Hydrological impacts of climate and land-use change on ﬂ ow regime variations in upper Indus basin

Investigating the effects of climate and land-use changes on surface runoff is critical for water resources management. The majority of studies focused on projected climate change effects on surface runoff, while neglecting future land-use change. Therefore, the main aim of this article is to discriminate the impacts of projected climate and land-use changes on surface runoff using the Soil and Water Assessment Tool (SWAT) through the lens of the Upper Indus Basin, Pakistan. Future scenarios of the land-use and climate changes are predicted using cellular automata arti ﬁ cial neural network and four bias-corrected general circulation models, respectively. The historical record (2000 – 2013) was divided into the calibration period (2000 – 2008) and the validation period (2009 – 2013). The simulated results demonstrated that the SWAT model performed well. The results obtained from 2000 to 2013 show that climate change (61.61%) has a higher in ﬂ uence on river runoff than land-use change (38.39%). Both climate and land-use changes are predicted to increase future runoff depth in this basin. The in ﬂ uence of climate change (12.76 – 25.92%) is greater than land-use change (0.37 – 1.1%). Global weather data has good applicability for simulating hydrological responses in the region where conventional gauges are unavailable. The study discusses that both climate and land-use changes impact runoff depth and concluded some suggestions for water resources managers to bring water environment sustainability.

variability of runoff. Land-use change refers to the human intervention on natural processes which badly impact river flow (Li et al. 2009;Liu et al. 2010;Wang et al. 2014;Birkinshaw et al. 2017;Zhang et al. 2017). Therefore, it is critical to quantify the impacts of climate and land-use changes on river runoff in order to assist policymakers and watershed managers in achieving watershed sustainability (Liu et al. 2010;Shang et al. 2019).
Pakistan is among one of the world's water-stressed countries, with an annual average rainfall of 240 mm. Rapid urbanization and population increase are the major sources of water quality impairment at the basin scale (Ghoraba 2015). Previously, the water supply systems were built considering climatic trends that cause variations in streamflow. Variations in precipitation pose a great challenge to water resources management. These negative consequences of anthropogenic activitiesboth qualitatively and quantitively on water resourcesrestrict the use of water to meet the growing demand of future population (Mukheibir 2007;Obuobie et al. 2012).
In this study, the watershed hydrological model, Soil and Water Assessment Tool (SWAT) incorporated in Geographic Information System (GIS) was applied. The SWAT model is frequently used nowadays because it is a river basin scale model capable to simulate the impacts of land management practices in large, complex watersheds, including the impacts that occur in the smaller modeling units, called hydrologic response units (HRUs). The SWAT has been termed by many studies as a computationally high efficient model for forecasting the effects of land use and climate change on streamflow (Neitsch et al. 2011). Numerous studies have evaluated the SWAT model's effectiveness while globally assessing hydrological processes in various areas (Khan et al. 2014;Shang et al. 2019).
The Intergovernmental Panel on Climate Change (IPCC) has developed the most advanced numerical-based general circulation models (GCMs) to understand the future global climatic systems, such as the atmosphere, the sea ice, and the ocean Mahmood & Babel 2013). These models are very efficient in forecasting climate changes across the globe; but, outputs obtained from these models are completely based on large grid sizes that range from 250 to 600 km, which restrict its use in the evaluation of hydrological impacts caused by climate change (Wilby et al. 2000;Gebremeskel et al. 2004). However, dynamical and statistical downscaling (SD) methods have been developed to include GCM data into hydrological models to predict climate change. SD is often used by various institutions working on climate change (Wilby et al. 2000;Fowler et al. 2007).
Perhaps, many researchers investigated water quality problems in Pakistan (Daud et al. 2017;Bashir et al. 2020)there is a deficiency in evaluating the impacts of both land use and climate changes on a large and complex watershed scale. The main purpose of this study is to understand how surface runoff responds to projected land use and climate change at a representative watershed scale to provide a solid background for long-term water resource prediction and planning in the upper region of the Indus River. Moreover, this study also separates the impacts of climate and land-use change on river runoff.

Land use
Land-use data of Moderate Resolution Imaging Spectroradiometer (MODIS) was downloaded from the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov/). The details of land-use classes and their percentages composition for the delineated watershed are demonstrated in Figure 2.

Soil data
Soil data was downloaded from the Food and Agriculture Organization (FAO) (http://www.fao.org/). The details of soil classes and their percentage compositions for the delineated watershed are demonstrated in Figure 2.

Hydroclimatic data sets
Meteorological data, which includes precipitation, temperature (maximum and minimum), humidity, sunshine duration, evapotranspiration, and wind speed, were collected from Pakistan Meteorological Department (PMD) from 1990 to 2013. Similarly, hydrological data which includes daily and average flow were obtained from Water and Power Development Authority (WAPDA) for the period of 1990-2013. Our study area includes a total of nine PMD stations, in which, only four stations data are available. Because of the unavailability of meteorological data, Global Weather data was used for simulation purposes. The PMD coordinates and WAPDA monitoring stations are depicted in Figure 1.
Daily precipitation and temperature (min and max) of four GCMs, namely, GFDL-ESM2G, HadGEM2-ES, MIROC5, and CanESM2 were used for simulations in the SWAT model from 2005 to 2010, which were obtained from Coupled Model

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Intercom-parison Project Phase 5 (CMIP5) archive. These models are frequently used in the Asian region (Babar et al. 2015;Prasanna 2015;Mahmood & Jia 2016). The description of four GCMs is listed in Table 1.

Bias correction of GCMs
Bias corrections are often applied to climate models when it does not show any/little similitude to observed data. GCMs are corrected by bias corrections which use a transformation algorithm to relate outputs of GCMs. The main objective of the  biases is to create a correlation between observed and simulated data, which will be the premise for correcting future projections of GCMs (Sun et al. 2011;Nguyen et al. 2020). Climate model data for hydrologic modeling (CMhyd) tool was used for biasing climate models (Rathjens et al. 2016b). There are plenty of biasing methods in the CMhyd tool, i.e. linear scaling (additive and multiplicative), delta change correction (additive and multiplicative), power transformation of precipitation, precipitation local intensity scaling, variance scaling of temperature, and distribution mapping of precipitation and temperature (Rathjens et al. 2016a). Utilizing GCM results at the regional scale is more straightforward and faster with linear scaling than with statistical or dynamic downscaling. These methods are used to correct biases in GCM or RCM findings. SD involves the development of empirical relationships between local-scale variables (e.g. temperature or precipitation) and large-scale variables (e.g. specific humidity, sea level pressure, and temperature of GCMs), and then, simulating future data using these relationships. However, bias correction successfully corrects the simulated GCM results (e.g. temperature and precipitation) (Fang et al. 2015). We have used linear scaling (additive and multiplicative) bias correction for our study area (Worku et al. 2020).

Description of SWAT model
The SWAT model is a watershed hydrological model incorporated in the GIS developed by the United States Department of Agriculture in the 1990s. It was designed to predict the effects of weather, different land-use patterns, and soil conditions on river runoff (Yi & Sophocleous 2011;Yu et al. 2013;Wisal et al. 2020). The SWAT model requires topographic (DEM), landuse/land-cover, soil, and daily weather (precipitation and temperature) data as their minimum inputs (Ficklin and Barnhart, 2014). The SWAT model uses water balance equation for simulation as follows: where SW t is the final soil moisture content, mm; SW 0 is the initial soil moisture content of the ith day, mm; t is the time, d; R day is the precipitation of the ith day, mm; Q surf is the surface runoff of day i, mm; E a indicates the amount of evapotranspiration on day i, mm; W seep indicates the amount of water entering the vale zone from the soil profile on day i, mm; and Q gw indicates the return flow amount on day i, mm.

SWAT model calibration and validation
The Doyian station at Astore River was selected for model calibration to evaluate the dominant parameters of the streamflow using SWAT-CUP (SWAT Calibration and Uncertainty Procedures) automatic calibration with the Sequential Uncertainty Fitting program algorithm (SUFI-2) (Shang et al. 2019). The SWAT-CUP model was calibrated using monthly data from 2000 to 2008. Calibration is a process to assess the goodness of fit between the observed and simulated data to get the optimum values of representative functions such as Nash-Sutcliffe efficiency (NSE), coefficient of determination (R 2 ), root-meansquare error to the standard deviation ratio (RSR), and percent bias (PBIAS). For RSR and PBIAS, the most optimum value is zero, whereas for R 2 and NSE, it is 1. The R 2 value ranges from 0 to 1; the closer to 1 value indicates the best results, while greater than 0.5 is in an acceptable range. Succeeding calibration, the model was validated for monthly data from 2009 to 2013. The model efficiency was tested in the validated period by evaluating the decision factors as shown in Table 2. The SWAT model was used to separate the percentage of climate change impacts from land use on river flow. The observed and simulated flow data were compared using preset climate and land-use scenarios. For instance, four scenarios were used in which scenario 1 and scenario 3 encountered the land-use change impacts on river runoff, while scenario 2 and scenario 4 encountered the climate change impacts on river runoff as given in Table 3. The contribution rate was computed by Equations (6) and (7) where Q 1 , Q 2 , Q 3, and Q 4 are the average river runoff obtained from the below-mentioned scenarios (Guo et al. 2016;Shang et al. 2019): Hypothetically, DQ is equal to DQ M , and thereafter, the impact of climate and land-use change on river runoff can be computed by h C climate change and h L land-use change: 2.3.5. Future land-use prediction

Model calibration and validation
Calibration (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) and validation (2009)(2010)(2011)(2012)(2013) were carried out on the observed flow data obtained from WAPDA at the Doyain outlet station at Astore River. A warm-up period of 2 years (1998)(1999)(2000) was used to initialize the calibration process. The model calibration process was followed by sensitivity analysis to choose parameters that govern the observed river runoff. A total of 24 most effective parameters were selected for model calibration as demonstrated in Table 4. The model performance was evaluated to predict the watershed conditions by statistical indicators such as R 2 , NSE, PBIAS, and RSR as given in Table 5, and simulated flow results are demonstrated in Figure 3.

Impacts of climate and land-use change on river runoff
The simulated annual streamflow results show the effects of climate and land-use change on river runoff over the preset scenarios as demonstrated in Table 6. The total variation observed in streamflow is 121.32 m 3 /s. In the aforementioned total It shows that climate change is the major driver in the understudy area which caused variation in the streamflow. Climate and land-use changes significantly alter the hydrological cycle. Adaptation methods for water management should consider how climatic variability will impact the hydrological regime and water supplies (Krysanova and White 2015). Tan et al. (2015) used the SWAT model to investigate the effects of land-use change and climatic variability in the Johor River basin, Malaysia where temperature and precipitation showed a significant increasing trend. The results demonstrated that the anticipated climatic variability showed a greater impact on streamflow and evaporation, in comparison to the projected land-use changes.

Climatic effects on streamflow
The SWAT model was used to assess the influence of climate change, i.e. precipitation and temperature (min and max) on the streamflow using four different GCMs. The analysis was carried out for four different periods (2007-2020, 2021-2034, 2035- 2048, and 2086-2099). Table 7 shows the future mean annual streamflow of four GCMs at Astore River, Doyian station where a remarkable increase was observed in future streamflow in comparison to its base period (2007-2020). Neupane et al. (2014) investigated the impacts of climate change on monsoon-driven streamflow in a central Himalayan watershed where predicted streamflow increased during the monsoon and post-monsoon seasons, while decreasing during the pre-monsoon season. Sood et al. (2013) used the SWAT model to assess the climate change impacts on the Volta River Basin in West Africa (2013), where rainfall showed decreasing trend while air temperature exhibited an increasing trend causing greater variability and reduced (40%) streamflow.

Land-use change effects on streamflow
Land use for the year 2050 was projected using QGIS plugin MOLUSCE. First, the land use of 2001 and 2010 was used to predict the land use of 2015, using CA-ANN. Later, the projected land-use map of 2015 was compared with NASA MODIS   Uncorrected Proof land use for correctness and kappa value, which were found to be 80.26 and 0.72%, respectively. After validation, the land use of 2001 and 2018 was used to predict the land use of 2050. Increase and decrease are observed in different land-use classes as demonstrated. Figure 4 and Table 8 depict the area in percentages of variant land-use classes in different years. Later, the projected land use of 2050 influence was evaluated on surface runoff using the SWAT model. The results showed that streamflow changes from 0.37 to 1.1% in response to the land use of 2018 and 2050.
The impacts of land-use/land-cover changes are evaluated using present trends and future scenarios. Getachew & Melesse (2012) studied the impacts of land-use change on the Angered watershed in Ethiopia (2012). Decades of landcover change affect runoff and reservoir inflow. Agricultural and urban areas increased, whereas forest and grassland areas shrank, affecting the mean monthly flow. Baker & Miller (2013) used the SWAT model to investigate how land development affects water resources in Kenya's River Njor. The results showed that dramatic alterations in land use have increased surface runoff and decreased groundwater recharge. Urbanization, agriculture, energy demand, and crop production are expected to grow in the Cedar River Basin of the Midwest, which will increase surface runoff and decrease baseflow (Wu et al. 2013). Schilling et al. (2014) examined the impacts of agricultural land use on flood risk reduction in Iowa's Raccoon River watershed. The study results unveiled that cropland conversion to permanent vegetation reduces the frequency of floods, but not their duration. This research study reported that climate change influence on the Indus flow regime overweighs land-use change. Climate change will increase water availability in the short term and decrease it in the long term. This dramatic change in flow regime is attributed to global warming. In the early 21st century, flow is expected to increase in summer followed by a decrease in the same in the late decades of the century. Summer and late spring discharges will increase in the short term because of excessive snowmelt, and in contrast, will decrease in long term because of the disappearance of glaciers; but their effect will be lessened by rainfall. Some current aspects need re-assessment to improve waters condition in the basin. The sustainability of the current water system in the basin is questionable. Water management practices are suggested to store water which will assist to lessen the influences of extreme conditions, i.e. flood and drought. The stored water will be used in the dry season for agriculture, hydropower, industrial, and domestic purposes. Furthermore, plantations are suggested in the study area to decline the increasing trend of air temperature as the sensitivity of streamflow is higher in the study area (Ahmad et al. 2020;Shah et al. 2021).

CONCLUSIONS
In this study, the SWAT model was used to evaluate the impacts of climate and land-use change on river runoff in the upper region of the Indus river basin. Future land-use and climate change scenarios are projected through the CA-ANN model and four bias-corrected GCM models, respectively. This research study reported the percentage contribution of climate and landuse changes on runoff depth for the adopted as well as projected scenarios. The conclusions can be summarized as follows: (1) The simulated results demonstrated that the SWAT model yields good statistical results during calibration (NSE ¼ 0.69, R 2 ¼ 0.7, RSR ¼ 0.56, and PBIAS ¼ 9.7) and validation (NSE ¼ 0.61, R 2 ¼ 0.66, RSR ¼ 0.62, and PBIAS ¼ 21.3).
(2) SWAT model was used to isolate the climate and land-use change impacts on river runoff in the upper region of the Indus river basin, Doyain station at Astore River, where the contribution rate of climate and land-use changes on surface runoff was 61.61 and 38.39%, respectively. (3) GCMs (CanESM2, GFDL-ESM2G, MIROC5, and HadGEM2) based on CMIP5 have good applicability in the study area for the assessment of future climate change. Simulations with the calibrated model were conducted for 2007-2020, 2021-2034, 2035-2048, and 2086-2099 periods, where an increase was observed in the average annual river runoff.