Climate Risk and Vulnerability Assessment Report

Madhya Pradesh Irrigation Efficiency Improvement Project (RRP IND 45371)

INDIA: MADHYA PRADESH IRRIGATION EFFICIENCY IMPROVEMENT PROJECT

CLIMATE RISK AND VULNERABILITY ASSESSMENT

FINAL REPORT

JANUARY 2017

This report was prepared by: Anthony Kiem (Climate Scientist) Olivier Drieu (Hydrologist) Andre Oosterman (Economist)

This consultant’s report does not necessarily reflect the views of the ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. All the views expressed herein may not be incorporated into the proposed project’s design.

CURRENCY EQUIVALENTS (as of 31 January 2017) Currency Unit – Indian rupee (₹) ₹1.00 = $0.014743 $1.00 ₹67.829

ABBREVIATIONS ADB – Asian Development Bank CAT – catchment area treatment CCA – cultivable command area CRVA – Climate Risk and Vulnerability Assessment DPR – Detailed Project Report EIA – environmental impact assessment ET0 – reference evapotranspiration GCM – global climate model ha – hectare Kc – crop coefficient KIP – Kundalia Irrigation Project KMMP – Kundalia Major Multipurpose Project km2 – square kilometer m3 – cubic meter Mm3 – million cubic meters MOM – management, operation and maintenance MPIEIP – Madhya Pradesh Irrigation Efficiency Improvement Project MPWRD – Water Resources Department Madhya Pradesh PMU – program management unit RCM – regional climate model SSIP – Sanjay Sarovar Irrigation Project TA – project preparatory technical assistance

GLOSSARY kharif – monsoon season, which lasts between June and October rabi – dry (winter) season, which lasts between November and March teshil – an administrative unit of local government at sub-district level

NOTES (i) The fiscal year (FY) of Government of India ends on 31 December. (ii) In this report, "$" refers to US dollars.

CONTENTS

EXECUTIVE SUMMARY ...... 1 I. INTRODUCTION ...... 3 I-A. Brief Project Description ...... 3 I-B. Climate Risk and Vulnerability Assessment (CRVA) ...... 6 II. CRVA SCOPE AND METHODOLOGICAL FRAMEWORK ...... 8 II-A. CRVA scope ...... 8 II-B. CRVA methodology ...... 8 II-C. CRVA work plan and schedule for deliverables ...... 14 III. DOCUMENTS AND DATA USED AS INPUT FOR THIS CRVA ...... 16 III-A. Existing documents and project reports relevant to this CRVA ...... 16 III-B. Baseline (observed) historical hydroclimatic data used in this CRVA ...... 17 III-C. Regional climate model projections used in this CRVA ...... 17 IV. ASSESSMENT OF CLIMATE CHANGE IMPACTS AND RISKS FOR THE KIP ...... 21 IV-A. Climate change impacts on monthly precipitation totals ...... 22 IV-B. Climate change impacts on daily precipitation maximums within each month .... 26 IV-C. Climate change impacts on maximum and minimum temperatures ...... 30 IV-D. Climate change impacts on reference evapotranspiration (ET0) ...... 38 IV-E. Climate change impacts on water demand (including peak water requirement) ...... 38 IV-F. Climate change impacts on water availability ...... 41 V. ECONOMIC IMPACT ANALYSIS OF CLIMATE CHANGE AND SELECTED CLIMATE CHANGE ADAPTATION MEASURES ...... 45 V-A. Assessment of economic impacts of climate change on the KIP ...... 45 V-B. Assessment of economic impacts of selected climate change adaptation measures for the KIP ...... 48 VI. RECOMMENDATIONS ...... 53 APPENDIX I – ACCLIMATISE AWARE FOR PROJECTS RAPID CLIMATE RISK SCREENING FOR THE MPIEIP ...... 57 APPENDIX II – LIST OF DOCUMENTS AND PROJECT REPORTS USED IN THIS CRVA ..... 67 A. Project documents ...... 67 B. ADB reports, Government of India reports and other publications relevant to this CRVA ...... 67

APPENDIX III – DAILY EXTRATERRESTRIAL RADIATION (RA) FOR DIFFERENT LATITUDES FOR THE 15TH DAY OF THE MONTH ...... 69 APPENDIX IV – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986-2005) AND FUTURE (2041-2060) MEAN MONTHLY PRECIPITATION TOTALS FOR DIFFERENT GCM/RCM OUTPUTS ...... 70 A. Under the RCP4.5 (“optimistic”) climate change scenario ...... 70 B. Under the RCP8.5 (“pessimistic”) climate change scenario ...... 75 APPENDIX V – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986-2005) AND FUTURE (2041-2060) DAILY PRECIPITATION MAXIMUMS WITHIN EACH MONTH FOR DIFFERENT GCM/RCM OUTPUTS ...... 80

A. Under the RCP4.5 (“optimistic”) climate change scenario ...... 80 B. Under the RCP8.5 (“pessimistic”) climate change scenario ...... 85 APPENDIX VI – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986-2005) AND FUTURE (2041-2060) MEAN MAXIMUM DAILY TEMPERATURE WITHIN EACH MONTH FOR DIFFERENT GCM/RCM OUTPUTS ...... 90 A. Under the RCP4.5 (“optimistic”) climate change scenario ...... 90 B. Under the RCP8.5 (“pessimistic”) climate change scenario ...... 95 APPENDIX VII – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986-2005) AND FUTURE (2041-2060) MEAN MINIMUM DAILY TEMPERATURE WITHIN EACH MONTH FOR DIFFERENT GCM/RCM OUTPUTS ...... 100 A. Under the RCP4.5 (“optimistic”) climate change scenario ...... 100 B. Under the RCP8.5 (“pessimistic”) climate change scenario ...... 105

EXECUTIVE SUMMARY

1. The Asian Development Bank (ADB) is supporting the Madhya Pradesh Irrigation Efficiency Improvement Project (MPIEIP) in India.

2. The MPIEIP will finance the development of approximately 125,000 hectares (ha) of new, highly efficient irrigation networks and productive command area under the Kundalia Irrigation Project (KIP) in Rajgarh and Shajapur districts in the northwestern part of Madhya Pradesh. The indicative cost of the KIP is $560 million.

3. The KIP has been screened for potential climate risks using the third-party “AWARE for Projects” rapid climate risk screening tool1 (see Appendix I) and also using the ADB climate risk checklist (see Appendix II). AWARE rated the overall climate-related risk associated with the KRRP as “high”, mainly because of high risks associated with temperature increase (and related evaporation increase), precipitation increase or decrease, flooding and medium risks associated wind speed increase.

4. In line with the ADB Climate Risk Management Framework, when a project receives a final AWARE rating of “medium-high risk” a further, more rigorous, CRVA is required. This report details the scope, methods and findings of the CRVA conducted for the KIP.

5. Based on the results of this CRVA and the associated economic impact analyses of climate change and selected climate change adaptation measures, the following recommendations relating to adaptation measures are made:

(i) Do not construct the Lakhundar Diversion or raise the Kundalia Dam as a stand- alone adaptation measure. (ii) Do not encourage the construction of groundwater recharge ponds. (iii) Optimize planting time of rabi crops. (iv) Improve application efficiency (mainly through additional investments at the farm level (e.g. better design of drip systems, better irrigation scheduling, leveling, etc.).

6. In summary, it is recommended to invest in optimizing the planting of rabi crops upon completion of the construction of the KIP. Because this measure will, by itself, not be sufficient to provide sufficient irrigation water to maintain the targeted cropping pattern until the end of the project’s economic lifetime, it should be combined with at least one other measure. If MPWRD decides to build the Lakhundar Diversion or raise the Kundalia Dam for reasons other than adapting to climate change, it will be economically feasible to divert part of the surface water made available by these projects to the project area. Alternatively, MPWRD may encourage farmers to invest in advanced drip and sprinkler installations, which would result in an increase in application efficiency. This measure would, however, only become economically viable in 2040.

7. Other adaptation options to consider, which were not included in the economic impact analyses of climate change and selected climate change adaptation measures but which may have other non-economic benefits include:

1 “AWARE for Projects” is a web-based, rapid climate risk screening tool developed for ADB by Acclimatise (a consulting firm based in the United Kingdom). Further details about “AWARE for Projects” are available at http://www.acclimatise.uk.com/index.php?id=4&tool=1. 2

(i) Mitigating excessive erosion, sediment transport, and/or local collapse of the banks of the Kalisindh River and its tributaries. (ii) Improving drought/flood management strategies. (iii) Improving groundwater management. (iv) Investigation into the impacts of climate change on water quality in the KIP area. (v) Investing in education and capacity building for understanding, implementing and extending the findings of this CRVA.

8. Further, given the substantial uncertainty associated with the climate change projections (within the KIP area and elsewhere in India and the wider West Asia region) the following recommendations relating to climate change impacts and climate risk assessment could also be considered to improve the quantification of, and resilience to, current and future climate-related risks for the KIP:

(i) Investigate climate change risks more rigorously; (ii) Assess robustness of adaptation measures given uncertainties associated with the climate change projections; (iii) Monitor performance of adaptation measures; (iv) Monitor economic viability of adaptation measures; and (v) Assess the potential impact of flooding on the KIP. 3

I. INTRODUCTION

I-A. Brief Project Description

1. The Asian Development Bank (ADB) is supporting the Madhya Pradesh Irrigation Efficiency Improvement Project (MPIEIP) in India.

2. The MPIEIP will finance the development of approximately 125,000 hectares of new, highly efficient irrigation networks and productive command area under the Kundalia Irrigation Project (KIP) in Rajgarh and Shajapur districts in the northwestern part of Madhya Pradesh (see Figure 1 and Figure 2). The indicative cost of the KIP is $560 million.

3. The MPIEIP will also potentially include the modernization and expansion of the Sanjay Sarovar Irrigation Project (SSIP)2 in the southeastern part of Madhya Pradesh. However, while the KIP and SSIP are linked, this Climate Risk and Vulnerability Assessment (CRVA) focuses on the KIP only.

Figure 1: Location of Madhya Pradesh within India (on the left) and the general location of the Kundalia Irrigation Project (KIP) (on the right).

2 The estimated total cost of the modernization and expansion of the Sanjay Sarovar Irrigation Project (SSIP) is given as $165 million in the Concept Paper, including $115 million of ADB financing and $50 counterpart funds from the Government of India. 4

Figure 2: Map of the Kundalia Irrigation Project (KIP) (Source: TA Interim Report).

4. The Water Resources Department Madhya Pradesh (MPWRD) is the executing agency of the KIP. The KIP is expected to improve the performance of irrigated agriculture and expand irrigated areas to ensure increasing agricultural productivity in the command area. Improved water productivity will enable irrigation expansion for increased food security, and increased allocation for domestic, municipal and industrial uses.

5. The project preparatory technical assistance (TA) was approved by India’s Department of Economic Affairs on 5 February 2016. The TA consultants began mobilizing to Madhya Pradesh on 29 February 2016 and implementation of the TA will occur during the 18 months from February 2016 until August 2017. The CRVA consultants commenced in June 2016 and the CRVA will be completed by October 2016.

6. The Kundalia Major Multipurpose Project (KMMP) consists of three main components: (i) the 44.5-meter high Kundalia Dam across the Kalisindh River (construction started in May 2015 and is funded by the State Government of Madhya Pradesh; the composite earth and concrete dam will create a 552.75 million cubic meters (Mm3) reservoir; the Kundalia Dam is an associated facility of the KIP; (ii) the KIP, which is being planned and designed to be a high efficiency piped distribution system with 100% pressurized irrigation systems (primarily sprinklers) at the farm level and 125,000 hectares (ha) of cultivable command area (CCA, which refers to the area which can be irrigated from a scheme such that the irrigation scheme ensures the area is fit for cultivation); (iii) the supply of water for potable and industrial use through a piped distribution system including, pump stations, sub-stations and power supply system, transmission pipelines, valves, controls and ancillary structures will be planned in 2016, with a turnkey contractor.

7. The Kalisindh River is non-perennial and usually runs dry between January and March. The construction of the Kundalia Dam will affect river discharge from the dam to its confluence with the Chambal River throughout the entire year. Whether the effect of the dam on river discharge is considered to be positive or negative depends on the time of year, how much water is being released from the dam, and the sector or potential impact of interest (e.g. flood risk will be reduced if the dam can even out peak discharges (i.e. flash flooding) or periods of high discharges associated with extreme rainfall events; drought risk will be reduced if the reservoir can store water from the monsoon season for use in the months when significant precipitation is rare; impacts for agriculture may be positive but impacts on the health of the river and its associated ecosystem may be negative if the natural regime of the river is altered – although sustaining an ‘environmental flow’ in a non-perennial river is likely to induce a positive effect on the environment). An environmental impact assessment (EIA) in progress to assess the effect of the KIP, and the expanded command area, on environmental flows and to inform plans to maintain environmental flows.

8. The dam and irrigation project are being constructed to contribute to the sustainable development of the project area, whose primarily rural population is already suffering from excessive groundwater overdraft, desertification, and recurring droughts that are expected to worsen with future climate change.3

3 TA 7417-IND: Support for the National Action Plan on Climate Change Support to the National Water Mission. Report prepared for Asian Development Bank, Government of India, and the Governments of Punjab, Madhya Pradesh and Tamil Nadu (Chapter IV and Appendix 3 in the Final Report published September 2011). 6

I-B. Climate Risk and Vulnerability Assessment (CRVA)

9. As part of the initial climate risk screening for the MPIEIP, ADB applied the “AWARE for Projects” rapid climate risk detailed screening tool4, referred to just as AWARE hereafter, to the project area to identify the potential climate risks.5 The AWARE tool evaluates risks for sixteen individual topics (i.e. hydroclimatic impacts) and provides an overall risk for a project. For the MPIEIP, the AWARE tool rated the overall climate related risk as “high” (refer to Appendix I for the full AWARE climate risk screening report for the MPIEIP). This “high risk” rating for the MPIEIP is mainly because of risks associated with: (i) Temperature increase: higher temperatures and heatwaves may increase crop water demand (due to higher evapotranspiration) and reduce crop productivity (due to temperature sensitivity). (ii) Precipitation increase may lead to increased seasonal runoff and therefore erosion and siltation of watercourses, reservoirs, and flooding and precipitation induced landslide events. (iii) Precipitation decrease may result in decreased seasonal runoff exacerbating pressures on water availability, accessibility, and quality; the variability of river runoff may be affected such that extremely low runoff events (i.e. drought) may occur much more frequently; pollutants from industry that would be adequately diluted could now become more concentrated; and increased risk of drought conditions could lead to accelerated land degradation, expanding desertification and more dust storms. (iv) Flooding: climate change is projected to increase the frequency and intensity of flood events and existing engineering designs may not take into consideration the risks associated with this. (v) Wind speed increase: could result in increases in incidences where current engineering design standards will not be sufficient. Increasing wind speed is also associated with increased evapotranspiration which would decrease the amount of water available.

10. It should be noted that while AWARE is a useful tool for gaining rapid insights into the potential climate related risks associated with a given project, it also has several limitations including: (i) the preliminary and generic nature of the climate risk assessments and recommendations; (ii) lack of insight into the role of natural climate variability; and (iii) lack of insight into possible adaptation strategies, their costs/benefits and how they should be implemented. AWARE, and other similar methods of conducting preliminary climate screening or rapid CRVAs, is not intended to replace or remove the need for more comprehensive climate risk assessments. Rather, tools such as AWARE are intended to quickly identify potential climate risks and vulnerabilities (and knowledge/information gaps) that need to be better understood and quantified via further, more detailed analytical and modelling work such as that conducted in this CRVA.

11. When a project receives a final AWARE rating of “high risk” ADB’s current procedures6 require a more detailed and more rigorous CRVA to be conducted. This is necessary to better

4 “AWARE for Projects” is a web-based, rapid climate risk screening tool developed for ADB by Acclimatise (a consulting firm based in the United Kingdom). Further details about “AWARE for Projects” are available at http://www.acclimatise.uk.com/index.php?id=4&tool=1. 5 ADB. 2015. Concept Paper: Proposed Multitranche Financing Facility – IND: Madhya Pradesh Irrigation Efficiency Investment Program. Manila. 6 ADB. 2014. Memorandum dated 3 March 2014 by Regional and Sustainable Development Department, Environment and Safeguards Division on Climate Risk Screening and Assessment of Projects. 7

understand and quantify the climate risks and vulnerabilities and also to identify and assess possible adaptation or mitigation options to reduce the identified risks and vulnerabilities.

9. Outputs of the CRVA, especially the adaptation measures, will be used to finalize the detailed design undertaken by the PPTA team, ensuring that the KIP is climate-proofed to the extent feasible.

10. The CRVA team is part of the overall PPTA team and reports to ADB Project Officer Mr. Arnaud Cauchois, Principal Water Resource Specialist, Environment, Natural Resources and Agriculture Division, South Asia Department. The CRVA team consists of three international experts engaged by ADB on an intermittent basis from June to December 2016:

(i) Climate Scientist/Applied Climate Scientist (Dr. Anthony Kiem); (ii) Hydrologist/Agricultural Hydrologist (Mr. Olivier Drieu); (iii) Economist/Applied Economist (Mr. Andre Oosterman). 8

II. CRVA SCOPE AND METHODOLOGICAL FRAMEWORK

II-A. CRVA scope

11. The CRVA describes the methodology and results of an assessment of the current (1966-2011) and future (2041-2060) climate related impacts, risks and vulnerabilities for the KIP. The CRVA also identifies and assesses possible adaptation measures that could be implemented to mitigate the identified climate related risks and vulnerabilities. The CRVA follows a logical and clear narrative from hazards to vulnerabilities to possible adaptation measures in order to demonstrate the climate risk management for the KIP. Even though the design for the KIP is already advanced, this CRVA can still be used to test the strengths and weaknesses of the proposed design and, where required, adjust the design or implement necessary adaptation measures accordingly.

12. The CRVA exclusively covers the KIP. It is envisaged that the budget for the MPIEIP will contain a provision for the detailed preparation for SSIP, including a CRVA, in parallel with the implementation of KIP.

II-B. CRVA methodology

13. The CRVA methodology consists of the following five tasks:

(i) Task 1: Develop a methodological framework and overall work plan for the CRVA. (ii) Task 2: Prepare relevant climate data and information. (iii) Task 3: Assess climate change impacts and risks to the KIP. (iv) Task 4: Identify and assess adaptive interventions. (v) Task 5: Report and disseminate.

14. The tasks were undertaken sequentially by the CRVA team as described below.

15. Task 1: Develop a methodological framework and overall work plan for the CRVA. This task involves the following activities:

(i) Review project documents and other technical resources and publications relevant to climate risk management within the context of the KIP (see Appendix II for a list of documents that were reviewed and utilized in this CRVA and Section III-A for a summary of key points emerging from the existing literature); (ii) Review (and where possible collect) relevant hydroclimatic data and information. This includes historical data, which is required to assess baseline or existing risks, as well as downscaled climate change scenario data for the State of Madhya Pradesh , especially the KIP, that is needed to understand how risks might change in the future (see Section III-B and Section III-C for details on the historical and future data used in this CRVA); (iii) Based on existing project documents, technical resources and data availability develop a methodological framework and overall work plan (refer to Section II-C).

16. Task 2: Prepare relevant climate data and information. This task involves the preparation and interpretation of the historical and future hydroclimatic data collected in Task 1. The focus here is on preparing data for (a) variables that form inputs to the water balance modelling (and other hydrological or economic modelling) required to inform design and 9

implementation of the KIP and (b) variables required to quantify and better understand the “high climate related risks” identified by the AWARE rapid climate screening (especially temperature increase and precipitation increase or decrease).

17. For the historical data, an analysis of all available hydroclimatic data is conducted to assess existing variability and risks associated with the 1966-2011 baseline period (i.e. the “without climate change” period). This is referred to as Scenario 1, and represents “current conditions” or a “no climate change” scenario. The baseline (observed) historical hydroclimatic data is further discussed in Section III-B.

18. For the climate change scenarios the period of interest is mid-21st century (2041-2060), which is in line with the assumed life of the KIP being 50 years starting in 2017. Uncertainty associated with future climate change projections is accounted for by considering outputs from different climate models and emissions scenarios, referred to as Representative Concentration Pathways (RCPs) for the Intergovernmental Panel on Climate Change’s (IPCC’s) Fifth Assessment Report (AR5).7 The four RCPs used in IPCC AR5 are outlined in Table 1 and are compared with the IPCC AR4 emission scenarios in Figure 3. The two emission scenarios used in this CRVA are RCP4.5 to represent an “optimistic” future (Scenario 2) and RCP8.5 to represent a “pessimistic” future (Scenario 3). Note that the IPCC recommends that all RCPs, and all climate model projections based on those RCPs, should be considered equally likely or plausible. This CRVA also needs climate change scenario information to be representative of the different climate patterns that exist across the KIP. This necessitates some form of downscaling due to the coarse resolution of the original General Circulation Model (also known as Global Climate Model or GCM) outputs (GCM grid size varies from 64-640 km x 64-640 km depending on which GCM is considered). Therefore, downscaled climate change scenario data was obtained from CORDEX (Coordinated Regional Climate Downscaling Experiment, http://www.cordex.org/) and used in this CRVA (see Section III-C for further details about the downscaled climate change scenario data).

19. The three climate change scenarios mentioned above are then used in Task 3 to test the strengths and weaknesses of the proposed KIP design. Task 4 then, where required, identifies and assesses possible adaptation measures that could be implemented to mitigate the identified climate related risks and vulnerabilities.

7 Further information about the Representative Concentration Pathways (RCP) process and all the RCP data is available at http://tntcat.iiasa.ac.at:8787/RcpDb/dsd?Action=htmlpage&page=welcome#intro. 10

Table 1: The Four Representative Concentration Pathways (RCPs).8 Name Radiative forcing Concentration (p.p.m) Pathway Source of RCP*

-2 RCP8.5 >8.5Wm in 2100 >1,370 CO2-equiv. in 2100 Rising MESSAGE -2 ~6Wm at stabilization ~850 CO2-equiv. (at Stabilisation RCP6.0 AIM after 2100 stabilization after 2100) without overshoot -2 ~4.5Wm at stabilization ~650 CO2-equiv. (at Stabilisation RCP4.5 GCAM after 2100 stabilization after 2100) without overshoot

Peak at ~490 CO2-equiv. Peak at ,3Wm-2 before RCP2.6 before 2100 and then Peak and decline IMAGE 2100 and then declines declines * MESSAGE, Model for Energy Supply Strategy Alternatives and their General Environmental Impact, International Institute for Applied Systems Analysis, Austria; AIM, Asia-Pacific Integrated Model, National Institute for Environmental Studies, Japan; GCAM, Global Change Assessment Model, Pacific Northwest National Laboratory, USA (previously referred to as MiniCAM); IMAGE, Integrated Model to Assess the Global Environment, Netherlands Environmental Assessment Agency, The Netherlands.

Figure 3: From Moss et al. (2010)8. Median temperature anomaly over pre-industrial levels and SRES versus RCP comparisons based on nearest temperature anomaly.

8 Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P. and Wilbanks, T.J. (2010): The next generation of scenarios for climate change research and assessment. Nature, 463, 747-756, doi:10.1038/nature08823. 11

20. Task 3: Assess climate change impacts and risks to the KIP. Model-based computations of the KIP will be conducted in collaboration with PPTA engineers to derive current and projected monthly water balances, based on projected changes in KIP climate and hydrology. The aim is to quantify and better understand any projected changes in the volume and timing of crop water demand (including peak water requirement) and discharge (water availability) – and to address any issues of system design and management that are identified. Three climate scenarios are assessed as introduced above:

(i) Scenario 1 – the baseline or current climate or “no climate change” scenario: uses available historical hydroclimatic data (for the 1966-2011 baseline period) to assess existing hydroclimatic risks to the KIP (i.e. risks due to natural year-to- year (or decadal) variability in hydroclimatic conditions); (ii) Scenario 2 – the “optimistic” climate change scenario: uses downscaled climate model outputs for the 2041-2060 period under RCP4.5 and compares it with downscaled climate model outputs for the baseline or current period (here current is represented by climate model outputs for the 1986-2005 period); (iii) Scenario 3 – the “pessimistic” climate change scenario: uses downscaled climate model outputs for the 2041-2060 period under RCP8.5 and compares it with downscaled climate model outputs for the baseline or current period (here current is represented by climate model outputs for the 1986-2005 period).

21. Task 3 involves:

(i) Presenting summary results (based on literature review and data analysis) from the assessment of the current (1966-2011) and future (2041-2060) climate conditions in the region containing the KIP. This will focus on the primary climate variables that have been identified as important for the KIP (i.e. changes to precipitation totals and intensity, changes to minimum and maximum temperature, and changes to reference evapotranspiration (ET0). (ii) Quantifying, for the KIP specifically, the baseline case (i.e. Scenario 1 information developed in Task 2) for the following variables that have been identified as critical to the KIP water balance and associated water availability and demand:  monthly 75% dependable precipitation;  monthly mean reference evapotranspiration (ET0) (derived from Hargreaves 9 ET0 equation) ;  monthly consumptive use (demand) for each crop (ETc) based on monthly 75% dependable precipitation and ET0;  monthly 75% dependable surface inflow. (iii) Repeating the previous step, but this time with climate change impacted inputs (based on Scenario 2 and Scenario 3 information developed in Task 2), to assess the projected impacts of climate change (mid-21st century (2041-2060) compared to current (1966-2011)) on the variables listed above and the overall KIP water balance and associated water availability and demand. (iv) Assessing the implications of the projected climate change impacts, and associated uncertainties, for the design and operations of the KIP, in particular:  direct precipitation;

9 Equation 52 at http://www.fao.org/docrep/X0490E/x0490e07.htm. 12

 crop water demand;  amount of water available for irrigation;  timing, reliability and uncertainty of water available for irrigation.

22. The methodology and data required to conduct the water balance assessments with and without climate change, and to assess the associated implications, have been made available to the CRVA team and are reported on in Volume II (Hydrology) and Volume IV (Water Planning) of the Detailed Project Report (DPR) that was first undertaken in 2013 by MPWRD, and then revised in 2015 by MPWRD. The PPTA Interim Report (September 2016) also presents further information relating to the PPTA consultants’ focus, amongst other activities, on reviewing and updating the DPR for the KIP as well as developing a spreadsheet to compute annual and monthly water balances based on data available in the catchment (precipitation gauges, river gauges, groundwater wells) and parameters such as ET0 and crop coefficients (kc) (obtained from technical publications produced by the Madhya Pradesh Irrigation Department (Technical Circular No. 25).10 This revised hydrological assessment is in Appendix 2 of the PPTA Interim Report and concentrates only on the current (“no climate change”) situation and uses only historical observations as inputs. In this CRVA, the water balance modelling and associated spreadsheets were reviewed again by the CRVA team and then used to redo the water balance and hydrological assessment for the KIP under Scenario 1 (to confirm the PPTA results) and also under the optimistic (Scenario 2) and pessimistic (Scenario 3) climate change scenarios.

23. Overall, this CRVA study provides an assessment of reliable projections of (i) demand for water for irrigation and (ii) water availability within the KIP as relevant to project design and suitable for establishing targets for project outputs (that are in the context of the identified climate-related risks).

24. Task 4: Identify and assess adaptive interventions. The adaptation measures appropriate to the project are derived through a three step process: impact, vulnerability and adaptation analysis. The previous task (Task 3) assessed climate related impacts and risks. The first aim of Task 4 is to identify current and future vulnerabilities and understand the key determinants of this assessed vulnerability. ‘Vulnerability refers to the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change.11 The approach to assessing vulnerability is to determine the sensitivity of the KIP to likely climate change driven changes in water availability and demand. Sensitivity testing for assessing adaptive interventions will therefore focus on: (i) increased water demand (including peak water requirement) and (ii) decreased water availability.

25. Task 4 also involves identifying potential adaptation measures that might be appropriate for mitigating the risks and vulnerabilities associated with the project. ADB (2012) states that “This includes the identification of strategies to minimize damages caused by the changing climate and to take advantage of the opportunities that a changing climate may present”.11

26. Potential climate adaptation options in the agriculture sector can be classified into four groups (ADB, 2012):11

(i) engineering (structural; specifications, design standards, irrigation etc.); (ii) non-engineering (management, operation, maintenance, capacity building etc.); (iii) biophysical (plant breeding);

10 Madhya Pradesh Irrigation Department. 1990. Design Series Technical Circular No. 25. Estimation of Crop Water Requirement and Irrigation Water Requirement. 11 ADB. 2012. Guidelines for Climate Proofing Investment in Agriculture, Rural Development & Food Security. Manila. 13

(iv) maintaining the status quo (i.e. ‘do nothing’).

27. Based on the generic guidance provided in the previous paragraphs, a preliminary assessment of various potential adaptation options unique to this project setting (including those already in practice) has been made by the CRVA team. The potential adaptation options for the KIP that have been identified to date include:

(i) Options for adapting to an increase in water demand, such as:  Improve irrigation efficiencies (engineering option) to reduce overall irrigation demand.  Reduce cropping intensities (non-engineering option) noting that, while a reduction in cropping intensity would reduce overall irrigation demand and be associated with relatively low administration costs, it would also have the negative effect of reducing project and farm benefits.  Change cropping regimes by, for example, promoting lower water demand and higher value crops. (ii) Options for adapting to a decrease in water availability, such as:  Reduce irrigation demand using the options listed in the previous point.  Increase water storage capacity within the project command area. (iii) Options for adapting to increased flood risk or more intense precipitation, such as:  Design standards: review current design standards for water infrastructure to verify compliance with potential changes to the timing, magnitude, frequency and location of flood events and, if necessary, recommend modifications to be taken into account in the KIP design.  Water resources management: improve catchment and river basin management (capacity, planning, monitoring, alert systems) to reduce flood risk.

28. This identification of adaptation options (from all four groups defined in paragraph 26) was via ongoing stakeholder consultation, including the focus group discussions that were conducted by members of the TA, and was also further informed by the results emerging from Tasks 2 and 3. The identified adaptation options will then be weighed against each other using a cost-benefit or cost-effectiveness analysis (or a qualitative equivalent in the case of data limitations) to assist in identifying and prioritizing the most appropriate adaptation measures. The likelihood of different climate change impacts, based on current conditions and projected future trends, will also be taken into account when assessing the most appropriate adaptation options. This is further described in the next two paragraphs.

29. Task 4 also involves an assessment of the economic impacts of climate change and the economic impacts of climate change adaptation measures for the KIP. As described in the Interim Report of the PPTA core team, it is anticipated that the economic benefits of the KIP will mostly consist of the benefits from increased agricultural production. The project’s net economic benefits will be estimated assuming an optimistic and pessimistic climate change scenario for the project area (these are Scenario 2 and Scenario 3, respectively). The net economic benefits of the project will also be estimated for the “without climate change” scenario (Scenario 1). Net economic benefits under the “with climate change” scenarios will likely be substantially lower than in a scenario “without climate change”, because in these scenarios: (i) higher temperatures will reduce water availability and thereby reduce the size of the irrigable area in the long run (overdrafts of groundwater aquifers may delay reduction in irrigable area, but not indefinitely), (ii) higher temperatures and higher variability in precipitation will result in 14

lower yields, (iii) higher variability in climatic conditions will increase the risk of irrigation failures, and thereby decrease total agricultural production during the project’s economic lifetime, and (iv) costs will be increased because of the need to invest in adaptive measures. The difference between net economic benefits in Scenarios 2 and 3 and the “without climate change” Scenario 1 is interpreted as the estimated cost of the adverse impacts of climate change on the project. The economic analysis of individual adaptation interventions is discussed further below.

30. The outcome of Task 4 (the impact, vulnerability and adaptation analysis) is a series of interventions that are likely to mitigate the adverse impacts of climate change on the performance of the project. An economic analysis will be conducted for each of the proposed interventions. Where possible, the economic benefits of each intervention will be quantified by comparing the net economic benefits of: (i) the project under climate change without adaptation measures, with (ii) the project under climate change with adaptation measures. A qualitative analysis will be prepared for interventions for which economic costs or benefits cannot be estimated with a reasonable degree of accuracy. The purpose of the analysis is twofold:

(i) To eliminate adaptation measures that are not economically feasible. These are interventions with an economic internal rate of return (EIRR) below ADB’s minimum economic opportunity cost of capital. (ii) To prioritize economically feasible adaption interventions. These consist of all other proposed adaptation interventions.

31. Based on this economic analysis, the CRVA team will recommend a series of viable adaptation measures for inclusion in the design and/or management of the KIP. Recognizing that investing in such measures may be costly and that future benefits may be uncertain, the CRVA team will also recommend on the timing for undertaking the investments.

32. Task 5: Report and disseminate. This task consists of:

(i) Preparing a CRVA Interim Report (this report) to summarize progress and detail any changes to the CRVA methodology or work plan that became necessary subsequent to submission of this CRVA Inception Report. (ii) Prepare a CRVA Final Report that details the CRVA scope, data, methodology, results and recommendations. This includes describing the potential impacts and risks of climate change for the region containing the KIP and/or KIP related infrastructure. Also included is an assessment of adaptation interventions, including practical advice on the use of assessment results in infrastructure design and operation to ensure sustainable and resilient irrigation planning. (iii) Disseminate the CRVA findings to the core team working on the KIP and MPIEIP, including key staff at MPWRD as well as any other relevant stakeholders. Members of the CRVA team travelled to Bhopal during the fourth quarter (Q4) of 2016 (19-29 November) to present the CRVA findings and discuss the recommendations and any associated implications for the KIP. Comments and suggestions emerging from these discussions have been incorporated in the CRVA Final Report. II-C. CRVA work plan and schedule for deliverables

33. The work plan to complete the CRVA, including the schedule for the deliverables, is outlined in Table 2. 15

Table 2: Work plan. Submission Period Activity Deliverable date Task 1: Develop a methodological framework and CRVA May-Aug 2016 overall work plan. Inception 15 Aug 2016 Report Task 2: Prepare relevant climate data and information. Task 3: Assess climate change impacts and risks to the CRVA KIP: July-Aug 2016 Interim 2 Sep 2016 - Address comments on CRVA Inception Report; Report - Prepare and submit CRVA Interim Report; - Provide inputs to PPTA draft Interim Report. Task 4: Identify and assess adaptive interventions: CRVA Draft - Address comments on CRVA Interim Report; 25 Nov 2016 Report Sep 2016 - - Prepare and submit CRVA Draft Report. Jan 2017 Task 5: Report and disseminate: CRVA Final - Address comments on CRVA Draft Report; 12 Jan 2017 Report - Prepare and submit CRVA Final Report. Task 5: Report and disseminate (continued): - The CRVA team travelled to Bhopal during Q4 2016 Disseminate Fourth Nov 2016 – to present the CRVA findings and discuss the CRVA quarter (Q4) Jan 2017 recommendations and any associated implications findings of 2016 for the KIP.

III. DOCUMENTS AND DATA USED AS INPUT FOR THIS CRVA

III-A. Existing documents and project reports relevant to this CRVA

34. The documents that were reviewed and utilized in this CRVA are listed in Appendix II. These documents support the findings of the preliminary AWARE climate risk screening. That is, that the KIP is prone to drought and associated water scarcity but also receives quite heavy rainfall during the monsoon season (or kharif, which typically lasts from June to October) and these conditions are likely be exacerbated in the future if, as expected, temperature and/or precipitation timing, frequency, location, and/or magnitudes were to change as a consequence of anthropogenic climate change. Refer to Volume IV, Appendix 3 in the National Action Plan on Climate Change – Support to the National Water Mission12 (which focuses on the Kshipra sub- basin, which is in Madhya Pradesh and located close to the KIP) for further information on hydroclimatic conditions in the KIP area.

35. Other important documents include:

(i) The “Crop evapotranspiration – Guidelines for computing crop water requirements – Food and Agriculture Organization (FAO) Irrigation and drainage paper 56” which is used to calculate reference evapotranspiration (ET0) in this CRVA;13 (ii) Volume II (Hydrology) and Volume IV (Water Planning) of the Detailed Project Report (DPR) that was first undertaken in 2013 by MPWRD, and then revised in 2015 by MPWRD. This is where the methodology and data required to conduct the water balance assessments used in this CRVA is obtained. As per paragraph 22, the water balance and hydrological assessment methodology for the KIP was originally developed by PPTA consultants and applied for the “no climate change” (Scenario 1) case only. In this CRVA we use the same water balance and hydrological assessment methodology and apply it under Scenario 1 (to confirm the PPTA results) and also under the optimistic (Scenario 2) and pessimistic (Scenario 3) climate change scenarios. (iii) The PPTA Interim Report (September 2016) also presents further information relating to the PPTA consultants’ focus, amongst other activities, on reviewing and updating the DPR for the KIP as well as developing a spreadsheet to compute annual and monthly water balances based on data available in the catchment. The revised hydrological assessment is in Appendix 2 of the PPTA Interim Report. (iv) India’s Second National Communication to the United Nations Framework Convention on Climate Change (http://unfccc.int/resource/docs/natc/indnc2.pdf) presents a comprehensive overview of the current and future climate change related challenges that are facing India. These include: (a) India’s large population, of which a significant proportion depends upon climate-sensitive sectors like agriculture and forestry for its livelihood; (b) adverse impacts on water availability due to recession of glaciers, decrease in rainfall and increased flooding in certain pockets would threaten food security, cause dieback of natural ecosystems including species that sustain the livelihood of rural households; (c) adverse impacts on coastal areas due to sea-level rise and increased extreme

12 Government of India, Governments of Punjab, Madhya Pradesh and Tamil Nadu, ADB (September 2011): Support for the National Action Plan on Climate Change Support to the National Water Mission. Report prepared by Asian Development Bank, Government of India, and the Governments of Punjab, Madhya Pradesh and Tamil Nadu. 13 Equation 52 at http://www.fao.org/docrep/X0490E/x0490e07.htm. 17

events; and (iv) adverse impacts across most of the country, and multiple sectors, associated with high temperatures and high evapotranspiration and associated issues like water scarcity and heat stress for plants and animals. III-B. Baseline (observed) historical hydroclimatic data used in this CRVA

12. Table 3 summarizes the baseline (observed) historical hydroclimatic data used in this CRVA.

Table 3: Baseline (observed) hydroclimatic data available for the Kundalia Irrigation Project (KIP). Locations (number of Temporal Period Variable Source gauges) resolution covered Astha 1935 - 2011 Agar 1965 - 2011 Bagli 1960 - 2011 MPWRD (district wide Sarangpur 1962 - 2011 precipitation data also Shajapur Monthly 1956 - 2012 available from 2004- Precipitation Dewas 1959 - 2012 2010 at India Water Khilchipur 1961 - 2012 Portal)14 Shujalpur 1944 - 2011 Sonkach 1927 - 2012 Zeerapur 1985 - 2011 MPWRD Fortnightly Susaner 1985 - 2011 Temperature District wide Monthly 1901 - 2002 India Water Portal15 1 site (Sarangpur) Streamflow upstream of the dam Daily 1976 - 2008 MPWRD of the KIP Humidity District wide Monthly 1901 - 2002 India Water Portal15 Sunshine India Water Portal15 District wide Monthly 1901 - 2002 duration Cloud cover District wide Monthly 1901 - 2002 India Water Portal15 Various wells in India Water Portal15 Groundwater Shajapur and Rajgarh 4 times per year 2005 - 2015 districts Evaporation District wide Monthly 1901 - 2002 India Water Portal15

III-C. Regional climate model projections used in this CRVA

13. Seneviratne et al. (2012)16 provide some generic information about projected regional changes in temperature and precipitation (including dryness) extremes in the West Asia region (i.e. the broader region that encompasses India). For the end of the 21st century there is: (i) high confidence that there number of warm (cold) days will increase (decrease); (ii) high confidence that there will be longer, more frequent and more intense heat waves; (iii) low confidence (i.e. high uncertainty) about if/how the characteristics (i.e. frequency, magnitude, duration, location,

14 http://www.indiawaterportal.org/data/rainfall-data-all-districts-india-2004-2010. 15 http://www.indiawaterportal.org/data/meteorological-datasets-13-climate-parameters-all-districts-india-1901-2002. 16 Seneviratne et al. (2012): Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.the information available at http://en.climate-data.org/location/386274/. 18

timing) of heavy precipitation events will change; and (iv) low confidence (i.e. high uncertainty) about if it will be drier or wetter and if/how drought/flood risk will change.

14. The available information about if/how recent and future hydroclimatic conditions have or might change in India is quite generic. Also, there is a lack of information in the existing literature that is specific to the KIP that could be used to inform this CRVA. Therefore, as part of this CRVA it was required to develop KIP-specific climate change scenario information.

15. Climate change scenario information from the following sources was checked for relevance and compliance with the requirements of this CRVA (e.g. based on climate model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), downscaled data to enable KIP location-specific impacts and risks to be assessed, variables required are available at appropriate temporal resolution and from multiple climate models and greenhouse gas emission scenarios or RCPs): • Coupled Model Intercomparison Project Phase 5 (CMIP5 data) available at http://cmip-pcmdi.llnl.gov/cmip5/ or https://climexp.knmi.nl/ – not selected as this data is not downscaled; • http://globalweather.tamu.edu/cmip – not selected as only data from CMIP3 was available; • http://sdwebx.worldbank.org/climateportal/ – not selected as raw data not available, only summary tables and plots showing annual or monthly averages; • Statistically downscaled data from http://www.ccafs-climate.org/data/ – not selected as only had limited data available for India; • United States National Aeronautics and Space Administration (NASA) Earth Exchange (NEX) Global Daily Downscaled Climate Projections (https://nex.nasa.gov/nex/projects/1356/ – not selected as some variables required (e.g. monthly precipitation totals, daily precipitation maximums within each month) were not directly available. It is possible to calculate the required variables but this would take extra time as would the need to download, store and manipulate projections for the entire globe rather than just the South Asia region as is possible with some of the other datasets available; • Dynamically downscaled climate change data from CORDEX (Coordinated Regional Climate Downscaling Experiment, http://www.cordex.org/) South (and West) Asia – this is the climate model data used in this CRVA as it has all variables required available for direct download and is a dataset that is being commonly used in this region.

16. The CORDEX vision is to advance and coordinate the science and application of regional climate downscaling through global partnerships. The CORDEX goals are to: (i) better understand relevant regional/local climate phenomena, their variability and changes, through downscaling; (ii) evaluate and improve regional climate downscaling models and techniques; (iii) produce coordinated sets of regional downscaled projections worldwide; (iv) foster communication and knowledge exchange with users of regional climate information. CORDEX South (and West) Asia focuses on the region illustrated in Figure 4 and is hosted by the Centre for Climate Change Research (CCCR) at the Indian Institute of Tropical Meteorology, Pune (IITM Pune). Further information about the CORDEX South Asia data available can be found at http://cccr.tropmet.res.in/cordex/index.jsp.17

17 A comparison between CORDEX South Asia models and non-downscaled GCM outputs for precipitation extremes over India is available in this paper: Mishra, V., D. Kumar, A. R. Ganguly, J. Sanjay, M. Mujumdar, R. Krishnan, 19

Figure 4: The CORDEX South Asia spatial domain.

17. For this CRVA, monthly precipitation totals, daily precipitation means, daily precipitation maximums, daily maximum temperature and daily minimum temperature were obtained at a 0.44° x 0.44° (~50 km x 50 km) spatial resolution from the CORDEX South Asia datasets for five different GCM/regional climate model (RCM) combinations for current (1986-2005) and two future (2041-2060) time horizons (i.e. one future under RCP4.5 conditions and the other future under RCP8.5 conditions). Note that outputs from four other GCM/RCM combinations beyond those assessed here are also available from the CORDEX South Asia datasets, however, given the time required to download and assess outputs from multiple GCM/RCM combinations, it was decided that outputs from five GCM/RCMs was sufficient for this CRVA. It should also be noted in addition to time constraints this decision to limit the number of GCM/RCMs projections used to five was further justified by the fact that water balance calculations were actually undertaken for two different optimistic scenarios and two different pessimistic scenarios (the first chose based on comparison of just three GCM/RCMs and the second, the one eventually used in this CRVA, based on give GCM/RCMs). The water balance results were similar in both cases suggesting that assessing outputs from more, subjectively chosen, GCM/RCMs is unlikely to significantly alter the CRVA findings and recommendations.

18. Reference evapotranspiration (ET0) was not available as a direct output of the climate models available in the CORDEX South Asia datasets and therefore was derived using 18 Hargreaves ET0 equation : 0.5 ET0 (in mm/day) = 0.0023 x (Tmean + 17.8) x (Tmax - Tmin) x Ra where Ra is extraterrestrial radiation (in mm/day) and is obtained by taking Ra values from the row for latitude 24°N in the table included in Appendix III (which shows Ra values in the units MJ -2 -1 m day ) and dividing by 2.45 to convert to mm/day. Note that Hargreaves ET0 equation is

and R. D. Shah (2014): Reliability of regional and global climate models to simulate precipitation extremes over India, J. Geophys. Res. Atmos., 119, 9301–9323, doi:10.1002/2014JD021636. 18 Equation 52 at http://www.fao.org/docrep/X0490E/x0490e07.htm. 20

known to underpredict ET0 under high wind conditions (wind speed measured at 2 m that is greater than 3 m/s) and to overpredict ET0 under conditions of high relative humidity.

19. The Volume II (hydrology) of the DPR provides a few basic characteristics of the current climate in the river basin which includes the KIP, based on available hydrometeorological data recorded at Guna station (located in the eastern part of the KIP area) by the India Meteorological Department (IMD). This information is used to compare, for Scenario 1 only, the ET0 estimates derived with Hargreaves ET0 equation with the ET0 estimates in the PPTA Interim Report (calculated using the Penman-Monteith (PM) method). The comparison (Figure 5) reveals that at the annual scale the difference is small (Hargreaves 13.2% higher) and that Hargreaves tends to predict higher ET0 values than the PM method in both the monsoon (kharif) season and the dry (rabi) season, by 10.6% and 30.8% respectively. Importantly, there is only one month of the year (May) where the Hargreaves methods possibly underpredicts ET0. For the purposes of this CRVA this acceptable as the more conservative approach (i.e. ET0 may be slightly overestimated but in terms of assessing the viability of the KIP and the costs/benefits of various adaptation options this is better than underestimating ET0).

Figure 5: Comparison between the Hargreaves and Penman-Monteith (PM) methods of calculated ET0.

IV. ASSESSMENT OF CLIMATE CHANGE IMPACTS AND RISKS FOR THE KIP

20. All figures shown in this section are for the State of Madhya Pradesh. The KIP is located to the northwest of Madhya Pradesh. Refer to Figure 1 and Figure 2 to visualize which part of the maps shown in this section should be focused on to infer climate change impacts for the KIP.

21. The figures shown in this section only include climate change projections from one GCM/RCM combination (maps for the other GCM/RCM combinations assessed are included in Appendix IV to Appendix VII).

22. The projections from the single GCM/RCM combination shown in the figures included in this section are the ones used to derive inputs for the water balance modelling used to determine climate change impacts on water demand (Section IV-E) and water availability (Section IV-F) which are in turn used to identify and assess climate change adaptation options (i.e. Task 4, for which results are included in Section V). The tables presented in this section show the projections obtained from all GCM/RCM combinations investigated as well as, to illustrate climate model consensus and/or uncertainty, the median, average and standard deviation across all GCM/RCM combinations investigated. Projected changes from an individual GCM/RCM combination, rather than the median or average across several models, is used in this CRVA so as to ensure coherence across variables (e.g. the change to rainfall indicated by a multi-model ensemble may not be linked to changes to temperature as it should be and as it generally is in individual GCMs) and also to maintain the physics relating to energy and water budgets within each individual set of GCM/RCM outputs.19,20,21

23. As previously discussed in paragraph 18, each GCM/RCM combination should be considered equally plausible and therefore the choice of which GCM/RCM outputs to use in this CRVA is largely subjective – the GCM/RCM combination selected is the one that, for all variables, is judged to be most consistently indicative of likely impacts of climate change for the KIP (i.e. in the middle of the range of possible projected impacts and never “unrealistically” diverging from other GCM/RCM results). More objective GCM/RCM selection methods do exist (e.g. Sperber et al., 201322), and there are also more sophisticated and more rigorous ways to utilize climate change information23, but these more detailed investigations are beyond what was possible in the time available to conduct the CRVA (these type of GCM/RCM evaluation and selection investigations, and if needed GCM/RCM bias correction, typically take several months to more than a year to complete).

19 Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A. (2010): Challenges in combining projections from multiple climate models. Journal of Climate, 23, 2739–2758. doi:10.1175/2009JCLI3361.1. 20 Kiem, A.S. and Verdon-Kidd, D.C. (2011): Steps towards ‘useful’ hydroclimatic scenarios for water resource management in the Murray-Darling Basin. Water Resources Research, 47, W00G06, doi:10.1029/2010WR009803. 21 Carbone, G.J. (2014): Managing climate change scenarios for societal impact studies, Physical Geography, 35(1), 22-49, doi: 10.1080/02723646.2013.869714. 22 Sperber, K.R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A., Wang, B. and Zhou, T. (2013): The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dynamics, 41(9-10), 2711-2744, doi:10.1007/s00382-012-1607-6. 23 Mortazavi-Naeini M, Kuczera G, Kiem AS, Cui L, Henley B, Berghout B, Turner E (2015) Robust optimization to secure urban bulk water supply against extreme drought and uncertain climate change. Environmental Modelling & Software 69:437-451, doi:410.1016/j.envsoft.2015.1002.1021. 22

IV-A. Climate change impacts on monthly precipitation totals

24. Figure 6 shows, for Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean monthly precipitation totals under the RCP4.5 (“optimistic”) climate change scenario for one GCM/RCM combination. Figure 7 shows the same information for the RCP8.5 (“pessimistic”) climate change scenario. Maps showing the same results for other GCM/RCM combinations are included in Appendix IV. In Figure 6 and Figure 7, the third column shows difference expressed as future minus current, while the fourth column expresses that difference as a percentage of the current value.

25. Table 4 summarizes, for the actual KIP study area only, the difference (future divided by current) in mean monthly precipitation totals under the RCP4.5 (“optimistic”) and RCP8.5 (“pessimistic”) climate change scenarios for all GCM/RCM combinations assessed in this CRVA.

26. In summary, the results for changes to mean monthly precipitation totals projected by the different GCM/RCMs suggest a high degree of uncertainty (both in the magnitude and direction of change and also in the seasonality of the changes (i.e. which months the greatest changes will occur).

27. From Table 4, there appears to be some consensus that the kharif monsoon season (June to October) will be wetter (more cells shaded blue than red) with the magnitude of monthly precipitation increase during the monsoon season ranging from 2% to 57% for Scenario 2 and from 5% to 66% for Scenario 3. Similarly, Table 4 shows some consensus that November to January will be drier (more cells shaded red than blue) with the magnitude of monthly precipitation decrease ranging from 8% to 80% for Scenario 2 and from 6% to 67% for Scenario 3. 23

Figure 6: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean monthly precipitation totals under the RCP4.5 (“optimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Figure 7: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean monthly precipitation totals under the RCP8.5 (“pessimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Table 4: For KIP only (as per Figure 1), the ratio of future (2041-2060) to current (1986- 2005) mean monthly precipitation totals under “optimistic” and “pessimistic” scenarios for the downscaled climate model outputs assessed in this study. Values greater (less) than 1.0 indicate future increases (decreases) and are shaded blue (red). Projected changes used in this CRVA are from GFDL-CM3_r1_CSIRO-CCAM-1391M (i.e. the first row in both the RCP4.5 and RCP8.5 cases). Optimistic scenario (RCP4.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 0.66 1.66 1.12 1.03 1.15 1.26 1.28 1.02 1.56 1.28 0.74 0.82 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 0.20 1.63 0.98 1.00 1.14 1.27 1.23 0.97 1.21 1.15 1.08 1.03 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 0.92 2.76 0.35 0.62 0.74 1.57 0.69 0.92 0.95 1.10 0.27 0.37 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 0.59 0.85 2.86 1.11 1.04 0.74 1.20 1.13 1.00 1.04 1.83 0.52

CNRM- CM5_r1_CSIRO- 3.17 5.32 1.63 0.84 0.47 1.17 0.93 1.35 1.24 1.54 0.85 1.61 CCAM-1391M Median 0.9 2.8 1.4 1.0 1.0 1.2 1.2 1.0 1.2 1.1 0.8 0.5 Average 1.2 2.7 1.4 1.0 0.9 1.2 1.1 1.1 1.2 1.3 0.9 0.8 Standard Deviation 1.2 1.7 0.9 0.3 0.3 0.3 0.2 0.2 0.3 0.3 0.7 0.5 Pessimistic scenario (RCP8.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 0.84 1.28 1.26 0.74 1.23 1.39 1.21 0.93 1.39 1.40 0.69 0.89 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 0.33 1.31 0.67 0.55 1.28 1.42 1.07 0.87 1.27 1.45 1.07 1.08 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 0.80 2.47 0.26 3.91 0.98 1.17 0.88 1.14 1.09 1.35 0.94 0.71 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 1.23 2.78 1.70 1.16 0.92 1.08 1.66 1.26 0.95 1.19 1.11 0.85

CNRM- CM5_r1_CSIRO- 2.49 2.07 1.66 1.24 0.69 1.30 0.95 1.32 1.05 1.09 1.25 0.90 CCAM-1391M Median 1.2 2.1 1.7 1.2 1.0 1.3 1.1 1.1 1.1 1.4 1.1 0.8 Average 1.2 2.0 1.2 1.6 1.0 1.3 1.2 1.1 1.2 1.3 0.9 0.8 Standard Deviation 0.8 0.7 0.7 1.3 0.2 0.1 0.3 0.2 0.2 0.1 0.4 0.2

IV-B. Climate change impacts on daily precipitation maximums within each month

36. Figure 8 shows, for Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) daily precipitation maximums within each month under the RCP4.5 (“optimistic”) climate change scenario for one GCM/RCM combination. Figure 9 shows the same information for the RCP8.5 (“pessimistic”) climate change scenario. Maps showing the same results for other GCM/RCM combinations are included in Appendix V. In Figure 8 and Figure 9, the third column shows difference expressed as future minus current, while the fourth column expresses that difference as a percentage of the current value.

37. Table 5 summarizes, for the actual KIP study area only, the difference (future divided by current) in daily precipitation maximums within each month under the RCP4.5 (“optimistic”) and RCP8.5 (“pessimistic”) climate change scenarios for all GCM/RCM combinations assessed in this CRVA.

38. In summary, even more so than changes to mean monthly precipitation totals the projected changes to daily precipitation maximums are very uncertain.

39. Table 5 suggests some consensus that daily precipitation maximums within the monsoon season will increase, especially under Scenario 3. The projected increase in daily precipitation maximums during the monsoon season exceeds 200% in some cases (i.e. more than three times the existing daily precipitation maximum). Potential for such drastic increases to already problematic extreme precipitation is obviously a concern, however, Table 5 also shows numerous cases where daily precipitation maximums during the monsoon season are projected to decrease (sometimes by more than half what they are currently). Figure 8 and Figure 9 (and the figures for the other GCM/RCM combinations included in Appendix V) also show minimal spatial coherence in the projected changes to daily precipitation maximums. This is in line with Seneviratne et al. (2012)24, and others, and is symptomatic of the low confidence (and high uncertainty) associated with if, how, when and where anthropogenic climate change will impact precipitation extremes (particularly at the daily or sub-daily time step).

24 Seneviratne et al. (2012): Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.the information available at http://en.climate-data.org/location/386274/. 27

Figure 8: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) daily precipitation maximums within each month under the RCP4.5 (“optimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Figure 9: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) daily precipitation maximums within each month under the RCP8.5 (“pessimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Table 5: For KIP only (as per Figure 1), the ratio of future (2041-2060) to current (1986- 2005) daily precipitation maximums within each month under “optimistic” and “pessimistic” scenarios for the downscaled climate model outputs assessed in this study. Values greater (less) than 1.0 indicate future increases (decreases) and are shaded blue (red). Projected changes used in this CRVA are from GFDL-CM3_r1_CSIRO- CCAM-1391M (i.e. the first row in both the RCP4.5 and RCP8.5 cases). Optimistic scenario (RCP4.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 0.76 5.46 0.88 0.92 1.26 2.62 2.11 0.81 1.40 1.91 0.20 0.60 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 0.35 0.83 1.07 0.31 5.33 1.90 1.60 0.52 0.74 0.34 1.21 1.73 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 0.62 3.00 0.09 1.50 1.46 1.46 0.64 0.78 0.77 1.73 0.31 0.37 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 0.58 1.39 2.87 1.34 0.65 0.57 1.99 2.99 1.78 0.23 2.23 0.57

CNRM- CM5_r1_CSIRO- 4.67 2.58 0.24 0.77 0.31 1.13 1.13 1.46 3.48 0.85 1.65 1.11 CCAM-1391M Median 0.6 2.6 0.9 0.9 1.3 1.5 1.6 0.8 1.4 0.9 1.2 0.6 Average 1.4 2.7 1.0 1.0 1.8 1.5 1.5 1.3 1.6 1.0 1.1 0.9 Standard Deviation 1.8 1.8 1.1 0.5 2.0 0.8 0.6 1.0 1.1 0.8 0.9 0.5 Pessimistic scenario (RCP8.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 0.62 2.20 7.12 1.43 1.00 1.51 2.23 0.55 1.63 0.37 0.13 0.86 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 0.83 0.79 0.33 0.32 3.27 1.75 1.21 1.45 1.45 0.42 1.28 1.44 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 0.62 2.16 0.19 7.80 4.02 1.02 0.74 1.14 1.00 2.23 0.85 0.75 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 1.22 3.81 1.43 1.23 0.43 1.87 4.85 4.22 0.65 1.40 0.91 1.52

CNRM- CM5_r1_CSIRO- 3.30 1.48 0.38 1.00 0.58 0.78 0.69 1.04 1.61 3.02 1.23 1.19 CCAM-1391M Median 0.8 2.2 0.4 1.2 1.0 1.5 1.2 1.1 1.5 1.4 0.9 1.2 Average 1.3 2.1 1.9 2.4 1.9 1.4 1.9 1.7 1.3 1.5 0.9 1.1 Standard Deviation 1.1 1.1 3.0 3.1 1.7 0.5 1.7 1.5 0.4 1.1 0.5 0.3

IV-C. Climate change impacts on maximum and minimum temperatures

40. Figure 10 shows, for Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean maximum daily temperature within each month under the RCP4.5 (“optimistic”) climate change scenario for one GCM/RCM combination. Figure 11 shows the same information for the RCP8.5 (“pessimistic”) climate change scenario. Maps showing the same results for other GCM/RCM combinations are included in Appendix VI. In Figure 10 and Figure 11, the third column shows difference expressed as future minus current, while the fourth column expresses that difference as a percentage of the current value.

41. Table 6 summarizes, for the actual KIP study area only, the difference (future minus current) in mean maximum daily temperature within each month under the RCP4.5 (“optimistic”) and RCP8.5 (“pessimistic”) climate change scenarios for all GCM/RCM combinations assessed in this CRVA.

42. In summary, it is very likely, under both Scenario 2 and Scenario 3, that mean maximum daily temperature will increase across the entire KIP area for all months of the year.

43. Table 6 suggests that the increase to mean maximum daily temperature during the monsoon season ranges from 0.1°C to 2.6°C for both Scenario 2 and Scenario 3.

44. Table 6 also shows that for the rabi season (November to March) the increase to mean maximum daily temperature during the monsoon season ranges from 0.7°C to 3.8°C for Scenario 2 and from 0.3°C to 4.3°C for Scenario 3. 31

Figure 10: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean maximum daily temperature within each month the RCP4.5 (“optimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Figure 11: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean maximum daily temperature within each month under the RCP8.5 (“pessimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Table 6: For KIP only (as per Figure 1), the difference (future (2041-2060) minus current (1986-2005) in mean maximum daily temperature (°C) within each month under “optimistic” and “pessimistic” scenarios for the downscaled climate model outputs assessed in this study. Values greater (less) than 0.0 indicate future increases (decreases) and are shaded red (blue). Projected changes used in this CRVA are from GFDL-CM3_r1_CSIRO-CCAM-1391M (i.e. the first row in both the RCP4.5 and RCP8.5 cases). Optimistic scenario (RCP4.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 3.4 2.9 2.2 2.3 2.3 2.1 0.9 0.6 0.0 0.7 2.4 2.9 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 2.0 2.1 0.7 1.7 2.1 2.0 0.7 -0.1 0.6 1.1 0.9 2.4 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 3.8 3.1 2.2 1.9 1.9 0.1 2.2 1.2 1.1 1.6 2.3 2.7 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 2.3 2.2 1.0 1.5 0.5 2.3 0.4 0.0 0.0 0.7 2.7 2.1

CNRM- CM5_r1_CSIRO- 0.8 -1.5 -0.1 1.4 3.8 2.6 1.0 -0.2 -0.6 -1.4 2.1 2.0 CCAM-1391M Median 2.3 2.2 1.0 1.7 2.1 2.3 1.0 0.0 0.6 0.7 2.3 2.4 Average 2.9 1.9 1.4 1.9 2.3 2.0 1.3 0.6 0.4 0.5 2.5 2.9 Standard Deviation 1.8 2.0 1.2 0.7 1.2 1.1 0.9 1.1 0.7 1.1 1.4 1.3 Pessimistic scenario (RCP8.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 3.7 3.6 2.3 3.2 2.9 2.0 0.6 1.2 0.7 0.4 3.5 4.3 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 2.1 2.4 1.2 2.4 1.5 1.4 0.3 0.8 0.8 0.6 2.3 2.9 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 3.9 3.1 2.9 2.1 2.5 0.5 2.6 1.7 1.6 2.2 2.9 3.3 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 1.6 1.2 0.7 1.9 2.4 1.7 -0.2 -0.1 -1.5 0.1 2.0 1.5

CNRM- CM5_r1_CSIRO- 1.6 0.3 0.4 1.8 2.7 1.8 0.5 -0.3 -0.2 1.0 2.4 3.2 CCAM-1391M Median 2.1 2.4 1.2 2.1 2.5 1.7 0.5 0.8 0.7 0.6 2.4 3.2 Average 2.9 2.3 1.7 2.4 2.7 1.6 0.8 0.7 0.3 0.8 2.9 3.3 Standard Deviation 1.6 1.7 1.4 0.9 1.0 0.8 1.1 0.9 1.2 0.8 1.1 1.5

45. Figure 12 shows, for Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean minimum daily temperature within each month under the RCP4.5 (“optimistic”) climate change scenario for one GCM/RCM combination. Figure 13 shows the same information for the RCP8.5 (“pessimistic”) climate change scenario. Maps showing the same results for other GCM/RCM combinations are included in Appendix VII. In Figure 12 and Figure 13, the third column shows difference expressed as future minus current, while the fourth column expresses that difference as a percentage of the current value.

46. Table 7 summarizes, for the actual KIP study area only, the difference (future minus current) in mean minimum daily temperature within each month under the RCP4.5 (“optimistic”) and RCP8.5 (“pessimistic”) climate change scenarios for all GCM/RCM combinations assessed in this CRVA.

47. In summary, as with mean maximum daily temperature, it is very likely, under both Scenario 2 and Scenario 3, that mean minimum daily temperature will increase across the entire KIP area for all months of the year.

48. Table 7 suggests that the increase to mean maximum daily temperature during the monsoon season ranges from 0.4°C to 2.1°C for Scenario 2 and from 0.8°C to 2.8°C for Scenario 3.

49. Table 7 also shows that for the rabi season (November to March) the increase to mean maximum daily temperature during the monsoon season ranges from 0.4°C to 3.2°C for Scenario 2 and from 0.5°C to 4.1°C for Scenario 3. 35

Figure 12: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean minimum daily temperature within each month the RCP4.5 (“optimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Figure 13: For Madhya Pradesh, the difference between current (1986-2005) and future (2041-2060) mean minimum daily temperature within each month under the RCP8.5 (“pessimistic”) climate change scenario (projections from GFDL-CM3 downscaled using CSIRO-CCAM).

Table 7: For KIP only (as per Figure 1), the difference (future (2041-2060) minus current (1986-2005) in mean minimum daily temperature (°C) within each month under “optimistic” and “pessimistic” scenarios for the downscaled climate model outputs assessed in this study. Values greater (less) than 0.0 indicate future increases (decreases) and are shaded red (blue). Projected changes used in this CRVA are from GFDL-CM3_r1_CSIRO-CCAM-1391M (i.e. the first row in both the RCP4.5 and RCP8.5 cases). Optimistic scenario (RCP4.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 3.2 2.3 2.5 2.1 2.0 1.7 1.2 1.3 1.2 1.9 2.6 2.7 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 2.4 2.4 1.2 2.0 1.6 1.3 1.1 1.1 1.1 2.1 2.8 2.9 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 1.7 1.6 1.9 1.2 1.1 1.3 1.7 1.3 1.9 1.9 1.2 0.9 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 2.2 1.6 1.8 1.6 1.0 1.1 0.4 0.8 1.0 1.7 3.2 1.3

CNRM- CM5_r1_CSIRO- 0.9 0.7 0.4 1.2 2.4 1.6 0.9 0.8 0.8 1.0 2.0 1.7 CCAM-1391M Median 2.2 1.6 1.8 1.6 1.6 1.3 1.1 1.1 1.1 1.9 2.8 1.7 Average 2.3 1.6 1.8 1.6 1.7 1.5 1.2 1.3 1.4 1.8 2.5 2.0 Standard Deviation 1.3 0.6 1.2 0.4 0.7 0.5 0.6 0.6 0.6 0.5 0.9 1.1 Pessimistic scenario (RCP8.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 4.1 3.6 2.9 2.5 2.6 2.1 1.7 1.9 2.1 2.6 3.5 4.1 CCAM-1391M MPI-ESM- LR_r1_CSIRO- 3.2 3.6 2.0 2.3 2.0 1.6 1.3 1.4 1.7 2.4 3.2 3.8 CCAM-1391M MPI-M-MPI-ESM- LR_r1i1p1_MPI- 1.8 2.0 2.8 2.1 2.6 1.9 2.2 1.8 2.8 2.6 3.7 2.4 CSC-REMO2009_v1 CCSM4_r1_CSIRO- CCAM-1391M 2.7 1.6 1.6 2.1 2.0 1.3 0.8 1.3 1.3 1.9 3.2 2.1

CNRM- CM5_r1_CSIRO- 1.3 1.3 0.5 2.0 2.2 1.4 1.0 1.0 1.1 1.9 3.0 2.6 CCAM-1391M Median 2.7 2.0 2.0 2.1 2.2 1.6 1.3 1.4 1.7 2.4 3.2 2.6 Average 2.8 2.4 2.1 2.2 2.4 1.8 1.5 1.6 1.9 2.3 3.4 3.0 Standard Deviation 1.4 1.1 1.2 0.3 0.5 0.6 0.6 0.5 0.8 0.4 0.4 1.0

IV-D. Climate change impacts on reference evapotranspiration (ET0)

50. Table 8 summarizes the difference between current (1986-2005) and future (2041-2060) mean monthly ET0 under the RCP4.5 (“optimistic”) and RCP8.5 (“pessimistic”) climate change scenarios for all GCM/RCM combinations assessed in this CRVA. No maps are included for ET0 25 since, as explained in paragraph 18, ET0 was derived based on Hargreaves ET0 equation which is dominated by temperature – hence maps showing the impact of climate change on ET0 under the RCP4.5 (“optimistic”) and RCP8.5 (“pessimistic”) scenarios would be very similar to the maps for temperature (i.e. as shown in Section IV-C, Appendix VII and Appendix VIII).

Table 8: For KIP only (as per Figure 1), the ratio of future (2041-2060) to current (1986- 2005) mean monthly ET0 within each month under “optimistic” and “pessimistic” scenarios for the downscaled climate model outputs assessed in this study. Values greater (less) than 1.0 indicate future increases (decreases) and are shaded red (blue). Projected changes used in this CRVA are from GFDL-CM3_r1_CSIRO-CCAM-1391M (i.e. the first row in both the RCP4.5 and RCP8.5 cases). Optimistic scenario (RCP4.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 1.13 1.12 1.07 1.08 1.08 1.08 1.02 0.97 0.98 1.00 1.08 1.12 CCAM-1391M Pessimistic scenario (RCP8.5) Climate model J F M A M J J A S O N D GFDL- CM3_r1_CSIRO- 1.14 1.14 1.08 1.13 1.11 1.07 0.97 1.01 0.99 0.97 1.12 1.17 CCAM-1391M

IV-E. Climate change impacts on water demand (including peak water requirement)

51. The calculations undertaken to assess the impacts of climate change on water demand have been based on the same assumptions as in the PPTA Interim Report: 75% dependable monthly rainfall, a scheme-level irrigation efficiency of 78%, irrigation requirements for the crops that peak in November, when land preparation is being done for medium and late wheat, and gradually diminish until the end of the rabi season in March, and a cropping pattern assuming early wheat is planted by the middle of October.

52. Table 9 presents monthly and annual mean reference evapotranspiration (ET0) over the entire command area for the three scenarios. The average percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 6.1% (7.3%), with the highest increases occurring in December to February.

25 Equation 52 at http://www.fao.org/docrep/X0490E/x0490e07.htm. 39

Table 9: Monthly and annual mean reference evapotranspiration (ET0) over the command area (125,000 ha) and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1).

Mean reference evapotranspiration ET0 (mm) J F M A M J J A S O N D Total Scen. 1 88 105 157 193 242 166 136 114 130 138 98 83 1,650 (current) Scen. 2* 100 118 168 209 262 180 139 111 127 138 106 93 1,750 (optimistic) Scen. 3* 100 120 169 218 268 178 132 115 129 134 110 98 1,770 (pessimistic) % Scenario - 12 7 - - 8 2 -3 -2 0 8 - 6 2 vs 1 % Scenario - 14 8 - - 7 -3 1 -1 -3 12 - 7 3 vs 1 25 * ET0 derived based on Hargreaves ET0 equation .

28. Table 10 presents the monthly and annual crop water requirements (Mm3) over the entire command area for the three scenarios. The average percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 2.8% (7.8%). The values are significantly less towards the end of the monsoon period in Scenarios 2 and 3 compared to Scenario 1.

Table 10: Monthly and annual crop water requirements (Mm3) over the command area (125,000 ha) and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). Crop water requirements (Mm3) over the command area (125,000 ha) J F M A M J J A S O N D Total Scen. 1 110 67 32 20 25 7 - - 52 18 108 79 519 (current) Scen. 2 124 80 35 22 28 6 - - 27 13 111 89 534 (optimistic) Scen. 3 125 83 35 23 28 6 - - 39 14 113 93 559 (pessimistic) % Scenario 13 19 8 8 8 -15 - - -48 -32 3 12 2.83 2 vs 1 % Scenario 14 23 10 13 11 -5 - - -26 -25 5 17 7.76 3 vs 1

29. Table 11 presents the monthly net irrigation water requirements (IWR) over the entire command area for the three scenarios. The net irrigation water requirements increase during the dry season for both Scenarios 2 and 3 compared to Scenario 1 while significant decreases take place towards the end of the monsoon period (September and October).

30. The total gross IWR for the scheme assuming wheat is planted by the middle of October every year is approximately 662, 681, and 713 Mm3 for Scenario 1, 2 and 3 respectively. 40

Table 11: Monthly net irrigation water requirements (l/s/ha) over the command area (125,000 ha) and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). Net irrigation water requirements (IWR) (l/s/ha) J F M A M J J A S O N D Scen. 1 0.33 0.22 0.10 0.06 0.08 0.02 - - 0.16 0.05 0.33 0.24 (current) Scen. 2 0.37 0.26 0.10 0.07 0.08 0.02 - - 0.08 0.04 0.34 0.26 (optimistic) Scen. 3 0.37 0.27 0.11 0.07 0.08 0.02 - - 0.12 0.04 0.35 0.28 (pessimistic) % Scenario 13 19 8 8 8 -15 - - -48 -32 3 12 2 vs 1 % Scenario 14 23 10 13 11 -5 - - -26 -25 5 17 3 vs 1

53. Table 12 presents the monthly and annual total water requirements over the entire command area for the three scenarios. The average percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 2.8% (7.8%). The values are significantly less towards the end of the monsoon period in Scenarios 2 and 3 compared to Scenario 1.

Table 12: Monthly and annual total water requirements (Mm3) over the command area (125,000 ha) (Mm3) over the command area (125,000 ha) and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). Total water requirements (Mm3) over the command area (125,000 ha) J F M A M J J A S O N D Total Scen. 1 140 86 41 26 32 9 - - 67 23 138 101 662 (current) Scen. 2 158 102 44 28 35 7 - - 35 16 142 113 681 (optimistic) Scen. 3 159 106 45 29 36 8 - - 50 18 144 119 713 (pessimistic) % Scenario 13 19 8 8 8 -15 - - -48 -32 3 12 2.8 2 vs 1 % Scenario 14 23 10 13 11 -5 - - -26 -25 5 17 7.8 3 vs 1

54. Table 13 presents the monthly and annual surface water requirements over the entire command area for the three scenarios. The average percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 2.5% (7.6%). The values are significantly less towards the end of the monsoon period in Scenarios 2 and 3 compared to Scenario 1. 41

Table 13: Monthly and annual surface water requirements (Mm3) over the command area (125,000 ha) and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). Surface water requirements (Mm3) over the command area (125,000 ha) J F M A M J J A S O N D Total Scen. 1 122 75 36 - - - - - 59 20 120 89 521 (current) Scen. 2 139 89 39 - - - - - 30 14 124 99 535 (optimistic) Scen. 3 139 93 39 - - - - - 43 15 127 104 561 (pessimistic) % Scenario 13 19 8 ------48 -32 3 12 2.5 2 vs 1 % Scenario 14 23 10 ------26 -25 5 17 7.6 3 vs 1

55. Table 14 shows the monthly and annual groundwater requirements over the entire command area for the three scenarios. The average percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 3.9% (8.5%). The values are significantly less towards the end of the monsoon period in Scenarios 2 and 3 compared to Scenario 1.

Table 14: Monthly and annual groundwater requirements (Mm3) over the command area (125,000 ha) and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). Groundwater requirements (Mm3) over the command area (125,000 ha) J F M A M J J A S O N D Total Scen. 1 17 11 5 26 32 9 - - 8 3 17 13 141 (current) Scen. 2 20 13 6 28 35 7 - - 4 2 18 14 146 (optimistic) Scen. 3 20 13 6 29 36 8 - - 6 2 18 15 153 (pessimistic) % Scenario 13 19 8 ------48 -32 3 12 3.9 2 vs 1 % Scenario 14 23 10 ------26 -25 5 17 8.5 3 vs 1

56. The peak design flow rate based on 22 hours per day of irrigation has been used by the PPTA team to calculate the peak irrigation demand for the design of the pump stations and pipeline network. For Scenario 1, the value is 0.46 l/s/ha, and 0.48 and 0.49 l/s/ha respectively for Scenarios 2 and 3, so an increase of 3.2% and 5% compared to Scenario 1.

IV-F. Climate change impacts on water availability

57. Table 15 presents the average monthly precipitation estimates of the months of the monsoon period, the total of the dry season, and the annual amount occurring over the upstream catchment up to the Kundalia Dam site for the three scenarios, and the percentage of change between them. The percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 21% (15%). The largest increases occur mostly in September and October, then 42

in June and July and are higher for Scenario 2 than Scenario 3. These changes highlight the higher intensity of the precipitation during these months with potential risks of frequent prolonged flooding events. Also, the higher intensity of the precipitation is likely to exacerbate erosion in the upstream catchment and watercourses and to increase the amount of sediments entering the reservoir, hence its siltation. In areas of the upstream catchment with steep slopes and sparse vegetation cover, the risk of landslides may also increase locally. During the dry season, precipitation will decrease in the future, especially for Scenario 3, thus exacerbating the risk of potential prolonged and more frequent dry periods (droughts) between intense monsoon periods. Both scenarios 2 and 3 confirm one of the generally widely admitted impacts of climate change: “the sharpening of the extremes” (i.e. more floods and droughts).

Table 15: Seasonal and annual precipitation estimates (mm) in the upstream catchment up to the Kundalia Dam site and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). Average monthly precipitation estimates over the upstream catchment up to the Kundalia Dam site (mm) Total Total dry J J A S O Total monsoon season Scen. 1 111.7 291.9 327.9 141.4 32.9 905.8 33.9 939.7 (current) Scen. 2 140.4 373.5 335.7 220.6 42.1 1112.2 26.2 1138.4 (optimistic) Scen. 3 154.9 354.2 303.4 196.6 46.1 1055.2 22.8 1077.9 (pessimistic) % Scenario 26 28 2 56 28 23 -23 21 2 vs 1 % Scenario 39 21 -7 39 40 16 -33 15 3 vs 1 % Scenario 10 -5 -10 -11 9 -5 -13 -5 3 vs 2

58. To mitigate excessive erosion, sediment transport, and/or local collapse of the banks of the Kalisindh River and its tributaries in the upstream catchment, river basin management measures such as mulching, reforestation, and erosion control of riverbanks (with gabions or geotextiles for example) could be implemented in the foreseeable future. To prevent sediments from excessively entering the Kundalia Dam, activities such as revegetation along the banks of the Kalisindh River to create buffer zones to trap sediments could be an accompanying measure of the construction of the Kundalia Dam. To start adaptation measures to limit the severe impacts that could be induced by droughts, drought response plans may be prepared as part of an overall drought management strategy.

59. Table 16 presents the monthly and annual 75% dependable surface water flow estimate for Kalisindh River into the Kundalia reservoir and average percentage of change between the scenarios. The average percentage of change of Scenario 2 (Scenario 3) compared to Scenario 1 is 26% (13%). The largest increases occur throughout the monsoon period (28% and 15%) with significant higher volumes of runoff entering the reservoir, especially for Scenario 2, highlighting potential risks of frequent flooding events. During the dry season volumes of runoff to the reservoir are expected to decrease in the future, highlighting the risk of potential droughts. 43

Table 16: Monthly and annual 75% dependable surface water flow (Mm3) estimate for Kalisindh River at the Kundalia Dam site and percentage of change (Scenario 2 compared to Scenario 1 and Scenario 3 compared to Scenario 1). 75% dependable surface water flow estimate for Kalisindh River at the Kundalia Dam site (Mm3) J F M A M J J A S O N D Total Scen. 1 - 15 4 - - 42 243 196 111 64 2 - 677 (current) Scen. 2 - 12 3 - - 53 311 250 142 81 2 - 854 (optimistic) Scen. 3 - 10 3 - - 48 280 225 128 73 1 - 768 (pessimistic) % Scenario 0 -23 -23 0 0 28 28 28 28 28 -23 0 26 2 vs 1 % Scenario 0 -33 -33 0 0 15 15 15 15 15 -33 0 13 3 vs 1

60. The increasing probability of floods and droughts, and other uncertainties in climate, may seriously increase the vulnerability resource-poor farmers to climate change. Early warning systems, especially for floods and droughts, and contingency plans can provide support to regional and national administration, as well as to local bodies and farmers to adapt. Also, policies that encourage crop insurance can provide protection to the farmers in the event their farm production is reduced due to natural calamities.

61. Based on the annual surface water inflow from the Kalisindh River to the Kundalia Dam (depicted in Table 16) and accounting for upstream projects, evaporation losses, and downstream releases for riparian users and environmental flows, the estimated annual surface water quantity available to be reliably pumped from the reservoir in 3 out of 4 years for irrigation is approximately: 521.5 Mm3 for Scenario 1, 689.8 Mm3 for Scenario 2 and 603.2 Mm3 for Scenario 3. The Scenario 2 and Scenario 3 values represent respectively an increase of 32.3% and 15.7% compared to Scenario 1.26

62. The TA team used the available groundwater data in the vicinity of the project and determined conservatively that upper limit of pumped groundwater to be 1,200 m3 per ha (150 Mm3) to avoid groundwater pumping overdraft. The groundwater resources have two components: static fresh groundwater reserves are in aquifer zones below the zone of groundwater table fluctuation, while the dynamic component is replenished annually. Since the southwest monsoon is the dominant source of rainfall in India, approximately 73% of country’s annual replenishable groundwater recharge takes place during the kharif period of cultivation. To mitigate the potential future climate change induced risk of over-exploiting the groundwater resources, especially the aquifer zones, during prolonged periods of dry, groundwater management measures such as frequent levels (and quality) of monitoring, and identification and protection of recharge zones are recommended. Some of these zones could be slightly excavated to enhance ponding.

63. Climate change impacts on water quality is also a potential issue. However, no water quality baseline data was available for the Kalisindh River or its tributaries in the vicinity of the

26 The system is designed to start with a “full reservoir” (live storage capacity of 521 Mm3) at the beginning of the season. So while there may be more runoff in the upstream catchment, it is more likely to benefit groundwater recharge to local aquifers than providing more useable water, since if it comes into a full reservoir the reservoir will just spill. So these values should be seen as theoretical rather than more available stored surface water. 44

KIP area. Therefore, the potential impacts of climate change on water quality could not be assessed. However, it is known that water quality is sensitive to both increased water temperatures and changes in precipitation and can change in response to climate change, through the increased mobility of chemical compounds, changes in hydrology and changes in timing of biological and meteorological patterns. Water quality in particular relates to nutrients, oxygen, natural organic matter and hazardous substances, contained in the water, as well as to the temperature of the water. Peak discharge, especially short intense flashy flood events, can cause water quality issues by causing a “flush” of high concentration of sediment or pollutants, or indirectly by causing a leaching of waste disposals and water treatment facilities. Flash events can also result in the spread of pathogens. As such, chemical products that farmers may use to enhance their crops ought to be stored in areas not prone to flooding. Further investigation, including collection of water quality data for the Kalisindh River and its tributaries, is also required so that the impacts of climate variability and change on water quality can be quantified. 45

V. ECONOMIC IMPACT ANALYSIS OF CLIMATE CHANGE AND SELECTED CLIMATE CHANGE ADAPTATION MEASURES

64. Introduction. The economic benefits of the Kundalia Irrigation Project (KIP) mostly consist of benefits from increased agricultural production.27 These benefits were estimated by taking into account adverse impacts of climate change on irrigation water requirements. As described elsewhere in this report, it is anticipated that the command area will enjoy an increase in annual rainfall (during 2041-2060, the 75% dependable yield will be 13% to 26% higher than the average yield during 1966-2011), which will increase the supply of irrigation water. At the same time, higher temperatures will increase evapotranspiration (which will be 6% to 7% higher during 2041-2060 than during 1966-2011), and thereby demand for irrigation water. The net effect of higher rainfall and higher temperatures is a projected increase in irrigation water requirements for a given cropping pattern. With the exception of a short transition period, the economic analysis prepared by the PPTA Consultant assumed that the cropping pattern would remain unchanged throughout the economic lifetime of the project (2020-2069), and that any shortfall in surface water available for the project would be met by an increase in pumped groundwater. This was deemed feasible because projected groundwater abstraction would remain below the sustainable amount of 1,200 m3 per hectare per year, or 150 million cubic meter (Mm3) per annum.28 There are, however, additional measures that can be considered to adapt to adverse impacts of climate change. This chapter (Section V) first assesses the economic impacts of climate change on the KIP, which essentially consists of the difference between the project’s pumping costs in a “with climate change” and “without climate change” scenario. It then presents an assessment of the economic impacts of selected climate change adaptation measures, also known as measures for climate proofing (CP).

V-A. Assessment of economic impacts of climate change on the KIP

65. Methodology for assessing economic impacts of climate change. ADB recently issued a report on the economic analysis of climate proofing investment projects.29 This report contains a useful figure that illustrates: (i) the economic impacts of climate change on a project, and (ii) the economic impacts of individual adaptation measures on a project. This figure, reproduced below as Figure 14, shows that the economic impact of climate change on a project as a whole can be measured by estimating the change in the project’s economic net present value (NPV) caused by climate change. This change can, in turn, be estimated by comparing the NPV of the project’s costs and benefits in a “with climate change” and “without climate change” scenario. In the economic analysis prepared by the PPTA Consultant, the economic benefits in the “with climate change” scenario are exactly the same as in the “without climate change” scenario. This is because the cropping pattern was fixed, and demand for surface water was fully met by available surface water and, where required, increases in groundwater abstraction, so that climate change did not affect the total agricultural production (or the composition thereof). Economic costs were also the same in both scenarios, with the exception of pumping costs, which were higher in the “with climate change” scenario. Estimating the change in the economic NPV of the project was therefore equivalent to estimating the change in the present value of the economic cost of pumping up groundwater. To quantify incremental pumping costs, estimates were prepared for: (i) the incremental volume of groundwater pumping, and (ii) the incremental cost of pumping up one unit of groundwater.

27 Economic Analysis of MPIEIP (PPTA Consultant, forthcoming). 28 India: Madhya Pradesh Irrigation Efficiency Improvement Project––Interim Report (Final) (PPTA Consultant, September 2016) 29 Economic Analysis of Climate-Proofing Investment Projects (ADB, 2015). 46

Figure 14: Economic impacts of climate change and of adaptation measures.

Source: ADB (2015) 29

66. Incremental volume of groundwater pumping. The economic lifetime of KIP was estimated at 50 years. Project implementation would start in 2020, the first year after completion of the dam, and end in 2069. In a world without climate change (CC), the gross irrigation water requirement would be 662 Mm3/year. This is the amount needed to supply a command area of 125,000 hectares with the cropping pattern shown in Table 17, assuming an application efficiency and distribution efficiency of 98% and 80%, respectively, and furthermore assuming that rainfall and evapotranspiration would continue to follow patterns observed during 1966- 2011. Of the annual requirement, approximately 521 Mm3 would be supplied by surface water and the remaining 141 Mm3 by groundwater. This is the amount of water that would need to be pumped up, every year, during the project’s 50-year economic life time, in the “without climate change” scenario (refer to Table 18 for further details).

Table 17: Proposed cropping pattern for KIP. Percent of Total ‘000 Hectares Crop Kharif Rabi Kharif Rabi Soybean 50 62.5 Maize 20 25.0 Pulse 5 6.3 Vegetable 5 15 6.3 18.7 Wheat* 40 50.0 Pulse 15 18.7 Mustard 5 6.3 Coriander 13 16.2 Orange 12 12 15.0 15.0 Source: Interim Report (PPTA Consultant, September 2016) * Divided into three staggered planting dates (early, medium, late) 47

67. Chapter IV of this report describes two equally likely “with climate change” scenarios: an optimistic and a pessimistic case. The economic analysis of the PPTA Consultant was prepared for a third “with climate change” scenario. This scenario was defined as having the average 75% dependable rainfall yield and average evapotranspiration levels of the optimistic and pessimistic scenarios. During 2041-2060, the annual amount of groundwater pumping required to maintain the proposed pattern in the “average with climate change” scenario would be about 149 Mm3, up from 141 Mm3 in the “without climate change” scenario (see Table 18). The annual increase of about (149.2 – 140.6 =) 8.7 Mm3 would be realized gradually. More specifically, it was assumed that the increase would be realized in 50 years—the time span between the end of the projection period for the “without climate change” and “with climate change” scenarios—by increasing the required amount of groundwater by a fixed amount of (8.7 / 50 =) 0.17 Mm3 per year. Based on this assumption, the incremental volume of groundwater pumped was estimated at about 212 Mm3 over the economic lifetime of the project for the “average with climate change” scenario. The annual incremental volumes in the “optimistic” and “pessimistic” scenarios are 133 Mm3 and 280 Mm3, respectively. In all scenarios, the maximum amount of groundwater abstraction was capped at the sustainable yield of 150 Mm3 per year. In the “pessimistic” scenario, groundwater supply was not sufficient to meet demand at the proposed cropping pattern from 2060 onwards. It was assumed that farmers would respond to the irrigation water shortage by reducing the area planted with oranges (this crop was selected because it requires more water than any other crop in the proposed cropping pattern, and is not a subsistence crop). The impact of the groundwater shortage would be modest; in the final year of the project’s economic lifetime the area planted with oranges in the “pessimistic” scenario would be about 230 hectares smaller (or 1.5% of the original orange cultivation area) when compared to the other scenarios.

Table 18: Demand and supply of irrigation water for KIP by climate change scenario. (Mm3 per year) Without CC With CC (2041-2060)

(1966-2011) Optimistic Average Pessimistic Gross water requirement – 662 681 697 713 Available surface water26 = 521 535 548 561 Required groundwater* 141 146 149 152 Sources: CRVA Consultant estimates, Interim Report (PPTA Consultant, September 2016) * Sustainable level of groundwater abstraction in the command area is estimated at 150 Mm3 per year.

68. Incremental cost of groundwater pumping. The PPTA Consultant estimates the economic cost of pumping up one cubic meter of groundwater at Rs1.25. The incremental pumping volumes were valued at this unit cost, and the present value of incremental pumping cost in the “average with climate change” scenario was estimated at about Rs14.7 million when discounted at the economic opportunity cost of capital (EOCC) of 12%.30 This is equivalent to less than $220,000 in mid-2016 prices. The present value was low because over 80% of the total increase in pumping cost is realized after 2040, by which time the discounted value of an increase has been reduced to less than 10% of its undiscounted value. For the “optimistic” and “pessimistic” scenarios, the present value of the incremental pumping cost was Rs9.2m and Rs20.1m, respectively.

30 This is ADB’s current estimate of the EOCC for all its developing member countries. 48

69. Economic impacts of climate change on KIP. The economic costs and benefits of KIP in a world without climate change were compared to the project’s costs and benefits in three “with climate change” scenarios: (i) optimistic, (ii) pessimistic, and (iii) average. As expected, in all three “with climate change” scenarios the present value of pumping costs was higher than in the “without climate change scenario”. The increase in the present value was equal to the reduction of the project’s net present value (NPV) in the “optimistic” and “average” scenarios. For the “pessimistic” scenario only, the project’s NPV was further reduced by a decrease in economic benefits from the production of oranges, which would be lower than in the “without climate change” scenario because of groundwater shortages during 2060-2069. Because these shortages would only materialize in the far future, the impact of on the project’s net present value was not significant. The total reduction of the project’s NPV attributable to climate change ranged from Rs9.2m in the “optimistic” scenario to Rs21.2m in the “pessimistic” scenario (Table 19). For all three scenarios, the reduction was equivalent to less than 1% of the project NPV, which was estimated by the PPTA Consultant at over Rs14.24 billion. This means that, once the project is completed, the economic cost of adapting to climate change is far lower than the economic benefits of undertaking the project vis-à-vis a “do nothing” scenario.

Table 19: Impact of CC on project NPV for selected climate change scenarios. (Rs million, constant mid-2016 prices) Adaptation Measure Optimistic Average Pessimistic Increase in economic cost of groundwater pumping -9.2 -14.7 -20.1 Reduction in economic benefits from orange production - - -1.1 Total impact of CC on project NPV -9.2 -14.7 -21.2 Source: CRVA Consultant estimates.

V-B. Assessment of economic impacts of selected climate change adaptation measures for the KIP

70. Methodology for assessing economic impacts of selected adaptation measures. As described in Section V-A, the economic feasibility analysis prepared by the PPTA Consultant assumed that farmers in the project area would respond to CC-induced irrigation water shortages by: (i) increasing groundwater pumping (up to the sustainable yield), and (ii) reducing cultivation area of oranges (after having exhausted the sustainable groundwater yield). These adaptation measures were selected because they describe the most likely response of farmers to climate change in the absence of interventions by the project’s Executing Agency. MPWRD may consider undertaking alternative measures to actively increase the availability of irrigation water and thereby support farmers to better adapt to adverse impacts of climate change, such as building a new diversion to supply more water, or train farmers to use water more efficiently. As shown in Figure 14, the economic impact of such measures is analyzed by comparing the NPV of the project without adaptation measures with the NPV of the project with a selected adaptation measure (as mentioned above, the NPV of the project with increased groundwater pumping is Rs14.24 billion in mid-2016 prices; without this measure the NPV would range from Rs14.22b to Rs14.23b). If an adaptation measure increases the NPV of the project as a whole, it should be undertaken. Conversely, if an adaptation measure reduces the NPV of the project, the economic benefits of the project are higher without the measure, which should therefore not be undertaken. An adaptation measure will only increase the NPV of the project as a whole if the present value of the benefits of the adaptation measure itself exceeds the present value of its costs. Economic benefits and costs were assessed for two sets of adaptation measures: 49

(i) Supply-side measures. These are measures for increasing the supply of irrigation water to the project area. (ii) Demand-side measures. These are measures aimed at reducing demand for irrigation water by making more efficient use of available irrigation water supplies.

71. Supply-side adaptation measures.

(i) S1. Construct the Lakhundar diversion. MPWRD has considered constructing a new barrage across the Lakhundar River, including an 18 km diversion channel. This measure would increase the surface water supply to the Kundalia Reservoir by about 105 Mm3. If constructed, MPWRD may decide to reserve part of the increased supply to help farmers in the project area adapt to climate change. (ii) S2. Raise the Kundalia Dam. MPWRD is currently constructing a dam across the Kalisindh River with a height of 44.5 meters. Upon completion, the dam would create a reservoir with a live storage capacity of about 553 Mm3. MPWRD could consider increasing the height of the dam to augment the storage capacity of the reservoir. An increase of 10 meters would add an additional 150 Mm3 in storage capacity and increase the available surface water supply by over 100 Mm3 (a lower increase was not considered given the very high fixed costs associated with a raising a dam). (iii) S3. Construct groundwater recharge ponds. This measure does not, by itself, increase the supply of irrigation water, but would enable farmers to smooth out variations in the annual supply of irrigation water by overdrafting groundwater in relatively dry years and replenishing the groundwater recharge ponds in relatively wet years.

72. Demand-side adaptation measures.

(i) D1. Optimize planting time of rabi crops. Planting rabi crops close to the end of the monsoon would enable these crops to utilize more residual soil moisture, thereby reducing demand for irrigation water. (ii) D2. Improve application efficiency (field level irrigation efficiency). Apart from the planting schedule, the total demand for irrigation water is determined by: (i) the size of the command area, (ii) the cropping pattern, (iii) crop coefficients, (iv) climatic conditions (notably rainfall and evapotranspiration), and (v) design efficiency. The size of the command area, cropping pattern, crop coefficients, and climatic conditions were all fixed.31 However, it would be possible to reduce demand for irrigation water by improving design efficiency. The overall design efficiency of KIP was estimated by the PPTA Consultant at 78.4%, the product of an application efficiency of 98% and a distribution efficiency of 80%. Although there is limited scope to further improve the already high distribution efficiency, investments in better sprinkler and drip irrigation systems could increase the application efficiency of the system, and thereby reduce demand for irrigation water.

73. Adaptation measures not considered. The Interim Report of the CRVA Consultant listed several other adaptation measures for which economic impacts were not assessed. These

31 More specifically, the size of the command area and the cropping pattern were fixed for the purpose of this analysis, and climatic conditions were fixed for the three scenarios under consideration (optimistic, pessimistic and the average of these two). Crop coefficients are constants, and can therefore not be varied. 50

measures, and the reasons for excluding these from the economic impact analysis, are the following:

(i) Change cropping pattern. The project was designed to supply sufficient irrigation water to a command area of 125,000 ha with the cropping pattern shown in Table 17. The cropping pattern was therefore fixed in advance, and adaptation measures were identified to overcome any potential shortfall in irrigation water supplies caused by climate change, so as to maintain the pre- defined cropping pattern. In this context, changing the cropping pattern in response to water shortages (by, for example, reducing the size of the cultivated area or by swapping high value-added crops for food crops that require less water) is not an adaptation measure but a failure to adapt to climate change. (ii) Reduce cropping intensity. The cropping intensity forms part of the cropping pattern, and reducing the intensity is therefore not an adaptation measure either. (iii) Improve design standards. In all three “with climate change” scenarios, the project would need to convey more surface water than without CC. However, the increase is less than 10% in all three cases, which is well within the design parameters of the proposed system. For this reason, there would be no economic benefit from further improving design standards. (iv) Improve water resources management. The design of KIP includes a smart water management system, consisting of: (i) a Supervisory control and data acquisition (SCADA) system for collecting information from remote sites in the command area, and (ii) a decision support system with an irrigation database. The proposed water resources management system is state-of-the-art, and the economic benefits from additional investments in the system would therefore, in all likelihood, not be significant.

74. Economic impacts of selected adaptation measures on project NPV. The economic costs and benefits of the five selected adaptation measures (S1, S2, S3, D1, D2) were estimated to assess their net impact on the NPV of the project as a whole if they would be undertaken in 2020, the first year of the project’s assumed economic lifetime. The economic cost of a measure was defined as the cost of ensuring that sufficient irrigation water would be available to maintain the targeted cropping pattern (see Table 17) until the end of the economic lifetime of the project, without pumping up additional groundwater—which is an adaptation in its own right—but otherwise assuming that the project would make surface water available in the quantities listed in Table 18. The economic benefit of a measure was defined as the avoided reduction in economic benefits from cultivating oranges (in any year in which irrigation water shortages were predicted, farmers were assumed to reduce the size of the cultivated area of oranges but maintain the cultivated area of all other crops). The benefits of the five adaptation measures were therefore almost identical (minor variations were caused by differences in timing; some measures would come on-line earlier than others), but there were major differences in economic costs.

75. Economic impacts of constructing the Lakhundar diversion (S1). The estimated investment cost of this measure is about Rs2.0 billion in constant mid-2016 prices. The present value of the economic costs is about Rs1.67 billion, including management, operations and maintenance costs. This is far higher than the economic benefits from avoided losses of orange production, which were estimated at about Rs0.30 billion (see Table 20 below). The diversion would admittedly provide much more irrigation water than is needed to maintain the targeted cropped pattern (105 Mm3 per year, which is almost ten times higher than expected shortfall of 11 Mm3 at the end of the projection period). However, it would be extremely costly—if at all 51

technically feasible—to downscale the proposed project so that it would only provide the increase needed to cover the shortfall caused by climate change. For this reason, the diversion is not economically feasible as an adaptation measure when undertaken on a stand-alone basis. If MPWRD would decide to implement the Lakhundar diversion project for other reasons, it may wish to allocate part of the increase in surface water to the project area. In that case, it would be reasonable to also consider part of the project cost as the economic cost of using the diversion as an adaptation measure. Assuming that the economic cost would be 10% of the total project cost, the measure would become economically feasible (as the present value of economic benefits is higher than 10% of the present value of the economic costs).

76. Economic impacts of raising the Kundalia Dam (S2). The estimated cost of this measure is about Rs1.5 billion (appr. $23 million), again in mid-2016 prices. It would not be economically feasible on a stand-alone basis for the same reasons as the Lakhundar Diversion: raising the dam provides far more irrigation water than is needed to maintain the targeted cropping pattern, and it would not be efficient to raise the dam by a small increment. If only the cost of the required increase (11 Mm3/year) instead of the total increase (100 Mm3/year) in surface water supplies is considered, the proposed measure would be feasible.

77. Economic impacts of constructing groundwater recharge ponds (S3). MPWRD estimates the average construction cost of a groundwater recharge pond at ₹350,000 (or approximately $5,000), and a single recharge at ₹50,000 (about $750). Such a pond would actually be an open well that would capture, store and slowly recharge the groundwater. A recharge pond of this type may yield up to 50 m3 per hour and would therefore quickly exhaust the sustainable yield of 1,200 m3 per hectare per year. Assuming a single recharge pond covers 5 hectares (the same as the average coverage of each of the 25,000 tube wells already in the 125,000 ha command area) this means an average yield of 6,000 m3 per year per pond. Assuming a 20-year economic lifetime and one recharge per four years the annual costs associated with a recharge pond is ₹17,500 per year capital cost (Rs350,000 / 20) plus ₹12,500 per year recharge cost (₹50,000 per recharge x 4 recharges divided by 20 years). Based on these calculations the cost of abstracting the sustainable yield would be about Rs5/m3 (₹30,000 / 6,000 m3) or several multiples higher than the average cost of groundwater abstraction through increased groundwater pumping (₹1.25/m3). It may be argued that groundwater recharge ponds offer an important benefit that increased pumping does not provide (i.e. the ability to save groundwater in relatively wet years). However, the newly proposed project (i.e. the KIP) includes a state-of-the-art water management system, which would provide these benefits already through improved management of available surface water supplies, which account for over 85% of total irrigation water supplies. Even though groundwater recharge ponds are more costly than increased groundwater pumping, the economic benefits of this adaptation measure (S3) still exceed the economic costs by a substantial margin (see Table 20 below).

31. Economic impacts of optimizing planting time of rabi crops (D1). The NPV of the project was estimated by the TA Consultant based on the assumption that current planting schedules would continue to prevail throughout the project implementation period. Planting rabi crops close to the end of the monsoon would enable these crops to utilize more residual soil moisture, and therefore reduce the demand for irrigation water. The most important rabi crop is wheat, which is assumed to cover 40% of the command area in the dry season (Table 17). If all wheat would be planted in October, instead of during October, November and December (as is presently the case and assumed to continue), the maximum shortfall in irrigation water in the “pessimistic” scenario would drop from 11 Mm3/year to 7 Mm3/year. Training and awareness raising events would be needed to encourage farmers to change their planting schedules, the 52

cost of which was estimated at ₹133 million for a five-year program. As shown in Table 20, the present value of the associated costs would be far lower than the associated benefits, and this adaptation measure would therefore be highly economically feasible.

32. Economic impacts of improving application efficiency (field level irrigation efficiency) (D2). A combination of investments in training, awareness raising and equipment could conceivably increase the application efficiency from 80% to a higher level, and thereby eliminate irrigation water shortage arising in any of the three “with climate change” scenarios that were considered. To ensure that sufficient irrigation water would be available for the targeted cropping pattern, the application efficiency would need to increase to about 86% during 2041-2060. Unsurprisingly, this increase comes at a cost. Unfortunately, there is no accurate information about the cost of increasing the application efficiency by 6%. According to a highly indicative estimate of the TA Consultant, the cost of increasing the application efficiency from 80% to 90% would be ₹100,000/ha (the average cost of advanced drip and sprinkler equipment would be ₹150,000/ha, against ₹50,000/ha to achieve the “baseline” adaptation efficiency of 80%). This means that, by the end of the project’s economic life, 60% of the command area must have the advance equipment in place to achieve a weighted average application efficiency of 86%.32 The total capital investment in equipment is therefore (60% x 125,000 x 100,000=) ₹7.5 billion, which is considerable. Assuming that this amount would be spread out over 50 years, and adding a 5% margin for training and awareness raising, the present value of the economic cost would be ₹1.3 billion, which is far higher than the present value of the economic benefits.

33. Economic impacts of increased groundwater pumping.33 This is the default adaptation measure. It provides the same benefits as groundwater recharge ponds (by avoiding a reduction in the area cultivated with oranges as long as groundwater requirements do not exceed the sustainable yield of 150 Mm3 per year), but at a much lower economic cost. Like groundwater recharge ponds, it is not a solution for the very long term, as irrigation water requirements will begin to exceed the sustainable yield in 2060. Increasing groundwater abstraction as a means to adapt to climate change also risks systematic overdrafting of groundwater reserves, which could eventually lower the sustainable yield, as observed elsewhere in the country.

Table 20: Economic impact on project NPV of selected adaptation measures. (₹ million, constant mid-2016 prices) Present Value (₹ million)** Undertake Adaptation Measure Benefits Costs Net Measure? S1. Construct Lakhundar Diversion 299 1,669 -1,370 Not on S2. Raise Kundalia Dam 299 1,252 -953 stand-alone basis S3. Construct groundwater recharge ponds 305 81 +225 Yes, but selectively D1. Optimize planting time of rabi crops 274 96 +178 Yes D2. Improve application efficiency 306 1,308 -1,002 Yes, but later Baseline: increase groundwater pumping 305 20 +285 Yes Source: CRVA Consultant estimates. * All figures presented in the table are for the “pessimistic” scenario. ** In constant mid-2016 prices, assuming that implementation of adaptation measures starts in 2020.

32 (60% x 90%) + (40% x 80%) = 86%. 33 Increased groundwater pumping is not a recommended adaptation measure but a measure that is assumed to best reflect the actual behavior of farmers in the absence of adaptation measures taken by MPWRD. 53

VI. RECOMMENDATIONS

78. Based on the results of this CRVA and the associated economic impact analyses of climate change and selected climate change adaptation measures, the following recommendations relating to adaptation measures are made:34

(i) Do not construct the Lakhundar Diversion or raise the Kundalia Dam as a stand-alone adaptation measure. These are both relatively costly investment projects that will increase the supply of surface water by a much higher amount than is needed for the targeted cropping pattern, even in the “pessimistic” scenario. Because of the nature of these projects, it would not be feasible to downscale the size so that they would supply the maximum shortfall of 11 Mm3 per year (this is the difference in groundwater required between the “without CC” and the “pessimistic with CC” scenario in Table 18). As a result, the economic cost of both measures are far higher than the economic benefits of avoiding the incremental pumping costs and loss of orange cultivation area, and should therefore not be undertaken on a stand-alone basis. Both measures may be feasible if MPWRD would implement the projects for other reasons (for example, to develop a new irrigated area in the region) and allocate part of the incremental surface water flows to the KIP command area. (ii) Do not encourage the construction of groundwater recharge ponds. Although this measure is economically feasible, it is nonetheless not recommended for widespread adoption in the command area, although it may be useful in locations with high variability in groundwater availability. The reasons are: (a) the economic costs of increasing groundwater pumping are far lower and provide the same benefits, and (b) encouraging farmers to construct recharge ponds is likely to result in abstraction of groundwater at unsustainable levels. Experience suggests that farmers will not stop extracting groundwater up to the sustainable yield, and once a tube-well—which is a capital-intensive investment—has been constructed the marginal costs of groundwater abstraction are very low. (iii) Optimize planting time of rabi crops. This adaptation measure is not only highly feasible, but also does not cost farmers anything once the behavioral change is in place. The disadvantage of this measure is that it cannot fully cover the shortfall between irrigation water supply and demand for the targeted cropping patterns, and should therefore be combined with at least one other measure. (iv) Improve application efficiency (mainly through additional investments at the farm level (e.g. better design of drip systems, better irrigation scheduling, leveling, etc.). When implemented in 2020, this measure is not economically viable. In later years, however, when climate change has caused a substantial increase in the demand for irrigation water, improve application efficiency would become economically viable. The “tipping point” would be reached in 2040. This is the first year in which the (then) present value of the economic benefits would exceed the economic costs.

79. In summary, it is recommended to invest in optimizing the planting of rabi crops upon completion of the construction of KIP. Because this measure will, by itself, not be sufficient to

34 Increased groundwater pumping is not a recommended adaptation measure but a measure that is assumed to best reflect the actual behavior of farmers in the absence of adaptation measures taken by MPWRD. 54

provide sufficient irrigation water to maintain the targeted cropping pattern until the end of the project’s economic lifetime, it should be combined with at least one other measure. If MPWRD decides to build the Lakhundar Diversion or raise the Kundalia Dam for reasons other than adapting to climate change, it will be economically feasible to divert part of the surface water made available by these projects to the project area. Alternatively, MPWRD may encourage farmers to invest in advanced drip and sprinkler installations, which would result in an increase in application efficiency. This measure would, however, only become economically viable in 2040.

80. Other adaptation options to consider, which were not included in the economic impact analyses of climate change and selected climate change adaptation measures but which may have other non-economic benefits, are detailed in Section IV-F and include:

(i) Mitigating excessive erosion, sediment transport, and/or local collapse of the banks of the Kalisindh River and its tributaries. River basin management measures such as mulching, reforestation, and erosion control of riverbanks (with gabions or geotextiles for example) could be implemented. To prevent sediments from excessively entering the Kundalia Dam, activities such as revegetation along the banks of the Kalisindh River to create buffer zones to trap sediments could be an accompanying measure of the construction of the Kundalia Dam. (ii) Improving drought/flood management strategies. To limit the severe impacts of droughts and floods (which are expected to increase in frequency, duration and magnitude), drought/flood warning, monitoring and response systems and plans ought to be prepared. Early warning systems, especially for floods and droughts, and contingency plans can provide support to regional and national administration, as well as to local bodies and farmers to adapt. Also, policies that encourage crop insurance can provide protection to the farmers in the event their farm production is reduced due to natural calamities. (iii) Improving groundwater management. To mitigate the potential future climate change induced risk of over-exploiting the groundwater resources, especially the aquifer zones, during prolonged periods of dry, groundwater management measures such as frequent levels (and quality) of monitoring, and identification and protection of recharge zones are recommended. Some of these zones could be slightly excavated to enhance ponding. (iv) Investigation into the impacts of climate change on water quality in the KIP area. Climate change impacts on water quality is a potential issue. However, no water quality baseline data exists for the Kalisindh River nor its tributaries in the vicinity of the KIP area. Therefore, the potential impacts of climate change on water quality could not be assessed in this CRVA. However, it is known that water quality is sensitive to both increased water temperatures and changes in precipitation and can change in response to climate change, through the increased mobility of chemical compounds, changes in hydrology and changes in timing of biological and meteorological patterns. Water quality in particular relates to nutrients, oxygen, natural organic matter and hazardous substances, contained in the water, as well as to the temperature of the water. Peak discharge, especially short intense flashy flood events, can cause water quality issues by causing a “flush” of high concentration of sediment or pollutants, or indirectly by causing a leaching of waste disposals and water treatment facilities. Flash events can also result in the spread of pathogens. As such, chemical products that farmers may use to enhance their crops ought to be stored in areas not prone to flooding. Further investigation, including collection of water quality 55

data for the Kalisindh River and its tributaries, is also required so that the impacts of climate variability and change on water quality can be quantified. (v) Investing in education and capacity building for understanding, implementing and extending the findings of this CRVA. This could involve communicating the CRVA methods and findings to the MPWRD, the farmers within the KIP and other stakeholders involved with the KIP and also educating farmers and decision makers about the adaptation measures and, where required, providing training for their implementation (e.g. training to use drip irrigation, SCADA etc.).

81. Further, given the substantial uncertainty associated with the climate change projections (within the KIP area and elsewhere in India and the wider West Asia region) the following recommendations relating to climate change impacts and climate risk assessment could also be considered to improve the quantification of, and resilience to, current and future climate-related risks for the KIP:

(i) Investigate climate change risks more rigorously. The GCM/RCM projections used in this CRVA were selected subjectively (i.e. as per paragraph 23, the GCM/RCM combination used for the water balance calculations is the one that, for all variables, is judged to be most consistently indicative of likely impacts of climate change for the KIP (i.e. in the middle of the range of possible projected impacts and never “unrealistically” diverging from other GCM/RCM results)). However, from the results presented in Section IV (and Appendices IV to VII) it is clear that a lot of uncertainty exists across the different GCM/RCM outputs (especially relating to projected changes in the timing and magnitude of precipitation). It is recommended that a more comprehensive evaluation of all available GCM/RCM outputs be conducted to objectively determine which GCM/RCMs most realistically represent hydroclimatic conditions in the KIP area, and also the climate influences important for the KIP area (e.g. the southwest monsoon, El Niño/Southern Oscillation etc.) – similar to the work done by Sperber et al. (2013)35 for the southeast Asian region. Then once the more realistic GCM/RCMs are identified only future scenarios from these GCM/RCMs should be considered. This recommendation should not be seen as just “doing more climate modelling” since, as per paragraph 17, subjectively increasing the number of GCM/RCM scenarios used in the water balance calculation is unlikely to significantly alter the CRVA findings and recommendations. However, if only outputs from the GCM/RCMs that perform well (i.e. the ones that can most realistically simulate observed historical conditions) for the KIP area are used then: (a) the water balance calculations may change; (b) the uncertainty associated with the future projections could reduce; and (c) it may be possible to more reliably quantify the likelihood of certain climatic changes occurring (or not) as opposed the current requirement of considering all changes projected by the different GCM/RCMs to be equally plausible. A critical first step in this GCM/RCM performance evaluation and selection is the collection and analysis of observed historical data with which to quantify the baseline hydroclimatic influences and impacts the GCM/RCMs will be tested against. Most of this observed historical data already exists for India, and the KIP area, so the collation and analysis of

35 Sperber, K.R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A., Wang, B. and Zhou, T. (2013): The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dynamics, 41(9-10), 2711-2744, doi:10.1007/s00382-012-1607-6. 56

that data in order to quantify baseline hydroclimatic influences and impacts is recommended followed by GCM/RCM performance evaluation and selection, similar to Sperber et al. (2013)35, and if needed GCM/RCM bias correction, but with the KIP area as the specific target. If the water balance calculations do change then the economic impact analyses of climate change and selected climate change adaptation measures should be repeated. As per recommendation (v) in paragraph 80, it is likely that capacity building activities will be required to enable the MPWRD, or similar government organization(s), to perform this type of work. (ii) Assess robustness of adaptation measures given uncertainties associated with the climate change projections. Undertake more adaptation planning and assessment of adaptation measures (including scheduling, costing and implementation) for more realistic (as opposed to just plausible) climate change scenarios for the KIP area (i.e. scenarios coming from the GCM/RCMs identified in the previous recommendation as performing well for the KIP area), rather than just the optimistic, pessimistic and average projections used in this CRVA. This should especially be the case for scenarios that seem unlikely now since proactively planning now for even the scenarios which seem unlikely could mean the costs of adapting to adverse impacts of climate change might be significantly reduced later. (iii) Monitor performance of adaptation measures. Perform follow-up investigations to assess whether the recommended adaptation measures actually do reduce risk (e.g. by re-assessing risk and vulnerability post- implementation of climate adaptation under an increased number of plausible scenarios of both climate variability and climate change). (iv) Monitor economic viability of adaptation measures. Develop monitoring measures to identify thresholds or ‘triggers’ (i.e. where and when) to proceed with the adaptation measures that are not recommended immediately but become feasible (and recommended) sometime in the future. For example, if locations within the KIP begin to more regularly exceed sustainable groundwater pumping levels this could be a trigger for the implementation of some of the more costly adaptation measures that it is recommended are deferred until later (e.g. improving application efficiency). (v) Assess the potential impact of flooding on the KIP. This CRVA focused specifically on assessing the impacts of climate change on the volume and timing of crop water demand (including peak water requirement) and discharge (water availability) – and to address any issues of KIP system design and management that were identified. The impact of flooding on the KIP, and more importantly the access roads and ability to carry out maintenance and repairs to pipes and other infrastructure associated with the KIP, was not assessed (either under current climate conditions or plausible future scenarios). It was suggested in the workshop held with MPWRD in November 2016 (the workshop where the findings of the CRVA were presented) that an investigation into the potential impacts of flooding on the KIP should be conducted, and that this investigation should account for both natural climate variability and also the impacts of longer- term climate change. This will require access to and analysis of daily and sub- daily rainfall and runoff data. This information exists but was not accessible, and the required flood risk analyses were not feasible, in the time allocated to this CRVA.

APPENDIX I – ACCLIMATISE AWARE FOR PROJECTS RAPID CLIMATE RISK SCREENING FOR THE MPIEIP

APPENDIX II – LIST OF DOCUMENTS AND PROJECT REPORTS USED IN THIS CRVA

A. Project documents

Source Date Title ADB ADB’s consultant report (during reconnaissance, pre-PPTA) ADB Nov 2015 AWARE climate risk screening for the KIP project (generated by ADB) ADB Dec 2015 Project Concept Paper ADB May 2016 Aide Memoire of TA9051-IND Inception Review Mission (1–12 May 2016) Madhya Pradesh Irrigation Efficiency Improvement Project - PPTA – ADB Aug 2016 Inception Report (Draft)

B. ADB reports, Government of India reports and other publications relevant to this CRVA

Source Date Title Reports from ADB ADB and International Building climate resilience in the agriculture sector in Asia and the Food Policy Research 2009 Pacific Institute. Technical Assistance for Innovations for More Food with Less ADB 2011 Water into Regional Cooperation (TA 7967-REG) ADB 2012 Water Operational Plan 2011–2020 Guidelines for Climate Proofing Investment in Agriculture, Rural ADB Nov 2012 Development, and Food Security ADB 2013 Cost-benefit analysis for development: A practical guide Support for the Implementation of the National Water Mission by ADB 2014 State Governments in India: Scoping Study for a National Water Use Efficiency Improvement Support Program Memorandum by Regional and Sustainable Development ADB Mar 2014 Department, Environment and Safeguards Division on Climate Risk Screening and Assessment of Projects. (internal document) Assessing the Cost of Climate Change and Adaptation in South ADB Jun 2014 Asia. M. Ahmed and S. Suphachalasai Climate Risk Management in ADB Projects - Publication Stock ADB Nov 2014 No. ARM146926-2 ADB 2015 Economic Analysis of Climate Proofing Investment Projects ADB Feb 2015 Guidance Note: Irrigation Subsector Risk Assessment Reports from Government of India (national and Madhya Pradesh) Madhya Pradesh Design Series Technical Circular No. 25. Estimation of Crop 1990 Irrigation Department Water Requirement and Irrigation Water Requirement Government of India, Drought Research Study of Dry and Wet Spells for Meteorological Subdivisions of Unit, India 2001 India by P.G. Gore Meteorological Department Government of India 2011 National Action Plan for Climate Change Government of India, National Water Mission 2012-2017 under the National Action Plan Ministry of Water 2011 on Climate Change Resources 68

Source Date Title Government of India, Support for the National Action Plan on Climate Change Support Governments of to the National Water Mission. Report prepared by Asian Punjab, Madhya Sep 2011 Development Bank, Government of India, and the Governments of Pradesh and Tamil Punjab, Madhya Pradesh and Tamil Nadu (Volume IV and Nadu, ADB Appendix 3 in the Final Report) Government of India, India Second National Communication to the United Nations Ministry of May 2012 Framework Convention on Climate Change Environment & Forests (http://unfccc.int/resource/docs/natc/indnc2.pdf) Government of India, 2012 Twelfth Five-Year Plan 2012-2017 Planning Commission Government of Detailed Project Report (DPR): Madhya Pradesh, 2013 & - version 1 (2013) for the original canal scheme; Water Resource 2015 - updated version (2015) for the 125,000 ha pressurized scheme. Department Government of Madhya Pradesh, Madhya Pradesh Water Resource Development Plan 2015-2025.

Water Resource Communication. Unpublished Department Reports from other sources 2007 & Climate Change 2007 & 2014: Impacts, Adaptation and IPCC 2014 Vulnerability Climate Change Impacts in Drought and Flood Affected Areas: World Bank Jun 2008 Case Studies in India Assessing Climate Change Impacts and Vulnerability: Making UNFCCC 2011 Informed Adaptation Decisions Climate risk screening tools and their application: A guide to the UNDP/UNEP Sep 2011 guidance Climate Risk and Financial Institutions - Challenges and IFC 2010 Opportunities Managing the Risks of Extreme Events and Disasters to Advance IPCC 2012 Climate Change Adaptation (SREX) - Special Report of the Intergovernmental Panel on Climate Change. The Global Climate 2001-2010: A decade of climate extremes. WMO 2013 Summary Report, WMO-No. 1119 Crop evapotranspiration - Guidelines for computing crop water FAO 1998 requirements - FAO Irrigation and drainage paper 56. http://www.fao.org/docrep/X0490E/x0490e00.htm. A Question of Development: Overextraction of Groundwater AFD Apr 2015 Resources: What Are The Solutions? Water Accounting in Selected Asian River Basins: Pilot studies in UNESCO-IHE Apr 2016 Madhya Pradesh (India) – Inception Phase Report. W. Bastiaanssen and E. Salvadore. Water Policy Research Highlight 01/2016. IWMI-Tata Water 2016 Har Khet Ko Pani? (Water to Every Farm?) Emulate Madhya Policy Program Pradesh's Irrigation Reform. T. Shah et. al.

APPENDIX III – DAILY EXTRATERRESTRIAL RADIATION (RA) FOR DIFFERENT LATITUDES FOR THE 15TH DAY OF THE MONTH

Information extracted from Table 2.6 at: http://www.fao.org/docrep/X0490E/x0490e0j.htm#annex%202.%20meteorological%20tables.

Values in MJ m-2 day-1. Values can be converted to equivalent values in mm/day by dividing by 2.45.

th Values for Ra on the 15 day of the month provide a good estimate (error < 1 %) of Ra averaged over all days within the month. Only for high latitudes greater than 55° (N or S) during winter are deviations possibly more than 1%.

Values used in this CRVA are taken from the row for latitude 24°N.

APPENDIX IV – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986- 2005) AND FUTURE (2041-2060) MEAN MONTHLY PRECIPITATION TOTALS FOR DIFFERENT GCM/RCM OUTPUTS

A. Under the RCP4.5 (“optimistic”) climate change scenario 71

MPI-ESM-LR_r1_CSIRO-CCAM-1391M

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1

CCSM4_r1_CSIRO-CCAM-1391M

CNRM-CM5_r1_CSIRO-CCAM-1391M

B. Under the RCP8.5 (“pessimistic”) climate change scenario

MPI-ESM-LR_r1_CSIRO-CCAM-1391M 76

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1 77

CCSM4_r1_CSIRO-CCAM-1391M

CNRM-CM5_r1_CSIRO-CCAM-1391M

APPENDIX V – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986- 2005) AND FUTURE (2041-2060) DAILY PRECIPITATION MAXIMUMS WITHIN EACH MONTH FOR DIFFERENT GCM/RCM OUTPUTS

A. Under the RCP4.5 (“optimistic”) climate change scenario 81

MPI-ESM-LR_r1_CSIRO-CCAM-1391M

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1

CCSM4_r1_CSIRO-CCAM-1391M

CNRM-CM5_r1_CSIRO-CCAM-1391M

B. Under the RCP8.5 (“pessimistic”) climate change scenario

MPI-ESM-LR_r1_CSIRO-CCAM-1391M 86

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1 87

CCSM4_r1_CSIRO-CCAM-1391M

CNRM-CM5_r1_CSIRO-CCAM-1391M

APPENDIX VI – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986- 2005) AND FUTURE (2041-2060) MEAN MAXIMUM DAILY TEMPERATURE WITHIN EACH MONTH FOR DIFFERENT GCM/RCM OUTPUTS

A. Under the RCP4.5 (“optimistic”) climate change scenario 91

MPI-ESM-LR_r1_CSIRO-CCAM-1391M

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1

CCSM4_r1_CSIRO-CCAM-1391M

CNRM-CM5_r1_CSIRO-CCAM-1391M

B. Under the RCP8.5 (“pessimistic”) climate change scenario

MPI-ESM-LR_r1_CSIRO-CCAM-1391M 96

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1 97

CCSM4_r1_CSIRO-CCAM-1391M

CNRM-CM5_r1_CSIRO-CCAM-1391M

100

APPENDIX VII – MAPS SHOWING THE DIFFERENCE BETWEEN CURRENT (1986- 2005) AND FUTURE (2041-2060) MEAN MINIMUM DAILY TEMPERATURE WITHIN EACH MONTH FOR DIFFERENT GCM/RCM OUTPUTS

A. Under the RCP4.5 (“optimistic”) climate change scenario 101

MPI-ESM-LR_r1_CSIRO-CCAM-1391M

102

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1

103

CCSM4_r1_CSIRO-CCAM-1391M

104

CNRM-CM5_r1_CSIRO-CCAM-1391M

105

B. Under the RCP8.5 (“pessimistic”) climate change scenario

MPI-ESM-LR_r1_CSIRO-CCAM-1391M 106

MPI-M-MPI-ESM-LR_r1i1p1_MPI-CSC-REMO2009_v1 107

108

CCSM4_r1_CSIRO-CCAM-1391M

109

CNRM-CM5_r1_CSIRO-CCAM-1391M