Hydrometeorological Responses to Climate and Land Use Changes in the Jhelum River Basin, Pakistan

Home , Jhelum River, Poonch River, Xin River

Faculty of Environmental Sciences

DISSERTATION

To obtain the academic degree

Doctor of Egineering (Dr. – Ing.)

Submitted to the

Faculty of Environmental Sciences Technische Universität Dresden, Germany

Naeem Saddique, M.Sc. born on December 20, 1986 in Kasur, Pakistan

Reviewers: Prof. Dr. Christian Bernhofer TU Dresden, Institut für Hydrologie und Meteorologie, Professur für Meteorologie Prof. Dr. Rudolf Liedl TU Dresden, Institut für Grundwasserwirtschaft Prof. (Associate) Dr. Muhammad Adnan Shahid University of Agriculture, Faisalabad, Pakistan, Department of Irrigation and Drainage

Date of defense: 03.02.2021

Erklärung des Promovenden

Die Übereinstimmung dieses Exemplars mit dem Original der Dissertation zum Thema: “Hydrometeorological Responses to Climate and Land Use Changes in the Jhelum River Basin, Pakistan” wird hiermit bestätigt.

Ort, Datum

Unterschrift (Naeem Saddique)

Abstract Climate change and land use transition are the main drivers of watershed hydrological processes. The main objective of this study was to assess the hydrometeorological responses to climate and land use changes in the Jhelum River Basin (JRB), Pakistan. The development of proper climate information is a challenging task. To date, Global Climate Models (GCMs) are used for climate projections. However, these models have a coarse spatial resolution, which is not suitable for regional/local impact studies such as water resources management in the JRB. Therefore, different downscaling methods and techniques have been developed as means of bridging the gap between the coarse resolution global models projection and the spatial resolution required for hydrological impact studies. Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG) are selected in this study for downscaling of temperature and precipitation. Both downscaling approaches consider three climate models and two emission scenarios (i.e., RCP4.5 and RCP8.5) in order to sample the widest range of uncertainties in climate projections. Current land use and land cover (LULC) maps are generated from Landsat imagery to analyze the pattern and dynamic of land use change. Both climate projections and LULC are fed into SWAT (Soil and Water Assessment Tool) hydrological model to investigate the streamflow dynamics. The results indicate good applicability of SDSM and LARS-WG for downscaling of temperature and precipitation in three future periods (2020s, 2050s and 2080s). Both models show an increase mean annual max temperature, min temperature and precipitation as 0.4-4.2°C, 0.3-4.2°C and 4.4-32.2% under both RCPs scenarios. Similarly, results of SWAT model suggest an increase in mean annual discharge about 3.6 to 28.8% under RCP4.5 and RCP8.5. The study also revealed that water yield and evapotranspiration in the eastern part of the basin (sub-basins at high elevation) would be most affected by climate change. The results of LULC change detection show that forest exhibited maximum positive change while agriculture showed maximum negative change during 2001-2018. SWAT model simulations suggested that implementation of afforestation in the watershed would reduce surface runoff and water yield while enhancing the evapotranspiration. It is recommended that authorities should pay attention to both climate change and land use transition for proper water resources management.

III

List of Abbreviations

AR3 Third Assessment Report AR5 Fifth Assessment Report CCI Commission of Climatology CDD Consecutive Dry Days CDFs Cumulative Distribution Function CLIVAR Climate Variability and Predictability CMIP5 Coupled Model Inter-Comparison Project 5 CWD Consecutive Wet Days DD Dynamical Downscaling DEM Digital Elevation Model DS Dual Simplex ENVI Environment for Visualization Images ET Evapotranspiration ETCCDI Expert Team on Climate Change Detection and Indices ETM Enhanced Thematic Mapper FAO Food and Agriculture Organization FRST Forest Mixed GCMs General Circulation Models GSA Global Sensitivity Analysis GWQ Ground Water Flow HRUs Hydrological Response Units HWSD Harmonized World Soil Database IBIS Indus Basin Irrigation System IMD Indian Meteorological Department IPCC Intergovernmental Panel on Climate Change JRB Jhelum River Basin KC Kappa Coefficient LARS-WG Long Ashton Research Station Weather Generator LULC Land Use and Land Cover MK Mann–Kendall MLRs Multiple Linear Regressions mslpgl Mean Sea Level Pressure NCAR National Center for Atmospheric Research NCEP National Center for Environmental Prediction NRMSE Normalized Root Mean Square Error NSE Nash-Sutcliff Efficiency OA Overall Accuracy OLS Ordinary Least Squares p1_fgl Surface Airflow Strength p1_thgl Surface Wind Direction p1_ugl Surface Zonal Velocity p1_vgl Surface Meridional Velocity

IV p1_zgl Surface Vorticity p1_zhgl Surface Divergence p5_fgl 500 hpa air flow strength p5_thgl 500 hpa wind direction p5_ugl 500 hpa zonal velocity p5_vgl 500 hpa meridional velocity p5_zgl 500 hpa vorticity p5_zhgl 500 hpa divergence p500gl 500 hpa geopotential height p8_fgl 850 hpa air flow strength p8_thgl 850 hpa wind direction p8_ugl 850 hpa zonal velocity p8_vgl 850 hpa meridional velocity p8_zgl 850 hpa vorticity p8_zhgl 850 hpa divergence PA Producer’s Accuracy Pbias Percent of Bias PET Potential Evapotranspiration PMD Pakistan Meteorological Department prcpgl Precipitation PRCPTOT Annual Total Precipitation from Days ≥1 mm R10mm Count the Number of Days where Rainfall ≥10 mm r500gl Relative Humidity at 500 hpa R95p Total Annual Precipitation from Days >95th Percentile RCMs Regional Climate Models RCP Representative Concentration Pathway RF Random Forest rhumgl Near Surface Relative Humidity RMSE Root Mean Square Error RNGE Grass+ Sparse Vegetation RX1 day Annual Maximum 1 Day Precipitation RX5 day Annual Maximum Consecutive 5 Day Precipitation s500gl Specific Humidity at 500 hpa SCS Soil Conservation Service SD Statistical Downscaling SDII Ratio of Annual Total Wet-Day Precipitation to the Number of Wet Days SDSM Statistical Downscaling Model SED Semi-Empirical Distribution SFTMP Snowfall Temperature shumgl Surface Specific Humidity SMFMN Minimum Melt Rate of Snow During the Year SMFMX Maximum Melt Rate of Snow During the Year SMTMP Snow Melt Base Temperature SRES Special Report on Emissions Scenarios SRTM Shuttle Radar Topography Mission

SU Annual Number of Days when Daily Max Temperature >25 °C SUFI-2 Sequential Uncertainty-Fitting, Version 2 SWAT Soil and Water Assessment Tool tempgl Mean Temperature at 2 m TIMP Snow Pack Temperature Lag Factor Tmax Maximum Temperature Tmin Minimum Temperature TN10p Percentage of Nights in a Year when Daily Max Temperature <10th Percentile TN90p Percentage of Nights in a Year when Daily Max Temperature >90th Percentile TNn Minimum Values of Daily Minimum Temperature TNx Maximum Values of Daily Minimum Temperature TX10p Percentage of Days in a Year when Daily Max Temperature <10th Percentile TX90p Percentage of Days in a Year when Daily Max Temperature >90th Percentile TXn Minimum Values of Daily Maximum Temperature TXx Maximum Values of Daily Maximum Temperature UA User's Accuracy URMD Residential-Medium Density USGS United States Geological Survey WAPDA Water and Power Development Authority WATR Water WDs Western Disturbances WGs Weather Generators WMO World Meteorological Organization WY Water Yield

List of Symbols

bmelt Melt factor Ea Amount of evapotranspiration O̅ Average of observed values Oi Observed values P̅ Average of modelled values Pi Modelled values Qgw Amount of baseﬂow Qsurf Amount of surface run-off Rdayi Amount of precipitation on day i snowcov. Fraction of the HRU covered by snow SNOWmelt Amount of snowmelt Swo Initial soil water content SWt Final soil water content Tmelt Base temperature above which snowmelt Tsnow Snowpack temperature Wseep Amount of water entering the vadose zone from the soil proﬁle Ϭ Standard deviation

VII

CONTENTS

Abstract ...... III List of Abbreviations ...... IV List of Symbols ...... VII 1 Introduction ...... 1 1.1 Overview ...... 1 1.2 Study Area ...... 3 1.2.1 Indus River basin ...... 3 1.2.2 Jhelum River basin ...... 5 1.3 Objectives ...... 6 1.4 Structure of Thesis ...... 6 1.5 List of Publications ...... 8 2 Publications ...... 9 2.1 Downscaling of CMIP5 Models Output by Using Statistical Models in a Data Scarce Mountain Environment (Mangla Dam Watershed), Northern Pakistan ...... 9 2.2 Trends in Temperature and Precipitation Extremes in Historical (1961-1990) and Projected (2061-2090) Periods in a Data Scarce Mountain Basin, Northern Pakistan ...... 26 2.3 Simulating the Impact of Climate Change on the Hydrological Regimes of a Sparsely Gauged Mountainous Basin, Northern Pakistan ...... 41 2.4 Quantifying the impacts of Land use/Land cover change on the water balance in the afforested River Basin, Pakistan ...... 67 3 General Conclusions and Recommendations ...... 80 3.1 Summary and Conclusions ...... 80 3.2 Recommendations and Outlook ...... 82 References ...... 83 Acknowledgments ...... 86 Erklärung ...... 87

VIII

1 Introduction 1.1 Overview Pakistan, a country with the sixth largest population (220 million) in the world (United Nations, 2019), is highly dependent on its agricultural based economy. Approximately, more than 60 percent of country's population living in rural areas is directly or indirectly dependent on agriculture for their livelihood. Moreover, this sector provides raw material to domestic agro- based industries, such as sugar, leather, and textiles. Hence, the importance of agriculture to the well-being of its people cannot be overstated. Sustainability of human beings and the elated food production is totally dependent on water. Currently, Pakistan is experiencing heavy impacts of climate variability including potential climate change. The country has faced drought during most of the years of the last few decades but in the recent past Pakistan has seen the worst floods ever in its history (Rehman et al., 2015). In addition, exponential growth in population and changes in land use have enhanced the climate-induced problems. Therefore, proper understanding of cause and effect processes is vital for sustainable water resources management in Pakistan.

A common approach used for assessing the impacts of climate change on water resources is first to project future climate change scenarios through the use of General Circulation Models (GCMs). The GCMs are used to simulate the effects of greenhouse gases (GHG) on climate variables such as precipitation, temperature and humidity. However, direct use of GCMs outputs in local hydrological studies is not recommended due to their coarse spatial resolution (Elsner et al., 2010; Leta et al., 2016). Therefore, different downscaling methods and techniques have been developed as means of bridging the gap between coarse resolution models projection and spatial resolution required for hydrological impact studies. Downscaling methods have been divided into two groups. First, dynamical downscaling is applied to regional climate modelling (RCMs) and is more feasible than statistical due to its reasonable consistency in physical processes. However, RCMs have several drawbacks in terms of computational expensive and large range of plausible boundary conditions (Fowler et al., 2007; Maraun et al., 2010). Second, statistical methods have developed empirical relationships between large scale variables and local weather such as precipitation and temperature (Wilby et al., 1998; Okkan and Kirdemir, 2016). These methods require less computational efforts as compared to dynamical methods. Because of this, they are considered to be more practical downscaling techniques in hydro-meteorology studies (Tripathi et al., 2006).

1 Different statistical approaches are used in statistical downscaling such as regression-based, weather generators and weather typing. Among the several statistical downscaling models, the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG) have demonstrated effective results. The SDSM is hybrid of regression and weather generator. Both models have been used in several hydrological studies to assess the impact of climate change on water resources (Yang et al., 2014; Mahmood and Jia, 2016; Al- Safi et al., 2017; Gebremeskel and Kebede, 2018). Moreover, several sources of uncertainty are involved in climate models for the future projections of local climate of a region such as emission scenarios and GCMs. Therefore, researchers are recommended to consider multiple GCMs and emission scenarios for future projections (Das et al., 2018; Zhu et al., 2019).

Indeed, land use/cover change is another key driver, altering the watershed hydrology; it has potential to exert pressure on water resources (Zhang et al., 2016; Jin et al., 2015; Wagner et al., 2013). Population growth raises the demand of food and housing. To meet these demands, enormous changes in land use have occured in many parts of the world (Verburg et al., 2013; Rizvi et al., 2018). According to the Food and Agriculture Organization (FAO) world will need to increase agriculture output by 70% to sustain a growing population (FAO, 2012). Deforestation, urban sprawl and expansion of industrial and agriculture activities are the most common land use/cover changes worldwide over the past half-century (Chen et al., 2014; Hosonum et al., 2012). At local and global scale, the mechanism of land use/cover changes is complex and requires a comprehensive knowledge about the processes and drivers behind these changes (Rudel et al., 2009). Assessment of land use and land cover changes is necessary for the modeling of past and future land use effects on watershed hydrology. Recently, satellite data at fine spatial and temporal resolution is available to assess the land use/cover changes at the local or global level. In the past, researchers used aerial photographs and conducted the local survey to get land use /cover information. Thus, examining the consequences of land use and land cover changes on hydrological process is vital for hydrologists, ecologists and water resources managers (Mallinis et al., 2014; Luan et al., 2018). Whereas changes in water demands from changing land use practices (e.g. irrigation and urbanization) can be assessed easily, assessment of changes in water supply and quality from altered hydrological processes infiltration, evapotranspiration, groundwater recharge and runoff is not straight-forward and requires; a deeper understanding of the complex watershed hydrology (DeFries and Eshleman, 2004). The individual impacts of climate change and land use change are necessary to understand the hydrological processes for better management of watersheds (Dey et al., 2016;

2 Wang, 2014). This understanding is helpful to frame the watershed management strategies and adaptations measures to mitigate the changes in water availability. The information is also useful for hydrologists who are interested in modelling future impacts of climate change and land use change on watershed hydrology for planning purposes (Liu et al., 2017; Hyandye et al., 2018). Therefore, it is important to study the hydrometeorological responses of the Jhelum River basin under climate and land use changes for better water management.

1.2 Study Area 1.2.1 Indus River basin The Indus River basin (Figure 1) is one of the largest basins in the world with drainage area about 1.1 million km2. It is shared by Pakistan, India, China and Afghanistan, however the largest area of the basin is in Pakistan (47 %) followed by India (39 %), China (8 %) and Afghanistan (6 %). The catchment of Indus River stretches between mountainous terrain of Himalayas, Karakoram and Hindukush in the North and productive lands of Pakistan in the southern plains. The Indus basin in Pakistan covers 65% of country territory and comprises two whole provinces (Punjab and Khyber Pakhtunkhwa) and the greater part of Sindh province and eastern part of Baluchistan. On the other hand, only 14, 11 and 1% territories of India, Afghanistan and China are covered Indus basin. It is lifeblood of roughly estimated 300 million people, with 61 % living in Pakistan, 35 % in India and 4% in China and Afghanistan (FAO, 2011; Khan et al., 2019). The drainage of the basin is ranked 12th largest in world. The annual discharge of the basin ranked 10th in the world, with 2×103 m3/s (FAO, 2011).

The climate of the basin is very diverse in nature. It varies from subtropical arid and semi-arid to temperate sub humid in the southern part (plains of Sindh and Punjab) to alpine in the mountainous highlands of the north. Annual precipitation ranges between 100 and 500 mm in the lowlands to a maximum of 2000 mm on mountain slopes. Snowfall at higher altitudes (above 2500 m) accounts for most of the river runoff. The climate in the Indus plains is arid to semi-arid. In the lower plain December to February is the cold season and mean monthly temperatures vary from 14 to 20 °C. Mean monthly temperatures during March to June vary from 42 to 44 °C. In the upper plain mean temperature ranges from 23 to 49 °C during summer and from 2 to 23 °C during winter (FAO, 2011).

3 The Indus River has two main tributaries, the Kabul on the right bank and the Panjnad on the left. The Panjnad is the flow resulting from five main rivers (literally Punjab means “five waters”): the Jhelum and Chenab, known as the western rivers with the Indus River, and the Ravi, Beas and Sutlej, known as the eastern rivers.

Figure 1. Location map of Indus River basin: source, FAO (2011)

4 1.2.2 Jhelum River basin The Jhelum River Basin (JRB) is considered as the study area in this research work. It is the major eastern tributary of the Indus River. Jhelum River originates from north-west side of Pir Punjal and continues parallel to Indus River with average elevation about 1676 m. It passes through alluvial soil of the Kashmir valley. After flowing through the Srinagar (capital of Kashmir), it joins Wular lake near Baramulla. Leaving the Wular lake, it flows through a distance of 120 km with sharp changes in elevation (6.28 m/km). One of the largest tributary (Neelum River) of Jhelum River joins near Muzaffarabad at the place called Domel. Kunhar River that is the second biggest tributary of Jhelum River joins the Jhelum 8 km downstream of the Domel. Two other tributaries, Poonch and Kanshi meet directly the Mangla reservoir. The Poonch River drains the southern side of Pir Punjal, joins the Jhelum at Tangrot while Kanshi River drains the Rawalpindi and Jhelum districts, and carries most of the monsoon and seepage water.

The Jhelum River catchment area situated upstream of the Mangla reservoir is called the Mangla watershed or upper Jhelum River basin (Figure 2). The geographical location of Mangla watershed is stretches between 73–75.62 °E and 33–35 °N. The total catchment area of the Mangla Basin is about 33,397 km2. This is a transboundary river catchment like other rivers in Pakistan (Indus, Chenab, Ravi and Sutlej) with 56% (18,966 km2) of its area situated in India, which makes data collection very difficult due to socio-political issues between the two countries. The altitude within the JRB ranges from 232 m in lowland areas to 6285 m in highland region and, as a result, the climate varies greatly within the basin. Approximately 1.5% (508 km2) of the total study area is covered by perennial glaciers and nearly 63% of the catchment is covered by seasonal snow cover during the winter months (October–March). There are two sources of precipitation in the Mangla Basin: Western Disturbances (WD) and monsoon precipitation that occurs due to the energetic southwestern winds moving from the Bay of Bengal and across the Arabian Sea towards the Himalayas. The WD take place from December to March and the latter from June to September. Both of these rainfall patterns and abrupt changes in elevation make the hydrology of this catchment peculiar. A dual rainfall distribution pattern exists in the Jhelum basin. In the southern part distribution of rainfall is bimodal with peaks in spring, particularly in March, and maximum peaks during the summer monsoon, specifically during July, and with minima in May and November. Conversely, in northern and western parts of the basin, a single rainfall peak occurs at Naran during spring and during summer at Srinagar significantly lower rainfall peaks occurs. However, in the whole

5 basin, during the period of 1961 to 2010, about 62% and 38% of total mean annual rainfall occurred during the summer and winter seasons, respectively.

Figure 2. Location map of Jhelum River basin and spatial distribution of climate stations and flow gauges.

1.3 Objectives

The main objectives of study include:

1. Estimating the changes (magnitudes and extremes) in precipitation and temperature over the Jhelum River basin

2. Simulate the impact of climate change on the hydrological regimes of basin by the application of hydrological model

3. Quantifying the dynamics of land use and land cover change and its effects on catchment water balance

1.4 Structure of the Thesis

The thesis comprises of three chapters including four research articles.

Chapter 1: First chapter provides a general introduction and overview of study. It describes the main objectives and outlines the study area chosen for this thesis.

6 Chapter 2: Second chapter consists of four research articles each with its own introduction, discussion and conclusion. All the papers have been published in peer reviewed journals.

Article I describes the downscaling of CMIP5 models outputs using the statistical downscaling models. We used two different downscaling models (SDSM and LARS-WG), three General Circulation Models (GCMs) and two Representative Concentration Pathways (RCP4.5 and RCP8.5) and projected the future pattern of precipitation and temperature for the period of 2011-2100. This chapter contains results in form of research article, which published in peer- reviewed journal (Saddique et al., 2019a).

Article II comprises of trends analysis of temperature and precipitation during historical and projected periods. We selected nine temperature and six precipitation indices for investigation. Man-Kendall and Sen’s slope test were used to detect the trends in climate extremes (Saddique et al., 2020a).

Article III mainly covers the impact of climate change on stream flow of Jhelum River basin using the SWAT hydrological model. The model was calibrated and validated by using the daily flow data of seven stations. This article addressed the second objective of study. We also highlight the uncertainties associated with future flow projections due to the downscaling methods, Global Climate Models and RCPs scenarios (Saddique et al., 2019b).

Article IV presents third main objective of this study. This article concerns about dynamics of land use and land cover change (using Landsat imagery) between 2001 and 2018 and its effects on surface runoff, base flow, evapotranspiration and water yield. Some extremes scenarios of forest dominant land use were also considered to explore the impacts on water resources of JRB. Further impact of land use change on evapotranspiration and water yield was assessed at sub-basin scale as at basin scale the positive and negative effects cancel each other (Saddique et al., 2020b).

Chapter 3: Third chapter summarizes the main findings of the overall study along with conclusions based on preceding chapters.

7 1.5 List of Publications

Saddique, N., Usman, M., Kronenberg, R., and Bernhofer, C. (2019a). Downscaling of CMIP5 Models Output by Using Statistical Models in a Data Scarce Mountain Environment (Mangla Dam Watershed), Northern Pakistan. Asia-Pacific Journal of Atmospheric Sciences, 55, 719- 735

Saddique, N., Khaliq, A., and Bernhofer, C. (2020a). Trends in Temperature and Precipitation Extremes in Historical (1961-1990) and Projected (2061-2090) Periods in Data Scarce Mountain Basin, Northern Pakistan. Stochastic Environmental Research and Risk Assessment, 34:1441-1455

Saddique, N., Usman, M., and Bernhofer, C. (2019a). Simulating the Impact of Climate Change on the Hydrological Regimes of a Sparsely Gauged Mountainous Basin, Northern Pakistan. Water, 11(10), 2141

Saddique, N., Mahmood, T., and Bernhofer, C. (2020b). Quantifying the impacts of Land use/Land cover change on the water balance in the afforested River Basin, Pakistan. Environmental Earth Sciences, 79:448

8 2 Publications Article I

9 720 N. Saddique et al. in upstream snow reserves are mostly affected by the changing canonical correlation, principal component analysis and artifi- climate in South Asia (Immerzeel et al. 2010). Projection of cial neural networks, have been used to develop empirical re- future climate change in South Asia is mandatory for explor- lationships between predictand and predictors (Conway et al. ing sustainable development and for devising future adapta- 1996; Huth 2002). The main strength of regression models is tion measurements to nature. that they are comparatively less difficult to apply. The trends of past, present and future climate change have The LARS-WG and SDSM are two famous statistical been explored by using General Circulation Models (GCMs). downscaling models used for downscaling GCM outputs such These GCMs simulate the global climate on the bases of pos- as temperature, precipitation and solar radiation. The SDSM sible future greenhouse gas scenarios. The horizontal resolu- model is a hybrid of transfer functions and WG methods. Both tion of a current GCM is approximately 150 Km, and the models have been universally used for the assessment of cli- spatial resolution of one GCM varies with respect to another mate change (Wilby et al. 2002; Huang et al. 2011; Somenov (Taylor et al. 2012). Direct use of GCM for decision-making is and Barrow 2002; Mahmood and Babel 2013; Hassan et al. not recommended because their output lacks fine spatial res- 2014; Meaurio et al. 2017; Gulacha and Mulungu 2017). The olution, which is necessary for local applications. To surmount main focus of current studies is the evaluation and comparison this gap, different downscaling techniques have been devel- of these models to simulate temperature and precipitation. oped, including statistical approaches such as regression, sto- Several findings suggest that both models perform well for chastic weather generators and relationships with weather pat- simulating mean daily rainfall, although wet and dry spell terns. These downscaling techniques establish a relationship lengths were better estimated by LARS-WG compared with among large-scale climate variables (predictors) such as rela- SDSM. Similar patterns of maximum and minimum daily tive humidity and wind speed to local-scale variables temperatures were found for both statistical models during (predictand) such as temperature and precipitation (Wilby calibration and validation. However, the two models did not et al. 2002; Mahmood and Babel 2013; Pervez and Henebry agree for future temperature and precipitation time series due 2014; Zhang et al. 2016). The statistical downscaling (SD) to different downscaling strategies (Dibike and Coulibaly approach does not depend on GCM boundaries, as with 2005; Hassan et al. 2014). Regional Climate Models (RCMs). In the SD approach the Few recent studies have been conducted for downscaling biases in climatic models can be easily corrected (Wilby temperature and precipitation using multiple GCMs under et al. 2002; Maraun et al. 2010; Sachindra and Perera 2016). new RCPs scenarios for Pakistan, in particular for the RCMs are used in the dynamic downscaling (DD) of climatic Jhelum River basin (Mahmood and Jia 2017; Mahmood variables and are mostly found to be heavily biased for a et al. 2018). The studies used the linear scaling method for specific region (Turco et al. 2011), which thus necessitates downscaling climatic variables. Moreover, no study has been the use of SD for climate change projections. conducted using SDSM and LARS-WG under RCP scenarios Stochastic weather generators (WGs) are probabilistic until now. The aim of this study is the evaluation of both models that are used to simulate weather data at a particular models for the study area and analysis of their output. For this place by analyzing historical climatic data and then generating purpose, two RCPs (4.5 and 8.5) and three GCMs of Coupled a time series of weather variables with statistical properties Model Inter-comparison Project 5 (CMIP5) were used with identical to the historical data (Wilks 1999). Daily weather data SDSM and LARS-WG to downscale temperature and precip- generated by these models have been used in various climate itation by the end of this century. The objective of this com- change studies in agriculture and hydrology (Semenov et al. parison was to select the statistical method to downscale cli- 1998; Charles et al. 2017). First order Markov chain models mate data that could be further used for different environmen- have been used in WGs that simulate the occurrence of rainy tal studies. The rest of manuscript is organized as follows: and dry days and use a gamma distribution for precipitation section 2 states the study area and data, and section 3 gives amount. Over time, second order (Mason 2004) and third order the detailed descriptions of the two downscaling methods. (Dubrovský et al. 2004) Markov chain models have been de- Section 4 demonstrates results and discussions. The last sec- veloped that more effectively reproduce precipitation occur- tion (section 5) illustrates the conclusions of this study. rences along with climate statistics. Regression-based downscaling methods involve establishing an empirical relationship between large scale variables 2 Study Area and Data (predictors) and the local past observational climate data (predictands) (Wigley et al. 1990; Dibike and Coulibaly 2.1 Description of the Study Area 2005). The success of this method is depend on the quality of the data used for calibration, selection of the effective predic- The Jhelum River basin (JRB) extends from 73 °E and 75.62 tors and choice of the transfer function. To date, different types °E and 33 °N and 35 °N (Fig. 1). The basin has an area of regression methods, such as linear and nonlinear regression, coverage of 33,397 km2. Its altitude ranges from 232 to

Korean Meteorological Society 10 Downscaling of CMIP5 Models Output by Using Statistical Models in a Data Scarce Mountain Environment... 721

6287 m. The upper Jhelum River is the largest tributary of the (Fig. 2a). The highest peak in the basin is in the month of Indus Basin after the Indus River. The Jhelum River drains its July, the result of the summer monsoon that occurs due to water into the Mangla reservoir, the second largest reservoir in the energetic southwestern winds moving from the Bay of Pakistan. The majority of water flows into the reservoir during Bengal and across the Arabian Sea towards the Himalayas. March to August, with a significant share during May due to The next peak (March) in the watershed is due to Western snowmelt. More than 75% of the total water enters the reser- disturbances (WDs) that bring sudden winter rain (Ahmad voir during March to August (Babur et al. 2016). The total et al. 2015). command area of the Mangla dam is 6 mha and the dam serves two purposes: it provides water for irrigation and a 6% share of the total electricity production in Pakistan (Archer and 2.2 Data Fowler 2008). Locations of precipitation and temperature stations are shown in Fig. 1. 2.2.1 Meteorological Data The Jhelum River basin is mostly covered by mountains whose altitude increases from south to north. In the southern Observed rainfall, maximum temperature (Tmax) and mini- part of the watershed, the temperature changes from subtrop- mum temperature (Tmin) data of 16 stations for the period of ical and falls below the freezing point in the northern parts of 1961–2012 were obtained from the Water and Power the watershed during winter. The hottest months are June and Development authority (WAPDA), Pakistan Meteorological July, with an average temperature of 30.5 °C, whereas the Department (PMD), and the Indian Meteorological coldest months (Dec-Jan) have an average temperature of Department (IMD). The temperature and rainfall data of 0.3 °C. The entire JRB has a long-term mean annual rainfall Gulmarg, Srinagar, Qaziqand, and Kupwara meteorological of approximately 1196 mm yr.−1 (Table 1). Two distinct peaks stations were obtained from the IMD and the remaining data can be observed based on the basin’s mean precipitation, from the PMD and the WAPDA.

Fig. 1 Location map of the study area and weather stations

Korean Meteorological Society 11 722 N. Saddique et al.

Table 1 Inventory of climate stations Station Name Lat Long Elve Tmax Tmin Precipitation (mm/ (deg.) (deg.) (m,MSL) (°C) (°C) year)

Jhelum 32.94 73.74 287 30.59 16.45 854 Gujar khan 33.26 73.30 458 28.02 13.98 830 Kotli 33.50 73.90 614 28.44 15.63 1245 Plandri 33.72 73.71 1401 22.73 11.30 1443 Rawalkot 33.87 73.68 1676 21.87 10.25 1397 Bagh 33.97 73.79 1067 26.80 12.86 1422 Murree 33.91 73.38 2213 16..55 8.83 1779 Garidoptta 34.22 73.62 814 25.34 12.16 1567 Muzaffarabad 34.37 73.48 702 27.32 13.55 1423 Balakot 34.55 73.35 995 25.06 11.97 1701 Naran 34.90 73.65 2362 11.13 1.15 1154 Astore 35.34 74.90 2168 15.35 3.94 534 Kupwara 34.51 74.25 1609 20.04 6.29 1245 Gulmarg 34.00 74.33 2705 11.38 1.75 1543 Srinagar 34.08 74.83 1587 19.78 7.31 721 Qaziqund 33.58 75.08 1690 19.24 6.47 1345

2.2.2 NCEP/NCAR Reanalysis Data period of 1961–2100 and have the same number of predictors for all the GCMs and period length. RCP4.5 illustrates a The daily reanalysis data for the time period of 1961–2005 medium-low RCP of radiative forcing rising to ~ 4.5 W.m−2 were obtained from the National Centers for Environmental until 2070, and RCP8.5 is the highest-level RCP, which leads Prediction/National Center for Atmospheric Research to radiative forcing of 8.5 W.m−2 by the end of twenty-first (NCEP/NCAR); the data are available at a spatial coverage century (Moss et al. 2010). These GCMs were selected on the of 2.5 degrees longitude and 2.5 degrees latitude and include basis of their good performance over South Asia. All the 26 atmospheric variables such as relative humidity, zonal ve- models well captured the peaks of rainfall, specially locity component, specific humidity, and vorticity. MICROC5 performance was outstanding in highly complex climate system (Babar et al. 2015; Prasanna 2015). These 2.2.3 RCP Scenario Data GCM data were interpolated at the same grid resolution (2. 5o × 2.5o) as the NCEP data to eliminate the biases that may Three GCM (i.e., BCC-CSM1.1, CanESM2 and MIROC5) have occurred by contradictions present at this scale. Then, the outputs with RCP4.5 and RCP8.5 scenarios were obtained NCEP and all the GCM predictors were normalized with the from the ESGF website (http://pcmdi9.llnl.gov/) for the mean and standard deviation acquired from the historical

Fig. 2 Monthly mean a precipitation and rainy days and b temperature Max and Min in the Jhelum River basin

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Table 2 Description of General Circulation Models (GCMs), the Development institute GCM Grid resolution Downscaling RCP downscaling methods and the model Representative Concentration Pathways (RCPs) used in this Beijing Climate Center, China BCC-CSM1–1 2.8125° × 2.8125° SDSM 4.5 study LARS-WG 8.5 4.5 8.5 Atmosphere and Ocean Research MIROC5 1.40625° × 1.40625° SDSM 4.5 Institute, University of Tokyo, Japan LARS-WG 8.5 4.5 8.5 Canadian Centre for Climate CanESM2 2.8125° × 2.8125° SDSM 4.5 Modelling and Analysis, Canada LARS-WG 8.5 4.5 8.5 period of 1961–1990. Table 2 provides the overview of each values for bias correction and variance inflation were 1 and GCM such as name, development institute and grid 12, respectively. Bias correction tunes the daily downscaled resolution. temperature and precipitation, and variance inflation changes the variance in downscaled climatic data to agree more closely with observed data. 3 Methodology 3.1.1 Screening of Predictors 3.1 SDSM Description The most essential component of statistical downscaling with SDSM is a hybrid of transfer functions and WGs (Wilby et al. SDSM is the screening of predictors (Wilby et al. 2002). The 2002; Wilby et al. 2006). SDSM is extensively used world- predictors selection method employed in the current study is wide to downscale weather parameters, such as temperature consistent with those of other similar studies (Wilby et al. and precipitation, to determine how climate change affects the 2002; Khan et al. 2006; Gulacha and Mulungu 2017). In water cycle (Chu et al. 2010). Multiple linear regressions SDSM4.2, more appropriate predictors were screened based (MLRs) establish empirical relationships between predictors on correlation coefficient, explained variance and the p value (such as geopotential height) and predictands (such as solar among the individual predictors and predictand. The daily radiations) and generate some parameters. Weather generators data of 26 predictors were employed to investigate correlation use these parameters to simulate a number ensemble to create between predictors and predictand. The predictor with the a strong correlation with station data. Three different types of highest correlation was selected as the first predictor sub-models can be constructed in SDSM such as monthly, (superpredictor), and the default values always had a signifi- seasonal and annual sub-models. The current study employs cance level of P < 0.05. After the selection of the first predic- only the monthly model; 12 different regression models were tor, the second and third predictors were ranked based on developed, one for each month of the year. A conditional highest correlation and explained variance. However, in the model was chosen for precipitation and an unconditional mod- case of precipitation, correlation between the predictand and el for temperature (Wilby and Dawson 2004). In uncondition- predictors was not high (Hashmi et al. 2011; Huang et al. al models a direct link is assumed between the large scale 2011; Meaurio et al. 2017). The predictor variables selected variables (predictors) and local scale variable (predictand) for precipitation and temperature in the present study are (e.g., local wind speeds may be a function of regional airflow shown in bold text in Table 3. indices). In conditional models, there is an intermediate pro- For the selected predictors, it was observed that for both cess between large scale forcing and local weather (e.g., local temperatures (Tmax and Tmin), temperature at 2-m height precipitation amounts depend on the occurrence of wet–days, was a superpredictor, whereas two superpredictors were found which in turn depend on large–scale predictors such as humid- for precipitation at 500 hpa, specific humidity (southwest part) ity and atmospheric pressure). A fourth order transformation and meridional wind velocity (upper part). The predictors se- was applied to precipitation data before its subsequent appli- lected in the current study are the same as in other similar cation in the regression analysis. For the optimization, the studies (Wilby et al. 2002; Huang et al. 2011; Mahmood and ordinary least squares (OLS) method was used while default Babel 2013).

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Table 3 Overview of all NCEP variables (the one in bold text were more than 20 years of climate data is recommended for cli- selected for the calibration of model) mate change studies (Mehan et al. 2017). The semi-empirical Nr. Variable Description distribution (SED) approach is used for the length of wet and dry day series as well as daily precipitation amount. SED is 1 ncepmslpgl Mean sea level pressure normally a cumulative probability density function that has 2 ncepp1_fgl Surface airflow strength constant numerical intervals of flexible length (Semenov and 3 ncepp1_ugl Surface zonal velocity Stratonovitch 2010). 4 ncepp1_vgl Surface meridional velocity In the current study, LARS-WG5.5 was used for downscal- 5 ncepp1_zgl Surface vorticity ing temperature and precipitation. The model has been used 6 ncepp1_thgl Surface wind direction with 23 intervals (ten in the old version) to illustrate the SED 7 ncepp1_ Surface divergence shape (Chen et al. 2013; Hassan et al. 2014). Therefore, the zhgl current version provides a broad range of distributions for the 8 ncepp5_fgl 500 hpa air flow strength more-correct simulation of precipitation statistics, and the sim- 9 ncepp5_ugl 500 hpa zonal velocity ulation of daily maximum and minimum temperature is con- 10 ncepp5_vgl 500 hpa meridional velocity trolled by the status of the day (i.e., whether it is wet or dry) 11 ncepp5_zgl 500 hpa vorticity (Al-Safi and Sarukkalige 2018). In the present study, the 12 ncepp5_thgl 500 hpa wind direction model-calibrated parameters were generated for each station 13 ncepp5_ 500 hpa divergence one by one by incorporating daily-observed data for 30 years zhgl 14 ncepp8_fgl 850 hpa air flow strength (1961–1990). For the model validation, a 10-years long daily 15 ncepp8_ugl 850hpa zonal velocity time series was generated using these calibrated parameters. 16 ncepp8_vgl 850 hpa meridional velocity Each of these generated daily time series was then examined 17 ncepp8_zgl 850 hpa vorticity to determine any statistically significant differences (at a 5% 18 ncepp8_thgl 850 hpa wind direction level) between the ground station and simulated weather data. 19 ncepp8_ 850 hpa divergence For the projection of future changes of precipitation and zhgl temperature for a weather station, the relative change factors 20 ncepp500gl 500 hpa geopotential height for each month were calculated from the GCM outputs by 21 ncepprcpgl Precipitation taking differences between the data for future periods (i.e., 22 nceps500gl Specific humidity at 500 hpa 2011–2040, 2041–2070 and 2071–2100) and the reference 23 ncepshumgl Surface specific humidity period (i.e., 1961–1990). To obtain daily time series of future 24 nceprhumgl Near surface relative humidity periods for each weather station, the calculated relative change 25 ncepr500gl Relative humidity at 500hpa factors were then employed for LARS-WG statistical param- 26 nceptempgl Mean temperature at 2 m eters of the baseline observed data. A complete overview of the different steps used in LARS-WG applications in the development of climate change was presented in Semenov and 3.2 Lars-WG Stratonovitch (2010).

LARS-WG is a stochastic WG used to simulate daily time 3.3 Climate Extreme Indices series data used in studies of the impacts of climate change (Wilks and Wilby 1999). The data simulation procedure in- The Expert Team (ET) on Climate Change Detection and volves three steps (Semenov and Barrow 2002). First, the Indices (ETCCDI) jointly sponsored by the World LARS-WG examines the ground station parameters by Meteorological Organization (WMO) Commission of obtaining their statistical properties on a monthly basis to gen- Climatology (CCI) and the Climate Variability and erate probability distribution parameters for that particular Predictability (CLIVAR) project developed 27 indices for site. Second, these parameters files are further used to synthe- monitoring the changes in climate extremes (Peterson 2005). size data having the same statistical properties as the station The computation of these indices requires daily precipitation data. LARS-WG is evaluated based on observed and simulat- and temperature (minimum and maximum) dataset. In this ed average monthly weather statistical indices, and relative study, we considered only 4 indices for models evaluation change factors from the GCMs outputs for each month are (listed in Table 4). then calculated. Finally, the calibrated parameters and relative change factors are used to project daily time series data (Wilks 3.4 Model Performance and Wilby 1999; Al-Safi and Sarukkalige 2018; Sharma et al. 2018). For LARS-WG, one-year data are adequate to simulate The performances of the SDSM and LARS-WG statistical the synthetic time series of climatic variables but the use of models were examined by comparing the downscaled and

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Table 4 Core climate indices calculated and analyzed in this Sr. Index Index long name Index detail Units study No.

1 SU Number of summer days Annual account of days when TX (daily maximum Days temperature) >25 °C 2 CWD Consecutive wet days Maximum number of consecutive days with Days RR ≥ 1 mm 3 CDD Consecutive dry days Maximum number of consecutive days with Days RR < 1 mm 4 R10 Number of heavy Annual account of days when RR ≥ 10 mm Days precipitation days

observed precipitation, Tmax and Tmin, by using different sta- 4 Results and Discussion tistics: the correlation coefficient (R), Bias, Percent of bias (Pbias), Root Mean Square Error (RMSE) and Normalized 4.1 Calibration of SDSM and LARS-WG Root Mean Square Error (NRMSE). In this study the calibration period for both models was 30 years N : ∑i 1 Pi−P Oi−O (1961–1990). The downscaled mean monthly precipitation, tem- R ¼ 1 ¼ 2 2 ð Þ perature maximum and minimum simulated by both models are ∑N P −P : ∑N O −O shown in Table 5. In the case of T and Tmin, both models rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii 1 i rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii 1 i max ¼ ¼ behaved very close to the observed data, whereas the RMSE N ∑i 1 Pi−Oi value of SDSM was lower than that of LARS-WG, which shows Bias ¼ ð Þ 2 ¼ N ð Þ the better performance of SDSM. However, the Pbias values for N SDSM and LARS-WG were − 0.127% and 0.021%, respectively. ∑i 1 Pi−Oi Pbias ¼ Nð Þ 100 3 With respect to precipitation, SDSM produced much better results ¼ ∑i 1Oi Â ð Þ ¼ than LARS-WG. The simulated average monthly precipitation N 2 values of SDSM and LARS-WG were approximately 2.35 mm ∑i 1 Oi−Pi RMSE qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi¼ ð Þ 4 and 6.91 mm higher compared with the mean (X̅) observed value. p 2 ¼ N ð Þ The values of RMSE and R represents the satisfactory perfor- RMSEffiffiffiffi mance of LARS-WG in downscaling the precipitation. NRMSE 100 5 ¼ O Â ð Þ Although the SDSM performed better in the statistical comparison, the predicted SDSM data were not fully able to where Oi and Pi are the observed and modeled values, respec- capture the observed mean monthly weather data (i.e., Fig. 3). tively,̅ O and̅ P are the means of the observed and modeled It is seen that both models overestimated the rainfall in almost values, respectively, and N is the number of data points. A all months. However, LARS-WG overestimated more as com- Taylor diagram (Taylor 2001) is used to quantify the statistical pared with SDSM in all months except February and relationship between observed and modeled data. In this dia- December. Two peaks can be observed in the trend, one in gram, the relationship is explained by the Pearson correlation March and the other in July. The March peak was well cap- coefficient (R), the standard deviation ( ) and the centered root tured by LARS-WG and the other peak by SDSM. In term of mean square difference (RMS). Tmax and Tmin, the LARS-WG and SDSM overestimated

Table 5 Performance of the 2 models during the calibration ̅X R RMSE Bias Pbias (%) SD period Precipitation (mm/month) OBSERVED 92.66 44.97 SDSM 95.01 0.994 3.86 2.31 2.50 44.70 LARS-WG 99.57 0.942 12.90 6.88 7.42 39.98 Tmax (°C/month) OBSERVED 21.856 7.515 SDSM 21.829 0.998 0.252 −0.027 −0.127 7.467 LARS-WG 21.861 0.995 0.721 0.004 0.021 7.271 Tmin (°C/month) OBSERVED 9.617 7.183 SDSM 9.659 0.998 0.260 0.041 0.442 7.157 LARS-WG 9.693 0.990 0.704 0.075 0.787 6.980

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Can, respectively. To validate the LARS-WG model the same statistical indicators of observed data in calibration were used without any forcing applied.

4.2.1 Comparisons of Model Performance

In the validation period, the performances of both statistical models were statistically assessed by using NRMSE and Pbias between the observed and downscaled data for precipitation, Tmax and Tmin. The results presented in Table 6 are the calculated mean values of all meteorological stations. It can be seen that daily precipitation simulated by SDSM and LARS-WG show mean NRMSE values of 100.8 and 107.8%, respectively. In monthly precipitation by SDSM, NRMSE and Pbias range from 66.3 to 73.0% and − 2.1 to −9.1%, respectively. Conversely, LARS-WG has NRMSE and Pbias values of 73.8% and − 5.7%, respectively. Higher value of NRMSE show that LARS-WG underperformed for downscaling both daily and monthly precipitation. With regard to mean monthly precipitation, LARS-WG has an NRMSE value again higher than SDSM, which indicates that SDSM performed better in downscaling the precipitation in the study area than LARS-WG. In the downscaling of monthly (10 years=10×12=120 months) and mean monthly (10 years = mean 12 months) precipitation, the NRMSE and Pbias of SDSM-BCC were less than those of SDSM- MICROC and SDSM-Can. In the case of daily temperature (both Tmax and Tmin) simulated by SDSM and LARS-WG, NRMSE ranged from 20.1 to 27.6% and 24.5 to 31.1%, respectively. In the simulation of monthly temperature (both Tmax and Tmin), SDSM, NRMSE and Pbias ranged from 8.5 to 18.8% and − 0.1 to 1.3%, respectively, and in LARS-WG these values ranged from 12.3 to 16.2% and 0.1 to 3.1%, respectively. In addition, for mean monthly Tmax and Tmin for SDSM-NCEP (including SDSM-BCC, MICROC and Can), NRMSE ranged from 2.7 to 10.6% and LARS-WG presented a good simulation, Fig. 3 SDSM and LARS-WG calibration results for a Precipitation, b with NRMSE ranging from 7.5 to 7.9%. In the downscaling Tmax and c Tmin in the Jhelum River basin of precipitation and temperature, BCC-CSM1–1 GCM performance was better than that of the other GCMs. SDSM performance was better with NCEP reanalysis data than from Can, temperatures in the months of January, October, November MICROC and BCC because NCEP data were used during and December and underestimated them in the months of model calibration. February, March, April, May and June. 4.2.2 Taylor Diagram

4.2 Validation of Downscaling Models The Taylor diagram provides a brief statistical summary of standard deviation, correlation coefficient and root mean Five datasets were generated for the historical period 1991– square difference. Figure 4 shows the performance of SDSM 2000 to validate the SDSM and LARS-WG models. For and LARS-WG for the downscaling of daily, monthly and SDSM forcing of NCEP, three GCM, BCC-CSM 1–1, mean monthly temperature and precipitation. For daily precip- MIROC5 and CanESM2 variables were used, denoted as itation, SDSM with NCEP has a correlation coefficient greater SDSM-NCEP, SDSM-BCC, SDSM-MIROC and SDSM- than 0.2, whereas LARS-WG has a correlation coefficient less

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Table 6 Models performance during the validation period Precipitation Tmax Tmin

NRMSE (%) Pbias (%) NRMSE (%) Pbias (%) NRMSE (%) Pbias (%)

Daily SDSM-NCEP 100.8 −5.1 27.6 0.6 20.1 6.4 LARS-WG 107.8 −3.4 31.2 1 24.5 8.4 Monthly SDSM-NCEP 67.1 −7.3 12.9 0.2 8.5 1.3 LARS-WG 73.8 −5.7 16.2 0.5 12.3 3.1 SDSM-Can 71.6 −8.1 17.6 −0.3 12.8 −1.2 SDSM-MICROC 73.0 −9.1 18.8 −0.7 13.1 −0.4 SDSM-BCC 66.3 −2.1 16.7 −0.1 12.1 0.1 Mean Monthly SDSM-NCEP 14.5 −0.1 3.6 0.2 2.7 0.6 LARS-WG 30.8 1.0 7.9 0.6 7.5 2.5 SDSM-CanESM2 28.6 −1.9 10.1 −0.3 9.1 −1.8 SDSM-MICROC5 28.4 −2.9 10.6 −0.6 7 −1 SDSM-BCC 26.9 −0.5 5.8 0.1 5.9 −0.7

Fig. 4 Taylor diagram for three GCMs during SDSM and LARS-WG validation for a Precipitation, b Tmax and c Tmin in the Jhelum River basin

Korean Meteorological Society 17 728 N. Saddique et al. than 0.15. As usual, the downscaling of daily time series pre- difference between simulated and observed data was less than cipitation is not easy, and the simulation results for daily pre- 0.2 (i.e., 6.96 mm), and the correlation coefficients of all cipitation in this study are comparable with those of a few past SDSM models (BCC, Can, and MICROC) were in the range studies (Huang et al. 2011; Hassan et al. 2014). Simulation of 0.95 to 0.99, which shows that the performance of SDSM is results for monthly precipitation by SDSM show correlation relatively better than that of LARS-WG. For daily Tmax and coefficient values in the range of 0.65 to 0.80, whereas these Tmin, SDSM and LARS-WG correlation coefficients were values are less than 0.65 for LARS-WG. For mean monthly greater than 0.95 and the normalized RMS differences were precipitation, the SDSM-NCEP model result standard devia- less than 0.4. Mean monthly temperature is thus better tion was very close to the observed data. The normalized RMS projected than monthly data.

Fig. 5 Scatter plot observed versus simulated values during SDSM and LARS-WG validation for a Precipitation, b Tmax and c Tmin in the Jhelum River basin

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4.2.3 Scatter Plot Tmax and Tmin), it can be observed that there is a satisfactory agreement between the observed and simulated daily values. Scatter plot between mean daily observed and simulated values are shown in Fig. 5. It is seen that all the data sets 4.2.4 Temporal Variation in Precipitation and Temperature (LARS-WG, SDSM-NCEP, SDSM-BCC, SDSM-MIROC and SDSM-Can) are not able to simulate the daily precipita- Figure 6 shows the comparison of monthly downscaled tion time series very well. In the case of temperature (both and observed precipitation. It is obvious that both the

Fig. 6 Monthly results of SDSM and LARS-WG during validation for a precipitation, b Tmax and c Tmin in the Jhelum River basin

Korean Meteorological Society 19 730 N. Saddique et al. models fairly captured the precipitation peaks (though in consecutive wet days (CWD) are overestimated by some years under estimation) and they well reproduce SDSM-NCEP while SDSM with other datasets well sim- temporal variation in precipitation. However, the other ulate the CWD. Consecutive dry days (CDD) were slight- three data sets of SDSM (i.e., BCC, MICROC and Can) ly overestimated by both the models. also display good overlapping with observed precipitation with small under estimation. The trend of LARS-WG in 4.3 Future Climate Change all months of the year was similar to the three data sets of SDSM, with comparatively more deviation from observed In this study, the future period was divided into three periods: precipitation. The datasets of SDSM and LARS-WG are 2011–2040 (2020s), 2041–2070 (2050s), and 2071–2100 (2080s). displayed to emulate Tmax and Tmin, climatologies with good precision, yet there are very small over estimated and under estimated biases. 4.3.1 Changes in Seasonal and Annual Precipitation

Table 7 shows the projected change in mean annual precipita- 4.2.5 Observed and Simulated Climate Indices tion. All GCMs projected an increase in precipitation under both downscaling methods, two RCPs and three future periods Figure 7 shows the comparison of mean values of climate relative to the baseline. For SDSM the mean annual precipi- indices of simulated temperature and precipitation with tation changes for the three GCMs ranged from 4.4 to 8.3%, observed data for the validation period. The summer days 7.3 to 10.6% and 6.4 to 12.9% under RCP4.5 during the (SU) and heavy precipitation days (R10) were well simu- 2020s, 2050s, and 2080s, respectively, whereas under lated by SDSM and LARS-WG. It can be seen that RCP8.5 these changes ranged from 4.5 to 8.7%, 7.9 to

Fig. 7 Mean values of extreme indices for the validation (1991–2000) climate

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Table 7 Annual changes in Precipitation (%) in three future Periods SDSM LARS-WG periods under three GCMs and two scenarios (RCP4.5 and CanESM2 BCC-CSM1– MICROC5 CanESM2 BCC-CSM1– MICROC5 RCP8.5) with two models 1 1

RCP4.5 2020s 8.3 6.9 4.4 14.6 16.4 11.6 2050s 10.6 8.6 7.3 17.9 21.6 16.4 2080s 12.9 6.4 8.3 22.5 25.3 20.7 RCP8.5 2020s 8.7 4.5 6.9 16.6 13.1 16.6 2050s 12.3 7.9 10.2 22.1 22.9 23.5 2080s 16.9 11.6 17.9 32.2 24.5 28.2

12.3%, and 11.6 to 17.9% during the three future periods, predicted change for the seasonal and annual mean precipita- respectively. The mean annual precipitation predicted by tion for the three GCMs and two scenarios using LARS-WG using LARS-WG showed a significant increase in precipita- were greater compared with SDSM. tion in all GCMs. The three GCMs generated increments ranging from 11.6 to 16.4%, 16.4 to 21.6%, and 20.7 to 25.3% 4.3.2 Changes in Seasonal and Annual Tmax under RCP4.5, whereas under RCP8.5 these changes ranged from 13.1 to 16.6%, 22.1 to 23.5%, and 24.5 to 32.2%, re- Table 8 illustrates the changes in Tmax during three future spectively, during the three future periods. Mean annual pre- periods using RCP4.5 and RCP8.5 for both models relative cipitation projected by the BCC-CSM1–1 GCM in RCP8.5 to a baseline (1961–1990). By using the SDSM and LARS- was slightly less in magnitude compared with RCP4.5 in both WG methods, the projected Tmax showed an increase ranging downscaling methods for two future periods. Among the fu- between 0.40 and 1.38 °C, 0.81 and 2.27 °C, and 1.07 and ture years, CanESM2, BCC-CSM1–1 and MICROC5 with 4.18 °C for the future periods, respectively (i.e., the 2020s, two downscaling methods and two RCPs showed the highest 2050s, and 2080s). A continuously increasing trend was change in values in the 2080s. projected for Tmax by all GCMs, two RCPs and both down- The changes in seasonal mean precipitation under two sce- scaling methods in the future periods. The RCP8.5 scenarios narios using SDSM and LARS-WG for the 2080s are shown using SDSM and LARS-WG projected higher increases in in Fig. 8. It is seen that under RCP4.5, the seasons in which Tmax than the future RCP4.5 scenarios except during the changes of mean precipitation would be extreme in the 2080s 2020s by MICROC5 in SDSM. The highest positive change were winter and autumn. During autumn, a dramatic increase in temperature was found in the 2080s and the change in in precipitation was found using both downscaling methods. magnitude for Tmax was higher for LARS-WG compared This increment would shift the monsoon period in the study with SDSM for all GCMs. area; similar findings are found under RCP8.5 but with differ- The seasonal changes of Tmax in the 2080s under two ences in magnitude for the change. The magnitude of the scenarios using SDSM and LARS-WG are shown in Fig. 9.

Fig. 8 Change in seasonal and annual mean precipitation in percentage (relative to baseline) with SDSM and LARS-WG in the 2080s under a RCP4.5 and b RCP8.5

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Table 8 Annual changes in Tmax (°C) using three GCMs and two Table 9 Annual changes in Tmin (°C) using three GCMs and two scenarios (RCP4.5 and RCP 8.5) with two models scenarios (RCP4.5 and RCP 8.5) with two models

Periods SDSM LARS-WG Periods SDSM LARS-WG

CanESM2 BCC MICROC5 CanESM2 BCC MICROC5 CanESM2 BCC MICROC5 CanESM2 BCC MICROC5

RCP4.5 RCP4.5 2020s 0.40 0.49 0.64 0.69 0.67 0.70 2020s 0.5 0.7 0.3 0.7 0.9 0.8 2050s 0.83 0.81 1.08 1.02 1.29 1.38 2050s 0.9 1.2 0.9 0.9 1.5 1.5 2080s 1.07 1.09 1.32 1.98 2.18 1.77 2080s 1.1 1.4 1.1 1.9 2.2 1.9 RCP8.5 RCP8.5 2020s 0.72 0.52 0.52 0.97 0.92 1.38 2020s 0.6 0.8 0.5 0.9 1.5 1.4 2050s 1.32 0.88 1.29 1.56 2.21 2.27 2050s 1.2 1.6 1.2 1.5 2.3 2.3 2080s 2.09 1.16 2.02 3.31 4.18 3.54 2080s 1.9 1.9 1.9 3.0 4.2 3.6

It can be seen that under RCP4.5 three seasons showed incre- seasons. Under RCP8.5, all seasons showed increments in ments in Tmax with different magnitudes but a decreasing Tmin in the 2080s, although with different magnitudes. trend was found in spring with SDSM in all GCMs. Under SDSM and LARS-WG predicted that the most-warmed sea- the RCP8.5 scenario for both downscaling models, the three son in the 2080s would be autumn, with a rise of mean Tmin GCMs projected increased temperature in all seasons except between 4.11 and 4.66 °C. for summer by BCC-CSM1–1 GCM. Autumn showed maximum increases in Tmax under both RCPs.

4.3.3 Changes in Seasonal and Annual Tmin 5 Conclusions

Table 9 represents the changes in Tmin in three future time The performances of a multi-regression model called periods relative to the baseline period using the SDSM and SDSM, a weather generator called LARS-WG and three LARS-WG models. The increase in projected Tmin ranged GCMs (CanESM2, BCC-CSM1–1 and MICROC5) were between 0.3 and 1.5 °C, 0.9 and 2.3 °C and 1.1 and 4.2 °C evaluated with respect to their ability to downscale pre- for the future periods, respectively (i.e., the 2020s, 2050s, and cipitation, Tmax and Tmin for two RCPs (RCP4.5 and 2080s). For annual mean Tmin, the BCC-CSM1–1 showed RCP8.5) for the Jhelum River basin. The screening of the largest increase during the 2080s under the two downscal- the most suitable predictors of SDSM was very challenging methods and all scenarios. ing in the study area due to complex topography. There is The seasonal changes of Tmin in the period of the 2080s little accordance of opinions on the best method of using RCP4.5 and RCP8.5 with the SDSM and LARS-WG selecting predictors for climatic variables, especially for models are shown in Fig. 10. Under RCP4.5, SDSM with all rainfall. The meridional velocity (at 500 hpa) was found the GCMs predicted decreases in Tmin in spring, and BCC- to be a superpredictor for rainfall for most weather sta- CSM1–1 predicted more increases of temperature in all tions in the Jhelum River basin.

Fig. 9 Change in seasonal and annual mean Tmax (relative to baseline) with SDSM and LARS- WG in the 2080s under a RCP4.5 and b RCP8.5

Korean Meteorological Society 22 Downscaling of CMIP5 Models Output by Using Statistical Models in a Data Scarce Mountain Environment... 733

Fig. 10 Change in seasonal and annual mean Tmin (relative to baseline) with SDSM and LARS- WG in the 2080s under a RCP4.5 and b RCP8.5

The results showed that during calibration, the simula- average) also reported an increase in precipitation in the tion of mean monthly precipitation SDSM performance was study area for the future time period (IPCC 2013). better than that of LARS-WG, although, graphically, both For temperature (Tmax and Tmin), both downscaling ap- models slightly overestimated the rainfall with different proaches showed annual increases in temperature in the study magnitudes. However, for Tmax and Tmin the simulated area with RCP4.5 and RCP8.5. However, the results showed results for both models were very close to observations. that the magnitudes of temperature changes using both ap- During validation, the precipitation downscaled by SDSM proaches were significantly different from each other. The with NCEP data was good compared with the results of results on a seasonal basis also showed increments in temper- LARS-WG, especially in modelling the mean monthly pre- ature in all seasons under RCP8.5; however, the temperature cipitation, for which the correlation value was higher than in spring would be decreased with both approaches and the 0.98. A good performance of the BCC-CMS1–1 global cli- three GCMs under RCP4.5. The results showed a maximum mate model was found in downscaling of the monthly and increment in temperature in the study area of 4.7 °C (autumn). mean monthly precipitation. Overall, the validation results In general, the SDSM and LARS-WG models well simu- show that SDSM and LARS-WG could be used for future lated the present day temperature, although the precipitation projection using all three GCM outputs. results of SDSM were reasonably better than those from The data were divided into three periods (the 2020s, LARS-WG. Future results for both the models were signifi- 2050s and 2080s) to observe the changes in future simu- cantly different from each other due to their different down- lated data. The results of the three future periods showed scaling strategies and concepts. The BCC-CSM1–1 GCM that annual mean basin precipitation increased with both model performed better than the other GCMs. From the down- statistical downscaling approaches, three GCMs and with scale results, it is concluded that the Jhelum basin will face RCP4.5 and RCP8.5. The predicted change in precipita- more rainfall in the future. All the GCMs also showed incre- tion with LARS-WG was higher in magnitude than that of ments in Tmax and Tmin, which will most likely result in a SDSM. In the 2080s, the annual mean precipitation of the hotter climate of the region in the future with respect to the Jhelum River basin under RCP4.5 and RCP8.5 would current climate. These results contribute to later scientific increase by 12.7% and 17.9%, respectively, with SDSM, work such as water resources planning and in the agriculture which were higher than the simulation results presented in sector. However much more research is needed with maxi- another study (Mahmood and Babel 2013) for the same mum number of GCMs to understand the uncertainties related region. Significant seasonal variations in precipitation to these climate models. change would be observed when compared with the baseline period; all the seasons except winter showed incre- Acknowledgements We are thankful to the Institute of Hydrology and ments in precipitation in the 2080s under both RCPs, Meteorology, Chair of Meteorology, Technical University Dresden for giving the opportunity to perform this research. Mr. Naeem Saddique also three GCMs with SDSM and LARS-WG. Autumn acknowledge the Higher Education Commission of Pakistan (HEC) showed a higher percentage increase in precipitation com- Pakistan and German Academic Exchange Service (DAAD) Germany pared with spring and summer. The BCC-CSM1–1 GCM for providing him financial support for his PhD studies. The financial projected less percentage increase under both RCPs with support for this project was extremely useful for the completion of this research endeavor and is greatly appreciated. The authors wish to extend a SDSM compared with the other two GCMs, and the re- special thanks to the organizations WAPDA, PMD and IMD for provid- verse was the case for LARS-WG. The AR5 (models ing access to meteorological data utilized in this research.

Korean Meteorological Society 23 734 N. Saddique et al.

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Korean Meteorological Society 25 Stochastic Environmental Research and Risk Assessment

https://doi.org/10.1007/s00477-020-01829-6 (0123456789().,-volV)(0123456789().,- volV)

ORIGINAL PAPER

Trends in temperature and precipitation extremes in historical (1961–1990) and projected (2061–2090) periods in a data scarce mountain basin, northern Pakistan

Naeem Saddique1,2 • Abdul Khaliq2 • Christian Bernhofer1

Ó The Author(s) 2020

Abstract This study investigates the trends of precipitation and temperature extremes for the historical observations (1961–1990) and future period (2061–2090) in the Jhelum River Basin. Future trends are estimated by using ensemble mean of three general circulation models under RCP4.5 and RCP8.5. Therefore, statistical downscaling model has been used to downscale the future precipitation and temperature. A total of 15 precipitation and temperature indices were calculated using the RClimdex package. Man-Kendall and Sen’s slope tests were used to detect the trends in climate extreme indices. Overall, the results of study indicate that there were significant changes in precipitation and temperature patterns as well as in the climate extremes in the basin for both observed as well as projected climate. Generally, more warming and increase in precipitation were observed, which increases from RCP4.5 to RCP8.5. For all the stations, increasing trends were found for both precipitation and temperature for twenty-first century at a 95% significance level. The frequency of warm days (TX90p), warm nights (TN90p), and summer days (SU25) showed significant increasing trends, alternatively the number of cold nights (TN10p) and cold days (TX10p) exhibited opposite behaviors. In addition, an increasing trend of warmest day (TXx) and coldest day (TNn) was observed. Our analysis also reveals that the number of very wet days (R90p) and heavy precipitation days (R10 mm) will likely increase in the future. Meanwhile, the Max 1-day (RX1-day) and 5-day (RX5-day) precipitation indices showed increasing trends at most of the stations of basin. The results of the study is of potential benefit for decision-makers to develop basin wide appropriate mitigation and adaptation measures to combat climate change and its consequences.

Keywords Temperature extremes Á CMIP5 Á Jhelum River basin Á Climate change Á Warm nights Á Sen’s slope

1 Introduction changes in mean climate values will have less negative impacts on human health and natural system than changes Intergovernmental Panel on Climate Change (IPCC) points in extreme events (Katz and Brown 1992; IPCC 2013). It is out that the climate change started to affect the frequency, reported in Fifth Assessment Report (AR5) that globally intensity, and duration of extreme climate events such as the number of warm days and nights has increased, droughts, floods and heat waves globally at the end of whereas the number of cold days and nights has decreased twentieth century which is likely to continue in future between 1951 and 2010. Climate change poses serious (IPCC 2013). Additionally, it is anticipated that any future threats to Pakistan. As such, it has caused various disasters in the form of droughts and floods in the country. In the aftermath of the 2010 floods, about 20% the country’s land & Naeem Saddique area was underwater; it severely damaged the infrastruc- [email protected] ture, crops and housing, impacted livelihoods over 20 million people. Due to the single flood event, estimated 1 Institute of Hydrology and Meteorology, Technische Universita¨t Dresden, Tharandt, Germany damages were about US$10 billion, half of which were losses in the agriculture sector (ADB and World Bank 2 Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan 2010). In addition, severe heat wave that occurred in

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Pakistan during 2015 and caused about 1233 deaths Saddique et al. 2019). The downscaling methods have been (Saleem et al. 2018). During this heat wave, around 65,000 divided into two main categories: statistical downscaling, people were treated in different hospitals because of the establishing a relationship between large scale climate heatstroke. variables (vorticity and sea level pressure) and local In the last decade, for analyzing the climate extremes observations like precipitation and temperature; dynamical changing patterns across the world a number of global and downscaling, using GCM output to drive regional climate local studies have been reported (Islam et al. 2009; Sill- models (RCMs) (Turco et al. 2011; Bao et al. 2015; Chen mann et al. 2013; Rashid et al. 2017; Pei et al. 2017; et al. 2018). A widely used statistical tool in climate Ongoma et al. 2018; Roxy et al. 2017; Cheong et al. 2018; change studies is SDSM (statistical downscaling model). Feyissa et al. 2018; Abbasnia and Toros 2018; Abatan et al. One study has been undertaken only on temperature 2018; Peng et al. 2018; Fenta Mekonnen and Disse 2018; extreme indices over the Jhelum River basin (Mahmood Salvador and de Brito 2018). However, the impact of cli- and Babel 2014) using one GCM (HADCM3) from third mate change is not uniform globally (Sillmann et al. 2013). Assessment report (AR3), noting an increase in tempera- Islam et al. (2009) assessed that the occurrence of annual ture. To the best of our knowledge, no prior studies have warm spells shows slight increasing trend while cold spells been reported to date that examine the extreme precipita- significantly decrease over Pakistan. Ongoma et al. (2018) tion events in Jhelum River basin. Precipitation is found a remarkable increase in maximum 1-day and increasingly important and relevant parameter to be studied maximum 5-day precipitation amount over Kenya and due to flash flooding and abnormal river flows in past few Uganda. Abbasnia and Toros (2018) analyzed that the in years in the Indus Basin including Jhelum River basin future rainfall events will be short and intense, the occur- (Rehman et al. 2015). Both temperature and precipitation rence of extreme temperatures could be more pronounced phenomenon are interlinked and to river flow in the region, in favor of hotter events, and atmospheric moisture content therefore, it is justified to study both variables together for will be increased over the Marmara Region, Turkey. Thus, clearer picture. Thus the specific objective of the present this suggests that in future there will be higher occurrence study is to investigate the past and projected changes of probabilities of heavy rainfalls and droughts. These kind of both temperature and precipitation climate extremes over intense climate events are always associated with social Jhelum River basin using the observed data and future and economic losses. The impact becomes more severe if outputs of three GCMs from CMIP5 (Coupled Model different extremes occur at the same time for instance Intercomparison Project from Phase 5). First, this study heatwaves and meteorological droughts (Sharma and investigates the trends of climate extreme indices over the Mujumdar 2017). study area during the observed period. After that, the Globally changes in climate extreme events associated downscaling method (SDSM) is employed to 2061–2090 with substantial effects on human health and the environ- CMIP5 runs under two RCPs scenarios and evaluates how mental system. Nevertheless, climate change has the ability much change occurs in climate Indices after 100 years. to alter both frequency and intensity of climate extremes Fifteen (15) precipitation and temperature indices are used events. More severe extreme events will be occurred due in the study defined by the Expert Team on Climate more severe climate changes, causing significant conse- Change Detection Monitoring and Indices (ETCCDMI). quences. Therefore, climate extremes prediction is very The rest part of this study outline is as follows: Sect. 2 essential to analyze the climate change impacts on human briefly explains the study area and data. Section 3 explains society and on the environmental system (Mahmood and the methodology used for downscaling GCMs outputs and Babel 2014). This kind of information is also important for climate Extremes Indices calculation. Section 4 presents effective planning at national and local level strategies for and discusses results, while the conclusion of the study is both adaptation and mitigation (Gu et al. 2012; Frias et al. described in Sect. 5. 2012; Adyeri et al. 2019). Global climate models (GCMs) are the major tools used to reproduce observed features of present climate and 2 Study area and data project future climate (Meehl et al. 2007). However, GCMs have coarse resolution and their outputs cannot directly 2.1 Description of the study area meet the needs of end-users who want to apply climate extremes changes in integrative studies, for instance con- The Jhelum River Basin (JRB) lies between 73–75.62 °E servation, hydrology, and climate change risk assessment and 33–35 °N with total basin area 33,397 km2. JRB has (Diaz-Nieto and Wilby 2005). To fill this gap, different significant variation in the elevation, which varies between downscaling techniques are developed and commonly used 232 and 6287 m. The whole river discharge flows into the (Wilby and Dawson 2004, 2013; Zhang et al. 2016; Mangla Dam reservoir, the second biggest reservoir of

123 27 Stochastic Environmental Research and Risk Assessment country. Locations of the study area and weather stations the Indian Meteorological Department (IMD). Precipitation (temperature and precipitation) are shown in Fig. 1. and temperature data of Srinagar, Gulmarg, Qaziqund, and Most of the water ﬂows into the reservoir in summer, Kupwara meteorological stations were obtained from the with a substantial share due to snowmelt during the month IMD. of May. Generally, more than 2/3 of the total water enters In this study, daily predictors (26) of three GCMs (i.e., the reservoir from March to August. The Mangla Dam CanESM2, BCC-CSM1.1, and MIROC5) from Phase 5 of reservoir can irrigate up to 6 million hectares land. the Coupled Model Intercomparison Project (CMIP5) for Reservoir water mainly used for two key purposes: supply the period of 1961–2100 are downloaded from ESGF water at downstream to irrigate the agriculture land and website (http://pcmdi9.llnl.gov/). We used two RCPs sce- generate 1000 MW electricity, which is approximately narios, RCP 4.5 and RCP 8.5, which assumes that radiative 15% of the total electricity production through hydroelec- forcing would increase to 4.5 Wm- 2 and 8.5 Wm- 2, tric plant in the country (Archer and Fowler 2008). respectively, towards the end of the twenty-ﬁrst century The whole basin has mean annual precipitation about (Moss et al. 2010). Daily predictors data of NCEP (Na- 1196 mm and mean annual temperature 13.2 °C. The cli- tional Centers of Environmental Prediction) for the his- mate of the Jhelum River basin (JRB) varies with eleva- torical period (1961–2005) is obtained from SDSM website tion; northern part of the basin is colder (high altitude) than (https://sdsm.org.uk/data.html). As the GCMs data are southern part. Naran with mean temperature 6.4 °C and available at different spatial resolutions, these data sets are Mangla with mean temperature 22.62 °C are the coldest interpolated at the grid resolution of NCEP (2.5° 9 2.5°). and hottest stations of Jhelum River basin. Murree and GCMs predictors have been normalized with respect to Balakot are two highly rainy stations with precipitation their means and standard deviations (SD) for the historical amounts more than 1700 mm/year (Saddique et al. 2019) period 1961–1990. Table 2 provides a detail description of (Table 1). global climate models.

2.2 Data description 3 Methodology Daily observed precipitation (Prec.), Temperature maximum (Tmax) and Temperature minimum (Tmin) data of 15 SDSM (statistical downscaling model) is a combination of stations for the period of 1961–2012 were obtained from multiple linear regression (MLR) and stochastic weather Pakistan Meteorological Department (PMD), Water and generator. In SDSM during calibration MLR establish Power Development Authority of Pakistan (WAPDA), and mathematical relationship between regional predictors (i.e.,

Fig. 1 Location map of Jhelum River Basin and weather stations

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Table 1 Inventory of weather Sr. no. Name Latitude (°) Longitude (°) Sr. no. Name Latitude (°) Longitude (°) stations used in the present study S1 Bagh 33.97 73.79 S9 Mangla 33.14 73.64 S2 Balakot 34.55 73.35 S10 Murree 33.91 73.38 S3 Garidhoptta 34.22 73.62 S11 Naran 34.90 73.65 S4 Gujar Khan 33.26 73.30 S12 Plandri 33.72 73.71 S5 Gulmarg 34.00 74.33 S13 Qaziqund 33.58 75.08 S6 Kotli 33.50 73.90 S14 Rawalkot 33.87 73.68 S7 Kupwara 34.51 74.25 S15 Srinagar 34.08 74.83 S8 M.Abad 34.37 73.48

Table 2 Description of three Model name Short name Spatial resolution Modelling countries Historical period Future period selected GCMs used in this study CanESM2 CanESM 2.812° 9 2.812° Canada 1961–2005 2006–2100 MIROC5 MIROC 1.406° 9 1.406° Japan 1961–2005 2006–2100 BCC-CSM1-1 BCC 2.812° 9 2.812° China 1961–2005 2006–2100 mean sea level pressure and specific humidity) and local analyze the changes in climate extremes (Peterson 2005). observations (i.e., Temperature and Precipitation) and The extreme indices are calculated using the ‘Rclimdex’ produced some statistical parameters. Weather generator package (Zhang and Yang 2004). For this study 6 precip- used these regression parameters along with GCM and itation indices and 9 temperature indices were selected to NCEP predictors and generate few daily time series (con- explore the changes in climate extremes of Jhelum River sider 25 in this study) that are closely fit with observed data basin (listed in Table 3). To understand how extreme (Saddique et al. 2019). temperature and precipitation changed in the past 30 years In SDSM for the selection of most suitable predictors and to predict how it will change after 100 years more from a set of 26 atmospheric predictors various indictors accurately, we used the observed dataset (1961–1990) and (correlation matrix, partial correlation, explained variance, future dataset (2061–2090) to evaluate the climate scatter plots, p value and histograms) are used for down- extremes. scaling of precipitation and temperature. Unconditional model was chosen for temperature and a conditional model 3.2 Trend analysis for precipitation (Wilby and Dawson 2004). As precipitation data is not normally distributed, before its application In the current study, non-parametric Mann–Kendall (MK) in the regression analysis a fourth order transformation was test (Mann 1945; Kendall 1975) is employed to determine applied. Ordinary least square (OLS) method was used for the trends in the temperature and precipitation extreme the optimization of model as this method is faster than dual indices. MK test is globally applied in hydrological and simplex (DS) and produces comparable results (Huang meteorological studies for detecting trends in time series et al. 2011). Figure 2 describes the main steps involved in while Sen’s slope estimator determines the magnitude of statistical downscaling method. trend. The MK Z-value [ 1.96 shows significantly The most important step in statistical downscaling increasing trend and \ - 1.96 shows the significantly methods is screening of predictors during calibration decreasing trend at 5% significance level. The null

(Huang et al. 2011). In present study correlation coefﬁ- hypothesis (H0) for the test signiﬁed no trend and the cient, partial correlation and p value are used for the alternate hypothesis suggest that there is increasing or selection of appropriate predictors. Predictor whose cor- decreasing trend present in the time series. Mathematical relation is high selected as a super predictor and other Eq. 1 was used to determine the MK trend statistic. predictors were placed at second and third position based mÀ1 m on highest correlation and explained variance. S ¼ sgnðx À x Þ ð1Þ X X j i i¼1 j¼iþ1 3.1 Climate extremes þwhere m: the number of observations, xi and xj the ith and jth observations, sgn: the sign function. The Expert Team (ET) on Climate Change Detection and sgn is calculated by Eq. 2; Indices (ETCCDI) developed a core set of 27 indices to

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Fig. 2 Flow chart illustrating the statistical downscaling method

Table 3 List of climate extreme indices used in the present study Code Description Indices deﬁnition

Temperature TXx Warmest days Maximum values of daily maximum temperature TNx Warmest nights Maximum values of daily minimum temperature TXn Coldest days Minimum values of daily maximum temperature TNn Coldest nights Minimum values of daily minimum temperature TX90p Warm days Percentage of days in a year when daily maximum temperature [ 90th percentile TN90p Warm nights Percentage of nights in a year when daily min temperature [ 90th percentile TX10p Cold days Percentage of days in a year when daily maximum temperature \ 10th percentile TN10p Cold nights Percentage of nights in a year when daily min temperature \ 10th percentile SU Summer days Annual number of days when daily maximum temperature [ 25 °C Precipitation RX1 day Max 1-day precipitation amount Annual maximum 1 day precipitation RX5 day Max 5-day precipitation amount Annual maximum consecutive 5 day precipitation R95p Very wet days Total annual precipitation from days [ 95th percentile SDII Simple daily intensity index The ratio of annual total wet-day precipitation to the number of wet days PRCPTOT Annual total wet-day precipitation Total annual precipitation from days C 1 mm R10mm Number of heavy precipitation days Count the number of days where rainfall C 10 mm

q 1 if xj À xi [ 0 mð m À 1 Þð2m þ 5 Þ À tpðtp À 1Þð2tp þ 5Þ 8 ÀÁ Varð S Þ ¼ Pp¼1 sgn xj À xi ¼ < 0 if xj À xi ¼ 0 ð2Þ 18 ÀÁ ÀÁ \ À1 if xj À xi 1 ð4Þ : ÀÁ The S statistic mean and variance are calculated as where q: the number of tied groups, tp: the number of (Eqs. 3 and 4, respectively); observations in pth group. EðS Þ ¼ 0 ð3Þ The MK Z statistic is calculated by (Eq. 5);

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S À 1 nights (TNn) have more increasing trends with magnitude for S [ 0 8 Varð S Þ 1.13 and 1.32 °C per decade. > Z ¼ <> 0 for S ¼ 0 ð5Þ In case of TXn and TNn about 90% stations are featured S þ 1 for S\0 by significant positive trends at 5% significance level while > Varð S Þ for TXx and TNx about 25% stations showed significant :> results. The results of prior studies by Mahmood and Babel (2014) also illustrated an upward trend (positive) in these 4 Results and discussion indices. The frequency indices (for instance- warm days 4.1 Observed extremes indices (TX90p), cold days (TX10p), warm nights (TN90p), and cold nights (TN10p) are presented in Fig. 4. Generally, TX10p and TN10p show significantly decreasing trends 4.1.1 Observed (historical) temperature indices with magnitude of - 1.30% and - 1.17% per decade respectively (- 4.74 and - 4.01 days per decade) while The 1961–1990 extreme observed (historical) temperature the TX90p and TN90p revealed significantly increasing trends showed (Fig. 3) that the intensity and frequency of trends with a mean rate of 3.6 and 2 days per decade temperature extremes significantly increase, while the respectively. Figure 4b, d showed that nearly all the sta- intensity and frequency of cold temperature extremes sig- tions exhibit a decreasing trend in cold days and nights, and nificantly decrease. Statistically, warming trends are approximately 90% of these stations have significant trends observed at 0.05 significance level at most of the stations of at the 0.05 level. In general, more than 95% of the stations Jhelum River basin. Compared to, warmest days (TXx) and showed increasing trend in warm days and nights at 95 % warmest nights (TNx), the coldest days (TXn) and coldest confidence interval (Fig. 4a, c). The Fifth Assessment

Fig. 3 Station trends (left) for a warmest day, b coldest day, signiﬁcant trends by gray circles; regional series (right) for c warmest night, d coldest night- positive trends are represented by e warmest days, f coldest days, g warmest nights, h coldest nights upward triangles, negative trends by downward triangles and for the period of 1961–1990

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Fig. 4 Station trends (left) for percentage of a warm days, b cold downward triangles and signiﬁcant trends by gray circles; regional days, c warm nights, d cold nights and e number of summer days— series (right) of f warm days, g cold days, h warm nights, i cold nights positive trends are indicated by upward triangles, negative trends by and j summer days for the period of 1961–1990

Report (AR5) of the IPCC has also been presented similar All the 15 stations of basin showed upward (increasing) conclusions for most land areas in the world (IPCC 2013). trend of RX1-day, four stations have significant positive In general, the number of summer days (SU) increased over trends (Fig. 5a). Similarly, maximum 5-day precipitation the study area as shown in Figure 4j, the mean increase in amount represents increasing trend with magnitude 1.38 SU was 4.47 days per decade over the study basin. It can be mm per year for the study area (Fig. 5h). Most of the seen that three stations (Kotli, Bagh, and Garidhoptta) stations showed increasing trend except Rawalkot and showed decreasing trends in summer days while the other Garidhoptta. The RX5-day series ranged between 118.87 stations presented increasing trends (Fig. 4e). mm to 236.47 mm during the different years of baseline period. It can be seen from Fig. 5i that the annual total wet 4.1.2 Observed (historical) precipitation indices day precipitation (PRCPTOT) substantially increased by 5.72 mm/year. All the stations showed increasing trends in The maximum 1-day precipitation amount (RX1) exhibited PRCPTOT (Fig. 5c). Murree, Muzaffarabad, and Qaziqund (Fig. 5g) significantly increasing trend with a magnitude of revealed significant positive trends at 0.05 significance 0.85 mm/year. The basin average RX1-day varied between level. Overall, if the rain intensity increases then frequency 77.89 and 134.64 mm for the baseline period 1961–1990.

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Fig. 5 Station trend (left) for a RX1-day, b RX5-day, c PRCPTOT, d R10mm, e R95p, f SDII; regional time series (right) for g RX1-day, h RX5-day, i PRCPTOT, j R10mm, k R95p, l SDII for the period of 1961–1990 of wet days was expected to decrease during the year trends except Mangla and Gujar khan. The results of this (Casanueva Vicente et al. 2014). study are consistent with the findings of Hartmann and The number of heavy precipitation days (R10 mm) Buchanan (2014) who analyzed the precipitation data for ranged between 32.00 and 37.87 days in different years of period the period 1979–2011 and found increasing trends in baseline period. The number of heavy precipitation days precipitation extremes indices in Indus Basin. showed significant positive trends at the 95% confidence interval at Murree, Balakot, Garidhoptta, Plandri, and 4.2 Projected precipitation and temperature Qaziqund, with an increase of 1.40, 0.75, 0.45, 0.85 and extremes indices 1.14 days per decade, respectively. Only one station (Rawalkot) showed decreasing trend at the rate of - 1.1 Future climate indices over the Jhelum River basin are days per decade (Fig. 5d). For the whole Jhelum River calculated using the ensemble of three GCMs under two basin average increase in R10 about 0.71 days per decade RCPs scenarios (RCP4.5 and RCP8.5) for one-time slice (Fig. 5j). The simple daily intensity index (SDII) increased (2061–2090). with annual trend of 0.09 mm/day/year. The basin average SDII ranged between 12.26 mm/day to 18.0 mm/day for the observed period. All the stations exhibited positive

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4.2.1 Temperature indices under RCP4.5 and RCP8.5 be seen that RX5-day also increase by 15.40 mm and 26.77 mm under both RCPs (Fig. 7b). Projected climate show Figure 6 represents the temperature indices for projected increase in number of heavy precipitation days (R10 mm) climate. The results show that average annual indices when compared with referenced period for most of the expected to be higher in future period (2061–2090) than stations (Fig. 7c). We found basin average increase of 1.57 those in baseline period (1961–1990). TX90p and TN90p days for RCP4.5 and 3.15 days for RCP8.5. A consistent expected to increase drastically under both emission sce- increase in SDII values for all the stations with both scenarios (Fig. 6a, b). The Basin average TX90p increased by narios (Fig. 7d). Comparing to referenced climate, SDII 8.89% under RCP4.5 and 14.43% under RCP8.5 after 100 increased by 1.51 mm/day and 3.25 mm/day under medium years of observed period. TN90p also shows a consistent and high emission scenarios, respectively. It is seen that increase of 10.83% and 18.09% for RCP4.5 and RCP8.5 PRCPTOT also increased under both emission scenarios respectively. On the other hand, TX10p and TN10p for all the stations except Kupwara (S7). Figure 7f showed expected to decrease for projected climate over the Jhelum that R95p (very wet days) increase under both scenarios River basin. It can be seen that cool days and nights but some stations (Bagh, Balakot, Garidhoptta, and Mur- decreased more drastically under RCP8.5 (high emission ree) have drastic increase. Under RCP 4.5, R95p increased scenario) as compared to RCP 4.5 (Fig. 6c, d). Garidhoptta by 75.30 mm and 94.16 mm under RCP8.5. Sharma and (S3) and Gujar Khan (S4) exhibited slightly increase in Goyal (2020) projected an increase in precipitation indices TX10p for RCP 4.5. We found that TXx, TXn, TNx, and such as R10 mm, R95p, RX1-day and RX5-day in Eastern TNn also increased for the projected climate. Maximum Himalayas using four GCMs under two RCPs scenarios for value of daily maximum temperature (TXx) and minimum the period of 1950–2100. temperature (TNx) rise by 1.22 and 1.35 °C under RCP4.5; similar findings are found under RCPs 8.5 but with higher 4.2.3 Trends analysis of projected climate under RCP4.5 magnitude (2.16 and 1.93 °C). TXn (minimum value of daily maximum temperature) and TNn (minimum values of Table 4 shows the temperature and precipitation indices daily minimum temperature) show slightly high-rise in trend analysis for the future period (2061–2090) under values under RCP8.5 as compared to TXx and TNx. There RCP4.5. The frequency of TX90p and TN90p are expected was an increase of summer days (SU) for all the stations of to increase across the whole basin. In this regard, all the basin. Three stations (Gulmarg S5, Murree S10, and Naran stations showed significant positive trend, with a highest S11) of basin have very few summer days as these stations increase of 3.79% (13.83 days) per decade. On the other lie at high altitudes (Fig. 6i). Islam et al. (2009) projected hand, frequency of TX10p and TN10p exhibits a decreas- an increasing trend in warm spell while decreasing trend in ing trend. In case of TN10p Gulmarg, Mangla, Rawalkot, cold spell using RCM PRECIS for the period 2071–2100 and Sarinagr showed significant decreasing trend. All the over Pakistan. They also found that increase in minimum stations revealed positive trends in TXx, TXn, TNx, and temperature was more as compared to maximum temper- TNn. TXx exhibited significant positive trend for all the ature. Mahmood and Babel (2014) also reported a large stations except Plandri (non-significant positive). We found increase in temperature extremes in the study area. For highest increase in maximum value of daily minimum instance, coldest nights (TNn) were found to increase by temperature (TNx) as compared to other three indices. 4.7 °C in winter whereas hot days (TX90P) and hot nights Number SU showed significant positive trends at most of (TN90P) were found to increase by 2.09 and 1.46 °C in the stations except Gulmarg, Naran, and Plandri with a spring at the end of this century. highest trend of 3.89 days per decade. In general, all precipitation indices showed positive 4.2.2 Precipitation indices under RCP4.5 and RCP8.5 trends at most of the stations. RX1-day and RX5-day indices had all positive trend except for the negative trend Figure 7 illustrates projected climate mean precipitation at Garidhoptta, M. Abad, Gujar Khan, and Naran stations. indices for 30-year (2061–2090) at all the stations. We However, these negative trends are not significant. In case observed an increase in precipitation for more than 90% of PRCPTOT, having the highest value of 39.71 mm per stations, the precipitation indices were also anticipated to decade except for Murree and Naran with negative trends increase. Generally, majority of the indices revealed of -9.31 and - 11.31 mm/decade. R10 mm and SDII also increase from RCP4.5 to RCP8.5. RX1-day increased showed positive trends for most of the climate stations for about 13.29 mm under RCP4.5 and about 17.39 mm under future time slice. R95p (very wet days) are observed to RCP8.5 relative to baseline (1961–1990). All the stations have positive trend in 2061–2090 with highest trend of (Fig. 7a) of JRB showed increase in RX1-day under both 44.20 mm per decade. The positive trends in R95p are RCPs scenarios except Garidhoptta (S3). Similarly, it can significant at Balakot, Kupwara, and Plandri.

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Fig. 6 Comparison of annual average extreme indices of temperature for the projected climate (2061–2090) and for the observed period (1961–1990) at all the stations of Jhelum River basin

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Fig. 7 Comparison of annual average extreme indices of precipitation for the projected climate (2061–2090) and for the observed period (1961–1990) at all the stations of Jhelum River basin

4.2.4 Trends analysis of projected climate under RCP8.5 increasing trend in summer days, especially for the Qaz- iqund that showed an increase of 6.67 days per decade. Table 5 illustrates significant positive trends in number of All the precipitation indices showed positive trends warm days and number of warm nights for all stations under RCP8.5 except for RX1 and RX5 with few stations except Garidhoptta, Naran, Murree, and Qaziqund while all negative trends. The highest positive trend of 58.92 mm per stations exhibited decreasing trends in frequency of cool decade was reported in annual total precipitation at nights and cool days. Positive trends in frequency of warm Garidhoptta. For the future time, slice RX1-day and RX5- days and nights (1.96–4.89% per decade) are seen at Jhe- day rainfall exhibited positive trends except for negative lum river basin while the negative trend in frequency of trends at Gujar khan, Mangla, and M. Abad. In case of cool days and nights (- 1.18 to - 2.89% per decade) is RX5-day highest positive trend of 13.12 mm was recorded observed at study area. TXx, TNx, TXn, and TNn exhibited at Garidhoptta. Heavy precipitation days had significant positive trends at all stations like RCP 4.5 but with higher positive trends at Garidhoptta, Mangla, and Rawalkot and magnitudes. In case of minimum value of daily minimum rainfall intensity index had significant positive trends at temperature, all stations showed significant positive trends Balakot, Garidhoptta, Mangla and Qaziqund. Regarding except Qaziqund. Results of future analysis show that the very wet days (R95p), four stations revealed significant positive trends in TXx, TXn, TNX, and TNn ranged positive trends; highest magnitude of 51.34 mm per decade between 0.23 and 0.96 °C per decade. There was a broad was found at Bagh. Different studies carried out in Indus Basin have also reported an increase in temperature and

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Table 4 Decadal trends of GCMs ensemble means extremes Indices under RCP4.5 during 2061–2090 Stations Temperature indices Precipitation indices TXx TXn TNx TNn TX90p Tx10p TN90p TN10p SU PRCP RX1 RX5 R10 SDII R90p

Bagh 0.19 0.20 0.60 0.38 1.38 2 1.47 2.09 - 1.22 2.86 19.21 4.00 4.74 - 0.21 0.23 42.20 Balakot 0.26 0.30 0.60 0.41 2.24 2 1.30 2.48 - 1.56 2.58 26.11 5.21 11.18 0.43 0.44 44.20 Garidoptta 0.22 0.23 0.53 0.35 2.51 - 1.11 2.79 - 1.89 2.86 39.71 - 0.28 1.50 0.40 0.36 24.00 Gujar khan 0.31 0.35 0.53 0.50 2.54 - 1.13 3.49 - 1.25 1.94 9.8 1.17 - 1.82 0.60 - 0.07 1.41 Gulmarg 0.29 0.21 0.50 0.45 1.99 2 1.10 2.80 2 2.17 2.78 23.98 5.53 7.35 0.56 0.29 26.08 Kotli 0.37 0.20 0.52 0.52 2.74 2 1.45 2.56 - 1.45 2.50 8.09 2.40 1.83 0.74 0.19 26.30 Kupwara 0.19 0.31 0.62 0.42 2.39 - 2.14 3.79 - 1.29 2.78 22.34 3.33 3.42 0.43 0.08 18.53 M.Abad 0.26 0.19 0.40 0.36 2.58 - 1.17 2.75 - 1.54 2.63 20.74 - 1.08 0.28 0.49 0.20 23.59 Mangla 0.25 0.31 0.45 0.23 1.54 2 2.27 3.18 2 2.67 2.38 14.21 1.32 1.44 0.87 0.24 5.81 Murree 0.41 0.33 0.54 0.36 2.74 2 1.80 2.74 - 2.45 1.35 - 9.31 0.97 0.83 0.25 0.33 26.97 Naran 0.18 0.35 0.45 0.45 1.75 - 1.22 1.52 - 1.25 1.16 - 11.31 0.15 - 0.31 0.13 0.17 28.14 Plandri 0.20 0.36 0.46 0.34 2.84 - 1.15 2.45 - 1.85 1.10 31.45 0.27 5.17 0.93 0.12 20.27 Qaziqund 0.33 0.15 0.53 0.46 1.34 - 2.21 2.97 - 1.16 3.33 34.45 4.28 8.92 0.67 0.54 36.04 Rawalkot 0.45 0.25 0.40 0.51 2.12 2 1.58 2.09 2 1.65 3.89 28.90 2.57 3.98 0.21 0.53 9.63 Sarinagr 0.28 0.25 0.56 0.41 2.34 - 1.12 3.04 2 1.27 2.37 13.45 2.87 5.79 0.11 0.29 12.63 Bold values represent signiﬁcant trends at 95% conﬁdence interval

Table 5 Decadal trends of GCMs ensemble means extremes Indices under RCP8.5 during 2061–2090 Stations Temperature indices Precipitation indices TXx TXn TNx TNn TX90p Tx10p TN90p TN10p SU PRCP RX1 RX5 R10 SDII R90p

Bagh 0.63 0.24 0.72 0.88 1.96 2 1.96 2.58 2 2.89 3.24 42.61 4.30 5.45 0.26 0.43 51.34 Balakot 0.27 0.60 0.51 0.52 2.91 - 1.58 2.63 - 1.78 3.25 44.53 6.27 13.12 0.47 0.55 47.98 Garidoptta 0.32 0.43 0.75 0.35 3.38 2 1.64 3.87 - 2.1 3.89 58.92 1.83 2.23 0.55 0.58 15.67 Gujar khan 0.42 0.41 0.65 0.36 3.58 - 1.63 2.96 - 1.54 2.18 15.86 2 2.82 2.92 0.32 0.16 8.54 Gulmarg 0.60 0.25 0.52 0.28 3.85 2 2.01 4.41 2 2.79 2.57 35.85 5.17 8.94 0.75 0.35 32.76 Kotli 0.42 0.29 0.45 0.25 2.48 - 1.89 2.85 2 1.46 3.67 37.76 4.80 2.33 0.83 0.28 37.28 Kupwara 0.29 0.39 0.54 0.52 3.40 - 1.55 2.98 - 2.01 5.33 30.31 5.33 4.48 0.49 0.13 24.54 M.Abad 0.31 0.88 0.61 0.48 3.48 - 2.83 4.85 - 1.60 3.33 36.56 2.79 - 1.13 0.61 0.24 28.33 Mangla 0.38 0.28 0.42 0.32 3.85 - 1.30 3.20 - 1.55 3.75 20.54 - 2.45 2.75 0.72 0.47 24.12 Murree 0.53 0.67 0.96 0.56 3.23 2 1.73 4.11 - 1.34 3.67 7.50 3.18 1.75 0.27 0.49 32.27 Naran 0.31 0.38 0.67 0.41 2.74 - 1.35 2.74 - 1.65 2.13 4.46 1.15 6.73 0.34 0.18 29.32 Plandri 0.38 0.90 0.73 0.35 3.40 - 1.37 2.56 - 1.96 2.00 29.46 3.85 5.71 0.96 0.17 39.58 Qaziqund 0.50 0.23 0.58 0.54 2.20 2 2.64 2.52 - 1.18 6.67 45.81 4.04 10.23 0.53 0.52 32.10 Rawalkot 0.62 0.31 0.54 0.70 2.52 - 1.23 4.27 2 2.2 2.50 31.20 4.36 4.23 0.39 0.65 14.60 Sarinagr 0.34 0.83 0.46 0.73 3.04 - 1.54 4.89 2 2.67 5.00 16.34 3.06 5.92 0.23 0.17 21.34 Bold values represent signiﬁcant trends at 95% conﬁdence interval

precipitation extremes (Rajbhandari et al. 2015; Wijngaard 5 Conclusion et al. 2017; Ali et al. 2019). In order to understand the vulnerability of river basins due to climate change, this study analyzed the impact of climate change on extreme climate indices in the Jhelum

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River basin using observed data of 15 stations for past assessment of climate change impacts on human health, climate extremes. For future trends of climate extremes, the agriculture, environment and water resources in order to ensemble mean of three Global Climate Models from enhance regional decision-making, adaptation and strategic CMIP5 were used with two Representative Concentration planning. Pathways (RCP) scenarios. Therefore, Statistical Down- scaling Model (SDSM) has been used to downscale the Acknowledgements Open Access funding provided by Projekt future precipitation and temperature. The selection of most DEAL. The first author was financially supported by the doctoral scholarship from the Higher Education Commission of Pakistan appropriate predictors for the calibration of SDSM was (HEC) Pakistan and German Academic Exchange Service (DAAD) very challenging in the study area due to complex terrain. Germany. The authors wish to acknowledge the Pakistan Meteoro- In general, the performance of SDSM was good during logical Department (PMD) and Indian Meteorological Department validation with NCEP and GCMs outputs (Saddique et al. (IMD) for providing data used in this study. The authors thank the two anonymous reviewers for their constructive comments and sugges- 2019). The results reveal that there is a warming trend in tions, which helped us to improve the manuscript considerably. temperature indices and increasing trends in precipitation indices at most of the stations of basin. Open Access This article is licensed under a Creative Commons The outcomes showed that the frequency of summer Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as days, warm nights and days increased for the projected long as you give appropriate credit to the original author(s) and the climate across the River basin. On the other hand, the source, provide a link to the Creative Commons licence, and indicate number of cold days’ and nights decreased at all the sta- if changes were made. The images or other third party material in this tions of basin under both RCPs scenarios. However, the article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not magnitudes of trends under RCP8.5 was higher as com- included in the article’s Creative Commons licence and your intended pared to RCP4.5. Furthermore, intensity indices (warmest use is not permitted by statutory regulation or exceeds the permitted days, warmest nights, coldest days and coldest nights) also use, you will need to obtain permission directly from the copyright showed positive trends. The decadal increment in warmest holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/. days and nights were projected to be higher than coldest nights (TNn) and coldest days (TXn) for both scenarios. Under RCP8.5, the warmest nights exhibited significant References positive trends with a mean increase of 0.60 °C per decade over the catchment for future time slice (2061–2090). This Abatan AA, Abiodun BJ, Gutowski WJ, Rasaq-Balogun SO (2018) shows, there is warming across the Jhelum River basin at Trends and variability in absolute indices of temperature extremes over Nigeria: linkage with NAO. Int J Climatol the end of twenty-first century. 38(2):593–612. https://doi.org/10.1002/joc.5196 The results of precipitation extremes indices indicated Abbasnia M, Toros H (2018) Analysis of long-term changes in an increasing trend in annual total precipitation amount, extreme climatic indices: a case study of the Mediterranean maximum 1-day precipitation, maximum 5-day precipita- climate, Marmara Region, Turkey. Pure Appl Geophys 175(11):3861–3873. https://doi.org/10.1007/s00024-018-1888-8 tion, heavy precipitation days, precipitation in very wet Adyeri OE, Lawin AE, Laux P, Ishola KA, Ige SO (2019) Analysis of days, and simple daily intensity index over the JRB. During climate extreme indices over the Komadugu-Yobe basin, Lake the future time period (2061–2090), the magnitudes of all Chad region: past and future occurrences. Weather Clim precipitation indices except precipitation during very wet Extremes 23:100194. https://doi.org/10.1016/j.wace.2019. 100194 days would increase but the average increase expected to Ali SHB, Shafqat MN, Eqani SAMA, Shah STA (2019) Trends of be smaller as compared to baseline period. In general, climate change in the upper Indus basin region, Pakistan: SDSM was able to reasonably simulate the extremes implications for cryosphere. Environ Monit Assess 191:51. indices in the JRB. However, much more research is nee- https://doi.org/10.1007/s10661-018-7184-3 Archer DR, Fowler HJ (2008) Using meteorological data to forecast ded for a clearer understanding of the future changes in the seasonal runoff on the River Jhelum, Pakistan. J Hydrol climate and uncertainties (downscaling methods and 361(1–2):10–23. https://doi.org/10.1016/j.jhydrol.2008.07.017 GCMs) associated with climate projections. Therefore, the Asian Development Bank (ADB), World Bank (2010) Pakistan floods authors would like to suggest further assessment of the 2010 damage and needs assessment. Pakistan Development Forum, Islamabad–Pakistan. Published on 12 Nov 2010. To be study area using large ensembles of GCMs/RCMs or the accessed from http://reliefweb.int/report/pakistan/pakistan- Coordinated Regional Climate Downscaling Experiments floods-2010-preliminary-damage-and-needs-assessment (CORDEX). CORDEX is developed to improve the Bao J, Feng J, Wang Y (2015) Dynamical downscaling simulation regional downscaling techniques and achieve valuable and future projection of precipitation over China. J Geophys Res Atmos 120:8227–8243. https://doi.org/10.1002/2015JD023275 conclusions in future regional climate projections. Casanueva Vicente A, Rodrı´guez Puebla C, Frıás Domıńguez MD, Overall, the results of present study help to illustrate the Gonza´lez Reviriego N (2014) Variability of extreme precipita- Jhelum River basin under the risk of climate extreme tion over Europe and its relationships with teleconnection events will serve as a convenient resource for the patterns. Hydrol Earth Syst Sci 18:709–725

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123 40 water

Article Simulating the Impact of Climate Change on the Hydrological Regimes of a Sparsely Gauged Mountainous Basin, Northern Pakistan

Naeem Saddique 1,3,*, Muhammad Usman 2,3 and Christian Bernhofer 1 1 Institute of Hydrology and Meteorology, Technische Universität Dresden, 01062 Dresden, Germany; [email protected] 2 Department of Remote Sensing Am Hubland, Institute of Geography and Geology, University of Wurzburg, 97080 Würzburg, Germany; [email protected] 3 Department of Irrigation and Drainage, University of Agriculture, Faisalabad 38000, Pakistan * Correspondence: [email protected]; Tel.: +49-173-935-3114

Received: 18 September 2019; Accepted: 12 October 2019; Published: 15 October 2019

Abstract: Projected climate changes for the 21st century may cause great uncertainties on the hydrology of a river basin. This study explored the impacts of climate change on the water balance and hydrological regime of the Jhelum River Basin using the Soil and Water Assessment Tool (SWAT). Two downscaling methods (SDSM, Statistical Downscaling Model and LARS-WG, Long Ashton Research Station Weather Generator), three Global Circulation Models (GCMs), and two representative concentration pathways (RCP4.5 and RCP8.5) for three future periods (2030s, 2050s, and 2090s) were used to assess the climate change impacts on flow regimes. The results exhibited that both downscaling methods suggested an increase in annual streamflow over the river basin. There is generally an increasing trend of winter and autumn discharge, whereas it is complicated for summer and spring to conclude if the trend is increasing or decreasing depending on the downscaling methods. Therefore, the uncertainty associated with the downscaling of climate simulation needs to consider, for the best estimate, the impact of climate change, with its uncertainty, on a particular basin. The study also resulted that water yield and evapotranspiration in the eastern part of the basin (sub-basins at high elevation) would be most affected by climate change. The outcomes of this study would be useful for providing guidance in water management and planning for the river basin under climate change.

Keywords: water balance; hydrological regime; evapotranspiration; uncertainties; climate change; SWAT

1. Introduction The fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC, AR5) points ◦ out that the earth air temperature has increased by 0.85 C for 1881–2012 and for each of the recent past three decades. More temperature has been recorded for every consecutive decade. More specifically, it ◦ has been projected that the mean temperature will increase over 1 C under the low emission scenario ◦ (RCP2.6) and over 4 C under the high emission scenario (RCP8.5) [1]. In many studies, an increasing temperature trend has been observed in the Himalayan basins [2–4]. For instance, a study conducted by Immerzeel et al. in the Himalayas catchment concluded that the availability of water in the 21st century is not likely to decrease due to the rise in glacier melt and increasing rainfall [5]. The most often used method for impact assessment of climate change on hydrological cycles is by forcing the outputs of Global Circulation Models (GCMs) into hydrological models [6]. Generally, an ensemble approach of different GCMs, which could show various scenarios, is recommended

Water 2019, 11, 2141; doi:10.3390/w11102141 www.mdpi.com/journal/water 41 Water 2019, 11, 2141 2 of 26 that provide a better foresight into future climate than just using single GCM [7]. Many studies have utilized climate projections using an ensemble approach employing three to six GCMs [8–11]. Nevertheless, GCMs have a very coarse spatial resolution, which makes it inconsistent with the hydrological boundaries of the models. To fill this gap, the statistical or dynamical downscaling method can be involved as a bridge. Uncertainties associated with the impact of climate change on hydrological regimes may arise from each of these four processes: GCMs, climate change scenarios, downscaling methods, and hydrological simulations. For instance, according to Chen et al., impact studies based on a single downscaling approach should be carefully assessed due to associated output uncertainties [12]. Wilby and Haris found that the more uncertainties emerge from the choice of GCMs and downscaling methods than the selection of hydrological models and climate scenarios [13]. Meaurio et al. concluded that the downscaling methods result in higher uncertainty than GCMs itself [14]. The uncertainty of hydrological models is cumulative as the inherent uncertainties of the downscaling methods used at each level keep on propagating to the next level and finally to the ultimate hydrological output [15]. For the simulation of future water cycles, hydrological models are used, GCMs project precipitation, and temperature data are used to force these models. The selection of the most suitable hydrological model to be employed depends on the spatial scale of the model, characteristics of basin, data availability, required accuracy and the objective of the study, and the ease of calibration [16]. The efficiency of the hydrological models is less in high-elevated and scarcely gauged basins, where snowmelt is a substantial part of water flow [17]. Therefore, it is quite challenging to estimate the runoff in such catchments. The Soil and Water Assessment Tool (SWAT) is a physically-based, semi-distributed hydrologic model, which has been proved to be an effective tool for assessing the climate change impacts on water resources, and non-point source pollution problems for a wide range of spatial-temporal scales and ecosystems across the entire globe [18]. For example, Perazzoli et al. concluded the climate change impacts on the river flow changes and sediment production in a river basin located in southern Brazil [19]. Shi et al. analyzed the changes in land-use and climate and assessed their effects on the Huai River in China [20]. Yin et al. applied the Coupled Model Intercomparison Project 5 (CMIP5) model’s outputs and SWAT to project the impacts of climate change on the Jinsha River Basin [21]. They ◦ observed a decrease in streamflow by 2% to 5% in response to a 1 C rise in temperature. Furthermore, Bajracharya et al. quantified the impact of climate change on the hydrological regime and water balance using the SWAT model under Representative Concentration Pathways (RCPs) scenarios [7]. The results showed that water balance components (water yield, snowmelt, and evapotranspiration) would be affected by climate change. The overall objective of the current study was to disintegrate the contribution of precipitation, snowmelt, and evapotranspiration for water yield, considering the water balance of the Jhelum River Basin. It is all valuable to understand the impact of future hydro-climatic variability due to its importance for flood management and energy production. Previous studies had mainly focused only on the impact of climate change on streamflow, often ignoring other water balance components [22]. The most notable objectives of this study were: (i) to calibrate and validate the SWAT for simulating daily streamflow in the basin using snow parameters at sub-basin scale, (ii) projecting future precipitation and temperature from the data of three GCMs by using SDSM (Statistical Downscaling Model) and LARS-WG (Long Ashton Research Station Weather Generator), (iii) to analyze uncertainties in streamflow results obtained under different climate scenarios, and (iv) to quantify the impacts of future climate projections on the basin’s water balance components.

42 Water 2019, 11, 2141 3 of 26

2. Study Area

Description of the Study Area The Jhelum River Basin (JRB) (33,397 Km2) is a principal tributary of the Indus Basin after the ◦ ◦ Indus River. The JRB is around 725 km long and lying between 73–75.62 E and 33–35 N. The elevation stretches from 232 m to 6287 m with permanent snow-capped mountains in the north. The Kunhar, Neelum, and Poonch are three main tributaries of JRB. The basin drains its water in the Mangla reservoir, the country’s second-largest reservoir. More than 75% of the water flows to the reservoir from March to August. Mangla reservoir has a total command area of 6 Mha, and it serves two key purposes: supply water to irrigate the agricultural land and contribute 6% share in total electricity generation of the country [23]. It is very common to have a low-density network of climatic stations in developing countries like Pakistan. Due to the very complex topography of JRB, there are only 16 meteorological stations ◦ (Figure 1). The basin’s annual average maximum temperature is 30.8 C (June and July), and the mean ◦ minimum temperature is 0.3 C (December and January). The extremely cold station of the basin is ◦ ◦ Naran, where average air temperature goes below 0 C in the winter season and does not exceed 16 C in summer. The mean annual precipitation of the basin is about 1196 mm/year from 1961 to 2012 [24]. The annual mean runoff of JRB at Azad Pattan is 833 m3/s that is calculated from 1976 to 2005 (Table 1). The maximum discharge at Azad Pattan is 1741 m3/s, which occurs in June, while the minimum runoff is 223 m3 /s, which happens in January.

Figure 1. Location map of Jhelum River Basin and spatial distribution of weather and hydrological stations.

43 Water 2019, 11, 2141 4 of 26

Table 1. Basic information about hydrological stations, discharge is for 1976–2005.

Latitude Longitude Elevation Catchment Discharge Sr. No. River Station ◦ ◦ ( N) ( E) (m) Area (km2) (m3/s) F1 Kunhar Garhi Habibulllah 34.40 73.38 810 2441 102 F2 Neelum Muzaﬀarabad 34.37 73.47 670 7420 327 F3 Jhelum Domel 34.36 73.47 695 14,390 335 F4 Jhelum Kohala 34.12 73.50 584 24,930 787 F5 Jhelum Azad Pattan 33.73 73.60 476 26,420 833 F6 Poonch Kotli 33.46 73.88 518 3742 133 F7 Kanshi Palote 33.22 73.43 400 869.7 6.5

3. Data Description

3.1. Hydro-Metrological Data Table 2 gives the geographic and basic information of meteorological stations. The measured daily data of precipitation, maximum and minimum temperature were acquired from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA) of Pakistan, and the India Meteorological Department (IMD). The hydrological data of seven stations were obtained from the WAPDA (Table 1). All hydrological and meteorological data are available on a daily time scale.

Table 2. Meteorological data (1961–2012) that were used in the SWAT (Soil and Water Assessment Tool) simulation.

Longitude Latitude Elvevation Precipitation Sr. No. Station Name ◦ ◦ ( E) ( N) (m, MSL) (mm/year) 1 Jhelum 73.74 32.94 287 854 2 Gujar khan 73.13 33.25 458 830 3 Kotli 73.90 33.51 614 1245 4 Plandri 73.71 33.72 1401 1443 5 Rawalkot 73.76 33.86 1676 1397 6 Murree 73.38 33.91 2213 1779 7 Bagh 73.78 33.98 1067 1422 8 Garidoptta 73.62 34.22 814 1567 9 Muzaﬀarabad 73.47 34.37 702 1423 10 Balakot 73.35 34.55 995 1701 11 Naran 73.65 34.90 2362 1154 12 Astore 74.90 35.33 2168 534 13 Kupwara 74.25 34.51 1609 1245 14 Gulmarg 74.33 34.00 2705 1543 15 Srinagar 74.83 34.08 1587 721 16 Qaziqund 75.08 33.58 1690 1345

3.2. Digital Elevation Data The topography is an integral part of hydrological studies and is employed for watershed delineation, analysis of topographical features, and exploring drainage patterns [25,26]. It inﬂuences the movement rate and ﬂow direction over the land surface [27]. In the present study, Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) of National Aeronautics and Space Administration (NASA) with 30 m spatial resolution was used.

3.3. Land-Use and Soil Data The land cover map was generated from the 30 m resolution Landsat 7 Enhanced Thematic Mapper (+ETM) data acquired in 2001. For the Land use/land cover (LULC) classiﬁcation, the Random Forest machine learning algorithm in R language was applied to classify the image into ﬁve major classes,

44 Water 2019, 11, 2141 5 of 26 including agriculture, forest, grass, settlement, and water. Principle land-use in JRB is grass-sparse vegetation (36.79%) followed by agriculture (30.81%), forest (27.88%), settlement (2.43%), and water (2.07%), which are shown in Figure 2a. Figure 2b provides the soil map of the study area. The soil data were obtained from the Food and Agriculture Organization (FAO) of the United Nations Harmonized World Soil Database (HWSD). The HWSD is a 30 arc-second raster database with over 15,000 diﬀerent soil-mapping units. Eleven diﬀerent types of soils are present in JRB, which are shown in Table 3. Gleyic Solonacks (covers about 49% of the basin area) is found in the high mountainous areas, Calaric Phaoeozems soil (covers almost 23% of the basin area) is found in the middle ranges, and Mollic Planosols soil (covers about 21% of the basin area) is in the northern valley areas.

Figure 2. (a) Land-use and (b) Soil map of the study area.

Table 3. Land-use, soil, and slope report.

Spatial Data SWAT Class Description % Area Area (km2) AGRL Agriculture 30.81 10,296.61 FRST Forest mixed 27.88 9311.08 Land-use RNGE Grass + sparse vegetation 36.79 12,286.75 WATR Water 2.07 691.31 URMD Residential-medium Density 2.43 811.54 GLACIER-6998 Gleysol 0.39 130.24 I-B-U-3712 Gleyic Solonchaks 48.52 16,166.51 Be78-2c-3679 Luvic Chernozems 0.69 232.42 Be72-3c-3672 Calcaric Phaoeozems 23.20 7765.82 Be73-2c-3673 Calcic Chernozems 1.03 344.48 Soil Be79-2a-3680 Mollic Planosols 20.72 6933.33 WATER-6997 Water 0.19 65.09 I-X-c-3512 Gelic Regosols 1.08 363.10 Lo44-1b-3799 Harplic Solonetz 1.40 470.19 Be71-2-3a-3668 Gleyic Solonetz 1.11 370.75 Rc40-2b-3843 Haplic Chernozems 1.66 555.48 1 0–30% 41.18 13,782.23 2 30–60% 36.13 12,020.94 Slope 3 60–90% 19.67 6581.96 4 >90% 3.02 1012.32

Water 2019, 11, 2141 6 of 26

3.4. Representative Concentration Pathway (RCPs) Scenarios Four RCPs scenarios, namely 2.6, 4.5, 6.5, and 8.5, are defined based on different radiative forcing (2.6 to 8.5 Watt/m2). These radiative forcing levels depend on the emission of carbon dioxide. For the current study, we chose two RCPs (4.5 and 8.5). These two RCPs scenarios have been extensively used in climate change impact studies [7,11,14,28]. RCP4.5 and RCP8.5 correspond to the Special Report on Emissions Scenarios (SRES) B1 and A1F1 in terms of temperature anomaly. Three GCM (i.e., MIROC5, BCC-CSM1.1, and CanESM2) were selected from the literature review based on their performance and have been used widely in climate change studies in South Asia [29–31]. Table A1 provides the detail description (development countries, spatial resolution, and time) of all the GCMs used in this study. Generally, GCMs outputs have a coarse resolution; such data cannot be directly used in SWAT and thus needs downscaling to fit the required finer resolution. Finer resolution can be achieved by using two types of techniques: (1) dynamical downscaling and (2) statistical downscaling. In this study, two statistical downscaling models were used - the Statistical Downscaling Model (SDSM), which uses multiple linear regression and other statistical relations between GSMs and existing weather station data, and the Long Ashton Research Station Weather Generator (LARS-WG), which uses a combination of GCM output and local weather station statistics. Daily climatic data (Tmax, Tmin, and Prec) were downscaled with SDSM and LARS-WG for the period 2006–2100. Different descriptive statistics, such as R2, PBIAS, Standard Deviation (SD), mean and Normalized Root Mean Square Error (NRMSE), were used to check the suitability of the GCMs data in the study area [24].

4. Methodology

4.1. Hydrological Modeling SWAT is a semi-distributed hydrological model used to simulate the quality and quantity of water for different climate regimes and to assess the potential impacts of land-use, climate change, and land management practices on surface and groundwater. In the SWAT model, the whole catchment is divided into sub-basins, and a sub-basin further delineated into multiple Hydrological Response Units (HRUs) that consist of homogeneous land-use, soil, and topography. The water balance equation (1) given by Neitsch et al. [18] is involved in simulating the hydrological cycle. t SWt = SWo + R − Q − Ea − Wseep − Qgw (1) X day sur f i=1 where, SWt; final soil water content (mmH2O) SWo; initial soil water content (mmH2O) t; time (days) Rday ; the amount of precipitation on the day i (mmH2O) Qsurf; the amount of surface run-off (mmH2O) Ea; the amount of evapotranspiration (mmH2O) Wseep; the amount of water entering the vadose zone from the soil profile (mmH2O) Qgw; the amount of baseflow (mmH2O) SWAT distinguish precipitation as snow or rain by the values of the daily temperature. The snowmelt in the SWAT model is a linear function of the difference between the average snow pack’s maximum air temperature and the base or threshold temperature for snowmelt [18]. The snowmelt on a given day is represented by the following Equation (2),

T + T SNOW = b × snow × snow max − T (2) melt melt cov. 2 melt

where, SNOWmelt; the amount of snowmelt (mm)

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bmelt; melt factor snowcov; the fraction of the HRU covered by snow ◦ Tsnow; snowpack temperature ( C) ◦ Tmax; maximum air temperature ( C) ◦ Tmelt; base temperature above which snowmelt ( C) The SWAT theoretical documentation [18] presents the individual components of the model: (a) Soil Conservation Service (SCS) curve number is employed to calculate the surface runoff, (b) the rate and velocity of overland in channel is obtained using Manning equation, and (c) lateral flow is simulated by kinematic storage model. For the estimation of evapotranspiration (ET), the Hargreaves method was employed because the wind speed, relative humidity, and solar radiation data were not available for the future. The Arc SWAT 2012 was employed for the simulation of the hydrological process of JRB using present and future climatic data. The watershed was delineated into 27 sub-basins (Figure 3), and these sub-basins are further sub-divided into 397 HRUs. For the generation of HRUs in SWAT, a soil map, land-use map, and slope classes were used as input. The soil and land-use maps were reclassified according to the SWAT database by using lookup tables. The slope was first divided into four classes (0–30%, 30–60%, 60–90%, and >90%). A 5% threshold value for each soil type, land-use, and the slope was used in each sub-basin. To account for the process of snowmelt and orographic effect on precipitation and temperature in SWAT, five elevation bands were generated for different sub-basins. SWAT allowed each sub-basin to split into 10 elevation bands. The SWAT model was run from 1995 to 2000 for calibration and 2001–2005 for validation. To stabilize the model before simulation, a three years warm-up period was used from 1992–1994.

Figure 3. Sub-basins generated during delineation.

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SWAT-CUP tools by the sequential uncertainty-fitting, version 2 (SUFI-2) was used for model parameters sensitivity analysis, calibration, and validation [32]. Before doing the calibration, Global Sensitivity Analysis (GSA) was performed to analyze the sensitivity of a number of parameters that were selected from the literature. In the current study, we used 30 parameters, ran 900 simulations, and selected the eighteen most sensitive parameters for calibration. The best simulation in SUFI-2 was obtained by the Nash–Sutcliff Efficiency (NSE) coefficient between observed and simulated streamflow as the objective function.

4.2. Model Performance Metrics The model performance was evaluated both statistically and graphically using the Percent Bias (PBIAS), Nash–Sutcliffe Efficiency (NSE), coefficient of determination (R2), and RMSE-observations Standard Deviation Ratio (RSR) because they are commonly used in hydrological models. R2 varies between zero and one, where zero shows a poor fit and one a perfect fit. NSE stretches from minus infinity to 1 and indicates how well the observed versus the simulated data match the 1:1 line. The optimal value of Percent Bias is 0, low magnitude values of PBIAS indicate accurate model simulation (Equation (3)). Negative values of PBIAS indicate model overestimation (simulated values are greater than observed), and positive values indicate model underestimation bias [33]. RSR (Equation (6)) is calculated as the ratio of the RMSE and standard deviation of observed discharge data (SDobs). The lower RSR, lower the RMSE, and better the model simulation performance [33]. As seen from Table A2, model simulation is satisfactory if NSE value is greater than 0.5, PBIAS within ± 25%, and RSR less than 0.7 [33]. The value of R2 greater than 0.5 is also considered acceptable [34].

n (S − O ) i=1 i i × PBIAS = P n 100 (3) i=1 Oi P n − 2 i=1(Oi Si) NSE = 1 − P (4) n − 2 i=1 Oi Oavg P n − − − 2 i=1 Oi Oavg Si Savg R2 = P (5) n − 2 n − 2 i=1 Oi Oavg i=1 Si Savg P P n − 2 RMSE q i (Oi Si) RSR = = P (6) SDobs n − 2 q i Oi Oavg P where Si and Oi are the simulated and observed discharge, Savg and Oavg are the mean value of simulated and observed discharge, respectively, and n is the number of data records.

5. Results and Discussion

5.1. Sensitivity Analysis and Parametrization Sensitivity analysis explores how sensitive is the model output to the changes in the input parameter values and can be performed by two different methods. Sensitivity analysis also helps to minimize the number of parameters in the calibration process. The results obtained from SWAT-CUP using the Global Sensitivity Analysis (GSA) exhibited that out of the 18 parameters selected for calibration, snow parameters (SUB_SFTMP, SUB_SMTMP, SUB_SMFMX, SUB_SMFMN, SUB_TIMP) were the most sensitive parameters for Kunhar and Neelum Basins, and these parameters were adjusted for elevation bands in the sub-basin. For other basins (Poonch, Lower Jhelum, and Kanshi), ALPHA BF, CN2, and GWQMN were found to be highly sensitive for simulated discharge. Table 4 gives the initial range of different parameters used during calibration and their best-fitted values for all the basins.

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Table 4. Initial and calibrated parameters selected for the SWAT model.

Upper Lower Neelum Poonch Kunhar Sr. No. Parameter Initial Range Jhelum Jhelum Basin Basin Basin Basin Basin 1 v_SUB_SFTMP.sno −5–5 −2.19 0.96 – – −2.77 2 v_SUB_SMTMP.sno −5–5 1.24 −2.11 – – 4.32 3 v_SUB_SMFMX.sno 0–10 3.99 8.15 – – 2.62 4 v_SUB_SMFMN.sno 0–10 5.06 4.39 – – 3.08 5 v_SUB_TIMP.sno 0–1 0.61 0.73 – – 0.50 6 r_CN2.mgt ±0.25 −0.23 0.16 0.10 0.12 −0.03 7 v_GW_DELAY.gw 0–500 417.03 207.77 99.5 102.34 – 8 v_GW_REVAP.gw 0.02–0.2 – 0.05 0.10 0.82 – 9 v_GWQMN.gw 0–5000 3308.45 2959.75 1105 2745.45 – 10 v_REVAPMN.gw 0–500 – – 322.5 210.67 – 11 v_RCHRG_DP.gw 0–1 – 0.48 0.44 0.51 – 12 v_ALPHA_BF.gw 0–1 0.45 0.05 0.001 0.007 0.31 13 v_CH_K2.rte 0.001–200 107.98 158.87 43.80 105.23 – 14 v_CH_N2.rte −0.01–0.3 0.03 0.19 0.07 0.05 – 15 v_EPCO.hru 0–1 – – 0.19 0.21 – 16 v_ESCO.hru 0–1 0.10 – 0.35 0.32 – 17 r_SOL_AWC.sol ±0.20 – – 0.08 0.12 – 18 r_SOL_K.sol ±0.20 −0.04 – 0.09 0.08 – v_ means the default parameter is replaced by a given value; r_ means the existing parameter value is multiplied by (1 + a given value); – default value.

In each model run, tuning of parameters is done to match the simulated flow with observed flow, followed by validation of the hydrological model. In the present study, Nash–Sutcliff Efficiency (NSE) was used as an objective function to identify the best simulation ranges of parameters due to its wide applicability in hydrological modeling [35].

5.2. Calibration and Validation of the SWAT Model Figure 4 presents the results of daily streamflow during the calibration and validation of SWAT. Calibration was performed for the period from 1995–2001, and validation was conducted from 2002 to 2005. The calibrated parameters were used in the validation to simulate the discharge. The value of evaluation parameters, such as R2, NSE, PBIAS, and RSR, are shown in Table 5. At the daily time scale, the values of NSE and R2 were ranged from 0.56 to 0.81 and 0.52 to 0.81 during calibration and validation. At all the gauging stations except Kotli, the value of NSE was greater than 0.65 at a monthly scale. At different hydrological stations, trends of observed flows were well modeled. However, there are also very small over-estimations and under-estimations. Based on the performance classification of Moriasi et al., the results of model simulation fall under a good category [33]. Garee et al. also reported similar results over the Upper Indus Basin (UIB) using the SWAT model [36]. The topography, soil, and land-use were kept constant throughout the simulation period, which has a significant effect on streamflow [37]. Simulating streamflow should be based on good data collection via meteorology, hydrology, multi-period soil data, and land-use.

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Figure 4. Cont.

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Figure 4. Comparison of the observed and simulated stream (m3/s) ﬂow at diﬀerent stations (a) Azad Pattan (b) Kohala (c) Garhi Habibullah (d) Domel (e) M. Abad for the calibration and validation in the Jhelum River Basin.

Table 5. Performance statistics for daily and monthly calibration and validation.

Calibration Validation Stations (Rivers) Time R2 NSE PBIAS RSR R2 NSE PBIAS RSR

Azad Pattan Daily 0.74 0.71 −13.6 0.53 0.73 0.71 1.1 0.53 (Jhelum) Monthly 0.85 0.81 −13.6 0.43 0.82 0.81 1.1 0.43 Daily 0.77 0.71 −14.2 0.54 0.74 0.70 −2.2 0.54 Kohala (Jhelum) Monthly 0.85 0.80 −14.6 0.45 0.84 0.81 −1.3 0.42 Daily 0.80 0.75 −12.3 0.49 0.81 0.77 5.6 0.47 Domel (Jhelum) Monthly 0.86 0.80 −12.4 0.44 0.89 0.86 5.6 0.37 Muzaﬀarabad Daily 0.71 0.56 −14.4 0.57 0.59 0.52 −12.9 0.60 (Neelum) Monthly 0.81 0.75 −14.3 0.49 0.77 0.70 −12.7 0.54 Garhi Habibulllah Daily 0.72 0.72 −4.6 0.52 0.61 0.57 13.1 0.59 (Kunhar) Monthly 0.82 0.80 −4.3 0.43 0.73 0.66 13.2 0.57 Daily ------Kotli (Poonch) Monthly 0.58 0.55 −10.3 0.67 0.56 0.52 −13.2 0.69

5.3. Future Temperature and Precipitation The analysis of statistical models outputs showed that Tmax and Tmin increased in the range ◦ ◦ ◦ ◦ of 0.4 C to 4.18 C and 0.3 C to 4.2 C, respectively, relative to reference period for the two-climate

51 Water 2019, 11, 2141 12 of 26 change scenarios with three GCMs. A decrease in seasonal temperature values was estimated in the spring under RCP4.5. SDSM and LARS-WG projected that the warmest season at the end of this ◦ ◦ century would be autumn, with a rise of mean minimum temperature between 4.11 C and 4.66 C[24]. Islam et al. and Garee et al. projected an increase in temperature over the UIB using RCM (PRECIS) and GCMs (EC-EARTH, CCSM4, and ECHAM6) outputs under different scenarios [36,38]. ◦ Mahmood et al. also reported an increase in temperature about 2.6 C at the end of this century over the Jhelum River Basin using the HADCM3 outputs [22]. Overall, the above studies suggest that the JRB would face more warming in the future but with small differences in seasonal trends. The precipitation projection using two RCPs scenarios for future periods is illustrated in Figure 5. A linear rising trend could be observed under both RCPs in JRB. The annual increment was higher under RCP8.5 as compared to RCP4.5. The modified Mann–Kendall test revealed that there was a significant positive trend in precipitation for RCP4.5 and RCP8.5 with p-values less than 0.01 at the 99% significance level. RCP 4.5 showed a positive slope (increasing) of 1 mm/year and 1.4 mm/year with SDSM and LARS-WG, while RCP 8.5 exhibited a slope of 2.1 mm/year and 2.8 mm/year with the Sen’s slope estimator. Immerzeel et al. projected an increase in precipitation by 25% for 2046 to 2065 based on five GCMs over the Upper Indus Basin for the SRES A1B scenario [39]. Mahmood et al. also projected an increase in precipitation over the Jhelum River Basin by 12% and 14% towards the end of this century for A2 and B2 scenarios [40]. Similarly, projected annual precipitation increase over JRB was reported by [41] to be 45.52 mm in the 2080s compared to baseline using five GCMs data. The future projection of precipitation mainly depends on the GCMs outputs. All the above-cited studies predict an increase in precipitation over the study area, while the used methodology and details differ.

Figure 5. Total annual future precipitation trends using SDSM (Statistical Downscaling Model) and LARS-WG (Long Ashton Research Station Weather Generator) (a) RCP 4.5 and (b) RCP 8.5.

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5.4. Precipitation, Temperature, and CO2 Sensitivity Scenarios Figure 6 shows the average annual water balance components of JRB under different climatic scenarios, and Table 6 describes the hypothetical climate scenarios used in this study. It was seen that the surface runoff, ET, snowmelt, and groundwater (GW) recharge would be affected to a varying degree by the changes in precipitation or temperature in the future. The doubling of CO2 concentration (scenario-1) without any changes in precipitation and temperature could be observed; also, ET declined by 11.29%, and surface runoff increased by about 4.37%. Increased CO2 concentration has substantial impacts on plant physiology [42] through the reduced opening of the plant stomata, known as physiological forcing [43]. Physiological forcing can reduce ET, and reduced ET leaves more water in the soil profile, increasing the soil water content [44–46]. Moist soils can raise the water yield by generating more surface runoff, lateral flow, and seepage, all of which contribute to increasing streamflow [47,48]. Temperature rise (scenarios 2 to 4) caused a substantial decrease of snowmelt, surface runoff, and groundwater recharge. The average annual snowmelt (as snowfall decrease) was predicted to decrease by more than 50% in response to an increase in temperature by about ◦ ◦ ◦ 6 C. Predicted ET showed an increase of 7.2% and 11.11% in response to 4 C and 6 C increase in temperature, respectively. However, under Scenarios 5 to 6 (increasing precipitation), the annual average surface runoff of basin would increase as well as the annual mean GW recharge. A large amount of precipitation is theƺ major cause of an increase in runoff [28]. Huang and Zhang found that the main factor affecting surfaceƺ runoff is precipitation and that any (positive or negative) trend in precipitation would affect runoff trends [49].

Figure 6. Values of water balance components (mm) under diﬀerent climate change scenarios. GW, groundwater; ET, evapotranspiration.

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Table 6. Climate sensitivity scenarios.

◦ Scenario Precipitation Change (%) Temperature Increase ( C) CO2 Concentration (ppm) 1 0 0 660 2 0 2 330 3 0 4 330 4 0 6 330 5 +10 0 330 6 +20 0 330 7 −10 0 330 8 −20 0 330 0 means no change.

5.5. Impact of Future Climate Change on Streamﬂow Future hydrological projections were divided into three periods (2020s, 2050s, and 2080s) for the assessment of the impact of climate change on the discharge of JRB (at Azad Pattan station), which were compared with the reference observed discharge (1976–2005).

5.5.1. Changes in Annual Flow The changes in annual mean discharge under different climate change scenarios are shown in Figure 7. Focusing on the two downscaling techniques (SDSM and LARS-WG), the annual change in discharge (increase) derived from LARS-WG was higher in magnitude than SDSM under all the three future periods. Under RCP4.5, changes in projected annual discharge varied from 3.60% to 15.96% and 5.28% to 26.65% with SDSM and LARS-WG, respectively. Under RCP8.5, changes in projected annual discharge varied from 6.48% to 17.28% and 4.80% to 28.78% with SDSM and LARS-WG, respectively. In the 2080s, the maximum change in projected discharge was expected by using BCC-CSM1-1 under RCP4.5, and under RCP8.5 by using CanESM2. Similar results were also found by [22], who assessed the impact of climate change in the Jhelum River Basin using the HADCM3 global climate model with SRES A2 and B2. The outcomes of the current study were also comparable with other regional and local climate change impact studies [36,39]. Most of the global climate model suggested that the annual streamflow would increase from 7.57% to 32.12% at the end of the 21st century. This increase in streamflow may have positive and negative effects on water management and planning. Irrigation water for the Indus Basin Irrigation Systems (IBIS), which is the largest irrigation network in the world, is controlled through two major storage reservoirs (Jhelum River has a Mangla reservoir, and Indus River has Tarbela reservoir). Any change in the upstream water supply to these reservoirs will have a significant influence on thousands of farmers downstream. Our results indicated a substantial increase in future streamflow. Dependency on groundwater would also reduce in the future due to the availability of more surface water for irrigated agriculture and food security. Mangla hydropower plant has a total installed capacity of 1000 MW, which is 15% of total electricity production through hydropower plants. The increase in projected streamflow can be used for electricity production. All the hydroelectric plants under construction are based on the Runoff River (ROR) design. The amount of electricity generated from ROR design depends on the daily flow. As the future streamflow is projected to increase, this type of hydroelectric plant can immensely benefit from climate change. A study conducted by [50] at Mangla hydropower plant showed that high streamflow is expected, which would result in an annual increase of 18.9% and 19.9% in electricity production under SRES A2 and B2 at the end of this century. The high electricity generation contribution is due to the increase in streamflow in winter.

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5.5.2. Changes in Seasonal Flow Figure 8 shows the changes in seasonal mean discharge under two scenarios using SDSM and LARS-WG for the three future time horizons. In winter and autumn, an increasing trend in streamflow was observed at Azad Pattan with both downscaling methods under RCP4.5 and RCP 8.5 in all the three periods. All the GCMs showed similar patterns (increasing) but different magnitudes. The LARS-WG simulated considerably higher discharge than SDSM in both seasons. In winter, the maximum increase of 26.56%, 31.89%, and 43.50% was calculated in the 2020s, 2050s, and 2080s, respectively, under RCP8.5. CanESM2 showed a maximum increase of 58.56% in autumn at Azad Pattan. In other seasons (spring and summer), the greatest differences in projected discharge could be observed between SDSM and

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LARS-WG. In the 2020s, the projected runoff would increase in winter, summer, and autumn, while would decrease in spring with both downscaling methods. SDSM and LARS-WG would behave differently in summer and spring in the 2080s. It can be seen by the passage of time that the discharge would significantly increase in spring and decrease in summer with LARS-WG. Under RCP8.5, changes in average seasonal flow were higher in magnitudes than RCP4.5 in three future time horizons.

5.5.3. Changes in Monthly Flow The impact of climate change on the monthly streamﬂow of the Jhelum River Basin is shown in Figure 9. The results showed that both of the downscaling methods predicted an increase in streamﬂow during the majority of months of the year.

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Streamflow variability was higher in May, June, and July; corresponding months of the year had higher discharge. Peak streamflow was found in June in the 2020s and 2050s with both downscaling methods, but this peak was found to shift in May (2080s) using LARS-WG climatic data. LARS-WG showed higher discharge variability as compared to SDSM in all the future time slices (2020s, 2050s, and 2080s). In the 2020s, predicted streamflow was less than observed flow in March, April, and May with all scenarios using SDSM, but at the end of the century (2080s), predicted flow was higher as compared to observed during these months. The highest increase in the flow would be expected in low flow months (October, November, December, January, and February) with both RCP scenarios and downscaling methods.

5.6. Uncertainty of Annual Flow Simulations Figure 10 presents the Cumulative Distribution Function (CDFs) of annual discharge with two RCPs scenarios, two downscaling methods, and three GCMs (i.e., BCC-CSM, CanESM2, and MIROC5) for the Jhelum River Basin. Uncertainties of future streamflow projections were associated with each potential uncertainty element (downscaling method, GCM, and RCP). Overall, the flow was increasing during 2071–2100 with both downscaling methods using RCP4.5 and RCP8.5. The uncertainty associated with downscaling methods (SDSM and LARS-WG) was more as compared to uncertainty within GCMs in quantifying the impact of climate change on streamflow. Under RCP8.5, SDSM showed very less uncertainty in the flow values, especially at low and high ends. LARS-WG predicted a larger change in annual flow than the SDSM. Figures A1 and A2 also present similar types of uncertainties during the 2020s and 2050s with both downscaling methods for RCP4.5 and RCP8.5. However, magnitudes of annual flow changes were relatively lower as compared to the 2080s.

Figure 10. CDFs (Cumulative Distribution Function) of annual changes in ﬂow under (a) RCP4.5, and (b) RCP8.5 between the period 2071–2100 and 1975–2005, reﬂecting the uncertainty of SDSM and LARS-WG.

5.7. Effects of Climate Change on Water Balance Components Water balance components contribute to the river discharge and overall hydrological cycle of the basin (Bajracharya et al. 2018) [7]. Table 7 shows the mean annual values of all water balance components with SDSM and LARS-WG under both RCPs scenarios. However, surface runoff (SURQ) and groundwater flow (GWQ) demonstrated the highest increment. Surface runoff increased from 3.7% to 25.6% under RCP4.5 and 4.3% to 29.2% under RCP8.5. Likewise, ET and Potential Evapotranspiration (PET) showed the increment in all the scenarios. PET showed an increase ranging between 4.6% and 8.1%, 5.7% and 11%, and 6% and 16.6%, respectively, for the three future time horizons (2020s, 2050s, and 2080s). The increase of PET might lead to decrease moisture of soil and increase the plants’ water stress. This might reduce agriculture productivity, vegetation cover destruction, and the intensification of desertification in the basin during the 21st century.

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Table 7. Comparison of mean annual water balance under diﬀerent scenarios.

Scenarios Periods SURQ (%) GWQ (%) LATQ (%) PERC (%) ET (%) PET (%) WYLD (%) SDSM 2020s 3.7 6.3 4.5 2.8 6.2 4.6 6.8 RCP4.5 2050s 5.6 9.4 5.9 5.8 7.9 5.7 7.7 2080s 11.0 13.3 8.5 9.6 12.0 6.08 11.8 2020s 4.3 8.4 5.3 4.9 8.6 4.8 7.0 RCP8.5 2050s 11.7 16.2 9.1 12.4 11.4 6.1 11.4 2080s 23.8 25.1 14.12 21.0 17.4 7.9 16.0 LARS-WG 2020s 18.5 7.0 6.7 3.2 4.5 7.1 12.6 RCP4.5 2050s 19.5 11.6 9.6 7.8 8.2 9.8 14.7 2080s 25.6 17.3 15.4 13.4 16.1 11.2 21.4 2020s 18.9 8.3 7.8 4.6 12.4 8.1 13.0 RCP8.5 2050s 25.5 16.7 14.1 12.8 15.9 11.0 19.8 2080s 29.2 22.5 18.7 18.3 19.5 16.6 23.7

Water yield is the net amount of water contributed by sub-basins and HRUs to the streamflow [7]. It is the combination of surface runoff, lateral flow, and groundwater flow with any deduction in transmission losses and pond abstraction [51]. However, SDSM showed an increase in water yield of about 6.8% to 16% under both scenarios, while LARS-WG represented about 12.6% to 23.7%.

5.7.1. Impact on Water Yield Figure 11 shows the spatial distribution patterns of percentage changes in mean annual water yield projected for diﬀerent sub-basins in the 2020s and 2080s under RCP4.5 and RCP8.5, using climatic data of SDSM and LARS-WG relative to the baseline. All the sub-basins showed an increase in water yield, and the changes ranged between 1.5% to 46.7% over the whole basin. RCP4.5 and RCP8.5 had similar spatial patterns of water yield changes for the two future periods; however, RCP8.5 showed a higher increase as compared to RCP4.5 with both downscaling methods. As can be seen that while the water yield projected by using the SDSM-downscaled data was more signiﬁcant in the southwest part of the basin (except two sub-basins), this pattern was more pronounced in the western part (except lower sub-basins) of a watershed using LARS-WG-downscaled data. It can also be observed that the sub-basins located in the eastern part of the watershed showed less increase in water yield as these sub-basins receive snowfall, which would decrease with a rise in temperature in the future.

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Figure 11. Spatial distribution of relative change rate (%) in water yield in the Jhelum River Basin with SDSM and LARS-WG under RCP4.5 and RCP8.5.

5.7.2. Impact on Evapotranspiration (Green Water Flow) Figure 12 presents the changes in the spatial distribution of average annual actual evapotranspiration (ET) as compared to baseline under RCP scenarios (RCP4.5 and RCP8.5) with SDSM and LARS-WG in three future time horizons. Due to the increase in precipitation and temperature in the future, the ET of the basin would be aﬀected signiﬁcantly. This increase in temperature would cause the ET to rise as well. By using projected climatic data of SDSM, the mean annual ET changes ranged from 2.1% to 23.1% under RCP4.5, whereas under RCP8.5, these changes ranged from 4.9% to 32% during the two future periods. The mean annual ET changes (positive) projected by using LARS-WG data could reach as much as 43.2% under RCP8.5 in the 2080s. These results are also consistent with prior studies in South Asia [7,52].

Figure 12. Spatial distribution of relative change rate (%) in actual evapotranspiration in the Jhelum River Basin with SDSM and LARS-WG under RCP4.5 and RCP8.5.

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In the case of SDSM, green water flow was more pronounced in the eastern part and southwest lower sub-basins, while for LARS-WG, these changes were more significant only in the eastern part under RCP4.5 and RCP8.5. This increase in green water flow was more substantial in the sub-basin at high elevation (basin cover with snow) as projected temperature increase caused a shortening of snow cover season.

5.7.3. Impact on Snowmelt Figure 13 illustrates the spatial distribution in percentage changes of snowmelt during the 2020s and 2080s compared to the reference period in different sub-basins of the Jhelum River Basin using SDSM and LARS-WG under RCP4.5 and RCP8.5. The sub-basins located at lower elevation have relatively higher temperatures due to which no snowfall is observed. The decrease in snowmelt would be progressive in the future period as the basin would face more warming. Under RCP4.5, a moderate decrease (ranging from −11.3% to −16.4%) in snowmelt with SDSM was observed during the 2020s. The maximum decrease in snowmeltƺ was projected ƺ during the 2080s with LARS-WG up to −52.4% under RCP8.5. Both RCPs scenarios projected a decrease in snowmelt, but this reduction was relatively higherƺ under RCP8.5. It can be seen that the western part of the basin was not affected by changes in snowmelt, as these sub-basins are snow-free throughout the year. The sub-basin in the eastern part of the watershed showed a significant decrease in snowmelt with both downscaling methods. Using the climatic data of SDSM, the decrease was more pronounced in the northeast part of the basin.

Figure 13. Spatial distribution of relative changes in snowmelt during the 2020s and 2080s over the JRB (Jhelum River Basin) under RCP4.5 and RCP8.5 using climatic data outputs of SDSM and LARS-WG; no SF—represents no snowfall.

6. Conclusions In this study, to investigate the impacts of projected climate change (2011–2100) on the hydrological regimes of Jhelum River Basin, 12 climate projections combining three GCMs (i.e., BCC-CSM1.1, CanESM2, and MIROC5), two statistical downscaling methods (SDSM and LARS-WG), and two RCPs emission scenarios (RCP4.5 and RCP8.5) were considered. The Soil and Water Assessment Tool (SWAT) was used for the simulation of hydrological regimes in JRB. The SWAT model was calibrated and validated using the snow parameters at the sub-basin scale. The SWAT model simulated discharge was compared with observed discharge at the ﬁve diﬀerent hydrological stations that lie at various

61 Water 2019, 11, 2141 22 of 26 geographical locations, for both calibration (1995–2001) and validation (2002–2005) periods. The NSE and R2 values (>0.65 at most stations) showed the model’s ability to reproduce streamflow, with reasonable accuracy. The calibrated model was employed to study the influence of projected climate on water resources. Under the IPCC RCP8.5 (high emissions) scenario, the annual mean temperature of ◦ ◦ the basin expected to increase by 1.8 C and 3.7 C with SDSM and LARS-WG, respectively. Similarly, the projected annual mean precipitation would increase by 15.4% and 28.3% during the late century (2071–2100) under RCP8.5. The results of climate sensitivity studies revealed that the hydrology of JRB is highly sensitive to climate change scenarios. Out of all scenarios, scenario 6 (precipitation increase 20%) represented a maximum increase in annual mean surface runoff, snowmelt, groundwater recharge, and ET from the baseline condition, whereas the scenario 7 (precipitation decrease 20%) showed a maximum decrease in all water balance components. The average annual snowmelt (as snowfall decrease) was predicted ◦ to decrease by more than 50% in response to an increase in temperature by about 6 C. The mean annual streamflow was projected to rise under RCP4.5 and RCP8.5 using the three GCMs with SDSM and LARS-WG. However, the prediction was different for all the 12 scenarios (downscaling methods, GCMs, and RCPs). CanESM2 predicted the highest increase in streamflow by about 28.78% under RCP8.5 with LARS-WG. Substantial variations in seasonal streamflow would be observed when compared with the baseline period (1976–2005). Winter and autumn showed an increase in predicted flow under all the scenarios in three future horizons (2020s, 2050s, and 2080s). The seasons with most inconsistent results, and so, the ones for which to draw clear conclusions was challenging, were summer and spring, for which either future streamflow could increase or decrease based on the projected climatic data (downscaling method). On a monthly scale, in the 2030s, the predicted streamflow would increase in all months except March, April, and May. According to LARS-WG, peak discharge would shift from June to May under both RCP scenarios. Climate change also has a significant effect on the spatial and temporal variation of water balance components in the Jhelum River Basin. The ensemble means of three GCMs exhibited an increase in mean annual surface runoff, groundwater recharge, lateral flow, ET, and water yield with SDSM and LARS-WG for RCP4.5 and RCP8.5. The spatial distribution of projected water yield showed an increase in all sub-basins of the watershed. However, this increase was more pronounced in the western part of the basin as these sub-basins are mostly dependent on rainfall (no snowfall). On the other hand, green water flow (ET) under RCP8.5 was more pronounced in the sub-basin at high elevation (basin cover with snow-eastern part) as projected temperature increase caused a shortening of snow cover season. Spatial distribution of snowmelt revealed that, in the future, snowmelt (as snowfall decrease) could be decreased in the eastern part of the basin due to the rise of temperature. In general, climate change is expected to affect more sub-basins at high-altitude. Different uncertainties sources (GCMs, downscaling techniques, and RCPs) were involved in the impact studies. The results showed that the uncertainty associated with downscaling methods (SDSM and LARS-WG) was higher than GCMs itself. Some conclusions can be drawn from the hydrological modeling results, even if the uncertainty quantification was not part of the present study. Under RCP8.5, SDSM showed very less uncertainty in the flow values, especially at low and high ends. LARS-WG predicted larger changes in annual flow than the SDSM. Our research outcomes would be helpful for the effective planning and management of water demand and supply in the Jhelum River Basin to consider the impact of climate change. The hydropower plant in operation and several potential hydropower projects would benefit from considering future changes in the regional hydrological cycle. More specifically, they could help to comprehend better the future hydro-climatic variability, which is vital to design the hydroelectric power plants. In general, the positive effect of climate change could be the availability of sufficient water from the catchment, but sometimes, a high amount of water leads to flooding.

62 Water 2019, 11, 2141 23 of 26

Author Contributions: Conceptualization, N.S. and C.B.; Methodology, N.S.; Model calibration and validation, N.S.; Formal Analysis, M.U.; Data curation, N.S.; Writing—Original Draft Preparation, N.S. and M.U.; Writing—Review and Editing, N.S. and C.B.; Supervision, C.B. Funding: This research received no external funding. Acknowledgments: The first author was financially supported by the doctoral scholarship from the Higher Education Commission of Pakistan (HEC), Pakistan, and German Academic Exchange Service (DAAD), Germany. The authors wish to acknowledge the Water and Power Development Authority (WAPDA), Pakistan Meteorological Department (PMD), and Indian Meteorological Department (IMD) for providing data used in this study. We extend our thanks to the Institute of Hydrology and Meteorology, Chair of Meteorology, Technische Universität Dresden for giving the opportunity to perform this research. Open Access Funding by the Publication Fund of the TU Dresden. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Table A1. Climate models used to project climate change scenarios.

◦ Development Spatial Resolution ( ) Time Span GCM Countries Longitude Latitude Historical Future (RCPs) BCC-CSM1-1 China 2.812 2.812 1950–2005 2006–2100 MIROC5 Japan 2.812 2.812 1950–2005 2006–2100 CanESM2 Canada 1.406 1.406 1950–2005 2006–2100

Figure A1. CDFs of annual changes in ﬂow under (a) RCP4.5 and (b) RCP8.5 between the period 2011–2040 and 1975–2005, reﬂecting the uncertainty of SDSM and LARS-WG.

Table A2. Model performance rating based on Moriasi et al. [33].

NSE RSR PBIAS Performance Rating 0.75–1 0–0.5 <±10 Very good 0.65–0.75 0.5–0.6 ±10 to ±15 Good 0.5–0.65 0.6–0.7 ±15 to ± 25 Satisfactory <0.5 >0.7 >±25 Unsatisfactory

Water 2019, 11, 2141 24 of 26

Figure A2. CDFs of annual changes in ﬂow under (a) RCP4.5 and (b) RCP8.5 between the period 2041–2070 and 1975–2005, reﬂecting the uncertainty of SDSM and LARS-WG.

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66 Environmental Earth Sciences (2020) 79:448 https://doi.org/10.1007/s12665-020-09206-w

ORIGINAL ARTICLE

Quantifying the impacts of land use/land cover change on the water balance in the aﬀorested River Basin, Pakistan

Naeem Saddique1,2 · Talha Mahmood3 · Christian Bernhofer1

Abstract Land use and land cover (LULC) change is one of the key driving elements responsible for altering the hydrology of a watershed. In this study, we investigated the spatio-temporal LULC changes between 2001 and 2018 and their impacts on the water balance of the Jhelum River Basin. The Soil and Water Assessment Tool (SWAT) was used to analyze the impacts on water yield (WY) and evapotranspiration (ET). The model was calibrated and validated with discharge data between 1995 and 2005 and then simulated with different land use. The increase was observed in forest, settlement and water areas during the study period. At the catchment scale, we found that afforestation has reduced the WY and surface runoff, while enhanced the ET. Moreover, this change was more pronounced at the sub-basin scale. Some sub-basins, especially in the northern part of the study area, exhibited an increase in WY due to an increase in the snow cover area. Similarly, extremes land use scenarios also showed significant impact on water balance components. The basin WY has decreased by 38 mm/ year and ET has increased about 36 mm/year. The findings of this study could guide the watershed manager in the development of sustainable LULC planning and water resources management.

Keywords Hydrology · Water yield · Evapotranspiration · SWAT · Aﬀorestation · LULC changes

Introduction LULC have significant effects on atmospheric elements like precipitation and temperature, key driving elements of the Land use and land cover (LULC) changes altering hydrologi- hydrological cycle. Thus, it changes the water balance of a cal processes and have the potential to exert a large influence watershed that comprises stream flow, base flow and evapo- on earth water (Wagner et al. 2013; Kaushal et al. 2017). transpiration (DeFries and Eshleman 2004; Shooshtari et al. Rapid socio-economic development causes LULC changes 2017). Therefore, examining the practices and consequences that include changes of land cover classes, for example, con- of changing LULC are vital for the hydrologists, ecologists version of agriculture or forest to industrialization and resi- and land managers (Stonestorm et al. 2009; Mallinis et al. dential area due to population growth, in addition alteration 2014). within classes such as a change of crop rotations or crops To investigate the impact of land use and land cover (Wagner et al. 2013). Land use/cover change has been rec- changes on the hydrology of watershed, spatially dispersed ognized as a key driver of hydrological processes such as hydrological models are employed including HEC-HMS surface runoff, ET and base flow (Zhao et al. 2013 and Garg (Younis and Ammar 2017; Koneti et al. 2018), InVEST et al. 2017). Juang et al. (2007) reported that the changes in (Geng et al. 2014; Li et al. 2018), VIC (Garg et al. 2017) and SWAT (Kumar et al. 2018; Li et al. 2019; Munoth and * Naeem Saddique Goyal 2020). The SWAT has proven its suitability under [email protected] conditions of limited data availability in hydrological studies (Stehr et al. 2008; Gassman et al. 2007). Therefore, it is an 1 Institute of Hydrology and Meteorology Technische appropriate model to analyze the impact of LULC changes Universität Dresden, 01737 Tharandt, Germany on the water resources in Indus Basin Pakistan. 2 Department of Irrigation and Drainage, University The impact of LULC changes on water resources has been of Agriculture, Faisalabad 38000, Pakistan assessed in many studies at the regional level. For instance, 3 Department of Geography and Geology, Martin Luther Li et al. (2018) found that the expansion of built-up area Universität, 06120 Halle, Germany

Vol.:(0123456789)1 3 67 448 Page 2 of 13 Environmental Earth Sciences (2020) 79:448 and decline of vegetation area in Jing-Jin-Ji, China led to 2016 and Saddique et al. 2019b) have assessed the impact of an increase of water yield (5%). Mango et al. (2011) con- climate change on the stream flow of JRB forcing the Global ducted research over the Upper Mara river basin, Kenya and Climate Models (GCMs) data. Therefore, this study filled reported that if forest cover were converted to grass land this knowledge gap using different time periods land use then surface runoff increased by 20% but ET decreased by data and employed SWAT hydrological model to simulate 2%. Furthermore, Zhu and Li (2014) quantified the impact how these changes may affect the water resources of the of land use and land cover change on the hydrology of Little basin. The main objectives of this study were to (1) assess River basin, Tennessee. The results showed a small increase the spatial–temporal LULC changes during the period of of 3% in streamflow but distinct spatial change across the 2001–2018 and (2) analyze the impacts of land use/cover basin. Ahiablame et al. (2017) investigated the impact of two change on the water balance of Jhelum River Basin. future land use (LULC-2055 and LULC-2090) under three simulation scenarios (A1B, A2 and B1) on stream flow of James River watershed, United States and found that climate Study area and data and land use changes would result in 12–18% and 17–41% increases in annual stream flow at the end of twenty-first Description of the study area century. In terms of temperature change, RCP8.5 is close to SRES A2, but below SRES A1FI. RCP4.5 follows SRES The Jhelum River Basin (Mangla Dam Watershed) is located B2 up to 2060, but then drops to track SRES B1. RCP6.0 between 73–75.62°E and 33–35°N and has total drainage has lower temperature change to start, following SRES B1, area about 33,397 km2. Figure 1 shows the location of the but then increases toward SRES B2 by 2100 (Burkett et al. study area and climate stations. The watershed topography 2014). Wagner et al. (2013) reported an increase of WY and characterized by mountainous with elevation varies from decrease of ET due to urbanization; whereas, increase of 232 m in the lowland area to 6285 m in the highland area. cropland led to rise in ET by up to 5.9% over Indian River The catchment drains its whole flow into the Mangla res- basin. Additionally, Welde et al. (2017) assessed the impact ervoir that is the second-largest reservoir of Pakistan. The of land use/land cover dynamics on the hydrology of Teke water of this reservoir is mainly used for two purposes: to watershed, Ethiopia and found that increasing bare and agri- irrigate 14.82 million acres of agriculture land and generate culture area resulted in increase in stream flow. 1000 MW electricity which is 15% of the total electricity Several studies on LULC changes have been conducted production through hydel power plants (Archer and Fowler in northern Pakistan and Kashmir (part of the upper Jhe- 2008). lum River basin). Hassan et al. (2016) have reported defor- The whole basin has mean annual precipitation about estation and urbanization from 1992 to 2012 in the city 1196 mm and mean annual temperature by 13.2 °C. (Sad- of Islamabad and its surroundings. Similarly, a study on dique et al. 2019a) The temperature of the basin decreases Simly watershed was conducted by Butt et al. (2015) and with increasing elevation (from south to north) but precipi- found an increase of settlement and a decrease of vegeta- tation does not follow a specific trend in such a complex tion. Mannan et al. (2019) have found increase of agriculture topography. More than 70% of rainfall occurs from March to and built-up area and decrease of forest area at the foothill August. The basin monthly average temperature ranges from of the Himalayas Mountains of Pakistan. Iqbal and Khan 4.9 °C in January (coldest month) to 24.3 °C in July (hottest (2014) conducted their research at the sub-division of Azad month). The JRB is characterized by highly heterogeneous Jammu and Kashmir to investigate the spatio-temporal land soil and land cover; main types of soil include Gleyic Sol- use/land cover change between 1998 and 2009 and found onacks (49%), Calaric Phaoeozems soil (23%), and Mollic a decrease of forest and bare vegetation area and increase Planosols soil (21%). The basin drainage area is covered by of settlement. Alam et al. (2014) have examined the LULC diverse land cover such as agriculture (31%), grass-sparse changes between 1992 and 2015 in Kashmir valley, India vegetation (37%), forest (28%), water (2%) and settlement and observed an increase of plantation and built-up area (2%) (Saddique et al. 2019b). while a decrease in agriculture. However, only one study has been carried out at the small area of Indus River to quantify Data description the impact of LULC change on discharge using HEC-HMS hydrological model. Younis and Ammar (2017) concluded The daily observed precipitation, maximum and minimum that overall change in discharge was negligible. temperature data of fifteen stations were collected from the Based on the review literature and as far as the authors are Pakistan Meteorological Department (PMD), Water and aware, no previous study has been reported to date regard- Power Development Authority (WAPDA) of Pakistan, and ing LULC changes impact on the hydrology of the Jhelum the India Meteorological Department (IMD). River dis- River Basin (JRB). However, few studies (Mahmood and Jia charge data of five stations were obtained from WAPDA for 1 3 68 Environmental Earth Sciences (2020) 79:448 Page 3 of 13 448

Fig. 1 Location of the Jhelum River Basin calibration and validation of SWAT. Landsat imagery for Methodology the years of 2001, 2009 and 2018 freely obtained from the United States Geological Survey (USGS). Table 1 describes LULC classification the Landsat images characteristics used in this study. Refer- ence/ground truth data were collected from 3 September to Satellite images are known to have distortion; hence, pre- 2 October 2018 using handheld GPS for image classification processing prior to the detection of change is required to and accuracy assessment of LULC of the study area. Besides build a more direct linkage between the acquired data and field visit and data obtained from the forest department of biophysical phenomena (Coppin et al. 2004). Environment Azad Kashmir, high-resolution Google Earth imagery was for Visualization Images (ENVI) was used for radiometric, also used to collect referenced points for classification (Mon- atmospheric and geometry correction of images. In addition, dal et al. 2015; Matlhodi et al. 2019). images mosaicking and sub-setting were done in R. Super- vised classification was applied for the image classification using Random Forest (RF) machine learning algorithm in R for the Jhelum River Basin land use categories (Mango et al. 2011). LULC was classified into five classes including Agriculture, Forest, Grass, Settlement and Water. Table 2 provides a detail description of different LULC classes. The

Table 1 Landsat images Year Satellite Sensor Path/Row Resolution Acquisition Cloud cover characteristics for the Jhelum date (day/ River Basin month)

2001 7 ETM + 148/37,149/36–37,150/36–37 30 m 2,4,11/09 < 10 2009 5 TM 148/37,149/36–37,150/36–37 30 m 5,10,12,19/09 < 10 2018 8 OLI 148/37,149/36–37,150/36–37 30 m 12,21/09 < 10

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Table 2 Description of diﬀerent LULC classes Class code LULC class Description

1 Agriculture Wheat, rice, fodder crops and vegetables 2 Forest Evergreen and deciduous forests and orchards like apple, pear, walnut, almond etc. 3 Grass This class includes mountainous rangelands, state owned grass lands and sparsely vegetated area 4 Settlement Residential areas, roads, industrial zones, barren land and dry stream channels 5 Water Rivers, lakes, ponds and snow cover

accuracy of LULC maps was assessed by calculating three = + + − Qyld Qsrf Qlat Qgw Tloss, (2) different accuracies and kappa coefficient from the confusion matrix (or error matrix). where Qyld is the amount of water yield (mm); Qsrf is the surface runoff (mm); Qlat is the amount of water contributed by lateral flow (mm); Qgw is the ground water flow contri- SWAT hydrological model bution (mm); Tloss is the loss of water through transmission process (mm). The SWAT model was used to simulate the discharge of JRB. In this study, a threshold of 5% was used for soil and slope It is a semi-distributed physical-based hydrological model in each sub-basin during HRUs definition in SWAT. The that has been commonly used for investigating the impacts of JRB was divided into 27 sub-basins (Fig. 2) and 627 HRUs. LULC change on water resources around the world (Githui The soil conservation service (SCS) curve number and the et al. 2010; Wagner et al. 2013 and Garg et al. 2017). SWAT Manning equation were used for the estimation of runoff, operates at a daily time step with complex terrain conditions flow rate and velocity. The ET was calculated with the Har- including different land use, soils and management practices. greaves method as the data of wind speed, solar radiation Two phases (land and routing) are involved for simulating and relative humidity were not available for the simulation the hydrological process in SWAT. The land phase controls time period (Neitsch et al. 2005). In SWAT model, two dif- the amount of water and other elements delivered to the ferent processes were taken into account such as snowmelt main channel from each sub-basin and routing phase is the movement of water, sediment and nutrients loadings through channel network and finally reach the outlet of watershed (Neitsch et al. 2005). A watershed is divided into multiple sub-basins during the delineation process in SWAT and after that, these sub-basins are further divided into Hydrological Response Units (HRUs). HRUs are composed of similar land cover, soil type and slope classification. The hydrological cycle in SWAT model is simulated by Eq. 1 of water balance (Neitsch et al. 2005).

t = + − − − − SWt SWo Rday Qsurf Ea Wseep Qgw, (1) i=1 where SW t is the final soil water contents (mm); SW o is the initial soil water content (mm); t is the time in days; Rday is the precipitation amount on day i (mm); Qsurf is the measure of surface runoff on day i (mm); Ea is the amount of ET (mm); Wseep is the amount of water that enters the vadose zone from soil profile (mm); Qgw is the amount of base flow on day i (mm). Water yield is one of the vital parameters calculated for sustainable water resources management of the watershed. Water yield in a catchment is calculated by the Eq. 2 (Arnold Fig. 2 Three major river basins in study area and sub-basins gener- et al. 2011). ated during delineation

1 3 70 Environmental Earth Sciences (2020) 79:448 Page 5 of 13 448 and orographic effect on precipitation and temperature; for Results and discussion this elevation bands were generated at the sub-basins scale. The SWAT model was run for 14 years, which included Land use and land cover classification 1992–1994 warm-up period, 1995–2000 calibration period and 2001–2005 validation period. Sensitivity analysis, cali- Results of accuracy assessment of five different classes are bration and validation were conducted using the SUFI-2 given in Table 4 in terms of user’s accuracy (UA), producer’s algorithm in SWAT-CUP. Global Sensitivity Analysis (GSA) accuracy (PU), overall accuracy (OA) and kappa coefficient technique was used to identify the sensitive parameters for (KC). discharge over JRB. Among the 30 parameters (Table 3) The spatial distribution of land use and land cover of the selected on the basis of the literature review, 18 parameters study area for the year of 2001, 2009 and 2018 is shown in were found sensitive for this study. Fig. 3. Table 5 presents the LULC change matrix for the period of 2001–2018 and Table 6 provides the quantitative changes in LULC within last 18 years. Major changes can be observed in agriculture (decrease) and forest (increase) class. These outcomes are comparable with previous studies (Kuchay

Table 3 List of parameters selected for sensitivity analysis Parameters Description Unit

1 GW_DELAY Groundwater delays Days 2 GW_REVAP Groundwater revap coefficient – 3 GWQMN Threshold depth of water in shallow aquifer required for return flow to occur mm 4 REVAPMN Threshold depth of water in shallow aquifer required for “revap” to occur mm 5 RCHRG_DP Deep aquifer percolation fraction – 6 ALPHA_BF Base flow alpha factor Days 7 CN2 Initial SCS runoff curve number for moisture condition II – 8 CH_K2 Effective hydraulic conductivity in the main channel alluvium mmh−1 9 CH_N2 Manning’s “n” value for the main channel – 10 EPCO Plant uptake compensation factor – 11 ESCO Soil evaporation compensation factor – 12 SOL_AWC Available water capacity of the soil mmmm−1 13 SOL_K Saturated hydraulic conductivity mmh−1 14 SUB_SFTMP Snowfall temperature °C 15 SUB_SMTMP Snow melt base temperature °C 16 SUB_SMFMX Maximum melt rate for snow on June 21 mm°C−1 day−1 17 SUB_SMFMN Minimum melt rate for snow on December 21 mm°C−1 day−1 18 SUB_TIMP Snow pack temperature lag factor – 19 SNOCOVMX Minimum snow water content that corresponds to 100% snow cover mm 20 SNO50COV Snow water equivalent that corresponds to 50% snow cover mm 21 SOL_D Soil bulk density gcm−3 22 SOL_Z Depth from soil surface to bottom of layer mm 23 ALPHA_BNK Base flow alpha factor for bank storage Days 24 CH_D Average depth of main channel m 25 CH_L Length of main channel m 26 CANMX Maximum Canopy storage mm 27 SURLAG Surface runoff lag coefficient – 28 PLAPS Precipitation lapse rate mmKm−1 29 TLAPS Temperature lapse rate °CKm−1 30 SHALLST Initial depth of water in the shallow aquifer mm

Bold text parameters were selected for calibration

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Table 4 Summary of LULC LULC class 2001 2009 2018 maps accuracies for 2001, 2009 and 2018 UA % PA % OA % KC % UA % PA % OA % KC % UA % PA % OA % KC %

Agriculture 93 91 90 89 93 92 92 90 95 94 95 94 Forest 94 93 92 94 96 97 Grass 83 90 82 88 95 94 Settlement 83 83 92 93 94 93 Water 91 93 93 94 100 100

UA user ’s accuracy, PA producer ’s accuracy, OA overall accuracy, KC kappa coeﬃcient

Fig. 3 Land use/land cover of the Jhelum River basin for the period of 2001, 2009 and 2018

2 Table 5 LULC change (Km ) LULC 2018 Total matrix for the period of 2001–2018 Agriculture Forest Grass Settlement Water

LULC 2001 Agriculture 6998 2130 716 346 102 10,292 Forest 323 8455 328 32 173 9311 Grass 507 1533 9594 198 455 12,287 Settlement 0 0 22 656 14 692 Water 0 0 16 28 771 815 Total 7828 12,118 10,676 1260 1515 33,397

Table 6 Area statistics and changes in LULC in 2001–2018 LULC class 2001 2009 2018 Change 2001–2018 Area (Km 2) Area (%) Area (Km 2) Area (%) Area (Km 2) Area (%) Area (Km 2) Area (%)

Agriculture 10,292 30.82 8455 25.31 7828 23.43 − 2463.88 − 6.39 Forest 9311 27.88 10,745 32.17 12,118 36.28 2806.87 8.4 Grass 12,287 36.79 11,989 35.90 10,676 31.97 − 1611.24 − 4.82 Settlement 692 2.07 1031 3.08 1260 3.77 567.84 1.7 Water 815 2.44 1177 3.52 1515 4.53 700.42 2.09 Total 33,397 33,397 33,397 1 3 72 Environmental Earth Sciences (2020) 79:448 Page 7 of 13 448 et al. 2016 and Alam et al. 2019) conducted in Kashmir Val- snow during the year (SMFMN), snowfall temperature ley (Upper Jhelum). They found that horticulture practices (SFTMP), snow melt base temperature (SMTMP), snow (apple trees) have replaced the agriculture area. Pakistan pack temperature lag factor (TIMP) were the most sensi- forest conservation policy is also playing key role to increase tive parameters for the Neelum and Kunhar basins, and the forest. Figure 4 shows the changes occurred within each ground water control parameters were sensitive at the class during diﬀerent periods (2001–2009, 2009–2018 and lower elevation basins. Table 7 gives the parameters ini- 2001–2018) across the JRB. It can be seen that high change tial ranges and their calibrated values at diﬀerent basins occurred during 2001–2009 as compared to 2009–2018 in of the watershed. all classes except grass.

Sensitivity analysis SWAT calibration and validation

The results obtained from sensitivity analysis in SWAT- Figure 5 exhibits the comparison of daily simulated and CUP using GSA revealed that the maximum melt rate observed flow for the calibration period (Jan-1995 to Dec- of snow during year (SMFMX), minimum melt rate of 2000) and validation period (Jan-2001 to Dec-2005). Table 8 provides the model evaluation indicators such as NSE, R2 and Pbias. The simulated flow data closely matched the observed flow over the entire period. However, there are small over-estimations or under-estimations in flow. It can be seen that at all the gauging stations NSE and R2 values were above 0.5 and Pbias values were in the range of ± 15. The SWAT model simulation results fall under a good category according to the performance criterion of Moriasi et al. (2007). However, the performance indices for the validation period are poor than the calibration period as seen at the Garhi Habibullah station (Due et al. 2009; Pinto et al. 2013; Fukunaga et al. 2015) because the parameter values are specifically optimized for the calibration period. Additionally, DEM, land use and soil were not changed during the entire Fig. 4 Percentage changes in land use/land cover within each class simulation period, which have a substantial effect on the (2001–2009, 2009–2018 and 2001–2018) hydrological process (Jing et al. 2015).

Table 7 Parameters initial Sr. no. Parameter Category Initial range Neelum basin Kunhar basin Upper Jhelum basin ranges and fitted values at different basins 1 GW_DELAY Groundwater 0–500 417.03 207.77 2 GW_REVAP Groundwater 0.02–0.2 0.05 3 GWQMN Groundwater 0–5000 3308.45 2959.75 4 REVAPMN Groundwater 0–500 5 RCHRG_DP Groundwater 0–1 0.48 6 ALPHA_BF Groundwater 0–1 0.45 0.31 0.05 7 CN2 Runoff ± 0.25 − 0.23 − 0.03 0.16 8 CH_K2 Channel 0.001–200 107.98 158.87 9 CH_N2 Channel − 0.01–0.3 0.03 0.19 10 EPCO Evaporation 0–1 11 ESCO Evaporation 0–1 0.10 12 SOL_AWC Soil ± 0.20 13 SOL_K Soil ± 0.20 − 0.04 14 SUB_SFTMP Snow − 5–5 − 2.19 − 2.77 0.96 15 SUB_SMTMP Snow − 5–5 1.24 4.32 − 2.11 16 SUB_SMFMX Snow 0–10 3.99 2.62 8.15 17 SUB_SMFMN Snow 0–10 5.06 3.08 4.39 18 SUB_TIMP Snow 0–1 0.61 0.50 0.73

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Impacts of LULC change on the hydrology of JRB in the rate of infiltration and transpiration, hence increase in base flow and ET. These results are similar to the previous Historical land use change impacts studies (Bi et al. 2009) which suggested that the increase in forest caused the increase in ET and decrease in WY. To investigate the impact of LULC changes on the water balance components in the JRB, land use of three different Extreme LULC scenarios impacts periods (2001, 2009 and 2018) was used in SWAT model independently, while during simulation all other inputs Although obvious changes in water balance components were kept similar. The assessment included surface runoff, have been observed with historical LULC in SWAT model, base flow, WY and ET under each LULC change scenario the impacts of some assumed scenarios need to be further (2001/2009/2018). determined. As the forest is the highest increasing class in The results of average annual surface runoff, base flow, the basin as compared to other classes. Alam et al. (2019) water yield and ET are given in Table 9. It can be seen that reported that people shift in land use practice from paddy surface runoff and WY decrease during 18 years’ period. On (agriculture) to apple (forest) cultivation as high economic the other hand, ET and base flow increased. This increase return. Plantation especially in the form of horticulture (e.g., can be partly attributed to the increase of the area of forest apple orchards) and social forestry (poplar and willow trees) and water. The increase in forest land leads to an increase is a LULC that has grown fast and extensively across the

Fig. 5 Daily simulated and observed discharge at Azad Pattan (station) for the calibration and validation

Table 8 SWAT model Stations (Rivers) Calibration Validation calibration and validation performance statistics NSE R2 Pbias NSE R2 Pbias

Azad Pattan (Jhelum) 0.71 0.74 − 13.6 0.71 0.73 1.1 Kohala (Jhelum) 0.71 0.77 − 14.2 0.70 0.74 − 2.2 Domel (Jhelum) 0.75 0.80 − 12.3 0.77 0.81 5.6 Muzaﬀarabad (Neelum) 0.56 0.71 − 14.4 0.52 0.59 − 12.9 Garhi Habibullah (Kunhar) 0.72 0.72 − 4.6 0.57 0.61 13.1

Table 9 Comparison of mean annual hydrological components using the three historical LULC in SWAT model over JRB Historical LULC Surface runoﬀ (mm) Base ﬂow (mm) Water yield (mm) Evapotranspiration (mm)

LULC 2001 539.03 222.56 927.67 462.22 LULC 2009 527.21 225.3 920.58 469.41 LULC 2018 508.15 232.88 910.87 479.60 Changes 2001–2018 − 30.88 (− 5.7%) + 10.32 (4.6%) − 16.80 (− 1.8%) + 17.38 (3.7%)

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Kashmir Valley. Horticulture contributing 7–8% to Gross in WY. The decline in surface runoff can be attributed to State Domestic Product (GSDP) has been the primary eco- increase infiltration (Benegas et al. 2014). Anderson et al. nomic activity of about 60% of people of valley. Addition- (2009) conducted an experimental study and found agrofor- ally, with serious efforts of different sections of society and estry buffer treatments increase infiltration and water storage law forcing agencies, the timber smuggling was curbed to a compared to row crop treatment areas. Moreover, change in large level (Alam et al. 2019). Also, forest growth and for- base flow (increase or decrease) is fundamentally dependent est conservation policies of Pakistan are playing a key role on the aquifer water budget (Bruijnzeel 2004). If the incom- to increase the forest area in the northern part of Pakistan ing water through infiltration exceeds the water abstraction (Shahbaz et al. 2011). The area under horticulture changed through tree roots, then the additional available water may from 14.37% to 27.02% during 1992 and 2015. Therefore, lead to increase in base flow. Consequently, WY which is three extremes forest dominant land use scenarios (all agri- aggregate sum of surface runoff, lateral flow and base flow culture converted to forest, all grass converted to forest and also reduced with an increase in the area under forest. all agriculture and grass converted to forest) were applied in this section to explore the impacts on water resources Impact of LULC changes on ET and WY of JRB. We consider the soil information during scenarios at the sub-basin scale generation. As more than 90% basin area is covered by three kinds of soils (mentioned in study area), we have observed Figures 6, 7 show the percentage changes in ET and WY that forest grow in all kinds of soils in historical periods. at the sub-basin scale. Furthermore, Table 11 showed Therefore, we implemented the LULC scenarios at all area the interaction between LULC changes and hydrological agriculture. All these scenarios are applied on the 2018 responses in sub-basins 2, 3, 15, 16 and 17. On the large LULC classification. scale, positive and negative effects cancel each other and Table 10 provides the impacts of extremes land use sce- resulted in small change in ET and WY during the 18-year narios on the surface flow, base flow, WY and ET. When period. The mean annual ET changes range from a decrease all the agriculture converted to forest land, it resulted in of − 2.15% in sub-basin 12 to increase of 7.79% in sub-basin decrease (25 mm/years) in water yield. However, this 16 in 2001–2009. The effect of land use change is more pro- decrease in WY was less as compared to the second scenario nounced in 2001–2018, where ET has increased by 11.61% because area under grass (10,676 km2) was greater than agri- in sub-basin 16 and decreased − 1.11% in sub-basin 3. A culture (7828 km2). Highest increase (in ET and base flow) major increase in ET can be observed in the eastern part and decrease (in surface runoff and WY) were occurred in (Kashmir valley) of the basin in 2001–2018. Main decrease the basin (Zhang et al. 2001; Xiao et al. 2019) when all in ET is obvious in the upper North and southern parts of agriculture and grass converted to forest, as in this condition catchment. This change in ET can be attributed to change in more than 90% area of the basin is cultivated under for- LULC in each sub-basin. The sub-basin 2 water cover area est. This suggested that forests could not only absorb water increases from 6 to 17% and grass area decreases from 62 through leaves and roots but also promote the infiltration of to 52% during the analysis of land use change at the sub- rainwater into the underground aquifer (shallow or deep). basin scale in 18 years. While, the basins 15 and 16 showed Implementation of extremes LULC scenarios suggests increase in forest and subsequent decrease in agriculture that JRB basin would face a decrease in WY in future. Fur- and grass. Larger increase in forest area increases ET which thermore, through these scenarios, the basin would be more attributing a decrease in WY. exposed to water stress because of high ET from expanded Figure 7 illustrates the change in WY at sub-basin scale in forest. The watershed managers should pay attention to sus- three periods. The changes in mean annual WY range from tainable LULC for proper water resources management. a decrease of − 13.91% (− 48.32 mm/years) in sub-basin 17 These findings showed agreement with other studies to increase 12.29% (76 mm/years) in sub-basin 2. (Suarez et al. 2014; Mwangi et al. 2016 and Guzha et al. This might have attributed due to the increase of leaf 2018) that have reported an increase in ET and decrease area index (LAI) and increased transpiration from more

Table 10 Comparison of mean Scenarios Surface Base ﬂow (mm) Water yield (mm) Evapotran- annual hydrological components runoﬀ spiration under extreme LULC scenarios (mm) (mm)

All agriculture converted to forest 465.63 247.98 885.84 504.0 All grass converted to forest 439.95 264.38 883.23 506.9 All grass + agriculture converted to forest 421.09 271.25 873.01 515.7

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Fig. 6 Percentage changes in ET in each sub-basin over the Jhelum River basin between 2001 and 2009, 2009 and 2018, and 2001 and 2018

Fig. 7 Percentage changes in WY in each sub-basin over the Jhelum River Basin between 2001 and 2009, 2009 and 2018, and 2001 and 2018 vegetated surface (forest cover increase from 19 to 29%) in However, a significant gain and lost was observed within sub-basin 17. On the other hand, in basin 2 water cover area each class like agriculture converted to forest and forest con- increased (from 6 to 17%) and vegetated surface decreased. verted to agriculture. The main cause of significant increase The decrease in WY is more pronounced in eastern part of in forest land was due to the increase of horticulture (cultiva- the basin (in basins 15 and 17) in 2001–2018. On the other, tion of apple) practices in catchment. increase in WY is significant in the northern part (basin 2 The SWAT model simulated discharge was compared and basin 3) of the catchment. The key factor involved in with observed discharge at the five different hydrological decreasing the WY is the forest cover gain. Ashagrie et al. stations that lie at various geographical locations, for both (2006) were conducted research at a large catchment and calibration and validation during the period of 1995–2005. found that the overall impact of land use changes in the The NSE and R 2 values greater than 0.5 at daily time scale Meuse basin is too small to be detected. In this study, impact showed that the SWAT hydrological model could effectively of land use changes on WY and ET was more pronounced reproduce discharge. The calibrated model was used to ana- at the sub-basins scale as compared to the catchment scale. lyze the impacts of land use changes on the hydrological components. Our analysis indicates that increase in forest (afforestation) Conclusion would decline the surface runoff and WY while accelerate the ET over the JRB. This decrease in surface runoff was largely This study analyzed the LULC changes and their impacts attributed to improve water infiltration and retention. The on hydrological components. The results showed that the relative change in the hydrological components was propor- Jhelum River Basin had experienced significant changes tional to the magnitude of forest change. Generally, decline in in LULC during the 18 years’ interval. Five major LULC WY was attributed to greater water absorb by trees from the classes were identified which included agriculture, forest, shallow or deep aquifer due to their deep roots and increase grass, water and settlement. Among these land use, forest transpiration because of larger aerodynamic conductance. class showed significant increase in the 2001–2018 change The spatial distribution of ET showed increase in most sub- period; this was at the expense of agriculture and grass. basins. However, this increase was more pronounced in the

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Table 11 LULC change and corresponding hydrological responses

Basin no. Fraction LULC class area ET (mm) WY (mm) 2001 2009 2018 2001–2009 2009–2018 2001–2018 2001–2009 2009–2018 2001–2018

2 (Kunhar Basin) AGRL:0.14 AGRL:0.09 AGRL:0.11 − 2.16 − 3.05 − 5.21 54.54 21.02 76.32 FRST:0.14 FRST:0.13 FRST:0.12 RNGE:0.62 RNGE:0.60 RNGE:0.52 URMD:0.04 URMD:0.05 URMD:0.08 WATR:0.06 WATR:0.13 WATR:0.17 3 (Neelum Basin) AGRL:0.11 AGRL:0.09 AGRL:0.10 − 0.50 − 2.51 − 3.00 24.02 55.15 59.04 FRST:0.29 FRST:0.29 FRST:0.29 RNGE:0.56 RNGE:0.55 RNGE:0.48 URMD:0.02 URMD:0.02 URMD:0.02 WATR:0.02 WATR:0.05 WATR:0.11 15 (Upper Jhelum AGRL:0.37 AGRL:0.37 AGRL:0.22 13.21 31.93 45.14 − 11.23 − 34.56 − 45.01 Basin) FRST:0.20 FRST:0.25 FRST:0.38 RNGE:0.38 RNGE:0.29 RNGE:0.28 URMD:0.04 URMD:0.07 URMD:0.10 WATR:0.01 WATR:0.02 WATR:0.02 16 (Upper Jhelum AGRL:0.53 AGRL:0.59 AGRL:0.66 39.44 19.94 59.38 − 11.99 − 23.23 − 35.22 Basin) FRST:0.12 FRST:0.17 FRST:0.21 RNGE:0.34 RNGE:0.22 RNGE:0.11 URMD:0.01 URMD:0.02 URMD:0.02 WATR:0.00 WATR:0.00 WATR:0.00 17 (Upper Jhelum AGRL:0.19 AGRL:0.15 AGRL:0.13 11.96 39.97 51.93 − 6.30 − 42.02 − 48.32 Basin) FRST:0.19 FRST:0.25 FRST:0.29 RNGE:0.54 RNGE:0.51 RNGE:0.49 URMD:0.04 URMD:0.04 URMD:0.05 WATR:0.04 WATR:0.05 WATR:0.04

AGRL agriculture, FRST forest, RNGE grass, URMD urban, WATR water eastern part of the basin. On the other hand, WY was more as you give appropriate credit to the original author(s) and the source, pronounced in the northern part of the basin due to increase provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are of snow cover. Our research findings would be helpful to water included in the article’s Creative Commons licence, unless indicated resources managers to consider the impact of land use changes otherwise in a credit line to the material. If material is not included in in the Jhelum River Basin. the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will Acknowledgements This study is part of the doctoral research work need to obtain permission directly from the copyright holder. To view a of the first author conducted at Technical University Dresden. Our copy of this licence, visit http://creativeco mmons .org/licen ses/by/4.0/ . deepest gratitude also extends to the Higher Education Commission of Pakistan (HEC) Pakistan and German Academic Exchange Service (DAAD) Germany for providing financial support to the first author for References his Ph.D. studies. The authors are thankful to the Pakistan Meteoro- logical Department (PMD), Water and Power Development Authority (WAPDA) and Indian Meteorological Department (IMD) for providing Ahiablame L, Sinha T, Paul M, Ji J, Rajib A (2017) Stream flow discharge and weather data used in this study. The authors thank the response to potential land use and climate changes in the James two anonymous reviewers for their constructive comments and sugges- River watershed, Upper Midwest United States. J Hydrol: tions, which helped us to improve the manuscript considerably. Regional Stud 14(2017):150–166. https ://doi.org/10.1016/j. ejrh.2017.11.004 Alam A (2019) Using Landsat, satellite data for assessing the land Funding Open Access funding enabled and organized by Projekt use and land cover change in Kashmir valley. GeoJournal. https DEAL. ://doi.org/10.1007/s10708-019-10037 -x Anderson SH, Udawatta RP, Seobi T, Garrett HE (2009) Soil water Open Access This article is licensed under a Creative Commons Attri- content and infiltration in agroforestry buffer strips. Agroforest bution 4.0 International License, which permits use, sharing, adapta- Syst 75:5–16. https ://doi.org/10.1007/s10457-008-9128-3 tion, distribution and reproduction in any medium or format, as long

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1 3 79 3 General Conclusions and Recommendations 3.1 Summary and Conclusions Climate change has substantial effects on hydrological cycle and cause considerable uncertainties in availability of water at global and local scale. Pakistan is experiencing some of the worst impacts of global climate change. In addition, exponential population growth and changes in land use has enhanced climate-induced problems. Therefore, proper understanding of cause and effect processes is vital for sustainable water resources management. This study investigates the changes in climate and land use and their effects on water resources of Jhelum River basin, which is second biggest tributary of Indus basin. The following conclusions are drawn based on our analysis:

 Precipitation and temperature outputs of Global Circulation Models (GCMs) were downscaled at each station by using the SDSM and LARS-WG. The screening of the most suitable predictors of SDSM was particularly challenging in the study area due to the complex topography in the region. During calibration and validation periods, temperature was more efficiently downscaled as compared to precipitation. The SDSM performance was slightly better than LARS-WG, particularly in case of precipitation.  Significant increase in temperature and precipitation were observed under both scenarios (RCP4.5 and RCP8.5) over the basin during the 21st century. This increase was pronounced under RCP8.5 as compared to RCP4.5.  Annual mean precipitation of the basin will increase by 12.9% and 17.9% under RCP4.5 and RCP8.5, respectively with SDSM while with LARS-WG this increase will be more pronounced (25.3% and 32.2%).  All the seasons except winter showed increments in precipitation in 2080s under both scenarios. In addition, results for the autumn season revealed higher percentage increase in precipitation than summer and spring.  In basin, significant increase in temperature extremes and precipitation extremes are observed during historical (1961-1990) and projected (2061-2090) periods using the ensemble mean of three GCMs.  With regard to temperature, warm days (TX90P) and warm nights (TN90P) are increased significantly while the cold days (TX10P) and cold nights (TN10P) decrease significantly in both periods.

80  Precipitation extremes indices indicted increasing trends in precipitation amount, maximum 1-day precipitation, maximum 5-day precipitation, heavy precipitation days, precipitation in very wet days, and simple daily intensity index at most of the stations of the basin. Increasing trends were more pronounced in historical period as compared to projected period.  Projected changes in climate (Temperature and Precipitation) will pose serious threats to water resources of basin.  The SWAT hydrological model was used to assess the impact of climate on streamflow using the projected outputs of SDSM and LARS-WG.  The mean annual streamflow was projected to increase under all the 12 scenarios (GCMs, downscaling methods, RCPs).  The spatial distribution of projected water yield showed an increase in all sub-basins of the watershed. However, this increase was more pronounced in the western part of the basin as these sub-basins are mostly dependent on rainfall (no snowfall).  Spatial distribution of snowmelt revealed that, in the future, snowmelt (as snowfall decreases) could be decreased in the eastern part of the basin due to the rise of temperature. In general, climate change is expected to affect more sub-basins at high- altitude.  Different sources of uncertainty (GCMs, downscaling techniques, and RCPs) were involved in the impact studies. However, the uncertainty associated with downscaling methods (SDSM and LARS-WG) was higher than GCMs itself.  Land use and land cover change was assessed by using the Landsat images taken between 2001 to 2018 in three consecutive intervals.  Forest, water and settlement areas were increased by 8.4%, 2.0% and 1.7%, respectively while agriculture and grass areas decrease continuously over the basin.  Hence, an increase in forest cover will decrease the surface runoff and water yield while accelerating the evapotranspiration flux.  Change in evapotranspiration (increase 11.61%) and water yield (decrease 13.91%) was more pronounced at the sub-basin scale during the study period.  In basin, considering both LULC and climate change scenarios will result in increases of the annual water yield. According to the LULC extremes scenario (all agriculture + grass converted to forest), water yield will decrease by 6% (927 to 873 mm) while the extreme climate change scenario (RCP8.5-ensemble mean-2080s); water yield will

81 increase about 16% (878 to 1020 mm). Therefore, more water will be available in the future at JRB.

3.2 Recommendations and Outlook Specific recommendations are given in respective chapters of this dissertation. The focus of this section is general recommendations of the entire study, particularly on watershed management and conservation.

 It is suggested to downscale the outputs of GCMs before using them in impact assessment and adaption studies at basin scale.  SDSM can be used to produce the local climate (precipitation and temperature) for impact studies and showed high accuracy to represent the current climate compared to GCMs. Therefore, it can be used to produce site-based projections.  As there is obvious evidence of significant changes in temperature, precipitation and stream flow, watershed managers must consider climate change scenarios for effective planning of water resources.  Global warming is a big challenge for the whole world. Government and public should take some serious steps for mitigation of global warming, like production of energy from renewable resources instead of fossil fuels and control industrial pollution.  As all climate scenarios showed that more water will be available at Jhelum basin in the future, new reservoirs should not suffer from water shortage. The construction of reservoirs is seen as essential for the survival of people and economy of Pakistan.  Apart from construction of reservoirs, water harvesting (conservation pond and roof rainwater harvesting) is a good option at local scale to store the water during monsoon days, and later in dry periods that water can be used for irrigation and for animals bathing.  Land use change was found to be another key driver that has substantial impact on water resources of Jhelum River basin and therefore watershed managers should prioritize and place more emphasis on reversing the degradation of the watershed. Afforestation is recommended as a practical management and conservation strategy for the watershed that would also raise the tree cover.  Existing cropping pattern should be reviewed, water intensive crops should be replaced with those crops which consumed less water and more resilient to climate change.  The present study only considers impacts of present land use /land cover changes, future land use modelling and their impacts must be investigated. Climate data availability

82 and quality must be improved by installing more stations or should use some high- resolution satellite or reanalysis datasets. Further research should be directed to study the future changes in stream flow under both climate change and land use change scenarios together. In this study only three GCMs are used, it is recommended to use more GCMs to properly consider the range of uncertainties.

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85 Acknowledgments

First, I would like to express my sincere gratitude to my supervisor Prof. Dr. Christian Bernhofer for his tremendous and commendable scientific support right from the concept development and throughout the study. It is because of his advice and guidance that I was able to complete my degree within time. My prayers and well wishes are always for his active and healthy life. It is my deep wish to continue my connection with him in future through further research and, for higher studies.

I appreciate the administrative support that I received from TU Dresden. Also I thank our helpful secretory Sylke Schirmer, who was always there to help when I needed personal or administrative help. I also thank my colleagues at the Institute, especially Dr. Sabine Hahn- Bernhofer who always eager to help me. I am extremely thankful to her for such wonderful hospitality and caring attitude. Guidance received from Dr. Rico Kronenberg and Dr. Muhammad Usman is also highly acknowledged.

In addition, I would like to extend my thanks for the Higher Education Commission (HEC) Pakistan and German Academic Exchange Service (DAAD) to award me scholarship to pursue my Ph.D. in Germany. Special thanks also go to my employer, University of Agriculture, Faisalabad, for granting me study leave.

Last but not the least, my heartfelt gratitude goes to my family and friends, particularly my dear wife, Huma Shaheen, and children, Muhammad Wahaj and Hooram for their unwavering support during my PhD studies, away from home. Thank you for your patience and prayers! Above everything else, all Glory to Allah, the source of my strength and grace that I needed to go through my studies.

86 ERKLÄRUNG

Hiermit versichere ich, dass ich die vorliegende Arbeit ohne unzulässige Hilfe Dritter und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt habe; die aus fremden Quellen direkt oder indirekt übernommenen Gedanken sind als diese kenntlich gemacht worden. Bei der Auswahl und Auswertung des Materials sowie bei der Herstellung des Manuskriptes habe ich Unterstützungsleistungen von folgenden Personen erhalten:

Prof. Dr. Christian Bernhofer TU Dresden, Institut für Hydrologie und Meteorologie, Professur für Meteorologie Dr. Rico Kronenberg TU Dresden, Institut für Hydrologie und Meteorologie, Professur für Meteorologie Dr. Muhammad Usman Martin Luther Universität, Halle, Department of Geography and Geology Dr. Abdul Khaliq University of Agriculture, Faisalabad, Pakistan, Department of Irrigation and Drainage Dr. Solomon Hailu School of Geography and Environmental Science of Gebrechorkos Southampton, United Kingdom

Weitere Personen waren an der geistigen Herstellung der vorliegenden Arbeit nicht beteiligt. Insbesondere habe ich nicht die Hilfe eines oder mehrerer Promotionsberater(s) in Anspruch genommen. Dritte haben von mir weder unmittelbar noch mittelbar geldwerte Leistungen für Arbeiten erhalten, die im Zusammenhang mit dem Inhalt der vorgelegten Dissertation stehen. Die Arbeit wurde bisher weder im Inland noch im Ausland in gleicher oder ähnlicher Form einer anderen Prüfungsbehörde zum Zwecke der Promotion vorgelegt. Ich bestätige, dass ich die Promotionsordnung der Fakultät Umweltwissenschaften der TU Dresden anerkenne.

Ort, Datum

Unterschrift Naeem Saddique