Zhob River Basin

Balochistan Water Resources Development Sector Project (RRP PAK 48098)

Supplementary Linked Document 25:

Climate Risk and Vulnerability Analysis Report

The Government of Balochistan

Balochistan Water Resources Development Project Preparatory Technical Assistance (TA 8800-PAK)

CLIMATE RISK AND VULNERABILITY ANALYSIS REPORT November 2017

Table of Contents 1 INTRODUCTION ...... 1

2 METHODOLOGY ...... 13

2.3.1 SWAT (Soil and Water Assessment Tool) ...... 15

3 RESULTS OF CLIMATE CHANGE ANALYSIS ...... 21

3.1.1 Statistical tests ...... 21 3.1.2 Extreme event analysis ...... 24 3.1.3 Water availability in extreme years of Zhob river basin ...... 26 3.1.4 Above and below average rainfall events ...... 28 3.1.5 Statistical tests ...... 29 3.1.6 Extreme event analysis ...... 31 3.1.7 Water availability in extreme years of Mula river basin ...... 33 3.1.8 Above and below average rainfall events ...... 34

3.2.1 Results of GCM Analysis ...... 39 4 VULNERABILITY ANALYSIS ...... 64

4.5.1 Sediment ...... 69

5 ADAPTATION STRATEGIES ...... 74

5.3.1 Dam additional freeboard ...... 77 5.3.2 Flood protection bund additional freeboard ...... 77 5.3.3 Watershed Management ...... 78 5.3.4 Water storage system...... 78 5.3.5 Cost of adaptations ...... 78

5.4.1 Flood ...... 79 5.4.2 Drought ...... 80 5.4.3 Community coping mechanisms and enhancement of disaster resilience...... 80 6 CONCLUSION AND MESSAGE FOR THE PPTTA TEAM...... 82 REFERENCES ...... 83 ANNEXURE A- HISTORIC FLOOD INUNDATION MAPS...... 86 ANNEXURE B-TEMPERATURE ANALYSIS GRAPHS ...... 92 ANNEXURE C-METHODOLOGY OF ANALYSIS ...... 122 ANNEXURE D-RESULTS OF STATISTICAL TESTS ...... 124 ANNEXURE E- RESULTS OF GCM BASED DROUGHT AND FLOOD ANALYSES ...... 130 ANNEXURE F- DESIGN ADAPTATIONS FOR CLIMATE CHANGE ...... 145

List of Tables Table 1 - Water balance of Balochistan for surface and groundwater resources (After ADB TA-4560 PAK 2008 by Ahmad 2016) ...... 4 Table 2 - Climate Change adaptation policy for Balochistan as suggested in “Framework for Implementation of Climate Change Policy 2014 - 2030” (GOP, 2013) ...... 10 Table 3 - List of available meteorological stations, and data length for Zhob and Mula river basins of Balochistan ...... 14 Table 4 - Calibrated Water balance for Zhob river basin of Balochistan ...... 17 Table 5 - Calibrated Water balance for Mula river basin of Balochistan ...... 18 Table 6 - Surface and groundwater availability under five flood years in the Zhob river basin of Balochistan ...... 25 Table 7 - Surface and groundwater availability under 5 drought years in Zhob river basin of Balochistan ...... 26 Table 8 - Surface and groundwater availability in flood years in the Mula river basin of Balochistan ...... 32 Table 9 - Results of drought scenarios in Mula river basin of Balochistan ...... 32 Table 10 - Slope values of future decadal precipitation based on linear regression trend lines for Mula watershed...... 41 Table 11 - Slope values of future monthly average precipitation based on linear regression trend lines for Mula watershed...... 42 Table 12 - Slope values of future monthly average temperature based on linear regression trend lines for Zhob watershed...... 47 Table 13 - Slope values of future decadal temperature based on linear regression trend lines for Zhob watershed...... 50 Table 14 - Slope values of future decadal temperature based on linear regression trend lines for Mula watershed...... 53 Table 15 - Summary of results for Aridity index in Zhob and Mula watersheds...... 58 Table 16 - Summary of SPI analysis...... 59 Table 17 - Extreme Events (return periods) throughout the century for Zhob and Mula Watershed ...... 61 Table 18 - Flood Events (return periods) throughout the century for Zhob and Mulla Watershed ...... 61 Table 19 - Flood inundation modeling results for Zhob river basin of Balochistan ...... 67 Table 20 - Flood inundation mapping for the Mula river basin of Balochistan ...... 67 Table 21 - Flood return periods for Zhob and Mula Watershed...... 70 Table 22 - Climate change vulnerability and impact matrix...... 72 Table 23 - Possible adaptation measures applicable in Zhob and Mula river basins...... 76 Table 24 - Proposed adaptations against possible climatic impacts ...... 77 Table 25 - Climate Change Adaptation Cost ...... 79

List of Figures Figure 1 - Major river basins of Balochistan ...... 3 Figure 2 - Agro-ecological zones with mean annual rainfall of Balochistan ...... 5 Figure 3 - Brief outline of the study methodology ...... 13 Figure 4 - Trend analysis of rainfall data for the Zhob river basin of Balochistan ...... 23 Figure 5 - Results of 3-year moving average of rainfall in the Zhob river basin...... 24 Figure 6 - Histogram of rainfall (mm) for the Zhob river basin of Balochistan ...... 25 Figure 7 - Internally generated floodwater in extreme flood, median and drought years in Zhob river basin of Balochistan...... 27 Figure 8 - Groundwater recharge in extreme flood year, median year and drought year in Zhob river basin of Balochistan...... 28 Figure 9 - Annual rainfall years having values above and below annual average rainfall of Zhob river basin of Balochistan...... 28 Figure 10 - Linear regression trend analysis on rainfall data for Mula river basin of Balochistan ...... 30 Figure 11 - 3-year moving average for rainfall in Mula river basin of Balochistan...... 31 Figure 12 - Histogram of rainfall (mm) for the Mula river basin of Balochistan...... 31 Figure 13 - Internally generated floodwater in extreme flood, median and extreme drought years of Mula River basin of Balochistan ...... 33 Figure 14 - Annual rainfall years having values above and below annual average rainfall of Mula river basin of Balochistan ...... 34 Figure 15 - Maximum and minimum temperature changes for 2080s (Islam et al., 2009) .... 37 Figure 16 - Future Daily Flow plotted against Daily precipitation for Mula watershed...... 40 Figure 17 - Trends for decadal future precipitation for Mula watershed...... 40 Figure 18 - Averaged monthly historic and future precipitation for Mula watershed...... 41 Figure 19 - Future Monthly average precipitation trends for Mula watershed...... 42 Figure 20 - Annual average historic and future precipitation for Mula watershed...... 43 Figure 21 - Future Daily Flow versus Precipitation for Zhob watershed...... 44 Figure 22 - Future Decadal precipitation trends for Zhob watershed...... 44 Figure 23 - Averaged monthly historic and future precipitation for Zhob watershed...... 45 Figure 24 - Future Monthly average precipitation for Zhob watershed...... 45 Figure 25 - Historic and Future Annual average precipitation for Zhob watershed...... 46 Figure 26 - Future Monthly average minimum temperature for Zhob watershed...... 47 Figure 27 - Future Monthly average maximum temperature for Zhob watershed...... 48 Figure 28 - Historic and future Daily Maximum Temperature for Zhob watershed...... 48 Figure 29 - Historic and future Daily Minimum Temperature for Zhob watershed...... 49 Figure 30 - Future decadal Maximum temperature for Zhob Watershed...... 49 Figure 31 - Future decadal Minimum temperature for Zhob Watershed...... 50 Figure 32 - Historic and future averaged monthly maximum temperature for Zhob watershed...... 51 Figure 33 - Historic and future averaged monthly minimum temperature for Zhob watershed...... 51 Figure 34 - Historic and Future daily maximum temperature for watershed...... 52

Figure 35 - Historic and Future daily minimum temperature for watershed...... 52 Figure 36 - Future decadal maximum temperature for watershed...... 53 Figure 37 - Future decadal minimum temperature for Mula watershed...... 54 Figure 38 - Maximum (A) and Minimum (B) temperature moving average for the projected time series for Zhob Watershed...... 55 Figure 39 - Maximum (A) and Minimum (B) temperature moving average for the projected time series for Mulla Watershed...... 56 Figure 40 - Maximum (A) and Minimum (B) temperature moving average for the projected time series for Mulla Watershed...... 57 Figure 41 - Highest seven Precipitation Years for Mula Watershed (A) ...... 60 Figure 42 - Highest seven Precipitation Years for Mula Watershed (B) ...... 60 Figure 43 - Sub Basin Level Point Flood Map of Zhob Watershed ...... 62 Figure 44 - Sub Basin Level Point Flood Map of Mula Watershed ...... 63 Figure 45 - Floods in Zhob and Mula river basins of Balochistan in last 35 years...... 67 Figure 46 - Droughts in Zhob and Mula river basins in last 35 years...... 68 Figure 47 - Sediment yield at watershed outlet (Zhob Watershed) ...... 70 Figure 48 - Sediment Yield at Nawar Dam Location (Zhob Watershed) ...... 70 Figure 49 - Sediment Yield at Nawar Dam location (Zhob Watershed)...... 71 Figure 50 - Sediment Yield at Watershed outlet (Mula Watershed)...... 71

1 Introduction

1.1 Climate of Balochistan

1. Balochistan is located in the south-western part of Pakistan and it is the largest province of Pakistan in terms of geographical area; covering approximately 44 percent of the country’s geographical area. Topography of Balochistan is extremely diverse ranging from extensive plateau to flat plain areas (Hughes et al., 1977). However, there are only three major plains in Balochistan, the Kachhi, Lasbela, and Dasht. The temperature regime in Balochistan is extremely variable and is directly related with the altitude. Areas above 2000 m high have cooler temperature; whereas, temperate climate can be observed in areas that have altitudes between 1,300 to 2,000 m. High altitude areas with cooler temperatures usually experience a mean annual temperature between 100C to 180C. These areas definitely experience one month with mean temperature below 5o C. Frost and snow prevail during winters.

2. The low altitude temperate climate region has mean annual temperature between 18 oC and 240C. Tropical temperature dominates in the low mountain belt and low land facing the Arabian Sea. Winters in this region are mild; the mean maximum temperature of the coldest month is about 130C. The mean annual temperature ranges between 290C and 37 oC (Rees et al., 1990; Burke et al., 2005).

3. The climate of Balochistan is generally arid (Rasul et al., 2012; Burke et al., 2005). The province can be divided into three broad climatic zones:

• Hyper-arid (<100 mm/year) - Chaghai, Makran coastal areas and south-east of Lasbela • Arid (100-250 mm/year) - Northeast of Zhob, Loralai, Sibi, Kachhi, Lasbela plains, and Pab- Mor ranges • Semi-arid (250 – 400 mm/year) - Sulaiman ranges covering Toba Kakari area, Marri Bugti areas, and Pab Khirther mountain ranges and Brahui ranges.

4. Approximately 40% of average rainfall in eastern and southern Balochistan occurs in the months of July and August (monsoon dominated environments). However, less than 10% of average rainfall occurs in monsoon in western parts of the province (temperate climate regions). This makes rainfall dependability throughout upland Balochistan generally low (Rees et al., 1990).

1.2 Water Resources of Balochistan

5. Balochistan has an arid climate with low dependence on rainfall (Rasul et al., 2012; Rees et al., 1990; Tareen et al., 2008). The province experiences frequent spells of droughts and occasional but torrential floods. Perennial rivers are rare in the region and life is mostly dependent on runoff farming (‘Khushkaba’) or Spate irrigation (flood water harvesting or ‘Sailaba’). Approximately 40% of irrigation water in Balochistan comes from the Indus River which irrigates only 5% of the province. This is because of rugged terrain and poor infrastructure.

6. Balochistan has inland drainage. No rivers carrying a large permanent flow of water are found in the province. For the greater part of the year the river beds contain merely a shallow 2

stream which frequently disappears in the pebbly bottom. After heavy rains the rivers become raging torrents. The largest rivers in the province are Hingol and Bolan. The northern part of the province is drained by the Zhob River on the east and the Pishin-Lora on the west. Further south Nari, Bolan, and Kachhi receive the drainage of the Loralai and Sibi districts. The rivers draining the Jhalawan area are the Mula, the Hub, and the Porali. In Makran the Dasht River carries the drainage to the south, while the Rakhshan, confluence with the Mashkel River, towards the north.

7. Balochistan has a poor Spate irrigation infrastructure and management of Spate irrigation system. Most of the floodwater is either wasted or causes destruction due to uncontrolled flow. Due to lack of perennial rivers, high climatic variability and frequent droughts, resulted in putting pressure on available groundwater resources, which is the only reliable source of water for drinking and livelihood purposes especially in drought periods. Groundwater abstractions are much higher than the recharge rates causing lowering of water table and depletion of groundwater.

8. The current population of Balochistan province, of around 10.5 million in 2016, lives in the 18 river basins (Figure 1) and is largely rural. Rural livelihoods are dependent on crops (fruit, wheat and vegetables) and livestock, the mainstay of Balochistan's economy, accounting for some 60 percent of the province's GDP and employing around 67 percent of the labor force. Crops and livestock contribute about 62 percent and 38 percent of gross farm income, respectively. This heavy economic dependence on an unproductive agriculture sector is associated with widespread poverty. The importance of agriculture as a source of livelihoods is proportionately greater in poorer rural communities. 3

Figure 1 - Major river basins of Balochistan

9. Some 87 percent of Pakistan's total available water is contributed by the Indus Basin Irrigation System (IBIS), but the Balochistan is having a minor portion of land commanded by the Pat Feeder and Khirther canals. The province receives an allocation of 4.78 billion m3 (BCM) from perennial canals in the IBIS; and 4.44 BCM are allocated as per Water Apportionment Accord of 1991 from the diversion of floodwater during the monsoon using mean flows. Yet only 41% of the apportioned water is used, due to inadequate canal infrastructure and deferred maintenance of the existing canal system. In addition, inefficient on-farm water conveyance and application methods cause wastage of water in conveyance and application and not available for crop consumptive use (Ahmad 2016).

10. Surface runoff is highly variable in time and in space across the province of Balochistan. According to the data available from the provincial Irrigation Department, internally generated surface water in Balochistan amounts to 10.79 BCM at 50% probability and 25.2 BCM at 25% probability, of which only about 20.6% is used at 50% probability (ADB. 2007).

11. While Pakistan's overall water economy is highly integrated and allows for risk pooling, Balochistan water economy is highly segmented, with 18 river basins (see Figure 1Given that 4

agriculture accounts for 95 percent of Balochistan total current water use, any strategy to deal with water management must necessarily assign this sector a very high priority.

12. The water balance presented in Table 1 indicates two things: one that there is a potential for further utilization of the floodwaters (balance of 79% of available resources) and that there is an imbalance in the utilization of surface- and groundwater at the expense of the groundwater, which is not sustainable.

Table 1 - Water balance of Balochistan for surface and groundwater resources (After ADB TA-4560 PAK 2008 by Ahmad 2016) Sources of Water Available or Current Balance Available Allocated Water Use for Further Water Development billion m3 (BCM) Indus Basin Perennial Water 4.78a 3.77b 1.01 Indus Basin Floodwater 4.44a 0.00b 4.44 Internally Generated 10.79c 2.22c 8.57c Floodwater Groundwater 2.21c 2.66c -0.45c Total 22.22 8.65 13.57 a Pakistan Water Apportionment Accord 1991. b Balochistan, Irrigation Department, Quetta. c Tareen, S., B. Sani, K. Babar and S. Ahmad. 2008. Re-assessment of Water Resources Availability and Use for the Major River Basins of Balochistan – Major Findings, Policy Issues and Reforms. Vol. (4), No. (7), ADB TA-4560 (PAK), Quetta, Pakistan. Data of rainfall used for the period of 1890 to 2005 for the climatic stations operated by Balochistan Irrigation Department and Pakistan Meteorological Department. Study for the assessment of water resources was conducted by the Messers Halcrow Pakistan and Cameos Consultants, 2008. This is the only study till today which covers all the 18 river basins of Balochistan.

13. The groundwater resource is around 9.9% of the total water resources available in the province. On overall provincial basis, there is rapid depletion of groundwater and it is severe in some of the basins like Pishin-Lora including the Quetta sub-basin. Therefore, the future development of water resources in the province must be focussed on the development of floodwaters in terms of Spate and minor perennial irrigation schemes and generation of livelihood.

1.3 Irrigation and Agriculture of Balochistan

14. Six agro-ecological zones are characterized for Balochistan (Saeed and Ahmad 2008) and are described as under Figure 2

• Highlands-I: comprise districts Ziarat and Kalat having an altitude of >2000 m above mean sea level. • Highlands-II: comprise districts Quetta, Qila Abdullah, Musa Khel, Barkhan, Qila Saifullah, Pishin, Loralai, Zhob and Mastung having an altitude of 1200–2000 m above mean sea level. • Sub-Highlands: comprise districts Khuzdar and Kohlu having an altitude of 900–1200 m above mean sea level. 5

• Deserts: comprise districts Chagai, Dalbandin, Noshki, Panjgur, Awaran and Kharan having an altitude of 700–900 m above mean sea level. • Plains: comprise districts Jhal Magsi, Naseerabad, Jafarabad, Bolan, Sibi and Dera Bugti having an altitude of 100–400 m above the mean sea level. • Coastal Zone: comprises districts Gwadar, Turbat and Lasbela having climate of mild to warm in winter and very hot in summer.

Figure 2 - Agro-ecological zones with mean annual rainfall of Balochistan

15. The six agro-ecological zones largely described on topography indicated that within each agro-ecological zone, there is wide variability in mean annual rainfall, where it varies from <50 mm to >381 mm in the province. However, in major part of the province it varies between 100- 150 mm Figure 2). Thus, the extremely low rainfall is an indicator that irrigation is an essential input for having successful farming in the province (Saeed and Ahmad 2008).

16. The province has been divided into 18 distinct river basins including the Balochistan part of the Indus river basin. The main irrigation water sources are surface water from Indus basin irrigation system, floodwater and perennial base flows in rivers, sub-surface flow through river 6

gravels, springs and groundwater through the development of Spate irrigation, perennial irrigation, Karezes, dug wells and deep tube wells.

17. The total estimated perennial irrigated area in the province was 1.020 million ha of which 0.569 million ha (52%) were irrigated by canals and restricted to the districts of Nasirabad, Jaffarabad and part of Jhal Magsi, which are fed by Pat Feeder, Desert and Khirthar canals emanating from the Gudu and Sukkur barrages on the Indus river, respectively. The area irrigated by tube wells, dug wells and Karez and springs were 0.42 million ha, 0.06 million ha and 0.04 million ha, which is 38 %, 5% and 5 %, respectively, of the total irrigated area. (Agriculture Statistics of Balochistan, 2015).

18. In addition, Spate irrigation, locally known as Sailaba farming is widely practiced in the province since ancient times. Rainwater Harvesting/Khushkaba is also practiced in the province since centuries and is basically dependent on incident rainfall and localized runoff from adjacent slopes. Approximately 0.87 million ha are under Sailaba and Khushkaba farming systems (Agricultural Statistics of Balochistan, 2015). Thus, total cultivated area in the province is 1.89 million ha. The Sailaba and Khushkaba farming is dependent on the reliability of available rainfall, runoff and floodwater in a given year and thus variability is quite high from year to year.

19. Presently, Sailaba and Khushkaba farming (Spate Irrigation and Runoff Farming) represents 46% of the total cultivated area of about 0.87 million hectares. The spate irrigation and runoff farming can be further increased up to 3.00 million ha if the balance quantum of floodwater is controlled and harnessed efficiently in different river basins of the province. Potential sites for Spate irrigation prospects exist at Nari, Kachhi, Mula (these three are now part of the NRB), Porali, Kaha, Hingol, Zhob and Rakhshan River basins. There exists high potential to promote Spate Irrigation which will not only supplements the agriculture production but also increase the recharge zone of the existing depleting aquifers, generate new ones and also help in flood mitigation (Ahmad 2016).

20. Land under Khushkaba farming is 0.26 million ha about 13.8% of the total cultivated area (Agriculture Statistics of Balochistan 2015 Agriculture Census of Pakistan 2010). Rain water harvesting practices are observed as of great importance in the arid and semi-arid regions and in the areas of remote and scattered human settlements. The water scarcity in the province calls for adapting comprehensive rain water harvesting and management strategies in order to meet the water requirement of the small settlements in the rural areas. Considerable scope exists to enhance water productivity through rainwater harvesting in the high, mid and low lands in Balochistan which receives rainfall in summer monsoon as well during the winters. Furthermore, the settlements in these areas are characterized as the poverty pockets of Balochistan and fragile environments due to lack of any sort of stream flows (Ahmad 2016).

1.4 Need for Climate Change Analysis

21. In the last few decades climate change has become an emergent concern to scientists from different parts of the world. Increasing frequency of extreme climatic events such as high intensity short duration rainfall, and prolonged droughts are basic indicators of climatic change 7

(Asimakopoulos et al., 2001). Hansen (2006) states in his study that global surface temperature has increased approximately 0.2 °C per decade in the past 30 years. According to a study conducted on climate change perspective in Pakistan states that there is climate change in Pakistan with variable effects on different parts of the country (Farooqui et al., 2005).

22. Balochistan is an arid region where annual total rainfall ranges between less than 50 mm to over 350 mm, occasionally a few heavy rainfall events in active monsoon period brings lot of rainfall which results in devastation instead of casting benefits. It has been clearly mentioned in the 4th Assessment Report of IPCC (2007) that it is very likely (more than 90% confidence) that the frequency and intensity of extreme events will increase due to climate change in 21st century. However, the scale of increase will differ from region to region. According to a recent study, such change is quite visible in Pakistan (Rasul et al., 2012). Many research results have shown that Balochistan is the most vulnerable region with high sensitivity and low adaptive capacity to climate change (Malik et al., 2012). An increased evapotranspiration is expected in Balochistan following the predicted increase in temperature and a 5% decrease in relative humidity in the province (Farooqui et al., 2005).

23. Sadiq and Qureshi (2010) used linear and quadratic models to evaluate the trends in temperature and precipitation for the five major urban cities of Pakistan. Conclusion of results obtained is that maximum temperature is increasing, the most in Quetta (0.075°C /Year), and least in Peshawar (0.018°C /Year); whereas results for Lahore show a decreasing trend of - 0.004°C/Year. All four cities show rise in annual precipitation except Karachi. This result does not provide a significant evidence as second decimal variation in temperature is due to the advances in computations and not the reality. Thermometers normally used in Pakistan does not provide results even at one decimal level.

24. In another study conducted for analyzing trends in precipitation for five different climatic zones in Pakistan (Salma et al., 2012), indicated a decreasing trend of -1.18mm/decade in precipitation throughout Pakistan. The stations located in the North, North West, West, and coastal areas show an overall decreasing trend; whereas plain areas and south west of the country have been observed with no significant trends.

25. Negative trends in precipitation have been observed in different studies in last few decades. Both annual and seasonal precipitation is decreasing in more than 70 % of the stations in Balochistan. Furthermore, frequency of severe and extreme droughts is higher in north-west, from central parts towards south, south-east and some coastal areas when analyzed quarterly at 3- month winter season and annually for 12-month dry–wet periods. Central-eastern, south-western, southern, and some isolated coastal areas in the south are more susceptible to severe droughts particularly during winter and dry–wet periods because of high variability in precipitation in these areas (Ashraf and Routray, 2015).

26. Spatial and temporal rainfall variability is very high in Balochistan. Approximately 56% reduction in rainfall was observed during the drought of 1998-05. The province experienced a 33% reduction in average crop production. Around 84% farmers in Balochistan have non-irrigated 8

agriculture who faced approximately 69% reduction in crop production during the drought period of 1998-2005 (Tareen et al., 2008).

27. The literature review shows that there are signs of change in climate in Pakistan, specifically Balochistan. Therefore, it is necessary to adapt to minimize the impacts of climate change and incorporate its effect on water resource management, health, industry, transportation, and ecosystem’s sustainability.

28. All water development projects at the river basin level should be screened for climate risks. Climatic parameters that can affect a project’s success might include increased maximum and minimum temperatures, extreme events like flood and droughts and others. In this section of the report a detailed study has been conducted on the risk of variation of climatic variables and their possible impacts on the goal of sustainable water development. This report aims to quantify the risk and identify adaptation options that can be integrated into the project design. Often the technical depth of a report containing such analyses as mentioned above depends upon the availability of observed data. This report consists of desk study based on already published work on the same context as well as assessment based on the available climatic data. Based on the climate change risk assessment and vulnerability analysis, relevant adaptation options have been suggested, initially on basin scale and later on scheme level.

29. Economy of both river basins (Zhob and Mula) under the subject study is dependent mainly on agriculture which makes it particularly susceptible to the effects of climate change. In addition, the area does not have adequate monitoring systems for the prediction of occurrence of extreme events, or the assessment of possible changes in weather patterns, thus making the task of sustainable livelihood development or management extremely difficult. Adaptation, or long term strategies to combat any possible adverse effect of changing climatic parameters is difficult to formulate unless detailed vulnerability and impact assessment studies are undertaken.

30. Impact assessment and adaptation studies were carried out on basin scale and then on scheme level. Basin scale parameters assessed for vulnerability cover agriculture, and settlements. Later, specific adaptation measures will be suggested based on scheme level socio- economic data, which is being collected at the level of 22 potential schemes identified for Zhob and Mula river basins.

1.5 Study Objectives

31. In the present situation of water availability and climatic variability, it is compulsory to implement certain adaptation measures to improve the livelihoods of rural population. The Asian Development Bank (ADB) has signed a contract with Techno Consult International (TCI) for the implementation of the project preparatory technical assistance under the ADB PPTA-8800 (PAK) for the project entitled “Balochistan Water Resources Development Project (BWRDP)” under the guidance of the Government of Balochistan Irrigation Department (BID). GOB is the executing agency (EA) for the subject PPTA. ADB is now assisting the EA in implementing the PPTA through TCI. 9

32. This part of the report consists of Climate Risk and Vulnerability Analysis for two river basins selected for this project. These river basins include Zhob and Mula. Terms of reference for the work are stated below:

33. The Specialist will collect, review, and analyze relevant agro-climatic data, and reports on climate change particularly those related to Balochistan, and examine the impact of climatic change with respect to:

a. increase in evapotranspiration resulting from higher temperatures and hence in irrigation water requirements b. increase in high intensity rains that may require increase in design peak flood and possible increase in capacity of the hydraulic structures c. more frequent droughts d. changes in the time of occurrence of rains.

34. The available information was evaluated, and stakeholders are being consulted to develop reasonable quantitative and qualitative assumptions about the climatic change and its impact. This was followed by adaptation assessment including technically feasible adaptation solutions and associated cost and benefits.

35. Adaptation options included the followings:

• Engineering options • Non-engineering options • Biophysical options.

36. The “do nothing” option was also considered in the light of technical and economic analysis of the adaptation options.

1.6 The Context of Climatic Variability

37. Under the National Climate Change Policy (NCCP), Climate Change Division, Government of Pakistan, formulated a report titled “Framework for Implementation of Climate Change Policy (2014 - 2030)” in November 2013. It is expected that this document will serve as a foundation to be used to prepare the detailed provincial and local climate change adaptation action plans in the future.

38. To address the impact of climate change on water resources and to help in enhancing water security, the plan of action was suggested for the province of Balochistan and shown in Table 2. Implementation of each action proposed has been designed into following four-time frames.

a. Priority Actions (PA) : within 2 years b. Short term Actions (SA): within 5 years c. Medium term Actions (MA): within 10 years d. Long term Actions (LA): within 20 years

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Table 2 - Climate Change adaptation policy for Balochistan as suggested in “Framework for Implementation of Climate Change Policy 2014 - 2030” (GOP, 2013) Project Team Strategy Action Timeline Suggestion • Identify areas for building new Proposed to be included rainwater harvesting in the project as part of infrastructure for irrigation and Watershed and range household including land management Development of local rainwater Balochistan and other interventions. Short harvesting measures vulnerable areas. term • Initiate programs aimed at promoting the use of flood water for irrigation in Balochistan. Protecting groundwater through Suggested and proposed management and technical in the project Construct delayed action and measures like regulatory Medium check dams for groundwater frameworks, water licensing, term recharging artificial recharge especially for threatened aquifers. Legislating and Enforcing laws Strong recommendations Cap the subsidy given to and regulations required for to be included in the agriculture tube wells and ban efficient water resource project reports installation of new in most priority management, conservation and threatened aquifers in groundwater regulatory Balochistan. framework. Encouraging farmers, Suggested • Develop and introduce climate particularly in rain-fed areas, to resilience late crop varieties in avoid monoculture and plant a Short Balochistan variety of heat and drought term • Introduce low delta crops, at resistant crops, to reduce the large scale, in Balochistan. risk of crop failure. Encouraging farmers to adopt Suggested agriculture drought Establish special financial grant management practices as part mechanism for farmer’s priority of highly variable climate, rather community in Balochistan. than as unusual natural disaster. • Set-up pilot project for Suggested demonstration and Improving crop productivity by introduction of high yielding increasing the efficiency of crop varieties in Balochistan. Short various agricultural inputs, in • Set-up pilot project for term particular the input of irrigation demonstration of water water. conservation techniques to the farmers of Balochistan. Provide incentives to Suggested Balochistan’s farmer for Improving farm practices by Short development of water channels, adopting modern techniques. term dairy sheds and market linkages. Undertake further strengthening Suggested Developing models for of Plant Protection Department Short assessment of climate change (PPD) particularly its activities term impacts on agricultural in Balochistan. 11

Project Team Strategy Action Timeline Suggestion production systems in all agro- ecological zones. Developing quality datasets on Suggested crop, soil and climate-related Enhance the present limited parameters to facilitate capacity of institutions in Short research work on climate Balochistan to promote tissue Term change impact assessment and culture. productivity projection studies. Enhancing research capacity of Suggested and relevant organizations to make Establish close collaboration performed in this study to reliable climate change between academic and some extent projections to assess the research organizations Short corresponding likely impacts on /institutions in Balochistan Term various agriculture products and particularly dealing in develop appropriate adaptation agricultural areas. measures. • Undertake remote sensing GIS techniques have mapping of existing and future been used in this study. Developing “Remote Sensing land use planning in Capacity building and GIS techniques” capacity to Short Balochistan. programs should include assess temporal land cover Term RS and GIS trainings changes. • Establish GIS laboratory for relevant sectors in Balochistan. Arrange awareness material, Highly recommended campaigns and exposure Enhancing the capacity of the workshops in all provinces farming community to take particularly in Balochistan for advantage of scientific findings priority Information dissemination to of the relevant research farmers about climate change organizations. threats.

• Set-up system to control the Suggested illegal import of pesticides and Reducing greenhouse gas for applying quarantine emissions through improved measures at dry/sea ports Short management and techniques in particularly in Balochistan. Term agriculture and livestock sector. • Proper disposal of obsolete pesticides/chemicals in Balochistan be ensured. Improving awareness of issues Develop drought adaptability of Suggested related to mitigation of climate the communities living in Short change induced disasters county’s drought prone areas Term through public participation. particularly in Balochistan. • Identify the drought vulnerable Performed to some extent areas in Balochistan and in this study Developing hazard zoning and develop mitigation strategies mitigation strategies through for vulnerable communities. Short management, formulation and • Undertake risk mapping of all Term enforcement of regulation and vulnerable areas in laws. Balochistan. • Update the flood plain maps for 100 years return period. 12

Project Team Strategy Action Timeline Suggestion Promoting development of Suggested renewable energy resources Identify potential wind corridor and technologies such as solar, in different parts of Balochistan Short wind, geothermal, small- for installing wind power Term hydropower and bio-fuel projects. energy.

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2 Methodology

39. The methodology adopted to evaluate the impacts of climate change on two selected river basins for this study is outlined in Figure 3 All possible meteorological data available for both river basins were collected and evaluated using different statistical tests. Simultaneously, hydrological modeling was done to obtain the water balance components that are further used in extreme scenario evaluation and hydraulic modeling. Future climate projection has been done using GCM and hydrological modeling. Inputs of all above mentioned analysis is used to evaluate the vulnerability of both river basins to possible climate changes and relevant adaptation measures are suggested that would help in reducing the adverse effect of possible climate change. Moreover, cost evaluation has also been done for development projects with and without the adaptation measures so that cost effective choices can be made.

Figure 3 - Brief outline of the study methodology

2.1 Description of Data Used in the Study

40. Available climatic data use in the study comprise of 9 meteorological stations, 6 in Zhob river basin and 3 in Mula river basin. The meteorological stations located in the Mula river basin are: Khuzdar, Kalat and Gandawa. The six meteorological stations located in the Zhob river basin are: Zhob Airport, Qila Saifullah, Badin Zai, Muslim Bagh, Murgha Kibzai, Sharan Jagozai. The later three stations have only precipitation data available; however, all other stations have both temperature and precipitation data. Duration of data is different for different stations. Details of the metrological data are provided in Table 3. 14

Table 3 - List of available meteorological stations, and data length for Zhob and Mula river basins of Balochistan River Meteorological Temperature Data Length Rainfall Data Length (Years) Basins Station (years) Khuzdar 1986-2015 (29 years) 2005-2015 (10 years) Mula Kalat 1986-2011 (25 years) 2002-2015 (13 years) Gandawa 1986-2015 (29 years) 2002-2015 (13 years) Zhob 1960-2011 ( 51 years) 1992-2015 (23 years) Qila Saifullah 1994-2015 (21 years) 2002-2015 (13 years) Badinzai 1990-2015 (25 years) 2002-2015 (13 years) Zhob 1901-1946 and 1969-1993 (69 Muslim Bagh - years) Murgha Kibzai 1911-1946 (35 years) - Sharan Jagozai 1975-1993 (18 years) -

2.2 Statistical tests

41. The benefits of using statistical tests for accurately evaluating trends in precipitation and temperature are well documented. However, Balochistan has complex topography and scarce meteorological information which makes interpretation of climatic behavior difficult (Ahmed et al., 2014). In this study, different statistical tests are used to understand the trend in climatic parameters like precipitation and temperature in Zhob and Mula river basins. Climatic data used for the tests is presented in Table 3. Tests used in this study include Linear Regression, 3-year Moving Average, and Man-Kendall trend test. Brief description of the background of the statistical tests is mentioned in Annexure C.

2.3 Hydrological Modeling

42. It is essential to assess the amount of water available in a river basin. In recent years, computer simulation modeling has become an effective approach for water resources management. Various types of surface water models are available to attain certain outputs. Some of the popular surface water models Hydrologic Simulation Program – Fortran (HSPF), Agricultural Non-point Source (AGNPS), Annualized Agricultural Non-point Source (AnnAGNPS), Gridded Surface Hydrologic Analysis (GSSHA), KINematic Runoff and EROSion (KINEROS2), Precipitation-Runoff Modeling System (PRMS) (USGS), DRAINMOD, Soil and Water Assessment Tool (SWAT) (USGS), MIKE 11, HEC-River Analysis Simulation (HEC-RAS), Storm Water Management Modeling System (SWMMS), etc.

43. All of these models have different requirements and qualities such as inputs, outputs and require expertise to run the model. Therefore, the selection of a model becomes very crucial for a study where it can provide the best outputs to achieve the objectives. Also, some of these models are user friendly and easy to use whereas some are complex. Surface water (Hydrological) model used for this study is SWAT.

44. Quantification of surface water availability depends on accurate estimation of the water balance components. Precipitation, as input to the river basin, gets divided into different 15

components as it reaches the surface of earth. These components are infiltration, evapotranspiration, surface runoff, deep percolation, and base flow. The water balance components have a specific range of percentage out of the total precipitation. Equation 1 shows the general ranges of these components.

Precipitation = Evapotranspiration + Surface Runoff + Base flow + Deep Percolation (1) 100% 50-60% 10-15% 30-35% 2-4%

45. Each water balance component varies based on the characteristics of the river basin. For example, for a steeper river basin the percentage of surface runoff would be higher as compared to a flatter river basin. Similarly, arid areas have more evapotranspiration as compared to semi- arid or humid regions.

46. A hydrologic model can simulate the river basin processes such as surface runoff, evapotranspiration, deep percolation, etc., by using climatic data (temperature, precipitation, humidity, etc.), Digital Elevation Model (DEM), channel properties, and land use layer as minimum requirements. These inputs are required to simulate various processes in the model to obtain water balance of the area.

2.3.1 SWAT (Soil and Water Assessment Tool)

47. SWAT is river basin or river basin scale model developed by Arnold et al. (1998) for the USDA Agricultural Research Centre (ARC), Blackland, Texas, US. The model is effective in predicting the impact of land management practices on water, sediment and agricultural fields with varying soils, land use, and management conditions over a long period of time.

48. The hydrologic cycle as simulated by SWAT is based on the water balance shown in equation 2.

t SWt = SW0 + ∑i=1(Rday − Qsurf − Ea − wseep − Qgw) (2)

Where; SWt=Final soil water content (mm) SW0= Initial soil water content on day i (mm) Rday=Amount of precipitation on day i (mm) Qsurf= Amount of surface runoff on day i (mm) Ea= Amount of evapotranspiration on day i (mm) wseep= Amount of water entering the vadoze zone from the soil profile on day i (mm) Qgw= Amount of return flow on day i (mm)

49. Potential Evapotranspiration in the model is calculated using different methods; the method selected for this study is the Penman-Monteith method. SWAT calculates surface runoff using the SCS method as well as the Green-Ampt infiltration equation presented by Eq. 3. 16

SCS-Method

ퟐ (퐑퐝퐚퐲−퐈퐚) 퐐퐬퐮퐫퐟 = (3) (퐑퐝퐚퐲−퐈퐚+퐒)

Where; Qsurf=Accumulated runoff or rainfall excess (mm) Rday=Rainfall depth for the day (mm) Ia= Initial abstraction (mm) S= Retention parameter (mm)

50. Green-Ampt Infiltration Equation presented Eq. 4 is used for the quantification of infiltration in SWAT.

횿퐰퐟.횫훗퐯 퐟퐢퐧퐟,퐭 = 퐊퐞. [ퟏ + ] (4) 퐅퐢퐧퐟,퐭

Where; finf,t=Infiltration rate at time t (mm/hr) Ke=Effective hydraulic conductivity (mm/hr) Ψwf= Wetting front potential (mm) Δφv= Change in volumetric moisture content across the wetting front (mm/mm) Finf,t= Cumulative infiltration at time t (mm)

51. Peak Runoff Rate is calculated using rational method which is given by Eq. 5.

퐂.퐢.퐀퐫퐞퐚 퐪 = (5) 퐩퐞퐚퐤 ퟑ.ퟔ

Where; 3 qpeak=Peak runoff rate (m /s) C=Runoff coefficient i= Rainfall intensity (mm/hr) Area= Sub basin area (Km2) 3.6= Unit conversion factor

52. Balochistan province has the largest percentage of area in Pakistan. It has land covered mostly with mountains having an arid climate with variable precipitation amounts in different regions of the province. The approximate water balance components (as simulated by the model) based on the specific conditions of Zhob and Mula river basins are presented in Table 4 and Table 5, respectively. The specific water balance components have been obtained by the calibration of hydrological model. The surface runoff component for Mula river basin is above the usual range. This is due to the fact that most of the part of the river basin is very steep that increases the total 17

amount of surface runoff; however, the flow rate decreases at the lower end of the river basin due to flatter slope.

Table 4 - Calibrated Water balance for Zhob river basin of Balochistan Annual Average Water Balance Component Percentage Volume (MCM) (mm) Precipitation 265 100 3566 Evapotranspiration 146 58 2068 Runoff 40 12 428 Deep Percolation 5 2 71 Base Flow 74 28 999

18

Table 5 - Calibrated Water balance for Mula river basin of Balochistan annual average Water Balance Component Percentage Volume (MCM) (mm) Precipitation 135 100 2000 Evapotranspiration 77 57 1100 Runoff 16 12 240 Deep Percolation 1.4 1 200 Base Flow 41 30 590

53. Calibrated hydrological models can provide a good picture of the existing scenario of the selected river basins, and can consequently help in estimating the potential of the area for development. Such models can also help in analyzing any climate change effect on the river basin, and hence help in assessing adequate adaptation measures. Following main hydrologic/hydraulic analyses are needed to be performed in order to achieve the main objective of this study:

1. Surface water assessment by using hydrologic models. 2. Extreme climatic event modeling, mapping, and analysis to identify existing prone areas.

54. Hydrological modeling mainly constitutes the steps of simulation, sensitivity analysis, and calibration. Sensitivity analysis helps in identifying the most sensitive parameters on which model output is dependent. Once identified, these parameters are used to calibrate the model.

2.4 Extreme Event Analysis

55. With an estimated population of over 190 million, Pakistan would face food crises in the years to come, if adequate water supplies were not available to increase crop production. The country was facing the most severe droughts that occurred during the years 2001–2002 and prolonged to 2005–2006. It has been reported that the annual per capita water availability has already decreased from 5600 m3 at the time of independence to 1014 m3 in 2016 in the country. The per capita water availability is based on the total water availability of 192.5 billion m3 covering water from the Indus river system both western and eastern rivers and rivers outside the Indus basin directly draining to the Mekran coast and the close basins of Balochistan (Ahmad 2016).

56. The situation is alarming, and it not only warns to aptly utilize all the possible surface and groundwater resources but also to optimally manage, conserve, and use them (Garg and Ali, 1998). Droughts are common occurrence in Balochistan. Early winter droughts are frequent in the north of Balochistan (Hanif et al., 2013). Early summer droughts occur more frequently in the east, and late summer droughts occur in the northeast. Rabi droughts are more frequently in the central and north-eastern regions of Balochistan, while more severe Kharif droughts occur primarily in the eastern regions (Ahmed, 2016). Extreme climatic events like droughts and floods cannot be avoided but their impact can be reduced by adopting a pro-active approach. Preparation against such extreme events is only possible if knowledge of the extent of damage 19

they can cause is recorded accordingly and taken into consideration while designing infrastructure.

57. Extreme event analysis has been performed using the output of hydrological model. Metrological data for three different stations of Zhob river basin (Zhob, Qila Saifullah, and Badin Zai) and three stations of Mula river basin (Khuzdar, Kalat, BadinZai) was input into the hydrological Model (SWAT), separately. SWAT distributes the rainfall data throughout the river basin by Thiessen polygon method and generates one time-series for the data. The duration of data ranges from 1982 to 2014. The model generated one single time series was used to perform the extreme event analysis. Five of the highest rainfall (flood scenario) and 5 of the lowest precipitation (drought scenario) years have been identified and their respective water balance have been estimated. Moreover, water use was compared with the water availability in average, flood, and drought years. This is done to evaluate the excess water or water shortage. Comparison has been performed for both groundwater and surface water. Groundwater value used for the analysis is the quantity of recharge expected, not the total water in the aquifer.

2.5 Hydraulic Modeling

58. The hydraulic model chosen for this study is HEC-RAS. Using channel properties and amount of water, this model simulates streams and consequently, overflow inundates the river banks. This model is capable of channel flow analysis and flood plain determination. It includes numerous data entry capabilities, hydraulic analysis components, data storage and management capabilities, and graphing capabilities. It models the hydraulics of water flow through natural rivers and other channels. In a study done by Maidment and Tate (1999) floodplain modeling was performed using HEC-RAS hydraulic model on Waller Creek, Austin, Texas. It enlightened that Geographical Information System (GIS) is an effective environment for flood plain modeling, and HEC-RAS provides good representation as an outcome with improved visualization.

59. HEC-GeoRAS is set of ArcGIS tools designed to process geospatial data for use with HEC- RAS. HEC-GeoRAS creates a file of geometric data for import into HEC-RAS and enables viewing of exported results from RAS. The import file is created from the data extracted from ArcGIS layers and Digital Elevation Model (DEM). The layers and DEM are referred to collectively as RAS layers. Geometric data are developed based on intersection of these RAS layers. This model is capable of modeling subcritical, supercritical, or mixed flow regimes. Hydraulic calculations are performed at each cross section to compute water surface elevation, critical depth, energy grade elevation, and velocities.

60. Relevant GIS layers of the river basin are imported to HEC-GeoRAS. HEC-GeoRAS works as the interface to develop GIS based HEC-RAS readable input dataset. Cross sections and river banks are marked, and channel properties are defined to the software for each cross section. Hydraulic modeling has been used in the climate change section to create flood inundation maps. These maps are drawn based on the 5 highest rainfall years for both the river basins – Zhob and Mula. The hydraulic model output was provided with stream flows at every junction of the stream channel along with the cross-sectional properties of the channel which resulted in relevant flood inundation maps. These maps are used in further vulnerability analysis. 20

2.6 Vulnerability Analysis

61. Definitions of vulnerability in the climate change related literature tend to fall into two categories viewing vulnerability either (i) in terms of the amount of (potential) damage caused to a system by a particular climate-related event or hazard (Jones and Boer, 2003), or (ii) as a state that exists within a system before it encounters a hazard event (Allen, 2003). Vulnerability analysis in this study is based on these two basic definitions.

62. Vulnerability analysis for BWRDP is done primarily based on the trends in the climatic variables (temperature, and precipitation) and their impact on the area. Moreover, flood inundation maps have been generated using the hydraulic model that are over laid the available features like cultivated area, roads, settlements etc., and the number of features/infrastructure being affected by the flood is calculated and documented. Water availability, water stress, and surplus calculations for drought, and flood conditions have also been done to assess the sensitivity of the river basin to extreme climatic events. 21

3 Results of Climate Change Analysis

3.1 Zhob River Basin

3.1.1 Statistical tests

3.1.1.1 Temperature trends

63. The seasonal trend analysis was undertaken for both maximum and minimum temperature. The statistical test performed include Man-Kendall (MK) and Linear Regression (LR). MK trend was verified by comparing the values of Kendal Tau and Sen’s slope. If P value is greater than 0.05 it means there is no trend in the data set. Sen’s slope is the slope of data set without outliers. This means that the software ignores any extremely high or low value in the data set then draws a trend line. However, Kendal Tau represents the real trend in the data set. Therefore, the signs of both variables represent the slope of the trend line, and its magnitude represents the degree of that change. Results of MK are preferred over LR since the former is more conservative in terms of analysis. Daily data analysis was not undertaken due to data limitation.

64. The data have multiple limitations. Foremost, daily data were not available for any of these stations, and the timeline of available data was not same for all the stations either. Therefore, the analysis was limited to the data availability. Annexure D-Results of statistical tests

65.

66. Table D-1 shows the results of temperature trend analysis on three different stations of Zhob river basin. These stations included Zhob Airport, Qila Saifullah, and Badinzai.

67. The winter maximum temperature showed contrasting result. Decreasing trend was obtained by linear regression analysis; whereas, MK analysis did not show any trend. Negative sign of Kendall tau and Sen’s slope are ignored in this case since the ‘p’ value is greater than 0.05. Unlike winter maximum temperature, minimum temperature in winter showed an increasing trend with linear regression but no trend by MK test. Summer minimum temperature showed a decreasing trend which was evident in the results of both the tests. Overall, temperature trend analysis for Zhob airport station showed no significant trend in any of the parameters other than a decreasing trend in summer minimum temperature. Other than spring, summer, and autumn minimum temperature which showed no trend as shown by MK trend test result, all other seasons showed an increasing trend at Qila Saifullah Station.

68. None of the seasons showed any trend in the temperature for Badinzai station. This is evident from the ‘p’ value which was greater than 0.05 for both minimum and maximum temperatures for all seasons in the area. Linear regression results were ignored unless they were in line with MK test results (Table 6). A dominant trend of increasing maximum temperatures had mean higher rates of evapotranspiration (ET) and shorter growing seasons in the area. Although, other stations of Zhob river basin have not showed any significant trend, significance of increasing 22

temperature in Qila Saifullah was a red flag showing the possibility of need of certain measures to cope with hotter climate in near future.

3.1.1.2 Rainfall trends

69. Rainfall analysis was undertaken on monthly, annual, and seasonal time lines. Data for rainfall is analyzed for 6 stations in the Zhob river basin. These stations include: Zhob Airport, BadinZai, Qila Saifullah, Muslim Bagh, Murgha Kibzai, and Sharan Jagozai. Summer rainfall showed an increasing trend at Zhob airport station. However, there was no significant trend in rainfall for the rest of the seasons as well as in monthly and annual analysis. Similarly, there is no trend in rainfall for Qila Saifullah and BadinZai stations either.

70. Data for Muslim Bagh station was divided into two time series, i.e. 1901 to 1946 and 1969 to 1993. Trend analysis was performed for both data sets using both MK and LR tests. In the earlier part of the data (1901 to 1946) there is an increasing rainfall in yearly, monthly, and winter periods. However, in the later part of the data (1969 to 1993), a decreasing trend was observed. Daily and monthly time lines showed a decreasing trend in rainfall at Muslim Bagh station for the period of 1969 to 1993. Therefore, it could be inferred that in the earlier part of the 20th century, the area experienced an increase in rainfall; however, for the later part of rainfall data showed a decreasing trend. This sinusoidal behavior of climatic variables was a typical characteristic of the data observed in many different parts of the world. Similar to Muslim Bagh and Murga Kibzai station data also showed an increasing rainfall trend in the earlier part of the century, i.e. 1911 to 1946. Data for later part of the century was not available at this station.

71. Sharan Jagozai station data was available for the years 1975 to 1993. The data showed a decreasing trend in daily and summer rainfall. This trend was similar to the one observed at Muslim Bagh station for the same time line. Overall, for later period of the 20th century, the frequency of a negative trend superseded the positive trend, so it can be concluded that there is more possibility of a decrease in the rainfall in the Zhob river basin. There is a need to implement water management techniques those will help in saving rain water more effectively to increase resilience of the local population against decreased rainfall or expected drought spells.

72. Temporal trend of rainfall for the Zhob river basin for all three stations (Zhob Airport, Badinzai, and Qila Saifullah) using the LR analysis Figure 4The length of data for Zhob Airport was the largest; it depicted an increase in rainfall amount in 25 years of data. Although, Badinzai data were short (15 years), they clearly showed that there was a significant increase in rainfall in the recent years. However, Qila Saifullah station showed slight decrease in 15 years of data. Overall it was observed that rainfall trend was very different across the river basin. 23

Zhob River Basin Annual Rainfall - Linear Regression Linear (Zhob airport) Linear (Qila Saifullah) Linear (Badinzai) 350 300 250 200 150 100

Precipitation (mm) Precipitation 50 0 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 Year

Figure 4 - Trend analysis of rainfall data for the Zhob river basin of Balochistan

73. The rainfall data for the Zhob river basin was further analyzed to evaluate the trend of the data set. A moving average is a method to have an overall idea of the trends in a dataset by averaging of any subset of numbers. The method is useful for forecasting long-term trends and it smooths the data set. Also, smoothing reduces the variance of the data set, and the longer the smoothing interval, the greater the reduction in variability. The results of 3-year moving average for rainfall for Zhob river basin are presented in Figure 5 The comparison of three datasets showed that the trend was similar for the most of the years. However, the magnitude of data points varied throughout the dataset for the same years. Also, Badinzai rainfall data authenticated the increasing trend after 2011. Due to the lack of data for the recent years, the increasing trend for the other two stations could not be confirmed. 24

Zhob River Basin Annual Rainfall - Moving Average 3 per. Mov. Avg. (Zhob airport) 3 per. Mov. Avg. (Qila Saifullah) 3 per. Mov. Avg. (Badinzai) 500 450 400 350 300 250 200 150 100

Precipitation (mm) Precipitation 50 0 1985 1990 1995 2000 2005 2010 2015 Year Figure 5 - Results of 3-year moving average of rainfall in the Zhob river basin

3.1.2 Extreme event analysis

74. Extreme event analysis was performed using the output of hydrological model. Metrological data for three different stations (Zhob, Qila Saifullah, and Badin Zai) were input into the hydrological model (SWAT). SWAT distributed the rainfall data throughout the river basin by Thiessen polygon method and generated one time series for the data. The duration of data was from 1982 to 2014.

75. The model-generated one single time series was used to perform the extreme event analysis. Five of the highest rainfall (flood scenario) and 5 of the lowest rainfall (drought scenario) years were identified and their respective water balance was calculated. 25

76. Skewness and Kurtosis were also calculated for the rainfall data used by the model. This

1500

1000

500 Frequency

0 0 5 10 15 20 25 30 35 Rainfall (mm)

Figure 6 - Histogram of rainfall (mm) for the Zhob river basin of Balochistan analysis was undertaken on daily rainfall data series generated by the model. Skewness quantifies the symmetry of the data, while kurtosis showed whether the shape of data distribution matches with Gaussian distribution. For the analysis, non-zero values were discarded because out of total 11,901 data points 75% are zero, which will introduce biasness in the results. This also showed that, on an average, Zhob district received rainfall every fourth day. Pearson skewness and Person Kurtosis for Zhob were 4.1 and 27.5, respectively. In addition, skewness of 4.1 showed that the distribution of data was asymmetrical with a long tail to the left. Kurtosis value of 27.5 showed that the data distribution was higher peak than a Gaussian distribution. Figure 6 shows the histogram of the same data. An extreme event of 69 mm rainfall is not shown in Figure 6

3.1.2.1 Flood scenario

77. The values of the 5 highest rainfall years and respective water availability (surface and groundwater) are shown in Table 6. The year having the highest rainfall amount was 1982 with 601 mm of rainfall. The rainfall of four years, 1982, 1984, 1985, and 1986, were responsible for consecutive occurrence of the flood conditions in the area. After these consecutive floods, 2010 showed a high-flow year. This can be concluded that there was a possibility of recurring flood for more than one year in a row in the area, and also a long gap until the next flood year.

Table 6 - Surface and groundwater availability under five flood years in the Zhob river basin of Balochistan Annual Surface Year Annual Precipitation Groundwater Precipitation Water mm (MCM) 1982 601 13300 1600 266 1984 352 7800 936 156 1985 388 8590 1030 172 1986 367 8130 976 163 2010 559 12400 1490 248

78. Annual rainfall in millimeters (mm) was changed into volume by multiplying the depth of rainfall with the area of the river basin. This is an approximate value. Moreover, surface water and 26

groundwater quantities were also calculated in the unit of Million Cubic Meters (MCM). This was done by using the approximate proportion of surface water and groundwater in the total water balance equation for Zhob river basin. These calculations were estimated to compare and calculate the water availability/excess in flood scenario results, and will be used in the later part of the report.

3.1.2.2 Drought

79. Drought scenario has been identified as the 5 lowest rainfall years and was generated by the hydrological model SWAT based on similar methodology as mentioned for flood scenario. Results of the 5 drought years identified and results are shown in Table 7. Surface water and groundwater calculations have been undertaken for every single drought year that has been identified. Most extreme drought year identified in this analysis was 2000 with annual rainfall of only 85 mm. Also, years 2000, 2001, and 2004 were closely spaced drought years. Before these recurrent drought years, a drought occurred 7 years back in 1993 with annual rainfall of 133 mm, and the later one after 10 years in 2014 with annual precipitation of 146 mm (Table 7). This shows the possibility of recurrent drought years which can cause extreme stress on the local population.

Table 7 - Surface and groundwater availability under 5 drought years in Zhob river basin of Balochistan Surface Annual Rainfall Annual Rainfall Groundwater Year Water (mm) (MCM) (MCM) (MCM) 1 1993 133 2950 354 59 2 2000 85 1880 226 38 3 2001 114 2530 303 50 4 2004 149 3300 396 66 5 2014 146 3230 388 65

3.1.3 Water availability in extreme years of Zhob river basin

80. Floods and droughts cannot be predicted or controlled; however, being prepared can minimize the effect of damage they can cause or can be managed with enhanced utilization of excess water either for storage in groundwater or in reservoirs. First step to preparedness is knowledge of the extent of damage that is possible. This part of the report presents the water availability in terms of surface water and groundwater and the excess or shortage of water in both flood and drought scenarios in Zhob river basin.

81. Water availability was calculated by taking the most extreme years (wettest and driest) for both flood and drought events. Water availability under the wettest and driest years for both surface and groundwater was estimated and presented in Figure 7 and 8, respectively. 27

Internally Generated Foodwater in Extreme Flood, Median and Extreme Drought years (MCM) in Zhob River Basin

Drought Year 226

Median Year 325

Flood Year 1600

0 200 400 600 800 1000 1200 1400 1600

Figure 7 - Internally generated floodwater in extreme flood, median and drought years in Zhob river basin of Balochistan.

82. The internally generated floodwater in the Zhob river basin during the extreme flood year was 1600 MCM, whereas it was reduced to 325 MCM in a median year and further reduced to 226 MCM in the extreme drought year Figure 7 These are the hydrologic limitations while designing water development interventions and responding to climate change adaptations.

83. The groundwater recharge for the Zhob river basin was also computed using extreme flood, median and extreme drought years of 266, 175 and 38 MCM, respectively Figure 8This showed an extreme variability in groundwater recharge in a wet, median and dry years. In the dry years, the contribution of groundwater was increased in agriculture due to increased abstractions, whereas in the wet year the contribution of rainfall and localized runoff to agricultural crops increased resulting in reduced dependence on groundwater. This analysis indicated that recharge to groundwater can be enhanced in the wet years to reduce the risks of drought years. 28

Groundwater Availability under Extreme Flood, Median and Drought Years in Zhob River Basin

Drought Year 38

Median Year 175

Flood Year 266

0 50 100 150 200 250 300

Figure 8 - Groundwater recharge in extreme flood year, median year and drought year in Zhob river basin of Balochistan.

3.1.4 Above and below average rainfall events

84. Annual Average rainfall for the Zhob river basin was calculated as 267 mm. To further analyze the data, all the yearly data having a total annual rainfall amount more or less than the annual average have been separated and presented in Figure 9 This means that there is more probability of having water shortage or drought than the probability of having floods. Moreover, despite having less probability of occurrence, magnitude of flood events can vary much higher than the average rainfall as compared to the magnitude of below average rainfall years. This means that rare but high magnitude flood events can be expected in the river basin that can cause heavy damage to the local population if preparation for flood/drought conditions in terms of water management is not done accordingly to save the livelihood in the study area.

Above and Below Annual Average Rainfall Years - Zhob River Basin Above Average Below Average 800

600

400 y = -0.6828x + 1714.3

200 y = -0.2021x + 590.1 0

Precipitation (mm) Precipitation 1980 1985 1990 1995 2000Year 2005 2010 2015 2020 Figure 9 - Annual rainfall years having values above and below annual average rainfall of Zhob river basin of Balochistan. 29

3.1.5 Statistical tests

3.1.5.1 Temperature trends

85. Similar to the temperature trend analysis done for Zhob river basin, temperature data for Mula river basin was analyzed using Linear Regression (LR) and Man-Kendall (MK) trend test. Results of temperature trend test are shown in Table D-3. At Khuzdar, summer and autumn max temperature showed a decreasing trend using MK test. However, summer min temperature was increasing. Results for summer season were supported by LR results. All other variables did not show any significant trend. Temperature at Gandawa station did not show any trend for any season. For Kalat station, a decreasing trend was observed for winter min, spring max, and summer max. Summer min temperature showed an increasing trend.

86. Reason of obtaining no trends in data could be the shortage of daily time series data. Therefore, not obtaining any trend did not necessarily mean that there was no climate change. In fact, due to the shortage of data statistical tests were not detecting any major trend in temperature at Gandawa station. Frequency of variables showing decreasing trend was more than the frequency of increasing trend in Mula river basin. Moreover, decreasing trend was mostly observed for maximum temperatures of summer, autumn, and spring. It was also observed that winter minimum temperature at Kalat station was decreasing. This might mean cooler winters around Kalat station. Increasing trend was only observed for summer minimum temperatures at Khuzdar and Kalat stations. This indicated that nights in this region are getting warmer and days are getting cooler. For Kalat, results for summer minimum and winter minimum temperatures were opposite to each other. There is a possibility of warmer summer nights and cooler winter nights in Kalat in future.

3.1.5.2 Precipitation trends

87. Rainfall trend analysis was undertaken for monthly, annual, and seasonal time lines using data for three stations, Khuzdar, Kalat, and Gandawa. Trend analysis results are shown in Table D-4. Shortage of data was a limitation for analyzing rainfall trend analysis for Mula river basin too. Most of the variables did not show any trend, but the ones that show trend were critical. This means that if variation occurs in those variables it would be a concern. Winter rainfall showed a decreasing trend at Khuzdar station, and summer rainfall showed a decreasing trend at Gandawa station. Autumn precipitation at Kalat station showed an increasing trend but that was not important because the region barely received any rainfall in autumn.

88. In summer season the study area received some rainfall from monsoon and in winters through the westward winds. These are the two major seasons in which rainfall is expected in the area. Both the seasons showed a decreasing trend which is an alarming situation. These results were verified by both MK and LR trend test. One inference can be made regarding decreasing summer rainfall and increasing autumn rainfall that the monsoon rainfall has shifted temporally. This indicated that the monsoon is now reaching this region later during these years as compared to when it used to. This can be only verified if rainfall data for closer metrological stations give 30

similar results. However, decreasing trend in summer and winter rainfall in Mula river basin require attention.

89. The rainfall data for Mula river basin was also analyzed to evaluate the temporal and spatial trendsFigure 10 depicts temporal trend for the three stations for Mula river basin using the LR. It clearly indicated that two stations (Khuzdar and Kalat) showed a decreasing trend for the study period. However, Gandawa station showed an increasing trend. It also showed the significant climatic variability in the river basin. In addition, it showed that the results of 3-year moving average of rainfall for Mula river basin are presented in Figure 11This method was found useful for forecasting long-term trends and it smoothed the data set by reducing the variance of the data set. The comparison of three datasets showed that the trends were not similar for most of the years. Also, the magnitude of data points varied throughout the dataset for the same years. Also, Gandawa station had steady increasing rains from 2003 to 2010; however, rainfall decreased in recent years. In addition, Khuzdar rain has recently shown the increasing trend. The recent trend for Kalat can’t be confirmed because of the lack of recent data.

Mula watershed annual rainfall-linear regression Linear (Gandwana) 350 Linear (Kalat) 300 250 Linear (Khuzdar) 200 150 100

Precipitation (mm) Precipitation 50 0 1985 1990 1995 2000 2005 2010 2015 Year

Figure 10 - Linear regression trend analysis on rainfall data for Mula river basin of Balochistan

31

Mula River Basin Annual Rainfall - Moving Average 3 per. Mov. Avg. (Gandwana) 3 per. Mov. Avg. (Kalat) 3 per. Mov. Avg. (Khuzdar) 500 450 400 350 300 250 200 150 100

Precipitation (mm) Precipitation 50 0 1985 1990 1995 2000 2005 2010 2015 Year Figure 11 - 3-year moving average for rainfall in Mula river basin of Balochistan.

3.1.6 Extreme event analysis

90. Extreme event analysis for Mula river basin was undertaken in a similar way as Zhob river basin. Non-zero values for Mula river basin were 73.6% out of total 11900 points. Pearson skewness and Pearson Kurtosis for the data were calculated as 6.9 and 63.5, respectively. As compare to Zhob river basin, Mula river basin rainfall data have longer tail, and also had higher peak. Longer tail to the left and higher peak showed that in Mula the rainfall events were of very little rainfall depth. Figure 12Howed histogram of rainfall for Mula river basin, where 74.5% of the total rainfall events were having rainfall depth of 0-1 mm.

Histogram Rainfall (mm) of Mula River Basin 2500

2000 1500

1000 Frequency 500 0 0 5 10 15 20 25 30 35

Rainfall (mm)

Figure 12 - Histogram of rainfall (mm) for the Mula river basin of Balochistan.

32

3.1.6.1 Flood

91. Model generated single time series rainfall data were utilized for flood analysis. From the available rainfall data, five highest rainfall years were identified as flood years. Quantity of surface and groundwater for all flood years were calculated and presented in Table 8. Results showed that three recurrent flood years (2007, 2008, and 2009) were experienced in Mula river basin with year 2007 having the highest magnitude rainfall of 248 mm. The flood occurred in 1997 which was 10 years earlier, and the next flood after recurrent year occurred in 2013 which was 4 years later. In addition, the highest magnitude flood was in 2013 with rainfall depth of 408 mm. This implies that floods have occurred in Mula river basin at all intervals i.e., 10-year difference, 4-year difference, or recurrent three years. Therefore, it can be assumed that floods in Mula river basin were extremely unpredictable and long-term preparedness is the best option to create resilience of local population against floods.

Table 8 - Surface and groundwater availability in flood years in the Mula river basin of Balochistan Annual Annual Surface Groundwater Year Precipitation Precipitation water (MCM) (MCM) (mm) (MCM) 1 1997 191 2800 336 28 2 2007 248 3600 436 36 3 2008 226 3300 397 33 4 2009 187 2700 329 27 5 2013 408 6000 717 59

3.1.6.2 Drought

92. Drought scenarios were identified as the 5 lowest rainfall years. The hydrological Model SWAT has generated the scenarios based on similar methodology as mentioned for flood scenario. Results of the 5 drought years identified are shown in Table 9. Surface water and ground water calculations were undertaken for every single drought year that has been identified. Most extreme drought year identified in this analysis was the year 2002 with annual rainfall of only 33 mm. All 5 drought years have an average difference of approximately 2 years in between them. Moreover, years 1999, 2000, and 2002 occurred very closely with very low annual average rainfall. This means that there is a strong possibility of expecting extreme drought conditions in recurrent years again in Mula river basin. Severity of drought was more intense in Mula river basin as compared to Zhob river basin since the annual rainfall amounts are much lower in Mula river basins than in Zhob river basin.

Table 9 - Results of drought scenarios in Mula river basin of Balochistan Annual Annual Surface Groundwater Year Precipitation Precipitation Water (MCM) (mm) (MCM) (MCM) 1 1992 68 996 119 10 2 1995 74 1080 130 11 3 1999 65 952 114 9 33

4 2000 41 600 72 6 5 2002 33 483 58 5

3.1.7 Water availability in extreme years of Mula river basin

93. The internally generated floodwater in the Mula river basin during the extreme flood year was 717 MCM, whereas it was reduced to 213 MCM in a median year and further reduced to 58 MCM in the drought year Figure 13These are the hydrologic limitations while designing water development interventions and responding to climate change adaptations.

Internally Generated Floodwater in Extreme Flood, Median and Extreme Drought Years (MCM) of Mula Rver Basin

Drought Year 58

Median Year 213

717 Flood Year

0 100 200 300 400 500 600 700 800

Figure 13 - Internally generated floodwater in extreme flood, median and extreme drought years of Mula River basin of Balochistan

94. The groundwater recharge for the Mula river basin was also computed using extreme flood, and extreme drought years of 59 and 5 MCM, respectively. This showed an extreme variability in groundwater recharge in wet and dry years. In the dry years, the contribution of groundwater increased in agriculture due to enhanced abstractions, whereas in the wet year the contribution of rainfall and localized runoff to agricultural crops increased resulting in reduced dependence on groundwater. This analysis indicated that recharge to groundwater can be enhanced in the wet years to reduce the risks of drought years.

95. Important thing to observe here was the difference between drought condition results for Zhob and Mula river basins. Surface water availability was more than the requirement for Zhob river basin; whereas, for Mula river basin drought was more severe because surface water availability was much lower. Groundwater recharge condition status was almost similar in both the river basins. The abstraction of groundwater is higher than the recharge to aquifer. Signs of depletion of groundwater are evident in both the river basins. To reduce the risks of droughts 34

efforts are needed to introduce groundwater recharge interventions in both the basin along with the development of surface water resource.

3.1.8 Above and below average rainfall events

96. Annual average rainfall calculated using Hydrological Model SWAT was 135 mm. Rainfall data were sorted for the years that had value above the annual average and also for the below annual average. The results are presented in Figure 14 The analysis showed that ratio of above average rainfall to below average is approximately 2:3. It can be concluded that the probability of water shortage is more than water surplus in Mula river basin. However, the frequency and magnitude of above average rainfall has been increased over the last 35 years. This was obvious from the circular data points (above average) that were more concentrated in the later part of the time series and also higher in magnitude.

Above and Below Annual Average Precipitation Years - Mula River Above Average Basin Below Average

450 400 350 300 y = 3.2431x - 6293.9 250 200 150

Precipitation Precipitation (mm) 100 50 y = -0.1743x + 433.03 0 1980 1985 1990 1995 2000 2005 2010 2015 Year

Figure 14 - Annual rainfall years having values above and below annual average rainfall of Mula river basin of Balochistan 35

3.2 Future climate projection using Global Circulation Model (GCM)

97. Use of Global Circulation Models for predicting future climatic changes have increased over the last decade (IPCC, Houghton et al., 2007). Multiple scenarios suitable to different areas of the world are available to predict the most realistic climate. A study conducted for the years 2040 to 2069 for the Karkheh catchment, a semi-arid region of Iran using two downscaling techniques, Statistical Downscaling Model (SDSM) and Artificial Neural Network (ANN). According to the projections, by year 2019, daily temperature will increase up to +0.58C (+3.90 %) and +0.48C (+3.48 %), and daily precipitation will decrease up to −0.1 mm (−2.56 %) and −0.4 mm (−2.82 %) for SDSM and ANN, respectively. Stream flow in the region also shows changes corresponding to the downscaled future projections. There is a reduction in mean annual flow of −3.7 cumecs and −9.47 cumecs using SDSM and ANN outputs, respectively. The results suggest a significant reduction of stream flow in both downscaling projections, particularly in winter (Samadi et al., 2013).

98. Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. Because general circulation models (GCMs) operate on a coarse scale, the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. A study was conducted on downscaling global monthly precipitation data to river basin scale for Malaprabha reservoir catchment, India, (Anandhi, 2007). The study area in the research is considered to be a climatically sensitive region. The results show that the precipitation is projected to increase in future for almost all the scenarios considered. Another study shows an increasing trend for maximum and minimum temperature for A1B, A2, and B1 scenarios, and the precipitation is projected to increase in the future for A2 and A1B scenarios, whereas no trend has been observed for pan evaporation in future in a semiarid region that is considered to be a climatically sensitive region in India (Goyal, 2012)

99. Few studies have been conducted for Pakistan to predict future climate change using GCM. A study conducted by Mahmood et al. (2013) for the Jhelum River basin, Pakistan and India, evaluated the statistical downscaling model (SDSM) for downscaling maximum and minimum temperature, and precipitation. This study also assesses future changes in climate in the Jhelum River basin. Changes in maximum, and minimum temperature, and precipitation are projected as 0.91 to 3.15%, 0.93 to 2.63%, and 6 to12 %, respectively. The values of change are 0.69– 1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s, respectively. These results show that the climate of the basin will be warmer and wetter relative to the baseline period.

100. In another study, precipitation simulation of regional climate model PRECIS for the baseline period for years 1960 to 1990 is evaluated and downscaled on a monthly basis for northwestern Himalayan Mountains and upper Indus plains of Pakistan. Predicted precipitation show overall decrease during winter and increase in spring and monsoon in the study area by the end of the twenty-first century. This analysis has been done under A2 and B2 scenarios. Both scenarios show similar pattern on spatial basis, but have a varying magnitude. It is predicted that the Himalayan southern regions will have more precipitation in monsoon, whereas northern areas and southern plains will face decrease in precipitation. Western and south western areas will 36

suffer from less precipitation throughout the year except peak monsoon months. The t-test results also show that changes in monthly precipitation over the study area are significant except for July, August, and December (Ashiq, 2010).

101. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of this century ranges from 2.5 to 5°C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh, and Myanmar. This has been claimed by Syed (2014) who conducted a climate change study on summer monsoon for South- Asia. Baseline simulation period for the study ranged from 1971 to 2000 and, for the future, from 2071 to 2100. GCM simulation was done under A1B emission scenario.

102. Rajbhandari et al. (2015) has made use of (ECHAM5) European Centre Hamburg, as a GCM model while Providing Regional Climates for Impacts Studies (PRECIS) and Regional Climate Model (RegCM4) have been selected as RCMs. Temperature and precipitation projections for future have been made for Indus Delta and other parts of Pakistan. Baseline data of 1961-2010 has been taken into account under scenario A1B1 of IPCC (Intergovernmental Panel on Climate Change) at a resolution of 25 km & 50 km. Temperature and precipitation projections for future show that there will be a rise of 4.5°C by the end of 21st century. The temperature projection for Indus delta is a 4°C rise in temperature over the deltaic plains expected by the end of this century. For precipitation projections for Indus delta the model output shows that Sindh province will have heavier rainfall than normal during Monsoon season. Future projections of rainfall also indicate that rainy season in Sindh may extend towards autumn.

103. An un published work done in 2013 at the Department of Civil Engineering, NED University of Engineering and Technology presented the fact that rainfall is showing an increasing trend in the future (by year 2015) for Hub watershed, Balochistan. Corresponding to the precipitation trend the hydrological model is simulating five to ten times higher stream flows as compared to the baseline data (1960’s to 1990’s). The study claims that predicting precipitation is a complex phenomenon as compared to predicting temperature.

104. A Regional Climate Model (RCM) simulation is used by Islam et al. (2009) to study the future variations in frequency of warm and cold spells duration over Pakistan. Simulation for the period 1961–1990 represents the baseline and simulation for the period 2071–2100 represents the future climate. Results show that summer daily minimum temperature is increasing more as compared to daily maximum temperature whereas in winter, the change in maximum temperature is higher than the minimum temperature. The occurrence of annual cold spells shows significantly decreasing trend while for warm spells there is slight increasing trend over Pakistan.

37

Figure 15 - Maximum and minimum temperature changes for 2080s (Islam et al., 2009)

105. Literature review based analysis on GCM modeling in Pakistan and other adjacent areas like India and Iran can be summarized as:

1. There is a possibility of increasing temperature in this century 2. Precipitation is also expected to increase in this century

106. These results are partially aligned with the results obtained through statistical tests performed for Zhob and Mula watersheds conducted in this study. An increasing trend in temperature has been observed in both the watersheds; however, there is a decreasing trend in the annual average precipitation amount.

107. There are multiple scenarios present in the literature of GCM that can be used for simulation of the model. The use and selection of these scenarios depends on the conditions in the area. Brief discussion of the latest GCM scenarios are as follows:

RCP 8.5 – High emissions

This RCP is consistent with a future with no policy changes to reduce emissions. It was developed by the International Institute for Applied System Analysis in Austria and is characterized by increasing greenhouse gas emissions that lead to high greenhouse gas concentrations over time.

Comparable SRES scenario: A1 F1. This future is consistent with:

• Three times today’s CO2 emissions by 2100 • Rapid increase in methane emissions 38

• Increased use of croplands and grassland which is driven by an increase in population • A world population of 12 billion by 2100 • Lower rate of technology development • Heavy reliance on fossil fuels • High energy intensity • No implementation of climate policies

RCP 6 – Intermediate emissions

This RCP is developed by the National Institute for Environmental Studies in Japan. Radiative forcing is stabilized shortly after year 2100, which is consistent with the application of a range of technologies and strategies for reducing greenhouse gas emissions.

Comparable SRES scenario: B2, This future is consistent with:

• Heavy reliance on fossil fuels • Intermediate energy intensity • Increasing use of croplands and declining use of grasslands • Stable methane emissions • CO2 emissions peak in 2060 at 75 per cent above today’s levels, then decline to 25 per cent above today

RCP 4.5 – Intermediate emissions

This RCP is developed by the Pacific Northwest National Laboratory in the US. Here radiative forcing is stabilized shortly after year 2100, consistent with a future with relatively ambitious emissions reductions.

Comparable SRES scenario: B1. This future is consistent with:

• Lower energy intensity • Strong reforestation programmes • Decreasing use of croplands and grasslands due to yield increases and dietary changes • Stringent climate policies • Stable methane emissions • CO2 emissions increase only slightly before decline commences around 2040

RCP 2.6 – Low emissions

This RCP is developed by PBL Netherlands Environmental Assessment Agency. Here radiative forcing reaches 3.1 W/m2 before it returns to 2.6 W/m2 by 2100. In 39

order to reach such forcing levels, ambitious greenhouse gas emissions reductions would be required over time.

Comparable SRES scenario: This feature would require:

• Declining use of oil • Low energy intensity • A world population of 9 billion by year 2100 • Use of croplands increase due to bio-energy production • More intensive animal husbandry • Methane emissions reduced by 40 per cent • CO2 emissions stay at today’s level until 2020, then decline and become negative in 2100 • CO2 concentrations peak around 2050, followed by a modest decline to around 400 ppm by 2100

108. The scenario chosen for this study is RCP 6, because it resembles to the condition of Pakistan the most.

3.2.1 Results of GCM Analysis

Precipitation Analysis

Mula Watershed

109. Future daily precipitation data show an increasing trend as depicted by the linear regression trend line drawn in Figure 16Assessment of decadal data is necessary in order to identify behavior of the data separately. Decadal trends for future precipitation in Mula watershed is shown in Figure 17It can be observed that the decade 2047 – 2056 shows highest rise in precipitation with a slope value of 0.0001. Only other decade showing an increasing trend is 2067 to 2076 (slope=0.0000004); however, the slope is not as steep as the first decade. The rest of the decades are showing a decreasing trend. The steepest decreasing trend can be observed in decade 2077 to 2086 (slope= -0.0004), the slope values are shown in Table 10. Despite all the decreasing trends, overall the trend in future precipitation is expected to increase. 40

Daily Flow vs Precipitation-Future (Mula) Precipitation (mm) Flow (cms) Linear (Precipitation (mm)) 0 14000 50 12000 100 10000 y = 9E-06x + 1.5107 150 200 8000 250

6000 300 Flow Flow (cms) 4000 350

400 precipitation(mm) 2000 450

0 500

2/1/2041 2/1/2009 2/1/2013 2/1/2017 2/1/2021 2/1/2025 2/1/2029 2/1/2033 2/1/2037 2/1/2045 2/1/2049 2/1/2053 2/1/2057 2/1/2061 2/1/2065 2/1/2069 2/1/2073 2/1/2077 2/1/2081 2/1/2085 2/1/2089 2/1/2093 2/1/2097 Date Figure 16 - Future Daily Flow plotted against Daily precipitation for Mula watershed.

Future Decadal Precipitation (Mula) Linear (2017-2026) 2.4 Linear (2027-2036) 2.3

2.2 Linear (2037-2046)

2.1 Linear (2047-2056) 2

1.9 Linear (2057-2066)

Precipitation(mm) 1.8 Linear (2067-2076) 1.7 Linear (2077-2086)

1.6

1

953 137 273 409 545 681 817

1225 1361 1497 1633 1769 1905 2041 2177 2313 2449 2585 2721 2857 2993 3129 3265 3401 3537 1089 Linear (2087-2099) Day

Figure 17 - Trends for decadal future precipitation for Mula watershed.

41

Table 10 - Slope values of future decadal precipitation based on linear regression trend lines for Mula watershed. Decadal Precipitation( Mula)

Decade Slope 17-26 -0.00006 27-36 -0.0001 37-46 -0.0001 47-56 0.0001 57-66 -0.00003 67-76 0.0000004 77-86 -0.0004

110. Figure 18 shows the averaged monthly precipitation for historic and future timelines for Mula watershed. It can be observed in Figure 18 that there is an increased amount of precipitation shown in the future data as simulated by GCM. The increase in amount of precipitation is between 3 mm in January to 20 mm in June. Overall, the increase in precipitation for winter months is not a lot (between 3mm to 5 mm); however, rest of the months and seasons show a lot more increase in the amount of precipitation. Maximum increase can be observed in spring, summer, and fall months.

Averaged Monthly Rainfall-Mula 45 Historic Precipitation (1982-2014) 40 35 Future Precipitation (2017-2099) 30 25 20 15

10 Precipitation(mm) 5 0 jan feb mar apr may jun jul aug sep oct nov dec month

Figure 18 - Averaged monthly historic and future precipitation for Mula watershed.

111. For the purpose of understanding the simulated precipitation of GCM monthly average precipitation graph has been developed as shown in Figure 19 The graph shows linear regression trend lines for future precipitation. Months of February, March, and April, (slopes: -0.0174, -0.017, and -0.0915 respectively) show decreasing trends in precipitation; however, the rest of the months show an increasing trend. The months of September (slope: 0.355) shows the most significant 42

increase in precipitation. Table 11 shows the slope values for linear regression analysis on future monthly average precipitation for Mula watershed.

Monthly Average Precipitation (Mula) 90 Linear (Jan) 80 Linear (Feb) 70 Linear (Mar) 60 Linear (Apr) Linear (May) 50 Linear (Jun) 40 Linear (July) 30 Linear (Aug)

Precipitation(mm) 20 Linear (Sep) Linear (Oct) 10 Linear (Nov) 0 Linear (Dec) 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 Month

Figure 19 - Future Monthly average precipitation trends for Mula watershed. Table 11 - Slope values of future monthly average precipitation based on linear regression trend lines for Mula watershed. Monthly Average Precipitation (Mula) Month Slope jan 0.0102 - feb 0.0174 mar -0.017 - apr 0.0915 may 0.061 jun 0.0656 jul 0.2091 aug 0.061 sep 0.355 oct 0.107 nov 0.1325 dec 0.1004

112. Similar result can be observed in Figure 20. Figure 20a graph that is showing linear regression trend lines for annual average historic and future precipitation in Mula watershed. Historic data is 32 years span; therefore the future data are also divided into three parts. The first two parts are 32 years each and the last one is 17 years. The results show that historic precipitation has an increasing trend. The first and the third parts of future data show an increasing 43

trend; however, the second part shows a decreasing trend. The range of average precipitation values in case of annual average is higher for the future data as compared to the historic data.

Annual Average Precipitation (Mula) Linear (Historic (1982-2014)) Linear (Future 1 (2017-2049)) Linear (Future 2 (2050-2082)) Linear (Future 3 (2083-2099)) 500 450 400 350 300 250 200 150

Precipitation(mm) 100 50 0 0 5 10 15 20 25 30 35 Year Figure 20 - Annual average historic and future precipitation for Mula watershed.

Zhob Watershed

113. Figure 21shows future daily precipitation and flow for Zhob watershed. Future Daily precipitation for the watershed has an increasing trend as shown by the trend line and its equation in Figure 21However, future decadal precipitation shows mixed results. Shown in Figure 22Decadal graph drawn for daily precipitation values shows that there is a decreasing trend in the precipitation for Zhob watershed for 4 out of 8 decades and the rest of the 4 have an increasing trend. The first decade (2017 to 2026) has a decreasing trend after which the last three decades depict a decreasing trend. There is a possibility that the precipitation in Zhob watershed might decrease in the current decade but increase for the next 30 years after that after which it might decrease in the last 30 years of the current century.

44

Future Daily Precipitation vs Flow (Zhob) Precipitation (mm) Flow (cms) Linear (Precipitation (mm)) 18000 0 16000 20 14000 40 12000 y = 7E-06x60 + 0.6606 10000 80 8000 100

Flow Flow (cms) 6000

4000 120 Precipitation(mm) 2000 140

0 160

2/1/2017 2/1/2081 2/1/2009 2/1/2013 2/1/2021 2/1/2025 2/1/2029 2/1/2033 2/1/2037 2/1/2041 2/1/2045 2/1/2049 2/1/2053 2/1/2057 2/1/2061 2/1/2065 2/1/2069 2/1/2073 2/1/2077 2/1/2085 2/1/2089 2/1/2093 2/1/2097 Date

Figure 21 - Future Daily Flow versus Precipitation for Zhob watershed.

Future Decadal Precipitation (Zhob)

1 Precipitation(mm)

1

1

110 219 328 437 546 655 764 873 982

1309 3598 1091 1200 1418 1527 1636 1745 1854 1963 2072 2181 2290 2399 2508 2617 2726 2835 2944 3053 3162 3271 3380 3489 3707 3816 3925 Day Linear (2017-2026) Linear (2027-2049) Linear (2037-2046) Linear (2047-2056) Linear (2057-2066) Linear (2067-2076) Linear (2077-2086) Linear (2087-2099) Figure 22 - Future Decadal precipitation trends for Zhob watershed.

45

Averaged Monthly Precipitation (Zhob)

80 Historic (1982-2014) 70 Future (2017-2099) 60 50 40 30

Precipitation (mm) Precipitation 20 10 0 jan feb mar apr may jun jul aug sep oct nov dec Month Figure 23 - Averaged monthly historic and future precipitation for Zhob watershed.

80 Monthly Average Precipitation (Zhob) 70

60

50

40

30 Precipitation(mm) 20

10

0 1 4 7 10 13 16 19 22 25 28 31 34 37 Month40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 Linear (Jan) Linear (Feb) Linear (Mar) Linear (Apr) Linear (May) Linear (Jun) Linear (Jul) Linear (Jul) Linear (Aug) Linear (Sep) Linear (Oct) Linear (Nov) Linear (Dec) Figure 24 - Future Monthly average precipitation for Zhob watershed.

46

Annual Average Precipitation (Zhob) 500 450 400 350 300 250 200

Precipitation(mm) 150 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Year

Linear (Historic (1982-2014)) Linear (Future 1 (2017-2049)) Linear (Future 2 (2050-2082)) Linear (Future 3 (2083-2099))

Figure 25 - Historic and Future Annual average precipitation for Zhob watershed.

114. Averaged monthly Precipitation graph presented in Figure 23shows that precipitation is expected to decrease in Zhob watershed for the months February, March, April, and May; however it is expected to increase in all rest of the months other than January which is almost same amount in the historic and future data. Most of the precipitation received by Zhob watershed is during monsoon or winters. The result of average monthly analysis shows an increasing trend in the rainy months of the watershed and a decrease in the non-rainy months. This might mean that the wet months will get wetter and the dry months will get drier in the future.

115. Monthly average precipitation graph (Figure 24supports the result of averaged monthly graph. As shown in Figure 24months August, September, November, and December show an increasing trend whereas almost all other months show a decreasing trend. According to the annual average precipitation analysis, historic data shows a decreasing trend; whereas the first and last parts of the future data show an increasing trend but the middle part (2050-2082) shows a decreasing trend. The results for annual average analysis are shown in Figure 25

TEMPERATURE ANALYSIS

Zhob Watershed

116. Averaged monthly analysis shows that both minimum and maximum temperatures are increasing in the future. Maximum temperature is rising in the range of 2oC to 5oC; whereas, minimum temperature has risen in the range of 1oC to 6oC. Least temperature rise is in the month of December and January (1oC) for maximum and minimum temperatures, respectively. Monthly average analysis shows that both maximum and minimum temperatures have increasing trend for all the months (Figures 26-27). Table 12 shows the slopes for linear regression analysis performed on monthly average temperature for Zhob watershed. The data shows the rise in temperature for all the months for maximum and minimum trends. 47

30 Future Monthly Average Minmum Temperature (Zhob) 25

20

15

10

5 Temperature Temperature (C) 0 0 500 1000 Day 1500 2000 2500 -5 Linear (Jan) Linear (Feb) Linear (Mar) Linear (Apr) Linear (May) Linear (Jun) Linear (Jul) Linear (Aug) Linear (Sep) Linear (Oct) Linear (Nov) Linear (Dec) Figure 26 - Future Monthly average minimum temperature for Zhob watershed.

Table 12 - Slope values of future monthly average temperature based on linear regression trend lines for Zhob watershed. Monthly average Minimum Temperature (Zhob) Month Slope Maximum Minimum 0.0009 jan 0.001 0.0008 feb 0.0007 0.001 mar 0.0009 0.0013 apr 0.0011 0.0011 may 0.0013 0.001 jun 0.0012 0.0005 jul 0.0008 0.0004 aug 0.0007 0.0004 sep 0.0008 0.001 oct 0.0012 0.0014 nov 0.0014 0.0008 dec 0.0009 48

Linear (Jan) Monthly Average Maximum Temperature Linear (Feb) 40 Linear (Mar)

35 Linear (Apr) Linear (May) 30 Linear (Jun)

25 Linear (Jul) Linear (Aug) 20 Temperature Temperature (C) Linear (Sep)

15 Linear (Oct) Linear (Nov) 10 Linear (Dec) 0 500 1000 1500 2000 2500 Month Figure 27 - Future Monthly average maximum temperature for Zhob watershed.

117. Figures 28-29 show the daily minimum and maximum temperature trend lines for Zhob watershed. The results indicate that minimum and maximum both temperatures have increasing trends similar to historical behavior.

Daily Maximum Temperature (Zhob)

Linear (Future (2017-2099)) 29 Linear (Historic (1979-2014))

26

23 Temperature Temperature (C)

20 0 5000 10000 15000 20000 25000 30000 Day

Figure 28 - Historic and future Daily Maximum Temperature for Zhob watershed. 49

Daily Minimum Temperature (Zhob) Linear (Future (2017-2099))

Linear (Historic (1979-2014))

14

7 Temperature Temperature (C)

0 0 5000 10000 15000 20000 25000 30000 Day Figure 29 - Historic and future Daily Minimum Temperature for Zhob watershed.

118. Table 13 shows the slope values for linear regression trend lines drawn on decadal time span. All decades in the future data show an increasing trend in both minimum and maximum temperatures (Figures 30 and 31). Decade 2087 to 2099 shows the most increase in minimum temperature (slope= 0.0007) and rise in maximum temperature is visible in the decade 2017-2026 (slope= 0.0007).

29 Decadal Maximum Temperature (Zhob) 28.5 28 27.5 27 26.5 26

25.5 Temperature Temperature (C) 25 24.5 24 0 500 1000 1500 2000 2500 3000 3500 4000 Day of the decade Linear (2017-2026) Linear (2027-2036) Linear (2037-2046) Linear (2047-2056) Linear (2057-2066) Linear (2067-2076) Linear (2077-2086) Linear (2087-2099) Figure 30 - Future decadal Maximum temperature for Zhob Watershed. 50

Table 13 - Slope values of future decadal temperature based on linear regression trend lines for Zhob watershed. Future Decadal Temperature (Zhob) slope Decade Maximum Minimum 17-26 0.0007 0.0002 27-36 0.0006 0.0002 37-46 0.0004 0.0002 47-56 0.0003 0.0002 57-66 0.0003 0.0004 67-76 0.0002 0.0004 77-86 0.0003 0.0005 87-99 0.0003 0.0007

16 Decadal Minimum Temperature (Zhob) 15.5 15 14.5 14 13.5 13

12.5 Temperature Temperature (C) 12 11.5 11 0 500 1000 1500 2000 2500 3000 3500 4000 Day of the decade Linear (2017-2026) Linear (2027-2036) Linear (2037-2046) Linear (2047-2056) Linear (2057-2066) Linear (2067-2076) Linear (2077-2086) Linear (2087-2099)

Figure 31 - Future decadal Minimum temperature for Zhob Watershed.

119. Overall, results show that it can be expected that Zhob watershed will get warmer in future. Warmer climate would mean more evapotranspiration in arid zone; hence, less water availability. The balance between precipitation and evaporation might exist in months where precipitation is also expected to increase, otherwise it can be expected that less water will be available in the future.

120. The trends for average monthly maximum and minimum temperatures show an increasing trend for Zhob watershed as shown in Figures 32-33, respectively. The range of increase is between 1 to 5oC for maximum temperature. However, the range of increase for minimum temperature is between 1 to 7oC. 51

Averaged Monthly Maximum Temperature (Zhob) Future 42 Historic 35

28

21

14 Temperature Temperature (C)

7

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 32 - Historic and future averaged monthly maximum temperature for Zhob watershed.

Averaged Monthly Minimum Temperature (Zhob) Future 30 Historic 25

20

15

10 Temperature Temperature (C)

5

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 33 - Historic and future averaged monthly minimum temperature for Zhob watershed.

Mula Watershed

121. Daily temperature analysis for Mula watershed shows that both maximum and minimum temperatures are increasing for both historic and future time lines (Figures 34-35). All other analyses done on future data set show an increasing trend for both maximum and minimum temperatures. 52

Daily Maximum Temperature Data (Mula) Linear (Future) Linear (Historic) 31

28 Temperature Temperature (C)

25 0 5000 10000 15000 20000 25000 30000 Day Figure 34 - Historic and Future daily maximum temperature for watershed.

Daily Minimum Temperature Data (Mula)

Linear (Future) 19 Linear (Historic)

16

13 Temperature Temperature (C)

10 0 5000 10000 15000 20000 25000 30000 Day Figure 35 - Historic and Future daily minimum temperature for watershed.

122. As shown in Figure 36Decade 2077 to 2086, which is the second last decade of the current century, is expected to have highest rise in minimum temperature. Contrary to the overall result, decade 2087 to 2099 is showing a negative trend in maximum temperature (slope= -0.00004). However, this negative trend does not have a significant effect on the overall trend for maximum decadal temperature. In case of minimum decadal temperature Figure 37 the second last decade (i.e. 2077-2086) shows the steepest slope (0.0005) in the increasing linear regression trend line. Therefore, it can be anticipated that watershed will get warmer in future with the most significant increase in temperature in the last 20 years of the current century. All linear regression slopes for decadal analyses are shown in Table 14. 53

32 Decadal Maximum Temperature (Mula) 31.5

31

30.5

30

29.5 Temperature Temperature (C) 29

28.5

28 0 500 1000 1500 2000 2500 3000 3500 Day of the decade Linear (2017-2026) Linear (2027-2036) Linear (2937-2046) Linear (2047-2056) Linear (2957-2066) Linear (2067-2076) Linear (2077-2086) Linear (2087-2099)

Figure 36 - Future decadal maximum temperature for watershed.

Table 14 - Slope values of future decadal temperature based on linear regression trend lines for Mula watershed. Future Decadal Temperature (Mula) slope Decade Maximum Minimum 17-26 0.0004 0.0002 27-36 0.0003 0.0001 37-46 0.0002 0.0002 47-56 0.0002 0.00007 57-66 0.0003 0.0003 67-76 0.00005 0.0002 77-86 0.0002 0.0005 87-99 -0.00004 0.0002

54

Decadal Minimum Temperature (Mula) 19

18.5

18

17.5

17

16.5 Temperature Temperature (C) 16

15.5

15 0 500 1000 1500 2000 2500 3000 3500 Day of the decade

Linear (2017-2026) Linear (2027-2036) Linear (2037-2046) Linear (2047-2056) Linear (2057-2066) Linear (2077-2086) Linear (2087-2099) Linear (2067-2076) Figure 37 - Future decadal minimum temperature for Mula watershed.

123. Smoothing of data set for long term trends in temperature variations is calculated by the taking the moving averages, 3- and 12-months, for both the watersheds, i.e. Zhob and Mulla for maximum and minimum temperature time series, separately as shown in Figure 38 and Figure 39

55

Max Temperature Moving Average Zhob Watershed Monthly 6-months moving average 45.00 12-months moving average 40.00 35.00 30.00 25.00 20.00

15.00 Temperature Temperature (C) 10.00 5.00

0.00

Jan-57 Jan-17 Jan-25 Jan-33 Jan-41 Jan-49 Jan-65 Jan-73 Jan-81 Jan-89 Jan-97

Sep-83 Sep-19 Sep-27 Sep-35 Sep-43 Sep-51 Sep-59 Sep-67 Sep-75 Sep-91 Sep-99

May-30 May-38 May-46 May-54 May-62 May-70 May-78 May-86 May-94 May-22 Figure 38 - Maximum (A) and Minimum (B) temperature moving average for the projected time series for Zhob Watershed. (A)

Min Temperature Moving Average

Zhob Watershed Monthly

30.00

25.00

20.00

15.00

10.00 Axis Axis Title

5.00

0.00

-5.00

Jul-22 Jul-33 Jul-44 Jul-55 Jul-66 Jul-77 Jul-88 Jul-99

Jan-17 Jan-28 Jan-39 Jan-50 Jan-61 Jan-72 Jan-83 Jan-94

Oct-41 Oct-19 Oct-30 Oct-52 Oct-63 Oct-74 Oct-85 Oct-96

Apr-25 Apr-58 Apr-47 Apr-69 Apr-80 Apr-91 Apr-36

(B) 56

Max Temperature Moving Average Mulla Watershed Monthly

6-months moving 50 average 45 40 35 30 25 20

15 Temperature Temperature (C) 10 5

0

Jul-22 Jul-33 Jul-44 Jul-55 Jul-66 Jul-77 Jul-88 Jul-99

Jan-39 Jan-17 Jan-28 Jan-50 Jan-61 Jan-72 Jan-83 Jan-94

Oct-19 Oct-30 Oct-41 Oct-52 Oct-63 Oct-74 Oct-85 Oct-96

Apr-25 Apr-36 Apr-47 Apr-58 Apr-69 Apr-80 Apr-91

Figure 39 - Maximum (A) and Minimum (B) temperature moving average for the projected time series for Mulla Watershed. (A) 57

Min Temperature Moving Average Mulla Watershed Monthly 6-months moving average 12-months moving average 35

30

25

20

15

Temperature Temperature (C) 10

5

0

Jul-25 Jul-42 Jul-59 Jul-76 Jul-93

Jan-34 Jan-68 Jan-17 Jan-51 Jan-85

Sep-22 Sep-56 Sep-39 Sep-73 Sep-90

Nov-19 Nov-36 Nov-53 Nov-70 Nov-87

Mar-31 Mar-48 Mar-65 Mar-82 Mar-99

May-45 May-28 May-62 May-79 May-96

Figure 40 - Maximum (A) and Minimum (B) temperature moving average for the projected time series for Mulla Watershed. (B)

58

Drought analysis

Aridity Index

124. Aridity is usually expressed as a generalized function of precipitation, temperature, and/or potential evapo-transpiration (PET). An Aridity Index (UNEP, 1997) can be used to quantify precipitation availability over atmospheric water demand. Aridity index can be measured using the following formula:

Aridity Index (AI) = MAP / MAE

Where: MAP = Mean Annual Precipitation and MAE = Mean Annual Potential Evapotranspiration Note: In the Global-Aridity dataset, following this formulation, Aridity Index values increase for more humid conditions, and decrease with more arid conditions (UNEP, 1997).

125. A summary of the results for AI for both the watersheds is presented in Table 15. The results show that the numbers of years for different intensities of droughts based on AI are almost same for both the watersheds. For Mula watershed, disastrous droughts are expected in years 2018, 2021, 2034, 2087, and 2099 (shown in Table E1 of Appendix E). This means that the worst droughts are expected to occur one year in the first three and last two decades of the projected data. The years expected to have disastrous drought in Zhob watershed are 2018, 2021, 2047, and 2081 (shown in Table E2). It can be observed that years 2018 and 2021 are common for both the watersheds. It can be expected that both the watersheds will experience some drought in the upcoming coming decades.

126. Results for AI performed for historic time span is also shown in Table 15. Compared to the number of years in both historic and future timespans, the results for all types of droughts is proportionate for both the watersheds.

Table 15 - Summary of results for Aridity index in Zhob and Mula watersheds.

Zhob Mula

Historic Future Historic Future No. of years of Disastrous Droughts 1 4 2 5 Expected No. of years of Large Droughts Expected 8 21 6 22 No. of years of Moderate Droughts 7 25 9 24 Expected No. of years of no Droughts Expected 17 33 15 32

No of years of data analyzed 33 83 33 83

59

Standard Precipitation Index (SPI)

127. Long term precipitation data is required to for the SPI analysis. These data are used to determine the probability distribution function which is then transformed to a normal distribution with mean zero and standard deviation of one. Standardized Precipitation Index (SPI) has gained importance in recent years as a potential drought indicator permitting comparisons across space and time. This is because SPI expresses the actual rainfall as a standardized departure with respect to rainfall probability distribution function and hence the index. Thus, the values of SPI are expressed in standard deviations, positive SPI indicating greater than median precipitation and negative values indicating less than median precipitation (Edwards and McKee, 1997).

128. In recent years SPI is being used increasingly for assessment of drought intensity in many countries (Vijendra et al., 2005; Wu et al., 2006; Vicente-Serrano et al., 2004). The drought interpretation at different time scales using SPI is proved to be superior to Palmer Drought Index (Guttman, 1998).

129. Results for the SPI analysis in Table 16 show that most of the historic and future periods show normal range of available water. There are few years with severely or extremely dry years and most of these years are not continuous; therefore, there is a possibility that even the worst drought would be manageable if enough water storage is available in the watersheds.

Table 16 - Summary of SPI analysis. Drought Summary (SPI) Zhob Mula Historic Future Historic Future Total No of Years 33 83 33 83 2 1 2 Observed Extremely Wet Years 2 3 1 4 Observed Very Wet Years 0 Observed Moderately Wet 8 1 7 1 Years 26 54 27 58 Observed Near Normal Years Observed Moderately Dry 2 11 2 5 Years 2 3 1 5 Observed Severely Dry Years 2 0 2 Observed Extremely Dry Years 0

Simulation of Future Flood Scenarios Based on Global Climatic Model (GCM) Forecasts

130. Data from Global Climatic Model for 83 years (2017-99) show that year 2018, 2047, 2031, 2027, 2017, 2059, and 2080 are the highest 7 years for rainfall and ultimately flows for Zhob 60

Watershed. Similarly form the same span of GCM simulations, 2018, 2027, 2031, 2038, 2047, 2059, and 2080 are identified to be the top seven high flow years. Figure 40 (a) and Figure 40 (b) represents highest seven years of precipitation data for Zhob and Mula watersheds.

Highest ten Years of Preceipitation in Mula Watershed 800 700 600 500 400 300

Precipitation (mm) 200 100 0 2018 2027 2031 2038 2047 2059 2080

Figure 41 - Highest seven Precipitation Years for Mula Watershed (A)

Highest ten Years of Preceipitation in Zhob Watershed 800 700 600 500 400 300 200

Precipitation (mm) 100 0 2017 2018 2027 2031 2047 2059 2080

Figure 42 - Highest seven Precipitation Years for Mula Watershed (B)

131. For reference, years of extreme rainfall events have been changed in to return period. This will help in understanding that how much water is expected in a single rainfall event. For Zhob watershed, extreme rainfall events in years 2020 and 2040 have been identified as 100-year return period precipitation event; however, for Mulla watershed, years 2064 and 2078 are identified as 100-year return period rainfall events. Table 17 represents the list of 20-, 30-, and 50-year return period event years for both the watersheds, respectively. 61

Table 17 - Extreme Events (return periods) throughout the century for Zhob and Mula Watershed †These return periods are assumed on the basis of recurrence of and interval in a data set (Future-82-

Return Period† Zhob Mulla

100 Yr. 2020, 2040 2064, 2078

50 Yr. 2020, 2060, 2068 2069, 2073, 2098

2040, 2060, 2062, 2068, 2069, 2043, 2048, 2056, 2057, 30 Yr. 2091, 2095 2061, 2062, 2085 2039, 2040, 2045, 2051, 2063, 2019, 2021,2028, 2035, 20 Yr. 2069, 2083, 2084, 2089, 2090, 2036, 2037, 2068, 2075, 2095, 2098 2087 years dataset)

Table 18 - Flood Events (return periods) throughout the century for Zhob and Mulla Watershed Return Period₸ Zhob Mulla

100-Yr 2020, 2098 2020, 2098

2020, 2045, 2054, 50-Yr 2020, 2060, 2068 2059, 2095, 2098,

₸These return periods are assumed on the basis of recurrence of and interval in a data set (Future-82- years dataset)

132. Based on the GCM simulated future data both watersheds are simulated for hydrologic distribution using climatic data and hydrologic model calibrated on historic data. The resulting attribute of interest from hydrological model is future stream flow for corresponding GCM data. 62

Figure 43 - Sub Basin Level Point Flood Map of Zhob Watershed

133. The results for the highest stream flow are summarized on sub basin level at significant outlets and a map is developed for better understanding of flood modeling of future scenarios Figure 41 Table E9 (Appendix E) also contains tabulated data from for all sub basins. Figure 39indicates the simulated flood flows at respective outlets for Zhob Watershed. In Zhob Watershed, the flow direction is from South-West to North-East. The last outlet in Zhob watershed receives enormous amount of flow in year 2059. Amongst all the top seven years, 2059 has significant high volume of flows observed at all the station. These flow parameters with significantly high amounts must be considered for future hydraulic structures for storage, flow, and diversions etc. In addition, these flows are also important for designing of transportation networks. Comparing the sub basins, the most upstream sub basin in Zhob watershed contributes 14.7 % to the outflow of complete watershed. 63

Figure 44 - Sub Basin Level Point Flood Map of Mula Watershed

134. Figure 42 presents simulation results for sub basin level for Mula Watershed. Contrary to Zhob watershed, the flow direction in Mula is towards South-East. The highest amount of flow is observed to be 10,000 CMS at the outlet of Mula River Basin. The Mula River basin has mixed terrain with high mountains in western region and plains in the East. Plains allow more amount to percolated because of the flatter slopes. Comparing the outflows of sub basins having mountainous terrain with plain topography, sub basins are observed producing relatively less flow which is justified by terrain.

135. Based on GCM data, for Zhob watershed, the highest and the second highest flood years for Zhob Watershed are 2059 and 2031, respectively. While, for Mula, the highest and the second highest flood years are 2059 and 2027.

136. Overall, Generation of GCM data for future climatic predictions and analysis of future hydrologic behavior for both watersheds give very clear directions for anticipated water structures for the development of the study area. The designing of hydraulic structures at sub basin scale needs water availability for wet and dry seasons and it has been pronounced accordingly in GCM data analysis.

64

4 Vulnerability Analysis

4.1 Sensitivity and Impact of Climate Change on Lives and Livelihood

137. There are multiple reasons that make Pakistan highly vulnerable to climate change. Some of these include the country’s geographic location, high dependence on agriculture and water resources, low adaptive capacity, and weak emergency response system. Studies indicate that with in Pakistan, Balochistan is the most vulnerable region with high sensitivity and low adaptive capacity. It is established that regions that are most deprived in terms of socio-economic indicators are at high risk. Special attention is required to lessen the vulnerability of regions like Balochistan to reduce the adverse effects of climate change (Malik et al., 2012).

138. As a consequence of climatic change, a significant impact is expected on hydrological parameters such as runoff, evapotranspiration, soil moisture, groundwater, etc. (Bultot et al., 1988). In Balochistan province there are significant rising trends of Diurnal Temperature Range (DTR). Diurnal Temperature Range is defined as the difference of daytime maximum temperature and night time minimum temperature. There is a warming trend of 1.15 oC in mean temperatures of Balochistan, and also increasing trend in daily maximum temperature, and annual minimum temperatures especially in Eastern Balochistan. Eastern Balochistan has been experiencing an increase in the number of heat wave days in the last decade. Cold waves have been increased in North-Eastern Balochistan and all mountainous regions.

139. Despite being an arid region, the province of Balochistan has several rivers, but only few are perennial. Steep gradients are a common feature because of which the rate of runoff is very rapid. River flow is characterized by quick runoff and flash floods. Droughts and floods comprise the major natural factors that have led to disasters in the province in the past. The bulk of the province‘s population is dependent on agriculture and livestock for livelihoods, which are constrained by limited water resources. The economy of Balochistan Province is sensitive and has a significant negative relation to climate change (Akram & Gulzar, 2013).

4.2 Floods, Droughts, and the most Vulnerable

140. Drought is a deceiving natural hazard that shows its effects when it’s already too late. Droughts originate from a deficiency of rainfall over an extended period of time, usually a season or more, which results in a water shortage for some activity, group, or environmental sectors (UNISDR, 2009). The main reason for droughts in Balochistan is lack of rainfall. Other factors include increasing maximum temperatures and low adaptive capacity.

141. Most severe recent droughts in Balochistan occurred in 1997–2002. These years were regarded as one of the worst drought years in the history of Balochistan. Effects of which include rise in food prices, widespread malnutrition, followed by disease which affected thousands of local communities, especially women and children. Almost 80 % of livestock and orchards perished completely other than another livelihood. These areas were declared as most severely affected by drought. Another effect of these droughts was widespread migration of people from affected areas. According to the Ministry of Finance, the drought cost PKR 25 billion to the national income 65

in 2000–2002 and was a major factor in slowing down the country‘s economic growth to 2.6 percent.

142. A substantial increase in the number of heat wave days was observed over the whole country. As compared to other areas, this increase was more pronounced over the province of Balochistan, especially western Balochistan. Following the devastating effects of 2010 flood, the Provincial Disaster Management Authority (PDMA) Balochistan, in coordination with the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), undertook a flood risk mapping exercise that found 12 out of the 30 districts to be most vulnerable to the risk of floods and flash floods. These districts include Nasirabad, Jaffarabad, Jhal Magsi, Sibi, Bolan, Qila Saifullah, Zhob, Loralai, Sherani, Ziarat, Kalat, Quetta, Musakhel, and Awaran. The floods of August 2010 severely affected the four eastern districts of Balochistan bordering Sindh and southern Punjab. Damage to agriculture and livestock was extensive. Damages in Balochistan contributed approximately 8% in the total damage to the agriculture, despite its sparse agriculture pattern (ADB, 2010).

143. Women and children are the most vulnerable members to natural disasters like floods and droughts in any society. Impacts of floods in Balochistan also include reduction in school going students during and post flood era. It has been reported that approximately 3% total enrolled students have been affected by damage to educational institutions, and this rate extended to a reduction of 4.9% in the net enrolment rate in Balochistan, as the opportunity cost of keeping children in school continues to rise in the post-flood scenario. This reduction in school enrolments will also affect gender parity indices, since girls are the first ones to be kept away from schooling if a family suffers loss of livelihoods.

144. It has been reported that the vulnerability of women and children had increased in the flood affected districts of Balochistan in 2010. The loss of houses during Flood 2010 made women and children vulnerable to gender based violence in Balochistan. Disaster emergencies often force large numbers of people to shift and take shelter with less affected areas. This migration can be life-threatening if environmental conditions are extreme for example exposure intense cold or heat. Loss of belongings can be traumatic too. Loss of privacy can cause serious psychological issues, especially for women. There is evidence that the incidence of violence against women increases in such disasters. Women are more vulnerable to water-borne diseases and are also transmitters of these diseases. This risk increases after disasters such as floods and earthquakes because water contamination is one of the most immediate effects of such events.

4.3 Migration Induced by Climate Change

145. Ahmad et al. (2003) stated in a climate change study that due to global warming effect the arid areas of South Asia are going to experience severe extreme events like droughts and floods. Also, the long-term drought conditions due to climate change can further deteriorate the situation of groundwater resources as well as migration of rural communities to the urban areas. The alarming level of water shortage in Balochistan without having water recharging points and new dams, there is a risk that the province would turn into a wasteland. 66

146. Climate-induced migrations are directly related to urbanization in developing countries (ADB, 2012). The reason is that most rural livelihoods are based on agriculture, and climate- induced losses in agricultural productivity have been shown to alter livelihoods (Barnett and Adger, 2007; Mueller et al., 2014). Changes in climatic variables like temperature and precipitation affect agricultural productivity. Increase in temperatures has specific effect on the productivity of winter wheat (Qin et al., 2014). A majority of rural migrants tend to move toward districts having high population densities. For example, 63% of the people who migrated in the last ten years moved to an urban area, with 56% moving to the provincial or the federal capital (Mahmud et al., 2010), mainly due to family networks and higher concentrated development in these areas.

4.4 Climate Change and Water Resources

147. Balochistan, a province used to be a water surplus area with lakes, waterfalls, and springs, now facing severe water shortage for the last three decades. The groundwater mining has affected the groundwater level to 300 m, drying up the ‘Karez system’. The ‘Karez’ system is comprised of a series of wells and linking ground channels that uses gravity to bring ground water to the surface after covering a distance from the source. The situation has worsened due to installation of more than 4,000 tube wells in Quetta, the capital of Balochistan – the largest landmass in Pakistan with an area of 343,000 Km2. Experts have already predicted that the province will run out of water in next decade or so. The vulnerable areas are Quetta, Pishin, Loralai, Qila Saifullah, Qila Abdullah, Ziarat and Gwadar. Agriculture system in the province has already been affected badly because of lack of hydraulic structures to cater water. Therefore, 13.0 million acre feet of water get wasted every year. However, the provincial government claims that feasibility studies costing of Rs. 315 million for the water recharging projects are already in progress to improve groundwater recharge (UNPO, 2015).

4.5 Flood Inundation Modeling and Vulnerability Analysis

148. This part of the report is focused on the analysis of observed and potential impacts of climate change in the study area. Results of flood inundation modeling are also presented in this section (Table 19 and Table 20). Flood inundation maps have been developed using the model HEC-RAS. These maps were used to calculate the extent of land being affected by flood conditions. Moreover, the extent of settlement, cultivated land, and length of roads if being affected are also reported. The flood scenarios are similar as reported in the previous parts of the report. Meteorological data for three stations have been used as an input into the hydrologic model (SWAT) and a single time series was generated by the model by distributing the rainfall throughout the river basin by Theissen polygon method. The five highest precipitation years were identified using the model generated data and flood simulation was undertaken using HEC-RAS. Flood maps for both the Zhob and Mula river basins are presented in Annexure 1. The results of flood inundation modeling for Zhob river basin are presented in Figure 41

67

Table 19 - Flood inundation modeling results for Zhob river basin of Balochistan Inundation Year Precipitation (mm) Roads Area (km2) Irrigated land (ha) (km) 1982 601 1993 172 56500 1984 352 1024 60 26900 1985 388 1307 81 35600 1986 367 1209 73 32700 2010 559 1712 130 48000

Table 20 - Flood inundation mapping for the Mula river basin of Balochistan Inundation Year Precipitation (mm) Area Roads Irrigated land (ha) (km2) (km) 1997 191 3758 152 52000 2007 248 3938 158 55200 2008 226 3948 158 55400 2009 187 3951 159 55500 2013 408 4071 162 57800

Floods in Zhob and Mula River Basins

700 600 Mula Zhob Linear (Mula) Linear (Zhob) 500 400 300

Rainfall (mm) 200 100 0

Date Figure 45 - Floods in Zhob and Mula river basins of Balochistan in last 35 years.

149. Figure 43 shows the floods in Zhob and Mula river basins. It can be observed that the magnitude of rainfall for Zhob river basin was much higher than Mula river basin. This was because of higher amount of annual rainfall in Zhob river basin compared to Mula river basin. Annual average rainfall in Zhob river basin was approximately 265 mm, whereas in Mula river 68

basin it was 135 mm. Despite much lower magnitude of rainfall in Mula river basin, the extent of damage was much higher (Table 19 and Table 20). Minimum area inundated in Zhob river basin for a rainfall amount of 352 mm was 1024 km2 whereas minimum area inundated for Mula river basin for a rainfall amount of 187 mm was 3758 km2. Similarly, the maximum area inundated in Zhob river basin for a rainfall amount of 601 mm was 1993 km2; whereas minimum area inundated for Mula river basin for a rainfall amount of 408 mm was 4071 km2. This difference was due to the unique river basin characteristics of both areas. Although 60 % of Mula river basin has approximately 10-15% slope but 40% area that is relatively a lot flatter (slope less than 5 %) mainly influencing these results. In contrast, most of the area through which the streams in Zhob river basin flows are steep (slope approximately 10 %) and no flat area intercepts the flow. Moreover, the flat area in Mula river basin is the one where most of the cultivation and settlements are. This is another important factor influenced the contrasting results for flood inundation and vulnerability analysis.

150. The flood data indicated an increasing trend in the frequency and magnitude of floods in Mula river basin; whereas a negative trend was observed in floods for Zhob river basin Figure 15 Following the same trend, the drought data showed a decreasing trend in Mula river basin whereas there was an increasing trend in the floods in this river basin Figure 16For Zhob river basin, there was an increasing trend in the occurrence of droughts in the last 35 years and decreasing trend was found in floods. These results imply the importance of devising flood management related adaptation measures in Mula river basin and drought management related adaptations in the Zhob river basin.

Drought in Zhob and Mula River Basin 160 Mula Zhob Linear (Mula) Linear (Zhob) 140 120 100 80 60

40 Precipitation Precipitation (mm) 20 0 1992 1993 1995 1999 2000 2001 2002 2004 2014 Year Figure 46 - Droughts in Zhob and Mula river basins in last 35 years. 69

4.5.1 Sediment

151. It is expected that if frequency and/or intensity of floods increases, the rate of erosion and sedimentation will also increase. Increasing sedimentation is expected to speed up the process of siltation in the reservoirs which will lead to a loss of the storage capacity much faster.

152. Model generated results of sediment deposit have been presented in this section. Numbers of Locations for which the results are shown are different for Zhob and Mula watersheds. The five flood years for both the watersheds have been chosen for sediment output.

153. Zhob watershed results are for three different locations. Two of these include the proposed dam locations of Sri Toi and Navar dam. Moreover, sediment transport results at the main outlet of the watershed have been presented for both the watersheds. The results are shown in Figure 45 to Figure 48 Since no dams are proposed in Mula watershed; therefore, only main outlet sediment has been presented in the results.

154. For reference, flood years have been changed in to return period. This will help in understanding that a larger amount of flow hence more sediment is expected for floods with bigger return period floods. For Zhob watershed flood years 1982 and 1984 have been identified as 3- year return period floods; however, 1985, 1986, and 2010 have been identified as 10-, 15-, and 30-year return period floods, respectively (Table 21). 70

Table 21 - Flood return periods for Zhob and Mula Watershed. Zhob Mula Return Period 1982, 1984 1997 3-year 1985 2007, 2009 10-year 1986 2008 15-year 2010 2013 30 year Sediment Yield (Watershed Outlet-Zhob)

4.E+05

4.E+05

3.E+05

3.E+05

2.E+05

2.E+05 Sediment Sediment (Tons) 1.E+05

5.E+04

0.E+00 1982 1984 1985 1986 2010 flood Year

Figure 47 - Sediment yield at watershed outlet (Zhob Watershed)

Sediment Yield (Nawar Dam-Zhob)

6.E+05

5.E+05

4.E+05

3.E+05

2.E+05 Sediment Sediment (Tons)

1.E+05

0.E+00 1982 1984 1985 1986 2010 flood Year

Figure 48 - Sediment Yield at Nawar Dam Location (Zhob Watershed) 71

Sediment Yield (Sri Toi Dam-Zhob)

1.E+06

1.E+06

8.E+05

6.E+05

4.E+05 Sediment Sediment (Tons)

2.E+05

0.E+00 1982 1984 1985 1986 2010 flood Year

Figure 49 - Sediment Yield at Nawar Dam location (Zhob Watershed).

Sediment Yield (Watershed outlet- Mula Watershed)

2E+05 2E+05 1E+05 1E+05 1E+05

8E+04 Sediment Sediment (tons) 6E+04 4E+04 2E+04 0E+00 1997 2007 2008 2009 2013 Flood Year

Figure 50 - Sediment Yield at Watershed outlet (Mula Watershed).

4.6 Vulnerability of Zhob and Mula River Basins

155. A vulnerability and impact matrix is developed to evaluate the possible climatic effects on both the river basins. Table 22 provides the details of vulnerability analysis for Zhob and Mula river basins. It was developed using the climate change analyses undertaken in the previous section, in addition to the review of secondary source information and data. The symbol ‘Y’ in 72

Table 22 referred to a strong chance of the change and ‘US’ means that the trend was not obvious. Symbol US was used in relation to the belief that data availability might be a strong factor in showing significant trends in climatic parameters.

156. It can be concluded from Table 22 that Zhob river basin is prone to losses associated with increasing temperatures, decreasing rainfall and more frequent droughts. Whereas, Mula river basin is characterized by a decreasing maximum temperature yet increasing minimum temperatures, decreasing rainfall in winters and summers (two main rainfall seasons) and an increasing trend in frequency and magnitude of floods.

Table 22 - Climate change vulnerability and impact matrix. Climatic Vulnerability Period Possible impacts activity Zhob Mula Increase in Winter Y US Increase in Evapotranspiration, crop Daily Maximum Spring Y US requirement, surface and groundwater 1 Temperature Summer Y US availability might reduce, shorter crop growing Autumn Y US periods, reduced crop yield Increase in Winter Y US Uncomfortable night time sleep for local Daily Minimum Spring US US 2 people in warmer seasons, changes in crop Temperature Summer US Y growing seasons Autumn US US Winter US US Decrease in Spring US Y 3 Daily Maximum Effect on crop yield Summer Y Y,Y Temperature Autumn Y Y Decrease in Winter US Y Cooler nights and severe winters in colder Daily Minimum Spring US US 4 season, shorter crop growing season, effect Temperature Summer Y US on crop yield Autumn US US Daily US US Annual US US Increase in Monthly US US More surface water, More frequent and 5 Rainfall Winter US US intense floods Spring US US Summer Y,Y US Autumn US Y Daily Y US Annual Y US Monthly Y,Y US Less surface water, more severe and 4 Decrease in Winter US Y frequent droughts, lowering of groundwater Rainfall Spring US US table, severe shortage of water Summer Y Y Autumn US US More surface water, more flash floods, 6 More Frequent Floods US Y economic loss due to loss of livestock and 73

agricultural land, potential to increase Sailaba farming Depletion of groundwater, failure of Sailaba 7 More Frequent Droughts Y US farming, loss of livelihood (Y=Yes, US=UnSure)

74

5 Adaptation Strategies

5.1 Adaptation Concepts and Measures

157. By definition adaptation is adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities. There are two major types of adaptation:

a. Autonomous adaptation or reactive adaptation tends to be what people and systems do as impacts of climate change become apparent b. Anticipatory or proactive adaptation are measures taken to reduce potential risks of future climate change

158. Naturally adaptation of living beings to environmental changes is yet long term process. People, animals, and plants have a natural instinct to adapt to environmental harshness or changes for the sake of survival; some flourish, some perish. In a modern world with some of the best scientific methods and technologies available to assess and predict changing environment, it has become much easier to adopt a proactive approach against natural disasters. A proactive approach helps in reducing the adverse impact of any extreme climatic event on the lives of people and their livelihood.

159. Adaptation measures are mostly localized. This is because adaptations are based on the changing environmental scenario locally and globally. For example, in a watershed, the amount of precipitation might be different from other areas. In addition, seasonal temperatures might be different or wind velocity could be variable. Therefore, the target areas in this study, Zhob and Mula river basins, have been evaluated for various climatic and hydrologic variables.

160. The areas of Zhob and Mula river basins, under consideration in this study, have shown quite a bit of variability in terms of climatic indicators and their effect on water resources. Moreover, both the river basins are subjected to relatively different kinds of climatic changes. Therefore, in terms adaptation measures, some adaptation measures will be more effective or not needed in the other river basins. Other than natural factors, the factor of cost effectiveness and sustainability also govern the selection of these adaptation measures.

161. Zhob and Mula river basins lie in the province of Balochistan which is an arid region. This means that the annual average rainfall is below 15 inches. Livestock industry is one of the main agricultural activities in the historic grazing lands of Balochistan. More than 50% of the native people of the province rely (completely or partially) on livestock. In case of both droughts and floods, this major source of livelihood is badly affected either by being washed away with excessive water or dying due to shortage of water and food.

162. The basic approach while adapting to climate change should be the economic development and capacity building of the community. The adaptation measures should be focused on showing proven benefits to the local people. This is one of the best ways of creating sustainability and ensuring long term benefits of any effort made in regard to adaptation. The adaptation measures 75

should be simple to expand and maintain for the local people so that they can reap real benefits out of them. One important step in creating effective resilience among the people is to show them real benefits of their effort of doing new things in their lives in terms of monitory and livelihood benefit.

163. The most real adaptation for the river basins of Zhob and Mula would be effective increase in groundwater recharge. One major reason for this strategy is that despite having many rivers and streams, these areas do not have a yearlong water flow. The flow in these regions is mainly seasonal that occurs due to recent precipitation events and disappears very quickly due to steep slopes and climate with high Evapotranspiration (ET). Long unfortunate drought spells have been experienced by both the river basins in recent past. Also, predicting nature’s behavior is a difficult task, especially when it comes to predicting the behavior of hydro-meteorological parameters. Groundwater has always been the source of water supply for the people in both the river basins, either in terms of tube wells or Karez systems. Increased groundwater levels will help in supplying water in the dry period as well as reviving the depleting Karez system.

164. Major loss of orchards has been experienced in earlier drought periods in both the river basis. Fruit orchards are sensitive to changes in climate. A better adaptation would be to replace these fruit orchards with crops that are easier to grow and more adaptable to harsh climate. An example would be to grow grass or fodder and promote cattle farming. Fodder and grass require a lot less water as compared to fruit orchards. Also, they are a lot less sensitive to changes in climate. Therefore, this practice will help in sustaining effective livelihood for the farmers.

165. Different areas should be evaluated for their individual capability of agriculture or plant production. Not every area has the same agricultural potential. There might be many reasons for this difference. However, it is suggested that instead of leaving a land barren because a certain type of crop does not grow or has very low yield, even very small amount of water is available, it is better to grow more tough species of plants that can survive even in the worst conditions. The least benefits that this adaptation measure can provide to the area are that the local people might have more fuel wood of their own and some grazing area for the livestock. Fuel wood prices are quite a burden on the pocket of a local farmer in the region of Zhob and Mula river basins. This measure will help in improving economic condition of the people in an indirect yet effective way.

5.2 Adaptation and Sustainability

166. With an expected increasing frequency and intensity of floods, the rate of erosion and sedimentation is also expected to increase. Increasing sedimentation is expected to speed up the process of siltation in the reservoirs which will lead to a loss of the storage capacity much faster. Few adaptation measures to avoid this situation is either do terracing of the steep slopes adjoining any reservoir to reduce erosion and also to promote plantation in the area to reduce erosion or intercept sediments and slowing down the rate of siltation in the reservoirs. Another effective yet inexpensive way of reducing rate of siltation is to create vegetated waterways. Vegetated waterways are unlined channels that have a special kind of grass or plants in the on the bed. These plants help intercepting the silt approaching the reservoir and slow down the process of siltation to a great extent (Table 23). 76

Table 23 - Possible adaptation measures applicable in Zhob and Mula river basins. River Possible Climatic Recommended Adaptations Basin Variation • Increase in water storage infrastructure, • Shifting to cash crop farming style • Reducing virtual water content from produce • Introduction of new groundwater recharge methods increasing temperatures, Opting for crops that are more genetically decreasing precipitation • Zhob adaptable to higher temperatures and more frequent droughts • Development of underground irrigation systems to reduce water loss due to evaporation • Creating new livelihood opportunities for economic growth • Creating livelihood opportunities for women

Decreasing maximum • Construction of water storage structures temperature yet • Erosion, and siltation reduction techniques like increasing minimum terracing, plantation, and vegetated waterways. temperatures, • Opting for crops that are more genetically decreasing precipitation adaptable to higher temperatures Mula in winters and summers Opting for crop types that are more resilient to (two main precipitation • harsh weather. seasons) and an increasing trend in • Creating new livelihood opportunities for frequency and magnitude economic growth of floods • Creating livelihood opportunities for women.

5.3 Adaptation at the Sub-Project Level

167. The adaptations taken to overcome possible impacts and vulnerabilities of different climatic activities are briefly described in Table 24. The basis for these adaptations is generally precautionary, with the level of precaution being set according to the expected risks.

168. Climate change adaptation for increase in rainfall include: additional freeboard for dam and flood protection bunds, better watershed management practices and provision of water storage systems.

169. Climate change adaptation for decrease in rainfall: construction of storage dams, lining of water courses and implementation of high efficiency irrigation system. These components have already been included in the original design and do not incur an additional cost. 77

Table 24 - Proposed adaptations against possible climatic impacts S. No. Climatic Possible Impacts Adaptations Activity 1. Increase in More surface water, more frequent and a. Dam additional freeboard. (1.5- rainfall intense floods meter-high parapet wall) b. Flood protection bund additional freeboard. (0.5 meter) c. Watershed management practices to reduce sedimentation. 2. Decrease in Less surface water, more severe and a. Selection of dam against other rainfall frequent droughts, lowering of options of development like weir and groundwater table, severe shortage of infiltration gallery. water b. Watershed management practices to increase ground water recharge. d. Lining of water courses to reduce water losses. e. implementation of high efficiency irrigation system.

5.3.1 Dam additional freeboard

170. Climate change will also increase magnitude of peak floods. Thus embankment failure due to overtopping will become more probable. In order to 1463.5 ensure safe passage of extreme floods, the spillway 1462 discharge capacity of Siri Toi Dam has been increased PMF WL 1461 from the Standard Design Flood (10,000-year) to the SDF WL 1459. 5 1 Probable Maximum Flood (PMF) . A 1.5-meter-high DAM parapet wall at the dam crest will ensure that the dam freeboard is maintained when the PMF passes through the spillway.

5.3.2 Flood protection bund additional Additional Freeboard freeboard MFL Bund 3 m 171. In Mula Basin, the proposed sub-projects: Kharzan-Hatachi Infiltration Gallery and Karkh River Development, will ensure that the more frequent floods in the basin due to climate change are used for agriculture, and surplus floods are safely diverted away from command areas. An extensive flood protection program for both sub-projects has been included in the design. The heights of

1 F.F Snyder, "Hydrology of Spillway Design; Large Structures-Adequate Data", ASCE, J. Hyd/ Div. 90 No. Hy3 (May 1964); 239-259. Design Standards No. 14 Appurtenant Structures for Dams (Spillways and Outlet Works) DS14(3.) Ross D. Zhou., C. R. Donnelly and David G. Judge. 2008. “On the Relationship between the 10,000-year flood and Probable Maximum Flood”. Hydrovision -HCI Publications. USACE EM-1110-2-1402 “Hydrological Engineering Requirements for Reservoirs” Manual on Estimation of Probable Maximum Precipitation (PMP), WMO No. 1045. (2009). 78

the proposed protection bunds, set for 5-year flood, have been raised by half a meter to ensure embankment safety for higher floods. Watershed management

5.3.3 Watershed Management

172. Adaptations for higher sedimentation rates due to increase in the magnitude and frequency of floods because of climate change, have been included for sub-projects in both basins. Watershed management practices like tree planation, spate irrigation, and provision of check dams at locations where sediment movement is expected will ensure that sediment movement is controlled.

5.3.4 Water storage system

173. In Zhob Basin, the proposed Siri Toi Dam will ensure resilience against more frequent droughts due to climate change. A water storage system for the community downstream of the dam is included in the design to ensure water for domestic use during these periods of droughts. The arrangement will prevent disease outbreak and forced migrations caused by water scarcity.

5.3.5 Cost of adaptations

174. Cost of climate change adaptations is briefed in Table 25. Detailed BOQ of flood protection, dam parapet wall and water storage system is attached Annexure F. Being primary component of the design, the cost of dam and lining of water courses is included in the design cost. Also, the cost of high efficiency irrigation system is the part of JFPR fund. Therefore, these costs are calculated as a part of climate change adaptations cost. 79

Table 25 - Climate Change Adaptation Cost Component Original Original Climate Climate Cost with Cost with Cost Cost Change Change Climate Climate Mil. Rs. Mil. $ Adaptations Adaptations Change Change Mil. Rs. (%) Mil. $ Adaptation Adaptation Mil. Rs. Mil. $ Flood 360.14 3.43 78.77 (22 %) 0.75 438.91 4.18 Protection Water Storage 81.25 0.77 8.75 (11 %) 0.08 90.00 0.86 System Dam 2,766 26.34 62 (2 %) 0.59 2,828.00 26.93 Freeboard Watershed 210.00 2.00 116 (55 %) 1.10 326.00 3.10 Management Total Cost of 9,542.48 90.88 265.52 (3 %) 2.53 9,808.00 93.41 the Project

5.4 Flood and Drought Risk Management (FDRM)

175. Floods and droughts are the two extreme hydro-climatological events that are a main focus in this study. An integrated approach to manage floods and droughts is required to minimize the adverse effect of both at the same time. Different adaptation measures can be used to reduce the impact of floods and droughts some of which are mentioned below:

5.4.1 Flood

176. Excess water received by the river basin during flood period is wasted due to inadequate storage facility. It is required to build adequate storage facilities with the provision of separating average flow water from the high flow water through specially designed canals and spillways. In this way the basic cost of the reservoir will not increase but the excess water during flood can be diverted to a supplemental storage area or dispersed on the ground to favor groundwater recharge.

177. As expected that more frequent high intensity precipitation events might be experienced by the area, as a consequence it is expected that higher magnitude floods will occur. This in turn will speed up the process of erosion and sedimentation. The situation of erosion and sedimentation is aggravated due to barren steep slope terrain that both the watersheds contain. Increased sedimentation will reduce the capacity of any storage facility already built in the area or planned in the future very quickly. Therefore, it is required to reduce the erosion and sedimentation process, or at least one of them so that the sustainability of the storage structures can be ensured.

178. A definite adaptation measure is to create new surface water storage reservoirs. This would help in storing the excess water in flood periods, and also help in dissipating the magnitude of destruction due to flash floods. Irrigation command area can be increased due to such reservoirs. Moreover, these reservoirs will help in storing water for the dry spells and will also increase the 80

rate of groundwater recharge hence help in raising the water table. In places with new storage facilities, it is suggested that certain hydraulic structures should be designed and constructed that can separate flood or above average flow from the average flow and divert it to another storage area or spread it out to a flatter area which will help in increasing the groundwater recharge even more as well as increase in cultivated area by increasing land surface area for Sailaba farming. This methodology will not increase the actual cost of the reservoir tremendously as would have happened in the case of higher height of reservoir just to store floodwater probability of which is not more than 1 in 4 years.

179. This can be done in multiple ways. Sediment reaching the reservoir can be trapped and stopped from reaching the reservoir. This can either be done by terracing of close steep slopes or creating filters, etc. An example of filter that can trap sediment and increase the life of a storage structure is vegetated waterways. Vegetated waterways are upstream channels that are planted with a certain type of grass and other plants to ensure that the sediment are intercepted by the vegetation and does not reach the reservoir as frequently as it would have in case the cannel was barren.

5.4.2 Drought

180. Balochistan is prone to drought and has been impacted severely during 1998–2002. The people are engaged in orchard and livestock farming activities for earning their livelihoods. During a survey farmers responded to believe that the climatic and environmental factors such as increased temperature, decreased precipitation, change in the timing of rainy season, inadequate supply of electricity for irrigation, over exploitation of groundwater, and population growth are culprits of aggravating the drought severity in the area. To cope with the drought, farmers have adapted a number of strategies at farm and off-farm levels that include crop and water management practices, adjustment in agricultural inputs, seeking off-farm employment, selling off assets, consumption smoothing, borrowing, and migrating to other places to seek alternative sources of income (Ashraf et al., 2013).

181. In some areas the emphasis was on saving vineyards which are considered drought- resistant as compared to other deciduous fruits grown in the uplands. Farmers were also observed working on increasing irrigation efficiency by using pipes to irrigate crops and trees, narrowing down the trees basins and avoiding irrigation during daytime.

5.4.3 Community coping mechanisms and enhancement of disaster resilience

182. Women and children are most sensitive to disasters. Moreover, health conditions of women affect the whole household. It is necessary to introduce adequate potable water supply and sanitation facilities at household level so that health of women and children can be ensured.

183. A lot of researchers have evaluated the drought coping mechanisms. Zamani et al. (2006) described two types of drought coping strategies. First is agricultural adjustments, most commonly the sale of livestock, early sowing of seeds, no sowing, livestock diversification, plant protection, storing of crop residue, purchase of forage, investment in shallow or deep tubewells, and 81

cultivating more water-efficient crops (Keenan & Krannig 1997; MacDonnel et al. 1995; Mortimor & Adams 2001; Zamani et al. 2006). Second is economic adjustments, where risks to food security are frequently anticipated and carefully planned. Economic coping strategies relate to asset management. During non-crisis years, two sorts of assets are acquired: a) savings and self- insurance through accumulation of small stock and jewelry, which can be liquidated in times of crisis; and b) investment in assets that play a key role in production and income generation, such as oxen and land, which can be riskier and less liquid.

184. There are more adaptation strategies to improve farmers' resilience for future droughts, such as to: a) develop, introduce and implement water harvesting practices at the community level; b) reduce wastage by improving irrigation management practices; and c) introduce crops which are classified as drought-resistant to release the pressure of water demand during drought conditions. The government’s capacity improvement will directly affect the resilience by implementing the strategies such as diversifying livelihoods, switching to more drought-resistant livestock species, and better rangeland management. The role of media such as television, radio, newspapers, and cell phones should be accordingly used to disseminate weather and other information about the current and future conditions with the adaptation practices for the said situation.

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6 Conclusion and Message for the PPTTA Team

185. One thing is worth mentioning in relation to adaptation strategy and measures that water users’ and farmers’ will adopt the adaptation measures which suit to their interest of having more profitable farming. Their interest is not in adaptation to climate change impacts rather to attain and sustain higher levels of productivity enhancement and profitability. Therefore, the measures in relation to crops, cropping pattern and land use practices can’t be dictated rather the market forces will determine the best choices and farmers preferences to adjust with the market pull. There is an emerging market for milk, meat, dairy products and skin and hides, which can be addressed and exploited by integrating irrigated agriculture with range and livestock along with recharging groundwater aquifers, which is the only source of water to reduce the risks of droughts.

186. Similarly, there is a message for the water development engineers that the structures for water development are the means to accomplish the objective or raising the farmers’ income. The concept of integrated land use and vegetative waterways suggested in this report needs to be undertaken while conducting feasibility and design of BWRDP schemes or sub-projects. They should be fully aware that there is not much water available to shift towards irrigated agriculture and therefore they have to come out of their traditional concepts of development of infrastructure towards water management at the level of river basin.

187. Lastly, we all know that in countries where river basins are in hydrological equilibrium are due to management of natural resources rather than conversion to irrigated agriculture in the fragile environments like Balochistan.

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References 1. ADB. 2007. Water Resources Assessment of Balochistan. ADB TA-4560 (PAK), Supporting Public Resource Management in Balochistan, Asian Development Bank, Government of Balochistan and Halcrow and Cameos Consultants, Quetta. 2. Ahmad, S. 2016. Situational Analysis of Water Sector of Pakistan. Paper prepared for UNDP. 3. Ahmad, S. 2016. Water Resources of Balochistan – Key Issues and Potential Actions. A paper prepared for the Balochistan Integrated Water Resources Management and Development Project, Government of Balochistan and World Bank based on the Policy Briefings under ADB TA-4560 (PAK). 4. Ahmad, S. 2007."Persistent drought of Balochistan and impacts on water availability and agriculture." Water for Balochistan: Policy Briefings 3, no. 6 (2007): 2010-11. 5. Ahmed, Kamal, Shamsuddin Shahid, and Sobri Bin Harun. 2014. "Spatial interpolation of climatic variables in a predominantly arid region with complex topography." Environment Systems and Decisions 34, no. 4 (2014): 555-563. 6. Ahmed, Kamal, Shamsuddin Shahid, Sobri bin Harun, and Xiao-jun Wang. 2016. "Characterization of seasonal droughts in Balochistan Province, Pakistan." Stochastic Environmental Research and Risk Assessment 30, no. 2 (2016): 747-762. 7. Arnold, Jeffrey G., Raghavan Srinivasan, Ranjan S. Muttiah, and Jimmy R. Williams. 1998. "Large area hydrologic modeling and assessment part I: Model development1." (1998): 73- 89. 8. Ashraf, Muhammad, and Jayant K. Routray. 2015. "Spatio-temporal characteristics of precipitation and drought in Balochistan Province, Pakistan." Natural Hazards 77, no. 1 (2015): 229-254. 9. Ashraf, Muhammad, and Jayant Kumar Routray. 2013. "Perception and understanding of drought and coping strategies of farming households in north-west Balochistan." International Journal of Disaster Risk Reduction 5 (2013): 49-60. 10. Asimakopoulos, D. N., V. D. Assimakopoulos, N. Chrisomallidou, N. Klitsikas, D. Mangold, P. Michel, M. Santamouris, and A. Tsangrassoulis. 2001. "Energy and climate in the urban built environment." M. Santamouris, University of Athens, Greece. ISBN 1873936907 (2001). 11. Burke, F., S. N. Huda, S. Hamza, and M. Azam. 2005. "Disparities in agricultural productivity in Balochistan-A GIS perspective." Pakistan Geographical Review 60, no. 1 (2005): 27-34. 12. Change, Climate. 2007. "Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change." (2007). 13. Farooqi, Anjum Bari, Azmat Hayat Khan, and Hazrat Mir. 2005. "Climate change perspective in Pakistan." Pakistan Journal of Meteorology 2, no. 3 (2005). 14. Hansen, James, Makiko Sato, Reto Ruedy, Ken Lo, David W. Lea, and Martin Medina- Elizade. 2006. "Global temperature change." Proceedings of the National Academy of Sciences 103, no. 39 (2006): 14288-14293. 15. Malik, Sadia Mariam, Haroon Awan, and Niazullah Khan. 2012. "Mapping vulnerability to climate change and its repercussions on human health in Pakistan." Globalization and health 8, no. 1 (2012): 1. 84

16. Malik, Sadia Mariam, Haroon Awan, and Niazullah Khan. 2012. "Mapping vulnerability to climate change and its repercussions on human health in Pakistan." Globalization and health 8, no. 1 (2012): 1. 17. Mirza, Monirul. 2007. "Climate change, adaptation and adaptive governance in water sector in South Asia." In Amsterdam Conference on Human Dimensions of Global Environmental Change, May in Amsterdam. 2007. 18. Qureshi, Asad Sarwar. 2011. "Water management in the Indus basin in Pakistan: challenges and opportunities." Mountain Research and Development 31, no. 3 (2011): 252-260. 19. Rahman, Arif, and Aneel Salman. 2013. A District Level Climate Change Vulnerability Index of Pakistan. No. 2013: 05. Pakistan Institute of Development Economics, 2013. 20. Rasul, G., A. Mahmood, A. Sadiq, and S. I. Khan. 2012. "Vulnerability of the Indus delta to climate change in Pakistan." Pakistan journal of meteorology 8, no. 16 (2012). 21. Rasul, G., Q. Z. Chaudhry, A. Mahmood, and K. W. Hyder. 2011. "Effect of temperature rise on crop growth and productivity." Pak. Journal of Meteorology 8 (2011): 53-62. 22. Rees, D. J., A. Samiullah, F. Rehman, C. H. R. Kidd, J. D. H. Keatinge, and S. H. Raza. 1990. "Precipitation and temperature regimes in upland Balochistan: their influence on rain-fed crop production." Agricultural and forest meteorology 52, no. 3 (1990): 381-396. 23. Reuveny, Rafael. 2007. "Climate change-induced migration and violent conflict." Political geography 26, no. 6 (2007): 656-673. 24. Saeed, M. and S. Ahmad. 2008. Promising Crops and Water Efficient Cropping Patterns for Irrigated Farming Systems of Balochistan, Pakistan – Key Issues and Policy Reforms. Vol. (4), No. (2), TA-4560 (PAK), Quetta, Pakistan. 25. Sathaye, Jayant A., and N. H. Ravindranath. 1998. "Climate change mitigation in the energy and forestry sectors of developing countries." Annual Review of Energy and the Environment 23, no. 1 (1998): 387-437. 26. Tareen, S., B. Sani, and K. Babar. "S., Ahmad, 2008. Re-assessment of Water Resource Availability and Use for the Major River Basins of Balochistan–Study Findings." Policy Issues and Reforms. Water for Balochistan 4, no. 7. 27. Tariq, Muhammad Atiq Ur Rehman, and Nick van de Giesen. 2012. "Floods and flood management in Pakistan." Physics and Chemistry of the Earth, Parts A/B/C 47 (2012): 11-20. 28. USACE, US. "Army Corps of Engineers.2006. "Engineer Research and Development Center- Building Information Modeling: A Road Map for Implementation To Support MILCON Transformation and Civil Works Projects within the US Army Corps of Engineers, USACE, Washington, DC(2006). 29. Van Vuuren, Detlef P., Elmar Kriegler, Brian C. O’Neill, Kristie L. Ebi, Keywan Riahi, Timothy R. Carter, Jae Edmonds et al. "A new scenario framework for climate change research: scenario matrix architecture." Climatic Change 122, no. 3 (2014): 373-386. 30. UNEP (United Nations Environment Programme), 1997. World atlas of desertification 2ED. UNEP, London. 31. Edwards D.C., McKee T.B., 1997, Characteristics of 20th century drought in the United States at multiple scales, Atmospheric Science, paper no. 634, May 1-30. 32. Vijendra K Boken, 2005, Monitoring and Predicting Agricultural Drought – A Global Study, 85

pp. 266-275, Oxford University Press. 33. Wu Hong, Svoboda D Mark, Hayes J. Michael, Wilhite A Donald and Fujiang Wen, 2006, Appropriate application of the standardizes precipitation index in arid locations and dry seasons, International Journal of Climatology, 27, pp. 65-79 34. Vicente-Serrano M Sergio, Gonzalez-Hidalgo Carlose Jose, Luis de Martin and Raventos Jose, 2004, Drought patterns in the Mediterranean area: the Valencia region (eastern Spain), Climate Research, 26, pp5-15. 35. Guttman B Nathaniel, 1998, Comparing the Palmer drought index and the standardized precipitation index, Journal of the American Water Resources Association, 34, 1, 113-121. 86

Annexure A- Historic Flood Inundation Maps

Figure A- 1. Flood inundation map of Mula River Basin for 1997.

87

Figure A- 2. Flood inundation map of Mula River Basin for 2007.

Figure A- 3. Flood inundation map of Mula River Basin for 2008.

Figure A- 4. Flood inundation map of Mula River Basin for 2009. 88

Figure A- 5. Flood inundation map of Mula River Basin for 2013.

89

Figure A- 6 Flood inundation map of Zhob River Basin for 1982.

Figure A- 7. Flood inundation map of Zhob River Basin for 1984.

90

Figure A- 8. Flood inundation map of Zhob River Basin for 1985.

Figure A- 9. Flood inundation map of Zhob River Basin for 1986.

Figure A- 10. Flood inundation map of Zhob River Basin for 2010

91

Figure A- 1. Flood inundation map of Mula River Basin for 1997...... 86 Figure A- 2. Flood inundation map of Mula River Basin for 2007...... 87 Figure A- 3. Flood inundation map of Mula River Basin for 2008...... 87 Figure A- 4. Flood inundation map of Mula River Basin for 2009...... 87 Figure A- 5. Flood inundation map of Mula River Basin for 2013...... 88 Figure A- 6 Flood inundation map of Zhob River Basin for 1982...... 89 Figure A- 7. Flood inundation map of Zhob River Basin for 1984...... 89 Figure A- 8. Flood inundation map of Zhob River Basin for 1985...... 90 Figure A- 9. Flood inundation map of Zhob River Basin for 1986...... 90 Figure A- 10. Flood inundation map of Zhob River Basin for 2010...... 90

92

Annexure B-Temperature Analysis Graphs

Zhob Annual Temperature_moving average 3 per. Mov. Avg. (Max-T)

3 per. Mov. Avg. (Min-T) 35

30

25

C) ° 20

15

Temperature Temperature ( 10

5

0 1991 1996 2001 2006 2011 2016 Year

Figure 0-1 Annual Temperature linear regression for Zhob

Zhob Annual Temperature_linear regression Linear (Max-T) Linear (Min-T) 35

30

25

C) ° 20

15

Temperature Temperature ( 10

5

0 1991 1996 2001 2006 2011 2016 Year

Figure 0-2 Annual temperature linear regression for Zhob watershed.

93

Zhob Monthly Temperature_moving average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 45 40

35 C) ° 30 25 20

15 Temperature Temperature ( 10 5 0 -5 May-1991 Nov-1996 May-2002 Oct-2007 Apr-2013 Month

Figure 0-3 Monthly temperature moving average for Zhob watershed

Zhob Monthly Temperature_linear regression Linear (Max-T) Linear (Min-T) 45 40 35

30

C) ° 25 20 15

Temperature Temperature ( 10 5 0 -5 May-1991 Nov-1996 May-2002 Oct-2007 Apr-2013 Month

Figure 0-4 Monthly temperature linear regression for Zhob watershed. 94

Zhob Winter Temperature_Linear Regression Linear (Max-T) 35 Linear (Min-T) 30

25 C) ° 20 15 10

5 Temperature Temperature ( 0 -5 -10 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-5 Winter temperature linear regression for Zhob watershed.

Zhob Winter Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 35 30

25 C)

° 20 15 10

5 Temperature Temperature ( 0 -5 -10 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-6 Winter temperature moving average for Zhob watershed. 95

Zhob Spring Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 50

40 C)

° 30

20

10 Temperature Temperature (

0

-10 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-7 Spring temperature linear regression for Zhob watershed

Zhob Spring Temperature_Moving Average 3 per. Mov. Avg. (Max-T)

50 3 per. Mov. Avg. (Min-T)

40 C)

° 30

20

10 Temperature Temperature (

0

-10 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-8 Spring temperature moving average for Zhob watershed 96

Zhob Summer Temperature_Linear Regression Linear (Max-T) 50 Linear (Min-T)

40

C) ° 30

20 Temperature Temperature ( 10

0 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-9 summer temperature linear regression for Zhob watershed

Zhob Summer Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 50 3 per. Mov. Avg. (Min-T) 45 40

35

C) ° 30 25 20

Temperature Temperature ( 15 10 5 0 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-10 Summer temperature moving average for Zhob watershed. 97

Zhob Autumn Temperature_Linear Regression Linear (Max-T) 50 Linear (Min-T)

40

C) ° 30

20 Temperature Temperature ( 10

0 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-11 Autumn temperature linear regression Zhob watershed.

Zhob Autumn Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 50

40

C) ° 30

20 Temperature Temperature (

10

0 Aug-1990 Mar-1994 Sep-1997 Apr-2001 Nov-2004 Jun-2008 Dec-2011 Jul-2015 Month

Figure 0-12 Autumn temperature moving average for Zhob watershed. 98

Qila Saifullah Annual Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 30

25 C) ° 20

15

10 Temperature Temperature (

5

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 0-13 Annual temperature linear regression for Qila Saifulla watershed.

Qila Saifullah Annual Temperature_Moving average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 30

25 C) ° 20

15

10 Temperature Temperature ( 5

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 0-14 Annual temperature moving average for Qila Saifulla watershed. 99

Qila Saifullah Monthly Temperature_Moving average 3 per. Mov. Avg. (Max-T) 45 3 per. Mov. Avg. (Min-T) 40

35 C) ° 30 25 20 15 Temperature Temperature ( 10 5 0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-15 Monthly temperature moving average for Qila Saifulla watershed.

Qila Saifullah Monthly Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 45 40

35 C) ° 30 25 20

15 Temperature Temperature ( 10 5 0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-16 Monthly temperature linear regression for Qila Saifulla watershed. 100

Qila Saifullah Winter Temperature_Linear Regression

Linear (Max-T) 25 Linear (Min-T)

20 C)

° 15

10

5 Temperature Temperature (

0

-5 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-17 Winter temperature linear regression for Qila Saifulla watershed.

Qila Saifullah Winter Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 25 3 per. Mov. Avg. (Min-T)

20 C)

° 15

10

5 Temperature Temperature (

0

-5 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-18 Winter temperature moving average for Qila Saifulla watershed. 101

Qila Saifullah Spring Temperature_Linear Regression Linear (Max-T) 35 Linear (Min-T)

30

25

C) ° 20

15

Temperature Temperature ( 10

5

0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-19 Spring temperature linear regression for Qila Saifulla watershed.

Qila Saifullah Spring Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 35 3 per. Mov. Avg. (Min-T) 30

25

C) ° 20

15

Temperature Temperature ( 10

5

0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-20 Spring temperature moving average for Qila Saifulla watershed. 102

Qila Saifullah Summer Temperature_Linear Regression Linear (Max-T) 45 Linear (Min-T) 40

35 C)

° 30

25

20

15 Temperature Temperature ( 10

5

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Month

Figure 0-21 Summer temperature linear regression for Qila Saifulla watershed.

Qila Saifullah Summer Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 45 3 per. Mov. Avg. (Min-T) 40

35 C)

° 30 25 20

15 Temperature Temperature ( 10 5 0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Month

Figure 0-22 Summer temperature moving average for Qila Saifulla watershed. 103

Qila Saifullah Autumn Temperature_Linear Regression

Linear (Max-T) 35 Linear (Min-T)

30

25

C) ° 20

15

Temperature Temperature ( 10

5

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Month

Figure 0-23 Autumn temperature linear regression for Qila Saifulla watershed.

Qila Saifullah Autumn Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 35 3 per. Mov. Avg. (Min-T) 30

25

C) ° 20

15

Temperature Temperature ( 10

5

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Month

Figure 0-24 Autumn temperature moving average for Qila Saifulla watershed. 104

Badinzai Annual Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 30

25 C)

° 20

15

10 Temperature Temperature (

5

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 0-25 Annual temperature linear regression for Badinzai

Badinzai Annual Temperature_Moving average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 30

25 C) ° 20

15

10 Temperature Temperature (

5

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 0-26 Annual temperature moving average for Badinzai 105

Badinzai Monthly Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 45 40

35 C)

° 30 25 20

15 Temperature Temperature ( 10 5 0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-27 Monthly temperature linear regression for Badinzai

Badinzai Monthly Temperature_Moving average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 45 40

35 C)

° 30 25 20 15 10 Temperature Temperature ( 5 0 -5 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-28 Monthly temperature moving average for Badinzai 106

Badinzai Winter Temperature_Linear Regression Linear (Max-T) 25 Linear (Min-T)

20

15

C) ° 10

5

Temperature Temperature ( 0

-5

-10 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-29 Winter temperature linear regression for Badinzai

Badinzai Winter Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 25 3 per. Mov. Avg. (Min-T)

20

15

C) ° 10

5

Temperature Temperature ( 0

-5

-10 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-30 Winter temperature moving average for Badinzai. 107

Badinzai Spring Temperature_Linear Regression Linear (Max-T) 40 Linear (Min-T) 35

30 C) ° 25

20

15 Temperature Temperature ( 10

5

0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-31 Spring temperature linear regression for Badinzai.

Badinzai Spring Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 40 3 per. Mov. Avg. (Min-T) 35

30

C) ° 25

20

15

Temperature Temperature ( 10

5

0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-32 Spring temperature moving average for Badinzai. 108

Badinzai Summer Temperature_Linear Regression Linear (Max-T) 45 Linear (Min-T) 40

35 C)

° 30 25 20

15 Temperature Temperature ( 10 5 0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-33 Summer temperature linear regression for Badinzai.

Badinzai Summer Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 45 3 per. Mov. Avg. (Min-T) 40

35 C) ° 30 25 20

15 Temperature Temperature ( 10 5 0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-34 Summer temperature moving average for Badinzai. 109

Badinzai Autumn Temperature_Linear Regression Linear (Max-T) 40 Linear (Min-T) 35

30 C) ° 25

20

15 Temperature Temperature ( 10

5

0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Month

Figure 0-35 Autumn temperature linear regression for Badinzai.

Badinzai Autumn Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 40 3 per. Mov. Avg. (Min-T) 35

30 C) ° 25

20

15 Temperature Temperature ( 10

5

0 Nov-2001 Mar-2003 Aug-2004 Dec-2005 Apr-2007 Sep-2008 Jan-2010 Month

Figure 0-36 Autumn temperature moving average for Badinzai. 110

Khuzdar Annual Temperature_Linear regression Linear (Max-T) 40 Linear (Min-T) 35

30 C) ° 25

20

15 Temperature Temperature ( 10

5

0 2004 2006 2008 2010 2012 2014 2016 Year

Figure 0-37 Annual temperature linear regression for Badinzai.

Khuzdar Annual Temperature_Moving average

3 per. Mov. Avg. (Max-T) 40 3 per. Mov. Avg. (Min-T) 35

30 C) ° 25

20

15

Temperature Temperature ( 10

5

0 2004 2006 2008 2010 2012 2014 2016 Year

Figure 0-38 Annual temperature moving average for Khuzdar. 111

Khuzdar Monthly Temperature_Moving average

3 per. Mov. Avg. (Max-T) 45 3 per. Mov. Avg. (Min-T) 40

35

C) 30 °

25

20

15 Temperature Temperature (

10

5

0 Jan-2005 Jun-2006 Oct-2007 Mar-2009 Jul-2010 Dec-2011 Apr-2013 Aug-2014 Jan-2016 Month

Figure 0-39 Monthly temperature moving average for Khuzdar.

Khuzdar Monthly Temperature_Linear regression Linear (Max-T)

45 Linear (Min-T)

40

35 C)

° 30

25

20

15 Temperature Temperature ( 10

5

0 Oct-2004 Mar-2006 Jul-2007 Nov-2008 Apr-2010 Aug-2011 Jan-2013 May-2014 Oct-2015 Month

Figure 0-40 Monthly temperature linear regression for Khuzdar. 112

Khuzdar Winter Temperature_Linear Regression

Linear (Max-T) 35 Linear (Min-T) 30

25 C) ° 20

15

10 Temperature Temperature ( 5

0

-5 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-41 Winter temperature linear regression for Khuzdar.

Khuzdar Winter Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 35 3 per. Mov. Avg. (Min-T) 30

25 C) ° 20

15

10 Temperature Temperature ( 5

0

-5 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-42 Winter temperature moving average for Khuzdar. 113

Khuzdar Spring Temperature_Linear Regression Linear (Max-T) 45 Linear (Min-T) 40

35

C) 30 °

25

20

15 Temperature Temperature ( 10

5

0 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-43 Spring temperature linear regression for Khuzdar.

Khuzdar Spring Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 45 3 per. Mov. Avg. (Min-T) 40

35 C)

° 30 25 20

15 Temperature Temperature ( 10 5 0 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-44 Spring temperature moving average for Khuzdar. 114

Khuzdar Summer Temperature_Linear Regression Linear (Max-T) 45 Linear (Min-T) 40

35 C)

° 30 25 20

15 Temperature Temperature ( 10 5 0 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-45 Summer temperature linear regression for Khuzdar.

Khuzdar Summer Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 45 3 per. Mov. Avg. (Min-T) 40

35 C)

° 30 25 20

15 Temperature Temperature ( 10 5 0 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-46 Summer temperature moving average for Khuzdar. 115

Khuzdar Autumn Temperature_Linear Regression Linear (Max-T) 30 Linear (Min-T)

25 C)

° 20

15

10 Temperature Temperature (

5

0 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-47 Autumn temperature linear regression for Khuzdar.

Khuzdar Autumn Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 30 3 per. Mov. Avg. (Min-T)

25 C)

° 20

15

10 Temperature Temperature (

5

0 Nov-2004 Jan-2007 Mar-2009 Jun-2011 Aug-2013 Oct-2015 Month

Figure 0-48 Autumn temperature moving average for Khuzdar. 116

Gandwana Annual Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 40

35

30

C) ° 25

20

15

Temperature Temperature ( 10

5

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 0-49 Annual temperature linear regression

Gandwana Annual Temperature_Moving average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 40

35

30

C) ° 25

20

15

Temperature Temperature ( 10

5

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 0-50 Annual temperature moving average for Gandwana. 117

Gandwana Monthly Temperature_Linear Regression

Linear (Max-T) Linear (Min-T) 60

50

C) 40 °

30

20 Temperature Temperature (

10

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-51 Monthly temperature linear regression for Gandwana.

Gandwana Monthly Temperature_Moving average

3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 60

50 C) ° 40

30

20 Temperature Temperature (

10

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-52 Monthly temperature moving average for Gandwana. 118

Gandwana Winter Temperature_Linear Regression

Linear (Max-T) Linear (Min-T) 30

25 C)

° 20

15

10 Temperature Temperature (

5

0 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Month

Figure 0-53 Winter temperature linear regression for Gandwana.

Gandwana Winter Temperature_Moving Average 30 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T)

25 C)

° 20

15

10 Temperature Temperature (

5

0 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Month

Figure 0-54 Winter temperature moving average for Gandwana. 119

Gandwana Spring Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 50 45 40

C) 35 ° 30 25 20

15 Temperature Temperature ( 10 5 0 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Month

Figure 0-55 Spring temperature linear regression for Gandwana.

Gandwana Spring Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 50 45

40 C)

° 35 30 25 20

15 Temperature Temperature ( 10 5 0 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Month

Figure 0-56 Spring temperature moving average for Gandwana. 120

Gandwana Summer Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 60

50 C) ° 40

30

20 Temperature Temperature ( 10

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-57 Summer temperature linear regression for Gandwana.

Gandwana Summer Temperature_Moving Average 3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 60

50 C) ° 40

30

20 Temperature Temperature ( 10

0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-58 Summer temperature moving average for Gandwana. 121

Gandwana Autumn Temperature_Linear Regression Linear (Max-T) Linear (Min-T) 45 40

35 C) ° 30 25 20 15 Temperature Temperature ( 10 5 0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-59 Autumn temperature linear regression for Gandwana.

Gandwana Autumn Temperature_Moving Average

3 per. Mov. Avg. (Max-T) 3 per. Mov. Avg. (Min-T) 45 40

35 C) ° 30 25 20

15 Temperature Temperature ( 10 5 0 Apr-2001 Sep-2002 Jan-2004 May-2005 Oct-2006 Feb-2008 Jul-2009 Nov-2010 Month

Figure 0-60 Autumn temperature moving average for Gandwana.

122

Annexure C-Methodology of Analysis

Simple Linear Regression

188. Linear Regression is a statistical test that attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Regression is used to estimate the unknown effect of changing one variable over another. Stock and Watson (2007) has given good description on the analysis of linear regression. Technically, linear regression estimates how much Y changes when X changes one unit.

189. The variable that needs to be predicted is called the criterion variable and is referred to as Y. The variable on which predictions are based is called the predictor variable and is referred to as X. When there is only one predictor variable, the prediction method is called simple regression. In simple linear regression, the predictions of Y when plotted as a function of X form a straight line. The straight line is said to show the best fit line generated through the points. This best-fit line is called a regression line. In this study, time series are predictor variables and meteorological parameters are criterion variables.

190. The formula for a regression line is:

Y = bx + A (1)

191. Where, Y is the predicted score, b is the slope of the line, and A is the Y intercept.

Mann-Kendall’s Rank Test

192. Mann Kendall test is a statistical test widely used for the analysis of trend in climatologic and in hydrologic time series. It is a non-parametric test which means it does not require the data to be normally distributed. This test shows whether the trends are significant or non-significant with respect to increase or decrease in temperature over the period of study. The test is based on various equations for finding parameters such as MK Statistic (S), Kendall’s Tau, Variance, p- value, and Alpha. Below is the equation for calculating MK’s Statistics;

dn − E(dn ) (2) U(dn ) = var(dn )

193. Where dn is sum of number of observations, for which difference between the observations and reference observation is positive, E (dn) is the expected value of dn, and U (dn) is test statistic value that measures the trend whether it is increasing, decreasing, or trend-less.

123

194. Mann- Kendall test is performed specifically to attain significance of hydrologic parameters. MK’s Statistics (S) is a pivotal variable that signifies trend attained by the variable. If the value is positive, this shows that there is an increasing trend, whereas significant negative value indicates a decreasing trend. Based on MK’s statistics (S), trends are identified for all the hydrologic parameters. MK’s Tau, Variance and p values (two-tailed test) are also calculated to verify the results accordingly. Hirsch (1982) performed MK Rank’s test for water quality data analysis and evaluated satisfactory results.

195. Hamed (2008) and Burn & Elnur (2002) in their studies suggested that out of all tests, MK is given preference due to its significant results for hydrological parameters. Their studies show that this test is an excellent tool for trend detection and verification of hydrological parameters.

Moving average

196. A moving average is a technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends. Moving average trend can be calculated for any period of time. For example, if there is temperature data for a twenty-year period, different analyses can be done like a five-year moving average, a four-year moving average, a three-year moving average and so on. Hydrologists often find 3 to 5 year moving average to analyze data.

197. An average represents the “middling” value of a set of numbers. The moving average is exactly the same, but the average is calculated several times for several subsets of data. For example, if a two-year moving average for a data set from 2000, 2001, 2002 and 2003 needed to be found averages for the subsets 2000/2001, 2001/2002 and 2002/2003 will be taken. Moving averages are usually plotted and are best visualized.

124

Annexure D-Results of statistical tests

Table D-1: Results of Man-Kendall and linear regression tests on temperature in the Zhob river basin of Balochistan Station Variable Kendall tau p-value Sen's slope MK Trend LR Winter Max -0.06 0.453 -0.01 × D Winter Min 0 0.984 0 × I Spring Max 0 0.966 0 × I Spring Min 0.08 0.353 0.034 × I Summer Max -0.04 0.6 0 × D Summer Min -0.17 0.041 -0.035 D D Zhob Autumn Max -0.02 0.861 0 × I Autumn Min -0.06 0.474 -0.031 × D Monthly Max -0.006 0.872 0 × I Monthly Min 0 0.995 0 × D Yearly Max 0.041 0.816 0 × D Yearly Min 0.052 0.758 0 × D Winter Max 0.62 0.0001 0.559 I I Winter Min 0.34 0.026 0.233 I I Spring Max 0.38 0.012 0.421 I I Spring Min 0.26 0.091 0.277 × I Summer Max 0.41 0.007 0.359 I D Qila Summer Min 0.14 0.369 0.117 × D Saifullah Autumn Max 0.39 0.011 0.56 I D

Autumn Min 0.13 0.403 0.1 × D Monthly Max 0.268 0 0.133 I I Monthly Min 0.286 0.399 0.703 × D Yearly Max 0.786 0.006 1.494 I I Yearly Min 0.141 0.043 0.061 I I Winter Max -0.11 0.492 -0.059 × I Winter Min -0.01 0.979 0 × I Spring Max 0.12 0.434 0.192 × I Spring Min 0 1 -0.008 × I Summer Max -0.11 0.492 -0.036 × I Summer Min -0.21 0.169 -0.143 × I BadinZai Autumn Max 0.00 1 0.008 × I Autumn Min -0.12 0.444 -0.15 × I Monthly Max 0.043 0.537 0.021 × D Monthly Min 0.01 0.885 0.005 × I Yearly Max 0.5 0.109 0.116 × I Yearly Min -0.071 0.905 -0.055 × D Winter Max -0.05 0.674 -0.024 × D Winter Min 0.10 0.445 0.03 × I Spring Max -0.04 0.744 -0.024 × D Khuzdar Spring Min 0.15 0.913 0 × I

Summer Max -0.29 0.022 -0.079 D D Summer Min 0.28 0.031 0.108 I I Autumn Max -0.22 0.078 -0.093 D I 125

Station Variable Kendall tau p-value Sen's slope MK Trend LR Autumn Min 0.14 0.277 0.121 × I Monthly Max -0.086 0.146 -0.021 × D Monthly Min 0.063 0.288 0.021 × I Yearly Max -0.308 0.247 -0.143 × I Yearly Min 0.224 0.365 0.125 × I Winter Max -0.268 0.081 -0.089 × I Winter Min -0.12 0.443 -0.033 × D Spring Max 0.128 0.412 0.284 × I Spring Min 0.144 0.354 0.179 × D Summer Max 0.224 0.146 0.133 × I Gandawa Summer Min 0.072 0.653 0.025 × I Autumn Max 0.136 0.383 0.155 × I Autumn Min 0.116 0.459 0.133 × D Monthly Max 0.101 0.153 0.036 × I Monthly Min 0.109 0.122 0.021 × I Yearly Max 0.143 0.72 0.038 × I Yearly Min 0.214 0.548 0.101 × I (D=Decreasing Trend, ×= No Trend, I= Increasing Trend) 126

Table D-2: Results of Man-Kendall and linear regression tests on rainfall in Zhob river basins of Balochistan. Sen's Station Time Line Kendall's tau MK Trend LR Trend slope Monthly 0.071 0 × I Yearly 0.134 1.342 × Winter 0.085 0.01 × I Zhob Spring 0.016 0 × D Summer 0.135 0.099 I I Autumn 0.083 0 × D Monthly -0.019 0 × D Yearly -0.067 -1.516 D Winter 0.148 0.084 × I Qila Saifullah Spring -0.026 0 × D Summer -0.012 0 × D Autumn -0.172 0 × D Monthly 0.053 0 × I Yearly 0.333 18.427 × Winter -0.109 -0.034 × I Badinzai Spring 0.183 0 × I Summer 0.055 0 × I Autumn 0.177 0 × I Yearly 0.257 0.107 I I Monthly 0.094 0 I I Muslim Bagh Autumn 0.044 0 × × (1901-1946) Summer 0.108 0 × I

Spring 0.1 0.001 × I Winter 0.178 0.005 I I Daily -0.023 0 D D Monthly -0.103 0 D D Muslim Bagh Yearly 0.018 0.033 × D (Zhob)(1969-1993) Winter -0.067 0 × I Spring -0.084 0 × I Summer -0.14 0 × I Autumn 0.096 0 × I Daily 0.015 0 I D Monthly -0.002 0 × D Murgha Yearly -0.111 -0.081 × D Kibzai(1911-1946) Winter 0.11 0.021 × I Spring -0.121 -0.027 × D Summer -0.036 -0.015 × D Autumn -0.106 0 × D Sharan Daily -0.002 0 × D Jagozai(1975- Monthly -0.116 0 D D 1993) Yearly -0.329 -0.129 × D Winter -0.053 -0.028 × D 127

Sen's Station Time Line Kendall's tau MK Trend LR Trend slope Spring -0.012 0 × D Summer -0.442 -0.144 D D Autumn 0.175 0 × I • (D=Decreasing Trend, ×= No Trend, I= Increasing Trend) 128

Table D-3 Results of Man-Kendall and Linear Regression tests on temperature in Mula river basin of Balochistan. Kendall Sen's Station Variable p-value MK Trend LR tau slope Winter Max -0.05 0.674 -0.024 × D Winter Min 0.1 0.445 0.03 × I Spring Max -0.04 0.744 -0.024 × D Spring Min 0.15 0.913 0 × I Summer Max -0.29 0.022 -0.079 D D Summer Min 0.28 0.031 0.108 I I Khuzdar Autumn Max -0.22 0.078 -0.093 D I Autumn Min 0.14 0.277 0.121 × I Monthly Max -0.086 0.146 -0.021 × D Monthly Min 0.063 0.288 0.021 × I Yearly Max -0.308 0.247 -0.143 × I Yearly Min 0.224 0.365 0.125 × I Winter Max -0.268 0.081 -0.089 × I Winter Min -0.12 0.443 -0.033 × D Spring Max 0.128 0.412 0.284 × I Spring Min 0.144 0.354 0.179 × D Summer Max 0.224 0.146 0.133 × I Summer Min 0.072 0.653 0.025 × I Gandawa Autumn Max 0.136 0.383 0.155 × I Autumn Min 0.116 0.459 0.133 × D Monthly Max 0.101 0.153 0.036 × I Monthly Min 0.109 0.122 0.021 × I Yearly Max 0.143 0.72 0.038 × I Yearly Min 0.214 0.548 0.101 × I Winter Max 0.1495 0.08 0.0472 × D Winter Min -0.15175 0.02 -0.041 D I Spring Max -0.154 0.001 -0.0348 D D Spring Min 0.15625 0.3 0.0286 × I Summer Max -0.1585 0.034 -0.0224 D D Summer Min 0.16075 0.006 0.0162 I I Kalat Autumn Max 0.163 0.383 0.01 × D Autumn Min 0.16525 0.459 0.0038 × I Monthly Max 0.1675 0.153 -0.0024 × D Monthly Min 0.16975 0.122 -0.0086 × I Yearly Max 0.172 0.72 -0.0148 × D Yearly Min 0.17425 0.548 -0.021 × I (D=Decreasing Trend, ×= No Trend, I= Increasing Trend) 129

Table D-4 Results of Man-Kendall and linear regression tests on rainfall in Mula river basin of Balochistan Kendall's Sen's MK Location Time p-value LR Trend tau slope Trend Khuzdar monthly -0.077 0.036 0 × D Yearly -0.242 0.063 -4.565 × I winter -0.146 0.048 -0.05 D D spring -0.079 0.291 0 × D summer -0.063 0.391 -0.052 × D autumn -0.055 0.481 0.481 × I Kalat monthly -0.053 0.181 0 × D Yearly -0.124 0.39 -3.81 × D winter -0.103 0.185 -0.103 × D spring -0.099 0.216 -0.01 × D summer -0.037 0.65 0 × D autumn 0.027 0.76 0 I I Gandwana monthly 0.09 0.023 0 × I Yearly 0.196 0.134 3.135 × I winter 0.084 0.295 0 × I spring 0.151 0.062 0 × I summer -0.037 0.0065 -0.45 D D autumn 0.027 0.76 0 × I (D=Decreasing Trend, ×= No Trend, I= Increasing Trend)

130

Annexure E- Results of GCM based drought and flood analyses

Table E1. Detailed result of aridity index for Mula watershed (Future).

Year Type of Drought 2018 Disastrous 2021 Disastrous 2034 Disastrous 2087 Disastrous 2099 Disastrous 2024 Large 2025 Large 2026 Large 2027 Large 2030 Large 2031 Large 2033 Large 2035 Large 2036 Large 2038 Large 2042 Large 2047 Large 2055 Large 2056 Large 2059 Large 2075 Large 2080 Large 2081 Large 2082 Large 2085 Large 2090 Large 2093 Large 2019 Moderate 2020 Moderate 2022 Moderate 2028 Moderate 2029 Moderate 2032 Moderate 131

2040 Moderate 2048 Moderate 2050 Moderate 2053 Moderate 2060 Moderate 2061 Moderate 2062 Moderate 2063 Moderate 2065 Moderate 2067 Moderate 2068 Moderate 2069 Moderate 2070 Moderate 2073 Moderate 2076 Moderate 2079 Moderate 2083 Moderate 2089 Moderate 2017 No Drought 2023 No Drought 2037 No Drought 2039 No Drought 2041 No Drought 2043 No Drought 2044 No Drought 2045 No Drought 2046 No Drought 2049 No Drought 2051 No Drought 2052 No Drought 2054 No Drought 2057 No Drought 2058 No Drought 2064 No Drought 2066 No Drought 2071 No Drought 2072 No Drought 2074 No Drought 2077 No Drought 132

2078 No Drought 2084 No Drought 2086 No Drought 2088 No Drought 2091 No Drought 2092 No Drought 2094 No Drought 2095 No Drought 2096 No Drought 2097 No Drought 2098 No Drought

Table E2. Detailed result of aridity index for Zhob watershed (Future).

Year Type of Drought 2081 Disastrous 2021 Disastrous 2018 Disastrous 2047 Disastrous 2026 Large 2093 Large 2085 Large 2075 Large 2030 Large 2082 Large 2061 Large 2088 Large 2020 Large 2040 Large 2060 Large 2087 Large 2042 Large 2034 Large 2038 Large 2099 Large 2031 Large 2027 Large 2080 Large 2083 Large 133

2059 Large 2022 Moderate 2044 Moderate 2033 Moderate 2025 Moderate 2048 Moderate 2070 Moderate 2065 Moderate 2037 Moderate 2023 Moderate 2057 Moderate 2049 Moderate 2091 Moderate 2077 Moderate 2090 Moderate 2069 Moderate 2043 Moderate 2024 Moderate 2063 Moderate 2053 Moderate 2074 Moderate 2089 Moderate 2076 Moderate 2054 Moderate 2067 Moderate 2086 Moderate 2056 No Drought 2058 No Drought 2046 No Drought 2041 No Drought 2019 No Drought 2029 No Drought 2073 No Drought 2097 No Drought 2071 No Drought 2052 No Drought 2039 No Drought 2032 No Drought 2017 No Drought 134

2096 No Drought 2064 No Drought 2078 No Drought 2035 No Drought 2036 No Drought 2045 No Drought 2050 No Drought 2084 No Drought 2072 No Drought 2051 No Drought 2068 No Drought 2055 No Drought 2079 No Drought 2062 No Drought 2028 No Drought 2066 No Drought 2094 No Drought 2095 No Drought 2098 No Drought 2092 No Drought

Table E3. Detailed result of aridity index for Zhob watershed (Historic).

Type of Year Drought 1982 Disastrous 1983 Large 1994 Large 1999 Large 2000 Large 2001 Large 2002 Large 2004 Large 2010 Large 1984 Moderate 1993 Moderate 1995 Moderate 1997 Moderate 2003 Moderate 2008 Moderate 135

2009 Moderate 1985 No Drought 1986 No Drought 1987 No Drought 1988 No Drought 1989 No Drought 1990 No Drought 1991 No Drought 1992 No Drought 1996 No Drought 1998 No Drought 2005 No Drought 2006 No Drought 2007 No Drought 2011 No Drought 2012 No Drought 2013 No Drought 2014 No Drought

Table E4. Detailed result of aridity index for Mula watershed (Historic).

Type of Year Drought 1997 Disastrous 2013 Disastrous 1999 Large 2000 Large 2002 Large 2007 Large 2011 Large 2012 Large 1982 Moderate 1984 Moderate 1987 Moderate 1991 Moderate 1993 Moderate 1994 Moderate 1995 Moderate 2003 Moderate 2009 Moderate 1983 No Drought 136

1985 No Drought 1986 No Drought 1988 No Drought 1989 No Drought 1990 No Drought 1992 No Drought 1996 No Drought 1998 No Drought 2001 No Drought 2004 No Drought 2005 No Drought 2006 No Drought 2008 No Drought 2010 No Drought

Table E5. SPI for Zhob historic

Year SPI 1982 Extremely Wet 2011 Extremely Wet 1993 Moderately Dry 2002 Moderately Dry 1985 Moderately Wet 1983 Near Normal 1984 Near Normal 1986 Near Normal 1987 Near Normal 1988 Near Normal 1989 Near Normal 1990 Near Normal 1991 Near Normal 1992 Near Normal 1994 Near Normal 1995 Near Normal 1996 Near Normal 1997 Near Normal 1998 Near Normal 1999 Near Normal 2003 Near Normal 2004 Near Normal 2005 Near Normal 137

2006 Near Normal 2007 Near Normal 2008 Near Normal 2009 Near Normal 2010 Near Normal 2012 Near Normal 2013 Near Normal 2014 Near Normal 2000 Severely Dry 2001 Severely Dry

Table E6. SPI for Zhob Future

Year SPI 2021 Extremely Dry 2081 Extremely Dry 2047 Extremely Wet 2080 Extremely Wet 2022 Moderately Dry 2025 Moderately Dry 2030 Moderately Dry 2033 Moderately Dry 2048 Moderately Dry 2057 Moderately Dry 2061 Moderately Dry 2070 Moderately Dry 2082 Moderately Dry 2085 Moderately Dry 2093 Moderately Dry 2027 Moderately Wet 2059 Moderately Wet 2060 Moderately Wet 2067 Moderately Wet 2076 Moderately Wet 2087 Moderately Wet 2092 Moderately Wet 2098 Moderately Wet 2017 Near Normal 2019 Near Normal 2020 Near Normal 2023 Near Normal 138

2024 Near Normal 2028 Near Normal 2029 Near Normal 2032 Near Normal 2034 Near Normal 2035 Near Normal 2036 Near Normal 2037 Near Normal 2038 Near Normal 2039 Near Normal 2040 Near Normal 2041 Near Normal 2042 Near Normal 2043 Near Normal 2045 Near Normal 2046 Near Normal 2049 Near Normal 2050 Near Normal 2051 Near Normal 2052 Near Normal 2053 Near Normal 2054 Near Normal 2055 Near Normal 2056 Near Normal 2058 Near Normal 2062 Near Normal 2063 Near Normal 2064 Near Normal 2065 Near Normal 2066 Near Normal 2068 Near Normal 2069 Near Normal 2071 Near Normal 2072 Near Normal 2073 Near Normal 2074 Near Normal 2077 Near Normal 2078 Near Normal 2079 Near Normal 2084 Near Normal 2086 Near Normal 139

2088 Near Normal 2089 Near Normal 2090 Near Normal 2091 Near Normal 2094 Near Normal 2095 Near Normal 2096 Near Normal 2097 Near Normal 2099 Near Normal 2026 Severely Dry 2044 Severely Dry 2075 Severely Dry 2018 Very Wet 2031 Very Wet 2083 Very Wet

Table E7. SPI for Mula historic

Year SPI 2013 Extremely Wet 2000 Moderately Dry 2002 Moderately Dry 2008 Moderately Wet 1982 Near Normal 1983 Near Normal 1984 Near Normal 1985 Near Normal 1986 Near Normal 1987 Near Normal 1988 Near Normal 1989 Near Normal 1990 Near Normal 1991 Near Normal 1992 Near Normal 1993 Near Normal 1994 Near Normal 1995 Near Normal 1996 Near Normal 1997 Near Normal 1998 Near Normal 1999 Near Normal 140

2001 Near Normal 2003 Near Normal 2004 Near Normal 2005 Near Normal 2006 Near Normal 2009 Near Normal 2010 Near Normal 2011 Near Normal 2012 Near Normal 2014 Severely Dry 2007 Very Wet

Table E8. SPI for Mula future

Year SPI 2021 Extremely Dry 2081 Extremely Dry 2047 Extremely Wet 2059 Extremely Wet 2022 Moderately Dry 2025 Moderately Dry 2030 Moderately Dry 2033 Moderately Dry 2082 Moderately Dry 2031 Moderately Wet 2034 Moderately Wet 2038 Moderately Wet 2067 Moderately Wet 2086 Moderately Wet 2092 Moderately Wet 2099 Moderately Wet 2017 Near Normal 2019 Near Normal 2020 Near Normal 2023 Near Normal 2024 Near Normal 2028 Near Normal 2029 Near Normal 2032 Near Normal 2035 Near Normal 2036 Near Normal 141

2037 Near Normal 2039 Near Normal 2040 Near Normal 2041 Near Normal 2042 Near Normal 2043 Near Normal 2045 Near Normal 2046 Near Normal 2048 Near Normal 2049 Near Normal 2050 Near Normal 2051 Near Normal 2052 Near Normal 2053 Near Normal 2054 Near Normal 2055 Near Normal 2056 Near Normal 2057 Near Normal 2058 Near Normal 2060 Near Normal 2061 Near Normal 2062 Near Normal 2063 Near Normal 2064 Near Normal 2065 Near Normal 2066 Near Normal 2068 Near Normal 2069 Near Normal 2070 Near Normal 2071 Near Normal 2072 Near Normal 2073 Near Normal 2074 Near Normal 2076 Near Normal 2077 Near Normal 2078 Near Normal 2079 Near Normal 2084 Near Normal 2087 Near Normal 2088 Near Normal 2089 Near Normal 142

2090 Near Normal 2091 Near Normal 2094 Near Normal 2095 Near Normal 2096 Near Normal 2097 Near Normal 2098 Near Normal 2026 Severely Dry 2044 Severely Dry 2075 Severely Dry 2085 Severely Dry 2093 Severely Dry 2018 Very Wet 2027 Very Wet 2080 Very Wet 2083 Very Wet

143

Table E9. Sub Basin Level flows for highest seven years of precipitation in Mula River Basin

Sub Basin ID Years 1 2 3 4 5 6 7 8 9 10 11 12 25 2018 16.1 15.4 42 44.8 8.17 9.21 77.3 6.05 26.8 85.2 41.1 99.3 1017 2027 184 191 533 644 276 308 1221 110 433 1343 452 119 4423 2031 80.9 89.4 235 286 156 174 539 68.2 196 613 188 121 2630 2038 77.4 85.3 223 269 53.8 59.8 509 56.8 186 571 175 187 2860 2047 92.8 98.8 274 333 62.7 70.3 641 68.5 223 716 221 165 2927 2059 401 441 1163 1388 406 458 2642 349 885 3022 933 367 10120 2080 78 84.6 230 273 89.1 100 520 61.1 188 587 182 157 2715

Sub Basin ID Years 13 14 15 16 17 18 19 20 21 22 23 24 26 2018 81.5 194 276 16.44 744.9 180.3 678.9 947.7 62.46 1010 6.76 13.7 163 2027 660 236 894 210.1 1973 419.2 1363 2439 1898 4341 75.54 271 194 2031 408 240 649 95.91 1424 304.8 1136 1753 831.8 2587 40.63 117 202 2038 234 364 600 91.83 1616 407.8 1338 2047 771.1 2820 37.99 113 309 2047 240 321 562 108.4 1543 383.8 1209 1953 927.3 2882 42.06 129 268 2059 1098 761 1825 473.7 4568 1159 3386 5844 4065 9918 190.2 506 651 2080 291 314 598 91.42 1503 357.6 1228 1884 789.8 2675 37.52 111 259

Table E10. Sub Basin Level flows for highest seven years of precipitation in Mula River Basin

Sub Basin ID Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 2080 1769 1246 932 289 118 112 782 35.6 38.9 126 551 416 104 59.8 42.9 2047 1631 1396 1256 123 52.8 50.5 1190 84.5 76.5 106 998 882 216 111 36.5 2031 2097 2081 2071 12 3.77 3.63 2069 203 176 22.3 2031 2010 498 236 7.02 2027 1884 1488 1250 206 89.5 86.1 1137 49.9 49.2 198 772 550 135 73.1 69.7 2017 1544 1075 814 265 99.3 88 696 32.5 50.2 76.4 561 490 122 76.2 24 2018 1611 1321 1156 157 62.8 56.3 1080 85.7 85.5 50.8 992 943 233 123 16.1 2059 9378 8157 7418 701 273 262 7084 500 537 560 6060 5478 1383 659 189

Sub Basin ID Year 16 17 18 19 20 21 22 23 2080 95.6 177 19.8 24.8 196 32.9 315 253 2047 188 355 45.8 47.2 403 75.3 664 521 2031 412 785 109 101 900 180 1514 1171 144

2027 121 226 27.5 30.8 254 45.6 416 329 2017 113 208 20 30.8 228 35 371 298 2018 204 383 47.6 51.9 432 79 713 561 2059 1086 2077 284 276 2376 479 4096 3141

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Annexure F- Design Adaptations for Climate Change Flood Protection Bund Flood Protection Bund, Cost for Climate Change Rate Area Length Volume S. No. Description Cost (Rs.) (sq.m) (m) (cu.m) Core sub projects 1 & 2 Kharzan and Karkh (a) Random Fill 625 1.027 21,725 22,311.83 13,944,895

(b) Grouted Stone Pitching 4,000 0.25 21,725 5,431.31 21,725,250

(c) Rip Rap 2,100 0.223 21,725 4,844.73 10,173,935

Total Core sub projects Rs. 45.84 mil.

Mula Basin

3 Churri 3,334 2,001 6,671,334

4 Pashta Khan & Garambowad 3,334 4,821 16,073,214

5 Manyalo 3,334 2,650 8,835,100

Rs. 31.58 mil.

Zhob Basin

6 Ahmedzai 3,334 241 803,494

7 Sabakzai 3,334 0 -

8 Siri Toi 3,334 0 -

9 Killi Sardar Akhtar 3,334 164 546,776

Rs. 1.35 mil.

Total Non-core sub projects Rs. 32.93 mil.

146

Water Supply System S. No. Component Unit Quantity Rate Cost 1 Length of pipe meter 4250 1000 4,250,000 2 Length of tank (wall) meter 20 2655 53,100 3 Width of tank (wall) meter 20 2655 53,100 4 Floor area of tank sq.m 400 1600 640,000 5 Total cost of 1 tank Rs. 746,200 6 Total cost of 2 tanks Rs. 4,477,200 7 Total budget Rs. 8,750,000

147

Dam Parapet Wall RATE AMOUNT ITEM. DESCRIPTION UNIT QUANTITY No. (Rs.) (Rs.)

DAM AND DYKE PARAPET WALL

1 Concrete class 10/20 under Wall Cu.m 60 8,000 480,000

2 Concrete class C 30/20 in Wall Cu.m 1,582 12,000 18,985,155

3 Reinforcement ( Grade - 60 ) M. Ton 158 143,000 22,594,000

4 Sealant in expansion joints. L.m 470 850 399,500

Water stop in expansion / contraction L.m 410 3,300 1,353,000 / construction joint ( Type A )

Application for bond breaking 5 Sq.m 1,000 1,100 1,100,000 compound at contraction joint.

Performed filler in expansion joints Sq.m 1,000 1,100 1,100,000 Providing and applying 2 coats of hot bitumen @ 1 kg per sq.m, each coat, 6 to the concrete surfaces in contact sq.m 2,000 500 1,000,000 with the earth and contraction joints, complete as directed by the Engineer.

Supplying and applying Safe elastic V 7 or similar approved joint sealant in Rm 1,000 500 500,000 contraction and expansion joints

APPROACH WALLS, ABUTMENTS ( SPILLWAY WALL RAISING)

8 Concrete class C 30/20 in walls. Cu.m 500 12,000 6,000,000

9 Reinforcement ( Grade - 60 ) M. Ton 50 143,000 7,150,000

10 Sealant in expansion joints. L.m 300 850 255,000 148

11 Performed filler in expansion joints Sq.m 100 1,100 110,000

Providing and applying 2 coats of hot bitumen @ 1 kg per sq.m, each coat, 12 to the concrete surfaces in contact sq.m 250 500 125,000 with the earth and contraction joints, complete as directed by the Engineer. Supplying and applying Safe elastic V 13 or similar approved joint sealant in Rm 1,200 500 600,000 contraction and expansion joints Water stop in expansion / contraction 14 L.m 200 3,300 660,000 / construction joint ( Type A )

Total 62,411,655

149

Watershed Management Interventions No. Watershed Management Interventions Quantity Unit Rate Amount (PKR)

Development of earthen micro-catchments 1 2110 Hectares (eyebrow terraces) – Contract to VWC 35,000 73,850,000

Digging of pit, addition of compost, termite

2 treatment and plantation of trees 955 Hectares 28,000 26,740,000 (timber/forest/arid fruits) – Contract to VWC

3 Seeding of native grasses – Contract to VWC 425 Hectares 3,000 1,275,000

Loose stone and Gabion Structures for

4 Critical Locations – small check structures – 144 Number 20,000 2,880,000 Contract to VWC

Water Storage Ponds for Establishing the

5 Plantation and recharge to groundwater – 75 Number 150,000 11,250,000 Contract to VWC

Total Cost of Watershed Management Interventions 115,995,000