CLIMATE VULNERABILITY and RISK ASSESSMENT REPORT Islam Barrage

Major Change of Trimmu and Panjnad Barrages Improvement Project (PAK- 47235) CLIMATE VULNERABILITY AND RISK ASSESSMENT REPORT Islam Barrage I. Introduction 1. The ADB approved the TPBIP on 22 September 2014 for a combined loan amount of $150 million, consisting of $50 million from ADB’s ordinary capital resources and $100 million equivalent from ADB’s Special Funds resources. The current project will rehabilitate and upgrade the Trimmu and Panjnad barrages. Additional financing will enable the upscaling of the project scope by adding another barrage, the Islam barrage. These barrages were built 80 to 90 years ago, and suffer from structural deterioration from aging, and reduced flood passage capacity. As a result, these barrages are now exposed to high risk of failure, which would result in the loss of irrigated farm land and downstream flooding, affecting the life and livelihood of some 2.5 million people in seven districts in Punjab.1 II. The Project and Climate Vulnerability and Risk Assessment (CRVA) Study A. Rationale 2. Major change in project scope is required to allow upscaling of the project by increasing the number of barrages for rehabilitation and upgrading from two to three. The third barrage, proposed for additional financing, is the Islam barrage, which is built some 90 years ago, and like Trimmu and Panjnad barrages, its structural stability and the capacity to safely pass through floods have deteriorated. The barrage faces significant risk of failure, which would lead to considerable loss of agricultural production in 42,000 ha of farm land to which the barrage provides water, and to devastation of some 0.3 million people due to flood. The barrage is in the same southern Punjab province as with the two barrages, and on the Sutlej River that joins the Chenab River about 200 km downstream where Panjnad barrage is located. B. Objective and Purpose of the CRVA study 3. The objective of this assignment is to help ADB and the Government to advance the project readiness for the proposed additional financing for the Trimmu and Panjnad Barrages Improvement Project (TPBIP). The assignment should lead to the delivery of a comprehensive assessment of technical feasibility, and financial and economic viability of the proposed additional financing, through careful review and validation of the available feasibility and detailed engineering study reports, as well as other relevant information to be gathered during the assignment to identify and analyze the information gaps in technical, financial and economical due diligence requirements, update the project cost, financial and economic analysis, and prepare a comprehensive investment proposal in the form of relevant project preparation documents required by ADB. 4. The CRVA study required a total of 1.25 person-months input over a duration of 3 months. The Climate Change Specialist required to work under the guidance and oversight of the international Water and Irrigation Specialist, with close collaboration with other experts to be engaged separately. They are Irrigation Specialist and a Project Economist. 1 They are districts of Bahawalpur, Jhang, Lodhran, Multan, Muzaffargarh, Rahimyar Khan, and Vehari. 2 III. DATA AND PROPOSED METHODOLOGY A. Historic Hydro-climate Datasets 5. Impact of climate change has been assessed using various datasets (such as hydro- climatic data) from different line departments. The datasets have been acquired in hard copies or in soft format (subject to availability). Data from hard copies have been digitized prior to further analysis. To assess the impact of climate change Islam barrage and Sulaimanki barrage upstream and downstream flow data together with link canals flow data have been acquired and analysed. Long-term available flow data (>25 years data) have been acquired from PMO barrages and PMIU Punjab. Climate stations precipitation and temperature data (>25 years data) have been acquired from Pakistan Meteorological Department (PMD) or through Global Change Impact Studies Centre (GCISC). 6. In addition to the above-mentioned datasets from line departments, gridded datasets from public domain have also been acquired. Main datasets from public domain are: i) daily precipitation datasets (historic and future projected), and ii) daily temperature datasets (historic and future projected), All these datasets have been acquired for free. Details of the above-mentioned datasets, their limitations/constraints are provided in this report. B. Projected Climate Datasets and Scenarios 7. General Circulation/Climate Models (GCMs): General Circulation Models (GCMs) are used to simulate the world climate at different spatial resolutions (~100km2 to ~250km2), and provide future projections of the climatic data till 2100. The present-day cutting edge GCMs are prepared in the 5th Coupled Model Intercomparing Project CMIP-5 (Taylor et al. 2012)2, which was utilized as a foundation by the Intergovernmental Panel On Climate Change (IPCC) for the initiation of its 5th Assessment Report (AR5). 8. Nowadays more than 61 GCMs provide future climatic data, and are publicly available. Accuracy assessment can be carried out for selection of the best available GCM/GCMs for the study area, prior to GCMs data use in the CRVA study. A basin-wide accuracy assessment approach is generally recommended. Reggiani and Rientjes (2014)19 suggested the best method to test the accuracy of precipitation data sets is by basin-wide mass balance assessment. They presented a relationship for such an analysis, provided in Equation 1: Q + ET = P ± S (1) where, Q is average annual flow (mm/yr),act P is total∆ annual precipitation (snow and rainfall) in mm/yr, ETact is actual evapo-transpiration (mm/yr), and ∆S is change in storage (mm/yr), which represents unaccounted losses. Unfortunately, most of the datasets for the current study inaccessible or unavailable. Therefore, based on the available literature (such as Ashfaq et al., 2016; Lutz et al., 2016) and availability of data, 14 GCMs (Table 1) have been selected for preliminary assessment. Data of the acquired GCMs have been downloaded from http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html, all for the period of 1976-2100. 2 Taylor, K. E., R. J. Stouffer & G. A. Meehl, 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society 93(4):485-498 doi:10.1175/bams-d-11-00094.1. 3 Table 1: List of acquired GCMs for the study area S.No GCM Resolution Source 1 CanESM2 2.7906ⅹ2.8125 Canadian Centre for Climate Modelling and Analysis 2 CCSM4 1.250ⅹ0.942 National Center for Atmospheric Research 3 CESM1-CAM5 1.250ⅹ0.942 National Center for Atmospheric Research 4 CMCC-CMS 1.875ⅹ1.865 Centro Euro-Mediterraneo per I Cambiamenti Climatici 5 CNRM-CM5 1.406ⅹ1.401 Centre National de Recherches Meteorologiques 6 EC-EARTH 1.1215ⅹ1.125 Irish Centre for High-End Computing (ICHEC), European Consortium 7 FGOALS-s2 2.813ⅹ1.659 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences 8 GFDL-ESM2G 2.500ⅹ2.023 Geophysical Fluid Dynamics Laboratory 9 GFDL-ESM2M 2.500ⅹ2.023 Geophysical Fluid Dynamics Laboratory 10 INM-CM4 2.000ⅹ1.500 Institute for Numerical Mathematics 11 MIROC-ESM- 2.7906ⅹ2.8125 National Institute for Environmental Studies, The CHEM University of Tokyo 12 MPI-ESM-LR 1.875ⅹ1.865 Max Planck Institute for Meteorology (MPI-M) 13 MPI-ESM-MR 1.875ⅹ1.865 Max Planck Institute for Meteorology (MPI-M) 14 NorESM1-M 2.500ⅹ1.895 Norwegian Climate Centre 9. GCMs have further been shortlisted based on five scenarios: i) wet and warm scenario, ii) wet and cold scenario, iii) dry and cold scenario, iv) dry and hot scenario, and v) average scenario. CCSM4 and INMCM4 have been excluded from the analysis as their projected data shown significant uncertainty. Changes in precipitation and temperature have been estimated based on comparison of 2011-2100 data with baseline data (1976-2005), see Figure 1. Based on the above-mentioned criteria, future projections of all GCMs, five GCMs have been selected for further analysis (see Table 2). 4 Figure 1: Changes in precipitation and temperature during 2011-2100 compared to 1976-2005. Figure shows selected GCMs (in dark colors) based on different scenarios. 3.5 Dry and Hot Wet and Warm 3 C) o 2.5 Average Dry and Cold2 Wet and Cold 1.5 1 Average Average Temperature Changes ( 0.5 0 -10 0 10 20 30 40 50 Annual Precipitation Changes (%) 10. Most of the GCMs underestimate the monsoon precipitation, in the HKH region and are not true representative for the region (Sperber et al. 2013)3. They can therefore not be used for the extreme events (floods) projection (Lee and Wang 2014)4. Therefore GCMs have first been downscaled (using statistical downscaling) and bias-corrected using climate stations data. Schematic diagram of GCMs selection, downscaling and use is shown in Figure 2. Table 2: List of selected GCMs together with changes in precipitation and temperature during 2011-2040, 2041-2070, 2071-2100, and 2011-2100 compared to 1976-2005. Precipitation Changes in Average Temperature Changes Selected Percent (RCP 4.5) in oC (RCP 4.5) Scenario GCMs 2011- 2041- 2071- 2011- 2011- 2041- 2071- 2011- 2040 2070 2100 2100 2040 2070 2100 2100 Wet and 7.1 34.4 42.8 28.1 1.4 2.4 2.3 2 FGOALS-S2 Warm Wet and 6.6 7.2 24.7 12.8 0.9 1.7 2.1 1.6 CNRM-CM5 Cold EC-EARTH Average -3.3 17.6 12.7 9 1 1.9 2.3 1.7 3 Sperber, K. R., H. Annamalai, I.-S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang & T. Zhou, 2013. The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century.

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