($) 44,045,575 87,956,539 121,468,073 (121,468,072.83) (77,422,497.98) (33,511,534.26) Value of firewood that can be harvested ($) - 63,603,287 154,776,223 (154,776,223.19) (154,776,223.19) (91,172,936.46) Total values of ecosystem services after the 20th year 1,129,148,229 1,129,750,668 1,336,184,416 1,557,856,999 1,829,113,681 (699,363,013) Average annual value of ecosystem services lost due to inaction to fully restore (32,594,207) (34,968,151) (24,646,463) (13,562,834) - TRUE TRUE the Aralkum Desert with trees and grass cover ($) (assuming 20 years of loss) % gain (+) or loss (-) lost due to inaction -36.60% -38.24% -26.95% -14.83% 0.00% Gain (+) or loss (-) as equivalent to % of Karakalpakstan’s GDP -1.54% -1.66% -1.17% -0.64% 0.00% Cost of action: First planting + first replanting + second replanting: assuming NA NA 470,413,150 470,413,150 470,413,150 50% and 25% of original cost for the first and second replanting, respectively ($) Annualized benefit: Cost ratio (assuming 20-year planning period) 0.44 0.91 1.49 Source: Authors’ estimates. 20 A CO2 price of $ 10/tonne is used and converted to carbon price as follows: (44/12)*$10 = $36.67/tonne (World Bank, 2019).. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 69 Annex E: Off-Site Impacts Annex Table E.1 Values of Total Production and Estimates of Specific Crop Values Lost Due to Inaction to Restore the Aralkum Value of Total Production Lost Due to SDS from the Aralkum ($) All Grains Cotton Potatoes Vegetables Melones and Gourds Value of Value of Value of Value of Value of Total Loss: Lower Loss: Upper Total Loss: Upper Total Total Loss: Lower Loss: Upper Total Loss: Upper Production Bound Loss: Bound Production Loss: Lower Loss: Bound Production Loss: Lower Loss: Loss: Upper Production Bound Loss: Bound Production Loss: Lower Loss: Bound District ($) ($) Average ($) ($) ($) Bound ($) Average ($) ($) ($) Bound ($) Average ($) Bound ($) ($) ($) Average ($) ($) ($) Bound ($) Average ($) ($) Nukus City 65,848 1,129 2,258 3,386 61,088 1,047 2,094 3,142 125,780 2,156 4,312 6,469 3,354,613 57,508 115,015 172,523 357,662 6,131 12,263 18,394 Amudaryo 18,917,821 50,673 101,345 152,018 17,550,314 47,010 94,020 141,029 5,200,880 13,931 27,862 41,793 44,629,687 119,544 239,088 358,631 9,760,526 26,144 52,289 78,433 Beruniy 8,110,886 21,726 43,451 65,177 7,524,577 20,155 40,310 60,465 2,695,334 7,220 14,439 21,659 19,844,902 53,156 106,312 159,468 8,170,161 21,884 43,769 65,653 Karauzyak 4,412,178 75,637 151,275 226,912 4,093,236 70,170 140,340 210,509 705,768 12,099 24,198 36,297 4,136,244 70,907 141,814 212,721 3,456,411 59,253 118,506 177,758 Kegeyli 4,578,157 78,483 156,965 235,448 4,247,217 72,809 145,619 218,428 501,165 8,591 17,183 25,774 5,907,586 101,273 202,546 303,819 3,592,764 61,590 123,180 184,771 Kungrad 5,907,343 101,269 202,537 303,806 5,480,320 93,948 187,897 281,845 1,109,663 19,023 38,046 57,068 9,042,115 155,008 310,015 465,023 2,080,983 35,674 71,348 107,022 Kanlykul 4,279,463 73,362 146,724 220,087 3,970,115 68,059 136,118 204,177 254,076 4,356 8,711 13,067 4,652,441 79,756 159,512 239,268 2,701,582 46,313 92,626 138,938 Moynaq 467,048 46,705 93,410 140,114 433,287 43,329 86,657 129,986 441,349 44,135 88,270 132,405 2,273,534 227,353 454,707 682,060 480,423 48,042 96,085 144,127 Nukus 5,656,508 141,413 110,479 79,545 5,247,618 131,190 102,493 73,795 895,557 22,389 17,491 12,594 14,119,385 352,985 275,769 198,554 3,383,774 84,594 66,089 47,584 Takhiatash 1,837,983 31,508 63,017 94,525 1,705,122 29,231 58,461 87,692 508,991 8,726 17,451 26,177 3,186,549 54,627 109,253 163,880 1,001,199 17,163 34,327 51,490 Tahtakupir 4,343,275 74,456 148,912 223,368 4,029,314 69,074 138,148 207,222 625,827 10,728 21,457 32,185 3,330,604 57,096 114,192 171,288 2,019,390 34,618 69,236 103,854 Turtkul 10,183,412 27,277 54,554 81,831 9,447,286 25,305 50,610 75,916 2,730,832 7,315 14,629 21,944 20,357,764 54,530 109,059 163,589 5,281,677 14,147 28,295 42,442 Khujayli 4,810,323 82,463 164,925 247,388 4,462,601 76,502 153,003 229,505 1,005,405 17,236 34,471 51,707 5,984,282 102,588 205,175 307,763 2,481,123 42,534 85,067 127,601 Chimbay 8,851,171 151,734 303,469 455,203 8,211,349 140,766 281,532 422,298 1,899,843 32,569 65,137 97,706 14,526,207 249,021 498,041 747,062 7,079,759 121,367 242,735 364,102 Shumanay 4,105,678 70,383 140,766 211,149 3,808,892 65,295 130,591 195,886 480,761 8,242 16,483 24,725 8,837,370 151,498 302,996 454,493 4,049,399 69,418 138,837 208,255 Elikkala 6,778,645 18,157 36,314 54,471 6,288,639 16,845 33,689 50,534 1,816,549 4,866 9,732 14,597 17,568,700 47,059 94,118 141,177 5,053,997 13,537 27,075 40,612 Total 93,305,741 1,046,374 1,920,402 2,794,429 86,560,974 970,735 1,781,582 2,592,429 20,997,782 223,580 419,873 616,166 181,751,982 1,933,907 3,437,613 4,941,320 60,950,830 702,412 1,301,724 1,901,037 As % of 4.42% 0.05% 0.09% 0.13% 4.10% 0.05% 0.08% 0.12% 0.99% 0.01% 0.02% 0.03% 8.60% 0.09% 0.16% 0.23% 2.88% 0.03% 0.06% 0.09% GDP Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 70 Annex E: Table 1 Values of Total Production and Estimates of Specific Crop Values Lost Due to Inaction to Restore the Aralkum (cont’d) Value of Total Production Lost Due to SDS from the Aralkum ($) Share in Value of Total Production Fruits Grapes Total of All Crops (%) Loss: Lower Loss: Upper Loss: Upper Value of Total Bound Bound Value of Total Loss: Lower Bound Value of Total Loss: Lower Loss: Upper District Production ($) ($) Loss: Average ($) ($) Production ($) Bound ($) Loss: Average ($) ($) Production ($) Bound ($) Loss: Average ($) Bound ($) Lower Bound Average Upper Bound Nukus City 300,077 5,144 10,288 15,433 246,445 4,225 8,450 12,674 4,511,513 77,340 154,680 232,021 1.71% 3.43% 5.14% Amudaryo 23,496,251 62,936 125,873 188,809 2,279,028 6,105 12,209 18,314 121,834,507 326,342 652,685 979,027 0.27% 0.54% 0.80% Beruniy 11,198,197 29,995 59,990 89,986 1,726,287 4,624 9,248 13,872 59,270,345 158,760 317,520 476,280 0.27% 0.54% 0.80% Karauzyak 1,102,400 18,898 37,797 56,695 177,206 3,038 6,076 9,113 18,083,443 310,002 620,004 930,006 1.71% 3.43% 5.14% Kegeyli 3,219,466 55,191 110,382 165,573 292,213 5,009 10,019 15,028 22,338,568 382,947 765,894 1,148,841 1.71% 3.43% 5.14% Kungrad 1,122,744 19,247 38,494 57,741 975,217 16,718 33,436 50,154 25,718,385 440,887 881,773 1,322,660 1.71% 3.43% 5.14% Kanlykul 718,404 12,315 24,631 36,946 185,420 3,179 6,357 9,536 16,761,501 287,340 574,680 862,020 1.71% 3.43% 5.14% Moynaq 146,224 14,622 29,245 43,867 15,256 1,526 3,051 4,577 4,257,121 425,712 851,424 1,277,136 10.00% 20.00% 30.00% Nukus 1,486,396 37,160 29,031 20,902 517,534 12,938 10,108 7,278 31,306,771 782,669 611,460 440,251 2.50% 1.95% 1.41% Takhiatash 690,430 11,836 23,672 35,508 257,007 4,406 8,812 13,217 9,187,281 157,496 314,993 472,489 1.71% 3.43% 5.14% Tahtakupir 1,097,314 18,811 37,622 56,433 627,848 10,763 21,526 32,289 16,073,571 275,547 551,094 826,641 1.71% 3.43% 5.14% Turtkul 8,463,177 22,669 45,338 68,008 1,117,217 2,993 5,985 8,978 57,581,365 154,236 308,472 462,707 0.27% 0.54% 0.80% Khujayli 1,907,266 32,696 65,392 98,088 485,848 8,329 16,658 24,986 21,136,848 362,346 724,692 1,087,038 1.71% 3.43% 5.14% Chimbay 3,992,544 68,444 136,887 205,331 485,848 8,329 16,658 24,986 45,046,721 772,230 1,544,459 2,316,689 1.71% 3.43% 5.14% Shumanay 1,088,413 18,659 37,317 55,976 123,222 2,112 4,225 6,337 22,493,736 385,607 771,214 1,156,821 1.71% 3.43% 5.14% Elikkala 5,801,904 15,541 31,082 46,622 969,350 2,596 5,193 7,789 44,277,785 118,601 237,202 355,804 0.27% 0.54% 0.80% Total 65,831,207 444,165 843,041 1,241,917 10,480,947 96,889 178,010 259,130 519,879,462 5,418,062 9,882,245 14,346,429 1.04% 1.90% 2.76% As % of 3.12% 0.02% 0.04% 0.06% 0.50% 0.00% 0.01% 0.01% 24.61% 0.26% 0.47% 0.68% GDP Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 71 Annex Table E.2 Summary of Health Impacts of SDS from the Aralkum—Lower Bound Total for District Nukus City Amudaryo Beruniy Karauzyak Kegeyli Kungrad Kanlykul Moynaq Nukus Takhiatash Tahtakupir Turtkul Khujayli Chimbay Shumanay Elikkala Karakalpakstan Total GDP in 2019 0.3559 0.2206 0.2120 0.0592 0.1011 0.1450 0.0569 0.0353 0.0555 0.0823 0.0451 0.2376 0.1369 0.1302 0.0629 0.1764 2.1129 (billion $) Mortality Due to Ambient Air Pollution (AAP) Caused by SDS from the Aralkum Cost High, $, billion 0.0003 0.0000 0.0000 0.0001 0.0002 0.0001 0.0001 0.0003 0.0001 0.0001 0.0000 0.0000 0.0002 0.0002 0.0001 0.0000 0.0015 Cost Low, $, billion 0.0001 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0005 Average Cost, $, billion 0.0002 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0002 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0010 Morbidity (Assumed to be 10% of Mortality Value) Cost High, $, billion 0.0000 0.0000 0.0000 5.282E-06 1.61E-05 1.034E-05 6.976E-06 2.53E-05 5.37E-06 7.4336E-06 4.5124E-06 0 1.609E-05 1.735E-05 7.125E-06 0 0.0001 Cost Low, $, billion 0.0000 0 0 1.652E-06 5.04E-06 3.235E-06 2.182E-06 7.92E-06 1.68E-06 2.325E-06 1.4113E-06 0 5.033E-06 5.427E-06 2.228E-06 0 0.0000 Average Cost, $, billion 0.0000 0 0.0000 3.467E-06 1.06E-05 6.788E-06 4.579E-06 1.66E-05 3.53E-06 4.8793E-06 2.9619E-06 0 1.056E-05 1.139E-05 4.676E-06 0 0.0001 Total Health Cost of AAP Due to SDS from the Aralkum Cost High, $, billion 0.0003 0.0000 0.0000 0.0001 0.0002 0.0001 0.0001 0.0003 0.0001 0.0001 0.0000 0.0000 0.0002 0.0002 0.0001 0.0000 0.0016 Cost Low, $, billion 0.0001 0.0000 0.0000 1.817E-05 0.0001 0.0000 0.0000 0.0001 1.85E-05 2.5576E-05 1.5525E-05 0.0000 0.0001 0.0001 0.0000 0.0000 0.0005 Average Cost, $, billion 0.0002 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 0.0002 0.0000 0.0001 0.0000 0.0000 0.0001 0.0001 0.0001 0.0000 0.0011 % GDP 0.055% 0.000% 0.000% 0.064% 0.115% 0.051% 0.089% 0.518% 0.070% 0.065% 0.072% 0.000% 0.085% 0.096% 0.082% 0.000% 0.051% Average Cost in $ 194,560 - - 38,135 116,303 74,672 50,366 182,815 38,794 53,673 32,580 - 116,179 125,285 51,441 - 1,074,803 Total Number of Deaths Due to SDS from the Aralkum Deaths IHD 0.11 0.0000 0.0000 0.2965 1.2117 0.3905 0.1174 1.2357 0.4344 0.2684 0.3858 0.0000 0.4158 0.9052 0.1178 0.0000 5.8875 Deaths stroke 0.86 0.0000 0.0000 0.1267 0.0000 0.1137 0.1078 0.0693 0.0316 0.2015 0.0000 0.0000 0.4290 0.3395 0.1623 0.0000 2.4460 Deaths COPD 0.01 0.0000 0.0000 0.0000 0.0000 0.1611 0.3481 0.1072 0.0093 0.0475 0.0000 0.0000 0.3736 0.2696 0.3064 0.0000 1.6325 Deaths LC 0.59 0.0000 0.0000 0.0108 0.1905 0.0070 0.0000 0.4287 0.0034 0.0071 0.0000 0.0000 0.0143 0.0000 0.0000 0.0000 1.2539 Deaths LRI 0.63 0.0000 0.0000 0.0034 0.0000 0.0069 0.0000 0.2494 0.0000 0.0237 0.0000 0.0000 0.0033 0.0000 0.0000 0.0000 0.9160 Deaths Diabetes 2 0.20 0.0000 0.0000 0.0330 0.0329 0.2421 0.0482 0.1654 0.0000 0.1140 0.0161 0.0000 0.1975 0.0315 0.0482 0.0000 1.1256 Total 2.40 0.0000 0.0000 0.4705 1.4350 0.9213 0.6214 2.2557 0.4787 0.6622 0.4020 0.0000 1.4335 1.5458 0.6347 0.0000 13.2615 Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 72 Annex Table E.3 Summary of Health Impacts of SDS from the Aralkum—Average Total for District Nukus City Amudaryo Beruniy Karauzyak Kegeyli Kungrad Kanlykul Moynaq Nukus Takhiatash Tahtakupir Turtkul Khujayli Chimbay Shumanay Elikkala Karakalpakstan Total GDP in 2019 0.3559 0.2206 0.2120 0.0592 0.1011 0.1450 0.0569 0.0353 0.0555 0.0823 0.0451 0.2376 0.1369 0.1302 0.0629 0.1764 2.1129 (billion $) Mortality Due to Ambient Air Pollution (AAP) Caused by SDS from the Aralkum Cost High, $, billion 0.0005 0.0000 0.0000 0.0001 0.0003 0.0002 0.0001 0.0003 0.0001 0.0001 0.0001 0.0000 0.0003 0.0003 0.0001 0.0000 0.0024 Cost Low, $, billion 0.0002 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0007 Average Cost, $, billion 0.0004 0.0000 0.0000 0.0001 0.0002 0.0001 0.0001 0.0002 0.0001 0.0001 0.0000 0.0000 0.0002 0.0002 0.0001 0.0000 0.0016 Morbidity (Assumed to be 10% of Mortality Value) Cost High, $, billion 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 Cost Low, $, billion 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 Average Cost, $, billion 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 Total Health Cost of AAP Due to SDS from the Aralkum Cost High, $, billion 0.0006 0.0000 0.0000 0.0001 0.0003 0.0002 0.0001 0.0003 0.0001 0.0001 0.0001 0.0000 0.0003 0.0003 0.0001 0.0000 0.0026 Cost Low, $, billion 0.0002 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0008 Average Cost, $, billion 0.0004 0.0000 0.0000 0.0001 0.0002 0.0001 0.0001 0.0002 0.0001 0.0001 0.0000 0.0000 0.0002 0.0002 0.0001 0.0000 0.0017 % GDP 0.109% 0.000% 0.000% 0.099% 0.184% 0.085% 0.149% 0.608% 0.104% 0.105% 0.108% 0.000% 0.138% 0.149% 0.135% 0.000% 0.081% Average Cost in $ 1,646,787 749,569 1,298,989 145,219 516,790 266,228 221,204 246,576 150,462 207,285 120,557 474,133 456,561 498,820 213,809 646,808 1,714,850 Total Number of Deaths Due to SDS from the Aralkum Deaths IHD 0.16 0.00 0.00 0.44 1.79 0.58 0.17 1.37 0.64 0.40 0.57 0.00 0.62 1.34 0.17 0.00 8.26 Deaths stroke 1.28 0.00 0.00 0.19 0.00 0.17 0.16 0.08 0.05 0.30 0.00 0.00 0.64 0.51 0.24 0.00 3.62 Deaths COPD 0.02 0.00 0.00 0.00 0.00 0.29 0.62 0.13 0.02 0.08 0.00 0.00 0.67 0.48 0.55 0.00 2.85 Deaths LC 1.38 0.00 0.00 0.03 0.44 0.02 0.00 0.57 0.01 0.02 0.00 0.00 0.03 0.00 0.00 0.00 2.49 Deaths LRI 1.57 0.00 0.00 0.01 0.00 0.02 0.00 0.33 0.00 0.06 0.00 0.00 0.01 0.00 0.00 0.00 2.00 Deaths Diabetes 2 0.36 0.00 0.00 0.06 0.06 0.45 0.09 0.17 0.00 0.21 0.03 0.00 0.36 0.06 0.09 0.00 1.94 Total 4.77 0.00 0.00 0.72 2.30 1.52 1.04 2.65 0.72 1.07 0.60 0.00 2.33 2.39 1.05 0.00 21.16 Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 73 Annex Table E.4 Summary of Health Impacts of SDS from the Aralkum—Upper Bound Total for District Nukus City Amudaryo Beruniy Karauzyak Kegeyli Kungrad Kanlykul Moynaq Nukus Takhiatash Tahtakupir Turtkul Khujayli Chimbay Shumanay Elikkala Karakalpakstan Total GDP in 2019 0.3559 0.2206 0.2120 0.0592 0.1011 0.1450 0.0569 0.0353 0.0555 0.0823 0.0451 0.2376 0.1369 0.1302 0.0629 0.1764 2.1129 (billion $) Mortality Due to Ambient Air Pollution (AAP) Caused by SDS from the Aralkum Cost High, $, billion 0.0008 0.0000 0.0000 0.0001 0.0004 0.0002 0.0002 0.0003 0.0001 0.0002 0.0001 0.0000 0.0004 0.0004 0.0002 0.0000 0.0033 Cost Low, $, billion 0.0003 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 0.0001 0.0000 0.0001 0.0000 0.0000 0.0001 0.0001 0.0001 0.0000 0.0010 Average Cost, $, billion 0.0005 0.0000 0.0000 0.0001 0.0002 0.0002 0.0001 0.0002 0.0001 0.0001 0.0001 0.0000 0.0002 0.0002 0.0001 0.0000 0.0021 Morbidity (Assumed to be 10% of Mortality Value) Cost High, $, billion 0.0001 0.0000 0.0000 1.09682E-05 3.5441E-05 2.36864E-05 1.6477E-05 3.4151E-05 1.0685E-05 1.6588E-05 8.98895E-06 0 3.6237E-05 3.6259E-05 1.64595E-05 0 0.0003 Cost Low, $, billion 0.0000 0 0 3.43056E-06 1.1085E-05 7.40849E-06 5.1537E-06 1.0681E-05 3.3421E-06 5.18829E-06 2.81151E-06 0 1.1334E-05 1.1341E-05 5.14809E-06 0 0.0001 Average Cost, $, billion 0.0001 0 0.0000 7.19937E-06 2.3263E-05 1.55475E-05 1.0816E-05 2.2416E-05 7.0136E-06 1.08881E-05 5.90023E-06 0 2.3785E-05 2.38E-05 1.08038E-05 0 0.0002 Total Health Cost of AAP Due to SDS from the Aralkum Cost High, $, billion 0.0009 0.0000 0.0000 0.0001 0.0004 0.0003 0.0002 0.0004 0.0001 0.0002 0.0001 0.0000 0.0004 0.0004 0.0002 0.0000 0.0036 Cost Low, $, billion 0.0003 0.0000 0.0000 3.77362E-05 0.0001 0.0001 0.0001 0.0001 3.6763E-05 5.70712E-05 3.09266E-05 0.0000 0.0001 0.0001 0.0001 0.0000 0.0011 Average Cost, $, billion 0.0006 0.0000 0.0000 0.0001 0.0003 0.0002 0.0001 0.0002 0.0001 0.0001 0.0001 0.0000 0.0003 0.0003 0.0001 0.0000 0.0024 % GDP 0.163% 0.000% 0.000% 0.134% 0.253% 0.118% 0.209% 0.698% 0.139% 0.146% 0.144% 0.000% 0.191% 0.201% 0.189% 0.000% 0.111% Average Cost in $ 579,143 - - 79,193 255,894 171,022 118,971 246,576 77,150 119,770 64,903 - 261,637 261,796 118,842 - 2,354,896 Total Number of Deaths Due to SDS from the Aralkum Deaths IHD 0.21 0.0000 0.0000 0.5808 2.3744 0.7663 0.2305 1.5011 0.8529 0.5266 0.7574 0.0000 0.8152 1.7789 0.2318 0.0000 10.6287 Deaths stroke 1.71 0.0000 0.0000 0.2543 0.0000 0.2255 0.2141 0.0864 0.0627 0.4018 0.0000 0.0000 0.8570 0.6744 0.3183 0.0000 4.7997 Deaths COPD 0.02 0.0000 0.0000 0.0000 0.0000 0.4136 0.8936 0.1471 0.0238 0.1220 0.0000 0.0000 0.9590 0.6920 0.7865 0.0000 4.0623 Deaths LC 2.16 0.0000 0.0000 0.0395 0.6945 0.0256 0.0000 0.7131 0.0125 0.0258 0.0000 0.0000 0.0523 0.0000 0.0000 0.0000 3.7219 Deaths LRI 2.52 0.0000 0.0000 0.0136 0.0000 0.0276 0.0000 0.4153 0.0000 0.0945 0.0000 0.0000 0.0132 0.0000 0.0000 0.0000 3.0794 Deaths Diabetes 2 0.53 0.0000 0.0000 0.0889 0.0885 0.6516 0.1297 0.1793 0.0000 0.3069 0.0435 0.0000 0.5316 0.0849 0.1297 0.0000 2.7640 Total 7.15 0.0000 0.0000 0.9771 3.1574 2.1102 1.4679 3.0424 0.9519 1.4778 0.8008 0.0000 3.2282 3.2302 1.4663 0.0000 29.0559 Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 74 Annex F: Total Impacts Annex Table F.1 Annual Values of On-Site and Off-Site Ecosystem Services Under Different Rehabilitation Scenarios—Lower Bound Annual Total Benefits of On-Site and Off-Site Ecosystem Services Annual Losses ($) Compared to the Base Case (Scenario 1) Annual BCR On-Site Off-Site Average NPV (assuming a Value Gained Average PV As % of Annual Cost Ecosystem from Action of Action Karakalpakst of Action ($, of Action 20-year Scenarios Services Crop Yields Health Total ($) ($/year) ($/year/ha) an GDP million) ($/year/ha) period) Min 24,384,066 5,418,062 1,074,803 30,876,931 Scenario 1 (Current Scenario): Avg 24,384,066 9,882,245 1,714,850 35,981,161 90% Bare (-) and 10% Bare (+) Max 24,384,066 14,346,429 2,354,896 41,085,391 Min 17,059,695 2,977,691 724,218 20,761,604 10,115,327 7.65 0.48% 23,104,132 (9.82) 0.44 Scenario 2: 50% Shrub (-) and Avg 17,059,695 5,085,176 995,959 23,140,830 12,840,332 8.42 0.61% 23,104,132 (9.06) 0.56 50% Shrub (+) Max 17,059,695 7,192,661 1,267,700 25,520,055 15,565,336 11.77 0.74% 23,104,132 (5.70) 0.67 Min 9,710,417 2,941,456 724,218 13,376,091 17,500,839 13.24 0.83% 23,104,132 (4.24) 0.76 Scenario 3: 50% Tree (-) Avg 9,710,417 5,014,055 995,959 15,720,431 20,260,730 14.03 0.96% 23,104,132 (3.45) 0.88 and 50% Tree (+) Max 9,710,417 7,086,654 1,267,700 18,064,771 23,020,620 17.41 1.09% 23,104,132 (0.06) 1.00 Min - 2,606,582 159,166 2,765,747 28,111,184 21.26 1.33% 23,104,132 3.79 1.22 Scenario 4: (best possible scenario): Avg - 4,356,036 713,433 5,069,468 30,911,693 22.08 1.46% 23,104,132 4.61 1.34 100% Tree (+) Max - 6,105,490 1,267,700 7,373,190 33,712,202 25.50 1.60% 23,104,132 8.02 1.46 Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 75 Annex Table F.2 Annual Values of On-Site and Off-Site Ecosystem Services Under Different Rehabilitation Scenarios—Upper Bound Annual Total Benefits of On-Site and Off-Site Ecosystem Services Annual Losses ($) Compared to the Base Case (Scenario 1) Annual BCR On-Site Off-Site Average NPV (assuming a Value Gained Average PV As % of Annual Cost Ecosystem from Action of Action Karakalpakst of Action ($, of Action 20-year Scenarios Services Crop Yields Health Total ($) ($/year) ($/year/ha) an GDP million) ($/year/ha) period) Min 34,968,151 5,418,062 1,074,803 41,461,016 Scenario 1 (Current Scenario): Avg 34,968,151 9,882,245 1,714,850 46,565,246 90% Bare (-) and 10% Bare (+) Max 34,968,151 14,346,429 2,354,896 51,669,476 Min 24,646,463 2,977,691 724,218 28,348,372 13,112,643 9.92 0.62% 24,300,409 (8.46) 0.54 Scenario 2: 50% Shrub (-) and Avg 24,646,463 5,085,176 995,959 30,727,598 15,837,648 10.68 0.75% 24,300,409 (7.70) 0.65 50% Shrub (+) Max 24,646,463 7,192,661 1,267,700 33,106,824 18,562,652 14.04 0.88% 24,300,409 (4.34) 0.76 Min 13,562,834 2,941,456 724,218 17,228,508 24,232,507 18.33 1.15% 24,300,409 (0.05) 1.00 Scenario 3: 50% Tree (-) Avg 13,562,834 5,014,055 995,959 19,572,848 26,992,397 19.12 1.28% 24,300,409 0.74 1.11 and 50% Tree (+) Max 13,562,834 7,086,654 1,267,700 21,917,188 29,752,288 22.50 1.41% 24,300,409 4.12 1.22 Min - 2,606,582 159,166 2,765,747 38,695,268 29.27 1.83% 24,300,409 10.89 1.59 Scenario 4: (best possible scenario): Avg - 4,356,036 713,433 5,069,468 41,495,777 30.09 1.96% 24,300,409 11.71 1.71 100% Tree (+) Max - 6,105,490 1,267,700 7,373,190 44,296,287 33.50 2.10% 24,300,409 15.12 1.82 Source: Authors’ estimates. The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 76 Annex G: Off-Site SDS Impact Assessment Using Regional Atmospheric Modeling System (RAMS): A Single Event Case Study Activity/Methodology Off-site SDS impact assessment was pursued through atmospheric modeling using RAMS (Cotton et al., 2003; Pielke et al., 1992) to simulate a selected SDS event observed on March 23, 2020 across the Aralkum (Annex Map G.1; left). RAMS generated the atmospheric wind trajectories and the consequential pick-up and suspension of surface sediments originating from the dry Aral Seabed and its surrounding areas. The single event analysis (Annex Map G.1) represents the SDS event observed through remote sensing and reveals: i) a reasonable match with the observation, and ii) a significant effect of hypothetical vegetation cover mimicking e.g., Scenario 3.2 (Annex Map G.1; right) tree- and shrub-based rehabilitation. The single- event atmospheric simulation was pursued in a parallel attempt to the simplistic radial dispersion modeling undertaken – and was used for visual verification of dust dispersion processes and area of impact estimation. Coupling the high-resolution on-site wind erosion model with the atmospheric trajectory modeling eventually allows a more detailed assessment of SDS fluxes and their potential impacts per specific context. Annex Map G.1 RAMS Simulation of Dust Concentration and Wind Field at 40 m Height During March 23, 2020 SDS Event Source: NASA MODIS (left); Institute of Accelerating Systems and Applications (IASA) affiliated with the Technical University of Athens, under Prof. George Kallos and his students, co-advised by Utrecht University and ICARDA (middle and right). Note: Remote sensing image of the SDS event on March 23, 2020 (left). Degraded/actual scenario of dry Aral Seabed (middle). Rehabilitated scenario with 50% shrub and vegetation cover (comparable to Scenario 3.2 Tree (+) tree- and shrub-based established ecosystem) (right). The Value of Landscape Restoration in Uzbekistan to Reduce Sand and Dust Storms from the Aral Seabed 77 www.worldbank.org