Climate Risk and Vulnerability Assessment Report

Irrigation Rehabilitation Project (RRP KAZ 50387)

Supplementary Document 21:

CLIMATE RISK AND VULNERABILITY ASSESSMENT REPORT

Kazakhstan Irrigation Rehabilitation Sector Project: Climate Risk and Vulnerability Assessment Report

December 2018

Authors: Peter Droogers Murari Lal

Client: Asian Development Bank

FutureWater Wageningen, The Netherlands www.futurewater.nl and RMSI Pvt. Ltd. A-8, Sector 18 Noida, India 201301 www.rmsi.com TABLE OF CONTENTS

LIST OF ACRONYMS AND ABBREVIATIONS I EXECUTIVE SUMMARY II I. PROJECT BACKGROUND AND AREA 1

A. BACKGROUND 1 B. KAZAKHSTAN CLIMATE CHANGE ISSUES, POLICIES AND INVESTMENTS 3 II. CLIMATE CHANGE PROJECTIONS AND EXTREMES 4

A. KAZAKHSTAN’S MEAN CLIMATE & SEASONAL VARIABILITY 4 1. General 4 2. Inter-annual Climate Variability in Kazakhstan and project provinces 7 3. Hydro-meteorological Disasters in Kazakhstan 13 B. SIMULATION OF FUTURE CLIMATE OF KAZAKHSTAN & PROJECT PROVINCES 14 1. Future Climate for Kazakhstan and project provinces 14 C. UNCERTAINTIES IN FUTURE PROJECTIONS 39 D. SUMMARY - KEY FINDINGS ON CLIMATE CHANGE 40 III. IMPACT OF CLIMATE CHANGE AND EXTREMES 44

A. ASSESSMENT TOOL WEAP 44 1. Background WEAP 44 2. WEAP Input Data 45 B. SCENARIOS ANALYZED 50 C. IMPACT AT PROVINCE LEVEL 50 1. Overall 50 2. East Kazakhstan 54 3. Karaghandy 56 4. Kyzlorda 58 5. Zhambyl 61 D. IMPACT AT SUB-PROJECT LEVEL 63 1. Methodology 63 2. East Kazakhstan subprojects 67 3. Karaghandy subprojects 70 4. Kyzlorda subprojects 74 5. Zhambyl subprojects 78 IV. ADAPTION AND INVESTMENTS TO CLIMATE CHANGE 83

A. ADAPTATION NEEDS AND BASIC PRINCIPLES 83 B. POTENTIAL ADAPTATION AND INVESTMENT MEASURES 84 C. SPECIFIC ADAPTATIONS AND INVESTMENT ACTIONS 88 D. CONCLUDING REMARKS 90 V. CONCLUSIONS AND RECOMMENDATIONS 92 VI. APPENDIX: PROJECTED CHANGES IN CLIMATE EXTREMES AS INFERRED FROM SELECTED CORE CLIMATIC INDICATORS 94

A. CLIMATIC INDICATORS 94 B. CHANGE IN NUMBER OF HOT DAYS 94 C. CHANGES IN NUMBER OF FROST DAYS 98 D. ANNUAL MEAN CHANGES IN NUMBER OF DRY DAYS 100 E. CHANGES IN NUMBER OF WET / VERY WEY DAYS 101

F. CHANGES IN MAXIMUM RAINFALL INTENSITY 103 VII. APPENDIX: INTRODUCTION OF GLOBAL CLIMATE MODELS USED IN THIS CVRA STUDY 109

A. MODEL GENERATED CLIMATE DATA 109 1. General 109 2. Bias Corrections to the Model Generated Datasets 110 3. The Earth System Model Datasets 111 A.2 DATA ANALYSIS TOOLS 112 4. Data and Choice of Metrics 113 5. Validation of Global Climate Models to Baseline Observed (CRU, UK) Climatology 114

TABLES

TABLE II-1: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER KAZAKHSTAN AT THREE TIME SLICES UNDER RCP 8.5 AND RCP4.5 PATHWAYS 15 TABLE II-2: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER THE FOUR PROJECT PROVINCES OF KAZAKHSTAN DURING 2020S FOR RCP 8.5 PATHWAY 22 TABLE II-3: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER THE FOUR PROJECT PROVINCES OF KAZAKHSTAN DURING 2050S FOR RCP 8.5 PATHWAY 23 TABLE II-4: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER THE FOUR PROJECT PROVINCES OF KAZAKHSTAN DURING 2080S FOR RCP 8.5 PATHWAY 24 TABLE II-5: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER THE FOUR PROJECT PROVINCES OF KAZAKHSTAN DURING 2020S FOR RCP 4.5 PATHWAY 25 TABLE II-6: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER THE FOUR PROJECT PROVINCES OF KAZAKHSTAN DURING 2050S FOR RCP 4.5 PATHWAY 25 TABLE II-7: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED WARMING AREA AVERAGED OVER THE FOUR PROJECT PROVINCES OF KAZAKHSTAN DURING 2080S FOR RCP 4.5 PATHWAY 26 TABLE II-8: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED DIURNAL TEMPERATURE RANGE AREA AVERAGED OVER KAZAKHSTAN DURING THE THREE TIME SLICES FOR RCP 8.5 AND 4.5 PATHWAYS 27 TABLE II-9: MONTHLY, SEASONAL AND ANNUAL MEANS OF PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER KAZAKHSTAN AT THREE TIME SLICES FOR RCP 8.5 AND RCP 4.5 PATHWAYS 28 TABLE II-10: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER PROJECT PROVINCES DURING 2020S UNDER RCP 8.5 PATHWAY 35 TABLE II-11: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER PROJECT PROVINCES DURING 2050S UNDER RCP 8.5 PATHWAY 36 TABLE II-12: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER PROJECT PROVINCES DURING 2080S UNDER RCP 8.5 PATHWAY 37 TABLE II-13: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER PROJECT PROVINCES DURING 2020S UNDER RCP 4.5 PATHWAY 38 TABLE II-14: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER PROJECT PROVINCES DURING 2050S UNDER RCP 4.5 PATHWAY 38 TABLE II-15: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN AREA AVERAGED PRECIPITATION (ΔP, PERCENT CHANGE) OVER PROJECT PROVINCES DURING 2080S UNDER RCP 4.5 PATHWAY 39 TABLE III-1: SUMMARIZED IMPACT OF CLIMATE CHANGE ON EAST KAZAKHSTAN PROVINCE 56 TABLE III-2: SUMMARIZED IMPACT OF CLIMATE CHANGE ON KARAGHANDY PROVINCE 58 TABLE III-3: SUMMARIZED IMPACT OF CLIMATE CHANGE ON KYZLORDA PROVINCE 61 TABLE III-4: SUMMARIZED IMPACT OF CLIMATE CHANGE ON ZHAMBYL PROVINCE 63 TABLE III-5: AVERAGE EXPECTED DATE OF PEAK RUNOFF UNDER CLIMATE CHANGE AND CHANGES IN ANNUAL AVERAGE RUNOFF COMPARED TO THE REFERENCE SITUATION (1961-1990). RESULT OBTAINED USING THE HYDRO-CLIMATE MODEL WEAP 68 TABLE III-6: AVERAGE EXPECTED DATE OF PEAK RUNOFF UNDER CLIMATE CHANGE AND CHANGES IN ANNUAL AVERAGE RUNOFF COMPARED TO THE REFERENCE SITUATION (1961-1990) [RESULT OBTAINED USING THE HYDRO-CLIMATE MODEL WEAP] 71 TABLE III-7: AVERAGE EXPECTED DATE OF PEAK RUNOFF UNDER CLIMATE CHANGE AND CHANGES IN ANNUAL AVERAGE RUNOFF COMPARED TO THE REFERENCE SITUATION (1961-1990) [RESULT OBTAINED USING THE HYDRO-CLIMATE MODEL WEAP] 75 TABLE III-8: AVERAGE EXPECTED DATE OF PEAK RUNOFF UNDER CLIMATE CHANGE AND CHANGES IN ANNUAL AVERAGE RUNOFF COMPARED TO THE REFERENCE SITUATION (1961-1990) [RESULT OBTAINED USING THE HYDRO-CLIMATE MODEL WEAP] 79 TABLE IV-1: DIFFERENCES BETWEEN MITIGATION AND ADAPTATION AS FUNCTION OF THE SPATIAL AND TEMPORAL SCALE AND THE MAIN SECTORS INVOLVED [ADAPTED FROM HTTP://WWW.CIFOR.ORG/] 84 TABLE VI-1: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED NUMBER OF HOT DAYS OVER KAZAKHSTAN AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 95 TABLE VI-2: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN NUMBER OF FROST DAYS OVER KAZAKHSTAN AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 97 TABLE VI-3: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN NUMBER OF FROST DAYS OVER KAZAKHSTAN AND THE FOUR PROJECT PROVINCES AT 2080S UNDER RCP 8.5 PATHWAY 98

TABLE VI-4: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN PEAK PRECIPITATION INTENSITY OVER KAZAKHSTAN AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 104 TABLE VI-5: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN PEAK PRECIPITATION INTENSITY OVER EAST KAZAKHSTAN PROVINCE AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 105 TABLE VI-6: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN PEAK PRECIPITATION INTENSITY OVER KARAGHANDY PROVINCE AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 105 TABLE VI-7: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN PEAK PRECIPITATION INTENSITY OVER KYZLORDA PROVINCE AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 106 TABLE VI-8: MONTHLY, SEASONAL AND ANNUAL MEAN PROJECTED CHANGE IN PEAK PRECIPITATION INTENSITY OVER ZHAMBYL PROVINCE AT 2020S, 2050S AND 2080S UNDER RCP 8.5 AND RCP 4.5 PATHWAYS 106 TABLE VII-1: LIST OF CORE CLIMATE INDICATORS 113 TABLE VII-2: BASELINE CLIMATOLOGY OF AVERAGE MONTHLY MEAN MAXIMUM TEMPERATURE (DEG C) OVER KAZAKHSTAN AS OBSERVED (CRU CLIMATOLOGY) AND SIMULATED BY CLIMATE MODEL ENSEMBLE (RMSE IS THE ROOT MEAN SQUARE ERROR BETWEEN THE OBSERVED AND SIMULATED SURFACE AIR TEMPERATURES) 117 TABLE VII-3: BASELINE CLIMATOLOGY OF AVERAGE MONTHLY MEAN MINIMUM TEMPERATURE (DEG C) OVER KAZAKHSTAN AS OBSERVED (CRU CLIMATOLOGY) AND SIMULATED BY MODEL ENSEMBLE (RMSE IS THE ROOT MEAN SQUARE ERROR BETWEEN THE OBSERVED AND SIMULATED MINIMUM SURFACE AIR TEMPERATURES) 118 TABLE VII-4: BASELINE CLIMATOLOGY OF AVERAGE MONTHLY PRECIPITATION (MM) OVER KAZAKHSTAN AS OBSERVED (CRU CLIMATOLOGY) AND SIMULATED BY CLIMATE MODEL ENSEMBLES (RMSE IS THE ROOT MEAN SQUARE ERROR BETWEEN THE OBSERVED AND SIMULATED PRECIPITATION) 119

FIGURES

FIGURE I-1: MONTHLY MEAN TEMPERATURES OVER KAZAKHSTAN IN 2015 ARE WARMER RELATIVE TO 1961-1990 MEAN CLIMATOLOGY (SOURCE: RSE “KAZHYDROMET”, 2016) 2 FIGURE II-1: GEOGRAPHICAL LOCATION OF KAZAKHSTAN AND ITS FOUR PROJECT PROVINCES WITH ELEVATIONS ABOVE SEA LEVEL 6 FIGURE II-2: THE 16 GROUPS OF THE IN 245 SCHEMES WHERE THE PROJECT WILL TAKE PLACE 7 FIGURE II-3: AVERAGE ANNUAL DISTRIBUTION (%) OF REPORTED DISASTERS IN KAZAKHSTAN (SOURCE: GFDRR, 2016) 13 FIGURE II-4: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL MEAN SURFACE AIR TEMPERATURE WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2020S UNDER RCP 8.5 PATHWAY 16 FIGURE II-5: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL MEAN SURFACE AIR TEMPERATURE WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2050S UNDER RCP 8.5 PATHWAY 17 FIGURE II-6: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL MEAN SURFACE AIR TEMPERATURE WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2080S UNDER RCP 8.5 PATHWAY 18 FIGURE II-7: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL MEAN SURFACE AIR TEMPERATURE WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2020S UNDER RCP 4.5 PATHWAY 19 FIGURE II-8: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL MEAN SURFACE AIR TEMPERATURE WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2050S UNDER RCP 4.5 PATHWAY 20 FIGURE II-9: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL MEAN SURFACE AIR TEMPERATURE WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2080S UNDER RCP 4.5 PATHWAY 21 FIGURE II-10: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL PRECIPITATION (PERCENT) WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2020S UNDER RCP 8.5 PATHWAY 29 FIGURE II-11: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL PRECIPITATION (PERCENT) WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2050S UNDER RCP 8.5 PATHWAY 30 FIGURE II-12: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL PRECIPITATION (PERCENT) WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2080S UNDER RCP 8.5 PATHWAY 31 FIGURE II-13: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL PRECIPITATION (PERCENT) WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2020S UNDER RCP 4.5 PATHWAY 32 FIGURE II-14: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL PRECIPITATION (PERCENT) WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2050S UNDER RCP 4.5 PATHWAY 33 FIGURE II-15: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN ANNUAL PRECIPITATION (PERCENT) WITH RESPECT TO BASELINE PERIOD (1961-90) OVER KAZAKHSTAN BY 2080S UNDER RCP 4.5 PATHWAY 34 FIGURE III-1: RELATION BETWEEN SPATIAL SCALE AND PHYSICAL DETAIL IN WATER ALLOCATION TOOLS. THE GREEN ELLIPSES SHOW THE KEY STRENGTH OF SOME WELL-KNOWN MODELS 44 FIGURE III-2: DEVELOPMENT IN DATA AVAILABILITY TO SUPPORT WATER ALLOCATION TOOLS OVER THE LAST 20 YEARS 46 FIGURE III-3: SCHEMATIZATION OF ONE SUB-CATCHMENT. EACH DEMONSTRATION CATCHMENT HAS BEEN DIVIDED IN FIVE TO SIX OF THESE SUB-CATCHMENTS 47 FIGURE III-4: MOST RELEVANT INPUT FIELDS FOR THE RIVER NODES IN A SUB-CATCHMENT 48 FIGURE III-5: MOST RELEVANT INPUT FIELDS FOR THE CATCHMENT NODES IN A SUB-CATCHMENT 48 FIGURE III-6: MOST RELEVANT INPUT FIELDS FOR THE GROUNDWATER NODES IN A SUB-CATCHMENT 49 FIGURE III-7: MOST RELEVANT INPUT FIELDS FOR THE DEMAND NODES IN A SUB-CATCHMENT 49 FIGURE III-8: MAIN TRANS-BOUNDARY RIVERS AND FLOW DIRECTIONS 51 FIGURE III-9: DIGITAL ELEVATION MODEL AND MAIN STREAMS AND RIVERS. 51 FIGURE III-10: TOTAL RIVER FLOWS AND FORECAST FOR THE ENTIRE COUNTRY 52 FIGURE III-11: HUMAN INDUCED REDUCTIONS IN RIVER FLOWS. 52 FIGURE III-12: PROJECTED CHANGES IN ACTUAL EVAPO-TRANSPIRATION IN 2050. NEGATIVE VALUES (ORANGE) INDICATE LESS EVAPO- TRANSPIRATION IN THE FUTURE; POSITIVE VALUES (GREEN) INDICATE MORE EVAPO-TRANSPIRATION IN THE FUTURE [SOURCE: WATER2INVEST (HTTP://W2I.GEO.UU.NL/NODE/12)] 53 FIGURE III-13: PROJECTED CHANGES IN RUNOFF IN 2050. NEGATIVE VALUES (ORANGE) INDICATE LESS RUNOFF IN THE FUTURE; POSITIVE VALUES (GREEN) INDICATE MORE RUNOFF IN THE FUTURE [SOURCE: WATER2INVEST (HTTP://W2I.GEO.UU.NL/NODE/12)] 53 FIGURE III-14: PROJECTED MONTHLY WATER DEMAND FOR THE IRRIGATION SECTOR IN EAST KAZAKHSTAN FOR THE FOUR TIME HORIZONS, WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 54

FIGURE III-15: PROJECTED ANNUAL UNMET WATER DEMAND FOR THE IRRIGATION SECTOR IN EAST KAZAKHSTAN FOR THE FOUR TIME HORIZONS, WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 55 FIGURE III-16: MEAN MONTHLY PROJECTED INFLOW INTO EAST KAZAKHSTAN PROVINCE FOR THE FOUR TIME HORIZONS, WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT IMPLEMENTED 55 FIGURE III-17: MEAN MONTHLY PROJECTED RIVER OUTFLOW FROM THE EAST KAZAKHSTAN PROVINCE FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT IMPLEMENTED 56 FIGURE III-18: PROJECTED MONTHLY WATER DEMAND FOR THE IRRIGATION SECTOR IN KARAGHANDY FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 57 FIGURE III-19: PROJECTED ANNUAL UNMET WATER DEMAND FOR THE IRRIGATION SECTOR IN KARAGHANDY FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 58 FIGURE III-20: PROJECTED MONTHLY WATER DEMAND FOR THE IRRIGATION SECTOR IN KYZLORDA FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 59 FIGURE III-21: PROJECTED ANNUAL UNMET WATER DEMAND FOR THE IRRIGATION SECTOR IN KYZLORDA FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 60 FIGURE III-22: MEAN MONTHLY PROJECTED INFLOW INTO KYZLORDA PROVINCE FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT IMPLEMENTED 60 FIGURE III-23: MEAN MONTHLY PROJECTED OUTFLOW FROM KYZLORDA PROVINCE FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT IMPLEMENTED 60 FIGURE III-24: PROJECTED MONTHLY WATER DEMAND FOR THE IRRIGATION SECTOR IN ZHAMBYL FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 62 FIGURE III-25: PROJECTED ANNUAL UNMET WATER DEMAND FOR THE IRRIGATION SECTOR IN ZHAMBYL FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT 62 FIGURE III-26: MEAN MONTHLY PROJECTED OUTFLOW FROM ZHAMBYL PROVINCE FOR THE FOUR TIME HORIZONS, AND WITHOUT AND WITH THE IRRIGATION REHABILITATION (REHAB) PROJECT IMPLEMENTED 63 FIGURE III-27: EXAMPLE OF CHANGES IN IRRIGATED AREAS BASED ON LANDSAT SATELLITE DATA USING MAXIMUM NDVI VALUES OVER 5 YEARS. 65 FIGURE III-28: MAIN SOIL WATER BALANCE PROCESSES (TOP) AND TYPICAL DATA INPUT SCREEN OF WEAP 66 FIGURE III-29: CHANGES IN CROP WATER REQUIREMENTS (ET REFERENCE) FOR THE SUBPROJECTS IN EAST KAZAKHSTAN PROVINCE. BASED ON PENMAN-MONTEITH CALCULATIONS USING THE WEAP MODEL 68 FIGURE III-30: AVERAGE SLOPES IN EACH SUBPROJECT AREA AS INDICATOR FOR FLOOD RISK UNDER CLIMATE CHANGE 69 FIGURE III-31: AVERAGE FLOW ACCUMULATION FOR EACH SUBPROJECT AREA IN EAST KAZAKHSTAN PROVINCE INDICATING DEPENDENCY OF UPSTREAM WATER RESOURCES IN TERMS OF CLIMATE IMPACT AND VULNERABILITY 69 FIGURE III-32: CHANGES IN PROJECTED MAXIMUM SNOW DEPTHS FOR THE SUBPROJECTS IN EAST KAZAKHSTAN PROVINCE. BASED ON HYDRO-CLIMATIC CALCULATIONS USING THE WEAP MODEL AND COMPARED TO REFERENCE CONDITIONS (1960-1990) 69 FIGURE III-33: RADAR PLOTS FOR EACH SUBPROJECT IN EAST KAZAKHSTAN PROVINCE INDICATING VULNERABILITY TO CLIMATE CHANGE FOR SIX TOPICS (A VALUE OF 1 MEANS LESS VULNERABLE, VALUE OF 5 INDICATES MORE VULNERABLE) 70 FIGURE III-34: CHANGES IN CROP WATER REQUIREMENTS (ET REFERENCE) FOR THE SUBPROJECTS IN KARAGHANDY PROVINCE (BASED ON PENMAN-MONTEITH CALCULATIONS USING THE WEAP MODEL) 71 FIGURE III-35: AVERAGE SLOPES IN EACH SUBPROJECT AREA AS INDICATOR FOR FLOOD RISK UNDER CLIMATE CHANGE 72 FIGURE III-36: AVERAGE FLOW ACCUMULATION FOR EACH SUBPROJECT AREA IN EAST KAZAKHSTAN PROVINCE INDICATING DEPENDENCY OF UPSTREAM WATER RESOURCES IN TERMS OF CLIMATE IMPACT AND VULNERABILITY 72 FIGURE III-37: CHANGES IN PROJECTED MAXIMUM SNOW DEPTHS FOR THE SUBPROJECTS IN KARAGHANDY PROVINCE [BASED ON HYDRO- CLIMATIC CALCULATIONS USING THE WEAP MODEL AND COMPARED TO REFERENCE CONDITIONS (1960-1990)] 73 FIGURE III-38: RADAR PLOTS FOR EACH SUBPROJECT IN KARAGHANDY PROVINCE INDICATING VULNERABILITY TO CLIMATE CHANGE FOR SIX TOPICS (A VALUE OF 1 MEANS LESS VULNERABLE, VALUE OF 5 INDICATES VERY VULNERABLE) 74 FIGURE III-39: CHANGES IN CROP WATER REQUIREMENTS (ET REFERENCE) FOR THE SUBPROJECTS IN KYZLORDA PROVINCE [BASED ON PENMAN-MONTEITH CALCULATIONS USING THE WEAP MODEL AND COMPARED TO REFERENCE CONDITIONS (1960-1990)] 75 FIGURE III-40: AVERAGE SLOPES IN EACH SUBPROJECT AREA AS INDICATOR FOR FLOOD RISK UNDER CLIMATE CHANGE 76 FIGURE III-41: AVERAGE FLOW ACCUMULATION FOR EACH SUBPROJECT AREA IN EAST KAZAKHSTAN PROVINCE INDICATING DEPENDENCY OF UPSTREAM WATER RESOURCES IN TERMS OF CLIMATE IMPACT AND VULNERABILITY 76 FIGURE III-42: CHANGES IN PROJECTED MAXIMUM SNOW DEPTHS FOR THE SUBPROJECTS IN KYZLORDA PROVINCE. BASED ON HYDRO- CLIMATIC CALCULATIONS USING THE WEAP MODEL AND COMPARED TO REFERENCE CONDITIONS (1960-1990) 77

FIGURE III-43: RADAR PLOTS FOR EACH SUBPROJECT IN KYZLORDA PROVINCE INDICATING VULNERABILITY TO CLIMATE CHANGE FOR SIX TOPICS (A VALUE OF 1 MEANS LESS VULNERABLE, VALUE OF 5 INDICATES VERY VULNERABLE) 78 FIGURE III-44: CHANGES IN CROP WATER REQUIREMENTS (ET REFERENCE) FOR THE SUBPROJECTS IN ZHAMBYL PROVINCE (BASED ON PENMAN-MONTEITH CALCULATIONS USING THE WEAP MODEL) 79 FIGURE III-45: AVERAGE SLOPES IN EACH SUBPROJECT AREA AS INDICATOR FOR FLOOD RISK UNDER CLIMATE CHANGE 80 FIGURE III-46: AVERAGE FLOW ACCUMULATION FOR EACH SUBPROJECT AREA IN EAST KAZAKHSTAN PROVINCE INDICATING DEPENDENCY OF UPSTREAM WATER RESOURCES IN TERMS OF CLIMATE IMPACT AND VULNERABILITY 80 FIGURE III-47: CHANGES IN PROJECTED MAXIMUM SNOW DEPTHS FOR THE SUBPROJECTS IN ZHAMBYL PROVINCE [BASED ON HYDRO- CLIMATIC CALCULATIONS USING THE WEAP MODEL AND COMPARED TO REFERENCE CONDITIONS (1960-1990)] 81 FIGURE III-48: RADAR PLOTS FOR EACH SUBPROJECT IN ZHAMBYL PROVINCE INDICATING VULNERABILITY TO CLIMATE CHANGE FOR SIX TOPICS (A VALUE OF 1 MEANS LESS VULNERABLE, VALUE OF 5 INDICATES VERY VULNERABLE) 81 FIGURE IV-1: SCHEMATIC OF CLIMATE CHANGE ISSUES AND THEIR LINKAGES [BASED ON HTTP://WWW.CIFOR.ORG/] 84 FIGURE VI-1: SPATIAL DISTRIBUTION OF PROJECTED NUMBER OF HOTTER DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 8.5 PATHWAY 96 FIGURE VI-2: SPATIAL DISTRIBUTION OF PROJECTED NUMBER OF HOTTER DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 4.5 PATHWAY 97 FIGURE VI-3: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN NUMBER OF FROST DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 8.5 PATHWAY 99 FIGURE VI-4: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN NUMBER OF FROST DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 4.5 PATHWAY 100 FIGURE VI-5: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN NUMBER OF DRY DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 8.5 PATHWAY 101 FIGURE VI-6: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN NUMBER OF WET DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 8.5 PATHWAY 102 FIGURE VI-7: SPATIAL DISTRIBUTION OF PROJECTED CHANGE IN NUMBER OF VERY WET DAYS OVER KAZAKHSTAN LIKELY BY 2080S UNDER RCP 8.5 PATHWAY 103 FIGURE VII-1: A COMPARISON OF MODEL SIMULATED AND OBSERVED CLIMATOLOGICAL (1961-1990) AVERAGE MONTHLY MEAN MAXIMUM (DAY TIME) AND MINIMUM (NIGHT TIME) SURFACE AIR TEMPERATURES AVERAGED OVER PROJECT PROVINCES IN KAZAKHSTAN 114 FIGURE VII-2: A COMPARISON OF MODEL SIMULATED AND OBSERVED CLIMATOLOGICAL (1961-1990) AVERAGE MONTHLY MEAN PRECIPITATION AVERAGED OVER PROJECT PROVINCES IN KAZAKHSTAN 116

LIST OF ACRONYMS AND ABBREVIATIONS

ADB Asian Development Bank AR5 Fifth Assessment Report of IPCC BCSD Bias-Corrected Spatial Disaggregation method CDF Cumulative Distribution Function CDI Climate Data Interface CDO Climate Data Operators Tool CMIP5 Coupled Model Inter-comparison project Phase 5 CRU Climate Research Unit CRVA Climate Risk and Vulnerability Assessment COP Conference of Parties CSA Climate Smart Agriculture DNA Designated National Authority DTR Diurnal Temperature Range ESM Earth System Model FAO-AQUAstat Food and Agriculture Organization - global information system on water and agriculture GCM Global Climate Model GEF Global Environmental Facility GFDRR Global Facility for Disaster Reduction and Recovery GHG Greenhouse gases GIS Geographical Information System INDC Intended Nationally Determined Contribution IPCC Intergovernmental Panel on Climate Change KAZHYDROMET National Hydro-meteorological Service of the Republic of Kazakhstan MDG Millennium Development Goals MEA Millennium Ecosystem Assessment NASA-NEX NASA Earth Exchange Global Daily Downscaled Datasets NC National Communication to UNFCCC NDCs Nationally Determined Contributions ODA Official Development Assistance RCPs Representative Concentration Pathways RSE Republican state-owned Enterprise SDGs Sustainable Development Goals SMEs Small and Medium Enterprises SRES Special Report on Emissions Scenarios TRTA TRansaction Technical Assistance UEA University of East Anglia, Norwich, UK UNCSD United Nations Commission on Sustainable Development UNDP United Nation Development Programme UNFCCC United Nations Framework Convention on Climate Change WEAP Water Evaluation and Planning Tool

EXECUTIVE SUMMARY

The Asian Development Bank (ADB) approved a transaction technical assistance (TRTA) for $1.1 million on 12 May 2017 to finance the feasibility studies (FSs) for rehabilitation and improvement of irrigation systems in four provinces of the Republic of Kazakhstan covering an area of about 171,106 ha. The four project provinces are East Kazakhstan, Karaghandy, Kyzlorda, and Zhambyl. The proposed 245 schemes are grouped into 16 subprojects. The funds for the FSs were provided as grant from ADB’s technical assistance special fund. Republican State Enterprise KazVodKhoz (KVK) is the Republic of Kazakhstan (RoK) counterpart agency responsible for supervision and administration of all project activities. KVK works under the Committee on Water Resources (CWR), which is under the Ministry of Agriculture (MoA).

The selection of the project area and estimation of the bills of quantities and costs for rehabilitation of the civil and associated works also requires consideration to climate change, water and land use patterns and intensities, crop yields, project benefit estimation, economic feasibility, land acquisition, farmers’ organizations, operation and maintenance (O&M), gender issues, and environmental aspects. In view of this, a comprehensive Climate Risk Assessment (CRA) has been undertaken for the 16 sub-projects within four provinces where the rehabilitation of irrigation facilities are to be implemented.

Observed climate over the past analysis show that the annual and seasonal mean observed surface air temperatures have a distinct warming trend over Kazakhstan between 1941 and 2016. Mean annual temperature has increased by 1.5°C since 1941 at an average rate of 0.3 °C per decade. Increase in annual mean temperatures is most pronounced in Kyzlorda province. The trend in annual mean surface air temperature in East Kazakhstan is the least of the four provinces considered here. The average number of ‘hot’ nights per year has increased between 1941 and 2016.

The annual mean precipitation over Kazakhstan had a discernible but only marginal increasing trend during 1941-2016. In general, in Kazakhstan, 2016 was a record year for the amount of precipitation. The annual amount of precipitation in 2016 on average 140% of normal or 441 mm. There was a trend of increasing annual amount of precipitation by 7 mm/10 years for the period 1976-2016. The precipitation in Kazakhstan and each of the four project provinces was characterized by high variability on inter‐annual and inter‐decadal time scales, which can make long‐term trends difficult to identify. In winter, an increase and in summer/autumn, a marginal decrease in seasonal total precipitation has been observed. Some unusually high intensity precipitation spells have occurred in the dry season in recent years (2000‐2016), but no consistent trend is discernible. Moreover, the length of dry spells has increased over Kazakhstan concurrently with observed increasing trends in precipitation intensity. projections for future climate were accessed using general climate models (GCMs). This assessment shows that the annual precipitation is expected to reduce between -5% (in East Kazakhstan province) to -15% (in Kyzlorda province). Reduction will be more pronounced during summer. Following the observed trends over recent decades, more precipitation is expected to fall as heavier precipitation events. Higher crop water requirements of about 8-10% are likely by 2020 and 15% by 2050 compared to the baseline period. Seasonal shift with earlier planting opportunities could be desirable and therefore irrigation demands earlier in the season are likely. Higher flooding risks are projected due to more frequent extremes in daily rainfall, especially in the more mountainous provinces. The retreat of glaciers in the mountains of southern Kazakhstan (Tien Shan – and surrounding area) as a consequence of rising air temperatures and rapidly- melting glaciers over the past few decades has also contributed to hydrometeorological hazards

iii

that affects the livelihood of large population in entire central Asia. About 75-80% of river runoff in the province comes from melting of glaciers and permafrost”, so their recession due to climate change threatens the water supply and thus the farming economy.

Impact of climate change combined with the effect of the rehabilitation project has been accessed using the impact model WEAP. Analyses focused on changes in water availability, water demand and water shortages. At province (oblast) level somewhat less water will become available as evapotranspiration of the natural vegetation will be higher (by increased temperatures) generating less runoff. A seasonal shift can be expected where peak flows will be 2-4 weeks earlier as a result of earlier snow melt. Water demand by the irrigated crop will also increase slightly by higher temperatures. In general, severe water shortage will not be expected, although unmet demand might happen in some year during the dry months.

At subproject level the impact of climate change has been evaluated and summarized by six indicators. Details per subproject are described in this report. The overall trend is that (i) crop water requirements will increase by about 15% in 2050; (ii) runoff from the landscape will reduce by around 10% for the subprojects in East-Kazakhstan and Zhambyl, 30% for Karaghandy and 35% for Kyzlorda subprojects, by 2050; (iii) total snow depths will increase substantially for all subprojects especially for the more southern located ones; (iv) risk on flooding will remain relatively low and might decrease even somewhat, although the subprojects in the more hilly provinces remain vulnerable; (v) subprojects relying on water resources from the bigger upstream rivers might will be more vulnerable for water shortage as those river flows will decrease; (vi) a substantial seasonal shift with earlier water availability and favorable temperatures for crops of 10-15 days can be expected; (vii) groundwater resources will reduce substantially by climate change for most subprojects and especially for the ones located in Karaghandy and East- Kazakhstan by lower rainfall and higher evaporation.

Adaptation and coping strategies to climate change are needed in the future and include pro- active adaptations and re-active responses. KazHydromet has reported for the agricultural sector some overall adaptation measures such as: plant-growing diversification, effective irrigation systems, improved equipment, extension services, and crop insurance. A more extensive overview of potential adaptation options is given in detail this report and is referred to as climate- smart agriculture. Climate-smart agriculture seeks to enhance productivity, water conservation, livelihoods, biodiversity, resilience to climate stress, and environmental quality. Finally adaptation should aim to enhance the climate resilience across all sectors and provinces through four major measures: (i) mainstreaming adaptation policies, plans, programs and projects into development planning; (ii) strengthening information systems and capabilities to facilitate the adaptation process; (iii) establishing the legal, regulatory, and institutional framework to support policy implementation, and (iv) promoting access to affordable financing for climate-resilient development.

I. PROJECT BACKGROUND AND AREA

A. Background

1. The Republic of Kazakhstan is by far the largest of the Central Asian states within the north-central Eurasia. With an area of about 2,724,900 square kilometers, Kazakhstan is the world's ninth biggest country by size, and it is more than twice the size of the other Central Asian states combined. It has borders with Russia, Peoples’ Republic of China, and the Central Asian countries of Kyrgyzstan, Uzbekistan, and Turkmenistan. Kazakhstan’s terrain is diverse, with the country situated in four climate zones: forest-steppe; steppe; semi-desert and desert. Kazakhstan has more deserts within its territory than any other Central Asian country, and approximately 66% of the national land is vulnerable to desertification in various degrees. Regulated runoff to rivers is one of the most critical factors for desertification. In all of the river basins, ground water levels have dropped and the land is drying.

2. The topography of the country is complex and diverse: about 10% highlands and the remaining comprising lowlands, plains, plateaus and heights. Widespread cutoff basins, deep depressions and dry hollows are typical of Kazakhstan’s relief. The vast size of Kazakhstan and distance from the ocean creates a sharp continental climate with a lack of precipitation. The foothill areas receive 500 to 1600 mm precipitation per year, 200 to 500 mm in the steppe and 100 to 200 mm in the desert. Four major landscapes are the forest-steppe, steppe, semi-desert, and desert. Kazakhstan’s 179.9 million hectares or 66% of the total area is prone to desertification1. Earthquakes are the dominant risk in Kazakhstan followed by floods, debris flow and landslides. Most provinces experience strong blizzard causing winds. Snow primarily falls in November and can continue until April. The frequent heavy blizzards disrupt transport and hinder work. Severe frosts of result in the re-planting of grains and crops. Degradation of fertile land has continued over decades, partly due to environmental reasons and the primitive technology for land cultivation2. Climatic conditions result in relatively low productivity – for examples with grains there is an average of 10 quintals per hectare a year.

3. During the years 1940-2016, Kazakhstan’s climate has been becoming warmer (see Figure I-1). The annual average temperature anomaly in 2016 was higher by 1.48 ºC relative to the 1961-1990 baseline period. With the exception of some local provinces, temperature increases were recorded in all seasons of the year. The average annual air temperature has increased by 0.34oC per decade3. The most rapid warming has taken place in winter – on average by 0.44oC/decade and 0.65oC/decade in the West, and in certain Northern and Central parts of the country. The studies also suggest that the main shifting characteristic is the increasing climate aridity in the areas of deserts and semi-deserts of Kazakhstan, as well as in adjacent areas to them4. The continued emissions of greenhouse gas (GHG) emissions in the atmosphere would cause increased weather volatility with the impacts increasing in intensity of the weather extremes

1 UNDP. 2009: Kazakhstan: Disaster Risk Reduction Review, Authored by Alexandr Kravchuk, Disaster Response Officer, July 2009, 12 pp. 2 UNCSD. 2005: Turning Political Commitments into Action, Statement by H.E. Mr. YERZHAN KH. KAZYKHANOV, Permanent Representative of the Republic of Kazakhstan to the United Nations, 13th Session of the United Nations Commission on Sustainable Development, New York, 21st April, 2005, 2 pp. 3 Kazhydromet, 2016: Ministry of Energy of the Republic of Kazakhstan Republican State Enterprise “Kazhydromet” Publication “ANNUAL BULLETIN OF CLIMATE CHANGE MONITORING IN KAZAKHSTAN: 2015”, Astana, Kaz., 51 pp. 4 Millennium Ecosystem Assessment (MEA), 2005: Ecosystems and Human Well-being: Desertification Synthesis. World Resources Institute, Washington, DC.

in coming years5. Anthropogenic warming of the climate system will increase the energy of the atmosphere, which will lead to more storminess and thus more extreme behavior. The negative impacts of climate change will disproportionally exacerbate problems such as the degradation of ecosystems, disasters, food security, increased disease outbreaks, and access to fresh water6.

4. While Kazakhstan has a rapidly growing economy, rural population, farmers and pastoralists outside of the main urban centers face significant climate change risks to their livelihoods stemming from increased aridity, water management challenges and extreme weather events. Degradation of glaciers has been recorded7. There were also an increasing number of forest fires detected between 2000 and 2006, wherein there were 6415 forest-fire cases recorded, resulting in 160,000 hectares of forest being burnt (UNDP, 2009). Climate change-induced effects in Kazakhstan are expected to have a far-reaching regional impact on fresh water resources. The agricultural, forest and water resource sectors are extremely vulnerable to climate change here. This is mainly associated with redistribution of precipitation and increasing severity and frequency of drought. The international experience of countries in addressing and adapting to climate change should be useful for Kazakhstan. It indicates a need to elaborate a National Strategy (Program) on climate change adaptation, an action plan and the creation of an institutional structure for the coordination of this process nationally. In addition to good development practice, incremental measures and actions will need to be considered to address specific risks and reduce vulnerability to the impacts of climate change.

Figure I-1: Monthly mean temperatures over Kazakhstan in 2015 are warmer relative to 1961-1990 mean climatology (Source: RSE “Kazhydromet”, 2016)

5 WMO (World Meteorological Organization). 2016: The Global Climate in 2011–2015, WMO-No. 1179, CH-1211 Geneva 2, Switzerland, 32 pp. 6 IPCC, 2013: Climate Change 2013 - The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. IPCC, Climate Change 2014: Impacts, Adaptation, and Vulnerability, Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2014) 7 Kotlyakov, V.M. and Severskiy, I. 2009: Glaciers of Central Asia: current situation, changes, and possible impact on water resources. In: Braun, L.N., Hagg, W., Severskiy, I.V., and Young, G. (eds). Assessment of Snow, Glacier and Water Resources in Asia. Koblenz: IHP/HWRP.

B. Kazakhstan Climate Change Issues, Policies and Investments

5. Kazakhstan is considered as a Non-Annex I party according to the UNFCCC (United Nations Framework Convention on Climate Change). Non-Annex I Parties are required to submit their first NC within three years of entering the Convention, and every four years thereafter. Kazakhstan submitted the Initial National Communication on 5-Nov-1998 and its Second National Communication on 4-Jun-2009.

6. In 2013 Kazakhstan published a report called to Third–Sixth National Communication which included the 3rd to the 6th communications to the UNFCCC for the period to 2012. In September 2015 Kazakhstan submitted its new climate action plan to the UNFCCC. This plan, referred to as Intended Nationally Determined Contribution (INDC), was prepared in the context of the UN climate conference in Paris in December 2015.

7. Kazakhstan has a number of key strategies, concepts, and related action plans that outline strategic directions for national climate change mitigation and adaptation actions. The government recently adopted the Kazakhstan 2050 Strategy, which includes a focus on the energy sector and a recognition that the country must develop alternative energy sources (notably solar and wind power) with the goal that by 2050 alternative and renewable energy sources must account for at least half of the country’s total energy consumption.

8. A draft National Concept on Adaptation to Climate Change was developed within the framework of the joint United Nations Development Programme and Ministry of Environment and Water Resources project “Strengthening the capacity in the field of sustainable development through integration of climate change issues into strategic planning in the Republic of Kazakhstan.” The objectives and tasks under this concept are to reduce the vulnerability of the population, economy, and natural resources to existing climate variability and forecasted climate change, and to reduce the most probable disaster risks that may lead to considerable humanitarian, economic, and environmental damages. In 2013, a draft State Program on Water Resource Management (2014-40) was also developed, with water security as a main goal.

II. CLIMATE CHANGE PROJECTIONS AND EXTREMES

A. Kazakhstan’s Mean Climate & Seasonal Variability

1. General

9. The climate of Kazakhstan (excluding south) is sharply continental with average surface air temperatures between -4°C and -19°C in January and between +19°C and +26°C in July. In winter temperature may decrease down to -45°C, and in summer rise up to +30°C. The highest temperature in Kazakhstan was registered in the city of Turkestan (South Kazakhstan) with +49°С. The lowest temperature was observed in the city of Atbasar (Akmola province) -57°С. The precipitation varies from less than 150 mm in the central desert areas to more than 1,500 mm in mountainous provinces. Average temperatures in January range from -18 °C in the north to -3°C in the south. The highest mountains on the border with Kyrgyzstan and China have an elevation at 6995m (7010m with ice cap) in Kazakhstan. The year-round run-off from snow-covered peaks is the source for most of Kazakhstan's rivers and streams. The rivers and streams are part of landlocked systems. They either flow into isolated bodies of water such as the Caspian Sea or simply disappear into the steppes and deserts of central and southern Kazakhstan. Many rivers, streams, and lakes are seasonal, evaporating in summer. The three largest bodies of water are Lake Balkhash, a partially fresh, partially saline lake in the east, near Almaty, and the Caspian and Aral Seas, both of which lie partially within Kazakhstan. Temperature increases in recent decades have caused the recession of glaciers in the mountains and changed mass water balances from positive to negative, threatening water supply and with it the farming economy and peace of the province, since many rivers are trans-boundary8 9. Even more dangerous, resulting glacial lakes dammed by unstable moraines drain in an uncontrolled way. The river network has had only limited development in spite of its importance for irrigation and power generation.

10. Kazakhstan is a semi-arid to arid country with a temperate climate, facing significant desiccation in the face of climate change, and significant exacerbation of baseline (non-climate) pressures. More than three-quarters of the country, including the entire west and most of the south, is either semi-desert (33.2%) or desert (44%). The terrain in these provinces is bare, eroded, broken uplands, or deserts with sand dunes, which occupy south-central Kazakhstan. The available agricultural land consists of 205,000 sq. km of arable land and 611,000 sq. km of land devoted to pastures and hay. Kazakhstan is the sixth-largest grain producer in the world. Wheat, barley, cotton, rice, and livestock, especially sheep-breeding are the most important agricultural commodities. Droughts are already a major problem in Kazakhstan affecting up to 66% of the country’s land and could become more prevalent with climate change10,11. The increasing aridity threatens to decrease the resilience of Kazakhstan’s ecosystems to land degradation pressures, constituting both a considerable threat to the natural environment, as well as to national development and poverty alleviation targets. Climate change impacts are expected to exacerbate existing land degradation pressures, by reducing ecosystem resilience to non- climate drivers of land degradation, while also contributing towards land degradation directly

8 UNDP, 2009: Kazakhstan: Disaster Risk Reduction Review, Authored by Alexandr Kravchuk, Disaster Response Officer, July 2009, 12 pp. 9 Usmanova, Z., Maria Shahgedanova, M., Severskiy, I., Nosenko, G. and Kapitsa, V, 2016: Assessment of Glacier Area Change in the Tekes River Basin, Central Tien Shan, Kazakhstan Between 1976 and 2013 Using Landsat and KH-9 Imagery, The Cryosphere Discuss. (EGU), doi:10.5194/tc-2016-82. 10 World Bank, 2009: Adapting to Climate Change in Eastern Europe and Central Asia, authored by Marianne Fay, Rachel Block, and Jane Ebinger (Washington, DC). 11 World Bank Group. 2015: World Bank. 2015. The World Bank Annual Report 2015. Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/22550, License: Creative Commons Attribution CC BY 3.0 IGO.

through desiccation and increased wind and water erosion. Land degradation already had significant adverse impact on rural communities, whose livelihoods are dependent on agriculture and livestock production. Climate change will increase the severity and geographic extent of land degradation pressures, threatening these livelihoods further12,13.

11. The Republic of Kazakhstan is a global leader in wheat production, but also has among the highest variation in annual yields of any major wheat-growing province globally due to widely varying climate, especially drought. Although rising temperatures in the coming decades could initially mean higher productivity, a projected shift in Spring/Summer precipitation suggests less available soil moisture during the critical growing season. Studies suggest that spring wheat yields may drop from current levels to as low as 63% by 2030, and 52% by 2050, unless adaptive measures are taken14. Cotton and rice production, while less dominant in the agriculture sector, do face productivity challenges now and will do so even more in the future. While expanded irrigation would be of benefit to mitigating shocks and shortages, overall water availability in many basins is projected to become even more problematic in the decades ahead.

12. The Republic of Kazakhstan has been divided into a total of 14 administrative provinces identified as provinces. This Climate Risk and Vulnerability Assessment (CRVA) report has been prepared under ADB project (as a sector loan) comprising multiple potential sub-projects across four provinces (oblasts) of Kazakhstan (Figure II-1). The core subprojects (Table 1; Figure 2-2) selected include: (i) Zharma subproject in East Kazakhstan province, (ii) Kyzlorda subproject in Kyzlorda province, (iii) Karaghandy subproject in Karaghandy province, and (iv) Talas subproject in Zhambyl province (the net not-irrigated project area is 174,101 ha). In total, the 245 schemes have been re-grouped into 16 subprojects. The CRVA will first assess the expected changes in the pattern and level of precipitation, groundwater, river flows, and flood risks for each project province. It will then undertake more detailed assessment and/or modeling at site level for the subprojects that will be developed during Transaction Technical Assistance (TRTA). These site- specific assessments will inform the sub-project designs, including (i) selection of target beneficiaries, (ii) off-take points and flood defenses, (iii) canal alignment and distribution options, (iv) alternative sources, and (v) command area development.

12 IPCC, 2007: Climate change: Impacts, adaptation and vulnerability, Contribution of WGII to IPCC Fourth Assessment Report, Parry et al. (eds), Cambridge University Press. 13 World Bank, 2012: World Development Indicators and Country Partnership Strategy for the Republic of Kazakhstan for the period FY12–FY17 (March 30, 2012) 14 World Bank Group. 2015: World Bank. 2015. The World Bank Annual Report 2015. Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/22550, License: Creative Commons Attribution CC BY 3.0 IGO.

Figure II-1: Geographical location of Kazakhstan and its four project provinces with elevations above sea level

Table 1-1: Summary of Final project Area identified for Rehabilitation province Number of Area to be schemes rehabilitated, ha East Kazakhstan 22 79,256 Karaghandy 10 27,900 Kyzlorda 11 28,974 Zhambyl 202 34,977 project Total 245 171,106 Source: KVK regional Office estimates

Figure II-2: The 16 groups of the in 245 schemes where the project will take place

2. Inter-annual Climate Variability in Kazakhstan and project provinces

13. An assessment of the historical and current climate variability in Kazakhstan and its four designated provinces has been attempted based on the data and analysis available in the Annual Bulletin of Climate Change published by Republican State Enterprise “Kazhydromet” of the Ministry of Energy of the Republic of Kazakhstan in Almaty in 2017. This bulletin provided the observed changes in air temperature and precipitation during the period from 1940 until 2016. Data of more than 190 weather stations were used to assess climatological normal for 30-year period from1961 to1990. The time series of daily maximum and minimum air temperatures and daily precipitation for more than 110 meteorological stations were also analyzed to assess trends and anomalies. The changes in intensity, frequency and duration of extreme temperatures and precipitations during the period from 1940-2015 are also inferred and reported in the said bulletin. The climatological normals for Kazakhstan and its four provinces were also derived by us from observed global monthly mean temperature and precipitation climatology available from Climate Research Unit (CRU) of the University of East Anglia (UEA), Norwich, UK. These monthly climatological normal of temperature and rainfall for Kazakhstan compared well with the climatological normal derived by Kazhydromet based on data from observing stations operated by them. The climatological dataset of the Climatic Research Unit (CRU) provides average monthly mean surface air temperatures and precipitation over Kazakhstan as depicted in Figure II-.

Figure II-4: Climatological (1961-90) average monthly mean temperatures and precipitation across Kazakhstan (based on CRU Climatology)

14. Kazakhstan has a high exposure to climate variability and extremes, which are expected to increase in frequency and intensity in the future. Figure II- depicts the observed inter-annual variability in annual and seasonal surface air temperatures over Kazakhstan. The trend analysis of mean annual temperature over land areas in Kazakhstan shows increase in annual mean temperatures between 1941 and 2016. In autumn, the highest rate of increase in mean air temperature over the country is in the range of 0.26 – 0.37 °C/decade. The average annual temperature in Kazakhstan has been rising by 0.28 ºC/decade. The rate of warming is more pronounced during spring / autumn seasons (@ 0.30 – 0.31 ºС/decade) than in the summer (@ 0.19ºC/decade) or winter (@ 0.28 ºC/decade) seasons. Moreover, this increase is observed in both daytime maximum and nighttime minimum temperatures with minimum temperatures warming more than the maximum temperatures with accelerated warming since the early 1990s.

2.2 Observed changes in air temperature in Kazakhstan Figures 2.10 - 2.11 show the time series and linear trends of the annual and seasonal air temperatures anomalies averaged over the territory of Kazakhstan and administrative regions for two periods: 1941-2016 and 1976-1966. Table 2.4 presents estimates of the change in air temperature for the period 1976-2016: the linear trend coefficient, which characterizes the average rate of change in the air temperature ano9mal y; and the coefficient of determination, which shows the contribution of the trend to the total variance. year ºС 2

-1

-2

-3

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 winter spring ºС ºС 4 5 3 3 2 1 1 -1 0 -1 -3 -2 -5 -3

-7 -4

8 0 4 8 2 6 0 4 8 2 6 0 4 2 6 0 4 8 2 6

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

8 4 4 4 5 5 6 6 6 7 7 8 8 9 9 0 0 0 1 1

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 summer autumn ºС ºС 3 3 2 2 1 1 0

0 -1 -2 -1 -3

-2 -4

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 8 2 6 0 4 8 2 6 0 4 8 6 0 4 8 2 6 0 2

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

4 4 5 5 6 6 6 7 7 8 8 8 9 0 0 0 1 1 4 9

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 9 9

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1

Figure II-F5:ig uTimere 2.10 series – Time and serie linears and trendslinear tr einnds the in annualthe annua andl and seasonal seasonal air air t etemperaturesmperatures anomaliesanomalies for thefor the period period 1941 1941-2016.-2016. (black(black li line)ne) a ndand fo rfor the the period period 1976 1976-2016- 2016(green (green line), line), averagedaveraged overover thethe ter territoryritory of ofKa zKazakhstan.akhstan. Anoma Anomalieslies are calcula areted calculated relative to relative the base peto riodthe ofbase period of 1961 -- 1990.1990. The The smoo smoothedthed curve curve repre serepresentsnts the 11-ye thear movi11-yearng a vmovingerage average (Source: RSE “Kazhydromet”, 2017). In bulletins published over the past years, estimates are given of the changes in the 15. averaFigurege annu IIal- anddepicts seaso nathel tempe observedratures ofinter surf-annualace air ovevariabilityr the past in76 annualyears (sinc meane 1941) surface. If to air temperaturesconsider ch aovernge of the air tefourmper provinceature fors shortein Kazakhstan.r period from Thethe mitrendddle 1analysis970 x ye aofrs ofmean the laannualst temperature over land areas in these provinces of Kazakhstan shows that increase in annual century when according to many experts, change of global climate became more intensive, then mean temperatures is most pronounced in Kyzlorda province between 1941 and 2016. The trend in annualestimate means of ra tessurface and so airmetim temperaturees even the in sig Eastn of Kazakhstantendencies is isdiff theere ntleast from of e stithemate fours duringprovince s considered here. Statistically significant trends of increase in total duration of heat waves (heat wave is recorded when the daily maximum temperature was above 90th percentile at least20 six consecutive days) have also been observed at over 70% of the 121 meteorological stations in Kazakhstan. The frequency of frost days when the daily minimum temperature is below 0 ºC, is also reported to be decreasing in Kazakhstan.

Kyzylorda South Kazakhstan

Zhambyl Almaty

EastKosta Kazanakhsty an KaParavlodarganda Kyzylorda South Kazakhstan ºС ºС 3 3 2 2 1 1 0 0 -1 -1 -2 -2

-3 -3

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

0 4 8 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 2

0 1

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 1

4 4 4 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1 5

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 9

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1

North Kazakhstan Akmola KAktobeyzylorda WSouthestZ Kaha Kambzazkhstaykhstl ana n Almaty ºС ºС 3 ºС ºС 3 2 2

2 1 1 1 0 0 0-1 -1 -1-2

-2-3 -2

0 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

-3 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 -3

8 8

0 4 8 2 6 0 4 8 2 6 0 4 2 6 0 4 2 6

4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

8 0 4 4 4 5 5 6 6 6 7 7 8 8 9 9 0 0 1 1

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

9 0 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1

FigureFig IIu-6:re Historical2.11Z –Atha Timeymbr atrendsuy lse rie ands and inter line-annualar trends variability of the annua in annualEal andMast Ka Almatseng meanazysonaastaukhsty surfacel a anir temper air atures Pavlodar temperatures observed in each of the four project provinces of Kazakhstan (Source: RSE anomaliesºС relative to 1961 – 1990 for 1941 – 20153 (black line) and 1976 – 2016 (green line) 3 “Kazhydromet”, 2017) averaged over the territory of the regions of Kazak2hstan. The smooth curve represents the 11- 2 16. The monthly mean precipitationyea duringr movi theng ayearvera1 2016ge List averaged 1 over for Kazakhstan was 1 mainly above normal, except February and August months.0 The precipitation in Kazakhstan is strongly0 controlled by topography and lack of marine influence. In the most part of Kazakhstan annual precipitation trends are mostly positive, but-1 insignificant. Long-time annual mean precipitation-1 trends over Kazakhstan do not exhibit any-2 significant increasing or decreasing trend

in general-2 (Figure II-). On an average, annual precipitation-3 has a marginally increasing trend on

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

a countrywide basis. In Kazakhstan, precipitation tends to9 marginally increase @ 0.7 mm per 22

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

-3 1

4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

decade.4 The annual increase in the amount of precipitation is observed in all provinces, except

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 for the 1 KyzylordaEa provincest Kaz a(khstFigurean II-), where the decrease in the amountNorthP ofKaavlodar precipitationzakhst an was 5.2 Akmola mm/10 years. In winter, spring and summer seasons, the territory of the republic also has a ºС ºС tendency toF igincreaseure 2.11 the – Timeamount se rieof sprecipitation and linear trbye ndsan 3averageof the a nnuaof 2.5l amm/10nd sea sonayears.l a Their te mperlargestatur es 3 significantanomalies rate reoflative increase to 196 in 1the – 1990amount for of 1941 precipitation – 20152 (bla (9 –c k24 li%ne/10) a ndyears) 1976 is – recorded 2016 (gr inee then li ne) southwest,2 north and southeast of the republic during winter. The decrease in the amount of averaged over the territory of the regions of Kaz1akhstan. The smooth curve represents the precipitation1 in the autumn period was 1.2 mm/10 years. 0 17. 0 A marginal (statistically insignificant)11-year movi increaseng ave rainge annual List 2 precipitation (@ 0.1 – 5.0 -1 mm/decade)-1 has been observed in East Kazakhstan and Karaghandy province (Figure II-). In other provinces considered here, precipitation has a statistically-2 insignificant decreasing trend @ -2

0.1 – 4.2S ummmm eperr tempe decaderatur duringes in the the period period 1941 1976-2016. -- 3201 Almost6. incr throughoutease at a thehig heterritoryr rate othanf the for a

0 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

-3 4

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

republic (65% of the total number of meteorological stations)4 there was a slight increase in the

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

longer period in the western and southern regions. I9 n the rest of the territory, trends have

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

maximum9 daily precipitation amount by 0.01 - 2.0 mm/10 years. An increase in the extreme

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 remained positive in recent decades, but they are mostly statistically insignificant. amount of precipitation during the summer leads to an increased risk of erosive processes, in In the aNorutumthn Kaof ztheakhst lastan four decades, the average rate of incrAkmolaease in air temperature in the northern, western and some southern regions increaseFdig aundre sli2.11ghtl – yTime decre seasrieeds inand the li neceantrr trael,nds of the annual and seasonal air temperatures ºС ºС easter3 n and south-eastern regions. anomalies relative to 1961 – 1990 for 1941 – 2015 (black line) and 1976 – 2016 (green line) 2 averaged over the territory of the regions of Kazakhstan. The smooth curve represents the 11- 1 Figure 2.12 provides more detailed information about changes in average annual, year moving average List 1 seasona0 l and monthly air temperatures (°C/10 years) for 1941 – 2015 in Kazakhstan. -1 -2

-3

0 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 23 22

Figure 2.11 – Time series and linear trends of the annual and seasonal air temperatures anomalies relative to 1961 – 1990 for 1941 – 2015 (black line) and 1976 – 2016 (green line) averaged over the territory of the regions of Kazakhstan. The smooth curve represents the 11- year moving average List 1

22 Linear trends of the monthly, seasonal and annual precipitation time series were estimated for 121 weather stations. For the last ten years there has been an alternation of short periods with positive and negative anomalies in the amount of precipitation. Long-term trends in the amount of precipitation during the study period are absent (Figure 3.7 and 3.8). On average in Kazakhstan for the period 1976-2016. there was a weak tendency to increase the annual amount of precipitation by 7 mm/10 years (Figure 3.7, Table 3.2). The annual increase in the amount of precipitation is observed in all oblasts, except for the Kyzylorda Aktobe 11 West Kazakhstan oblast, where the decrease in the amount of precipitation was 5.2 mm/10 years. In winter, spring and summer seasons, the territory of the republic also has a tendency to increase the amount of mountainous areas - mudflows of rain genesis, and in the winter - to an increase in the danger of precipitationavalanches. by an a Thever aconsecutivege of 2.5 dry mm days/10 (CDD ye) aindexrs. whichThe representsdecrease the in maximum the amount length of oftime a tmospheric when precipitation was less than 1 mm (duration of consecutive dry days), is very important in the precipitationarid in climatethe autu of Kazakhstan.mn period Statistically was 1.2 significantmm/10 decreaseyears (Fs ihavegure occurred 3.8, Ta inbl thee northern3.2). All and the trends of annual and northseasona-easternl pr provinceecipitations of Kazakhstan amount bys a1c –ros 5 dayss the every ter ritor10 years.y of The th evalues republi of thec CDDare statistically index are an important characteristic of the climate, especially for agriculture. insignificant.

Atyrau Kazakhstan Mangystau % 40

-20 Figure 3.7 – Time series and linear trends of annual precipitation anomalies (%) for 1940 – 2016

(black lin-e)40 and for 1976 - 2016 (blue line) calculated relative to the 1961 – 1990 baseline for the

4 0 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

territory of Kaza4 khstan and its regions. The smooth curve shows the 11–year moving average.

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 List 3 Figure IIK-7:y zTimeylo rdaseries and linear trends of annual precipitation Sanomalouth Kay (%)za forkhst 1940an – 2016 (black line) and for 1976 - 2016 (blue line) calculated relative to the 1961 – 1990 baseline for Kazakhstan (Source: RSE “Kazhydromet”, 2017) winter spring % % 40 80 60 20 40 0 20 -20 0 -20 -40 -40

-60 -60

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 summer autumn % % 80 60 60 40 40 20 20 Figure 3.7 – Time series and linear trends of annu0al precipitation anomalies (%) for 1940 – 2016 0 -20 (black line) and-20 for 1976 - 2016 (blue line) calculated relative to the 1961 – 1990 baseline for the -40 -40

-60 -60

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

territory of Kaz0 akhstan and its regions. The smooth curve shows the 11–year moving average.

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 L ist 1 FigFigureure 3.8 II–- 8:Time Historical series a ndtrends linea rand trends inter of- annualseasona lvariability precipitation in anomaseasonallies precipitation(%) for 1940 – observed over Kazakhstan (Source: RSE “Kazhydromet”, 2017) 2016 (black line) and for 1976 - 2016 (blue line) calculated relative to the 1961 – 1990 baseline for the territory of Kazakhstan. The smooth curve shows the 11–year moving average.

36 Zhambyl Almaty

East Kazakhstan Pavlodar

Linear trends of the monthly, seasonal and annual precipitation time series were estimated for 121 weather stations. For the last ten years there has been an alternation of short periods with positive and negative anomalies in the amount of precipitation. Long-term trends in the amount of precipitation during the study period are absent (Figure 3.7 and 3.8). On average in Kazakhstan for the period 1976-2016. there was a weak tendency to increase the annual amount of precipitation by 7 mm/10 years (Figure 3.7, Table 3.2). The annual increasNore inth the Ka amountzakhst ofan precipitation is observed in all oblasts,Akmola except for the Kyzylorda oblast, where the deZchareambse yinl the amount of precipitation was 5.2 mm/10Almat yeayrs. In winter, spring and summer seasons, the territory of the republic also has a tendency to increase the amount of precipitation by an average of 2.5 mm/10 years. The decrease in the amount of atmospheric precipitation in the autumn period was 1.2 mm/10 years (Figure 3.8, Table 3.2). All the trends of annual and seasonal precipitation amounts across the territory of the republic are statistically insignificant. 12 Kazakhstan % EastKosta Kaznaakhsty an PKaavlodarraganda % % 60 60 40 40 20 20 0 0 -20 -20 -40

-40 -60

4 0 8 6 0 4 2 6 0 4 8 2 6 0 4 8 2 6

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 4 5 6 6 7 7 8 8 8 9 9 0 0 0 1 1

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 NorthK yKazyzloardakhst an SouthAkmolaZ Kahazmbakhsty l an Almaty Figure 3.7 – Time series and linear trends of annual precipitation anomalies (%) for 1940 – 2016 % 400 % (bl360ack line) and for 1976 - 2016 (blue line) calculate80d relative to the 1961 – 1990 baseline for the 320 ter280ritory of Kazakhstan and its regions. The smoot60h curve shows the 11–year moving average. 240 200 40 160 List 2 120 20 80 40 0 0 -40 -20 -80

-120 -40

0 4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2 6

4 4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1

Kostanay Karaganda Figure II-9: Historical trends and inter-annual variability in observed annual mean Pavlodar % East Kazakhstan Figureprecipitation 3.7 – Time se rieins each and li neof ather tre fournds of project annual 60preprovincecipitations of anoma Kazakhstanlies (%) f or(Source: 1940 – 201 RSE6 (black line) and for 1976 - 2016 (blue li“Kazhydromet”,ne) calculated40 relative 201 to7 the) 1961 – 1990 baseline for the

territory of Kazakhstan and its regions. The smooth20 curve shows the 11–year moving average. 18. It is evident from Figure II- that the annual and seasonal mean observed surface air 0 temperature s suggest a warming trend overList Kazakhstan.1 Figure II- illustrates that the annual37 mean precipitation over Kazakhstan has a discernible-20 but only marginal increasing trend during 1941 -2016. More detailed analysis of daily surface-40 air temperature and precipitation data over

project provinces (obtained from CRU gridded climatological-60 datasets) provided the following key

4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1

9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0

climatological features: 9

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 • Mean annual temperature has increased by 1.48°C since 1941 an average rate of 0.34 °C (ranging between 0.13 to 0.67°C) per decade. Fig•u re Available3.7 – Time data serie indicatess and line athatr tre ndsthe ofaverage annual prenumbercipitation of ‘hot’ anoma nightslies (%per) fyearor 19 4averaged0 – 2016 over (black Kazakhstanline) and for 1976 has - increased2016 (blue betweenline) calcula 1941ted reandlative 2016 to the Nor(maximum 1961th Ka –z 1990akhst duration aban seli neof forheat the waves Akmola was more than 70 days in year in far western province). territory of Kazakhstan and its regions. The smooth curve shows the 11–year moving average. • The maximum duration of cold waves (15 - 16 days) were limited to in East Kazakhstan36 and Karaghandy provinces during winterList 2 of 2016. • The trend analysis of mean annual temperature over land areas in the project provinces of Kazakhstan shows that increase in annual mean temperatures is most pronounced in Kyzlorda province between 1941 and 2016. The trend in annual mean surface air temperature in East Kazakhstan is the least of the four provinces considered here. • In general, in Kazakhstan, 2016 was a record year for the amount of precipitation. The annual amount of precipitation in 2016 on average in the territory of Kazakhstan made 140% of norm or 441.3 mm. This is the maximum amount of precipitation observed during the period from 1941 to 2016. Kostanay Karaganda % 37 60 40 20 0 -20 -40

-60

4 8 2 6 0 4 8 2 6 0 4 8 2 6 0 4 8 2

4 4 5 5 6 6 6 7 7 8 8 8 9 9 0 0 0 1

9 9 9 9 9 9 9 9 9 9 9 9 9 9 0 0 0 0

1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1

Figure 3.7 – Time series and linear trends of annual precipitation anomalies (%) for 1940 – 2016 (black line) and for 1976 - 2016 (blue line) calculated relative to the 1961 – 1990 baseline for the territory of Kazakhstan and its regions. The smooth curve shows the 11–year moving average. List 2

37 13

• A weak tendency has been observed in Kazakhstan of increase in the annual amount of precipitation by 7 mm/10 years for the period 1976-2016. • The precipitation in Kazakhstan and each of the four project provinces was characterized by high variability on inter‐annual and inter‐decadal time scales, which can make long‐ term trends difficult to identify. • No statistically significant decreases in annual mean precipitation have been observed between 1941 and 2016 in Kazakhstan but in winter, an increase and in summer/autumn, a marginal decrease in seasonal total precipitation is noted. • Some unusually high intensity precipitation spells have occurred in the dry season in recent years (2000‐2016), but no consistent trend is discernible.

Figure II-3: Average annual distribution (%) of reported disasters in Kazakhstan (Source: GFDRR, 2016) 3. Hydro-meteorological Disasters in Kazakhstan

19. Kazakhstan is prone to natural hazards including floods, mudflows, landslides, steppe winds, and earthquakes (Figure II-3). Floods in the country are the result of river overflows - a combination of heavy rains and insufficient drainage infrastructure – leading to water intrusion into agricultural lands. At the same time, rapid urbanization has caused an explosion of buildings and roads that have reduced infiltration and exacerbated the impacts of floods. Droughts are also common particularly in southern parts of the country as a result of climate variability that manifests itself by a late onset of the rainy season, irregular spatial distribution of rains, and an early end to the rainy season. Droughts can cause a significant drop in crop yield and can contribute to annual revenue drops from millet/sorghum. The country has evidenced in recent decades an increasing frequency of natural hazards, devastating droughts and progressive desertification15 (GFDRR,

15 GFDRR (Global Facility for Disaster Recovery and Reduction), 2011: Guidance Notes for Estimation of Damage and Losses after Disasters, The World Bank, Washington, D.C., 2011.

2011). Climate change is expected to exacerbate hydro-meteorological disasters, adversely affecting small and medium enterprises (SMEs), and farmers. Agriculture plays a prominent role in the national economy and makes the country highly vulnerable to the risks of climate change. In the plains, spring floods fed by rain and snowmelt occur and mountainous provinces suffer mudflows triggered by rainfall or breaches of glacial lakes. About 13% of the country’s area containing over 26% of its population is prone to mudflows. An increase in intensity of precipitation or accelerated snow melt will aggravate mudflows, landslides, and avalanches.

B. Simulation of Future Climate of Kazakhstan & project provinces

1. Future Climate for Kazakhstan and project provinces

20. Predicting future climate, emissions scenarios are used to explore how much humans could contribute to future climate change given uncertainties in factors such as population growth, economic development, and development of new technologies. Representative Concentration Pathways (RCPs) are time and space dependent trajectories of concentrations of greenhouse gases and pollutants resulting from human activities, including changes in land use etc which influence the Earth’s climate16. Four RCPs, namely, RCP2.6, RCP4.5, RCP6, and RCP8.5, are identified based on a possible range of radiative forcing values in the year 2100 relative to pre- industrial values (+2.6, +4.5, +6.0, and +8.5 Wm-2, respectively). The word “representative” signifies that each RCP provides one of many possible scenarios that would lead to the specific radiative forcing pathway. These four greenhouse gas concentration (not emissions) trajectories adopted by the IPCC for its fifth Assessment Report (AR5) in 2014 supersedes the Special Report on Emissions Scenarios (SRES) projections published in 2000. In RCP 8.5, emissions continue to rise throughout the 21st century (Business-As-Usual scenario). In RCP 4.5, emissions peak around 2040 and then decline to achieve specific climate change targets (such as limiting change to 2°C) towards a “risk management” approach that combines reductions in emissions (one of the strategies to mitigate climate change) and adaptation to reduce climate change damages.

21. Parties to the U.N. Framework Convention on Climate Change (UNFCCC) reached a landmark agreement on December 12, 2015 in Paris, charting a fundamentally new course in the two-decade-old global climate effort. The agreement, which came into force on November 4th, 2016, reaffirms the goal of keeping average warming below 2°C, while also urging parties to “pursue efforts” to limit it to 1.5°C, a top priority for developing countries highly vulnerable to climate impacts. With the Paris Agreement’s entry into force, the focus has now shifted towards its implementation—in particular, countries, including Kazakhstan are looking at how to meet the mitigation commitments they set out in their Nationally Determined Contributions (NDCs) while strengthening Integrated Adaptation Policies, Plans and Strategies for adaptation (to combat climate change and limit future climate risks thus reducing the vulnerability to climate change) and resilience.

22. Whether climate change will be addressed effectively, and the goals of the Paris Agreement will be met will depend on other factors which govern present and future policies. These include energy and commodity markets, migration and economic policies. There are also important links to the implementation of the United Nations Sustainable Development Goals (SDGs) and the Sendai Framework for Disaster Risk Reduction. In view of this, the RCP 8.5 and RCP 4.5 represent the most plausible global concentration pathway range for the future and can

16 Meinshausen, M. S.J. Smith, K. Calvin, J.S. Daniel, M.L.T. Kainuma, and et al., 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213-241.

provide a range of possibilities in the vulnerability due to nature of future extremes. The climate change data analysis for inferring projections of future climate change in Kazakhstan and its four provinces are undertaken on four time scales, namely, baseline (1961-1990), near term (2020s i.e., 2000 to 2039), medium term (2050s i.e., 2040 to 2069) and long term (2080s i.e., 2070 to 2099).

Changes in Surface Air Temperature

23. Table II-1 provides a snapshot of annual and seasonal means of projected warming area averaged over Kazakhstan for RCP 8.5 and RCP 4.5 pathways at the three selected time slices with respect to the baseline. Mean annual temperatures are projected to increase by 2.47°C by the 2020s, 4.48°C by the 2050s and 7.02°C by the 2080s under RCP 8.5 concentration pathway. Under RCP 4.5 concentration pathway, the projected rise in surface temperature is restricted to 2.19°C by the 2020s, 3.69°C by the 2050s and 4.44°C by the 2080s. The projected average warming in Kazakhstan at all time slices during this century is higher during winter than during the summer, spring or autumn seasons (least during autumn). This is similar to IPCC findings in that the projected warming is likely to be most pronounced in winters. A clearer view on the future projections appears from Table II-1 which suggests that the peak warming averaged over Kazakhstan occurs in March by the middle of this century and beyond.

Table II-1: Monthly, seasonal and annual means of projected warming area averaged over Kazakhstan at three time slices under RCP 8.5 and RCP4.5 pathways Average surface air temperature change (deg C)

ΔT, 2020s ΔT, 2050s ΔT, 2080s ΔT, 2020s ΔT, 2050s ΔT, 2080s

RCP 8.5 RCP 4.5 January 2.68 5.27 8.29 2.90 3.90 4.90 February 3.14 5.62 8.90 3.03 4.53 5.53 March 3.29 5.59 8.66 3.04 5.04 6.04 April 2.53 4.23 6.74 2.20 4.20 4.20 May 1.98 3.52 5.27 1.41 2.91 3.41 June 2.16 3.81 6.05 1.62 3.12 3.62 July 2.54 4.62 7.12 2.39 3.39 4.39 August 2.74 4.80 7.57 2.41 3.91 4.41 September 2.54 4.40 6.58 2.54 3.54 4.54 October 2.18 3.97 6.03 1.73 3.23 3.73 November 1.72 3.53 6.03 1.31 2.81 3.81 December 2.10 4.43 7.02 1.68 3.68 4.68 Seasonal / Annual Change Winter 2.64 5.10 8.07 2.54 4.04 5.04 Spring 2.60 4.45 6.89 2.21 4.05 4.55 Summer 2.48 4.41 6.91 2.14 3.47 4.14 Autumn 2.15 3.97 6.21 1.86 3.19 4.02 Annual 2.47 4.48 7.02 2.19 3.69 4.44

24. Keeping in view that the future emissions of greenhouse gases may not be the Business- as-Usual (RCP 8.5) if all the nations follow stringent mitigation actions and take concerted efforts to reduce their emissions as per the commitments given under their NDCs, the warming projections for Kazakhstan may be lower than those projected above. Under RCP 4.5 pathway, the warming projections for Kazakhstan for the three time slices, namely 2020s, 2050s and 2080s are likely to be much lower (peaking to just about 4.44°C by 2080s as given in Table II-1 above.

25. The spatial distribution of projected rise in annual mean surface air temperatures over Kazakhstan (for RCP 8.5 pathway) at the three time slices are depicted in Figure II-4 to Figure II-6 below. It is evident from these maps that the annual mean rise in surface air temperature would be higher towards the western side of Kazakhstan relative to eastern Kazakhstan during all time slices. The range extends from 2.1°C over the southeast side (near the Caspian Sea) to 2.6°C over the northwest side of the country during 2020s (Figure II-4). During the period from 2050s to 2080s, the gradient in rise in surface air temperature over Kazakhstan from south to north is projected to have a significantly high range from 1.5°C to 3.0°C (Figure II-5 and Figure II-6).

Figure II-4: Spatial distribution of projected change in annual mean surface air temperature with respect to baseline period (1961-90) over Kazakhstan by 2020s under RCP 8.5 pathway

Figure II-5: Spatial distribution of projected change in annual mean surface air temperature with respect to baseline period (1961-90) over Kazakhstan by 2050s under RCP 8.5 pathway

Figure II-6: Spatial distribution of projected change in annual mean surface air temperature with respect to baseline period (1961-90) over Kazakhstan by 2080s under RCP 8.5 pathway

26. The spatial distribution of projected rise in annual mean surface air temperatures over Kazakhstan (for RCP 4.5 pathway) at the three time slices are depicted in Figure II-7 to Figure II-9 below. The annual mean rise in surface air temperature would be higher towards the western side of Kazakhstan relative to eastern Kazakhstan during all time slices. The range extends from 2.0°C over the southeast side (near the Caspian Sea) to 2.3°C over the northwest side of the country during 2020s (Figure II-7). During the period from 2050s to 2080s, the gradient in rise in surface air temperature over Kazakhstan from south to north is projected to have a range from 0.5°C to 1.0°C (Figure II-8 and Figure II-9).

Figure II-7: Spatial distribution of projected change in annual mean surface air temperature with respect to baseline period (1961-90) over Kazakhstan by 2020s under RCP 4.5 pathway

Figure II-8: Spatial distribution of projected change in annual mean surface air temperature with respect to baseline period (1961-90) over Kazakhstan by 2050s under RCP 4.5 pathway

Figure II-9: Spatial distribution of projected change in annual mean surface air temperature with respect to baseline period (1961-90) over Kazakhstan by 2080s under RCP 4.5 pathway

27. The monthly, seasonal and annual changes in surface air temperature projected over the four provinces in Kazakhstan at the three future time slices for RCP 8.5 pathway are summarized in Table II-2 to Table II-4. On an annual mean basis, the peak projected warming of 2.46°C is likely to occur in Karaghandy province around 2020s (February/March would likely to be warmest months). The least annual mean warming of 2.18°C could be expected in Zhambyl province by 2020s. Within the four seasons, winter is projected to be warmest around 2020s in Kyzlorda province but the peak warming occurs during the summer season in the remaining three provinces.

Table II-2: Monthly, seasonal and annual means of projected warming area averaged over the four project provinces of Kazakhstan during 2020s for RCP 8.5 pathway

Average surface air temperature change (deg C) Eastern Kazakhstan Karaganda Kyzylorda Zhambyl ΔT, 2020s ΔT, 2020s ΔT, 2020s ΔT, 2020s January 2.88 2.64 2.37 2.14 February 2.51 3.04 3.56 2.82 March 2.82 3.35 2.87 2.34 April 2.41 2.55 2.19 1.89 May 1.87 1.96 1.94 1.73 June 2.22 2.18 2.12 2.01 July 2.58 2.56 2.58 2.47 August 2.98 2.81 2.67 2.7 September 2.54 2.56 2.55 2.45 October 1.97 2.12 2.29 2.07 November 1.98 1.75 1.73 1.69 December 2.19 2.06 2.08 1.92 Seasonal / Annual Change Winter 2.53 2.58 2.67 2.29 Spring 2.37 2.62 2.33 1.99 Summer 2.59 2.51 2.45 2.39 Autumn 2.16 2.14 2.19 2.07 Annual 2.41 2.46 2.41 2.18

28. The peak warming over the four provinces shifts to the winter season with annual mean warming of about 4.48°C in Karaghandy province and 4.11°C in Zhambyl province around 2050s (Table II-3; February would likely to be warmest month). During the 2080s, annual mean warming of about 7.35°C in East Kazakhstan province and 6.62°C in Zhambyl province is expected. Peak warming continues to occur during the winter season in all the four provinces in 2080s (February would continue to be warmest month). These seasonal patterns of projected warming over the project provinces are consistent with that projected over the country.

Table II-3: Monthly, seasonal and annual means of projected warming area averaged over the four project provinces of Kazakhstan during 2050s for RCP 8.5 pathway

Average surface air temperature change (deg C) Eastern Kazakhstan Karaganda Kyzylorda Zhambyl ΔT, 2050s ΔT, 2050s ΔT, 2050s ΔT, 2050s January 5.36 5.40 4.78 4.51 February 5.02 5.48 5.50 4.86 March 5.41 5.79 4.83 4.44 April 4.21 4.06 3.74 3.41 May 3.57 3.54 3.62 3.23 June 3.84 3.80 3.95 3.75 July 4.55 4.68 4.77 4.62 August 5.03 4.81 4.74 4.82 September 4.44 4.33 4.44 4.46 October 3.59 3.82 4.12 3.84 November 3.91 3.55 3.46 3.30 December 4.78 4.54 4.09 4.10 Seasonal / Annual Change Winter 5.05 5.14 4.79 4.49 Spring 4.40 4.46 4.06 3.70 Summer 4.47 4.43 4.49 4.40 Autumn 3.98 3.90 4.01 3.87 Annual 4.48 4.48 4.34 4.11

Table II-4: Monthly, seasonal and annual means of projected warming area averaged over the four project provinces of Kazakhstan during 2080s for RCP 8.5 pathway

Average surface air temperature change (deg C) Eastern Kazakhstan Karaganda Kyzylorda Zhambyl ΔT, 2080s ΔT, 2080s ΔT, 2080s ΔT, 2080s January 8.83 8.52 7.47 7.44 February 8.71 9.05 8.50 7.96 March 8.90 9.08 7.27 7.05 April 7.47 6.58 6.02 5.60 May 5.57 5.23 5.35 5.14 June 5.99 6.04 6.40 6.25 July 7.17 7.18 7.22 7.26 August 8.44 7.77 7.41 7.82 September 6.80 6.54 6.68 6.86 October 5.94 5.92 6.17 6.06 November 6.56 5.96 5.82 5.58 December 7.82 7.14 6.27 6.38 Seasonal / Annual Change Winter 8.45 8.24 7.41 7.26 Spring 7.31 6.96 6.21 5.93 Summer 7.20 6.99 7.01 7.11 Autumn 6.43 6.14 6.22 6.17 Annual 7.35 7.08 6.72 6.62

29. The monthly, seasonal and annual changes in surface air temperature projected over the four provinces in Kazakhstan at the three future time slices for RCP 4.5 pathway are summarized in Table II-5 to Table II-7. On an annual mean basis, a peak projected warming of 2.33°C is likely to occur in Karaghandy province around 2020s (February/March would likely be the warmest months). The least annual mean warming of 1.99°C could be expected in Zhambyl province by 2020s. Within the four seasons, winter is projected to be warmest around 2020s in all the four provinces.

Table II-5: Monthly, seasonal and annual means of projected warming area averaged over the four project provinces of Kazakhstan during 2020s for RCP 4.5 pathway

Average surface air temperature change (deg C) Eastern Kazakhstan Karaganda Kyzlorda ΔT, Zhambyl ΔT, ΔT, 2020s ΔT, 2020s 2020s 2020s January 2.95 3.03 2.80 2.97 February 2.22 3.25 3.14 2.50 March 2.92 3.08 2.42 2.12 April 2.18 2.35 1.85 1.49 May 1.87 1.74 1.42 1.55 June 1.44 2.27 2.13 2.09 July 2.27 2.10 2.34 2.26 August 2.88 2.27 2.01 2.04 September 2.33 2.45 2.46 2.16 October 1.71 1.87 2.23 1.50 November 0.95 1.45 1.47 0.90 December 2.80 2.07 1.86 2.28 Seasonal / Annual Change Winter 2.66 2.78 2.60 2.58 Spring 2.33 2.39 1.90 1.72 Summer 2.20 2.21 2.16 2.13 Autumn 1.66 1.92 2.05 1.52 Annual 2.21 2.33 2.18 1.99

Table II-6: Monthly, seasonal and annual means of projected warming area averaged over the four project provinces of Kazakhstan during 2050s for RCP 4.5 pathway

Average surface air temperature change (deg C) Eastern Kazakhstan Karaganda Kyzlorda ΔT, Zhambyl ΔT, ΔT, 2050s ΔT, 2050s 2050s 2050s January 4.45 4.03 3.80 3.47 February 3.72 4.25 4.14 4.00 March 4.92 5.08 3.92 3.62 April 3.68 3.35 3.35 2.49 May 2.87 2.74 2.92 2.55 June 3.44 3.27 3.63 3.09 July 3.27 3.60 3.84 3.76 August 3.88 4.27 4.01 4.04 September 3.83 3.45 3.46 3.66 October 2.71 3.37 3.23 3.50 November 2.95 2.45 2.47 2.90 December 3.80 3.57 2.86 3.28 Seasonal / Annual Change Winter 3.99 3.95 3.60 3.58 Spring 3.83 3.72 3.40 2.89 Summer 3.53 3.71 3.83 3.63 Autumn 3.16 3.09 3.05 3.35 Annual 3.63 3.62 3.47 3.36

Table II-7: Monthly, seasonal and annual means of projected warming area averaged over the four project provinces of Kazakhstan during 2080s for RCP 4.5 pathway

Average surface air temperature change (deg C) Eastern Kazakhstan Karaganda Kyzlorda ΔT, Zhambyl ΔT, ΔT, 2080s ΔT, 2080s 2080s 2080s January 5.45 5.53 4.80 4.97 February 5.72 5.75 5.64 5.00 March 5.92 6.08 4.92 4.62 April 4.68 4.35 3.85 3.49 May 3.37 3.24 3.42 3.05 June 3.44 3.27 3.63 3.09 July 4.27 4.60 4.34 4.26 August 4.88 4.77 4.51 5.04 September 4.33 4.45 4.46 4.66 October 3.71 3.37 4.23 3.50 November 3.95 3.45 3.47 3.40 December 5.30 5.07 4.36 4.28 Seasonal / Annual Change Winter 5.49 5.45 4.93 4.75 Spring 4.66 4.56 4.06 3.72 Summer 4.20 4.21 4.16 4.13 Autumn 4.00 3.76 4.05 3.85 Annual 4.59 4.49 4.30 4.11

30. The projected warming in this study is on the higher side than those projected in the earlier studies reported elsewhere (The III-VI National Communication of the Republic of Kazakhstan to the UNFCCC, 2013). This could be attributed to improved earth system models and the emission scenarios used in this study. Given the model validation exercises undertaken before generating the projected scenarios and the skills exhibited by the models individually and collectively as ensembles, the warming projections reported here could be regarded as most plausible with the inherent uncertainties still existing in climate science.

Change in Diurnal Temperature Range

31. In order to examine the likely plausible changes in diurnal temperature range (DTR) with time over Kazakhstan, we have also analyzed the warming projections in diurnal temperature range (difference between maximum and minimum surface air temperatures at each of the three time scales) for the two greenhouse gas concentration pathways. The simulated diurnal temperature range at the baseline and that projected over Kazakhstan at the three time slices are listed in Table II-8. It is evident from this Table that no significant change in the annual mean DTR is expected in the future at all time scales. However, during winter months, a marginal decline in DTR is simulated in the future suggesting that rise in maximum surface air temperature is likely to be lower than the minimum surface air temperature in winter during all time slices in the future. During summer months, a marginal increase in DTR is simulated in the future suggesting that the rise in maximum (day time) surface air temperature is likely to be higher than the minimum (night time) surface air temperature in summer during all time slices in the future.

Table II-8: Monthly, seasonal and annual means of projected diurnal temperature range area averaged over Kazakhstan during the three time slices for RCP 8.5 and 4.5 pathways

Changes in Precipitation

32. The monthly, seasonal and annual changes in precipitation projected over Kazakhstan at the three future time slices are summarized in Table II-9 for RCP 8.5 and RCP 4.5 pathways. On an annual mean basis, the projected change in precipitation averaged over the country is a decline in precipitation by about -2% of the baseline value around 2020s, no change from the baseline value around 2050s and an increase in precipitation of 4% of the baseline value around 2080s (under RCP 8.5 pathway). Within the four seasons, the model results suggest a significant increase in precipitation of about 24% during winter by the end of this century. Similar findings of increases in winter precipitation over arid central Asia have been reported elsewhere17 (Song & Bai, 2016). However, during summer season, a modest decline of 13% in precipitation is projected. The temperature water vapor feedback at higher warming should induce more pronounced convective instabilities which should lead to an increase in precipitation around late century. A marginal decline in autumn precipitation is also projected for the future. A modest increase of as much as about 12% may be expected in spring season precipitation by the end of this century. On a monthly basis, August is likely to be when precipitation could be expected to decline substantially throughout during this century leading to drought conditions in some of the drought prone areas of Kazakhstan. Peak increase in precipitation (31%) is projected in the January month.

17 Song, Shikai and Jie Bai, 2016: Increasing Winter Precipitation over Arid Central Asia under Global Warming, Atmosphere, 7, 139; doi:10.3390/atmos7100139.

Table II-9: Monthly, seasonal and annual means of projected change in area averaged precipitation (ΔP, percent change) over Kazakhstan at three time slices for RCP 8.5 and RCP 4.5 pathways

Average precipitation change (%)

ΔP, 2020s ΔP, 2050s ΔP, 2080s ΔP, 2020s ΔP, 2050s ΔP, 2080s RCP8.5 RCP4.5 January 2% 12% 31% 18% 21% 23% February 5% 12% 23% 10% 16% 21% March 4% 8% 18% 4% 10% 17% April 1% 5% 7% 9% 12% 20% May 0% 10% 12% 11% 12% 15% June 2% 3% 5% 10% 14% 16% July -8% -15% -17% 2% 1% -4% August -20% -25% -28% -3% -7% -9% September -18% -19% -21% -2% -5% -8% October -7% -15% -16% 8% 9% 9% November 2% 10% 17% 11% 14% 15% December 6% 11% 18% 11% 14% 18% Seasonal / Annual Change Winter 4% 12% 24% 13% 17% 21% Spring 2% 8% 12% 8% 11% 17% Summer -9% -12% -13% 3% 2% 1% Autumn -7% -8% -7% 6% 6% 5% Annual -2% 0% 4% 7% 9% 11%

33. It is interesting to note from Table II-9 that, under the RCP 4.5 pathway, the model projections indicate an increase in precipitation in all seasons. On an annual basis, an increase in precipitation of as much as 11% could occur by the end of this century. In such a future scenario, recurrence of floods in the flood prone areas of Kazakhstan is likely in spring when the snow melt starts.

34. Figure II-10 to Figure II-12 suggest that the projected annual mean decline in precipitation over Kazakhstan at each time slice under RCP 8.5 pathway is likely to be most pronounced in the east and central provinces of the country.

Figure II-10: Spatial distribution of projected change in annual precipitation (percent) with respect to baseline period (1961-90) over Kazakhstan by 2020s under RCP 8.5 pathway

Figure II-11: Spatial distribution of projected change in annual precipitation (percent) with respect to baseline period (1961-90) over Kazakhstan by 2050s under RCP 8.5 pathway

Figure II-12: Spatial distribution of projected change in annual precipitation (percent) with respect to baseline period (1961-90) over Kazakhstan by 2080s under RCP 8.5 pathway

35. Figure II-13 to Figure II-15 depicts the spatial distribution of changes in annual precipitation (in percent) at the selected three time slices of the 21st century under RCP 4.5 pathway. These maps suggest that the annual mean precipitation over Kazakhstan is likely to increase across the country at each time slice under RCP 4.5 pathway and is likely to be most pronounced in the northern provinces of the country.

Figure II-13: Spatial distribution of projected change in annual precipitation (percent) with respect to baseline period (1961-90) over Kazakhstan by 2020s under RCP 4.5 pathway

Figure II-14: Spatial distribution of projected change in annual precipitation (percent) with respect to baseline period (1961-90) over Kazakhstan by 2050s under RCP 4.5 pathway

Figure II-15: Spatial distribution of projected change in annual precipitation (percent) with respect to baseline period (1961-90) over Kazakhstan by 2080s under RCP 4.5 pathway

36. The monthly, seasonal and annual changes in precipitation projected for the four project provinces in Kazakhstan at the three future time slices are summarized in Table II-10 to Table II-12. On an annual mean basis, the projected decline in precipitation over these provinces range between -5% (in East Kazakhstan province) to -15% (in Kyzlorda province) with respect to the baseline value around 2020s under RCP 8.5 pathway (Table II-10). This decline in precipitation is most pronounced during summer (particularly in August month) in all the provinces. Around 2050s, on an annual basis, a marginal increase (2%) is likely in East Kazakhstan province, while a decline in precipitation of between -4% and -13% is possible in the other three re provinces (Table II-10). The winter precipitation around this decade would likely increase to a maximum of 11% (in East Kazakhstan province) except in Kyzlorda province where no change is expected. During the spring season, a modest decrease in precipitation of -8% at Kyzlorda province and - 6% in Zhambyl province is likely around 2050s. However, the summer (as much as -28% at Zhambyl province) and autumn (as much as -21% in Kyzlorda province) seasons would have a significant decline (-25% to -40% in August month) in precipitation around 2050s.

37. The annual mean precipitation in project provinces around 2080s is projected to be within +10% (East Kazakhstan province) to -16% (Kyzlorda province) of the baseline value (Table II-12). Within the four seasons, the model results suggest a significant decline of between -16% (in Karaghandy provinces) to -36% (in Zhambyl province) in summer precipitation and an increase in winter precipitation of +20% or more (with the exception of Kyzlorda province where it could be restricted to only about +6%). On a monthly basis, June to October months are likely to be the

five months when precipitation could be expected to decline by the end of 21st century in most of the provinces considered in this study.

Table II-10: Monthly, seasonal and annual mean projected change in area averaged precipitation (ΔP, percent change) over project provinces during 2020s under RCP 8.5 pathway Average precipitation change (%)

East Kazakhstan Karaganda Kyzylorda Zhambyl ΔP, ΔP, 2020s ΔP, 2020s ΔP, 2020s 2020s January 0% 1% -1% -3% February -1% -6% -11% -5% March -11% -10% -19% 2% April -2% -6% -8% 3% May 11% 3% -7% -11% June -8% -3% -2% -11% July -18% -24% -27% -22% August -22% -23% -38% -32% September -15% -16% -26% -18% October 4% -6% -27% -2% November 6% -7% -11% 10% December -3% -4% -9% -4% Seasonal / Annual Change Winter -2% -3% -7% -4% Spring 2% -5% -11% -2% Summer -16% -15% -23% -22% Autumn -2% -10% -21% -3% Annual -5% -9% -15% -8%

Table II-11: Monthly, seasonal and annual mean projected change in area averaged precipitation (ΔP, percent change) over project provinces during 2050s under RCP 8.5 pathway

Average precipitation change (%)

East Kazakhstan Karaganda Kyzylorda Zhambyl ΔP, ΔP, 2050s ΔP, 2050s ΔP, 2050s 2050s January 11% 11% 8% 12% February 23% 4% -8% 5% March 6% 2% -14% 18% April 14% 4% -7% 3% May 20% 10% -11% -8% June -1% -2% -6% -13% July -16% -23% -26% -30% August -26% -26% -31% -40% September -18% -14% -27% -22% October -3% -19% -39% -11% November 16% 5% 2% 18% December 7% 2% -4% 11% Seasonal / Annual Change Winter 11% 5% 0% 9% Spring 11% 6% -8% -6% Summer -14% -17% -21% -28% Autumn -2% -9% -21% -5% Annual 2% -4% -13% -8%

Table II-12: Monthly, seasonal and annual mean projected change in area averaged precipitation (ΔP, percent change) over project provinces during 2080s under RCP 8.5 pathway

Average precipitation change (%)

East Kazakhstan Karaganda Kyzylorda Zhambyl ΔP, ΔP, 2080s ΔP, 2080s ΔP, 2080s 2080s January 34% 33% 15% 30% February 44% 24% 1% 22% March 20% 10% -13% 25% April 19% 0% -15% 6% May 28% 16% -19% -17% June -3% -7% -21% -34% July -22% -16% -21% -31% August -35% -24% -43% -42% September -22% -21% -45% -36% October 7% -7% -34% -10% November 10% 0% -13% 11% December 20% 18% 1% 18% Seasonal / Annual Change Winter 33% 25% 6% 23% Spring 28% 11% -9% 18% Summer -20% -16% -28% -36% Autumn 2% -9% -31% -12% Annual 10% 3% -16% -1%

38. The projected monthly, seasonal and annual changes in precipitation over the four project provinces for three time slices under RCP 4.5 pathway are detailed in Table II-13 to Table II-15 below.

Table II-13: Monthly, seasonal and annual mean projected change in area averaged precipitation (ΔP, percent change) over project provinces during 2020s under RCP 4.5 pathway

Change in Precipitation (%) Eastern Kazakhstan Karaganda Kyzylorda Zhambyl ΔP, ΔP, 2020s ΔP, 2020s ΔP, 2020s 2020s January 20% 17% 16% 12% February 14% 4% 2% 13% March 2% 0% -10% 7% April 9% 17% 25% 21% May 15% 14% -1% -2% June 11% 7% 3% -11% July 0% 0% -6% -16% August -16% -4% -16% -23% September -2% 3% -25% 5% October 22% 24% 10% 26% November 16% 21% 24% 35% December 16% 25% 22% 22% Seasonal / Annual Means Winter 17% 15% 14% 16% Spring 8% 7% 6% 14% Summer -2% 1% -6% -17% Autumn 12% 16% 3% 23% Annual 8% 10% 4% 9%

Table II-14: Monthly, seasonal and annual mean projected change in area averaged precipitation (ΔP, percent change) over project provinces during 2050s under RCP 4.5 pathway

Change in Precipitation (%) Eastern Kazakhstan Karaganda Kyzylorda Zhambyl ΔP, ΔP, 2050s ΔP, 2050s ΔP, 2050s 2050s January 29% 29% 27% 27% February 21% 11% 9% 20% March 13% 14% 5% 19% April 27% 18% 14% 17% May 20% 18% -6% 5% June 8% 13% 12% 9% July -3% 6% -4% -8% August -16% -3% -18% -24% September -5% 1% -28% -9% October 18% 8% -7% 14% November 21% 26% 17% 38% December 20% 21% 20% 33% Seasonal / Annual Means Winter 23% 20% 19% 27% Spring 20% 14% 9% 19% Summer -4% 6% -3% -8% Autumn 11% 12% -6% 14% Annual 13% 13% 5% 13%

Table II-15: Monthly, seasonal and annual mean projected change in area averaged precipitation (ΔP, percent change) over project provinces during 2080s under RCP 4.5 pathway

Change in Precipitation (%) Eastern Kazakhstan Karaganda Kyzylorda Zhambyl ΔP, ΔP, 2080s ΔP, 2080s ΔP, 2080s 2080s January 27% 28% 23% 30% February 29% 23% 13% 30% March 25% 25% 11% 32% April 28% 28% 24% 30% May 23% 28% 15% 8% June 12% 22% 15% 5% July -10% -6% -8% -23% August -15% -4% -2% -11% September 6% 13% 12% -1% October 16% 9% -12% 12% November 16% 16% 14% 32% December 25% 27% 20% 26% Seasonal / Annual Means Winter 27% 26% 18% 29% Spring 28% 25% 16% 31% Summer -4% 4% 2% -10% Autumn 13% 13% 5% 14% Annual 16% 17% 10% 16%

39. The findings in Table II-13 to Table II-15 suggest that, under RCP 4.5 pathway, the projected annual mean increases in precipitation in project provinces of Kazakhstan at each time slices are likely to be between +10% to +17% by the end of this century. Winter precipitation increases are projected to be most pronounced (+29% in Zhambyl province) by the end of 21st century (Table II-15).

C. Uncertainties in Future projections

40. Confidence in climate projections is a measure of how plausible the projected range of change is for a given emission scenario. Confidence comes from multiple lines of evidence including physical theory, past climate changes and climate model simulations. Three major sources of uncertainty exist which should be considered while using the future projections presented in this report. These include the choice of climate model, the choice of emissions scenario, and the internal variability of the modeled climate system. The relative contributions to uncertainty depend on the climate variable considered, as well as the province and time horizon. Internal variability is roughly constant through time, and the other uncertainties grow with time, but at different rates18. Internal variability and inter-model uncertainty are more important for the near-term. The uncertainty due to the choice of emissions scenario becomes more important toward the end of the twenty-first century. For precipitation projections across the globe, the RCP scenario uncertainty is relatively small when compared to the other sources of uncertainty. For midcentury precipitation projections, model internal variability is more important and this persists in some provinces into the late century.

18 Knutti, R, Allen, MR, Friedlingstein, P, Gregory, JM, Hegerl, GC, Meehl, GA, Meinshausen, M, Murphy, JM, Plattner GK, Raper, SCB, Stocker, TF, Stott, PA, Teng, H, Wigley, TLM, 2008: A review of uncertainties in global temperature projections over the twenty-first century, J. Clim. 21: 2651–2663. doi:10.1175/2007JCLI2119.1

41. Dealing with uncertainty is a fundamental task when using climate projections into a decision making context. Even where there is high confidence in the response of the climate system to forcings, there is uncertainty from natural variability and our incomplete knowledge of how climate changes affect the context of the decision. Also, there will always be some inevitable uncertainty in the processes involved and their impacts. In case where the direction of change is clear and we have High Confidence in the approximate ranges of change for a given scenario, such as for temperature, then the main risk management decisions may be about emission scenarios. In cases where there is considerable uncertainty in the response of the climate system to forcings, such as regional precipitation change, projections can still be useful but the decision making framework must be adaptive and put more emphasis on sampling the range of uncertainty in response.

42. We have provided here the future projections of the climate change in Republic of Kazakhstan and its four project provinces that would result from a given set of assumptions, or future scenarios, regarding human activities (including changes in population, technology, economics, energy, and policy). Future changes also have some uncertainty due to natural variability, particularly over shorter time scales and limitations in scientific understanding of exactly how the climate system will respond to human activities. The relative importance of these three sources of uncertainty changes over time. Which type of uncertainty is most important also depends on what type of change is being projected: whether, for example, it is for average conditions or extremes, or for temperature or precipitation trends. In view of this, the projections of key climate variables and core indicators reported here should be used with caution.

D. Summary - Key Findings on Climate Change

43. Salient features of projected annual mean climate change over Kazakhstan and its project provinces for the future based on our analysis are as highlighted below:

• The trend analysis of mean annual temperature over land areas in Kazakhstan shows increase in annual mean temperatures between 1941 and 2016. In autumn, the highest rate of increase in mean air temperature over the country is in the range of 0.26 – 0.37 °C/decade. The average annual temperature in Kazakhstan has been rising by 0.28 ºC/decade. The rate of warming is more pronounced during spring / autumn seasons (@ 0.30 – 0.31 ºС/decade) than in the summer (@ 0.19ºC/decade) or winter (@ 0.28 ºC/decade) seasons.

• The nighttime minimum temperatures have risen more than the daytime maximum temperatures since the early 1990s suggesting a decrease in diurnal temperature range.

• Increase in annual mean temperatures is most pronounced in Kyzlorda province between 1941 and 2016. The trend in annual mean surface air temperature in East Kazakhstan is the least of the four provinces considered here.

• More frequent hot days (when the daily maximum temperature was above 90th percentile at least for six consecutive days) have also been observed at over 70% of the 121 meteorological stations in Kazakhstan during the past three decades.

• During the past three decades, the frequency of frost days with daily minimum temperature below 0ºC is reported to be decreasing in Kazakhstan.

• The length of dry spells has increased over conterminous Kazakhstan concurrently with observed increasing trends in precipitation intensity.

• Long time annual mean precipitation trends over Kazakhstan do not exhibit any significant increasing or decreasing trend. On an average, annual precipitation has a marginally increasing trend on a countrywide basis. In Kazakhstan precipitation tends to slightly decrease @ 0.7 mm per decade in all seasons except winter when precipitation tends to increase @ 1.5 mm per decade and are significant during the period from 1940-2016.

• A marginal (statistically insignificant) increase in annual precipitation (@ 0.1 – 5.0 mm/decade) has been observed in Karaghandy province. In other provinces considered here, precipitation has a statistically insignificant decreasing trend @ 0.1 – 4.2 mm per decade during the period 1941-2016.

• Beyond the middle of this century, global and regional temperature changes will be determined primarily by the rate and amount of various emissions released by human activities, as well as by the response of the Earth’s climate system to those emissions. Efforts to rapidly and significantly reduce emissions of heat-trapping gases (e.g., RCP 4.5 pathway or still lower emission trajectory) can limit the global temperature increase to 2ºC relative to the 1961-1990 time period. However, significantly greater temperature increases are expected if emissions follow higher scenarios associated with continuing growth in the use of fossil fuels (e.g., RCP 8.5 pathway).

• Following the observed trends over recent decades, more precipitation is expected to fall as heavier precipitation events. In many mid-latitude provinces, including the Republic of Kazakhstan, there will be fewer days with precipitation but the wettest days will be wetter.

• Mean annual temperatures in Kazakhstan are projected to increase by 2.47°C by the 2020s, 4.48°C by the 2050s and 7.02°C by the 2080s under RCP 8.5 concentration pathway. Under RCP 4.5 concentration pathway, the projected rise in surface temperature is restricted to 2.19°C by the 2020s, 3.69°C by the 2050s and 4.44°C by the 2080s.

• The projected average warming in Kazakhstan at all time slices during this century is higher during the winter than during the summer, spring or autumn seasons (least during autumn). The peak warming averaged over Kazakhstan is likely to occur in March by the middle of this century and beyond.

• The annual mean rise in surface air temperature would be higher towards the western side of Kazakhstan relative to eastern Kazakhstan during all time slices. The range extends from 2.1°C over the southeast side (near Caspian Sea) to 2.6°C over the northwest side of the country during 2020s under RCP 8.5 pathway. During the period from 2050s to 2080s, the gradient in rise in surface air temperature over Kazakhstan from south to north is projected to have a significantly high range from 1.5°C to 3.0°C.

• On an annual mean basis, the peak projected warming of 2.6°C under RCP 8.5 pathway is likely to occur in Karaghandy province around 2020s (February/March would likely be the warmest months). The least annual mean warming of 2.2°C could be expected in Zhambyl province by 2020s. Within the four seasons, winter is projected to be warmest around 2020s in Kyzlorda province but the peak warming occurs during summer season in the remaining three provinces. The peak warming over the four provinces shifts to winter season with annual

mean warming of about 4.5°C in East Kazakhstan province and 4.1°C in Zhambyl province around 2050s. During the 2080s, annual mean warming of about 7.3°C in East Kazakhstan province and 6.6°C in Zhambyl province is expected. Peak warming continues to occur during the winter season in all the four provinces in 2080s.

• During winter months, a marginal decline in diurnal temperature range is projected for the future suggesting that rise in maximum surface air temperature is likely to be lower than the minimum surface air temperature in winter during all time slices in the future.

• On an annual mean basis, the projected change in precipitation averaged over the country is a decline in precipitation by about -2% of the baseline value around 2020s, no change from the baseline value around 2050s and an increase in precipitation of 4% of the baseline value around 2080s (under RCP 8.5 pathway). Within the four seasons, the model results suggest a significant increase in precipitation of about 24% during the winter by the end of this century. However, during the summer season, a modest decline of -13% in precipitation is projected.

• A significant increase in area averaged precipitation of as much as about 12% may be expected in spring season precipitation by the end of this century. On a monthly basis, August is likely to be when precipitation could be expected to decline (between -20% to -28%) substantially throughout this century leading to drought conditions in some of the drought prone areas of Kazakhstan. Peak increases in precipitation is projected in January month.

• Under the RCP 4.5 pathway, the model projections indicate an increase in precipitation in all seasons (likely to be most pronounced in the northern provinces of the country) but in summer, no appreciable change is likely. On an annual basis, an increase in precipitation of about +11% could occur by the end of this century. In such a future scenario, recurrence of floods in flood prone areas of Kazakhstan is likely in spring when the snow melt starts.

• The projected decline in annual precipitation over the four provinces would range between - 5% (in East Kazakhstan province) to -15% (in Kyzlorda province) with respect to the baseline value around 2020s (under RCP 8.5 pathway). This decline in precipitation is most pronounced during summer (particularly in August month) in all the provinces. Around 2050s, a marginal increase (+2%) is likely in East Kazakhstan province, while a marginal decline of - 4% to -13% (East Kazakhstan province is an exception with about 2% increase) is possible in the other provinces. The winter precipitation around this decade would likely increase between +3% (in Karaghandy province) to +11% (in East Kazakhstan province). During spring season also, a modest increase in precipitation in three of the four provinces is likely around 2050s. However, the summer and autumn precipitation would experience a significant decline (-25% to -40% in August month) in precipitation around 2050s. May to October months are likely to experience a decline in precipitation by the end of the 21st century in most of the provinces considered in this study.

• Kazakhstan is likely to experience as many as 100 hotter days by 2020s, 190 days by 2050s and over 260 days in a year by 2080s under the RCP 8.5. The hotter days would likely only be marginally more during summer season than winter season (peak in number of hot days would be in July). Autumn season will experience relatively lower hot days than any other season.

• A decrease in number of frost days in Kazakhstan is projected for the future. The peak in lesser number of frost days are likely in spring and autumn seasons of the year under both

the RCP 8.5 and 4.5 pathways. The maximum change in number of frost free days in a year by the end of this century is projected to be in Zhambyl province (55 days) under RCP 8.5 pathway. The average duration of the frost-free season is expected to be about 40 days longer across Kazakhstan (20 days under RCP 4.5 pathway) by the end of this century than it was in the early 20th century.

• The dry days over the four provinces in Kazakhstan are likely to increase by between 12 to 16 days in a year by the end of this century under RCP 8.5 pathway. This suggests that the projected warming will accelerate land surface drying with increases in the potential incidence and severity of droughts.

• No significant changes are likely in the number of wet / very wet days (increase of only between 1 to 3 days) over Kazakhstan by the end of this century under RCP 8.5 pathway.

• More precipitation is expected to fall as heavier precipitation events across Kazakhstan in the future. A 56% increase in maximum precipitation intensity on an annual basis may occur under RCP 8.5 pathway but may be restricted to just about 38% under RCP 4.5 pathway by the end of this century. Hence, the projected increases in heavy precipitation are expected to be greater under higher emissions scenarios as compared to lower ones. Changes in peak precipitation intensity are significantly different in each province for each of the two RCP pathways thus highlighting the spatial as well as temporal variability in the trends of precipitation-related extremes.

III. IMPACT OF CLIMATE CHANGE AND EXTREMES

A. Assessment Tool WEAP

1. Background WEAP

44. A standardized approach to climate change impact and vulnerability assessment does not exist yet. Various methods and guideline exist and ADB, amongst others, are in the process of refining the approach to climate risk assessment19, 20, 21, 22.

45. Various climate change impact assessment tools exist and appropriate selection of the most relevant is essential for the success of a project (Figure III-1). For the impact assessment analysis at the regional level and at sub-project level the WEAP framework can be considered as the most suitable.

Figure III-1: Relation between spatial scale and physical detail in water allocation tools. The green ellipses show the key strength of some well-known models23

46. Conventional supply-oriented catchment simulation models are not adequate for exploring the full range of climate change impact analysis. Over the last decade, an integrated approach to water development has emerged which places water supply projects in the context of demand- side management, regarding climate change impact analysis. WEAP incorporates these values into a practical tool for water resources planning by balancing supply and demand. Moreover, WEAP is very scalable and therefore able to incorporate processes into detail where necessary and scoping where possible.

19 Asian Development Bank (ADB). 2017. Guidelines for Climate Proofing Investment in the Water Sector: Water Supply and Sanitation. https://www.adb.org/sites/default/files/ institutional-document/219646/guidelines-climate-proofing-water.pdf. 20 Asian Development Bank (ADB). 2014. “Climate Risk Management in ADB projects.” 21 Asian Development Bank (ADB). 2012. Addressing Climate Change and Migration in Asia and the Pacific. Asian Development Bank. http://www.adb.org/publications/addressing-climate-change-and-migration-asia-and-pacific. 22 Economic Commission for Europa. 2009. Guidance on Water and Adaptation to Climate Change. https://www.unece.org/fileadmin/DAM/env/water/publications/documents/ Guidance_water_climate.pdf. 23 Droogers and Bouma, 2014. Simulation modelling for water governance in basins. International Journal of Water Resources Development. p. 475-494.

47. A detailed discussion on WEAP can be found in the WEAP manual which can be freely downloaded from the WEAP website (http://www.weap21.org/). In summary, WEAP have the following features: • Integrated Approach: Unique approach for conducting integrated water resources planning assessments. • Stakeholder Process: Transparent structure facilitates engagement of diverse stakeholders in an open process. • Water Balance: A database maintains water demand and supply information to drive mass balance model on a link-node architecture. • Simulation Based: Calculates water demand, supply, runoff, infiltration, crop requirements, flows, and storage, and pollution generation, treatment, discharge and in- stream water quality under varying hydrologic and policy scenarios. • Policy Scenarios: Evaluates a full range of water development and management options, and takes account of multiple and competing uses of water systems. • User-friendly Interface: Graphical drag-and-drop GIS-based interface with flexible model output as maps, charts and tables. • Model Integration: Dynamic links to other models and software, such as QUAL2K, MODFLOW, MODPATH, PEST, Excel and GAMS. Links to all other models can be developed quite easily since WEAP can read and write plain text files similar as SWAT, SPHY, SWAP, Mike11, HEC-HMS, HEC-RAS and Geo-SFM.

2. WEAP Input Data

48. WEAP have options to setup the analysis in a more conceptual mode or a more physical based one. In general, a mixture will be used based on the questions to be answered and data availability. Accuracy of the analysis for a specific area depends on the availability of data. For this specific study, it is important to realize the difference between “absolute” accuracy and “relative” accuracy. “Absolute” accuracy relates to how well the model represents reality; “relative” accuracy relates to the accuracy of comparing different scenarios. It has been proven that even if “absolute” accuracy is low, “relative” accuracy can be still high.24

49. Availability and access to good quality of data is essential for IWRM analysis using WEAP. Required input data can be divided into the following main categories: • Model building o Static data25 ▪ Land use, land cover ▪ Irrigation ▪ Population ▪ Reservoir operational rules o Dynamic data ▪ Climate (rainfall, temperature, reference evapo-transpiration) ▪ Evapo-transpiration by crops and natural vegetation ▪ Water demands by all sectors ▪ Reservoir releases

24 Droogers, P., A. Van Loon, W. Immerzeel. 2008: Quantifying the impact of model inaccuracy in climate change impact assessment studies using an agro-hydrological model. Hydrology and Earth System Sciences 12: 1-10 25 Nota that static data can still vary over longer time frames, but are fairly constant over days/weeks

• Climate change impact o Precipitation changes o Temperature

50. Each of the above categories can be refined depending on availability and accessibility of data. The WEAP framework is flexible in level of details of data availability. A typical example is that water demands can be included as a total amount of water, but can be also estimated by WEAP using for example the population, their daily required intake and daily and/or monthly variation. Similarly, climate data can be entered at annual, monthly, 10-days or daily level. The more refined the input dataset is, the higher the accuracy of the WEAP model scenarios will be. Input data can be from local sources or from the public domain (Figure III-2).

Figure III-2: Development in data availability to support water allocation tools over the last 20 years • Land use/land cover o GlobCover: European Space Agency Global Land Cover Map • Irrigated Area o National statistics (compiled by FAO-AQUAstat) o Global Map of Irrigated Areas • Climate reference o CRU • Climate projections o RCP • Streamflow o National statistics (compiled by

51. Some typical examples of how the WEAP Schematic input was created can be seen in the following Figures: • River Nodes (Figure III-4) • Catchments Nodes (Figure III-5) • Groundwater Nodes (Figure III-6) • Demand Nodes (Figure III-7)

52. Detailed description of model setup, data and scenarios is provided hereafter.

Figure III-3: Schematization of one Sub-Catchment. Each demonstration catchment has been divided in five to six of these Sub-Catchments

Figure III-4: Most relevant input fields for the River Nodes in a Sub-Catchment

Figure III-5: Most relevant input fields for the Catchment Nodes in a Sub-Catchment

Figure III-6: Most relevant input fields for the Groundwater Nodes in a Sub-Catchment

Figure III-7: Most relevant input fields for the Demand Nodes in a Sub-Catchment

B. Scenarios analysed

53. Scenario analysis has been used to assess the climate risk and vulnerability of the project areas and project activities. The following set of scenarios, based on the projected climate change as described in the previous Chapter, has been defined and further analyzed. • 00_Reference: Base line conditions at the start of the project. These represent the baseline climate as defined in Chapter 2 and are the weather conditions as occurred between 1961 and 1990. • 01_CC_2020: Climate conditions as projected around 2020 for the RPC 8.5 conditions, assuming that the irrigation rehabilitation project has not taken place. This scenario is useful to evaluate the impact of climate change only. • 02_CC_2050: Same as above, but for the climate conditions around 2050. • 03_CC_2080: Same as above, but for the climate conditions around 2080. • 04_Ref_Rehab: Base line climate conditions and assuming that the project would have been implemented already. This scenario is used to see the impact of the project assuming that climate change would not happen. • 05_2020_Rehab: Climate conditions as projected around 2020 for the RPC 8.5 conditions, and assuming that the irrigation rehabilitation project has been completed. • 06_2050_Rehab: Same as above, but for the climate conditions around 2050. • 07_2080_Rehab: Same as above, but for the climate conditions around 2080.

C. Impact at province Level

1. Overall

54. The previous Chapter described how the future climate will develop in terms of projected changes in temperature and precipitation. Those changes will affect many sectors that are water depending. Here, focus will be put on the irrigation sector, but given it dependency on overall water resources, these are discussed as well.

55. Water availability depends on the one hand on what a certain province flows in from other province and/or countries (Figure III-8). In general, half of the water is from local origin and the other half is runoff from sources inside the country. On the other hand, is water availability a function of local rainfall, evapotranspiration, groundwater recharge, surface runoff and base flow. Kazakhstan has quite some internal water resources and these are available for use (Figure III-9). Precipitation in Kazakhstan varies considerably, but overall a substantial amount of water is available in the country. Of all water resources, only a relatively small portion is abstracted. Figure III-11 shows that for most provinces less than 20% of the river flow is altered by human interference. Only in the Syr-Darya province and the Esil province over 20% of river flow is used.

Figure III-8: Main trans-boundary rivers and flow directions

Figure III-9: Digital elevation model and main streams and rivers.

Figure III-10: Total river flows and forecast for the entire country26

Figure III-11: Human induced reductions in river flows27.

56. Higher temperatures due to climate change will lead to an increased water demand by agriculture and by natural vegetation as well. This higher demand (evapotranspiration) by the natural vegetation might lead to a lower amount of runoff to rivers and streams. At the same time, changes in precipitation due to climate change must be considered as well. Figure III-12 indicates the changes in actual evapotranspiration by the vegetation using advanced hydrological modeling. The changes in expected runoff indicate that for most of the country somewhat lower runoff can be expected with the exception of the eastern part of the country (Figure III-13). These

26 Abishev et al. 2015: Committee on Water Resources 27 Abishev et al. 2015: Committee on Water Resources

changes as very relevant for the rehabilitation project, as most water for irrigation is generated within the province. Kyzlorda is the exception where most water comes from the Syr Darya.

Figure III-12: projected changes in actual evapo-transpiration in 2050. Negative values (orange) indicate less evapo-transpiration in the future; positive values (green) indicate more evapo-transpiration in the future [Source: Water2Invest (http://w2i.geo.uu.nl/node/12)]

Figure III-13: projected changes in runoff in 2050. Negative values (orange) indicate less runoff in the future; positive values (green) indicate more runoff in the future [Source: Water2Invest (http://w2i.geo.uu.nl/node/12)]

2. East Kazakhstan

57. Water demand will increase by about one third for East Kazakhstan by the proposed irrigation rehabilitation. At the same time will the projected increase in temperature as a result of global climate change also alter water demand. In Figure III-14 the mean annual water demand for irrigation is shown indicating that both climate change and the project itself will increase the demand for irrigation. The figure also shows that the impact of climate change is relatively small on water demand compared to the impact of the project itself.

58. Annual water shortage (unmet demand) is shown in Figure III-15. Important to note is that the Figure represents water shortage assuming that after project implementation (Rehab in the Figure) the delivery infrastructure is implemented according to the current design criteria. Water shortage can be mainly attributed to climate change and can be as high as 30 MCM per year around 2050 and even substantially higher around 2080. Although this seems to be substantially considering total demand this is only a small fraction. Moreover, Irtysh river and local resources are abundant available as long as the irrigation infrastructure will be designed and constructed taking this into consideration.

59. Inflow and outflow of water out of the East Kazakhstan province is mainly related to Irtysh river (Figure III-16 and Figure III-17). The analysis shows that total inflow will be reduced. More importantly is that there is a clear shift in expected inflows where the peak inflow will shift from May to April. Main reason is higher temperatures so earlier snow melt in the upstream catchments. Interesting is that local water resources are expected to be somewhat higher as a result of increased precipitation.

60. Table III-1 summarizes the main findings of the impact of the project itself and of climate change. For East Kazakhstan province the main issues to consider during the project design and implementation are: shift in the peak runoff and higher runoff rates.

Figure III-14: projected monthly water demand for the irrigation sector in East Kazakhstan for the four time horizons, without and with the irrigation rehabilitation (Rehab) project

Figure III-15: projected annual unmet water demand for the irrigation sector in East Kazakhstan for the four time horizons, without and with the irrigation rehabilitation (Rehab) project

Figure III-16: Mean monthly projected inflow into East Kazakhstan province for the four time horizons, without and with the irrigation rehabilitation (Rehab) project implemented

Figure III-17: Mean monthly projected river outflow from the East Kazakhstan province for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project implemented

Table III-1: Summarized impact of climate change on East Kazakhstan province MCM/year % change Demand by irrigation Demand current 876 Demand after rehabilitation 1165 33% Demand in 2050 1230 6% Unmet demand irrigation Unmet demand 0 0% Unmet demand after rehabilitation 0 0% Unmet demand in 2050 16 1% Inflow into province Inflow current 7,941 Inflow 2020 6,894 -13% Inflow 2050 7,461 -6% Outflow out of province Outflow current 30,790 Outflow after rehabilitation 26,111 -15% Outflow in 2050 27,932 7%

3. Karaghandy

61. Current water demand for irrigation in Karaghandy province is relatively low and is expected to increase substantially after project implementation. At the same time will the projected increase in temperature as a result of global climate change also alter water demand. This is shown in Figure III-18 demonstrating that the impact of climate change is relatively small on water

demand compared to the additional demand by the extension of area that will be served by irrigation water.

62. Annual water shortage (unmet demand) is shown in Figure III-19. Important to note is that the Figure represents water shortage assuming that after project implementation (Rehab in the Figure) the delivery infrastructure is implemented according to the current design criteria. Water shortage will be relatively small but might increase in 2050 by climate change. This projected shortage will be on average around 5% by the year 2050, but substantial year-to-year and monthly variation can occur.

63. Table III-2 summarizes the main findings of the impact of the project itself and of climate change. For Karaghandy province the main issues to consider during the project design and implementation is: the considerable additional water demand due to the project; while climate change issues will be rather modest.

Figure III-18: projected monthly water demand for the irrigation sector in Karaghandy for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project

Figure III-19: projected annual unmet water demand for the irrigation sector in Karaghandy for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project

Table III-2: Summarized impact of climate change on Karaghandy province MCM/year % change Demand by irrigation Demand current 352 Demand after rehabilitation 593 68% Demand in 2050 608 2% Unmet demand irrigation Unmet demand 1 0% Unmet demand after rehabilitation 9 2% Unmet demand in 2050 22 4%

4. Kyzlorda

64. Kyzlorda water demand will increase by a small amount by the proposed irrigation rehabilitation project. At the same time will the projected increase in temperature as a result of global climate change also alter water demand. In Figure III-20 the mean annual water demand for irrigation is shown indicating that both climate change and the project itself will increase the demand for irrigation. The figure also shows that the impact of climate change is about the same order as the additional demand required by the project activities itself.

65. Annual water shortage (unmet demand) is shown in Figure III-21. Important to note is that the Figure represents water shortage assuming that after project implementation (Rehab in the Figure) the delivery infrastructure is implemented according to the current design criteria. Water shortage can be attributed to climate change and to project implementation as well can be as high as over 100 MCM per year around 2050 and even substantially higher around 2080. Water resources and therefore water shortages in Kyzlorda are highly dependent on the flows in

Syr Darya. The result presented here take into account changes in climate but assume no changes in upstream reservoir operations.

66. Inflow and outflow of water out of the Kyzlorda province are mainly related to Syr Darya river (Figure III-22 and Figure III-23). The analysis shows that total inflow will be reduced. More importantly is that there is a clear shift in expected inflows where the peak inflow will shift from May to April. Main reason is higher temperatures so earlier snow and glacial melt in the upstream catchments. Outflows out of the province are expected to be reduced even further.

67. Table III-3 summarizes the main findings of the impact of the project itself and of climate change. For Kyzlorda province the main issues to consider during the project design and implementation are: potential water shortages; seasonal shifts in inflows, and reduced outflows to downstream areas and Aral Sea.

Figure III-20: projected monthly water demand for the irrigation sector in Kyzlorda for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project

Figure III-21: projected annual unmet water demand for the irrigation sector in Kyzlorda for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project

Figure III-22: Mean monthly projected inflow into Kyzlorda province for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project implemented

Figure III-23: Mean monthly projected outflow from Kyzlorda province for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project implemented

Table III-3: Summarized impact of climate change on Kyzlorda province MCM/year % change Demand by irrigation Demand current 1478 Demand after rehabilitation 1762 19% Demand in 2050 1803 2% Unmet demand irrigation Unmet demand 14 1% Unmet demand after rehabilitation 56 3% Unmet demand in 2050 101 6% Inflow into province Inflow current 1,984 Inflow 2020 1,677 -15% Inflow 2050 1,800 -9% Outflow out of province Outflow current 3,993 Outflow after rehabilitation 2,306 -42% Outflow in 2050 2,373 3%

5. Zhambyl

68. Water demand is influenced by both project implementation as well as climate change. In Figure III-24 the mean annual water demand for irrigation is shown indicating that both climate change and the project itself will increase the demand for irrigation. The figure also shows that the impact of climate change is smaller than that of the project itself.

69. Annual water shortage (unmet demand) is shown in Figure III-25. Important to note is that the Figure represents water shortage assuming that after project implementation (Rehab in the Figure) the delivery infrastructure is implemented according to the current design criteria. Water shortage can be mainly attributed to climate change and can be as high as 30 MCM per year around 2050 and even substantially higher around 2080. Although this seems to be substantially considering total demand this is only a small fraction. Moreover, Irtysh river and local resources are abundant available as long as the irrigation infrastructure will be designed and constructed taking this into consideration.

70. Outflow of water out of the Zhambyl province is mainly related to Shu river (Figure III-26). The analysis show that total outflow will be reduced and that a clear seasonal shift in outflow can be expected. Main reason is higher temperatures so earlier snow melt in the higher areas of the province.

71. Table III-4 summarizes the main findings of the impact of the project itself and of climate change. For Zhambyl province the main issues to consider during the project design and implementation are: a reduction in outflows to the downstream areas.

Figure III-24: projected monthly water demand for the irrigation sector in Zhambyl for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project

Figure III-25: projected annual unmet water demand for the irrigation sector in Zhambyl for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project

Figure III-26: Mean monthly projected outflow from Zhambyl province for the four time horizons, and without and with the irrigation rehabilitation (Rehab) project implemented

Table III-4: Summarized impact of climate change on Zhambyl province MCM/year % change Demand by irrigation Demand current 1253 Demand after rehabilitation 1563 25% Demand in 2050 1600 2% Unmet demand irrigation Unmet demand 20 2% Unmet demand after rehabilitation 66 4% Unmet demand in 2050 108 7% Outflow out of province Outflow current 6,336 Outflow after rehabilitation 5,317 -16% Outflow in 2050 5,631 6%

D. Impact at sub-project level

1. Methodology

72. For each of the subprojects the climate risk and vulnerability has been assessed. Summarizing factors to assess this climate risk and vulnerability at subproject level have defined: • Crop water requirements o The crop water requirements are influenced by changes in temperature. In general higher temperatures will increase the water demand by the crop. This vulnerability was assessed using the Penman-Monteith equation and was calculated with the WEAP assessment tool. • Water shortage o projected future water shortage is a function of changes in crop water requirements and projected changes in water availability from local sources, upstream sources

and groundwater availability. GIS and the WEAP assessment tool has been used for this. • Flooding o Vulnerability of flooding is a complex function of changes in rainfall patterns, runoff, and slopes. This has been assessed using the WEAP assessment tool combined with GIS analysis. • Upstream dependency o Some of the subprojects are for their water resources more dependent of upstream sources that others. Using so-called average flow accumulation based on GIS analysis this factor has been assessed. • Seasonal shift o It can be expected that as a result of climate change a seasonal shift in cropping patterns and water demand for irrigation will happen. Using the WEAP model this change in potential cropping season has been assessed. • Groundwater o Availability of groundwater can be influenced to a large extent as rainfall patterns will change and higher evapo-transpiration by natural vegetation and crops can be expected. Using WEAP changes in groundwater under climate change have been assessed.

73. The main tools to assess the climate risk and vulnerability are detailed GIS analysis and the WEAP assessment tool. A typical example of changes that occurred in the irrigation sector over the last 25 years is shown in Figure III-27. Obviously, these changes provide an integrated picture of potential climate change and of deterioration in irrigation infrastructure.

74. Analyses with GIS were using a combination of local data sets, global available data sets and remote sensing derived products. Most relevant are data on elevation, slopes, stream flow network and flow accumulated areas. GIS and remote sensing, in fact data in general, provide only information over the past. Therefore, the WEAP assessment tool was used to combine those data with climate change projections as discussed in Chapter 2.

Figure III-27: Example of changes in irrigated areas based on Landsat satellite data using maximum NDVI values over 5 years.

Precipitation (+ snowmelt) Irrigation Evapotranspiration

Surface runoff

Soil water

Interflow (drainage)

Percolation

Z1 (%)Z1 Soil water capacity (mm) capacity water Soil

Deep water

Base flow

Z2 (%)Z2 Deep water water (mm) capacity Deep

Figure III-28: Main soil water balance processes (top) and typical data input screen of WEAP

2. East Kazakhstan subprojects

75. An overview of the area and the location of the proposed subprojects in East Kazakhstan province is shown in the Table and Figure below. A total of five rather large subprojects has been defined in the province. Based on the analysis using WEAP and GIS, the various Tables and Figures hereafter provide a good picture of the risk and vulnerability of the subprojects. The most important conclusions that can be drawn based on these analyses are: • Crop water requirements will increase by about 8% by 2020 compared to the reference situation (1990) up to about 15% by 2050. No much variation between the subprojects in East Kazakhstan can be expected. • Reduction in runoff will be in the order of 20% by 2020. Interesting is that by 2050 reduction in runoff is less compared to 2020 as a result of changes in precipitation in the province. This is especially noticeable for the two southern subprojects Tarbagatayskiy and Urdzharskiy. The average date with highest runoff, mainly from snow melt, is expected to shift by 10 days in 2050 for the two northern located subprojects Kurchumskiy and Zharminskiy. • Changes in maximum snow depths and therefore in water stored for spring crops is not substantial till 2050. Only for Urdzharskiy with its more southern location maximum snow depths will decrease substantially. • Finally, the radar plots for each subproject provide an overview of the main issues regarding climate change impact and assessment for the subprojects. In summary, the following conclusions can be drawn for the East Kazakhstan province: o Potential changes of somewhat more flooding can be expected especially for Kurchumskiy and to a lesser extent for Tarbagatayskiy. o Crop water requirement is expected to increase and should be addressed during design phase. o Expected changes are quite similar for all subprojects in the province. o Reduction in groundwater recharge is an import issue for most subprojects. o Other impacts of climate change, such as dependency on upstream water resources, water shortages and seasonal shifts are not assed as severe.

Figure III-29: Changes in crop water requirements (ET reference) for the subprojects in East Kazakhstan province. Based on Penman-Monteith calculations using the WEAP model

Table III-5: Average expected date of peak runoff under climate change and changes in annual average runoff compared to the reference situation (1961-1990). Result obtained using the hydro-climate model WEAP Average expected peak runoff Changes in runoff subprojects Reference 2020 2050 2080 2020 2050 2080 Kurchumskiy 04-May 26-Apr 28-Apr 22-Apr -23% -17% -21% Tarbagatayskiy 22-Apr 19-Apr 19-Apr 17-Apr -19% -9% -9% Urdzharskiy 13-Apr 21-Apr 15-Apr 11-Apr -18% -5% 1% Zharminskiy 22-Apr 19-Apr 12-Apr 12-Apr -20% -10% -9%

Figure III-30: Average slopes in each subproject area as indicator for flood risk under climate change

Figure III-31: Average flow accumulation for each subproject area in East Kazakhstan province indicating dependency of upstream water resources in terms of climate impact and vulnerability

Figure III-32: Changes in projected maximum snow depths for the subprojects in East Kazakhstan province. Based on hydro-climatic calculations using the WEAP model and compared to reference conditions (1960-1990)

Figure III-33: Radar plots for each subproject in East Kazakhstan province indicating vulnerability to climate change for six topics (A value of 1 means less vulnerable, value of 5 indicates more vulnerable)

3. Karaghandy subprojects

76. Subprojects in Karaghandy provinces are shown in the Table and Figure below. The largest subproject area is located in Bukhar-Zhyrauskiy. Based on the analysis using WEAP, climate change scenarios and GIS, various Tables and Figures hereafter provide a good picture of the risk and vulnerability of the subprojects. The most important conclusions that can be drawn based on these analyses are: • Crop water requirements will increase by about 8% by 2020 compared to the reference situation (1990) up to about 15% by 2050. No much variation between the subprojects in Karaghandy province can be expected. • Changes in runoff will be substantial and in the order of 35% by 2020 and 30% by 2050. These changes are mainly a result of increased evapo-transpiration and changes in precipitation. Not much variation between subprojects is projected regarding runoff. Interesting is that the average date with highest runoff does not show a consistent trend. Most likely is that earlier snow melt by higher temperatures is influenced by changing rainfall patterns.

• Changes in maximum snow depths are in the order of 20% by 2020 as well as by 2050. By 2080 reductions in maximum snow depth of 25 to 40% can be expected. Not much variation between the subproject in Karaghandy is foreseen. • Finally, the radar plots for each subproject provide an overview of the main issues regarding climate change impact and assessment for the subprojects. In summary, the following conclusions can be drawn for the Karaghandy province: o Additional crop water requirements is an important issue for all subprojects and needs attention during the design phase. o Reductions in groundwater recharge is an import issue for the for the norther located subprojects: Abayskiy, Bukhar-Zhyrauskiy, Nurinskiy, and Osakarovskiy. o Other impacts of climate change, such as flooding, dependency on upstream water resources, seasonal shifts are not assed as severe.

Figure III-34: Changes in crop water requirements (ET reference) for the subprojects in Karaghandy province (Based on Penman-Monteith calculations using the WEAP model)

Table III-6: Average expected date of peak runoff under climate change and changes in annual average runoff compared to the reference situation (1961-1990) [Result obtained using the hydro-climate model WEAP] Average expected peak runoff Changes in runoff subprojects Reference 2020 2050 2080 2020 2050 2080 Abayskiy 19-Apr 21-Apr 25-Apr 18-Apr -32% -30% -27% Bukhar-Zhyrauskiy 04-May 26-Apr 25-Apr 22-Apr -34% -32% -31% Nurinskiy 25-Apr 22-Apr 21-Apr 15-Apr -32% -29% -26% Osakarovskiy 06-May 30-Apr 02-May 25-Apr -34% -33% -31% Ulytauskiy 16-Apr 13-Apr 07-Apr 04-Apr -28% -21% -14% Zhanaarkinskiy 16-Apr 17-Apr 12-Apr 10-Apr -29% -24% -18%

Figure III-35: Average slopes in each subproject area as indicator for flood risk under climate change

Figure III-36: Average flow accumulation for each subproject area in East Kazakhstan province indicating dependency of upstream water resources in terms of climate impact and vulnerability

Figure III-37: Changes in projected maximum snow depths for the subprojects in Karaghandy province [Based on hydro-climatic calculations using the WEAP model and compared to reference conditions (1960-1990)]

Figure III-38: Radar plots for each subproject in Karaghandy province indicating vulnerability to climate change for six topics (A value of 1 means less vulnerable, value of 5 indicates very vulnerable)

4. Kyzlorda subprojects

77. A summary of the Kyzlorda province subprojects is shown in the map and table hereafter. A total of eight subprojects has been defined in the province. Based on the analysis using WEAP, climate change scenarios and GIS the various Tables and Figures hereafter provide a good picture of the risk and vulnerability of the subprojects. The most important conclusions that can be drawn based on these analyses are: • Crop water requirements will increase by about 7% by 2020 compared to the reference situation (1990) up to about 13% by 2050. Variation between the subprojects in Kyzlorda province is small. • Expected reduction in runoff will be substantial and in the order of nearly 40% by 2020 as a result of higher evaporation and changes in rainfall. The average date with highest runoff will not change substantially, since snow fall and snow melt is limited in the province. • Changes in maximum snow depths are large. By 2080 it can be expected that snow will be very unusual in the province. This is positive from a cropping season perspective, but at the same time will put more emphasis on delivery of irrigation water earlier in the season. • Finally, the radar plots for each subproject provide an overview of the main issues regarding climate change impact and assessment for the subprojects. In summary, the following conclusions can be drawn for the Kyzlorda province: o The overall expected changes are quite similar for all subprojects in Kyzlorda province. o Dependency on upstream water resources is a major aspect to consider during design and operations of irrigation infrastructure. o A substantial seasonal shift can be expected with earlier growing seasons and therefore irrigation water demand. o Water shortage might be increasing due to climate change.

Figure III-39: Changes in crop water requirements (ET reference) for the subprojects in Kyzlorda province [Based on Penman-Monteith calculations using the WEAP model and compared to reference conditions (1960-1990)]

Table III-7: Average expected date of peak runoff under climate change and changes in annual average runoff compared to the reference situation (1961-1990) [Result obtained using the hydro-climate model WEAP] Average expected peak runoff Changes in runoff subprojects Reference 2020 2050 2080 2020 2050 2080 Aral`skiy 12-Apr 17-Apr 15-Apr 06-Apr -41% -42% -52% Karmakchinskiy 11-Apr 08-Apr 03-Apr 08-Apr -39% -38% -47% Kazalinskiy 13-Apr 16-Apr 26-Apr 24-Apr -39% -39% -48% Qyzylorda 07-Apr 09-Apr 04-Apr 01-Apr -37% -35% -43% Shieliyskiy 05-Apr 02-Apr 03-Apr 08-Apr -36% -33% -40% Terenozekskiy 10-Apr 12-Apr 07-Apr 02-Apr -38% -36% -44% Zhanakorganskiy 09-Apr 07-Apr 07-Apr 08-Apr -38% -36% -45% Zhalagashskiy 03-Apr 04-Apr 04-Apr 29-Mar -36% -33% -40%

Figure III-40: Average slopes in each subproject area as indicator for flood risk under climate change

Figure III-41: Average flow accumulation for each subproject area in East Kazakhstan province indicating dependency of upstream water resources in terms of climate impact and vulnerability

Figure III-42: Changes in projected maximum snow depths for the subprojects in Kyzlorda province. Based on hydro-climatic calculations using the WEAP model and compared to reference conditions (1960-1990)

Figure III-43: Radar plots for each subproject in Kyzlorda province indicating vulnerability to climate change for six topics (A value of 1 means less vulnerable, value of 5 indicates very vulnerable)

5. Zhambyl subprojects

78. Subprojects in Zhambyl province are shown in the Table and Figure below. The largest subproject areas are Shuskiy and Talasskiy. Based on the analysis using WEAP, climate change scenarios and GIS various Tables and Figures hereafter provide a good picture of the risk and vulnerability of the subprojects to climate change. The most important conclusions that can be drawn based on these analyses are: • Crop water requirements will increase by about 7% by 2020 compared to the reference situation (1990) up to about 13% by 2050. No much variation between the subprojects in the Zhambyl province is expected. • Changes in runoff will be quite small and between 2020 and 2050 an increase of runoff can be expected, although still somewhat below the reference situation (1990). Interesting is that the average date with highest runoff does not show a consistent trend. Most likely is that earlier snow melt by higher temperatures is influenced by changing rainfall patterns. • Changes in maximum snow depths are in the order of 30% by 2020 and 40%-50% by 2050. By 2080 reductions in maximum snow depth of even 70% can be expected.

• Finally, the radar plots for each subproject provide an overview of the main issues regarding climate change impact and assessment for the subprojects. In summary, the following conclusions can be drawn for the Zhambyl province: o Additional crop water requirement is an important issue for all subprojects and needs attention during the design phase. o Seasonal shifts, earlier growing season, can be expected for all subprojects. o Potentially higher changes on flooding due to climate change are foreseen for Lugovskoy. Especially the steeper slopes with changing rainfall patterns are the main reasons for this. o Other impacts of climate change, such as water shortage and dependency on upstream water resources are projected not to be major concerns of climate change.

Figure III-44: Changes in crop water requirements (ET reference) for the subprojects in Zhambyl province (Based on Penman-Monteith calculations using the WEAP model)

Table III-8: Average expected date of peak runoff under climate change and changes in annual average runoff compared to the reference situation (1961-1990) [Result obtained using the hydro-climate model WEAP] Average expected peak runoff Changes in runoff subprojects Reference 2020 2050 2080 2020 2050 2080 Lugovskoy 17-Apr 11-Apr 07-Apr 04-Apr -16% -8% -11% Sarysuskiy 10-Apr 07-Apr 03-Apr 30-Mar -14% -3% -2% Shuskiy 16-Apr 09-Apr 07-Apr 08-Apr -17% -11% -15% Talasskiy 08-Apr 07-Apr 02-Apr 08-Apr -12% 1% 3%

Figure III-45: Average slopes in each subproject area as indicator for flood risk under climate change

Figure III-46: Average flow accumulation for each subproject area in East Kazakhstan province indicating dependency of upstream water resources in terms of climate impact and vulnerability

Figure III-47: Changes in projected maximum snow depths for the subprojects in Zhambyl province [Based on hydro-climatic calculations using the WEAP model and compared to reference conditions (1960-1990)]

Figure III-48: Radar plots for each subproject in Zhambyl province indicating vulnerability to climate change for six topics (A value of 1 means less vulnerable, value of 5 indicates very vulnerable)

IV. ADAPTION AND INVESTMENTS TO CLIMATE CHANGE

A. Adaptation Needs and Basic Principles

79. As reported above, global warming will heighten the exposure to drought in Kazakhstan. Overall, Kazakhstan will become more drought-prone in the spring and summer and more humid during the remainder of the year. Arid areas could encroach on semi-arid zones, and the sub- humid grain belt will shrink. The quantity of water resources in North and East Kazakhstan may likely remain stable or reduce in future. The Ishim Basin seems most vulnerable. The number of dry days is anticipated to increase everywhere. The number of frost days would also decrease, while heat waves will increase in the more continental areas. Because of heightened aridity, grain crop yields may fall while pasture productivity may increase in initial stages of increase in temperature, but will decline after the temperature threshold is exceeded. The temperature increases in these provinces may benefit pasture and livestock production, although summer heat stress and changing disease patterns may be a factor of concern. Deforestation, soil erosion, soil salinization, and desertification will intensify, and forest fires will become more frequent.

80. As the impacts of climate change become more clearly understood, so too does the need for people to effectively respond and adapt to these changes. The semi-arid provinces of Kazakhstan are particularly vulnerable to climate-related impacts and risks. Across stakeholders and scales In Kazakhstan, water is perceived and reported as a scarce resource. However, responses to managing it are mostly focused on augmenting supply rather than managing demand, and the processes used to govern it are mostly reactive with very few examples of flexible, forward-looking decision-making. The subproject sites in project provinces in Kazakhstan are characterized by frequent drought and flood hazards and associated food insecurity problems, and decreases in the volume of irrigation water.

81. To date, most adaptation efforts have focused on reactive, short-term and site-specific solutions to climate-related vulnerabilities. Although important, these responses often fail to address the root causes of neither vulnerability, nor shed light on how to proactively spur larger- scale and longer-term adaptation that has positive effects on socio-economic development. The capacity and resource deficits at the sub-national and local levels limit support for water and adaptation governance.

82. The general principles of adaptation strategies to climate change are discussed extensively in many publications, reports and scientific publications. Most important is to distinguish the main topics related to climate change: (i) climate change projections, (ii) impacts, (iii) adaptation, and (iv) mitigation. In this report are the climate change projections discussed in Chapter 2, and impacts in Chapter 3. Mitigation issues are beyond the scope of this specific report, but might be addressed at higher policy levels. This chapter deals with the adaptive responses on the impacts of climate change.

83. Climate proofing needs to be considered appropriately in the project design stage. Although climate proofing could increase the upfront costs of the infrastructure projects (such as water reservoirs etc.), such higher costs could be economically justified by lower total life cycle costs over the long lifetime of most infrastructure and by the high probability of climate-related damage. A robust adaptation pathway is one that allows for flexibility in dealing with future climate vulnerability in the face of newly available evidence. It requires the continued monitoring and review of ongoing climate change measures to avoid locking in long-term investments in potentially inefficient undertakings.

84. It has been discussed widely that these responses can be re-active or pro-active. Given uncertainty in climate change projections a mixture of pro-active adaptation and re-active responses are often applied.

Figure IV-1: Schematic of climate change issues and their linkages [Based on http://www.cifor.org/]

Table IV-1: Differences between mitigation and adaptation as function of the spatial and temporal scale and the main sectors involved [Adapted from http://www.cifor.org/] Mitigation Adaptation

Spatial Scale Primarily an international Primarily a local issue, as issue, as mitigation provides adaptation mostly provides global benefits benefits at the local scale

Time Scale Mitigation has a long-term Adaptation can have a effect because of the inertia short-term effect on the of the climatic system reduction of vulnerability

Sectors Mitigation is a priority in the Adaptation is a priority in the energy, transportation, water and health sectors, industry, waste coastal or low lying areas, management, and and agricultural sectors agricultural sectors

B. Potential Adaptation and Investment Measures

85. A broad range of adaptation measures to overcome negative impact of climate change has been described in various reports. The third to sixth national communication of Kazakhstan

to the UNFCCC (2013) summarizes the following impact and adaptation strategies for the agricultural sector and is summarized here:

86. Climate change contemplates both positive and negative consequences for plant-growing of the Republic of Kazakhstan. The following main negative consequences of expected climate changes were determined on the basis of conducted researches (KazHydromet, 2013): • increase in number of days with high air temperature; • enhancement of climate dryness and increase in drought repetition; • increase in share of rain-shower precipitation; • increase in cases of hail fallout; • reduction in period with snow cover; • increase in inter-annual and intra-seasonal variability of weather pattern; • increase in repetition of anomalous cold winters and hot summers; • dislocation of agro-climatic wetting zones to the north; • decrease in yield capacity of cereal crops; • development of infectious diseases and insects of agricultural crops, distribution of weed vegetation.

87. The KazHydromet publication also summarized the main adaptation measures that are implemented or can be implemented: • no-till farming • plant-growing diversification • implementation of effective irrigation systems • optimization (adjustment) of terms for performance of agro-technical activities to weather pattern • re-equipment of agricultural vehicle and equipment fleet • preparation and professional improvement of agricultural specialists • improvement of plant-growing insurance system

88. A recent publication regarding climate change adaptation28 is based on an extensive inventory of existing and well-proven measures in the agricultural sector. This overview is very relevant for the proposed irrigation rehabilitation project.

28 Iglesias, A., L. Garrote, 2015: Adaptation strategies for agricultural water management under climate change in Europe - Agricultural water management, 155: 113-124

Adaptation needs Measure Mechanism to overcome the impacts of climate change I. Improve resiliency (1) Implement regional adaptation Enhances effectiveness of adaptation and adaptive capacity plans measures (2) Improved monitoring and early Mitigates consequences of adverse events warning (3) Improve coordination planning Enhances effectiveness of adaptation measures (4) Innovation and technology Improves effectiveness of adaptation measures and reduces costs II. Response to (5) Innovation: water use efficiency Increases water availability changes in water (6) Improve soil moisture retention Increases water use efficiency availability capacity (7) Small-scale water reservoirs on Increases water management flexibility at farmland the local level (8) Improve the reservoir capacity Increases management flexibility and water availability at regional level (9) Water reutilization Increases water availability (10) Improve water charging and Decreases inefficient use of water trade (11) Re-negotiation of allocation Improves water use efficiency agreements (12) Set clear water use priorities Improves water use efficiency (13) Integrate demands in Increases management flexibility and water conjunctive systems availability III. Response to floods (14) Create/restore wetlands Reduces flood peaks and droughts (15) Enhance flood plain Reduces flood vulnerability management (16) Improve drainage systems Reduces extent and duration of flooding (17) Farmers as ‘custodians’ of flood Decreases risk of flood damages plains (18) Hard defenses Decreases risk of flood damages (19) Increase rainfall interception Reduces flood peaks at the local level capacity (20) Introduce drought resistant Improves agronomic water use efficiency crops (21) Insurance to flood or drought Decreases economic losses to the farmer IV. Response to (22) Change in crops and cropping Decreases economic risk to farmers increased irrigation patterns requirements (23) Improve practices to retain soil Decreases the need for additional water to moisture crops (24) Develop climate change Mitigates impacts of climate change resilient crops V. Response to (25) Relocation of farm processing Maintains industrial activity changes in agricultural industry land use (26) Addition of organic material into Recovers soil functions soils (27) Introduce new irrigation areas Develops new agricultural land VI. Response to (28) Improve nitrogen fertilization Reduces agricultural diffuse pollution deterioration of water efficiency and soil quality (29) Soil carbon management and Reduces soil erosion and improves soil zero tillage water retention capacity (30) Protect against soil erosion Reduces land degradation VII. Response to loss of (31) Increase water allocation for Improves ecosystem services, effective at biodiversity ecosystems the global level (32) Maintain ecological corridors Improves biodiversity with positive global consequences (33) Improve crop diversification Improves biodiversity

89. Regulated runoff to rivers is one of the most critical factors for desertification in Kazakhstan. In all of the river basins, ground water levels have dropped and the land is drying. Land degradation and desertification would be worse if the flow of the trans-boundary rivers such as Ili, Syr Darya, Irtysh, and Ural were to be further regulated within the territories of the neighboring countries.

90. Any strategy to adapt agriculture and food systems to a changing climate must exploit the diversified means of climate resilient strategies29, including irrigation agriculture. Variability and extreme rainfall events have the potential to transform agricultural production systems (rain-fed or irrigated) and sub-sectoral diversifications of agricultural production (including crops, live-stock, forestry and fisheries) as well as downstream production activities (like processing, marketing and off-farm activities) which could help to smoothen agricultural consumption and production along the value chain30, 31. The ability to circumvent the negative impact of climate and weather variability in agricultural production is also an important consideration for climate-smart agriculture (CSA) and for maximizing its benefits of enhancing agricultural livelihoods and economic development.

91. CSA is an emerging concept and practice that seeks to adapt agricultural production to climate change and weather variability, while maintaining agricultural productivity, biodiversity and the ecosystem that sustains food security, livelihoods and economic development. CSA seeks to enhance productivity, water conservation, livelihoods, biodiversity, resilience to climate stress, and environmental quality32, 33, 34. The overall outcome of CSA is to improve agricultural livelihood incomes, promote food security and sustainable agricultural development by ensuring that agricultural production systems are best suited to respond to the challenges of climate change and variability. The resilience of agriculture to climatic changes and variability could boost agricultural production and broadly contribute to sustainable development.35

92. Technologies and practices do exist or have been developed in different parts of the world, to facilitate adaptation to climate change in the agriculture sector. These range from improved weather forecasts to water conservation, drip irrigation, sustainable soil management, better livestock management, and change in crop types and planting, among others. Some of these measures may need investment while the others primarily require improving awareness and building capacity to deal with new practices. The technologies that allow for and promote diversity are more likely to provide a strategy, which strengthens agricultural production in the face of uncertain future climate change scenarios. It is important here to understand local contexts – especially social and cultural norms – when working with sub-national and local stakeholders to make informed decisions about appropriate technology options. Some of these are:

29 Vermeulen, S.J., Aggarwal, P.K., Ainslie, A., Angelone, C., Campbell, B.M., Challinor,A.J., Hansen, J.W., Ingram, J.S.I., Jarvis, A., Kristjanson, P., Lau, C., Nelson, G.C.,Thornton, P.K., Wollenberg, E., 2012: Options for support to agriculture and food security under climate change. Environ. Sci. Policy 15, 136–144. 30 Liverman, D.M., Kapadia, K., 2010: Food systems and the global environment: anoverview. In: Ingram, J.S.I., Ericksen, P.J., Liverman, D. (Eds.), Food Security andGlobal Environmental Change. Earthscan, London. 31 Nelson, G.C., Rosegrant, M.W., Koo, J., Robertson, R., Sulser, T., Zhu, T., Ringler, C., Msangi, S., Palazzo, A., Batka, M., Magalhaes, M., Valmonte-Santos, R., Ewing, M., Lee, D., 2009: Climate Change: Impact on Agriculture and Costs of Adaptation. Food Policy Report #19, IFPRI, Washington, DC. 32 FAO, 2013: Climate-Smart Agriculture Sourcebook. Food and Agriculture Organization of the United Nations, Rome. 33 FAO, 2010: Climate-Smart Agriculture−Policies, Practices and Financing for Food Security, Adaptation and Mitigation. Food and Agriculture Organization of the United Nations, Rome. 34 Neufeldt, H., Kristjanson, P., Thorlakson, T., Gassner, A., Norton-Griffiths, M., Place, F., Langford, K., 2011: Making Climate-Smart Agriculture Work for the Poor. World Agroforestry Centre (ICRAF). Policy Brief 12, Nairobi, Kenya. 35 Nwanze, K.F., Fan, S., 2016: Climate change and agriculture: strengthening the role of smallholders. In: Global Food Policy Report. International Food Policy Research Institute, Washington, DC, pp. 13–21.

• Planning for climate change and variability, • Sustainable water use and management, • Soil management, • Sustainable crop management, • Sustainable livestock management, • Sustainable farming systems, and • Capacity building and stakeholder organization.

C. Specific Adaptations and Investment Actions

93. The following specific adaptation and investment actions towards climate change can be considered, based on the main conclusions of the projected climate change as described in Chapter 2: • Higher temperatures are to be expected with on average 0.3oC for each 10 years. This increase is somewhat more pronounced during spring and autumn. Kyzlorda show a slightly higher increase and East Kazakhstan slightly lower. In general, these seasonal and spatial differences between seasons and province are relatively small. • Precipitation is expected to reduce between -5% (in East Kazakhstan province) to -15% (in Kyzlorda province). Reduction will be more pronounced during summer. At the same time are higher daily rainfall intensities projected.

94. Using these climate change projections in the assessment tools (described in Chapter 3) leads to an integrated impact. Main conclusions (see Chapter 3 for details): • Higher crop water requirements of about 8-10% by 2020 and 15% by 2050 compared to the reference (1990). Not much variation between provinces and subprojects is projected. • Seasonal shift with earlier planting opportunities and therefore irrigation demands earlier in the season. • Substantial reduction in runoff to streams and rivers as a result of higher evaporation rates and precipitation reductions. • Higher flooding risks due to projected extremes in daily rainfall, especially in the more mountainous provinces. • Falling groundwater tables as a result of lower recharge.

95. Appropriate adaptation and investment actions for the project in general have been identified. More detailed fine-tuning for each subproject adaptation and investment needs can be assessed from the specific results described in Chapter 3. In summary: • Crop water requirements are in general 10-15% higher compared to the 1990 climate data. Required canals capacity is therefore also 10-15% higher. • Storage capacity needs to be substantially higher to compensate for the projected decrease in base flows and increase in demand. Exact numbers should be subproject and project level determined. • In general flooding is not considered as the most threatening consequences of climate change in the project areas. However, flood resilience should receive additional attention for those areas in mountainous areas with steep slopes. • Training of water managers at KVK is essential to ensure implementation of these measures and to be prepared during management of the system over the coming decades.

• Awareness raising to farmers is needed to make them aware that changes in climate will influence water delivery and will gradually lead to a change in cropping patterns and farm water management. • Water pricing will in most cases not lead to reduction in water consumption, but will help to make water services more reliable.

Climate Adaptation Plans within the Project Adaptation Activity / Target Estimated Cost Justification Measures Climate Risk Cost T ‘000 Rehabilitation Changes in 116,193 To keep record of water delivery data in the /construction of water surface flow whole project area. This will forewarn any metering structures patterns and abnormal water delivery trends resulting from precipitation climatic changes. Establishment of 24 Droughts and 775,286 Centralized data acquisition and remote- system control and floods controlled operation of key water distribution data acquisition structures will facilitate immediate and (SCADA) systems, appropriate actions in case of extreme one in each district adverse hydrologic event in any project district. Capacity Changes in 91,000 Awareness of beneficiaries and KVK staff development of temperature improved regarding monitoring of climate beneficiaries and and changes, climate risk management, and KVK staff precipitation developing adaptation measures. Concrete lining of Changes in 7,994,920 Concrete lining of water channels would water conveyance precipitation reduce conveyance losses. Thus, 25% of the channels and total investments for concrete lining was temperature considered climate change adaptation. Geogrid has been introduced instead of steel, as reinforcement material, which will reduce costs and, hence, improve returns under uncertain and variable future climatic conditions. Drip irrigation Droughts and 6,190,700 With drip irrigation, water requirements are changes in reduced to less than 50% compared to precipitation conventional irrigation systems together with and increased yields and improved product quality. temperature In addition, it will also save fertilizer and insecticide use and reduce labor requirements. Total 15,168,099 ($40,127,246) T = Kazakh tenge.

D. Concluding Remarks

96. The scaling up climate adaptation efforts in Kazakhstan would need to be based on consensus-building among communities, and assisting capacity development in Kazakhstan and its project provinces to effectively respond to climate change. Specifically, it should aim to enhance the climate resilience across the country and mitigate the impacts of natural disaster risks in project provinces through four major measures: (i) mainstreaming adaptation policies, plans, programs and projects into development planning; (ii) strengthening information systems and capabilities to facilitate the adaptation process; (iii) establishing the legal, regulatory, and institutional framework to support policy implementation, and (iv) promoting access to affordable financing for climate-resilient development. A robust adaptation pathway is one that allows for flexibility in dealing with future climate vulnerability in the face of newly available evidence. It requires the continued monitoring and review of ongoing climate change measures to avoid locking in long-term investments in potentially inefficient undertakings. Regardless of the climate scenario and modeling approach, the estimated cost of climate change adaptation is

considerable. Kazakhstan will require substantial increases in investment, supported as appropriate with financial and technical support from the international community.

97. Adaptation to climate change impacts is increasingly being observed in both physical and ecological systems as well as in human adjustments to resource availability and risk at different spatial and societal scales36. The adaptation activities that require climate risk information are new infrastructure, resource management, retrofit, behavioral, institutional, sector, communication, and financial. In addition, the elements of effectiveness, efficiency, equity, and legitimacy are also important for the success of sustainable development into an uncertain future. The degree of success would depend on the capacity to adapt and the distribution of that capacity.

98. To address underlying causes of disaster risk and climate vulnerability would require limiting the maximum increase in warming below if not well below 1.5°C, a peaking of global emissions by 2020 at the latest, and the achievement of net carbon neutrality by the 2050s in realization of the Paris Agreement.

36 Droogers, P., J. Aerts. 2005: Adaptation strategies to climate change and climate variability: a comparative study between seven contrasting river basins. Physics and Chemistry of the Earth 30: 339-346

V. CONCLUSIONS AND RECOMMENDATIONS

99. This report presents future projections of the climate change in Kazakhstan and its four provinces that would result from a given set of assumptions, or future scenarios, regarding human activities (including changes in population, technology, economics, energy, and policy). Future changes also have some uncertainty due to natural variability, particularly over shorter time scales and limitations in scientific understanding of exactly how the climate system will respond to human activities. The relative importance of these three sources of uncertainty changes over time. Which type of uncertainty is most important also depends on what type of change is being projected: whether, for example, it is for average conditions or extremes, or for temperature or precipitation trends. In view of this, the projections of key climate variables and core indicators reported here should be used with caution.

100. The scaling up climate adaptation efforts in Kazakhstan would need to be based on consensus-building among communities, and assisting capacity development in Kazakhstan and its project provinces to effectively respond to climate change. Specifically, it should aim to enhance the climate resilience across the country and mitigate the impacts of natural disaster risks in project provinces through four major measures: (i) mainstreaming adaptation policies, plans, programs and projects into development planning; (ii) strengthening information systems and capabilities to facilitate the adaptation process; (iii) establishing the legal, regulatory, and institutional framework to support policy implementation, and (iv) promoting access to affordable financing for climate-resilient development. A robust adaptation pathway is one that allows for flexibility in dealing with future climate vulnerability in the face of newly available evidence. It requires the continued monitoring and review of ongoing climate change measures to avoid locking in long-term investments in potentially inefficient undertakings. Regardless of the climate scenario and modeling approach, the estimated cost of climate change adaptation is considerable. Kazakhstan will require substantial increases in investment, supported as appropriate with financial and technical support from the international community.

101. Adaptation to climate change impacts is increasingly being observed in both physical and ecological systems as well as in human adjustments to resource availability and risk at different spatial and societal scales. The adaptation activities that require climate risk information are new infrastructure, resource management, retrofit, behavioral, institutional, sector, communication, and financial. In addition, the elements of effectiveness, efficiency, equity, and legitimacy are also important for the success of sustainable development into an uncertain future. The degree of success would depend on the capacity to adapt and the distribution of that capacity.

102. To address underlying causes of disaster risk and climate vulnerability would require limiting the maximum increase in warming below if not well below 1.5°C, a peaking of global emissions by 2020 at the latest, and the achievement of net carbon neutrality by the 2050s in realization of the Paris Agreement. Beyond the middle of this century, global and regional temperature changes will be determined primarily by the rate and amount of various emissions released by human activities, as well as by the response of the Earth’s climate system to those emissions. Efforts to rapidly and significantly reduce emissions of heat-trapping gases (e.g., RCP 4.5 pathway) might still limit global warming to or below 2ºC relative to the 1901-1960 time period. However, significantly greater temperature increases are expected if emissions follow higher scenarios associated with continuing growth in the use of fossil fuels (e.g., RCP 8.5 pathway).

103. Climate change is projected to have significant impacts on agriculture in Kazakhstan. The impacts of climate change may likely be skewed against the mountainous areas of south and southeast Kazakhstan, where it is expected that precipitation during the growing season will

decrease, and freshwater limitations and water stress will be experienced in the coming decades due to changes in snow and glacier melt. In southeast Kazakhstan agricultural production is mixed (e.g. livestock and crops), small and medium landholdings predominate, and the adaptive capacity of farmers is often low, due to the deterioration of existing irrigation infrastructures in the post-Soviet era and lack of financial and other resources.

104. Specific impact and adaptation and investment measures are described in detail for each of the subprojects. Some overall conclusions and recommendations are: (i) design crop water requirements and delivery infrastructure should be about 10-15% higher compared to the 1990 reference period; (ii) storage capacity (reservoir capacity) should be substantially higher to compensate for the projected decrease in base flows and increase in demand; (iii) flood resilience should receive additional attention for those areas in mountainous areas with steep slopes; (iv) training of water managers at KVK on climate change should be included in the project; (v) awareness raising on climate change issues to farmers is needed; (vi) water pricing should be introduced to make water services more reliable.

105. Finally, during project design and operational phase of the project adaptation to climate change is essential. At the same time are the required investments modest compared to overall project costs and essential to make the project sustainable over the coming decades.

VI. APPENDIX: PROJECTED CHANGES IN CLIMATE EXTREMES AS INFERRED FROM SELECTED CORE CLIMATIC INDICATORS

A. Climatic Indicators

106. Climate extremes play an important role in influencing society and natural systems. The objective of this analysis was to assess the spatial distribution and temporal trends of extreme precipitation and temperature events responses to global warming on the project provinces of Kazakhstan. The temperature extremes exhibited a significant warming trend, consistent with global warming. Warming trends in autumn and winter were greater than in spring and summer. Besides, precipitation extremes also exhibited statistically increasing trends, such as peak precipitation intensity and number of wet/very wet days, etc. In the following sub-sections, we shall present and discuss the key findings on the likely changes in selected core climatic indicators (measure of extremes).

B. Change in Number of Hot Days

107. As we go forward in time and the impacts of climate change become more significant, the colder temperatures are expected to warm more rapidly than the mean temperatures. This means that climate change may be most obvious in the warmer winter nights and hotter summer days to come. Table VI-1 lists the outcome of our analysis of number of days with hotter temperatures at different future time slices averaged over Kazakhstan under RCP 8.5 and 4.5 pathways.

Table VI-1: Monthly, seasonal and annual mean projected number of hot days over Kazakhstan at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

Average increase in number of hot days (Tmax > 90th percentile)

Hotter Hotter Hotter Hotter Hotter Hotter days in days in days in days in days in days in 2020s 2050s 2080s 2020s 2050s 2080s RCP 8.5 RCP 4.5 January 10 21 27 10 16 22 February 11 19 25 10 14 20 March 6 13 21 5 11 14 April 6 10 17 5 8 10 May 6 12 18 4 10 11 June 10 18 25 8 15 17 July 17 25 27 14 24 26 August 15 23 27 13 20 23 September 8 14 20 6 12 13 October 5 10 15 4 8 9 November 3 8 17 1 6 7 December 6 16 25 6 12 18 Seasonal / Annual Change Winter 27 56 76 26 43 61 Spring 17 34 56 14 29 35 Summer 42 66 78 35 59 66 Autumn 16 32 52 12 26 29 Annual 102 189 263 87 156 191

108. This analysis suggests that, broadly, Kazakhstan is likely to experience as many as 100 hotter days by 2020s, 190 days by 2050s and over 260 days in a year by 2080s under the RCP 8.5 pathway (Figure VI-1). The hotter days would likely only be marginally more during the summer season as compared to the winter season (peak in number of hot days would be in July). Autumn season will experience relatively lower hot days than any other season. Kazakhstan is likely to experience only around 190 hotter days in a year by 2080s under the RCP 4.5 pathway (Figure VI-2).

Figure VI-1: Spatial distribution of projected number of hotter days over Kazakhstan likely by 2080s under RCP 8.5 pathway

Figure VI-2: Spatial distribution of projected number of hotter days over Kazakhstan likely by 2080s under RCP 4.5 pathway Table VI-2: Monthly, seasonal and annual mean projected change in number of frost days over Kazakhstan at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

Average change in frost daysΔFrost (Tmin < ΔFrost0°C) ΔFrost ΔFrost days ΔFrost days ΔFrost days days in days in days in in 2020s in 2050s in 2080s 2020s 2050s 2080s RCP 8.5 RCP 4.5 January 0 -1 -2 -1 0 0 February 0 -1 -3 -1 0 0 March -2 -5 -10 -1 -3 -4 April -4 -6 -9 -5 -7 -8 May 0 -1 -1 -1 -1 -1 June 0 0 0 0 0 0 July 0 0 0 0 0 0 August 0 0 0 0 0 0 September -1 -1 -1 -1 -1 -1 October -4 -7 -10 -5 -7 -8 November -2 -4 -8 0 -2 -2 December 0 -1 -3 -1 0 0 Seasonal / Annual Change Winter 0 -3 -8 -3 0 0 Spring -6 -12 -20 -7 -11 -13 Summer 0 0 0 0 0 0 Autumn -7 -12 -19 -6 -10 -11 Annual -13 -27 -47 -16 -21 -24

C. Changes in Number of Frost Days

109. The length of the frost-free season (and the corresponding growing season) has been increasing nationally since the 1980s and is projected to continue to lengthen. The model simulations agree with the observed pattern for late twentieth century of a greater decrease in number of frost days in Kazakhstan in the future. The peak in lesser number of frost days are likely in the spring and the autumn seasons of the year under both the RCP 8.5 and 4.5 pathways (Table VI-2).

110. The maximum change in the number of frost free days in a year by the end of this century is projected to be in Zhambyl province (55 days) while the least would be in Karaghandy province (42 days) under RCP 8.5 pathway (Table VI-3).

Table VI-3: Monthly, seasonal and annual mean projected change in number of frost days over Kazakhstan and the four project provinces at 2080s under RCP 8.5 pathway

Average change in frost days (Tmin < 0°C)

East Kazakhstan Karaganda Kyzlorda Zhambyl January 0 0 -4 -5 February 0 -1 -6 -7 March -6 -8 -14 -15 April -13 -11 -4 -3 May -3 -1 0 0 June 0 0 0 0 July 0 0 0 0 August 0 0 0 0 September -4 -1 0 0 October -15 -13 -6 -6 November -5 -6 -13 -12 December -1 -1 -6 -7 Seasonal / Annual Change Winter -1 -2 -16 -19 Spring -22 -20 -18 -18 Summer 0 0 0 0 Autumn -24 -20 -19 -18 Annual -47 -42 -53 -55

Figure VI-3: Spatial distribution of projected change in number of frost days over Kazakhstan likely by 2080s under RCP 8.5 pathway

100

Figure VI-4: Spatial distribution of projected change in number of frost days over Kazakhstan likely by 2080s under RCP 4.5 pathway

111. Figure VI-3 and Figure VI-4 depict the spatial distribution of projected change in number of frost days over Kazakhstan likely by 2080s under RCP 8.5 and RCP 4.5 pathways respectively. The numbers of frost days are most consistently related to sea level pressure, with more frost days occurring when high pressure dominates on the monthly time scale in association with clearer skies and lower nighttime minimum temperatures. Spatial patterns of relative changes of frost days are, thus, indicative of regional scale atmospheric circulation changes that affect nighttime minimum temperatures. The average duration of the frost-free season is expected to be about 40 days longer across Kazakhstan (20 days under RCP 4.5 pathway) by the end of this century than it was in the early 20th century. While there will continue to be variations in the amount of frost-free days from year to year, climate change is contributing to an overall increase in the number of days without frost in Kazakhstan.

112. The longer the time without a frost, the longer would be the growing season. While this may seem good — more time should lead to a larger crop yield — it could possibly have detrimental effects on the crops we grow. Warmer weather helps pests survive longer which can wreak havoc on crops. Rising temperatures are also expected to contribute to a shift in which areas are most agriculturally productive and what crops grow there.

D. Annual Mean Changes in Number of Dry Days

113. The frequency of dry days affects regional hydrology and ecosystems and potentially influences agriculture and transportation. Future changes in the number of dry days per year can

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either reinforce or counteract projected increases in daily precipitation intensity as the climate warms. The analysis of dry tails of regional precipitation distributions is normally focused on understanding the probability of long-term drought, dry spells, or dry months. Looking at changes in the number of dry days per year facilitates our understanding as to how climate change will affect us that goes beyond just annual or seasonal mean precipitation changes, and allows us to better adapt to and mitigate the impacts of local hydrological changes. The length of dry spells has increased over conterminous Kazakhstan concurrently with observed increasing trends in precipitation intensity. The future projections suggest that the dry days over the project provinces in Kazakhstan could increase by between 12 to 16 days in a year by the end of this century under RCP 8.5 pathway (Figure VI-5). This analysis suggests that the projected warming will accelerate land surface drying with increases in the potential incidence and severity of droughts.

Figure VI-5: Spatial distribution of projected change in number of dry days over Kazakhstan likely by 2080s under RCP 8.5 pathway

E. Changes in Number of Wet / Very Wey Days

114. Observations show that changes are occurring in the amount, intensity, frequency and type of precipitation over Kazakhstan. These aspects of precipitation generally exhibit large natural variability and are associated with increased water vapor in the atmosphere arising from the warming of the world’s oceans. There are also increases in some provinces in the occurrences of both droughts and floods. The warmer climate increases risks of both drought − where it is not raining − and floods − where it is − but at different times and/or places. The spatial distribution of

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changes in the annual count of wet days and very wet days over Kazakhstan by the end of this century under RCP 8.5 pathway are illustrated in Figure VI-6 and Figure VI-7 respectively.

Figure VI-6: Spatial distribution of projected change in number of wet days over Kazakhstan likely by 2080s under RCP 8.5 pathway

115. The analysis suggests that no significant changes are likely in the number of wet / very wet days (increase of only between 1 to 3 days) in a year over Kazakhstan by the end of this century under RCP 8.5 pathway.

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Figure VI-7: Spatial distribution of projected change in number of very wet days over Kazakhstan likely by 2080s under RCP 8.5 pathway

F. Changes in Maximum Rainfall Intensity

116. Theoretically speaking, future increases in precipitation intensity are likely to be an important aspect of climate change, since warming will tend to accelerate the overall hydrological cycle, intensifying the wet extremes (IPCC, 2012). A well-established physical law (the Clausius - Clapeyron relation) determines that the water-holding capacity of the atmosphere increases by about 7% for every 1°C rise in temperature. Because precipitation comes mainly from weather systems that feed on the water vapor stored in the atmosphere, this has generally increased precipitation intensity and the risk of heavy rain and snow events. Increased water vapor would lead to more intense precipitation events even when the total annual precipitation is reduced slightly and with prospects for even stronger events when the overall precipitation amounts increase. An understanding of the ongoing changes in climate and climate-related extremes is, therefore, of central importance for adopting adaptation measures to mitigate negative impacts of climate change (World Bank, 2009).

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Table VI-4: Monthly, seasonal and annual mean projected change in peak precipitation intensity over Kazakhstan at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

117. As is depicted in Table VI-4, the peak precipitation intensity across Kazakhstan is projected to increase with time albeit at a different rate under both the RCP pathways. It is likely that a 56% increase in maximum precipitation intensity on an annual basis may occur under RCP 8.5 pathway but may be restricted to just about 38% by the end of this century. Hence, we can conclude that more precipitation is expected to fall as heavier precipitation events across Kazakhstan in the future. Also, the projected increases in heavy precipitation are expected to be greater under higher emissions scenarios as compared to lower ones.

118. The area averaged changes in maximum precipitation intensity in each of the four project provinces are given below. It is seen that the changes in peak precipitation intensity are significantly different in each province for each of the two RCP pathways thus highlighting the spatial as well as temporal variability in the trends of precipitation-related extremes. The peak change in maximum precipitation intensity is expected in Zhambyl province under RCP 8.5 pathway.

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Table VI-5: Monthly, seasonal and annual mean projected change in peak precipitation intensity over East Kazakhstan province at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

Table VI-6: Monthly, seasonal and annual mean projected change in peak precipitation intensity over Karaghandy province at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

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Table VI-7: Monthly, seasonal and annual mean projected change in peak precipitation intensity over Kyzlorda province at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

Table VI-8: Monthly, seasonal and annual mean projected change in peak precipitation intensity over Zhambyl province at 2020s, 2050s and 2080s under RCP 8.5 and RCP 4.5 pathways

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VII. APPENDIX: INTRODUCTION OF GLOBAL CLIMATE MODELS USED IN THIS CVRA STUDY

A. Model Generated Climate Data

1. General

119. The scientific assessment reports of the Intergovernmental Panel on Climate Change have projected an increase by up to 3 to 5% in winter precipitation across the Central Asia by the end of this century37. This increase is also likely to be associated with a shift in precipitation patterns; heavy rains could be concentrated in shorter and more violent spells. No significant trends are, however, projected for annual precipitation in Central Asia.

120. Before any downscaled data can be ingested into estimating the specific impacts of climate change over a country or province, we need to validate the performance of the models in simulating the historical climatology to attain a degree of confidence in simulations for the future. The performance of a set of 9 ESMs out of 17 ESMs, for which downscaled data is available from NASA website38 were evaluated over Central Asia region by RMSI Private Limited as part of an internal evaluation of model skills in simulation of regional historical climatology. In this study, we have attempted to update the future projections based on four of the best performing state-of-the- art Earth System Models (over Central Asia region). The dataset used for inferring these projections include those generated under RCP 8.5 and RCP 4.5 scenarios for which daily model simulated gridded surface maximum and minimum air temperature and precipitation data were produced under CMIP539 and used in IPCC 5th Assessment Report. In the current scenario of uncertainty in global agreement on mitigative actions for restricting the greenhouse gas emissions (target of stabilizing the emissions at 2°C could still be far away), the RCP 8.5 and RCP 4.540 represent the most plausible global concentration pathway range for the future. Moreover, since policy makers and decision makers at country level and at municipal level in any developing country are interested in higher and lower ends of the possibilities in the vulnerability due to future extremes, we shall opt for considering the best choice of the RCP 8.5 and RCP 4.5 pathways in our vulnerability assessment.

121. Although GCMs are valuable predictive tools, they cannot account for fine-scale heterogeneity of climate variability and change due to their coarse resolution. Numerous landscape features such as mountains, water bodies, infrastructure, land-cover characteristics, and components of the climate system such as convective clouds and coastal breezes, have scales that are much finer than 100–500 kilometers. Such heterogeneities are important for decision makers who require information on potential impacts on crop production, hydrology, species distribution, etc. at scales of 10–50 kilometers.

37 IPCC, 2007: Climate change: Impacts, adaptation and vulnerability, Contribution of WGII to IPCC Fourth Assessment Report, Parry et al. (eds), Cambridge University Press. 38 Thrasher, B., E. P. Maurer, C. McKellar, and P. B. Duffy, 2012: Technical Note on Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314, www.hydrol- earth-syst-sci.net/16/3309/2012/ doi:10.5194/hess-16-3309-2012 39 Taylor, Karl E., Ronald J. Stouffer, Gerald A. Meehl, 2012: An Overview of CMIP5 and the Experiment Design. Bull. Amer. Meteor. Soc., 93, 485–498. 40 Meinshausen, M. S.J. Smith, K. Calvin, J.S. Daniel, M.L.T. Kainuma, and et al., 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213-241.

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122. Various methods have been developed to bridge the gap between what GCMs can deliver and what society/businesses/stakeholders require for decision-making41. The derivation of fine- scale climate information is based on the assumption that the local climate is conditioned by interactions between large-scale atmospheric characteristics (circulation, temperature, moisture, etc.) and local features (water bodies, mountain ranges, land surface properties, etc.). It is possible to model these interactions and establish relationships between present-day local climate and atmospheric conditions through the downscaling process. The downscaling process adds information to the coarse GCM output so that information is more realistic at a finer scale, capturing sub-grid scale contrasts and in-homogeneities.

2. Bias Corrections to the Model Generated Datasets

123. The Bias-Correction step “corrects” the bias of the GCM data through comparisons performed against historical data (Thrasher et al., 2012). For each climate variable in a given day, the algorithm generates the cumulative distribution function (CDF) for the observed data and for the retrospective GCM simulations, respectively, by pooling and sorting the corresponding source values (day of year ± 15 days) over the period from 1950 through 2005. It then compares the two CDFs at various probability thresholds to establish a quantile map between the GCM data and the historical climate data. Based on this map, GCM values in any CDF quantile (e.g., p=90%) can be translated to corresponding GMFD values in the same CDF quantile. Assuming that the CDF of the GCM simulations is stable across the retrospective and the prospective periods, to “correct” the projected future climate variations the algorithm simply looks up the probability quantile associated with the predicted climate values from the estimated GCM CDF, identifies the corresponding observed climate values at the same probability quantile in the observed CDF, and then accepts the latter as the adjusted climate predictions. The climate projections adjusted in this way shall have the same CDF as the GMFD data; therefore, the possible biases in the statistical structure (the variance, in particular) of the original GCM outputs are removed by this procedure.

124. The Spatial-Disaggregation step spatially interpolates the Adjusted GCM data to the finer resolution grid of the 0.25-degree observed gridded data. Other than simple linear spatial interpolation, multiple steps shall be adopted in the SD algorithm to preserve spatial details of the observational data. First, the multi-decade daily climatologies of the Observed data variables (temperature and precipitation) shall be generated at both native and GCM resolutions. The climatology for the SD step is the average for each day of the year calculated over the reference period, 1950-2005. Second, for each time step, the algorithm shall compare the Adjusted GCM variables with the corresponding observed climatology to calculate “scaling factors”. In particular, the scaling factors can be calculated as the differences between the bias-corrected GCM and the observed data for temperature, but as the quotients (between the two datasets) for precipitation to avoid negative values for the latter. Third, the coarse-resolution scaling factors shall be bi- linearly interpolated to the fine-resolution observed data grid. Finally, the scaling factors would be applied, by addition or “shifting” for temperatures and by multiplication for precipitation, on the fine-resolution observed climatologies to obtain the desired downscaled climate fields. Thus, in principle, the algorithm essentially shall merge the observed historical spatial climatology with the relative changes at each time step simulated by the GCMs to produce the final results. 125. The Bias-Corrected Spatial Disaggregation (BCSD) method has been applied to data sets of downscaled CMIP5 climate projections for its use at the country, province or local level

41 TGICA (Task Group on Data and Scenario Support for Impact and Climate Assessment), 2007: General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment, IPCC-DDC website, 71 pp.

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assessment of climate change impacts. This approach used in generating the downscaled dataset inherently assumes that the relative spatial patterns in temperature and precipitation observed from 1950 through 2005 will remain constant under future climate change. Other than the higher spatial resolution and bias correction, this dataset does not add information beyond what is contained in the original CMIP5 scenarios, and thus preserves the frequency of periods of anomalously high and low temperature or precipitation (i.e., extreme events) within each individual CMIP5 scenario.

3. The Earth System Model Datasets

126. The downscaled climate scenarios for the globe that are derived from the ESM runs conducted under the Coupled Model Inter-comparison project Phase 5 (CMIP5) are available as NASA Earth Exchange Global Daily Downscaled projections42 dataset and have now been archived at our data base for some of the ESMs. The dataset available with us includes projections for RCP 8.5 and RCP 4.5 scenarios for which daily scenarios were produced under CMIP5. Each of the climate projections includes daily maximum surface air temperature, minimum surface air temperature, and precipitation for the periods from 1950 through 2100. This dataset facilitates in conducting climate change vulnerability assessment and sector specific impacts at local to regional scales, and thus to enhance our understanding of possible future global climate patterns at the country, province or local spatial scale. Each of these climate projections is downscaled at a spatial resolution of 0.25 degrees x 0.25 degrees (approximately 25 km x 25 km). The key climate variables include daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (“Retrospective Run”) and from 2006 to 2100 (“Prospective Run”). During the downscaling process, the retrospective simulations serve as the baseline data, and are compared against the observational climate records. We have extensively used the downscaled data sets in this study.

127. We have identified four out of nine better performing global climate models over Central Asia and evaluate the model-simulated near surface air temperatures and precipitation for the re province under study and compare these with the average monthly observed climatology (CRU, UK) for the baseline period of 1961-1990. The standard deviation of model simulated surface air temperature and precipitation over the province (averaged over land points only) are compared with CRU-based precipitation and temperature climatology (Climate Research Unit, Norwich-UK) for the period 1961-1990 to examine the skill performance of these models in simulating the seasonal and annual cycle of surface air temperature. For precipitation, RMS errors in the model simulations of annual/seasonal cycle and correlation with observations have been analyzed to obtain the skill score. The simulations of daily intensity of precipitation is also analyzed to finalize the choice of the selected ESMs to be adopted in this study as most appropriate for obtaining climate variability and climate change scenarios at a time scale of a century or shorter.

128. Each of the four climate projection data sets includes daily maximum surface air temperature, minimum surface air temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the climate model based bias-corrected dataset used for scenario development in this study is 0.25 degrees x 0.25 degrees (approximately 25 km x 25 km). This high resolution dataset facilitates in conducting climate change vulnerability assessment and sector specific impacts at local to regional scales, and thus to enhance our understanding of plausible future global climate patterns at the country, province or local spatial scale. These

42 NEX- GDDP (NASA Earth Exchange Global Daily Downscaled projections), 2015: Data Access URL - https://cds.nccs.nasa.gov/nex-gddp/

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downscaled CMIP5 climate projections preserve the frequency of periods of anomalously high and low temperature or precipitation (i.e., extreme events) within each emission scenario and are now being widely used at the country, province or local level assessment of climate change impacts. We have opted not to use RCM generated high resolution data sets in this study as dynamical downscaling through a RCM nested within ESM can facilitate us to visualize relevant, fine-resolution climate features; a tradeoff is that uncertainty and error are difficult to quantify.

A.2 Data Analysis Tools

129. The Climate Data Operators (CDO)43 software is a collection of many operators (more than 600 operators available) for standard processing of climate and forecast model output (gridded observational and climate model data on daily and monthly basis including time series data or horizontal gridded data). The operators include simple statistical and arithmetic functions, data selection and sub-sampling tools, and spatial interpolation. CDO was developed to have the same set of processing functions for GRIB and netCDF datasets in one package. The tool has been developed at Max Planck Institute for Meteorology in Hamburg). CDO has very small memory requirements and can process files larger than the physical memory. CDO is open source and released under the terms of the GNU General Public License v2 (GPL). CDO is distributed as source code - it has to be compiled and installed by the user. CDO should easily compile on every Posix compatible operating system like IBM's AIX, HP-UX, Sun's Solaris as well as on most Linux distributions, BSD variants and cygwin. Thus, it is possible to use CDO similarly on general purpose PCs and unix-based high performance clusters. In case of HPC, it is quite common to install software via source code compilation, because these machines tend to be highly tuned beasts. Special libraries, special compilers, special directories make binary software delivery simply useless even if operating systems support package management systems like rpm (e.g. AIX). That's why CDO uses a customizable building process with autoconf and automake. CDO offers a scripting interface for Ruby and Python.

130. The Climate Data Interface (CDI) is used for the fast and file format independent access to GRIB and netCDF datasets. The local data formats SERVICE, EXTRA and IEG are also supported. CDO is a multi-threaded application. When we use chaining operators then all these operators are running in parallel on different threads. Therefore, all external libraries should be compiled thread safe. Using non-thread safe libraries could cause unexpected errors. Especially, netCDF4 (HDF5) in combination with operator chaining cause problems, if the HDF5 library is not compiled thread safe. CDO process only the data variables and the corresponding coordinate variables of a netCDF file. All coordinate variables and dimensions which are not assigned to a data variable will be lost after processing with CDO.

131. Supported data formats are GRIB 1/2, netCDF 3/4, SERVICE, EXTRA and IEG. There are more than 600 operators available. There are some limitations for GRIB and netCDF datasets. A GRIB dataset has to be consistent, similar to netCDF. That means all time steps need to have the same variables, and within a time step each variable may occur only once. NetCDF datasets are supported only with 2-dimensional, 3-dimensional and 4-dimensional variables and the attributes should follow the GDT, COARDS or CF Conventions. The user interface and some operators are similar to the PINGO package. There are also some operators with the same name as in PINGO but with a different meaning.To order to cut specific areas such as basins or countries from the data, there is a cdo command called ifthen. This command needs a mask with

43 CDO User’s Guide, 2016: Climate Data Operators (CDO) software, Developed by Uwe Schulzweida, MPI for Meteorology, 200pp.

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zeros/ones at the same grid as the data. When comparing two data sets with different spatial resolutions, we always remap the data with higher grid resolution to the lower one. To remap precipitation data, use remapcon (first order conservative remapping) in order to be sure that no precipitation amount will get lost. Further information on CDO is available from https://code.zmaw.de/projects/cdo. Documentation on CDO is available at: https://code.zmaw.de/embedded/cdo/1.6.1/cdo.html. Reference card is available at: https://code.zmaw.de/files/cdo/html/1.6.1/cdo_refcard.pdf.

132. The CDO data selection, sub-sampling, statistical and arithmetic functions have been deployed over CMIP5 model data available in netcdf format (post-script files). Additional operators were used to compute climate indices of daily temperature and precipitation extremes. The definition of these climate indices are from European Climate and Assessment (ECA) project. All computed climatology were finally converted to ascii format, which were plotted in ArcGIS 10.2 (http://www.esri.com/).

4. Data and Choice of Metrics

133. The climate change data analysis for inferring projections of future climate change for Kazakhstan and the four project provinces has been undertaken on four time scales. These time slices are baseline (1961-1990), near term (2020s i.e., 2010 to 2039), medium term (2050s i.e., 2040 to 2069) and long term (2080s i.e., 2070 to 2099).

134. A set of core climatic indicators (https://www.ncdc.noaa.gov/indicators/) have been calculated from the bias corrected data sets for baseline and future time slices at each model grid-point before averaging over the total grid points within the province of interest (Kazakhstan and four project provinces). The focus is to infer vital information required for country (or project provinces) level decision-making in structuring future development plans in relevant sectors. The key core Indicators that we have derived for this assignment are given in Table VII-1.

Table VII-1: List of core climate indicators Core Climate Indicators Based on temperature Based on Precipitation (rainfall + snowfall) Warm nights (TN90p;% of days when Tmin > Wet days (R3mm; annual count of days when Rtot 90th percentile) ≥ 3 mm) Hot days (TX90p;% of days when Tmax > 90th Very wet days (R5mm; annual count of days percentile) Heat wave spell duration (HSDI; annual when Rtot ≥ 5 mm) count of days with at least 6 consecutive days Wet day Rainfall/Snowfall (R95pTOT; annual when Tmax > 90th percentile) total rainfall on days when Rtot > 95th percentile) Cold nights (TN10p;% of days when Tmin < Very wet day Rainfall/Snowfall (R99pTOT; 10th percentile) annual total rainfall on days when Rtot > 99th Cold days (TX10p;% of days when Tmax < 10th percentile) percentile) Rainfall/Snowfall intensity (SDII; average

Cold spell duration (CSDI; annual count of rainfall/Snowfall on days when Rtot ≥ 1 mm) days with at least 6 consecutive days when Longest wet spell (CWD; annual maximum Tmin < 10th percentile) Frost days (FD; annual count of days when number of consecutive days when Rtot ≥ 1 mm) Tmin < 0 °C) Dry days (Rnnmm; annual count of days when Rtot = 0 mm or another pre-selected threshold) Longest dry spell (CDD; annual maximum number of consecutive days when Rtot < 1 mm)

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TN (Tmin) refers to daily minimum surface air temperature; TX (Tmax) refers to daily maximum surface air temperature, Rtot refers to total daily rainfall. Also, total rainfall when Rtot > 95th percentile is counted as total snowfall for days with sub-zero temperatures.

5. Validation of Global Climate Models to Baseline Observed (CRU, UK) Climatology

135. Confidence in ESM projections is based on well-understood physical processes and laws, the ability of ESMs to accurately simulate past climate, and the agreement in results across models. Multiple model comparisons unanimously project warming of globally averaged near- surface temperature over the next few decades in response to increased greenhouse gas emissions. However, at regional scales, the magnitude of this increase varies from one model to another. Additionally, in certain provinces, different models project opposite changes in precipitation amount, which highlights the uncertainty of future climate change projections even when sophisticated state-of-the art ESM tools are used. We have undertaken a validation exercise to ascertain that the national and regional level climatology simulated by a select list of ESMs for the baseline period compares well with the baseline (1961-90) observed (CRU, UK) climatology. The outcome of these exercises is presented and discussed below.

Average Monthly Mean Temperature for Baseline Period (1961 - 1990) 30 Kazakhstan 25 Eastern Kazakhstan Observed (CRU) Simulated (Model) 20 Karanganda

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136. Figure VII-1 depicts comparison of monthly mean model simulated and observed maximum and minimum surface air temperatures averaged over Kazakhstan for the baseline period. It is encouraging to find that the simulated maximum as well as minimum temperature ensembles for each month are within ≤ 1.0°C of the observed climatological means. The models are also able to realistically simulate the observed annual cycle of surface temperatures. While each of the four models reproduces the annual cycle of the maximum and minimum surface temperatures realistically, the simulated monthly mean climatology is within ±1.0°C or less for

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each month when compared with observed climatology. The deviation of simulated maximum and minimum surface temperatures for each of the four models considered here are insignificant which provides support for our choice of ESMs to generate ensemble future projections for Kazakhstan.

137. The model-simulated ensemble of monthly mean precipitation averaged over all grid points over Kazakhstan for the baseline period also compares well with the observed climatology (Figure 6-2). The seasonality in the observed precipitation climatology over Kazakhstan is very well captured in the simulations of baseline climatology in each of the four models. The maximum deviation between the simulated ensemble and observed precipitation is observed in the month of October where the model simulates monthly total precipitation lower by about 4 mm compared to observed climatology. The model-simulated precipitation in the four provinces within Kazakhstan for the baseline period is marginally lower than the observed climatological precipitation during June to November. The maximum inter-model differences in simulated monthly precipitation in any of the four provinces are within ±10 mm.

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Average Monthly Mean Precipitation for Baseline Period (1961 - 1990) 30 Kazakhstan Observed (CRU) Simulated (Model) 1961-1990

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Average Monthly Mean Precipitation for Baseline Period (1961 - 1990) Average Monthly Mean Precipitation for Baseline Period (1961 - 1990) 25 45 Kyzylorda Observed (CRU) Zhambyl Observed (CRU) Simulated (Model) 1961-1990 40 Simulated (Model) 1961-1990 20

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138. Table VII-2 and Table VII-3 summarize the comparative skill in ensemble of selected ESMs in simulating the observed monthly mean maximum and minimum surface air temperatures. In general, the GFDL model simulations of monthly mean maximum or minimum surface air temperatures are best among the four models when compared with observed climatology for Kazakhstan.

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Table VII-2: Baseline climatology of average monthly mean maximum temperature (deg C) over Kazakhstan as observed (CRU Climatology) and simulated by climate model ensemble (RMSE is the root mean square error between the observed and simulated surface air temperatures)

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Table VII-3: Baseline climatology of average monthly mean minimum temperature (deg C) over Kazakhstan as observed (CRU Climatology) and simulated by model ensemble (RMSE is the root mean square error between the observed and simulated minimum surface air temperatures)

139. Table VII-4 summarizes the comparative skill in ensemble of selected ESMs in simulating the observed monthly precipitation climatology and the seasonal cycle. It is rather difficult to match the model simulated monthly precipitation with the observed climatology individually during the baseline as out of four models - some underestimate the precipitation in a particular month while others simulate a marginal excess precipitation. In general, each of the four models simulate marginally lower than observed precipitation during June to November, although the difference between the observed and simulated monthly precipitation totals is ±10 mm.

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Table VII-4: Baseline climatology of average monthly precipitation (mm) over Kazakhstan as observed (CRU Climatology) and simulated by climate model ensembles (RMSE is the root mean square error between the observed and simulated precipitation)

140. The findings summarized in Table VII-4 suggest that during October to May months (winter, spring and autumn seasons) when the precipitation is relatively low relative to the summer season, the skill of simulated ensemble is rather limited for both Kazakhstan as a country as well as project provinces in Kazakhstan, when, in general, models are simulating marginally lower than the observed monthly precipitation.

141. In this assessment, we have presented the projected climate change for Kazakhstan and four project provinces as inferred from ensembles of the four ESMs selected for time slices centered around 2020s (i.e., 2010 to 2039 - near term), 2050s (i.e., 2040 to 2069 - medium term) and 2080s (i.e., 2070 to 2099 - long term) relative to the baseline period of 1961-1990 under section 2 of this report.