ANNEXE 5B Sahiwal Hydrology and Modeling for Climate Risk and Vulnerability Assessment (CRVA) Pre-Feasibility Study for Sialkot and Sahiwal Cities

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1 REG-8556 ANNEXE 5B Sahiwal Hydrology and Modeling for Climate Risk and Vulnerability Assessment (CRVA) Pre-Feasibility Study for Sialkot and Sahiwal Cities REG-8556 ANNEXE 5B Page i

2 TABLE OF CONTENTS EXECUTIVE SUMMARY INTRODUCTION METHODOLOGY ASSESSMENT OF CLIMATE CHANGE IMPACTS ON HYDROLOGY, FLOOD CONTROL AND URBAN DRAINAGE IN SIALKOT Hydrology Extreme Events Generalized Extreme Value (GEV) Analysis for Precipitation in Sialkot Baseline Data Availability Statistical Model To Estimate Sub-Daily Intensity-Duration-Frequency Baseline Intensity-Duration-Frequency Analysis - Sialkot Assumptions Results GEV Model and Climate Change Vulnerability Analysis - Sialkot Analysis Minute duration curves for baseline scenario and climate change of 2050 scenario and 2100 Scenarios for Low, Medium and High Daily duration curves for baseline scenario and climate change of 2050 scenario and 2100 Scenarios for Low, Medium and High Analysis of Impact of Climate Change on Rainfall Frequency Curves Change in return periods for various frequency durations Conclusion Floods and Flood Control in Sialkot Chenab River Frequency Analysis of Peak Flow of Chenab at Marala Streams (Nullahs) Frequency Analysis Floods in Nullah Aik and Palkhu Analysis of Rainfall intensity in the catchment area of Nullah Aik Analysis Screening Level Watershed Model for Aik Nullah Aik Nullah Catchment Rainfall Series Model Runs Results Based on Sialkot Met Station Based on Catchment Rainfall Based on 95% Upper Confidence Limit of Catchment Rainfall Detail Results Simulated Discharge from Aik Nullah for all the return Periods Error Analysis Comparison of Results with Actual Data Discussion Screening Level Watershed With and Without Climate Change Method Model Runs REG-8556 ANNEXE 5B Page ii

3 TABLE OF CONTENTS (continued) Results Catchment Station Time Series with upper confidence limit Analysis Changes in Return Period Durations Conclusion Screening Level Storm Water Management Model for Urban Areas of Sialkot Setup of Model Assumptions Model Runs Results Screening Level Storm Water Management Model for Urban Areas of Sialkot with Climate Change Scenarios Model Runs with Climate Change Analysis for Impact of Climate Change 15-Minute Intensity and 5-Year Return Period Increase in Simulated Discharge In Time or Flow Regime Change With Climate Change Conclusion ASSESSMENT OF CLIMATE CHANGE IMPACTS ON WATER RESOURCES IN SIALKOT Glaciers and Snowmelt Contribution to Water Supply Storm Water and Recharge Calculations in Urban Areas of Sialkot by Mass Balance Area and Land use Draining channels Methodology used for various Land use areas Depth of Ground water Brief on HELP Model Climate Data Properties of Various Areas as put in the HELP model Soil and Design Data Results and Analysis Conclusion Impacts of climate change on Storm Water and Recharge (Water Supply) in Sialkot using Mass balance approach Properties of Various Areas as put in the HELP model Results Conclusion Water Supply -Sialkot Groundwater Draw dawn Analysis Recharge to Groundwater Analysis of Recharge and Discharge in Un-Commanded Area of Rechna Doab- Groundwater Mass Balance Model Discharge Water Use by Agriculture Water use by Water Supply Recharge to Aquifer Recharge from Rainfall REG-8556 ANNEXE 5B Page iii

4 TABLE OF CONTENTS (continued) Recharge from River Chenab Recharge from M-R link Canal Recharge from Nullahs Recharge from Field Percolation Sustainability of Water Supply Impacts of climate change on Water supply from upper catchments Increase in Estimated Flows of River Chenab Estimates for Potential Increased Seepage to Aquifer near Sialkot Sensitivity of Velocity to Seepage Impact of Climate Change on Seepage and Inflow Impact of Increases Water Inflow of River Chenab on Sialkot Ground Water Supply in ASSESSMENT OF CLIMATE CHANGE IMPACTS ON HYDROLOGY FLOOD CONTROL AND URBAN DRAINAGE IN SAHIWAL Hydrology Generalized Extreme Value (GEV) Analysis for Precipitation - Sahiwal Data Availability Statistical Model To Estimate Sub-Daily Intensity-Duration-Frequency Curves Baseline Intensity- Duration - Frequency Analysis - Sahiwal GEV MODEL And Vulnerability Analysis - Sahiwal Results Analysis of Impact of Climate Change on Rainfall Frequency Curves Change in return periods for various frequency durations Conclusion Screening Level Storm Water Management Model for Urban Areas of Sahiwal Setup of Model Assumptions Model Runs Results Screening Level Storm Water Management Model for Urban Areas of Sahiwal with Climate Change Scenarios Model Runs with Climate Change Analysis for Impact of Climate Change ASSESSMENT OF CLIMATE CHANGE IMPACTS ON WATER RESOURCES IN SAHIWAL Water Supply and Groundwater analysis - Sahiwal Water Resources Analysis Groundwater Depth Trend Compounded Trend in Groundwater Levels Recharge Source of Groundwater Recharge Sources and the aquifer response Conclusion ADAPTATION REFERENCES Annexure-A... A-1 Annexure-B... B-1 Annexure-C... C-1 REG-8556 ANNEXE 5B Page iv

5 TABLE OF CONTENTS (continued) Annexure-D... D-1 Annexure-E... E-1 Annexure-F... F-1 Annexure-G... G-1 Annexure-H... H-1 REG-8556 ANNEXE 5B Page v

6 TABLES Table Punjab vulnerable districts to various kind of flood Table Mean Temperature and Precipitation of Sialkot Table Relationship of Daily Rainfall intensity to 12, 6, and 3 Hour Rainfall Intensity Table Fitted Ratios of 24 Hour Rainfall Intensity to 12, 6, and 3 Hour Rainfall Intensity. 19 Table Calculated Ratios of Sub-Hourly Rainfall Intensity to 24-Hourly Rainfall Intensity. 19 Table Summary Table of GEV Distribution Tests Table Goodness of Fits Details Table GEV-Three Parameter Rainfall Distribution for Sialkot Table GEV-Three Parameter Rainfall Distribution 95% Confidence (lower) for Sialkot Table GEV-Three Parameter Rainfall Distribution 95% Confidence (upper) for Sialkot.. 22 Table Sialkot GEV parameters change due to climate change impact (%) Table Baseline and Climate Change 5-Minute Duration Rainfall Intensity Table Baseline and Climate Change Daily Duration Rainfall Intensity Table Percentage Change in Daily Rainfall Intensity of Rainfall in Sialkot with Climate Change Table Annual Peak Flow in River Chenab at Marala Table Peak Discharge in River Chenab versus Return Periods Table Limits of Flood Levels for Various Kind of Floods Table Historical Peak Flows on Nullah Aik Table Peak Discharges versus Return Period, GEV-Three Parameter Distribution 39 Table Goodness-of-Fit - Summary Table Goodness-of-Fit Details of GEV-Three parameter distribution Table Location of NCEP Analysis Points Table Simulated Discharge based on Data from Sialkot Met Station Data Table Simulated Discharge based on Data from Catchment Data Table Simulated Discharge based on Data from 95% Upper Confidence Limit value of Catchment Data Table Error Analysis Table Simulated Discharge of Nullah Aik with and without climate Change Table Percentage Vhange in Simulated Discharges of Nullah Aik and Nullah Palkhu for 25 Year Return Periods with and without climate change Table Simulated Discharges of Nullah Aik and Nullah Palkhu for 25 Year Return Periods with and without climate change Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Aik over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Bhaid over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Aik over Time from 30-Minute 5 Year Return Period Rainfall - Baseline.. 73 Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 30-Minute 5 Year Return Period Rainfall - Baseline REG-8556 ANNEXE 5B Page vi

7 TABLES (continued) Table Percentage increase in Simulated Discharge in Nullah Aik - 15 Minute Intensity and Return Period of 5-Year Table Percentage increase in Simulated Discharge in Nullah Bhaid - 15 Minute Intensity and Return Period of 5-Year Table Percentage increase in Simulated Discharge in Nullah Palkhu - 15 Minute Intensity and Return Period of 5-Year Table Percentage increase in Simulated Discharge in Nullah Aik - 30 Minute Intensity and Return Period of 10-Year Table Percentage increase in Simulated Discharge in Nullah Bhaid - 30 Minute Intensity and Return Period of 10-Year Table Percentage increase in Simulated Discharge in Nullah Palkhu- 30 Minute Intensity and Return Period of 10-Year Table Percentage increase in Simulated Discharge in Nullah Aik- 6 Hour Intensity and Return Period of 10-Year Table Percentage increase in Simulated Discharge in Nullah Bhaid - 6 Hour Intensity and Return Period of 10-Year Table Percentage increase in Simulated Discharge in Nullah Palkhu - 6 Hour Intensity and Return Period of 10-Year Table Type of Areas in Sialkot Table Soil Parameters Table Simulated Annual Water Mass Balance for the City of Sialkot Table Simulated Average Daily Water Mass Balance for the City of Sialkot Table Simulated Maximum Daily Runoff and Seepage on Peak Wet Day Table Simulated Average Selected Rainy Days Daily Water Mass Balance for the City of Sialkot Table Baseline and Climate Change Scenario-2050 Monthly Precipitation at Sialkot Table Baseline and Climate Change Scenario-2050 Monthly Average Temperature at Sialkot Table Simulated Average Daily Runoff from Storms from Storms CC Scenario 2050 High at Sialkot Table Simulated Average Daily Evaptranspiration and Percolation from Storms CC Scenario 2050 High at Sialkot Table Simulated Maximum Daily Runoff and Percolation from Storms in 100 Years Table Simulated Average Selected Rainy Days Daily Water Mass Balance for the City of Sialkot Table Simulated Average Selected Rainy Days Daily Water Mass Balance for the City of Sialkot Table Groundwater Depth (bgs) of Monitoring wells in and near Sialkot Table Estimated Contribution of Glacier Melt and Snow Melt to Discharge of River Chenab Table Increase in Flow of Chenab with Temperature Change Table Sensitivity of Velocity of River Chenab to Seepage Table Impact of Climate Change on Seepage from River Chenab Table Adjusted Impact of Climate Change on Seepage from River Chenab Table Impact of Climate Change on Seepage from Recharge Sources Table Impact of Climate Change on Seepage from Recharge Sources in Table Average Monthly Temperature of Sahiwal, Punjab REG-8556 ANNEXE 5B Page vii

8 TABLES (continued) Table Average Monthly Precipitation of Sahiwal, Punjab Table Relationship of Daily Rainfall intensity to 12, 6, and 3 Hour Rainfall Intensity Table Fitted Ratios of 24 Hour Rainfall Intensity to 12, 6, and 3 Hour Rainfall Intensity 122 Table Calculated Ratios of Sub-Hourly Rainfall Intensity to 24-Hourly Rainfall Intensity. 122 Table Calculated Parameters of Sub-Daily Rainfall Intensities to Daily Rainfall Intensity 123 Table GEV-Three Parameter Rainfall Distribution for Sahiwal Table GEV-Three Parameter Rainfall Distribution 95% Confidence (lower) for Sahiwal. 124 Table GEV-Three Parameter Rainfall Distribution 95% Confidence (upper) for Sahiwal 124 Table Sahiwal GEV parameters change due to climate change impact (%) Table Minute duration Rainfall curves Data for base scenario and Low, Medium and Table High of Hour duration Rainfall curves Data for base scenario and Low, Mid and High of Table Percentage Increase in the 30-Minute Intensity of Rainfall for CC Scenario 2050 (Low, Middle and High) and Scenario 2100 (Low, Middle and High) Table Percentage Increase in the 30-Minute Intensity of Rainfall for CC Scenario 2050 (Low, Middle and High) and Scenario 2100 (Low, Middle and High) Table Table Table Table Table Maximum Simulated Discharge from Various Areas for Various Duration of Rainfall and Return Period, Baseline Scenario, Sahiwal, Punjab Percentage Increase in the Simulated Discharge from Various Areas, Climate Change Scenario, Sahiwal, Punjab - 30 Minute Duration and Return Period of 5 Year Percentage increase from Baseline Scenario in Simulated Discharge from Various Areas, Climate Change Scenario, Sahiwal, Punjab - 30 Minute Duration and Return Period of 10 Year Percentage increase from Baseline Scenario in Simulated Discharge from Various Areas, Climate Change Scenario, Sahiwal, Punjab - 1 Hour Duration and Return Period of 10 Year Percentage increase from Baseline Scenario in Simulated Discharge from Various Areas, Climate Change Scenario, Sahiwal, Punjab - 3 Hour Duration and Return Period of 10 Year Table Percentage increase from Baseline Scenario in Simulated Discharge from Various Areas, Climate Change Scenario, Sahiwal, Punjab - 3 Hour Duration and Return Period of 25 Year Table Latest Reported Groundwater Depth of Monitoring Wells REG-8556 ANNEXE 5B Page viii

9 FIGURES Figure 1.1 Map of Pakistan Figure 1.2 Indus River Systems Figure Precipitation Intensity Duration Frequency for various Time Intervals using GEV- 3 Distribution Figure Baseline and Climate Change 2050 Scenario (Low, Middle and High) 5-Minute duration Rainfall Intensity Figure Baseline and Climate Change 2100 Scenario (Low, Middle and High) 5-Minute Duration Rainfall Intensity Figure Changes in Return Periods for Various Climate Change Scenarios for 15-Minute Duration Rainfall Intensity for Baseline Return Period of 5 Year Figure Changes in Return Periods for Various Climate Change Scenarios for 6-Hour Duration Rainfall Intensity for Baseline Return Period of 50 Year Figure Changes in Return Periods for Various Climate Change Scenarios for 6-Hour Duration Rainfall Intensity for Baseline Return Period of 50 Year Figure Changes in Return Periods for Various Climate Change Scenarios for 6-Hour Duration Rainfall Intensity for Baseline Return Period of 100 Year Figure Changes in Return Periods for Various Climate Change Scenarios for 24-Hour Duration Rainfall Intensity for Baseline Return Period of 100 year Figure Peak Discharge Frequency Analysis - Chenab at Marala Figure Historic Annual Peak Discharge of Nullah Aik at Ura Figure Nullah Aik Discharge Frequency Analysis - Generalized Extreme Value Distribution Figure Nullah Aik Discharge at Ura versus Return Period with 95% Confidence values.. 40 Figure Maximum Daily Rainfall (Mm) During & Part Of 2014 Sialkot and the Vicinity Figure Generalized Extreme Value Distribution, Daily Precipitation at Aik Catchment Figure Daily Precipitation versus Return Period, Daily Precipitation at Aik Catchment.. 44 Figure Catchment Areas of Nullah Aik and Nullah Palkhu Figure Schematic Map of Nullah Aik Basin on GIS map Figure Schematic Map of Nullah Aik Basin Figure Simulated Discharge in Nullah Aik at Ura and Daily Total Rainfall at Sialkot meteorological Station vs Return Periods Figure Simulated Discharge in Nullah Aik at Ura and Daily Total Rainfall at Catchment Point vs Return Periods Figure Simulated Discharge in Nullah Aik at Ura and 95% Upper Confidence Limit of Catchment Rainfall Daily Total Rainfall at Catchment Point vs Return Periods 52 Figure Simulated Discharge in Nullah Aik at Ura vs 25 Year Return Periods based on Sialkot Data Figure Simulated Discharge in Nullah Aik at Ura vs 50 Year Return Periods based on Sialkot Data Figure Simulated Discharge in Nullah Aik at Ura vs 25 Year Return Periods based on Catchment Data Figure Simulated Discharge in Nullah Aik at Ura vs 50 Year Return Periods based on Catchment Data Figure Simulated Discharge in Nullah Aik at Ura vs 25 Year Return Periods based on 95% UCL of Catchment Data REG-8556 ANNEXE 5B Page ix

10 FIGURES (continued) Figure Simulated Discharge in Nullah Aik at Ura vs 50 Year Return Periods based on 95% UCL of Catchment Data Figure Simulated Discharge in Nullah Aik At Ura Vs Various Return Periods - Sialkot Data Figure Simulated Discharge in Nullah Aik At Ura Vs Various Return Periods - Aik Catchment Data Figure Simulated Discharge in Nullah Aik At Ura Vs Various Return Periods - Aik Catchment Data 95% UCL Figure Model Results and Actual Data Figure Simulated Discharges in Nullah Aik at Ura with and without climate change Scenarios Figure Simulated Discharges in Nullah Aik at Ura with and without climate change Scenarios Figure Reduction in Baseline 5 year Return Periods with climate change-2100 Scenarios Figure Reduction in Baseline 25 year Return Periods with climate change Scenarios Figure Reduction in Baseline 50 year Return Periods with climate change Scenarios Figure Schematic and Model Domain Figure Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Aik over Time from 30-Minute 5 Year Return Period Rainfall Baseline Figure Simulated Storm water Discharge Loading from Sialkot and Peri-Urban areas to Nullah Aik from 30-Minute 5 Year Return Period Rainfall Baseline Figure Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Bhaid over Time from 30-Minute 5 Year Return Period Rainfall Baseline Figure Simulated Storm water Discharge Loading from Sialkot and Peri-Urban areas to Nullah Bhaid from 30-Minute 5 Year Return Period Rainfall - Baseline Figure Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Figure Simulated Storm water Discharge Loading from Sialkot and Peri-Urban areas to Nullah Palkhu from 30-Minute 5 Year Return Period Rainfall - Baseline Figure Location of Points Selected for Assessment of Climate Change Figure Location of Special Points Selected for Assessment of Climate Change Figure Simulated Urban Discharges in Nullah Aik - 15 minute Intensity and Return Period of 5-Year Figure Simulated Urban Discharges in Nullah Bhaid - 15 Minute Intensity and Return Period of 5-Year Figure Simulated Urban Discharges in Nullah Palkhu - 15 Minute Intensity and Return Period of 5-Year Figure Simulated Discharge in Nullah Aik at Link 109 with 15 Minute Rainfall Intensity and Return Period of 5-Year Figure Simulated Discharge in Bhaid at Link 203 with 15 Minute Rainfall Intensity and Return Period of 5-Year Figure Simulated Discharge in Nullah Aik at Link 109 with 6-Hour Rainfall Intensity and Return Period of 25-Year REG-8556 ANNEXE 5B Page x

11 FIGURES (continued) Figure Simulated Discharge in Nullah Bhaid at Link 203 with 6-Hour Rainfall Intensity and Return Period of 25-Year Figure Histogram of Data Generated by HELP Figure Location of Municipal Wells Figure Location of Monitoring Wells Figure Location of Monitoring Wells In and Near Sialkot and groundwater Depth in October Figure Location of Monitoring Wells In and Near Sialkot and groundwater Depth in October Figure Average Historic Groundwater Depth in June in Monitoring Wells in and Sialkot City Figure Average Historic Groundwater Depth in June in Monitoring Wells and Trend in and Sialkot City Figure Average Historic Groundwater Depth In June and Trend Monitoring Wells in and near Sialkot City Figure Historic Groundwater Depth Change in Monitoring Wells in and near Sialkot City 105 Figure Historic Discharge in River Chenab at US Marala and Groundwater Depth Change at City of Sialkot and Vicinity Figure Historic Discharge in Aik Nullah at Ura and Groundwater Depth Change at City of Sialkot And Vicinity Figure Monthly Total Rainfall at Sialkot and Average Monthly Groundwater Depth Change at City of Sialkot and Vicinity Figure Average Groundwater Depth and Trend in Sialkot, Baseline & with Climate Change - Scenario High in Figure Precipitation Intensity Duration Frequency for various Time Intervals using GEV- 3 Distribution Figure Minute duration Rainfall curves for base scenario and Low, Medium and High of Figure Minute duration Rainfall curves for base scenario and Low, Medium and High of Figure Hour duration Rainfall curves for base scenario and Low, Middle and High of Figure Hour duration Rainfall curves for base scenario and Low, Mid and High of Figure Reduction in Design Return Period of 10- Minute Duration Rainfall with Climate Change Scenarios (Low, Mid and High) Figure Reduction in Design Return Period of 3-Hour Duration Rainfall with Climate Change Scenarios (Low, Mid and High) Figure Reduction in Design Return Period of 6- Hour Duration Rainfall with Climate Figure Change Scenarios (Low, Mid and High) Reduction in Design Return Period of 6- Hour Duration Rainfall with Climate Change Scenarios (Low, Mid and High) Figure Model Domain Figure Setup of Model Figure Various Points for Assessment of Simulated Discharge Figure Simulated Storm water Discharge from various Areas with 30-minute Rainfall intensity and 5-Year Return Period REG-8556 ANNEXE 5B Page xi

12 FIGURES (continued) Figure Simulated Storm water Discharge from various Areas with 1-hour Rainfall intensity with 10-Year Return Period Figure Simulated Storm water Discharge from various Areas with 3-hour Rainfall intensity with 25-Year Return Period Figure Simulated Discharges from Area A with and without Climate Change Scenario for 30 Minutex Duration and 5 Year Return Period Figure Location of Monitoring Wells Figure Latest Reported Groundwater Depth of Monitoring Wells Figure Latest Reported Groundwater Depth of Monitoring Well MTN_WL_ Figure Average Groundwater Depths in Monitoring Wells - North of Sahiwal Figure Average Groundwater Depths in Monitoring Wells - East of Sahiwal Figure Average Groundwater Depths in Monitoring Wells - South of Sahiwal Figure Average Groundwater Depths in Monitoring Wells - West of Sahiwal Figure Average Groundwater Depth Difference in Monitoring Wells near Sahiwal Figure Typical Groundwater Depth in Monitoring Wells near Sahiwal and Ravi River Discharges Figure Typical Groundwater Depth in Monitoring Wells near Sahiwal and Ravi River Discharges REG-8556 ANNEXE 5B Page xii

13 ACRONYMS AND ABBREVIATIONS ADB bgs cfs CC CDS CRVA EPA FPS GCISC GEV GoP GCM HELP ICDS Mha PICIIP PKR PMD PPTA SWMM TMA UCL UCCRTF WASA Asian Development Bank below ground surface cubic feet per second Climate Change City Development Strategy Climate Risk, Vulnerability and Adaptation Assessment Environmental Protection Agency the Pre-Feasibility Study Global Change Impact Studies Centre Generalized Extreme Value Government of Pakistan General Circulation Models Hydrological Evaluation of Landfill Performance the Integrated City Development Strategy Million Hectare Punjab Integrated Cities Improvement Investment Program Pakistani Rupee Pakistan Meteorological Department Project Preparatory Technical Assistance Storm Water Management Model Town Municipal Administration Upper Confidence Limit Urban Climate Change Resilience Trust Fund Water and Sanitation Agency REG-8556 ANNEXE 5B Page 1

14 CONTRIBUTORS TO THIS REPORT This report is prepared by Munir A. Bhatti, International Consultant, Kitchener, Ontario, Canada and with the help and guidance from the following members of the CRVA team: 1. Mr. Wei Ye. Climate Scientist (Team Leader/International Consultant) 2. Mr. Saadullah Ayaz. National Climate Change Specialist (National Consultant) 3. Mr. Mohammad Ali Shaikh. Engineer/Urban Planner (National Consultant) 4. Mr. Huma Daha. Engineer/Urban Planner (National Consultant) 5. Mr. Rey Guarin. Climate Change Specialist/PICIIP Coordinator (International Consultant) REG-8556 ANNEXE 5B Page 2

15 ACKNOWLEDGEMENTS I wish to express my gratitude to Urban Unit, Government of Punjab for their great support. I also wish to express my indebtedness to Dr. Edward A. McBean, Professor of Engineering and Canada Research Chair of Water Supply Security, University of Guelph for providing valuable guidance during the study. REG-8556 ANNEXE 5B Page 3

16 EXECUTIVE SUMMARY The predominant scientific opinion based on the evidence currently available is that human activities have changed the atmospheric composition with the result that the meteorological processes that define climate have been altered. The resulting gradual changes in weather patterns, increasing climate variability and anticipated increases in weather extremes are expected to affect hydrologic conditions and the hydrologic responses of watersheds. This report is focused on the assessment on climate change impacts on hydrology within the project area along with associated risks, in particular in the cities of Sialkot and Sahiwal. This assessment reflects impact and implications of changes in the intensity, timing and/or overall accumulation of precipitation on run-off patterns This study assesses the risks, vulnerability and adaptations associated with climate change in Sialkot, Sahiwal and four other cities contesting for infrastructure and institutional development under multiple tranches. To examine impacts of hydrological changes on infrastructure design, operations and management due to change in climate-sensitive variables (e.g. frequency, depth, duration and patterns of extreme rainfall and runoff), various reports, documents and literature are reviewed. GEV model, groundwater mass balance model, and screening level EPA-SWMM model were used with the available information from various sources to quantify some of the impacts of climate change on water supply and flood management. This assessment analyzed the intensity, timings and/or over-all accumulation of precipitation on run-off alterations in snow and ice dynamics in the headwater regions of major river systems draining to the project area; and its effects on the dimensions of urban water resources (e.g. groundwater recharge). Though use of the mass balance model, it provides the significance and influence of non-climatological factors (e.g., increase of impermeable surface area; drainage design) on urban flooding. GEV model, water mass balance model, and storm water model are used to simulate the flows to Sialkot City to reflect projected changes in climate and hydrology. Furthermore, the changes in flow patterns and reduction in annual recurrence intervals of various extreme events are indicated with resultant enhancing of the floodplain boundaries and needed re-assessment for the risks to critical infrastructure including water supply and waste water treatment plants. For the City of Sialkot, various models are used for this assessment: a) Statistical Model to determine the sub-daily precipitation intensity for Sialkot, b) Generated Extreme Value (GEV) model to estimate the Intensity-Duration-Frequency Curves for Sialkot, c) GEV Model for the Catchment areas of Nullah Aik, Sialkot, d) Log-Pearson Type-III Model for the Catchment Areas of Sialkot, e) Water Mass Balance Model for Water Balance of Sialkot City, f) Watershed Model (EPA-SWMM) for the Nullah Aik Watershed, g) Storm Water Management Model (EPA-SWMM) for the Urban Areas of Sialkot, h) EPA-SWMM model for Aik Watershed with Climate Change, i) EPA-SWMM model for Urban Areas of Sialkot with Climate Change, j) Water Mass Balance REG-8556 ANNEXE 5B Page 4

17 Model for Areas up-gradient of M-R Link Canal Areas of Rechna Doab and k) Water Mass Balance Model for the City of Sialkot with climate change, Furthermore, the groundwater trends and quantities are estimated by analyzing the groundwater levels of the Punjab Irrigation Development Agency's monitoring wells. For the City of Sahiwal, various models are used: a) Statistical Model to determine the sub-daily precipitation intensity for Sahiwal, b) Generated Extreme Value (GEV) model to estimate the Intensity- Duration- Frequency Curves for Sahiwal, c) Storm Water Management Model (EPA-SWMM) for the Urban Areas of Sahiwal, and d) EPA-SWMM model for Urban Areas of Sahiwal with Climate Change. The groundwater trends are estimated by analyzing the groundwater levels of the Punjab Irrigation Development Agency's monitoring wells. This assessment indicates that vulnerability concerns of extreme weather events are increased with climate change. These impacts will a) disrupt infrastructure services, often cascading across infrastructures because of extensive interdependencies and would, as a result, b) threaten the local structures of health and economies. This impact would be severe in the urban areas with concentrated, human populations and economic activity especially in the City of Sialkot. This assessment indicates that both Sialkot and Sahiwal are vulnerable to climate change impacts on fluvial floods, urban flash floods, drainage systems, and on structures associated with flood controls, drainage systems, culverts and bridges on streams and etc. Groundwater is the only source of water for both cities and aquifers underlying these cities are being already over-exploited as groundwater levels are dropping. Climate change may increase the amount of recharge to groundwater due to increase in rainfall and increase in river inflows but increased intensities of rainfall may make increased recharge fairly minimal. Sialkot The GEV Model predicts that in Sialkot city the 5- minute duration rainfall intensity with return period of five years of mm/hr will be mm/hr in 2100 scenario (High). The daily rainfall intensity will increase by 20% with Climate Change scenario of 2050-High and by 42% with Climate Change scenario of 2100-High for Sialkot as per GEV model For the City of Sialkot, 15- minute duration or 6-hour duration design storms which are based on 5 year return period will have 4.4 year recurrence interval in low 2050 scenario and 3.8 years with High 2050 scenario 3- hour design storms which are based on 25 year return period will now have 20 year return period in 2050 scenario-low and 14 years with 2050 scenario-high normally the culverts converted and managed by using this intensity. Therefore the design lives of these structures will be reduced. Due to climate change the 50 year return period of 6 hour duration design storms will now have 40 year recurrence interval in 2050 scenario-low and 25 years with 2050 scenario-high as per GEV model. These findings indicate that if this scenario is realized, then return frequency will be reduced by 10-years. REG-8556 ANNEXE 5B Page 5

18 The peak flows from Nullah Aik and Nullah Palkhu are going to increase by 15% with 2050 scenario-high and by 32% with 2100 scenario-high. The storm waters from the urban systems which are already causing frequent flooding in the City of Sialkot will increase with climate change. For example the Sialkot urban area storm waters feeding to nullah will have 12% greater discharge with climate change-2050 low scenario and 30% more with climate change-2050 high scenarios from 15-minute with 5-Year return as predicted by simulation model The drinking water source for the City of Sialkot is groundwater and the city is pumping from the aquifer lying under the Sialkot. This aquifer is part and parcel of the main aquifer in Rechna Doab. This aquifer is being over-exploited mainly by the agriculture sector and the groundwater levels near Sialkot are dropping at an average rate of 192 mm per year. On an annual basis, in the Sialkot area on average 62 million m 3 (Mm 3 ) rainfall occurs out of which 56 million m 3 finds its way to surface runoff, 4 million m 3 is evaporated and an amount of 2 million m 3 is percolated and finds its way to groundwater. On an average annual basis with the climate change scenario-2050, the seepage from the green areas of Sialkot City will increase by 27.5% according to the simulation model. Therefore, there are opportunities to harvest water from groundwater recharge as per the water mass balance model. Glaciers and snowmelt provide a significant contribution to annual discharge of Chenab. The relatively higher dependence of Chenab on melt-water is due to the large glaciered fraction and persistent snow cover in the large expanses at higher altitudes, providing melt-water storage during the warmer seasons. The magnitude of the changes in snowmelt runoff in Chenab depends on the climate conditions over the basin and, in general, it is going to increase with the increase in temperature. Increase in the rainfall amounts in monsoon season will also be a main factor for the peak flows in river Chenab. This increase in inflow will increase the recharge to aquifer underlying Sialkot but minimally. Sahiwal The GEV Model predicts that in Sahiwal, the 30 minute design storm will increase by 27% with climate change scenario high and 52% with climate change scenario 2100-high. 6-Hour duration design storms which are based on 25 year return period will become 18 year recurrence interval in 2050 scenario-low and 12 years with 2050 scenario-high. The climate change will have its impacts on the urban drainage systems as the 10-minute duration design storms of 5-year return period will now have 4-year return period in 2050 scenario-low and 2.9 years return period with 2050 scenario-high as per GEV model. REG-8556 ANNEXE 5B Page 6

19 The discharge from the urban drainage system with 30-minute storm of 5 year return period will increase 18% with climate change scenario low, 28% climate change scenario middle and 43% to 51% climate change scenario high as per the simulation model. This will cause huge pressure on the existing inadequate drainage system of the City. Ground water is the sole source of potable water exploited in Sahiwal City. The water table average is about feet below the ground level and upper level produce limited quantities of mineralized water Vulnerabilities are especially large where infrastructures are subject to multiple stresses beyond climate change alone; when they are located in areas vulnerable to extreme weather events; and if climate change is severe rather than moderate. Regarding implications of climate change for infrastructures are: a. Extreme weather events associated with climate change will increase disruptions of infrastructure services in some locations such as low lying areas of Nullah Aik and Bhaid in Sialkot b. A series of less extreme weather events associated with climate change such as low flow periods of rivers which are recharging groundwater, the only water resource for water supply for both Sialkot, Sahiwal and possibly for other cities in Punjab, occurring in rapid succession may cause shortages of drinking water. Therefore, the potential adaptations for sustainable water resources management to be planned and implemented are: stopping leakage from the pipes by using water balance methods, maximizing the infiltration of runoff to the subsurface, water harvesting to replenish the depleting aquifers, reducing hard surfaces, construct permeable pavements for ground water recharge, creation of natural eco-system buffers for vulnerable water bodies, low-lying areas, Improvement of land-use planning, increased use of stormwater retention ponds, constructed wetlands and swales, this option will provide benefits for both flash flood control and increased recharge to groundwater, protecting existing wetlands and constructing more wetlands to hold runoff and recharge groundwater. The following adaptations have been identified for both Sialkot and Sahiwal with regard to urban drainage and flood control: developing rainfall intensity duration (IDF) curves for each city and design and plan structure design based on these curves as they are required with, or without, climate change, improving existing storm water and sewer systems, expanding the capacity of storm sewers to manage extreme weather events, separating storm water and sewer systems and retrofitting these systems to enhance capacity, flood-proofing of buildings in vulnerable locations, constructing buildings from flood-resilient materials that can withstand direct contact with floodwaters without sustaining significant damage Implementation of sustainable urban drainage systems, constructing green roofs to increase on-site retention of storm water REG-8556 ANNEXE 5B Page 7

20 Climate change is a complex issue, and it is essential to establish consistent practices for integrating climate change into water, sanitation and flood control decisions and planning processes. Municipalities and planners should take various measures to further their understanding of climate change so that they can manage climate risk. TMAs would require development of data and engineering resources to achieve steps such as a) revising design standards, b) establishing met stations in cities, c) introducing water demand management, and d) regulating ground water abstraction REG-8556 ANNEXE 5B Page 8

21 1. INTRODUCTION The intermediate cities of Punjab are potentially at-risk from increasingly severe flooding caused by extreme weather events, including intensified monsoon rains, which may be compounded by rapid snow and glacial melt due to increases in temperatures; and potentially by other projected impacts of climate change. This assignment was carried out to support and assess the above broader objectives by characterizing and (to the extent possible) quantifying the projected impacts and associated risks from climate change on Punjab's intermediate cities and on important urban service delivery sectors and forming a basis for "climate-proofing" of urban infrastructure, inclusive of water supply and sanitation, solid waste management and transport infrastructure particularly for the cities of Sialkot and Sahiwal. One of the most feared consequences of climate change in Pakistan is the likelihood of increased frequency, occurrence and severity of extreme events such as floods, droughts and cyclones with disastrous economic and social impacts. The country is particularly vulnerable to such events which have come from the large-scale destruction experienced in the recent past(ddma, 2008). Climate change raises many concerns for urban water management because of the effects on all aspects of the hydrological cycle. Urban water infrastructure has traditionally been designed using historical observations and assuming stationary climatic conditions. The capability of this infrastructure, whether for storm-water drainage, or water supply, may be under-designed for future climatic conditions. In particular, changes in the frequency and intensity of extreme rainfall events will have the most acute effect on storm-water drainage systems. Therefore, it is necessary to take future climatic conditions into consideration in engineering designs in order to enhance water infrastructure investment planning practices. This report is presented in seven sections. Section-1 provides the introduction while Section-2 discuses the methodology used. Section-3 discusses the assessment of climate change impacts on hydrology, flood control and urban drainage in Sialkot. The setup and runs of the Generalized Extreme Value (GEV) Model for Precipitation in Sialkot for the baseline and with climate change scenarios. It also provides the analysis of the results and quantifies the impact of climate change on rainfall quantities, patterns and the intensities. It also discusses about the risks of floods in Sialkot from River Chenab, Nullah Aik and Palkhu. The frequency analysis of the catchment of Nullah Aik is discussed. The setup of the screening level watershed model and its runs with and without climate change is presents. The analysis of the results is presented. A screening level storm water model for the urban areas of Sialkot is discussed. It analyzes the impacts of climate change on the storm water systems of the city. Section-4 presents the review of water resources of the city, glaciers and snowmelt contribution to water supply, a water mass balance model is presented which analyzes the quantities of the REG-8556 ANNEXE 5B Page 9

22 storm water and seepage from city with, and without, climate change. The tendencies of the groundwater levels are presented by analyzing recent groundwater levels of the monitoring wells in and near Sialkot. Analysis of recharge and discharge in the un-commanded area of Rechna Doab through the use of groundwater mass balance model. It also briefly discusses the impacts of climate change on water supply from upper catchments. Section-5 discusses the assessment of climate change impacts on hydrology, flood control and urban drainage in Sahiwal. The setup and runs of the Generalized Extreme Value (GEV) Model for Precipitation in Sahiwal for the baseline and with climate change scenarios. The setup of the screening level watershed model and its runs with and without climate change is presented. The analysis of the results is presented. A screening level storm water model for the urban areas of Sahiwal is discussed. It analyzes the impacts of climate change on the storm water systems of the city. Section-6 discusses the water supply and groundwater levels analysis and the various recharge sources and possible impacts of climate change. Section-7 presents some of the potential identified adaptation measures for both cities. Generalized Extreme Value Distribution fitted curves for Sialkot are presented in Annexure-A. Climate Change induced Rainfall Intensity for various durations for Sialkot are attached at Annexure-B. The paths of maximum daily rainfall for various years in Sialkot and the Vicinity are presented in Annexure-C. Three synthetic series generated for Watershed model are shown in Annexure-D. Schematic and model domain for Nullah Aik in urban area of Sialkot are presented in Annexure-E. Generalized Extreme Value Distribution fitted curves for Sahiwal are presented in Annexure-F. Climate Change induced Rainfall Intensity for various durations for Sahiwal are attached at Annexure-G. Schematic and model domain for urban area of Sahiwal are presented in Annexure-H. Pakistan Pakistan covers an area of 880,000 km 2 with climate varying from arid to humid sub-tropical. Pakistan lies in an arid and semi-arid climate zone. The northern segment of the country is bounded by the western ranges of the majestic Himalaya, Karakoram, and Hindukush, which include some of the world s highest mountain peaks such as K-2 (8,611 meters) and largest glaciers including Siachen (70 kilometers (km)) and Biafo (63 km) as shown in Figure 1.1. Average temperatures are strongly dependent on this topography, with coolest annual temperatures below zero in the far North (the Himalayan region), and higher average temperatures in the lower-lying south-east. In the warmest months (May to September) average temperatures in the north do not exceed 15 C, whilst in the south they can reach up to 35. In the coolest months (November to February) temperatures are well below zero in the highest altitudes, and range from C in the low-lying south (McSweeney et al, 2010). REG-8556 ANNEXE 5B Page 10

23 Pakistan s river system consists of more than 60 small and large rivers. Indus River, with an overall length of around 3200 Km and total estimated annual flow of 207 billion cubic meters, is Pakistan s longest and largest river as shown in Figure 1.2.The five rivers of Pakistan are Jhelum, Chenab, Ravi, Sutlej and Indus. The Indus system comprises mainly five rivers that pass mostly through the Punjab province; therefore the name 'Punjab'- 'panj' meaning five and 'aab' meaning water. Figure 1.1 Map of Pakistan REG-8556 ANNEXE 5B Page 11

24 Figure 1.2 Indus River Systems Extreme Events The first decade of the 21st century saw several extreme weather events including the worst floods in Pakistan s history in These floods resulted from a rain intensity that reached 300 mm in a 36-hour period contributing to the highest water levels in 110 years in the Indus River in the northern part of the country. An analysis of data from 52 meteorological stations in Pakistan over a 40-year period ( ) shows that the frequency of occurrence of highest daily temperature and heaviest rainfall events in 24 hour have gradually increased in the recent REG-8556 ANNEXE 5B Page 12

25 decades. Moreover, at the turn of the century, the country has experienced its worst drought in history. Vulnerability and Threats Many parts of Pakistan are already experiencing increasing water stress, including water shortages and deterioration of water quality due to rapid growth of population and economic activity. The impact of climate change would further intensify these problems. Misuse and overexploitation of water resources have already depleted aquifers, lowered water tables, shrunk inland lakes, and diminished stream flows generally to ecologically unsafe levels. The major climate change related threats to water security as identified by the Task Force on Climate Change (GOP 2010) are given below: Changes in river flows due to increases in the variability of monsoon and winter rains and loss of natural reservoirs in the form of glaciers; Changes in the seasonal pattern of river flows due to the early start of snow and glacier melting at elevated temperatures and the shrinkage of glacier volumes with serious implications for storage of irrigation water and its supply for cropping; Increased degradation of surface water quality due to increase in extreme weather events like floods and droughts; Deficiency of current knowledge and monitoring efforts on climate change impacts in the HKH region. The impacts of climate change on water infrastructure could result in a myriad of problems such as a) increased water demand, b) water apportionment issues, c) loss of potable water, d) increased water quality problems, e) increased risk of flooding, and f)sewer overflows (He et al, 2006). Punjab Punjab is the most densely populated province of the country consisting of 36 different administrative districts. Punjab covers 205,344 square kilometers and a large proportion of this area is arable owing to the integrated irrigations system of the Province. These rivers traverse the Province from north to south and due to the presence of these water channels, the land of Punjab is amongst the most heavily irrigated areas on the planet. Punjab is among the most urbanized regions of South Asia and is experiencing a consistent and long-term demographic shift of the population to urban regions and cities. The urban population in 2011 is projected as 48% of the total on the basis of the inter-census rate of 3.4% per annum recorded in While Lahore, the capital of Punjab and its largest city, is currently home to about 8 million people. Punjab has four other cities with populations in excess of one million, namely Faisalabad (3 million), Gujranwala and Rawalpindi (2 million each), and Multan (1.7 million). In REG-8556 ANNEXE 5B Page 13

26 addition, three other large cities (Sialkot, Bahawalpur and Sargodha) are poised to cross the one million mark (World Bank, 2012). Vulnerability of Punjab from Floods Punjab is vulnerable primarily from floods. The types of floods which are impacting Punjab are: fluvial Floods (river floods), flash floods and urban floods. Fluvial floods are natural phenomena which cannot be prevented. However, human activity such as clearing forests in the upper catchment areas, straightening of rivers and suppression of natural flood plains and inadequate drainage practices are contributing to an increase in the likelihood of extreme flood events. The geographical distribution of fluvial floods is highly dependent on the hydrological system and the adjacent human activities. However, the increased occurrence of extreme flood events cannot be exclusively ascribed to climate change. Flash floods are triggered by intense local precipitation events. Urban drainage flooding occurs primarily because of inadequate sewer systems and/or city planning and if the sewer systems are combined sewers then by intense local precipitation events. However, flash floods and urban floods, triggered by intense local precipitation events, are most likely to be more frequent throughout Punjab. Vulnerable districts from various types of floods are presented in the following Table 1.1 Table 1.1. Punjab's vulnerable districts with regards to various kinds of floods Flood Type River Vulnerable Districts River Indus Mianwali, Layyah, Muzaafargarh, DG Khan and Rajanpur Jhelum & Chenab Ravi Sutlej Jhelum, Sargodha, Khushab, Gujrat, Chiniot, Jhang, Khanewal, Lodhran and Multan Lahore / Shahdara, Gujranwala, Okara and Sialkot Pakpattan, Vehari and Bahawalpur Flash Urban Dera Ghazi Khan, Rajanpur, Mianwali, Sialkot, Sheikhupura and Lahore Rawalpindi, Lahore, Gujranwala and Faisalabad REG-8556 ANNEXE 5B Page 14

27 2. METHODOLOGY The methodology used involves four steps: Step-1 involves the review and analysis of the existing (baseline) conditions of hydrology, flood control and water resources of Pakistan and Punjab (in general) and of Sialkot and Sialkot (in specific) by literature review. Step-2 involves the identifying and setting up of the requisite statistical, watershed, urban storm water management model, water mass balance models, and groundwater mass balance model. Step- 3 involves the application of these models for assessing the baseline and climate change scenarios as described below: For assessment of the climate change impact on hydrology in Sialkot and its vicinity, the GEV model is utilized to establish the baseline scenario and the frequency analysis of the floods in streams and their catchment areas. The possible impact of climate change on water resources is discussed by assessing changes in glacier and snow melt by climate warming. Further the present groundwater conditions in Sialkot area and upper Rechna Doab are analyzed. A screening level storm water model for the Aik Nullah is employed for the baseline scenarios and a water mass balance model is used for estimating the groundwater recharge and storm water, Furthermore a screening model for the urban areas of Sialkot is used to assess the present conditions. All these models are later employed to assess the impact of climate change with 2050 (low, medium and high) scenario and 2100 (low, medium and high) scenario. A detailed analysis is carried out to assess the impact of climate change on the reduction of various return periods which would be used for designing various developmental projects. An analysis is carried out to estimate the recharge to groundwater through various sources and possible impact of climate change on the water supply infrastructure of Sialkot. For assessment of the climate change impact on hydrology in Sahiwal and its vicinity, GEV model is utilized for the baseline scenario and the present groundwater conditions in and around Sahiwal are analyzed.gev model for is again employed again to assess the impact of climate change with 2050 (low, medium and high) scenario and 2100 (low, medium and high) scenario on the intensity and duration of rainfall. An analysis is carried out to assess the impact of climate change on the reduction of various return periods which are used for designing various developmental projects. Step-4 involves identifying and reporting the potential adaptations to address the issues and impacts of climate change. REG-8556 ANNEXE 5B Page 15

28 3. ASSESSMENT OF CLIMATE CHANGE IMPACTS ON HYDROLOGY, FLOOD CONTROL AND URBAN DRAINAGE IN SIALKOT Sialkot is an industrial district of the Province of Punjab and is located at 125km from Lahore and in the north-east of Pakistan along the Indian border. Floods are normal occurrences in the district due to heavy rains in the region Hydrology Sialkot is situated in the Upper Rechna Doab, which is bounded by the Ravi and Chenab rivers. The Chenab River flows to the northwest of Sialkot, and the Marala-Ravi Link Canals flow to the west. It sits over abundant shallow and deep groundwater aquifers which are used by both the city water supply system and inhabitants with wells for their water supplies. Sialkot experiences significant seasonal variations in temperature with the average monthly temperature varying from 11.6 o C in January to 32.2 o C in June with an average temperature over the coldest three months of the year of about 12.7 o C. Table provides the historic temperature data and precipitation data (GHK, 2011). Table Mean Temperature and Precipitation of Sialkot Month Temperature Mean Value o C Mean precipitation mm Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec REG-8556 ANNEXE 5B Page 16

29 3.1.1 Extreme Events The maximum daily rainfall was reported on 26th July 2012 because of cloud burst and the rainfall reported for Sialkot Cantt. was 128mm and that for Sialkot Air Port was 76mm (PMD, 2012). This kind of event causes urban flooding. Urban flooding, which occurs during monsoon is due to increased inflows in Nullah Aik, Nullah, Bhaid and Nullah Palkhu and due to inadequate storm and sewer system within city which causes storm overflows in various areas of the city. Sialkot District faces the onslaught of flood causing devastation and disruption of normal life activity during the peak flood season. Sialkot city is threatened by the flood in Aik, Bhaid and Palkhu Nullahs. Due to the twin onslaught of Nullahs, aggravated with intensive rains, Sialkot City has faced the disaster of flood many a times over the past years ( DDMA, 2008). A generalized extreme value analysis was conducted to estimate the frequencies of extreme precipitations in Sialkot. The model and its results are described in next subsection. REG-8556 ANNEXE 5B Page 17

30 3.2 Generalized Extreme Value (GEV) Analysis for Precipitation in Sialkot Baseline Data Availability For Sialkot, Pakistan the meteorological data are available as annual daily maximum precipitation from the year 1965 to From 2008 to a part of 2014 continuous three hourly and daily data was available. The problem is to define values in smaller time intervals such as 10 minute, 15 minute, 30 minute, and one hour. Daily precipitation values have been successfully disaggregated using the cascade-based disaggregation, short-time intensity disaggregation methods and K-nearest neighbor approach by others Statistical Model To Estimate Sub-Daily Intensity-Duration-Frequency Curves The approach described below was used to develop intensity-duration-frequency curves for 15- minute, 30-minute, and one hour duration by statistical methods Six hourly and 12 hourly precipitation data was determined by adding the preceding three hour rainfalls together. Columns were created for monthly maximum 3-hourly, 6-hourly, 12-hourly data and daily (24 hour) data. There were eighty points as 28 months have no precipitation. Statistical analysis (linear fit) was used to estimate a relationship between maximum 3-hourly, 6- hourly and 12-hourly in the form of the equation: Y = a x D Where Y is the sub-daily intensity of precipitation (mm/hr) and D is the daily precipitation intensity (mm/hr) The results are shown in Table Table Relationship of Daily Rainfall intensity to 12, 6, and 3 Hour Rainfall Intensity Relationship of Intensity Equation R² (mm/hr) 12 hrs to 24 hrs Y= x D hrs to 24 hrs Y= x D hrs to 24 hrs Y = x D The next step was to estimate an equation from the above relationship to determine 5-minute, 15-minute, 30-minute and hourly precipitation intensity (mm/hr). The following relationship can be discovered from the data as shown in Table REG-8556 ANNEXE 5B Page 18

31 Table Fitted Ratios of 24 Hour Rainfall Intensity to 12, 6, and 3 Hour Rainfall Intensity Ratio to 24 Hrs Fitted ratio to 24 hrs Precipitation intensity (mm/hr) The equation was developed for developing a relationship for various fitted values of 3-hourly, six-hourly and 12-hourly intensity values to daily Intensity value of mm/hr to extrapolate the other time durations and the following equation was developed: K= ln(dt) , R² = for dt < 0.5 only where K is the ratio of precipitation intensity to daily precipitation intensity and dt is the ratio of time period in hours to 24 hours. The logic was to extrapolate the ratios of the observed three, six and twelve hour s ratios and determine hourly and sub-hourly rainfall intensity. The results are presented in Table Sub-hourly duration rainfall intensity at this location and frequency analysis was carried out. Table Calculated Ratios of Sub-Hourly Rainfall Intensity to 24-Hourly Rainfall Intensity Duration Ratio to 24 Hrs Actual Ratio Calculated Ratio 12 hr hr hr hr minute minute minute Baseline Intensity-Duration-Frequency Analysis - Sialkot GEV- 3 parameters distribution was fitted to the selected time series of 5-minute, 10-minute, 30- minute, one-hour, three-hour, six-hour, twelve-hour, and daily intensity series. Goodness of fit parameters were determined by using Easy Fit Software (Public Domain) and the Kolmogorov REG-8556 ANNEXE 5B Page 19

32 Smirnov, Anderson Darling and Chi-Squared values were , and (Table 3.2.4) and the fitting hypothesis was not rejected as shown below in summary and detail tables (Table 3.2.4). The individual fitted curves are shown in Annexure A as Figure A.1, A 2, A 3, A 4, A 5, A 6, A 7 and A 8. Table Summary Table of GEV Distribution Tests Summary Table Gen. Extreme Value Distribution Anderson Darling Kolmogorov Smirnov Chi- Squared Statistic Statistic Statistic Table Goodness-of-Fits Details Goodness-of-Fit - Details Gen. Extreme Value Kolmogorov-Smirnov Sample Size 41 Statistic P-Value Rank Critical Value Reject? No No No No No Anderson- Darling Sample Size 41 Statistic Rank Critical Value Reject? No No No No No Chi-Squared Deg. of freedom 3 Statistic REG-8556 ANNEXE 5B Page 20

33 P-Value Rank Critical Value Reject? No No No No No Assumptions Two assumptions were made which are: 1. The maximum three-hourly, six-hourly, twelve-hourly and daily rainfall time series of 2008 to 2014 behave in the same fashion and represents the daily rainfall time series of The statistical relationships established are mimicking the real phenomenon Do not use the rainfall frequency curves developed in this analysis for the design of the real time infrastructures and more detailed analysis should be carried out Results The intensity duration frequency results are presented in Table % confidence results are presented in Table and Table below. The results are presented in Figure The rainfall frequency curves developed in this analysis should be used with caution for the design of the real time infrastructures and more detailed analysis should be carried out. Table GEV-Three Parameter Rainfall Distribution for Sialkot Return Period 5- Minute 10- Minute GEV-Three Parameters Distribution Precipitation (mm/hr) 15- Minute 30-Minute Hourly Three Hourly Six Hourly 12- Jourly Daily REG-8556 ANNEXE 5B Page 21

34 Table Return Period 5- Minute GEV-Three Parameter Rainfall Distribution 95% Confidence (lower) for Sialkot GEV Three Parameters Distribution Precipitation (mm/hr)-95% Confidence (lower) Three Minute Minute 30-Minute Hourly Hourly Six Hourly 12- Jourly Daily Table GEV-Three Parameter Rainfall Distribution 95% Confidence (upper) for Sialkot Return Period 5- Minute Three Parameters Precipitation (mm/hr)-95% Confidence (upper) Three Minute Minute 30-Minute Hourly Hourly Six Hourly 12- Jourly Daily REG-8556 ANNEXE 5B Page 22

35 Figure Precipitation Intensity Duration Frequency for various Time Intervals using GEV-3 Distribution Precipitation Intensity (mm/hr) Return Periods 5- Minutes 15- Minutes 30-Minutes Hourly Three Hourly Six Hourly 12-Jourly Daily REG-8556 ANNEXE 5B Page 23

36 3.3 GEV Model and Climate Change Vulnerability Analysis - Sialkot Climate change is a reality that planners and designers of drainage infrastructures must consider. The cumulative effects of gradual changes in hydrology due to climatic change are expected to alter the magnitude and frequency of peak flows over the service life of drainage infrastructure. Potential future changes in rainfall intensity are expected to alter the level of service of drainage infrastructure, with increased rainfall intensity likely resulting in more frequent flooding of storm sewers and surcharging of culverts. For analyzing these changes, the following parameters developed by the Climate Change Expert of CRVA are used for the development of these curves. They are provided in Table Note that the location, scale and shape are the attributes of the frequency distribution Analysis Parameters shown in table were used to determine the changes in the baseline frequency distributions of rainfall presented in earlier parts. Frequency curves for baseline scenario, and climate change scenarios of low, medium and high scenarios of 2050 and 2100 are developed. These curves are determined for 5- minute, 10- minute, 15 minute, 30 minute, one hour, 3-hour, 6-hour, 12 hour and daily durations. 5-minute and daily curves and tables are shown in next section. The other curves and tables are presented in Annexure B Minute duration curves for baseline scenario and climate change of 2050 scenario and 2100 Scenarios for Low, Medium and High The data of the curves are presented in Table and depicted in Figure and in Figure It could be seen 5- minute duration rainfall intensity with return period of five years of mm/hr will be mm/hr in 2100 scenario (High). REG-8556 ANNEXE 5B Page 24

37 Table Sialkot GEV parameters change due to climate change impact (%) 2050 scenario 2100 scenario Low Mid High Low Mid High location Daily Scale Shape Location hour Scale Shape Location hour Scale Shape Location hour Scale Shape Location hour Scale Shape Location min Scale Shape Location min Scale Shape Location min Scale Shape REG-8556 ANNEXE 5B Page 25

38 Table Baseline and Climate Change 5- Minute Duration Rainfall Intensity Return Period Baseline 2050 scenario 2100 scenario Low Mid High Low Mid High mm/hr mm/hr mm/hr mm/hr mm/hr mm/hr Daily duration curves for base scenario and Low, Medium and High of 2050 scenario and 2100 Scenarios The curves are presented in Table Table Baseline and Climate Change Daily Duration Rainfall Intensity Return Period Baseline 2050 scenario 2100 scenario Low Mid High Low Mid High REG-8556 ANNEXE 5B Page 26

39 Figure Baseline and Climate Change 2050 Scenario (Low, Middle and High) 5- Minute Duration Rainfall Intensity Figure Baseline and Climate Change 2100 Scenario (Low, Middle and High) 5- Minute Duration Rainfall Intensity REG-8556 ANNEXE 5B Page 27

40 3.3.2 Analysis of Impact of Climate Change on Rainfall Frequency Curves With the advent of inevitable climate change, the impact of climate change can be seen in the percentage increase in the intensity of rainfall for various durations and with different return periods. These changes will directly impact the hydraulic structures such as culvert designs, bridges design, flood zones delineation maps and flood management infrastructures in Sialkot. These changes for the daily rainfall intensity with various return periods are shown in Table Table Percentage Change in Daily Rainfall Intensity of Rainfall in Sialkot with Climate Change Percentage Change in Daily Rainfall Intensity of Rainfall in Sialkot with Climate Change Return Period 2050 scenario 2100 scenario Low Mid High Low Mid High % 11.47% 18.08% 11.46% 17.92% 36.83% % 10.94% 18.92% 10.91% 18.47% 39.89% % 10.74% 19.27% 10.70% 18.70% 41.18% % 10.61% 19.52% 10.57% 18.87% 42.14% % 10.58% 19.59% 10.53% 18.92% 42.40% % 10.50% 19.78% 10.45% 19.05% 43.13% % 10.44% 19.94% 10.39% 19.17% 43.75% The use of statistically determined design floods based on past hydrometric records by themselves will not be adequate for Sialkot, the table as indicated above may be consulted for any future designs of hydraulic structures such as culverts Change in return periods for various frequency durations Based on the data presented above, the impact of climate change was determined on the return periods. 15- minute duration or 6-hour duration design storms which are based on 5 year return period will now have 4.4 year return period in low 2050 scenario and 3.8 years with High 2050 scenario as can be seen in Figure REG-8556 ANNEXE 5B Page 28

41 3-hour design storms which are based on 25 year return period will now have 20 year return period in 2050 scenario-low and 14 years with 2050 scenario-high as can be seen in Figure hour duration design storms which are based on 50 year return period will now have 40 year return period in 2050 scenario-low and 25 years with 2050 scenario-high as shown in Figure hour or daily duration design storms which are based on 100 year return period will now have 75 year recurrence interval in 2050 scenario-low and 48 years with 2050 scenario-high as can be seen in Figure However, considering 2100 climate change scenarios, 6 hour or daily duration design storms which are based on 100 year return period will now have 74 year recurrence interval in 2100 scenario-low and 22 years with 2100 scenario-high as can be seen in Figure Figure Changes in Return Periods for Various Climate Change Scenarios for 15- Minute Duration Rainfall Intensity for Baseline Return Period of 5 Year REG-8556 ANNEXE 5B Page 29

42 Figure Changes in Return Periods for Various Climate Change Scenarios for 3- Hour Duration Rainfall Intensity for Baseline Return Period of 25 Year Figure Changes in Return Periods for Various Climate Change Scenarios for 6- Hour Duration Rainfall Intensity for Baseline Return Period of 50 Year REG-8556 ANNEXE 5B Page 30

43 Figure Changes in Return Periods for Various Climate Change Scenarios for 6- Hour Duration Rainfall Intensity for Baseline Return Period of 100 Year Figure Changes in Return Periods for Various Climate Change Scenarios for 24- Hour Duration Rainfall Intensity for Baseline Return Period of 100 year REG-8556 ANNEXE 5B Page 31

44 3.3.4 Conclusion With the climate change already taken place, the design return periods on which certain structures are designed and constructed will see the reduction in their lives and the TMA has to consider and develop a strategy to retrofit or replace them at appropriate times. The adaptation of the effects of climate change on existing urban drainage infrastructure is more difficult and perhaps can be best achieved through the designation of space for remedial measures when space becomes available through urban redevelopment and the flood-proofing of existing development and infrastructure where possible. The planning and design of development in general and urban drainage infrastructure in particular should ideally be performed in a manner that integrates adaptive responses to climate change with sustainable environmental stewardship and minimization of the adverse effects of urbanization. REG-8556 ANNEXE 5B Page 32

45 3.4 Floods and Flood Control in Sialkot The city of Sialkot is potentially prone to floods in river Chenab and various nullahs passing through the city or the boundaries of the city Chenab River Chenab Basin located in the western Himalayas, is one of the main five tributaries of Great Indus river system. Chenab is a 1,086 km (675 mi) long river which originates in the Kulu and Kangra Districts of Himachal Pradesh in India. The major part of the Chenab River basin lies in India, while the lower part including its outfall into main Indus River lies in Pakistan. After covering a distance of 144 kms in Himachal Pradesh state, it enters into Jammu and Kashmir and flows southwest up to Akhnoor, and then finally it enters into Pakistan through the Sialkot district. The hilly catchment area above Marala Barrage is about 32,670 km 2, out of which 28,170 km 2 is in occupied Jammu and Kashmir and 4,500 km2 in Pakistan. The maximum discharge of 1.1 million ft³/s (31,000 m³/s). Sialkot is one of big districts of Punjab in the northeast side at the foothills of the snow-covered peaks of Kashmir near the Chenab River. Sialkot District is situated at an altitude of 829 feet above sea level. Floods in Chenab result from heavy rainfall in the upper drainage basin, which falls under the most active monsoon belt. Pir Punjal range beyond Akhnoor is ideally located to cause the necessary orographic lifting along its windward slopes. The snow melt contributions on the average 40% of the total flow in July when the peak melt rates are attained. Hence it synchronizes with the early monsoon in July, but not with the peak values occurring in August and September. During the monsoon, particularly the Jammu and Munawar Tawis contribute considerably to the flood flows at Marala (WMO/GWP, 2003). The historical extreme events in the downstream Marala were recorded in 1988, 1995, 1996 and Floods from River Chenab to Sialkot City are a major potential concern. However, Provincial Flood Contingency Plan of 2012, Punjab Disaster Response Plan of Provincial Disaster Management Authority, Government of Punjab do not categorize city of Sialkot as vulnerable to Chenab Floods. The City of Sialkot is approximately km away from River Chenab. The crest level of Marala Barrage is: Length [m] 1,363 Crest Level [m] Under Sluice Weir Pond level [m] Capacity [m 2 /s] 31,148 REG-8556 ANNEXE 5B Page 33

46 It means that the City of Sialkot is not under threat directly from Chenab Floods and there is no history of inundation of the city of Sialkot by flood in Chenab. However, the rise of water levels in Chenab may produce backwater heads in Nullah Palkhu stream which carries water from the areas of Sialkot and upstream areas of Sialkot. Two major water channels originate at the Marala headworks the Marala-Ravi Link Canal and the Upper Chenab Canal. Historic peak floods in River Chenab is presented in Table Table Annual Peak Flow in River Chenab at Marala Annual Peak Floods in Chenab at Marala Year Discharge In Year Discharge In m 3 /sec m 3 /sec , , , , , , , , , , , , , could not obtain , could not obtain , could not obtain , could not obtain , could not obtain , could not obtain , could not obtain , could not obtain , , , , , , , , , , , , , , , , ,457 REG-8556 ANNEXE 5B Page 34

47 Frequency Analysis of Peak Flow of Chenab at Marala As seen the from Table above, the continuous peak flood data is available from 1970 to 2001 so the same data was used to conduct the frequency analysis using Log-Person type III distribution. The results are presented in Table and Figure Table Peak Discharge in River Chenab versus Return Periods Peak Flood In Chenab at Marala Return Period Discharge in m 3 /sec 2 8, , , , , , ,217 Figure Peak Discharge Frequency Analysis - Chenab at Marala REG-8556 ANNEXE 5B Page 35

48 3.4.2 Streams (Nullahs) Sialkot is traversed by three seasonal streams, comprising Aik Nullah, to the south of the city, Bhaid Nullah, between the Cantonment and the rest of the city, and Palkhu Nullah, north of the Cantonment. The Sialkot Cantonment is primarily flanked by two rivulets called nullahs in vernacular i.e. nullah Palkhu towards the north and nullah Bhaid towards the south, both originating from the Indian Kashmir. Nullah Bhaid is administered by the city of Sialkot whereas nullah Palkhu and nullah Aik is administered by Punjab Irrigation department. Nullah Aik and Nullah Palkhu, two main natural tributaries of River Chenab pass through the Sialkot. These streams originate from Lesser Himalayas in the Jammu and Kashmir, at an altitude 530 m and 290 m, respectively. Nullah Aik and Nullah Palkhu drain about 1,875 km 2 catchments area and travel a total distance of about 131.6km and 98km respectively (Qadir, 2009). Nullah Aik also receives water from springs in eastern parts of Jammu district. The catchment area of Nullah Aik and Nullah Palkhu is part of upper Rechna Doab (Region between River Chenab and Ravi). The catchment area experiences five distinct seasons viz., summer (pre monsoon; April to mid-june), rainy season (monsoon; mid-june to mid- September), autumn (post monsoon; mid-september to November), winter (December to February) and a short spring (March). The climate is hot and humid during summer and cold during winter. June is the hottest month of the year with maximum daily temperature soaring to 40oC and above. The temperature during winter is usually around 4 o C but occasionally may even decline to freezing point during the month of January. The mean annual rainfall in the catchment is above 950 mm of which maximum precipitation (~80%) occurs during the monsoon season. During frequent rainfall in monsoon season, rain water flows into streams through surface runoff and cause flooding which usually have devastating effects on the area. According to Qadir (2009) Nullah Aik exhibited maximum width in upper catchment area that gradually narrows down in lower catchment area. During monsoon season, heavy rains occur and rainwater drains into Nullah Aik and Nullah Palkhu. Sometimes these streams cannot retain the rainwater that ultimately flow out and spread over the vast area. The discharge capacity of Nullah Palkhu increases as the stream moves from upstream towards downstream. At upstream, it has limited discharge capacity and collects rain water from its upper catchment area. Sometimes, in early summer season, it becomes dry and water inside the stream can be seen as isolated ditches and pools. The Aik, Bhaid and Palkhu nullahs receive the municipal and industrial wastes from the scores of industries in Sialkot. The Aik and Bhaid nullahs carry approximately m 3 /sec during the normal flow periods and around 7.1 m 3 /sec during winter. During the normal flow, the three nullahs degrade the environment of the villages, towns and cities downstream since they carry the effluents from some 130 odd leather industries and just as many metal industries, which keep discharging their toxic effluents into them. GHK, 2010 reported that the maximum flow in the Aik nullah is reported to reach 940 m3/sec, greater than the peak carrying capacity so that REG-8556 ANNEXE 5B Page 36

49 flooding can occur for short periods during the summer monsoon. The flow in the Aik nullah during dry weather conditions is reported to be 19m 3 /sec. Flows in the Bhaid Nullah are much lower, ranging from 0.9 m 3 /sec to 2.7 m 3 /sec. The limits of the type of floods by Federal Flood Commission (FFC)/ Pakistan Meteorological Department (PMD) are presented in Table S. No. Name of Nullah Table Place Limits of Flood Levels for Various Kind of Floods Low Flood Medium Flood High Flood Very High Flood Exceptionally High Flood 1 Aik Ura and above 2 Palkhu Wazirabad and above Data could be obtained for only Aik nullah upstream Sialkot, therefore the following analysis is done for only this nullah. The historic peak flood data is presented in Table and Figure Sialkot being situated in hazard prone region is exposed to many risks and uncertainties that can affect both life and property. Among these risks flood is the major calamities. Sialkot City has faced the disaster of flood many times over the recent years: a) In 1983, Hajipura Band on Aik Nullah got breached with flood directly hitting city of Sialkot. b) According the daily Dawn report on 9/7/2014, the flooded Aik, Palkhu and Bhaid nullahs submerged the Sialkot city. The city streets were under three to four feet water. Table Historical Peak Flows on Nullah Aik Nullah Aik Historic Peak Floods Year cusecs m 3 /sec , , , , , , , , , , , , ,711 1, ,100 1,305 REG-8556 ANNEXE 5B Page 37

50 Figure Historic Annual Peak Discharge of Nullah Aik at Ura 1,400 1,200 Average Discharge (cu.meter/sec) 1, Maximum Peak Carrying Capacity 833 CMS (GHK, 2010) Month-Year The government of Punjab reports that following actions were taken to reduce the impacts of various nullahs floods. Protecting flood protection bund RD IR along Aik Nullah in Sialkot City Remodeling Mallah Chak flood protection bund RD to RD along Aik nullah to protect Sialkot city & cantonment Remodeling flood protection bund along Aik nullah to protect Sialkot city RD to RD Constructing Hajipura flood protection bund along Aik nullah for Sialkot city RD to RD Construction of guide bank spurs on Munawar Tawi to protect agricultural land abadies of village Lashkari, Chak Barmala & Surkhpur, District Gujrat Channelization of Aik Nullah and Improving Drainage System in Distt. Sialkot REG-8556 ANNEXE 5B Page 38

51 Frequency Analysis The frequency analysis for nullah Aik was carried out by GEV three parameter distribution. The distribution were accepted by the test. The results are presented in Table 3.4.5, Figure and Figure for GEV three parameter distribution. The goodness of fit statistics are shown below In Table The details of the Goodness- of- fit for GEV-Three parameter distribution are shown are shown in Table Table Peak Discharges versus Return Period by Using GEV III Distribution Peak Discharges at Nullah Aik using Log- GEV Type-III distribution Pak Discharge in Return Period im 3 /sec , , , ,752 Table Goodness- of- Fit - Summary # Distribution Kolmogorov Smirnov Anderson Darling Chi-Squared Statistic Statistic Statistic 1 Gen. Extreme Value REG-8556 ANNEXE 5B Page 39

52 Figure Nullah Aik Discharge Frequency Analysis - Generalized Extreme Value Distribution 2,500 2,000 Discharge (cumecs) 1,500 1, Reduced Variate Figure Nullah Aik Discharge at Ura versus Return Period with 95% Confidence Value Discharge (cumecs) Return Period REG-8556 ANNEXE 5B Page 40

53 Table Goodness- of- Fit Details of GEV Three parameter distribution Gen. Extreme Value Kolmogorov-Smirnov Sample Size Statistic P-Value a Critical Value Reject? No No No No No Anderson-Darling Sample Size Statistic a Critical Value Reject? No No No No No Chi-Squared Deg. of freedom Statistic P-Value a Critical Value Reject? No No No No No REG-8556 ANNEXE 5B Page 41

54 3.4.3 Floods in Nullah Aik and Palkhu Analysis of Rainfall intensity in the catchment area of Nullah Aik An analysis was carried out for the rainfall in the catchment area of Nullah Aik. Data was downloaded from the National Centre of Environmental Prediction (NCEP), Climate Forecast System Reanalysis (CFSR) for the following sites at coordinates a) 75, , b) , , c) 74.06, , d) 74.06, , e) , , f) , , g) 75, h) , and i) , Contours were drawn to see the path and intensity of daily maximum rainfalls at various points in the catchment of Aik. They are called A, B.C.D.E.F, G.H, and I. The following charts show the contours of maximum daily rainfall in the catchment. The first figure is composite figure of daily maximum rainfall from 1979 to The subsequent figures show the year wise pattern. The names and coordinates of various points plotted on figures are provided in Table below: Table Location of NCEP Analysis Points Location Easting Northing A B C D E F G H I Annual maximum daily rainfall at these locations over the years and the reported historical maximum annual daily rainfall are shown in figures below. Figure shows the historical maximum annual daily rainfall the other Figures for different years are presented in Annexure- C. REG-8556 ANNEXE 5B Page 42

55 Figure MAXIMUM DAILY RAINFALL (MM) DURING & PART OF 2014 SIALKOT AND THE VICINITY Data Source: The National Centers for Environmental Prediction (NCEP), Climate Forecast System Reanalysis,(CFSR) Data, Analysis It can be seen from the figures that use of Sialkot met station data is not appropriate for analysis of the flood from the Aik Catchment. Therefore, Station G (75, ) was also used in the Aik Flood analysis along with the Sialkot met data. A frequency analysis of this location was carried out and the GEV- three parameter distribution is shown in Figure and extreme daily precipitation versus return period is shown in Figure From the analysis described in the previous section, the daily data disaggregated to sub-hourly durations was used in the model. Six to eight hour duration distribution of daily precipitation was made making sure that the total hourly precipitation matches the hourly intensity and that the total daily precipitation amount remains the same. The following three synthetic series were used in the model as rainfall input are described in next section as presented in. These series are shown in Annexure D in Table D-1, Table D-2 and Table D-3. REG-8556 ANNEXE 5B Page 43

56 Figure Generalized Extreme Value Distribution, Daily Precipitation at Aik Catchment Figure Daily Precipitation versus Return Period, Daily Precipitation at Aik Catchment REG-8556 ANNEXE 5B Page 44

57 The above selections of time series are appropriate as the heavy rainfall of more than 200 millimeters (7.9 in) recorded during the wet spell of September 1 to 5, 2014 in northern Pakistan based on data from the Pakistan Meteorological Department. City Stations Rainfall (mm) Rainfall (in) Sialkot Cantonment (city) Sialkot Airport This extreme event also broke several 24 hour Rainfall records of Sialkot, which can be seen in the main article. The numbers presented in the news item below dictates that 95% upper confidence interval of the catchment area be used to estimate the maximum discharge in Aik. Source: REG-8556 ANNEXE 5B Page 45

58 3.5 Screening Level Watershed Model for Aik Nullah The purpose of developing this model is to highlight the impact of climate change and not to provide the detailed design. The detailed development of the model requires detailed data from the site conditions and this model is based on GIS and default values of the soils and the area. Therefore, it should be only used for policy making and not the design of various infrastructure components to reduce the impact of floods in the area. The slopes and areas of various subcatchments were provided by GIS expert. EPA Storm Water Management Model - Version 5.1 (Build ) was used in this assignment. This software is free and was downloaded from EPA site. Following options were used: Rainfall and Runoff analysis was used, no snowmelt process was used. For this preliminary analysis no groundwater contribution to runoff was used. Horton method was used for infiltration and Kinematic wave method was adopted for the flow routing. No water quality parameters were used in the analysis. Some of parameters and the assumptions used are described below. The soil type is reported as 70% salty sand (GHK, 2010), therefore due to silt the channel bed porosity and conductivity should not be very high. The effective porosity is assumed as Initially percentage of impervious is used as 30% which may be increased. However, it was changed according to the location of sub-catchments. SWMM tables the range for manning n value for irregular sections with small pools and width <30 meter (natural/ hilly channel) is given as A moderate value of 0.07 is selected for roughness for all conduits. A value of 0.4 is used for n-impervious for the sub catchments (range for the natural lands without considering under bush) and a corresponding value of n- pervious soils of 0.3 is selected (range ) 0.3. Infiltration parameter data is not available and Horton method was used with maximum infiltration as 2.5 mm/hour and minimum infiltration of 0.25 mm/hr and decay constant of 4 and drying time of % area was assumed to contribute towards runoff. Appropriate data and estimates for the stream geometry within the modeled catchments are not available. In the modeled catchments of the streams, the natural stream is generally in the hilly/steep regions/valleys. Such conditions tell about meandering and sloped streambeds. Different transects were used for the channel geometry ensuring that the flow occurs. Naming convention- n- prefix is added to the model elements located on the nullah and w- prefix added to the model elements located over the watershed areas. Estimates of the areas of sub catchments are estimated from numbers provided by GIS and from the map. Width of the sub catchment refers to the minimum length across the flow towards the node in the nullahs. The SWMM model suggests using the width parameter to calibrate the model. The width parameter is generally relative to the shape (area) of the sub catchments. The length of the sub catchment REG-8556 ANNEXE 5B Page 46

59 refers to the longest path along the flow towards the node or towards other sub catchment and were estimated are estimated graphically from the map and with the help of Google Earth. Because of large variations in slopes and terrain characteristics, each catchment is divided into sub-catchments in accordance to the flow pattern towards Nullah Aik. The catchment has been dived into 95 sub-catchments, 57 nodes (junctions) and 57 conduit links. There is one outfall node and one rain gauge. The areas and slope were adjusted by consulting Google map though generally in line with GIS determination. There was an increase in the total area of the catchment. The overland flow over the natural wooded lands will have great impact and the default values of the manning's value for overland surface flows as given in by SWMM tables were used. The depression storage for pervious and impervious and pervious soils are suggested by SWMM in the range of 0.1 in to 0.2 in and 0.2 is used. Meandering in the model is the ratio of the meandered length to the nominal length. This value is used only to adjust the roughness coefficient. For the higher stream the meandering value is used as Aik Nullah Catchment The catchment of Nullah Aik as determined by GIS is shown in Figure Figure Catchment Areas of Nullah Aik and Nullah Palkhu Note that in this figure the Nullah Aik and Nullah Palkhu are connected in this GIS derived map where as the ground reality is that they are separate streams. An effort was made to make them two distinct streams through the use of local maps and Google Earth. The developed figure is presented below as Figure which also shows the estimated catchment area of Nullah Aik. REG-8556 ANNEXE 5B Page 47

60 Figure Schematic Map of Nullah Aik Basin on GIS map The general setup of the model with all sub-catchments, streams, links and nodes is presented in Figure Figure Schematic Map of Nullah Aik Basin REG-8556 ANNEXE 5B Page 48

61 3.5.2 Rainfall Series As discussed in the previous section, the met station at Sialkot may not cover the entire area of the catchment of Aik Nullah, therefore three sets of rainfall series were used. This is a screening level undertaking therefore it was appropriate to do so. The three sets of rainfalls sets are obtained from the frequency analysis of Sialkot Met station data, data obtained from the National Centre of Environmental Prediction (NCEP), Climate Forecast System Reanalysis (CFSR) for the following sites at coordinates of 75, Frequency analysis was conducted for maximum daily precipitation time series and values for 2, 5, 10, 25, 50 and 100 years values were obtained. The third series used was upper 95% confidence interval of time series at the coordinates of 75, A twelve hourly rainfall distribution was generated by assuring that the values of hourly precipitation intensity determined by the frequency analysis is not violated. The total maximum daily precipitation was distributed in twelve hours with the maximum being in two hours and the other six hours are distributed on both the sides. An argument can be made that this is an arbitrary distribution but it is determined that for a screening level model, this judgmental distribution pattern is appropriate. The Set-1 will be reported as Sialkot met station, set-2 and set-3 will be reported as catchment station and 95% UCL of catchment station. All these sets were described in the previous section Model Runs The model was runs six times for each set, the results and the error analysis is presented below Results REG-8556 ANNEXE 5B Page 49

62 Results of the discharge of Nullah Aik with reference various rainfall intensities are provided below: Based on Sialkot Met Station Results are presented in Table and Figure Table Simulated Discharge based on Data from Sialkot Met Station Data Return Period Rainfall Intensity Simulated Discharge (m 3 /sec) Year mm/day Sialkot Met Station Figure Simulated Discharge in Nullah Aik at Ura and Daily Total Rainfall at Sialkot Meteorological Station vs. Return Periods Discharge (Cubic meter per second) Rainfall Intensity (mm/day) Return Period Discharge at Ura Rainfall Based on Catchment Rainfall Results are presented in Table and Figure REG-8556 ANNEXE 5B Page 50

63 Table Simulated Discharge based on Data from Catchment Data Return Period Rainfall Intensity Simulated Discharge (m 3 /sec) Year mm/day Catchment Rainfall Figure Simulated Discharge in Nullah Aik at Ura and Daily Total Rainfall at Catchment Point vs. Return Periods Discharge (Cubic meter per second) Rainfall Intensity (mm/day) Return Period Discharge at Ura Rainfall Based on 95% Upper Confidence Limit of Catchment Rainfall Results are presented in Table and Figure REG-8556 ANNEXE 5B Page 51

64 Table Simulated Discharge based on Data from 95% Upper Confidence Limit value of Catchment Data Return Period Rainfall Intensity Simulated Discharge (m 3 /sec) Year mm/day UCL of Catchment Rainfall Figure Simulated Discharge in Nullah Aik at Ura and 95% Upper Confidence Limit of Catchment Rainfall Daily Total Rainfall at Catchment Point vs. Return Periods Discharge (Cubic meter per second) Return Period Rainfall Intensity (mm/day) Discharge at Ura Rainfall Detail Results REG-8556 ANNEXE 5B Page 52

65 Results of the discharge of Nullah Aik with reference to 25 year and 50 year rainfall events are provided below: Based on Sialkot Met data The results are shown in Figure for 25 years and in Figure for 50 years. Based on Catchment Rainfall The results are shown in Figure for 25 years and in Figure for 50 years. Based on 95% UCL of Catchment rainfall The results are shown in Figure for 25 years and in Figure for 50 years. Figure Simulated Discharge in Nullah Aik at Ura vs. 25 Year Return Periods based on Sialkot Data Discharge (cumecs) Rainfall (mm/hr) Hours Figure Simulated Discharge in Nullah Aik at Ura vs. 50 Year Return Periods based on Sialkot Data REG-8556 ANNEXE 5B Page 53

66 Discharge (cumecs) Rainfall (mm/hr) Hours Figure Simulated Discharge in Nullah Aik at Ura vs. 25 Year Return Periods based on Catchment Data Discharge (cumecs) Rainfall (mm/hr) Hours Figure Simulated Discharge in Nullah Aik at Ura vs. 50 Year Return Periods based on Catchment Data REG-8556 ANNEXE 5B Page 54

67 Discharge (cumecs) Rainfall (mm/hr) Hours Figure Simulated Discharge in Nullah Aik at Ura vs. 25 Year Return Periods based on 95% UCL of Catchment Data Discharge (cumecs) Rainfall (mm/hr) Hours REG-8556 ANNEXE 5B Page 55

68 Figure Simulated Discharge in Nullah Aik at Ura vs. 50 Year Return Periods based on 95% UCL of Catchment Data Discharge (cumecs) Rainfall (mm/hr) Hours Simulated Discharge from Aik Nullah for all the return periods The results are all the three time series versus various return periods are shown in Figure , Figure and Figure for Sialkot Met station, Catchment data point and 95% UCL points respectively. Figure Simulated Discharge in Nullah Aik At Ura Vs. Various Return Periods - Sialkot Data REG-8556 ANNEXE 5B Page 56

69 Discharge (cumecs) Hours (Rain starts at Hour 2) 2-Year Return Period 5-Year Return Period 10-Year Return Period 25-Years Return Period 50-Year Return Period 100 Years Return Period REG-8556 ANNEXE 5B Page 57

70 Figure Simulated Discharge in Nullah Aik At Ura Vs. Various Return Periods - Aik Catchment Data Discharge (cumecs) Hours (Rain starts at Hour 2) 5-Year Return Period 10-Year Return Period 25-Years Return Period 50-Year Return Period 100 Years Return Period Figure Simulated Discharge in Nullah Aik At Ura Vs. Various Return Periods - Aik Catchment Data 95% UCL Discharge (cumecs) Hours (Rain starts at Hour 2) 2-Year Return Period 5-Year Return Period 10-Year Return Period 25-Years Return Period 50-Year Return Period 100 Years Return Period REG-8556 ANNEXE 5B Page 58

71 Error Analysis A total of eighteen runs of the model were made i.e. six for each set of data set. There six runs were for 2, 5, 10, 25, 50 and 100 year storms. The overall error for runoff continuity ranged from % to % and for flow routing continuity ranged from % to %. These error numbers are provided in Table below: Table Error Analysis Runs Return Period Set Runoff Continuity Error (%) Flow Routing Continuity Error (%) Set-2 Runoff Continuity Error (%) Flow Routing Continuity Error (%) SEt-2 Runoff Continuity Error (%) Flow Routing Continuity Error (%) Comparison of Results with Actual Data The following figure ( Figure ) shows the results of EPA-SWMM model and the actual data observed, it could be seen that the extreme event the model results are mimicking the actual data for extreme values. Therefore it could be used for the analysis and comparison with CC based simulations. REG-8556 ANNEXE 5B Page 59

72 Figure Model Results and Actual Data 2,000 1,800 1,600 Discharge (cumecs) 1,400 1,200 1, ACTUA DATA (Ranked) Reduced Variate Discussion The results of the simulations mimic the actual discharges which are observed in Nullah Aik. Since this exercise is used to determine the impact of climate change, this screening level model can provide information for policy making with reference to climate change and future investments. REG-8556 ANNEXE 5B Page 60

73 3.6 Screening Level Watershed Model for Aik Nullah With and Without Climate Change Method As discussed and reported in section-3.5, the time series used in the baseline scenarios were adjusted to accommodate the climate change impacts of the 2050 scenario (low, mid and high) and the 2100 scenario (low, mid and high). All the parameters used in the model for the baseline scenario were employed. Three sets of rainfall time series were used, first, based on the frequency analysis of data from Sialkot Met station, second, based on the data of a point ( coordinates 75, ) in the catchment of Aik Nullah obtained from SWAT model data base and the third based on upper confidence level of the catchment data point. According to Pakistan Met station data, a maximum flood of 1,305 m3/sec was recorded in Nullah Aik in 2014 and Nullah Palkhu reported a maximum peak flood of 1, 374 m 3 /sec (Daily Dawn news report). Nullah Aik was selected to model and the results were correlated to Nullah Palkhu. Results of the model and the analysis of results are presented in proceeding sections. It should be mentioned again that there was some uncertainty in the rainfall data so three time series were used for the analysis Model Runs The EPA-SWMM model already setup for the baseline scenario was run for the three rainfall series for the 2050-scenarios of low, middle and high and 2100-scenarios of low, middle and high. Eighteen (18) model runs were made. As determined in the baseline scenario simulation, the upper confidence limit (UCL) data series of catchment point (Time Serise-3) produced the results which fits the actual data, therefore the results of six simulation runs of time-series 3 are presented for comparison Results The results of the model runs are presented below in section for the upper confidence limit (UCL) of catchment point (Time Serise-3). It should be mentioned that in baseline scenario the simulation results of time series of upper confidence limit (UCL) of catchment point mimicked the actual floods in Aik Nullah Catchment Station Time Series with upper confidence limit As mentioned earlier, the results of the time series of Catchment met station with upper confidence limit data was used to make forty two runs of the model for various return periods and with various scenarios. The results are presented in Table and Figure and Figure Figures also show the results of the baseline scenarios. REG-8556 ANNEXE 5B Page 61

74 Table Simulated Discharge in Nullah Aik at Ura with and without climate change Nullah Aik Discharge in m 3 /sec Return Period Baseline Climate Change 2050 scenario 2100 scenario Year Low Mid High Low Mid High ,018 1,044 1,099 1,044 1,095 1, ,249 1,314 1,350 1,436 1,349 1,430 1, ,502 1,580 1,620 1,722 1,620 1,714 1, ,771 1,865 1,912 2,044 1,911 2,033 2, ,081 2,198 2,254 2,419 2,296 2,405 2,851 Figure Simulated Discharges in Nullah Aik at Ura with and without climate change Scenarios Discharge (cu. meter/sec) Return Period (years) Baseline 2050 Scenario - Low 2050 Scenario - Mid 2050 Scenario - High REG-8556 ANNEXE 5B Page 62

75 Figure Simulated Discharges in Nullah Aik at Ura with and without climate change Scenarios Discharge (cu. meter/sec) Return Period (years) Baseline 2100 Scenario - Low 2100 Scenario - Mid 2100 Scenario - High Analysis An analysis was performed to determine the impact of climate change on the simulated discharge of nullah Aik and the implications on recurrence intervals of various floods. All the results as mentioned above were used to determine the percentage change in simulated discharge of Aik Nullah due to climate change and are shown in Table Assuming that the discharges observed in Nullah Aik and Nullah Palkhu are of 25 year return period (an assumption) then the 25 year discharges of Nullah Aik and Palkhu with climate change are provided in Table Changes in Return Period There will be reductions in time period of the baseline design periods return intervals for example the discharge which is realized in the baseline case at 5 year return period with the climate change scenarios of 2050 low, mid and high will be realized at return periods of 4.6 years, 4.26 Years and 3.85 years respectively as sown in Figure The discharge which is realized in the baseline case at 25 year return period with the climate change scenarios of 2050 low, mid and high will be realized at return periods of years, 18.5 Years and 15.5 years respectively as shown in Figure REG-8556 ANNEXE 5B Page 63

76 Table Percentage Change in Simulated Discharge of Nullah Aik with Climate Change Percentage Change in Simulated Discharge in Nullah Aik with and without climate change in percent (%) from base Return Period Climate Change 2050 scenario 2100 scenario Year Low Mid High Low Mid High 2 2.0% 3.7% 5.7% 3.7% 5.6% 11.7% 5 1.8% 4.0% 7.9% 3.9% 7.7% 19.2% % 6.7% 12.3% 6.7% 11.9% 27.7% % 8.1% 14.9% 8.0% 14.4% 32.2% % 7.9% 14.7% 7.9% 14.1% 32.5% % 7.9% 15.4% 7.9% 14.8% 34.7% % 8.3% 16.2% 10.3% 15.6% 37.0% Table Simulated Discharges of Nullah Aik and Nullah Palkhu for 25 Year Return Periods with Climate Change Observed Discharge Simulated Discharge 2050 scenario 2100 scenario Low Mid High Low Mid High m 3 /sec Nullah Aik ,372 1,411 1,499 1,409 1,493 1,725 Nullah Palkhu ,444 1,485 1,579 1,484 1,572 1,816 REG-8556 ANNEXE 5B Page 64

77 Figure Reduction in Baseline 5 year Return Periods with climate change-2050 Scenarios Discharge (cu. meter/sec) Return Period (years) Baseline 2050 Scenario - Low 2050 Scenario - Mid 2050 Scenario - High Figure Reduction in Baseline 25 year Return Periods with climate change Scenarios Discharge (cu. meter/sec) Return Period (years) Baseline 2050 Scenario - Low 2050 Scenario - Mid 2050 Scenario - High REG-8556 ANNEXE 5B Page 65

78 The discharge which is realized in the baseline case at 50 year return period with the climate change scenarios of 2050 low, mid and high will be realized at return periods of 40 years, 36 Years and 30 years respectively as shown in Figure Figure Reduction in Baseline 50 year Return Periods with climate change Scenarios Discharge (cu. meter/sec) Return Period (years) Baseline 2050 Scenario - Low 2050 Scenario - Mid 2050 Scenario - High Conclusion The results indicate that discharges in various nullahs will increase with climate change. This may cause frequent flooding to low lying areas. The return periods of the extreme floods on which certain infrastructures are designs will be reduced. The utilities in Sialkot should accept a gradual decrease in the level of service provided by the drainage infrastructure. However, if there is periodic flooding and the incurrence of minor damage associated with flooding are acceptable, then the city should consider the replacement of various infrastructures such as bridges, culverts and flood control measures. Various control measures which are adopted by the city officials have to be revisited. REG-8556 ANNEXE 5B Page 66

79 3.7 Screening Level Storm Water Management Model for Urban Areas of Sialkot The purpose of developing this model is to highlight the impact of climate change and not to provide a detailed design. The detailed development of the model requires detailed data from the site conditions and this model is based on GIS and default values of the soils and the area. Therefore, it should be only used for policy making and not the design of various infrastructure components to reduce the impact of floods in the area. The slopes and areas of various subcatchments were provided by GIS expert. EPA Storm Water Management Model - Version 5.1 (Build ) was used in this assignment. This software is free and was downloaded from the EPA site. The following options were used: Rainfall and Runoff analysis was used, no snowmelt process was used. For this preliminary analysis no groundwater contribution to runoff was used. Horton method was used for infiltration and Kinematic wave method was adopted for the flow routing. No water quality parameters were used in the analysis. Some of parameters and the assumptions used are described below. The soil type is reported as 70% salty sand (GHK, 2010), therefore due to silt the channel bed porosity and conductivity should not be very high. The effective porosity is assumed as Initially percentage of impervious is used as 80% for the buildup area, 70% for the areas with less constructed areas and 30% to 50% for the peri-urban areas. However, they were adjusted during the runs of the model. Appropriate data and estimates for the stream geometry within the modeled area are not available. Based on the various supplied maps and Google earth different transects were used for the channel geometry ensuring that the flow occurs. It is pertinent to report these transects needs to be adjusted to the ground truths. The open channel, closed conduits and the surface flow links are selected as reported in the reports and they sizes are approximate when not reported in the literature supplied by UU maps Setup of Model The schematic and the model domain is shown in Figure As the city of Sialkot, Cantonment area and the peri-urban area of Sialkot is traversed by three draining channels, Nullah Palkhu, Nullah Bhaid and Nullah Aik, therefore based on the contours provided by GIS, allocation of areas to various sub-catchments was made by personal judgment. It is appropriate to state that no information or data was provided in this regard. Therefore, considerable caution should be used for applying this model for design purposes. The slopes of the sub-catchments were also approximated by inspecting Google Earth. No information was provided about the sizes and the cross section of these three big draining channels. The sizes of the channels and the x-sections were also approximated. The model is setup in such a way that the ground truths can be added when available. REG-8556 ANNEXE 5B Page 67

80 Figure Schematic and Model Domain There are 112 sub-catchments, 85- junction nodes and 85 conduits links. It also includes three outfall nodes, one for each draining channel. The schematic of the nodes, links and outfalls are presented are presented in Annexure-E Assumptions 1. Sub-catchment areas are approximated from Google Earth and GIS maps. 2. Channel sizes are approximate from the scanty reports and Google earth. 3. Channel cross sections are approximated. 4. Land use areas are approximated from Google earth and GIS maps. This assessment is used for the allocation of imperviousness percentage of the catchment. 5. The length and area of each sub-catchment is approximated from Google Earth. 6. Slopes are approximated from the Google Earth. 7. Sewerage flows are not added as were not provided 8. Nullah discharges at the entry points of the city are not added as not provided so the discharge reported is only from the storm in the model domain Model Runs Frequency analysis of data acquired from Pakistan Met. Department was used to statistically determine the 5- minute, 15-minute, 30- minute, 1-hour, 3-hour, 6-hour and daily rainfall intensity for return periods of 5, 10, 25, 50 and 100 years. These numbers were used in the model and rainfall amount was used as an input as well. The rainfall intensity data was applied REG-8556 ANNEXE 5B Page 68

81 in such a fashion for the 15- minute intensity, and one mm rainfall was entered before that. This approach is appropriate as the main purpose of the model is to assess the impact of climate change. Two kind of the model runs were made, one as a baseline scenario and the other with climate change. The climate change runs were further divided in low, medium and high scenarios of The model runs were made for 15- minute, 30- minute and 6-hourly intensity for 5, 10, 50 years return periods Results 30-minute Rainfall intensity -The baseline results for 30-minute Rainfall intensity with 5 year return periods are shown in Figure , Figure 3.7.4, and Figure for Aik, Bhaid, and Palkhu streams. An effort was made to estimate the amount of discharge to the draining channels as it traversed through the city. This analysis is shown in Figure 3.7.5, Figure 2.8.8, and Figure and in Table 3.7.1, Table 3.72 and Table respectively for Nullah Aik, Nullah Bhaid and Nullah Palkhu. Note the sections indicated in Figure 3.7.3, Figure and Figure refers to the sections of Nullahs and areas mentioned in Figure 2.8.6, Figure and Figure refers to sub-catchment areas along the nullahs. Figure Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Aik over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Discharge (cu. meter/sec) Hours since onset of Rainfall Section-100 Section-101 Section-102 Section-103 Section-104 Section-105 Section-106 Section-107 Section-108 Section-109 Section-110 Section-111 Section-112 Section-113 Section-114 Note: the simulated discharge shown in the figure does not account for inflows from catchment and sewerage flow. REG-8556 ANNEXE 5B Page 69

82 Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Aik over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Nullah Aik Urban Contribution Minute 5 year Catchment Area Contribution from Area m 3 /sec Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area Figure Simulated Storm water Discharge Loading from Sialkot and Peri-Urban areas to Nullah Aik from 30-Minute 5 Year Return Period Rainfall - Baseline Discharge (cu. meter/sec) Contribution from Area REG-8556 ANNEXE 5B Page 70

83 Figure Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Bhaid over Time from 30-Minute 5 Year Return Period Rainfall - Baseline 5 4 Discharge (cu. meter/sec) Hours since the onset of Rainfall Section-200 Section-201 Section-202 Section-204 Section-208 Section-209 Section-210 Section-211 Note: the simulated discharge shown in the figure does not account for inflows from catchment and sewerage flow. Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Bhaid over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Nullah Bhaid Urban Contribution Minute 5 year Catchment Area Contribution from Area m 3 /sec Area Area Area Area Area Area Area Area Area Area Area Area Area REG-8556 ANNEXE 5B Page 71

84 Figure Simulated Storm water Discharge Loading from Sialkot and Peri-Urban areas to Nullah Bhaid from 30-Minute 5 Year Return Period Rainfall - Baseline Discharge (cu. meter/sec) Area-200 Area-201 Area-202 Area-203 Area-204 Area-205 Area-206 Area-207 Area-208 Area-209 Area-210 Area-211 Area-212 Contribution from Area Figure Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Discharge (cu. meter/sec) Hours since the onset of Rainfall Section -301 Section -303 Section -304 Section -306 Section -308 Section -310 Section -311 REG-8556 ANNEXE 5B Page 72

85 Note: the simulated discharge shown in the figure does not account for inflows from catchment and sewerage flow Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 30-Minute 5 Year Return Period Rainfall - Baseline Nullah Palkhu Urban Contribution Minute 5 year Catchment Area Contribution from Area m 3 /sec Area Area Area Area Area Area Area Area Area Area Area Area Figure Simulated Storm water Discharge Loading from Sialkot and Peri-Urban areas to Nullah Palkhu from 30-Minute 5 Year Return Period Rainfall - Baseline Discharge (cu. meter/sec) Area -300 Area -301 Area -302 Area -303 Area -304 Area -305 Area -306 Area -307 Area -308 Area -309 Area -310 Area -311 Area Contribution from Area REG-8556 ANNEXE 5B Page 73

86 6-Hour Rainfall intensity The baseline results for 6-hour Rainfall intensity with 25 year return periods are shown in Table , Table 3.7.5, and Table for Aik, Bhaid, and Palkhu streams. Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Aik over Time from 6-Hour 25 Year Return Period Rainfall - Baseline Nullah Aik Urban Contribution - 6-Hour 25 year Catchment Area Contribution from Area m 3 /sec Area Area Area Area Area Area Area Area Area Area Area Area Area Area Area The model results show run-on water in sub-catchments numbers: 101, 107, 110, 114, 118, 125, 130, 134, 137, 232, 200-b, 306, 307, 310, 311, 314, 315, 319, 320, 323, 325, 328, 329, 331, and 332. These run-on areas do not change with the increase in the intensity of rainfall. Since the purpose is to assess the impact of climate no further investigation was made. REG-8556 ANNEXE 5B Page 74

87 Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Bhaid over Time from 6-Hour 25 Year Return Period Rainfall - Baseline Nullah Bhaid Urban Contribution - 6-Hour 25 year Catchment Area Contribution from Area m 3 /sec Area Area Area Area Area Area Area Area Area Area Area Area Area Table Simulated Storm water Discharge from Sialkot and Peri-Urban areas to Nullah Palkhu over Time from 6-Hour 25 Year Return Period Rainfall - Baseline Nullah Palkhu Urban Contribution - 6-Hour 25 year Catchment Area Contribution from Area m 3 /sec Area Area Area Area Area Area Area Area Area Area Area Area REG-8556 ANNEXE 5B Page 75

88 3.8 Screening Level Storm Water Management Model for Urban Areas of Sialkot with Climate Change Scenarios The climate change impact on the urban area of Sialkot was assessed by simulated the storm discharges by using three set of intensity curves were developed for 2050, low, medium and high and these were used in the analysis. These data sets are prepared by using the scale, shape and location parameters for Sialkot as described earlier. Three to four points along the draining channels are used to assess the impact of climate change for the maximum discharge. These points are shown in Figure For assessing the impact of climate through the passage of storm in time, three points in the city were selected. These points are shown in Figure and are highlighted red. Figure Location of Points Selected for Assessment of Climate Change REG-8556 ANNEXE 5B Page 76

89 Figure Location of Special Points Selected for Assessment of Climate Change Model Runs with Climate Change Storm water Urban simulation model, as set up in the baseline scenario, was run with and without climate change for 15-minute, 30 minute and 6 hour for the return periods of 5 and 10 years. The points along the streams were analyzed, the percentage change in discharge are shown in the table and also depicted in figure for three streams. Note, the discharge mentioned in the figure does not include the sewerage and watershed flows. These are just the add-ons from the urban storm system Analysis for Impact of Climate Change 15-Minute Intensity and 5-Year Return Period The percentage increases for Nullah Aik, Nullah Bhaid and Nullah Palkhu at the selected points are shown in Table 3.8.1, Table and Table and in Figures 3.8.3, Figure and Figure REG-8556 ANNEXE 5B Page 77

90 Table Percentage increase in Simulated Discharge in Nullah Aik - 15 minute Intensity and Return Period of 5-Year CC Scenarios Points along Nullah Aik Path in Sialkot Link O1-L-104 Link O1-L-109 Node O1-O minute 5 Year Low 12.44% 11.28% 11.53% 15 minute 5 Year Middle 14.92% 13.49% 13.76% 15 minute 5 Year High 30.62% 27.76% 30.18% The increase in simulated discharge are different at points along the path of Aik Nullah. Table Percentage increase in Simulated Discharge in Nullah Bhaid - 15 minute Intensity and Return Period of 5-Year CC Scenarios Points along Nullah Bhaid Path in Sialkot Link O2-L-201 Link O2-L-203 Link O2-L-208 Node O2-O minute 5 Year Low 11.28% 11.45% 8.47% 12.22% 15 minute 5 Year Middle 13.85% 13.66% 10.08% 14.66% 15 minute 5 Year High 28.72% 28.19% 22.58% 31.20% Table Percentage increase in Simulated Discharge in Nullah Palkhu - 15 minute Intensity and Return Period of 5-Year CC Scenarios Points along Nullah Palkhu Path in Sialkot Link O3-L-303 Link O3-L-306 Node O3-O minute 5 Year Low 12.09% 15.37% 12.54% 15 minute 5 Year Middle 15.13% 18.49% 14.91% 15 minute 5 Year High 34.75% 38.24% 29.46% REG-8556 ANNEXE 5B Page 78

91 Figure Simulated Urban Discharges in Nullah Aik - 15 minute Intensity and Return Period of 5-Year Discharge(cu. meter/sec) Link O1-L-104 Link O1-L-109 Node O1-O-115 Links and Outfall Nodes 15 miunte 5 Year Baseline 15 miunte 5 Year Low 15 miunte 5 Year Middle 15 miunte 5 Year High Figure Simulated Urban Discharges in Nullah Bhaid - 15 minute Intensity and Return Period of 5-Year 5 Discharge (cu. meter/sec) Link O2-L-201 Link O2-L-203 Link O2-L-208 Node O2-O-212 Links or Outfall Nodes 15 miunte 5 Year Baseline 15 miunte 5 Year Low 15 miunte 5 Year Middle 15 miunte 5 Year High REG-8556 ANNEXE 5B Page 79

92 Figure Simulated Urban Discharges in Nullah Palkhu - 15 minute Intensity and Return Period of 5-Year Discharge (cu. meter/sec) Link O3-L-303 Link O3-L-306 Node O3-O-312 Links and Outfall Nodes 15 miunte 5 Year Baseline 15 miunte 5 Year Low 15 miunte 5 Year Middle 15 miunte 5 Year High 30-Minute Intensity and 10-Year Return Period The results are provided in Table 3.8.4, Table and Table The results are selfexplanatory and the increase in discharges can be viewed. Table Percentage increase in Simulated Discharge in Nullah Aik - 30 minute Intensity and Return Period of 10-Year CC Scenarios Points along Nullah Aik Path in Sialkot Link O1-L-104 Link O1-L-109 Node O1-O minute 10 Year low 10.28% 13.94% 12.01% 30 minute 10 Year Medium 12.39% 16.80% 14.42% 30 minute 10 Year High 25.13% 34.36% 28.42% REG-8556 ANNEXE 5B Page 80

93 Table Percentage increase in Simulated Discharge in Nullah Bhaid - 30 minute Intensity and Return Period of 10-Year CC Scenario Points along Nullah Aik Path in Sialkot Link O2-L-201 Link O2-L-203 Link O2-L-208 Node O2-O minute 10 Year low 11.51% 10.93% 9.78% 9.35% 30 minute 10 Year Medium 14.00% 13.61% 11.74% 11.27% 30 minute 10 Year High 28.67% 29.28% 26.81% 22.91% Table Percentage increase in Simulated Discharge in Nullah Palkhu- 30 minute Intensity and Return Period of 10-Year CC Scenario Points along Nullah Palkhu Path in Sialkot Link O3-L-303 Link O3-L-306 Node O3-O minute 10 Year low 14.15% 11.79% 12.26% 30 minute 10 Year Medium 17.15% 14.20% 14.73% 30 minute 10 Year High 35.92% 29.00% 29.35% 6-Hour Intensity and 10-Year Return Period The results are provided in Table 3.8.7, Table and Table The results are selfexplanatory and the increase in discharges can be viewed. Table Percentage increase in Simulated Discharge in Nullah Aik- 6-Hour Intensity and Return Period of 10-Year CC Scenario Points along Nullah Palkhu Path in Sialkot Link O1-L-104 Link O1-L-109 Node O1-O Hour 10-Year Low 7.07% 7.00% 7.05% 6-Hour 10-Year Middle 8.59% 8.49% 8.56% 6-Hour 10-Year High 17.51% 17.32% 17.43% REG-8556 ANNEXE 5B Page 81

94 Table Percentage increase in Simulated Discharge in Nullah Bhaid - 6-Hour Intensity and Return Period of 10-Year CC Scenario Points along Nullah Palkhu Path in Sialkot Link O2-L-201 Link O2-L-203 Link O2-L-208 Node O2-O hour 10 Year Low 11.28% 11.45% 8.47% 12.22% 6-hour 10 Year Middle 13.85% 13.66% 10.08% 14.66% 6-hour 10 Year High 28.72% 28.19% 22.58% 31.20% Table Percentage increase in Simulated Discharge in Nullah Palkhu - 6-Hour Intensity and Return Period of 10-Year CC Scenario Points along Nullah Palkhu Path in Sialkot Link O3-L-303 Link O3-L-306 Node O3-O Hour 10-Year Low 7.00% 7.03% 7.10% 6-Hour 10-Year Middle 8.50% 8.54% 8.62% 6-Hour 10-Year High 17.34% 17.40% 17.55% Increase in Simulated Discharge in Time or Flow Regime Change With Climate Change Analysis was carried to assess the impact of climate change in increase in timing or flow regime change with climate change at selected locations in Nullah Aik and Nullah Bhaid in the centre of the city. These locations are shown in Figure Figure to Figure The increase in discharge with climate change can be observed in these figures. REG-8556 ANNEXE 5B Page 82

95 Figure Simulated Discharge in Nullah Aik at Link 109 with 15 Minute Rainfall Intensity and Return Period of 5-Year Discharge (cu-meter/sec) Hour Baseline-2050 Low-2050 Middle High-2050 Figure Simulated Discharge in Bhaid at Link 203 with 15 Minute Rainfall Intensity and Return Period of 5-Year Discharge ( cu. meter/sec) Hours Baseline-2050 Low-2050 Middle High-2050 REG-8556 ANNEXE 5B Page 83

96 Figure Simulated Discharge in Nullah Aik at Link 109 with 6-Hour Rainfall Intensity and Return Period of 25-Year Discharge (cu-meter/sec) Hour Baseline Low Middle High Figure Simulated Discharge in Nullah Bhaid at Link 203 with 6-Hour Rainfall Intensity and Return Period of 25-Year Discharge ( cu. meter/sec) Hours Baseline Low-2050 Middle-2050 High-250 REG-8556 ANNEXE 5B Page 84

97 3.8.3 Conclusion The results indicate that with climate change there will be increased incidences of storm and combined sewers unable to accommodate more intense rainfall. Frequent urban flooding may happen more frequently and may result in costs associated with repairs to damaged structures. There will be increased damage and disruption of vulnerable critical utilities and infrastructure resulting in increased costs for repair. A key concern is that design criteria for infrastructure such as storm sewers, culverts, and bridges need to be updated to reflect what the climate change models are projecting in terms of storm event intensity and frequency. TMA should factor extreme weather into the design of infrastructure scheduled for refurbishment and should factor extreme weather into the design of new infrastructure. REG-8556 ANNEXE 5B Page 85

98 4. ASSESSMENT OF CLIMATE CHANGE IMPACTS ON WATER RESOURCES IN SIALKOT 4.1 Glaciers and Snowmelt Contribution to Water Supply Glaciers and snowmelt provide a significant contribution to annual discharge of Chenab and other rivers in Indus Basin. Few analyses have described the role of glaciers in the hydrologic regime of these mountains, in large part due to the inaccessibility and altitude (4,000 7,000 meters ) of Himalayan glaciers. The two principal sources of runoff from the upper Indus Basin (UIB) are winter precipitation, as snow that melts the following summer, and glacier melt. Winter precipitation is most important to the seasonal snow runoff volume, while summer temperature contributes most to glacier melt volume. Drawing a clear distinction between the runoff volumes resulting from snow melt and glacier melt is difficult. The primary zone of melt water from both sources is maximized at around 4,000 5,000 meters, as a result of a combination of maximum terrain surface area, maximum glacier surface area, and maximum snow water equivalent deposition occurring at these levels (World Bank, 2013). There are multiple approaches for quantifying the proportional contribution of glacial melt water. These can be classified into one of five different categories that: a) compare measured discharge at the glacier snout with measured downstream discharge (direct discharge measurement), b) estimate the water produced by changes in glacier mass (glaciological approaches), c) estimate glacier melt water discharge by solving for other components of the hydrological balance (hydrological balance equations), d) utilize hydro chemical tracers to solve the hydrological balance, and e) employ hydrological models (Frenierre et al., 2014). Glacier and snowmelt contribution to annual discharge are reported in World Metrological Organization /Global Water Partnership (2003) - 40%, World Bank (2013)- 85 %, and Gupta et al., %. However, Bookhagen, 2010 did a comprehensive analysis and reported that in Indus basin about 40% of the melt water originates from glaciers, 60% from snowpack. The melt water contribution to annual discharge for the major southern Himalayan catchments where the rivers flow into the plains (at the foot of the Himalayas)are estimated to be 66% for the Indus, 25% for the Jhelum, 43% for the Chenab, 16% for the Ravi, 21% for the Beas and 57% for the Sutlej river. The relatively higher dependence of Chenab on melt-water is due to the large glaciered fraction and persistent snow cover in the large expanses at higher altitudes, providing melt-water stores during the warmer seasons. River Chenab passes just west of the city of Sialkot and is major source of recharge to the groundwater for the water supply of the city. The watershed of river Chenab has 2,774 glaciers which covers an area of 3,007 km 2 having an elevation range of 4,842 to 5,299 meters and a mean slope of 27.9%. The debris covers of glaciers is 16%. The glaciers in Chandra and Bhaga (rivers which make the Chenab river) have lost 20 and 30% glacier area (Kukarni et al 2011). REG-8556 ANNEXE 5B Page 86

99 The snow melt contribute on the average 40% of the total flow in July when the peak melt rates are attained. Hence it synchronizes with the early monsoon in July, but not with the peak values occurring in August and September. During the monsoon, particularly the Jammu and Munawar Tawis contribute considerably to the flood flows at Marala (WMO/GWP, 2003). The spatial and seasonal variations in precipitation in Chenab river catchment is estimated by dividing the area into three categories based on altitude. They reported that in Greater Himalaya ranges (higher altitudes), about 75% precipitation occurs in pre-monsoon and monsoon seasons, while about 15% precipitation occurs in winter in the form of snowfall. In Middle Himalaya ranges, about 65% precipitation occurs in pre-monsoon and monsoon periods and about 26% in winter. In Outer Himalaya ranges, about 36% precipitation occurs in winter, but most of it is not in the form of snow due to lower altitudes and tropical climate, and thus forms a major source of contribution to river flow during winter season in the form of seasonal winter floods (Singh et al., 1995). Based on the analysis done by others, it is estimated that 30-40% of the melt-water in River Chenab originates from glaciers and % of the melt water originates from snowmelt which itself is around 43% of total discharge in River Chenab. REG-8556 ANNEXE 5B Page 87

100 4.2 Storm Water and Recharge Calculations in Urban Areas of Sialkot by Mass Balance Area and Land use The area of the city is around sq km which is divided into various types of areas for the model purposes as described Table below: Table Type of Areas in Sialkot Type of Land Use Percent Area Area Remarks % km 2 Green Areas 5.50% 3.5 Assumed Typical Bare Ground/ Unpaved Area 8.00% 5.1 Assumed Typical Pavement Area 4.00% 2.6 Assumed Typical Street/ Roads 2.00% 1.3 Assumed Typical Water Bodices 0.50% 0.3 Assumed Typical Built Up Area 80.00% 51.4 Assumed Typical Total % Draining channels Sialkot is traversed by three water channels or Nullah: the Bhaid, Palkhu Nullah and Aik mullahs. The Bhaid Nullah drains the northern part of the municipal area and part of the Cantonment while the Aik Nullah drains the southern part of the municipal area. The Bhaid Nullah joins the Palkhu Nullah, which runs north of the Cantonment, some distance outside the municipal limits Methodology used for various Land use areas. For the land use type as agricultural areas, green areas, bare grounds, pavement and street/ roads, and built up area, HELP model is employed. The Hydrologic Evaluation of Landfill Performance (HELP) model version 3.07 (Schroeder, 1994a and 1994b) was used to provide an estimate of the infiltration rate through the various land use areas of Sialkot City (Site). The Hydrologic Evaluation of Landfill Performance (HELP) model is a lumped parameter, quasi 2- dimensional water balance model originally developed by the US EPA. The model was originally developed to estimate the water balance for municipal landfills, but has since been updated to apply to a variety of other settings including modeling groundwater recharge in southern BC (Modelling Climate Change Impacts on Groundwater Recharge in a semi-arid region, Southern Okanagan, BC, Toews, University of Calgary, 2003). REG-8556 ANNEXE 5B Page 88

101 4.2.4 Depth of Ground water GHK, 2010 reports that the available information suggests that the depth of the water table (shallow groundwater) is typically ft bgs. It was assumed that groundwater is 25 ft bgs for this assessment Brief on HELP Model The Hydrologic Evaluation of Landfill Performance (HELP) computer program is a quasi-twodimensional hydrologic model of water movement across, into, through and out of landfills. The model accepts weather, soil and design data and uses solution. It uses techniques that account for surface storage, snowmelt, runoff, infiltration, vegetative growth, evapotranspiration, soil moisture storage, lateral subsurface drainage, leachate recirculation, unsaturated vertical drainage, and leakage through soil, geomembrane or composite liners. The HELP model uses many process descriptions that were previously developed, reported in the literature, and used in other hydrologic models. The optional synthetic weather generator is the WGEN model of the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) (Richardson and Wright, 1984). Runoff modeling is based on the USDA Soil Conservation Service (SCS) curve number method presented in Section 4 of the National Engineering Handbook (USDA, SCS, 1985). Potential evapotranspiration is modeled by a modified Penman method. Evaporation from soil is modeled in the manner developed by Ritchie (1972) and used in various ARS models including the Simulator for Water Resources in Rural Basins (SWRRB) and the Chemicals, Runoff, and Erosion from Agricultural Management System (CREAMS). Plant transpiration is computed by the Ritchie s (1972) method used in SWRRB and CREAMS. The vegetative growth model was extracted from the SWRRB model. Evaporation of interception, snow and surface water is based on an energy balance. Interception is modeled by the method proposed by Horton (1919). Snowmelt modeling is based on the SNOW-17 routine of the National Weather Service River Forecast System (NWSRFS) Snow Accumulation and Ablation Model. The frozen soil sub model is based on a routine used in the CREAMS model (Knisel et al., 1985). Vertical drainage is modeled by Darcy s (1856) law using the Campbell (1974) equation for unsaturated hydraulic conductivity based on the Brooks- Corey (1964) relationship. Saturated lateral drainage is modeled by an analytical approximation to the steady-state solution of the Boussinesq equation employing the Dupuit-Forchheimer (Forchheimer, 1930) assumptions. Leakage through geomembranes is modeled by a series of equations based on the compilations by Giroud et al. (1989, 1992). The processes are linked together in a sequential order starting at the surface with a surface water balance; then evapotranspiration from the soil profile and finally drainage and water routing, starting at the surface with infiltration and then proceeding downward through the landfill profile to the bottom. The solution procedure is applied repetitively for each day as it simulates the water routing throughout the simulation period. REG-8556 ANNEXE 5B Page 89

102 4.2.6 Climate Data The climatic data was synthetically generated using the temperature and precipitation normals of Sialkot, Punjab, Pakistan with specified latitude of o N (Google Earth) of the site. The precipitation, temperature, and radiation data were synthetically generated over a period of 100 years using the precipitation and temperature normals of Sialkot (and are shown previously in Section 2.1. Average annual wind speed was taken from the meteorological station located at Sialkot, Punjab, Pakistan and is 2 m/hr. Quarterly average relative humidity data was also taken from the weather data of the above mentioned station at Sialkot, Punjab, Pakistan (PMD Data for ) and are 71.2, 48.76, 75.44, and percent for first, second, third, and fourth quarters, respectively. Other climatic coefficients are taken from the HELP database for Phoenix, Arizona as the city lies at the same latitude as Sialkot and these are: P(W/W), P(W/D), a, and b which are probability of wet day after a wet day, probability of wet day after a dry day, and distribution parameters Properties of Various Areas as put in the HELP model As discussed earlier, the areas where the HELP model is employed are: green areas, bare grounds, pavement and street/roads, and built up areas. The following additional parameters were used in the analysis: A surface slope of 3 percent, and a slope length of 200 meters. Evaporative depth was taken as 120 cm. The start of the growing season was assumed to be day 1 (Julian Date) and the end of the growing season was assumed to be day 365 (Julian Date). For green areas and parks, the ground cover was assumed as Poor Stand of Grass and Maximum Leaf Area Index was assumed as Soil and Design Data Two layers were assumed for the recharge from surface to groundwater. A top layer of 15 cm which has less permeability and a bottom layer of 745 cm thickness. K values of various layers are presented below in Table REG-8556 ANNEXE 5B Page 90

103 Table Soil Parameters Bare Soil Parks and Green Areas Pavement/Street/Roads Built-up Area Layer Depth (cm) K (cm/sec) 1.9 x x x x Layer-2 Depth (cm) K (cm/sec) 5.8 x x x All these areas are modeled separately. HELP model generate 100 years of synthetic data. The histogram of the synthetic data is shown in Figure below. HELP model tries to assign steady state conditions in start to all the layers Figure Histogram of Data Generated by HELP No of Years Precip. mm/year Results and Analysis Baseline - Annual On an annual basis, in the Sialkot area on average 62 million m 3 rainfalls occurs out of which 56 million m 3 finds its way to surface runoff, 4 million m 3 is evaporated and an amount of 2 million m 3 is percolated and finds its way to groundwater. The distribution of each type of area is presented in Table below: REG-8556 ANNEXE 5B Page 91

104 Table Simulated Annual Water Mass Balance for the City of Sialkot Total Average Annual Type of Area Area Precipitation Runoff Percolation Evapotranspiration m 2 Mm 3/ year Mm 3/ year Mm 3/ year Mm 3/ year Green Areas 3,531, Bare Ground/ Unpaved Area 5,136, Pavement Area 2,568, Street/ Roads 1,284, Built Up Area 51,360, Daily Basis- Baseline Average Based on the help model results, the daily runoff and percolation of each type of area is presented in the table below such as 0.58 mm per day is produced as a runoff from the green area and 0.76 mm is percolated to groundwater. The total contribution of average daily storm water runoff is 145,778 m 3 of water as shown in Table Table Simulated Average Daily Water Mass Balance for the City of Sialkot Average Daily Rate Total Runoff Percolation Evapotranspiration Runoff Percolation mm mm mm m 3 m 3 Green Areas ,981 3,927 Bare Ground/ Unpaved Area , Pavement Area , Street/ Roads Built Up Area , ,778 4,601 Maximum Daily Runoff and Seepage on Peak Wet Day REG-8556 ANNEXE 5B Page 92

105 Based on the help model results, the daily runoff and percolation of each type of area is presented in the table below such as 2 Mm 3 as runoff from the agricultural area and Mm 3 mm is percolated to groundwater. The total contribution of this maximum daily storm of 100 year towards water runoff is 43 Mm 3 of water and towards percolation is 0.44 Mm 3 of water as shown in Table Table Simulated Maximum Daily Runoff and Seepage on Peak Wet Day Runoff Percolation Runoff Percolation Peak daily Rate Peak Daily Total mm/m 2 Mm/m m 3/ day m 3/ day Green Areas ,945,267 38,669 Bare Ground/ Unpaved Area ,425,702 4,420 Pavements ,756, Street/ Roads , Built Up Area ,461,923 0 Total 43,468,083 44,448 Average Daily Runoff and Seepage on Selected Rainy Days The simulated daily precipitation is sorted and the highest 5% rainy days were selected to assess the wa ter mass balance. The mass balance results are presented in Table Table Simulated Average Selected Rainy Days Daily Water Mass Balance for the City of Sialkot Average on Selected Rainy Days Daily Land Use Rate Total Runoff Percolation ET Runoff Percolation mm mm mm Mm 3 Mm 3 Green Areas ,999 2,569 Bare Ground/ Unpaved Area ,336 1,175 Pavement Area , Street/ Roads , Built Up Area ,545, ,933,038 4,098 REG-8556 ANNEXE 5B Page 93

106 Conclusion Based on this analysis it is estimated the storm drainage systems percolates around 4,503 m 3 of water on average daily basis and on selected rainy days it can contribute up to 2.9 Mm 3 of water. Therefore on rainy day there are opportunities to recharge the much exploited groundwater. REG-8556 ANNEXE 5B Page 94

107 4.3 Impacts of climate change on Storm Water and Recharge (Water Supply) in Sialkot using Mass balance approach. As discussed in section 4.2 that the Hydrologic Evaluation of Landfill Performance (HELP) computer program was used to the surface water balance which uses the climatic parameters to quantify the evapotranspiration from the soil profile, drainage and water routing and infiltration/percolation to soil. To assess the impact of climate change 2050 scenario data was used. The monthly average for temperature and rainfall were estimated and are presented in Table and Table Table Baseline and Climate Change Scenario-2050 Monthly Precipitation at Sialkot Baseline Scenario 2050 Low Mid High mm mm mm mm Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total REG-8556 ANNEXE 5B Page 95

108 Table Baseline and Climate Change Scenario-2050 Monthly Average Temperature at Sialkot Baseline Scenario 2050 Low Mid High o C o C o C o C Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Properties of Various Areas as put in the HELP model As discussed earlier, the areas where the HELP model is employed are: agricultural areas, green areas, bare grounds, pavement and street/roads, and built up areas. The same soil and design data was used. For various kinds of areas various model runs were made separately. HELP model generate 100 years of synthetic data. HELP model tries to assign steady state conditions in start to all the layers Results Average Daily Based on the help model results, the daily runoff and percolation of each type of area is presented in the table below such as 0.84 mm per day is produced as a runoff as opposed to 0.58 mm in baseline scenario from the green area and 0.97 mm is percolated to groundwater as opposed to 0.76 mm in baseline scenario. The average runoff amounts are shown in Table The total contribution of average daily storm water runoff is 170, 934 m 3 of water as opposed to 145,778 m 3 in baseline scenario are shown in Table and Table The percentage increase or decrease is also shown in the table. It can be seen the percolation of REG-8556 ANNEXE 5B Page 96

109 water to groundwater will be higher in bare grounds areas as opposed to pavement such as 4.74% and 2.74% for bare ground and pavement area respectively. Table Simulated Average Daily Runoff from Storms from Storms CC Scenario 2050 High at Sialkot Baseline CC-2050 High Changes in Runoff Runoff Runoff Daily Rate Daily Rate % Increase mm/m 3 mm/m 3 Green Areas % Bare Ground/ Unpaved Area % Pavement Area % Street/ Roads % Built Up Area % Table Simulated Average Daily Evaptranspiration and Percolation from Storms CC Scenario 2050 High at Sialkot Baseline Baseline CC-2050 High CC-2050 High Changes in Seepage ET Seepage ET Seepage ET Daily Daily % % Rate Rate Daily Rate Increase Increase mm/m 3 mm/m 3 mm/m 3 Green Areas % -2.35% Bare Ground/ Unpaved Area % 4.70% Pavement Area % 2.46% Street/ Roads % 2.46% Built Up Area % Maximum Daily Runoff and Seepage in 100 Years Based on the help model results, the daily runoff and percolation of each type of area is presented in the Table below. REG-8556 ANNEXE 5B Page 97

110 Table Simulated Maximum Daily Runoff and Percolation from Storms in 100 Years Baseline 2050 Scenario (High) Increase/Decrease Runoff Seepage Runoff Seepage Runoff Seepage Daily Daily % % Rate Rate Daily Rate Daily Rate Increase Increase mm/m 3 mm/m 3 mm/m 3 mm/m 3 Green Areas % 52.83% Bare Ground/ Unpaved Area % 5.70% Pavement Area % 5.67% Street/ Roads % 5.67% Built Up Area % - Average Daily Runoff and Seepage on Selected Rainy Days The simulated daily precipitation is sorted and the highest 5% rainy days were selected to assess the wa ter mass balance. The mass balance results are presented in Table and Table Table Simulated Average Selected Rainy Days Daily Water Mass Balance for the City of Sialkot Baseline CC-2050 High Change Runoff Runoff Runoff Daily Rate Daily Rate % Increase mm/m 3 mm/m 3 Green Areas % Bare Ground/ Unpaved Area % Pavement Area % Street/ Roads % Built Up Area % REG-8556 ANNEXE 5B Page 98

111 Table Simulated Average Selected Rainy Days Daily Water Mass Balance for the Baseline Baseline City of Sialkot CC-2050 High CC-2050 High Change Seepage ET Seepage ET Seepage ET Daily % % Rate Daily Rate Daily Rate Increase Increase mm/m 3 mm/m 3 mm/m 3 Green Areas % -7.11% Bare Ground/ Unpaved Area % 3.16% Pavement Area % 6.76% Street/ Roads % 6.76% Built Up Area % Conclusion Based on this analysis it is estimated the storm drainage systems amounts will increase with climate change though its increase will be different from various kind of land use areas. Climate change will increase the amount of percolation from various land use areas though the maximum increase will be in green and agricultural areas. REG-8556 ANNEXE 5B Page 99

112 4.4 Water Supply -Sialkot Sialkot is situated in the Upper Rechna Doab, which is bounded by the Ravi and Chenab rivers. It sits over abundant shallow and deep groundwater aquifers which are used by both the city water supply system and inhabitants with wells for their water supplies. The Chenab River flows to the northwest of Sialkot, and the Marala-Ravi Link Canals flow to the west. Sialkot is traversed by three seasonal streams, comprising Aik Nullah, to the south of the city, Bhaid Nullah, between the Cantonment and the rest of the city, and Palkhu Nullah, north of the Cantonment. The Sialkot water supply relies on groundwater. The piped water system in Sialkot is supplied by tubewells, from which water is pumped directly into supply. The aquifer is recharged from the Chenab River, which is located some 20 km north-west of the centre of Sialkot from the water channels and bodies within the city which are heavily polluted and to a lesser degree from precipitation. The aquifer below Sialkot is part the deep aquifer of Rechna Doab. The Rechna Doab is underlain by a deep, unconfined, high yielding aquifer that is 300m thick, relatively homogeneous and highly anisotropic. The strata are generally heterogeneous with little vertical or lateral continuity, suggesting that aquifers at different depths are interconnected. The bulk of the alluvial is composed of silt and fine sand, or mixtures thereof, with an absence of thick layers of pure clay, except for a few meters thick local clay lenses. The material is highly porous and is capable of storing and transmitting water readily, the horizontal permeability being greater than vertical. The latest monitoring results of the wells suggest that the depth to the water table is typically meters below the surface. Figure provides the locations of the municipal wells. The location of the monitoring wells are shown in Figure Figure depicts the water table levels based on the monitoring wells. Recent groundwater depths of the monitoring wells are shown in Figure for October, 2010 and Figure for October, 2012 REG-8556 ANNEXE 5B Page 100

113 REG-8556 ANNEXE 5B Page 101

114 Figure Location of Municipal Wells Figure Location of Monitoring Wells REG-8556 ANNEXE 5B Page 102

115 Figure Location of Monitoring Wells In and Near Sialkot and groundwater Depth in October 2010 Figure Location of Monitoring Wells In and Near Sialkot and groundwater Depth in October 2012 REG-8556 ANNEXE 5B Page 103

116 There are 72 tubewells with TMA, located at intervals throughout the town. TMA also acquired another 25 tubewells which have been installed by PHED, of which about 17 are working and 8 are not working. Most tubewells are nominally rated at 1.5 cusecs (42.5 l/sec or 153m3/hr) and water is provided for 12 hours per day (GHK, 2010). Assuming that all the wells are or will be in working condition then the total amount of water withdrawn is as: Total Water withdrawn = 97 x 42.5 x m 3 /sec = 4.11 m 3 /sec Annual withdrawal = 365 x4.11 m 3 /sec x 12 x 60 x 60 = 64,850,630 m 3 = million m 3 The Cantonment is supplied by 15 tubewells, with rated discharges ranging from 0.5 cusecs to 2 cusecs and a total rated discharge of 21.5 cusecs. The average rated discharge is thus 1.43 cusecs per tube well. In addition, one 1 cusec tubewell is not functioning because of bore failure and two 1.5 cusec tubewells have yet to be commissioned. The Cantonment Board says that the tubewells are normally operated for 8 hours per day. Total Water withdrawn is = 15 x 1.43 x m 3 /sec = 0.61 m 3 /sec Annual withdrawal = 365 x0.61 m 3 /sec x 8 x 60 x 60 = 6,412,320 m 3 = 6.4 million m 3 Total annual Water withdrawn by both city and cantonment from the aquifer = million m Groundwater Draw dawn Analysis Monitoring wells (maintained by Irrigation Department) with prefix of DLR NO_ and with names of 84UN, 146UN, 174UN, 175 UN, 176UN, 177UN and 178UN were used to estimate the trend of groundwater depth. Table provides the data of these monitoring wells. It could be seen that average groundwater levels from June 2010 to June 2012 dropped by 1.26 ft ( ) in two year at a rate of 0.63 ft per year recently. June was the month selected as this is the month where the recharge from rainfall, river flows and streams flows have already occurred in previous season. The average groundwater depth of these wells in the month of June is shown in Figure Figure shows the trend of drop in groundwater depths of these monitoring wells. The long term groundwater depth for these wells along with wells 96U, 120U,121U, 122U,123U, 173UN, UN from June 2006 to June 2012 is shown in Figure depicting that groundwater depth over time is increasing. REG-8556 ANNEXE 5B Page 104

117 Table Groundwater Depth (bgs) of Monitoring wells in and near Sialkot Groundwater Depth (bgs) of Monitoring wells in near Sialkot City DLR NO_ June_10 Oct_10 June_11 Octb_11 June_12 Oct_12 84UN UN UN UN UN UN UN Average Figure Average Historic Groundwater Depth in June in Monitoring Wells in and Sialkot City Groundwater Depth (bgs in ft) Jun/2006 Jun/2007 Jun/2008 Jun/2009 Jun/2010 Jun/2011 Jun/2012 Date REG-8556 ANNEXE 5B Page 105

118 Figure Average Historic Groundwater Depth In June and Trend Monitoring Wells in and near Sialkot City 0 2 Groundwater Depth (bgs in ft) y = x R² = Jun/2006 Jun/2007 Jun/2008 Jun/2009 Jun/2010 Jun/2011 Jun/2012 Date Figure Average Historic Groundwater Depth In June and Trend Monitoring Wells in and near Sialkot City 0 5 Groundwater Depth (ft BGS) Date REG-8556 ANNEXE 5B Page 106

119 4.4.2 Recharge to Groundwater Monitoring wells 84UN, 146UN, 174UN, 175UN, 176UN, 177UN and 178UN were used to analyze the trend in groundwater depth change and various recharge sources in the area. Figure depicts the trend in groundwater change where Figure 4.4.9, Figure , and Figure show the impact of recharge sources to the change in groundwater depth trends. It could be ascertained that there is groundwater recharge occurs with all the three sources and there is lag. The contribution of each source towards total recharge was not ascertained. Figure Historic Groundwater Depth Change in Monitoring Wells in and near Sialkot City 8 6 Groundwater Depth Change (ft bgs) Month-Year REG-8556 ANNEXE 5B Page 107

120 Figure Historic Discharge in River Chenab at US Marala and Groundwater Depth Change at City of Sialkot and Vicinity Groundwater Depth Change (ft bgs) Discharge (cms) Month-Year 0 Figure Historic Discharge in Aik Nullah at Ura and Groundwater Depth Change at City of Sialkot And Vicinity 8 60 Groundwater Depth Change (ft bgs) Discharge (cms) Month-Year 0 REG-8556 ANNEXE 5B Page 108

121 Figure Monthly Total Rainfall at Sialkot and Average Monthly Groundwater Depth Change at City of Sialkot and Vicinity Groundwater Depth (ft bgs) Discharge (cms) Month-Year 4.5 Analysis of Recharge and Discharge in Un-Commanded Area of Rechna Doab- Groundwater Mass Balance Model The aquifer lying under the Sialkot is part and parcel of the main aquifer in Rechna Doab. This analysis is performed on mass balance which is primarily: Recharge - Discharge = change in storage Negative change is storage means that the aquifer is being overexploited. A major assumption in this analysis is that groundwater which is flowing from the up-gradient of un-commanded area of Rechna Doab (groundwater flowing from Jammu i.e. River Tawi) is flowing out of the area at the same rate Discharge Water Use by Agriculture Agriculture is the sector which uses the largest amount of water of the aquifer underlain in upper Rechna Doab. In the agriculture area of upper Rechna Doab, the tubewell intensity ranges from REG-8556 ANNEXE 5B Page 109

122 per 1000 hectares (Asghar, et al, 2002). There are 36,161 tubewells in Sialkot district. Since the exact numbers of the tubewells in the upper Rechna Doab up gradient of MR-Link canal are not known, therefore it is difficult to estimate the water withdrawal from this section of the aquifer. However, Ahmed et al., 2009 estimated that 4,278 x 106 meters 3 amount of evapotranspiration is required for irrigating 0.45 million hectors of the out of command area of Rechna Doab which primarily lies up gradient of M-R link canal. They further pointed that out of this total demand 2,777 x 106 m 3 is provided by rainfall in the area and 1,501 million m 3 is withdrawn from the aquifer Water use by Water Supply As described earlier, the city of Sialkot on average is withdrawing million m 3 annually from the aquifer. Assuming that this same amount is extracted by other population centers in upper Chenab area, the withdrawal from the aquifer overall is more than 140 million m 3 of water Recharge to Aquifer Recharge from Rainfall A preliminary analysis was carried out by using the HELP model to estimate percolation from the top 15 meters of the vadoze zone of the aquifer near Sialkot. The estimated contributions of rainfall to recharge from the bare soil, parks and green area and pavement/ bricked open floor were 8.10%, 27.91% and 6.51% of the total rainfall respectively. Recharge to groundwater from rainfall in irrigated agricultural areas varies from 10 to 24% of the total annual rainfall (Basharat and Tariq, 2011). The total area of the un-commanded potion of the Rechna Doab is more than 0.45 million hectors so the average recharge from rainfall (annual amount 958 mm) is estimated to be million m 3.at a rate 17% of the rainfall Recharge from River Chenab Darcy's Law was used to estimate the recharge from River Chenab (after Hassan et al., 1995) which is RR = 10-7*T*i*L*t where RR is the recharge from river in million hectare meter (MHM) per season, L is the length of fiver contributing for recharge in km, i is the hydraulic gradient, T is the aquifer transmissivity in m 2 /day, t is the time in days per season for which recharge from the fiver will occur and 10-7 is the conversion factor. The average annual flow of river Chenab from 2010 to 2014 is 17,755 m 3 /second. The transmissivity of the aquifer has been estimated as 5818 m 2 /days (Hassan et al., 1995). The approximate length of river Chenab along the western boundary of the Marla Link Canal is around km. RR = 10-7 X 5,818 m 2/ day x 1/15 x 10 x 365 = 1,416 million m 3 per year REG-8556 ANNEXE 5B Page 110

123 Recharge from M-R link Canal The MR Link Canal carries on average flow of cumecs between 2010 to The canal is unlined and the total length of the canal is 101 km. The loss coefficient of link canals has been estimated as 5.8% (after Hassan, 1995). Assuming that the link canal runs throughout the year. The total loss or recharge to groundwater from the MR Link Canal is: x 365 x 24 x 60 x 60 x = 520,375,536 m 3 = million m 3 as it will recharge on both sides of canal half of the total recharge is assumed. Total recharge from M-R link canal = 260 million m 3 per year Recharge from Nullahs Based on the data from 2010 to 2014, the average discharge of Nullah Aik and Nullah Palkhu are 484 ft 3 per and 418 ft 3 per respectively. The following equation was used to estimate the seepage from these streams: Nullah Aik S = (5.0 X (Q ) x P x L)/10 6 = 5 x x 44 x 5280 = ft 3 /sec per mile and assuming the nullah traverses 21.2 miles from the catchment to MR-Link canal then recharge per year Nullah Palkhu = 32.9 million m 3 per year = 5 x x 44 x 5280 = ft 3 /sec per mile and assuming the nullah traverses 21.2 miles from the catchment to MR-Link canal then recharge per year is = 32.6 million m 3 per year Assuming that the same amount of recharge is occurring from Deg Nullah Recharge from Field Percolation Most of the field percolation losses come from rice fields, as about 59% of the total water applied is lost as deep percolation, while deep percolation rates for wheat and sorghum fields are only 5.6% and 31.2%, respectively (Tyagi et al., 2000a, 2000b). Back of the envelope calculations were made as below: Typically around mm is a typical amount of water needed for irrigated rice and around 450 to 650 mm is typically required for irrigated wheat. Assuming that out of 0.45 million REG-8556 ANNEXE 5B Page 111

124 hectares in Rechna doab around 50% is under crop and 75% is under rice and 50% under wheat and rice. The maximum percolation which could occur is 0.45 x 1000,000 x 10,000 x 0.5 x 0.75 x 1.3 x 0.59 = 1294 million m 3 from rice and 0.45 x 1000,000 x 10,000 x 0.5 x 0.5 x 0.45 x 0.06 = 30 million m 3 from wheat. Therefore, the total recharge from field percolation is 1324 million m Sustainability of Water Supply The balance of groundwater recharge and discharge is negative as presented in the mass balance calculations below: IN = (Aik) 32.6 million m 3 +( Palkhu) 32.9 million m 3 +(MR) 261 million m 3 + (Chenab) 1,416 million m 3 +(rain) 733 million m 3 + (field) 1,324 million m 3 + (Deg) 32.6 = 3,858 million m 3 OUT = (Water supply) 140 million m 3 + (Agriculture) 4278 million m 3 = 4,418 million m 3 This analysis suggest that there is an extra pressure of approximately 452 million m 3 on an area of 0.45 million hectares. There is shortage of 420 million m 3 over 0.45*10000 million m 3 of un-commanded area of Rechna doab and it translates to 420/4500 = meter or 93 mm of water per sq meter. This calculation is in line with Ahmed et al., 2009 determinations. It has been reported that groundwater levels are dropping near the centre of the city requires that a detailed study of groundwater be made (GHK, 2010). This fact is further reported by Ahmed et al., 1994 which notes that on average mm of water over and above the safe yield is being withdrawn from the area near Sialkot. This analysis also provides the information that in recent years there has been a 192 mm drop in groundwater levels per year. REG-8556 ANNEXE 5B Page 112

125 4.6 Impacts of climate change on Water supply from upper catchments The estimates of glacier melt volume by the World Bank (2013) are based on a summer-season freezing level of 5,000 m, above which some melt may occur but there is no measureable runoff. It is estimated that glacier runoff contributes approximately 2.3 million acre-feet (MAF) to the total mean annual flow of Chenab basin of 26.1 MAF so the percentage of glacier melt is 2.3/26.1 or 9% and snowmelt is 19.9 MAF or 76% as shown in Table Table Estimated Contribution of Glacier Melt and Snow Melt to Discharge of River Chenab Estimated Contribution of Glacier melt and snowmelt to total runoff for Chenab Basin based on World Bank(2013) Basin Chenab Area, (km 2 ) 22,503 Glacier, (km 2 ) 2,708 Ice melt (MAF) 2.3 Snowmelt (MAF) 19.9 MAF = million acre feet. The annual total stream flow runoff in Chenab varies linearly with increase in temperature (Arora et al, 2008). The snowmelt runoff increases linearly with increase in temperature. However, the magnitude of the changes in snowmelt runoff depends on the climatic conditions over the basin. The average value of increase in snowmelt runoff for T+1 C, T+2 C and T+3 C scenarios are 10, 28 and 43% and the average value of increase in total stream flow runoff for T+1 C, T+2 C and T+3 C are estimated to be 7, 19 and 28%, respectively (Arora et al., 2008).From the above the increase in snowmelt and runoff for the short term for river Chenab can be estimated as: Increase in snow melt = 16.5 X (increase in degree change C in temperature) - 6 Increase in runoff = 10.5 x (increase in degree change C in temperature) - 3 As reported by Bookhagen, 2010 there is general consent that in the Indus basin climate change will result in increased water availability in the short term. However in the long term water availability will decrease. However, the authors indicate that these changes are considerable; this reduction may be partly compensated for by increased mean upstream rainfall (Indus +25%). Regardless of the compensating effects of increased rainfall, summer and late spring discharges are eventually expected to be reduced consistently and considerably around 2046 to 2065 after a period with increased flows due to accelerated glacial melt. REG-8556 ANNEXE 5B Page 113

126 4.6.1 Increase in Estimated Flows of River Chenab. An analysis was carried to estimate the increased discharge of river Chenab with climate change which is based on the relationship developed by Arora et. al., The average annual flow of river Chenab from 1937 to 1995 was 35,078 cubic meters per year ( MAF) and it equates to an average flow of 1,112 cubic meters per second. The increase in runoff with the change in temperature is given in Table Table Increase in Flow of Chenab with Temperature Change Increase in Temperature in o C Discharge cu meter/sec Percentage Increase 0 1, , % 1 1, % 1.5 1, % 2 1, % 2.5 1, % 3 1, % Estimates for Potential Increased Seepage to Aquifer near Sialkot The seepage for the river is calculated by the following formula after McDonald and Harbaugh, 1988 as: Seepage = CRIV x delta H where delta H is limited to stage if the bottom of the river is above the water table and CRIV = KLW/M where CRIV = Riverbed conductance Kv = vertical hydraulic conductivity of the riverbed, L = reach length, W= reach width, and M = thickness of the riverbed. REG-8556 ANNEXE 5B Page 114

127 Data: Kv = 1.63 m 2 /day ( Khan et al, 2003) L = 10 km - estimated W = 900 meter (Awan, 2003) Thickness of river bed = 2 meter assumed CRIV= 1.63 x 10,000 x 900/2 = 7,335,000 m 2 /day Average Annual Q = m 3 / sec (from 1937 to 1995) (Bhatti, MA 1999) delta H or stage is calculated by assuming that the average velocity is 1 m/sec, an assumption to calculate so delta H = Q/(W x V) delta H = /(900 x 1) = 1.24 meter Baseline Seepage = 7,335,000 m2/day x 1.24 m = 9,095,400 m 3 /day = 3,319,821,000 m 3 /year As half of it will be received on one side the seepage to Sialkot area of Rechna doab will be = 1,660 million m Sensitivity of Velocity to Seepage Sensitivity analysis was carried to observe the relationship of velocity to seepage. The results are presented in Table REG-8556 ANNEXE 5B Page 115

128 Table Sensitivity of Velocity of River Chenab to Seepage Conductance Width Average Annual Velocity Discharge depth Seepage meter/sec meter 3 /sec meter million m 3 /year 7,335, , ,654 7,335, , ,504 7,335, , ,324 7,335, , ,103 7,335, , ,335, , ,335, , ,335, , Impact of Climate Change on Seepage and Inflows As reported above, the runoff in river Chenab will change with the increase in temperature, doing the same calculations the amounts and assuming that the velocity is constant, the percentage increase in recharge versus temperature change are provided in Table Table Impact of Climate Change on Seepage from River Chenab Temperature Increase Discharge depth Seepage % Increase o C meter 3 /sec meter million m 3 /year 0 1, , % 0.5 1, , % 1 1, , % 1.5 1, , % 2 1, , % 2.5 1, , % 3 1, , % REG-8556 ANNEXE 5B Page 116

129 It can be noticed that the percentage increase in seepage is same as the increase in discharge though actual velocity increases with increase in discharge but the relationship is not linear. An arbitrary velocity based on the judgment was introduced and the results are presented below in Table Table Adjusted Impact of Climate Change on Seepage from River Chenab Temperature Increase Average Annual Velocity Discharge depth Seepage % Increase o C meter/sec m 3 /sec meter million m 3 /year % 0 1 1, , % , , % , , % , , % , , % , , % , , % From the above analysis it could be concluded that with the increase of discharge due to temperature increase, the seepage to aquifer underlying Sialkot will increase up to 5.38% with a 1.0 o C rise Impact of Increases Water Inflow of River Chenab on Sialkot Ground Water Supply in 2035 Assuming the average temperature will be 25.6 o C so the temperature will rise by 1.3 o C and increase in the inflow in Chenab will Follow equations provided by Arora et al., 2008 so there will be an increase of 1.32 % in river recharge to Rechna Doaab and increase in irrigated fields and green areas increase 27% in 50 Years so 0.54% per years from the Mass balance model Therefore, per year increase in seepage from these sources are provided in Table Table Impact of Climate Change on Seepage from Recharge Sources Increase in Seepage per Year Rechana Doaab Up gradient MR Canal Chenab 0.39% Irrigated Areas 0.54% Green areas 0.54% REG-8556 ANNEXE 5B Page 117

130 Also assuming that Nullah Aik, Palkhu and Nullah Deg will follow the Chenab then the increase in seepage in 2035 is given by Table and shown in Figure Table Impact of Climate Change on Seepage from Recharge Sources in 2035 Average Recharge Mm 3 Average Recharge per year m Percentage of Recharge Increase in Recharge because of CC % Increase in feet/year Aik % 0.39% 7.90E-06 Palkhu % 0.39% 7.90E-06 MR % 0.00E+00 Chenab % 0.39% 1.49E-02 Rain % 0.54% 5.52E-03 Field % 0.54% 1.80E-02 deg % 0.39% 7.90E % 3.85E-02 Figure Average Groundwater Depth and Trend in Sialkot, Baseline & with Climate Change - Scenario High in with Cllimate Change Scenario-2050 High) 15 Groundwater Levels (bgs) Baseline y = x R² = Year REG-8556 ANNEXE 5B Page 118

131 As mentioned earlier, there are 2,774 glaciers in the upper catchment of Chenab and shrinking of these glaciers at various rates is reported. The small size glaciers for example, 127 glaciarates and ice fields less than 1 km 2 have retreated by 38 per cent since 1962 (UNEP, 2009), possibly due to a small response time. This clearly states that there will be an increase in runoff initially and after many decades when many smaller glaciers have totally lost their areas, the total stream flow may decrease. There is general consent among scientists that in the Indus basin climate change will result in increased water availability in the short term. However in the long term water availability will decrease. Immerzeel et al. (2010), for example, indicate a decrease in mean upstream water supply from the upper Indus ( 8.4 %) by with respect to the reference period These changes are considerable, but they are less than the decrease in melt water production would suggest, because this reduction is partly compensated for by increased mean upstream rainfall (Indus +25 %). Regardless of the compensating effects of increased rainfall, summer and late spring discharges are eventually expected to be reduced consistently and considerably around 2046 to 2065 after a period with increased flows due to accelerated glacial melt (Immerzeel et al. (2010). Global warming is expected to dramatically alter the flow regime of the Upper Indus river. The predicted change in flow regime is an initial increase in summer flows in the early decades of 21st century followed by a sharp decline of the same during the latter parts of the century. Similar results were found for the Sutlej river (Singh and Bengtsson, 2004). Although the effects of climate change will not be as visible in the short term, they will be prominent on the long term. Reduced water availability will be the most profound during the spring and summer months. Water from the snow and glacier melt will appear earlier than the main monsoon. Climate change will affect the temperatures, amount of snow and ice in the Himalayan region as well as rainfall patterns in the densely populated downstream regions such as Punjab. This may have enormous significance for livelihood and well being of the people. There is a need to prepare the people, institutions and countries of the region to anticipate the consequences of climate change and evolve suitable and cost-effective adaptation responses. REG-8556 ANNEXE 5B Page 119

132 5. ASSESSMENT OF CLIMATE CHANGE IMPACTS ON HYDROLOGY, FLOOD CONTROL AND URBAN DRAINAGE IN SAHIWAL Sahiwal is located in central Punjab, Pakistan. Sahiwal is approximately 180 km from the major city Lahore and is located between Lahore and Multan. With a population of 207,388 (1998 Pakistan Census), it is the 14th largest city in the Punjab and the 22nd largest city in Pakistan. Sahiwal City is divided into North Sahiwal & South Sahiwal by Lower Bari Doab Canal (LBDC), where North Sahiwal is almost 70 % of the Sahiwal City. 5.1 Hydrology The average maximum and minimum temperatures (worldsweather online, 2015) are shown in Table Table Average Monthly Temperature of Sahiwal, Punjab Month Temperature Average Max Value o C Temperature Average Min Value o C Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Sahiwal receives very small amount of precipitation and average monthly precipitation is shown in the following Table REG-8556 ANNEXE 5B Page 120

133 Table Average Monthly Precipitation of Sahiwal, Punjab Month Average monthly precipitation (mm) Jan 7.31 Feb Mar Apr May Jun July Aug Sep Oct 8.23 Nov 5.16 Dec 6.58 Extreme events The only water body is the lower Bari Doab canal crossing the city, however there are some ponding which happens either from sewer outflows or from rains though these ponded areas are either dry themselves out within few days or water is pumped out (Personal communication) Water supply Ground water is the sole source of portable water exploited in Sahiwal City. The water table average is about feet below the ground level and upper level produce limited quantities of mineralized water. At depths of feet greater quantities of good quality ground water are available and this is where the city s supplies are derived. Sewerage and Drainage The wastewater is generally disposed off to the nearby water bodies via sewer system, which is not only polluting the environment but also causing many water born diseases. The existing system covered 90 % area of Sahiwal through approximately 40 km long sewerage network. The residential area is rapidly expanding to north side of the town including slow expansion to REG-8556 ANNEXE 5B Page 121

134 the east side which has reduced the coverage of facility to approximately 20-30%. There are no separate storm water drains in the whole city, which results in excessive pressure on sewerage system in rainy season. The sewerage lines burst frequently due to insufficient capacity. South Sahiwal is planned without adequate sewerage infrastructure. Previously, the sewage was disposed in to the LBDC Canal, Balloki Sulemanki (BS) Link Canal& the agriculture land outside the city to the south. REG-8556 ANNEXE 5B Page 122

135 5.2 Generalized Extreme Value (GEV) Analysis for Precipitation - Sahiwal Data Availability For Sahiwal, Pakistan the meteorological data are available as from 2008 to a part of 2014 continuous three hourly and daily data. No data was available for other years though it may be received later. However, PMD data was available from 1965 to 2005 for Lahore (a city 170 km northeast) and for Multan (180 km south) from National Oceanic Atmospheric Administration (NOAA), National Climate Data Center (NCDC) for the station GHCND:PKM Multan International, Pk. The data for Multan was partial. However by averaging the annual daily maximum precipitation of Lahore and Multan, a data set from 1973 to 2005 was created Statistical Model To Estimate Sub-Daily Intensity-Duration-Frequency Curves The problem is to define values in smaller time such as 10 minute, 15 minute, 30 minute, and one hour. Daily precipitation values have been successfully disaggregated using the cascadebased disaggregation, short-time intensity disaggregation methods and K-nearest neighbor approach by others. The approach described below was used to develop intensity-duration-frequency curves for 15- minute, 30 -minute, one hour duration by statistical methods. Six-hourly and 12-Hourly precipitation data was determined by adding the preceding three hour rainfalls together. Columns were created for monthly maximum 3-hourly, 6-hourly, 12-hourly data and daily (24 hour) data. There were eighty points as 28 months have no precipitation. Statistical analysis (linear fit) was used to estimate a relationship between maximum 3-hourly, 6-hourly and 12- hourly in the form of the equation: Y = a x D Where Y is the sub-daily intensity of precipitation (mm/hr) and D is the daily precipitation intensity (mm/hr) The results are shown in Table Table Relationship of Daily Rainfall intensity to 12, 6, and 3 Hour Rainfall Intensity Relationship of Intensity (mm/hr) Equation R² 12 hrs to 24 hrs Y= 1.45 x D hrs to 24 hrs Y= 2.46x D hrs to 24 hrs Y =3.89 x D 0.58 REG-8556 ANNEXE 5B Page 123

136 The next step was to estimate an equation from the above relationship to determine 5-minute, 15 minute, 30 minute and hourly precipitation intensity (mm/hr). The following relationship exists shown in Table from the actual data Table Fitted Ratios of 24 Hour Rainfall Intensity to 12, 6, and 3 Hour Rainfall Intensity Ratio to 24 Hrs Fitted ratio to 24 hrs Precipitation intensity (mm/hr) An equation was developed for developing a relationship for various fitted values of 3-hourly, six-hourly and 12-hourly intensity values to Daily Intensity value of mm/hr to extrapolate the other time durations, the following equation was developed: K = -1.76ln(dt) R² = for dt < 0.5 only where K is the ratio of precipitation intensity to daily precipitation intensity and dt is the ratio of time period in hours to 24 hours. The logic was to extrapolate the ratios of the observed three, six and twelve hours ratios and determine hourly and sub-hourly rainfall intensity. The results are presented in Table Table Calculated Ratios of Sub-Hourly Rainfall Intensity to 24-Hourly Rainfall Intensity Duration Ratio to 24 Hrs Actual Raton Calculated Ratio 12 hr hr hr hr minute minute minute REG-8556 ANNEXE 5B Page 124

137 5.2.3 Baseline Intensity- Duration - Frequency Analysis - Sahiwal Sub-hourly duration rainfall intensity at this location and frequency analysis was carried out. GEV- 3 parameters distribution was fitted to the selected time series of 5 minute, 10 minute, 30 minute, one hour, three hour, six hour, twelve hour, and daily intensity series. The Shape (k), scale (α) and location (ξ) parameters of various fits are presented in Table Table Calculated Parameters of Sub-Daily Rainfall Intensities to Daily Rainfall Intensity Parameters k α ξ 5-Minute Minute Minute Hour Hour Hour Hour Daily The fitted curves are shown in Annexure F as Figure F-1, F-2, F-3, F-4, F-5, F-6, F-7 and F- 8.The intensity duration frequency results are presented in Table % confidence results are presented in Table and Table below. The results are presented in Figure Return Period Table GEV-Three Parameter Rainfall Distribution for Sahiwal 5- Minute 10- Minute GEV-Three Parameters Distribution Precipitation (mm/hr) Minute Minute Hourly Three Hourly Six Hourly 12- Jourly Daily REG-8556 ANNEXE 5B Page 125

138 Table GEV-Three Parameter Rainfall Distribution 95% Confidence (lower) for Sahiwal Return Period 5- Minute 10-Minute 15- Minute GEV-Three Parameters Distribution Precipitation (mm/hr)-95% Confidence 30- Minute Hourly Three Hourly Six Hourly 12- Jourly Dail y Table GEV-Three Parameter Rainfall Distribution 95% Confidence (upper) for Sahiwal Return Period 5-Minute 10-Minute 15 Minute GEV-Three Parameters Distribution Precipitation (mm/hr)-95% Confidence 30- Minute Hourly Three Hourly Six Hourly 12- Jourly Daily REG-8556 ANNEXE 5B Page 126

139 Figure Precipitation Intensity Duration Frequency for various Time Intervals using GEV-3 Distribution 100 Precipitation Intensity (mm/hr) Return Periods 5- Minutes 15- Minutes 30-Minutes Hourly Three Hourly Six Hourly 12-Jourly Daily REG-8556 ANNEXE 5B Page 127

140 5.3 GEV Model And Vulnerability Analysis - Sahiwal Climate change is a reality that planners and designers of drainage infrastructures must consider. The cumulative effects of gradual changes in hydrology due to climatic change are expected to alter the magnitude and frequency of peak flows over the service life of drainage infrastructure. Potential future changes in rainfall intensity are expected to alter the level of service of drainage infrastructure, with increased rainfall intensity likely resulting in more frequent flooding of storm sewers and surcharging of culverts. For analyzing these changes, the following parameters developed by the Team leader were used for the development of these curves. They are provided in Table Note that the location, scale and shape are the attributes of the frequency distribution. REG-8556 ANNEXE 5B Page 128

141 Table Sahiwal GEV parameters change due to climate change impact (%) Sahiwal GEV parameters change due to climate change impact (%). Daily 2050 scenario 2100 scenario Low Mid High Low Mid High location Scale Shape hour Location Scale Shape hour Location Scale Shape hour Location Scale Shape hour Location Scale Shape min Location Scale Shape min Location Scale Shape min Location Scale Shape Results The parameters shown in the table were used to determine the changes in the baseline frequency distributions of rainfall presented in earlier parts. Frequency curves for baseline scenario, low, medium and high scenarios of 2050 and 2100 for 5-minute, 10-minute, 15- REG-8556 ANNEXE 5B Page 129

142 minute, 30-minute, one hour, 3-hour, 6- hour, 12 hour and daily durations are determined. 5- Minute for 5 Year return period Table and shown in Figure 3.51 and Figure are presented below. Tables for all other durations are attached in Annexure G. 5-Minute duration Rainfall curves for base scenario and Low, Medium and High of 2050 scenario and 2100 Scenarios The data for curves are presented in Table and depicted in Figure and Figure Table Minute duration Rainfall curves Data for base scenario and Low, Medium and High of 2050 Return Period Baseline 2050 scenario 2100 scenario Low Mid High Low Mid High mm mm mm mm mm mm mm REG-8556 ANNEXE 5B Page 130

143 Figure Minute duration Rainfall curves for base scenario and Low, Medium and High of Rainfall Intensity (mm/hr) Return Period (yrs) 2050 scenario - Low 2050 scenario - Mid 2050 scenario - High Baseline Figure Minute duration Rainfall curves for base scenario and Low, Medium and High of Rainfall Intensity (mm/hr) Return Period (yrs) 2100 scenario - Low 2100 scenario - Mid 2100 scenario - High Baseline REG-8556 ANNEXE 5B Page 131

144 Daily duration curves for base scenario and Low, Mid. and High of 2050 scenario and 2100 Scenarios The data for curves are presented in Table and depicted in Figure and Figure Table Hour duration Rainfall curves Data for base scenario and Low, Mid. and High of 2050 Return Period Baseline 2050 scenario 2100 scenario Low Mid High Low Mid High mm mm mm mm mm mm mm REG-8556 ANNEXE 5B Page 132

145 Figure Hour duration Rainfall curves for base scenario and Low, Middle and High of Rainfall Intensity (mm/hr) Return Period (yrs) 2050 scenario - Low 2050 scenario - Mid 2050 scenario - High Baseline Figure Hour duration Rainfall curves for base scenario and Low, Mid. and High of Rainfall Intensity (mm/hr) Return Period (yrs) 2100 scenario - Low 2100 scenario - Mid 2100 scenario - High Baseline REG-8556 ANNEXE 5B Page 133

146 5.3.2 Analysis of Impact of Climate Change on Rainfall Frequency Curves With the advent of inevitable climate change, the impact of climate change can be seen in the percentage increase in the intensity of rainfall for various durations and with different return periods. These changes will directly impact the hydraulic structures such as culvert designs, bridges design, flood zones delineation maps and flood management infrastructures in Sialkot. These changes are shown in Table and Table for 30 minute, and Daily rainfalls. Table Percentage Increase in the 30-Minute Intensity of Rainfall for CC Scenario 2050 (Low, Middle and High) and Scenario 2100 (Low, Middle and High) 2050 Percentage Increase in 30- Minute Duration Rainfall Intensity Return Period Baseline 2050 scenario 2100 scenario Low Mid High Low Mid High % 18.57% 31.47% 18.50% 25.60% 57.72% % 15.40% 27.32% 15.35% 23.05% 52.00% % 14.00% 25.29% 13.93% 21.74% 48.89% % 12.89% 23.61% 12.80% 20.57% 46.14% % 12.55% 23.11% 12.50% 20.23% 45.30% % 11.67% 21.69% 11.58% 19.18% 42.82% % 10.90% 20.43% 10.82% 18.20% 40.46% Table Percentage Increase in the 30-Minute Intensity of Rainfall for CC Scenario 2050 (Low, Middle and High) and Scenario 2100 (Low, Middle and High) 2050 Percentage Increase in Daily Duration Rainfall Intensity Return Period Baseline 2050 scenario 2100 scenario Low Mid High Low Mid High % 11.26% 22.94% 11.26% 22.51% 51.52% % 10.53% 21.35% 10.53% 21.05% 47.66% % 10.30% 20.84% 10.07% 20.37% 45.67% % 9.81% 19.81% 9.62% 19.23% 43.46% % 9.60% 19.57% 9.60% 19.02% 42.57% % 9.12% 18.69% 9.12% 18.09% 40.58% % 8.76% 17.91% 8.76% 17.27% 38.53% REG-8556 ANNEXE 5B Page 134

147 5.3.3 Change in return periods for various frequency durations An analysis was carried out to determine the impact of climate change on the duration of average recurrence intervals of storms of various durations. With Climate change, the 10- minute duration design storms which are based on 5 year return period will now have 4 year return period in 2050 scenario-low and 2.9 years with 2050 scenario-high as can be seen in Figure Figure Reduction in Design Return Period of 10- Minute Duration Rainfall with Climate Change Scenarios (Low, Mid. and High) Rainfall Intensity (mm/hr) Return Period (yrs) 2050 scenario - Low 2050 scenario - Mid 2050 scenario - High Baseline 3-Hour duration design storms which are based on 10 year return period will now have 7.5 year recurrence interval in 2050 scenario-low and 6.5 years with 2050 scenario-high as can be seen in figure Hour duration design storms which are based on 25 year return period will now have 18 year recurrence interval in 2050 scenario-low and 12 years with 2050 scenario-high as can be seen in figure REG-8556 ANNEXE 5B Page 135

148 Figure Reduction in Design Return Period of 3-Hour Duration Rainfall with Climate Change Scenarios (Low, Mid. and High) Rainfall Intensity (mm/hr) Return Period (yrs) 2050 scenario - Low 2050 scenario - Mid 2050 scenario - High Baseline. Figure Reduction in Design Return Period of 6- Hour Duration Rainfall with Climate Change Scenarios (Low, Mid. and High) Rainfall Intensity (mm/hr) Return Period (yrs) 2050 scenario - Low 2050 scenario - Mid 2050 scenario - High Baseline REG-8556 ANNEXE 5B Page 136

149 Daily duration design storms which are based on 50 year return period will now have 36 year recurrence interval in 2050 scenario-low and 22 years with 2050 scenario-high as can be seen in figure Figure Reduction in Design Return Period of Daily Duration Rainfall with Climate Change Scenarios (Low, Mid. and High) Rainfall Intensity (mm/hr) Return Period (yrs) 2050 scenario - Low 2050 scenario - Mid 2050 scenario - High Baseline Conclusion It can be seen from the results that infrastructure of water in Sahiwal is vulnerable to climate change. The accommodation of the effects of climate change on existing urban drainage infrastructure is more difficult and perhaps can be best achieved through the designation of space for remedial measures when space becomes available through urban redevelopment and the flood-proofing of existing development and infrastructure where possible. The planning and design of development in general and urban drainage infrastructure in particular should ideally be performed in a manner that integrates adaptive responses to climate change with sustainable environmental stewardship and minimization of the adverse effects of urbanization. REG-8556 ANNEXE 5B Page 137

150 5.4 Screening Level Storm Water Management Model for Urban Areas of Sahiwal The purpose of developing this model is to highlight the impact of climate change and not to provide the detailed design. The detailed development of the model requires detailed data from the site conditions and this model is based on GIS and default values of the soils and the area. Therefore, it should be only used for policy making and not the design of various infrastructure components to reduce the impact of floods in the area. The slopes and areas of various subcatchments were provided by GIS expert. EPA Storm Water Management Model - Version 5.1 (Build ) was used in this assignment. This software is free and was downloaded from EPA site. Following options were used: Rainfall and Runoff analysis was used, no snowmelt process was used. For this preliminary analysis no groundwater contribution to runoff was used. Horton method was used for infiltration and Kinematic wave method was adopted for the flow routing. No water quality parameters were used in the analysis. Some of parameters and the assumptions used are described below. The effective porosity is assumed as Initially percentage of impervious is used as 70% for the buildup area, 60% for the areas with less constructed areas and 30% to 50% for the periurban areas. However, they were adjusted during the runs of the model. Appropriate data and estimates for the stream geometry within the modeled area are not available. Based on the various supplied maps and Google earth different transects were used for the channel geometry ensuring that the flow occurs. It is pertinent to report these transects needs to be adjusted to the ground truths. The open channel, closed conduits and the surface flow links are selected as reported in the reports and they sizes are approximate when not reported in the literature supplied by UU maps Setup of Model The setup of the model is shown in Figure and Figure As the city of Sahiwal and the peri-urban area of Sahiwal is intersected by Lower Bari Doab Canal, therefore based on the contours provided by GIS, allocation of areas to various sub-catchment are made by judgment as no information was available. Therefore, considerable caution should be used for applying this model for design purposes. The slopes of the sub-catchments were also approximated by inspecting Google Earth. No information was provided about the sizes and the cross section of these three big draining channels. The sizes of the channels and the x-sections are also approximated. The model is setup in such a way that the ground truths can be added when available. There are 44 sub-catchments, 42 junction nodes and 42 conduits links. It also include five outfall nodes, one for each draining area. The schematic of the nodes, links and outfalls are presented in Annexure-H. REG-8556 ANNEXE 5B Page 138

151 Figure Model Domain REG-8556 ANNEXE 5B Page 139