Abstract

Here, a regional climate model (RCM) RegCM4 and Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs) such as Coupled Physical Model (CM3), Coupled Climate Model phase 1 (CM2P1) and Earth System Model (ESM-2M) with their representative concentration pathway (RCP) datasets were utilized in projecting hydro-climatological variables such as precipitation, temperature, and streamflow in Teesta River basin in north Sikkim, eastern Himalaya, India. For downscaling, a ‘predictor selection analysis’ was performed utilizing a statistical downscaling model. The precision and applicability of RCM and GCM datasets were assessed using several statistical evaluation functions. The downscaled temperature and precipitation datasets were used in the Soil and Water Assessment Tool (SWAT) model for projecting the water yield and streamflow. A sequential uncertainty parameter fitting 2 optimization algorithm was used for optimizing the coefficient parameter values. The Mann–Kendall test results showed increasing trend in projected temperature and precipitation for future time. A significant increase in minimum temperature was found for the projected scenarios. The SWAT model-based projected outcomes showed a substantial increase in the streamflow and water yield. The results provide an understanding about the hydro-climatological data uncertainties and future changes associated with hydrological components that could be expected because of climate change.

INTRODUCTION

An assessment of climate change impacts and available water resources as per 21st-century demands is essential for long-term sustainable development at national or regional level. The energy balance of our climate system has been changing due to the profusion of greenhouse gases and aerosols in the atmosphere, which has further increased radiative forcing (Taylor et al. 2012; IPCC 2013). Many studies have indicated that the impacts of climate change on natural and human systems have been more pronounced in the last few years (IPCC 2013). As per the Intergovernmental Panel on Climate Change (IPCC) fifth climate assessment report (AR5; IPCC 2013), the realistic future climatic scenarios (under representative concentration pathways such as RCP 2.6, 4.5, 6.0, and 8.5) were generated in consideration of radiative forcing (Taylor et al. 2012; IPCC 2013). The global climate models (GCMs) under Coupled Model Intercomparison Project phase 5 (CMIP5)-based generated scenarios are available to analyze the freshwater-related risks of climate change (Taylor et al. 2012; Field et al. 2014; Das et al. 2016). CMIP5 models have been used for analysis of the various climatological and hydrological applications (Kharin et al. 2013; Akhter et al. 2016; Das et al. 2016). As per the CMIP5 GCMs and their RCP experiments, it was projected that there would be a decrease in water resources in dry subtropical regions and at mid latitudes; however, there will be an increase in water resources in humid mid-latitude and at high latitudes regions (IPCC 2013).

The Coordinated Regional Climate Downscaling Experiment (CORDEX) and the CMIP5 model data have been constructing varied meteorological information at both regional and global scales under multiple RCP experiments (Gao et al. 2012; Park et al. 2013; Lee et al. 2014). To overcome the limitations of GCMs, i.e., coarser resolution (grid size ∼100 km), regional climate model (RCM)-based projections are generally adopted, which have a relatively finer spatial resolution (Park et al. 2013). RCM-based future projections of temperature changes were found to be efficient in highlighting the local scale variability (Park et al. 2013). However, a few studies have also highlighted the bad performance of RCMs in simulating precipitation for different locations (Zou & Zhou 2013). The performance of the RCMs (e.g., RegCM4), as part of the CORDEX project in the simulation of precipitation at various geographical locations has been validated (Gao et al. 2012; Oh et al. 2014). However, the relative performance of RegCM4 RCM and CMIP5 GCMs together at local scale has not been considered, especially over Himalayan catchments.

This study primarily demonstrates an integrated analysis framework for an assessment of the Regional Climate Model 4 (RegCM4) and CMIP5 Coupled Physical Model (CM3), Coupled Climate Model phase 1 (CM2P1), and Earth System Model (ESM-2M), and their applicability in future climate projections of hydro-climatological variables at different locations over Teesta River Himalayan catchments. The RegCM4 has been considered as the finest resolution climate model (Park et al. 2013; Xue-Jie et al. 2013). The CMIP5 GCMs have already shown their usefulness in various climate change studies by various researchers around the world at different spatial scales (Kharin et al. 2013). Understanding water cycle and water balance components (e.g., streamflow, precipitation, snowmelt, and water yield) and their responses to future climate with enhanced temperature was the secondary objective of this study. A physical and meteorological weather generator parameter-based deterministic hydrological Soil and Water Assessment Tool model (SWAT; Arnold et al. 1998) was used in the simulation and projection of water balance components.

In hydrological modeling, several uncertainties (e.g., parameter-based uncertainty) generally arise, which affect the performance of the model in accurately simulating the water balance components (Zhang et al. 2014; Singh et al. 2015). The SWAT Calibration and Uncertainty Program (SWATCUP)-based Sequential Uncertainty Parameter Fitting version 2 (SUFI2; Abbaspour et al. 2007; Abbaspour 2011) was used for model calibration and parameterization sensitivity analysis. Uncertainties make GCM datasets unreliable (Taylor et al. 2012; Kharin et al. 2013). This study does not consider GCM data uncertainties. However, a statistical method of bias correction has been applied under the presence of observed variables to reduce the gaps between large-scale variables and local-scale variables. The bias correction method, initially utilized by Mahmood & Babel (2012), was applied to reduce the uncertainties in the downscaled variables. The Mann–Kendall (MK) test (Kendall 1975; Mann 1975) was applied to detect significant trends in the projected precipitation and temperature scenarios (2006–2060).

STUDY AREA AND DATA

Study area

For the present study, the Teesta River catchment (up to Chungthang gauge location) was selected, which is a part of north Sikkim eastern Himalayas, India. The catchment area is around 2,552.57 km2 and it exhibits diverse topographical characteristics (Figure 1). Teesta River originates from Khangchung Chho, which is a highly elevated glacial lake situated in the eastern Himalayas. The river flows southwards through gorges in the northeastern Sikkim Himalayas over a 309 km stretch. During its course through the Himalayas, the river is fed by many rivulets. Lachung River is the largest tributary of the Teesta River, and meets it at Chungthang. Originating from the Lachung Chhu glacier, Lachung River's catchment corresponds to extreme elevation variations (Figure 1). The upstream parts of the catchment correspond to extreme elevation peaks (around 7,400 m), while the lower parts of the catchment correspond to moderate elevation peaks (around 1,500 m) (Figure 1). The predominant monsoon climate of the Teesta catchment supports the evergreen rainforests, including grasses. In this region, about 80% of the annual rainfall is brought by southwest monsoon during the June to September months (Singh & Goyal 2016). The maximum, minimum, and average annual average precipitation variations across all the sub-catchments (SB) are 3,238.60 mm to 3,354.60 mm, 1,399 mm to 1,569 mm, and 2,346.71 to 2,460.05 mm, respectively. The high elevated upstream parts of the Teesta River catchment are mostly covered by snow and glaciers and the moderate elevated lower parts are supplied with rainfall during the monsoon and summer seasons.

Figure 1

Study area map and elevation variations over the Teesta River catchment part of north Sikkim, eastern Himalayas, India.

Figure 1

Study area map and elevation variations over the Teesta River catchment part of north Sikkim, eastern Himalayas, India.

Data used

Fine resolution gridded (0.5° × 0.5°) daily precipitation and minimum–maximum temperature datasets (0.5° × 0.5°) were used for this study. These datasets were collected from the Indian Institute of Tropical Meteorology (IITM) and Indian Meteorological Department (IMD) for the period of 1981 to 2005. The datasets were prepared by the utilization of quality-controlled observed precipitation data collected from more than 1,800 gauges. Additionally, measured daily precipitation datasets (from 1981 to 2005), available at the Lachung and Chungthang gauging stations, were utilized for this study. For SWAT model calibration and validation, about 15 years' daily observed discharge data sets (1991–2005), available at the Central Water Commission (CWC), were used. Out of the 15 years' available data, the first 10 years' data were used for calibration and the remaining 5 years were used to validate the model. The other meteorological variables, such as humidity, wind speed, and solar radiation (1981–2005), were obtained from the global weather data of SWAT (https://globalweather.tamu.edu/). The landuse/landcover (LULC) map and soil map of the study area were downloaded from the WaterBase website (http://www.waterbase.org/download_data.html).

METHODOLOGY

Spatial adjustment of observed meteorological, RegCM4 and CMIP5 GCM datasets

To highlight the topographical variations, the whole catchment was divided into seven sub-catchments (SB), which also highlighted the spatial variations in climatological factors (e.g., temperature and precipitation). Due to the extreme to moderate elevation differences, the temperature lapse rate (TLR) and precipitation lapse rate (PLR) varied significantly from the upstream parts to downstream parts of the catchment (Singh & Goyal 2016). Seven climate stations–Chopta valley (SB1), Thangu (SB2), Muguthang (SB3), Lachen (SB4), Yumthang (SB5), Lachung (SB6), and Chungthang (SB7)–were defined based on the elevation variation to project the local scale changes (Figure 1). The gridded and measured minimum–maximum temperature and precipitation were adjusted at each SB by calculating the TLR and PLR (Gardner et al. 2009; Singh & Goyal 2016). In this study, due to large variations in the topography and climate of the Teesta, we observed significant variations from SB1 to SB7 (Das et al. 2012). For the computation of TLR and PLR, the elevation differences within the SBs were calculated from Cartographic Satellite (CARTOSAT) Digital Elevation Model (DEM), which was downloaded from Bhuwan portal, ISRO, India (www.bhuwan.nrsc.gov.in).

For the downscaling of daily temperature and precipitation datasets, the Statistical Downscaling Model (SDSM; Wilby et al. 2002) was used on a daily scale at each SB utilizing RegCM4 (RCP4.5), ESM-2M (RCP4.5), CM3 (RCP4.5), and CM2P1 (RCP4.5) climate models. To adequately cover the various circulation domains, 6 GCM grids (2.5° × 2.5°) and 12 RegCM4 grids (0.5° × 0.5°) covering the Teesta catchment were chosen as the spatial domain of the 18 most relevant GCM predictors and 9 most relevant RCM predictors (Figure 1 and Table 1). These 6 GCM and 12 RCM grid points were spatially interpolated (point scale) at each climate station using an inverse distance weighted averaging (IDWA) technique. The IDWA interpolates the GCM and RCM grid points (for each predictor) as per the weighted average of each grid point at the known observed point (predictand, such as temperature and precipitation).

Table 1

GCM (e.g., CM3, CM2P1, and ESM-2M) and RCM (e.g., RegCM4) predictors and their descriptions

Predictors Description Unit Models 
clt Total cloud fraction GCMs 
hfls Surface upward latent heat flux W m−2 GCMs 
hfss Surface upward sensible heat flux W m−2 GCMs 
pr Precipitation flux kg m−2 s−1 GCMs, RCM 
prc Convective precipitation flux kg m−2 s−1 GCMs 
prClim Convective precipitation kg m−2 s−2 GCMs 
prsn Snowfall flux kg m−2 s−1 GCMs 
rhs Near-surface relative humidity GCMs 
rhsmax Surface daily maximum relative humidity GCMs 
rhsmin Surface daily minimum relative humidity GCMs 
rlds Surface downwelling longwave radiation W m−2 GCMs 
sfcwind Daily-mean near-surface wind speed m s−1 GCMs 
ta Air temperature GCMs 
tas Near-surface air temperature GCMs, RCM 
tasmax Daily maximum near-surface air temperature GCMs, RCM 
tasmin Daily minimum near-surface air temperature GCMs, RCM 
uas Eastward near-surface wind m s−1 GCMs, RCM 
vas Northward near-surface wind m s−1 GCMs, RCM 
huss Near-surface specific humidity RCM 
psl Sea level pressure Pa RCM 
rsds Surface downwelling shortwave radiation W m−2 RCM 
Predictors Description Unit Models 
clt Total cloud fraction GCMs 
hfls Surface upward latent heat flux W m−2 GCMs 
hfss Surface upward sensible heat flux W m−2 GCMs 
pr Precipitation flux kg m−2 s−1 GCMs, RCM 
prc Convective precipitation flux kg m−2 s−1 GCMs 
prClim Convective precipitation kg m−2 s−2 GCMs 
prsn Snowfall flux kg m−2 s−1 GCMs 
rhs Near-surface relative humidity GCMs 
rhsmax Surface daily maximum relative humidity GCMs 
rhsmin Surface daily minimum relative humidity GCMs 
rlds Surface downwelling longwave radiation W m−2 GCMs 
sfcwind Daily-mean near-surface wind speed m s−1 GCMs 
ta Air temperature GCMs 
tas Near-surface air temperature GCMs, RCM 
tasmax Daily maximum near-surface air temperature GCMs, RCM 
tasmin Daily minimum near-surface air temperature GCMs, RCM 
uas Eastward near-surface wind m s−1 GCMs, RCM 
vas Northward near-surface wind m s−1 GCMs, RCM 
huss Near-surface specific humidity RCM 
psl Sea level pressure Pa RCM 
rsds Surface downwelling shortwave radiation W m−2 RCM 

Downscaling of daily precipitation and minimum–maximum temperature

The SDSM downscaling method involves the empirical relationships between the large-scale predictor(s) and local-scale predictands (Wilby et al. 2002; Mahmood & Babel 2012). SDSM utilizes linear regression for estimation of the parameters of daily precipitation and minimum–maximum temperatures, separately. However, SDSM also incorporates conditional and stochastic weather generators. Therefore, SDSM is known to be sophisticated compared to a simple regression model (Wilby et al. 2002). In SDSM, regression coefficients for linear regression are computed using the ordinary least-square method (Mahmood & Babel 2012). Selection of predictors plays an important role in statistical downscaling. The predictor selection was carried out using correlation and partial correlation analysis. First, the highly correlated predictors were selected from the 18 GCM predictors and 9 RCM predictors with reference to predictand (as listed in Table 1); 12 GCM and 9 RCM most highly correlated predictors were finally used (as shown in Tables 2 and 3, respectively, for GCM and RCM). Further, a quantitative method was adopted for final selection of the most suitable predictors, as discussed by Mahmood & Babel (2012). In this method, correlation matrices between 12 predictors for GCMs and 9 predictors for RCM (as shown in Tables 2 and 3) and the predictand (observed data) were prepared. The predictors with high correlation coefficients were taken and arranged in descending order. The highest correlated or first ranked predictor was taken as the first suitable super predictor (FSSP). The absolute correlation (R) between predictand and predictor variables and the coefficient of correlation between individual predictor variables were also calculated. We should not chose highly correlated predictors to avoid multi-co-linearity in the predictors. Thus, the other highly correlated predictors were not considered to reduce multi-co-linearity among the predictor variables. For precipitation and temperature, the thresholds were taken as 0.5 and 0.7 between the two predictors, respectively. The selection of the other predictors was carried out using percentage reduction in an absolute partial correlation (PRP) with respect to absolute correlation, as discussed by Mahmood & Babel (2012). The smallest value (more negative values) shows minimal co-linearity among the predictors. As per the correlation matrix among the predictors, the second most suitable predictor was identified in the same way such that the predictor corresponded to minimum correlation (or the minimum PRP in partial correlation) (Tables 2 and 3). The minimum PRP predictor variable had nearly no or a very insignificant multi-co-linearity with the FSSP. The third, fourth, and following predictors were selected by reiterating the same procedure.

Table 2

Statistics of objective functions used in predictor's selection for GCMs

Variables Stations Statistics hfls pr prc rhs rhsmax rhsmin rlds sfcwind tas tasmax tasmin vas 
Precipitation SB1 0.38 0.52 0.32 0.29 0.21 0.31 0.41 0.11 0.62 0.41 0.42 0.24 
Partial r 0.03 0.01 0.01 −0.03 0.01 0.04 0.01 0.01 −0.02 0.04 0.04 
PRP −0.91 −0.97 −0.97 −1.1 −0.95 −0.88 −0.98 −0.95 −1.04 −0.91 −0.9 −1 
SB2 0.37 0.31 0.32 0.28 0.2 0.3 0.4 0.1 0.42 0.4 0.42 0.24 
Partial r 0.03 0.01 0.01 −0.02 0.01 0.03 0.01 0.01 −0.02 0.03 0.05 
PRP −0.93 −0.97 −0.97 −1.09 −0.96 −0.89 −0.99 −0.95 −1.04 −0.93 −0.88 −0.99 
SB3 0.37 0.31 0.31 0.27 0.19 0.3 0.4 0.1 0.53 0.4 0.42 0.24 
Partial r 0.03 0.02 0.01 −0.02 0.03 0.01 −0.02 0.03 0.05 
PRP −0.91 −0.95 −0.97 −1.09 −0.98 −0.89 −0.98 −0.98 −1.03 −0.94 −0.88 −1 
SB4 0.38 0.32 0.32 0.28 0.2 0.31 0.41 0.1 0.43 0.41 0.43 0.63 
Partial r 0.03 0.01 0.01 −0.02 0.01 0.03 0.01 0.01 −0.02 0.03 0.05 
PRP −0.92 −0.96 −0.97 −1.08 −0.96 −0.89 −0.99 −0.95 −1.04 −0.94 −0.89 −1 
SB5 0.4 0.34 0.34 0.31 0.21 0.33 0.44 0.11 0.44 0.42 0.45 0.27 
Partial r 0.04 0.02 0.01 −0.02 0.04 −0.01 0.02 0.05 0.01 
PRP −0.89 −0.95 −0.96 −1.08 −0.99 −0.88 −0.99 −1.03 −1.01 −0.96 −0.89 −0.95 
SB6 0.38 0.32 0.65 0.28 0.2 0.31 0.41 0.1 0.43 0.41 0.43 0.24 
Partial r 0.03 0.01 0.01 −0.03 0.01 0.03 0.01 0.01 −0.02 0.03 0.05 0.01 
PRP −0.92 −0.96 −0.98 −1.09 −0.95 −0.89 −0.97 −0.95 −1.04 −0.93 −0.89 −0.95 
SB7 0.38 0.56 0.32 0.28 0.19 0.3 0.41 0.1 0.43 0.41 0.43 0.24 
Partial r 0.02 0.01 0.01 −0.02 0.01 0.03 −0.01 0.02 0.05 0.01 
PRP −0.94 −0.98 −0.96 −1.07 −0.96 −0.91 −1 −0.97 −1.01 −0.96 −0.88 −0.95 
Max temperature SB1 0.73 0.51 0.52 0.5 0.39 0.54 0.77 0.1 0.85 0.82 0.83 0.39 
Partial r −0.04 −0.06 0.05 −0.07 0.15 0.07 −0.07 0.02 0.13 −0.03 0.06 −0.05 
PRP −1.05 −1.12 −0.9 −1.15 −0.63 −0.88 −1.1 −0.8 −0.85 −1.04 −0.93 −1.12 
SB2 0.73 0.51 0.52 0.5 0.38 0.53 0.77 0.07 0.86 0.83 0.84 0.39 
Partial r −0.03 −0.05 0.05 −0.08 0.14 0.06 −0.07 0.15 −0.06 0.08 −0.03 
PRP −1.03 −1.1 −0.91 −1.16 −0.64 −0.89 −1.09 −0.95 −0.83 −1.07 −0.91 −1.07 
SB3 0.73 0.5 0.51 0.49 0.36 0.52 0.77 0.08 0.86 0.83 0.83 0.39 
Partial r −0.02 −0.05 0.04 −0.08 0.13 0.06 −0.07 −0.01 0.16 −0.07 0.08 −0.02 
PRP −1.02 −1.09 −0.91 −1.17 −0.65 −0.89 −1.09 −1.1 −0.82 −1.09 −0.9 −1.05 
SB4 0.74 0.51 0.52 0.5 0.38 0.53 0.77 0.08 0.86 0.83 0.83 0.38 
Partial r −0.03 −0.06 0.05 −0.08 0.14 0.06 −0.08 0.01 0.15 −0.06 0.07 −0.03 
PRP −1.04 −1.11 −0.91 −1.15 −0.64 −0.88 −1.1 −0.91 −0.83 −1.07 −0.91 −1.09 
SB5 0.72 0.5 0.51 0.49 0.35 0.52 0.77 0.08 0.86 0.82 0.83 0.39 
Partial r −0.02 −0.05 0.05 −0.08 0.12 0.06 −0.07 −0.01 0.16 −0.08 0.09 −0.02 
PRP −1.02 −1.09 −0.91 −1.16 −0.65 −0.89 −1.1 −1.14 −0.81 −1.09 −0.9 −1.04 
SB6 0.73 0.51 0.52 0.5 0.38 0.53 0.77 0.08 0.85 0.83 0.83 0.38 
Partial r −0.03 −0.05 0.05 −0.07 0.14 0.06 −0.07 0.01 0.14 −0.05 0.07 −0.03 
PRP −1.04 −1.1 −0.91 −1.15 −0.64 −0.89 −1.1 −0.88 −0.83 −1.06 −0.91 −1.09 
SB7 0.66 0.45 0.46 0.44 0.33 0.47 0.69 0.05 0.78 0.75 0.75 0.33 
Partial r −0.02 −0.05 0.05 −0.08 0.13 0.06 −0.07 −0.01 0.16 −0.07 0.08 −0.02 
PRP −1.03 −1.1 −0.9 −1.17 −0.62 −0.88 −1.1 −1.13 −0.8 −1.09 −0.89 −1.05 
Min temperature SB1 0.81 0.58 0.58 0.59 0.45 0.62 0.85 0.11 0.91 0.87 0.9 0.44 
Partial r 0.1 0.02 −0.09 0.13 0.12 −0.02 −0.05 0.04 0.07 0.13 −0.05 
PRP −0.88 −0.96 −1.01 −1.16 −0.72 −0.81 −1.02 −1.42 −0.95 −0.92 −0.86 −1.11 
SB2 0.8 0.58 0.58 0.58 0.44 0.62 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r 0.1 0.03 −0.1 0.12 0.12 −0.02 −0.06 0.08 0.03 0.15 −0.03 
PRP −0.87 −0.95 −1.01 −1.17 −0.73 −0.81 −1.02 −1.68 −0.92 −0.97 −0.84 −1.07 
SB3 0.79 0.57 0.57 0.58 0.42 0.61 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r 0.11 0.03 −0.1 0.11 0.12 −0.02 −0.06 0.1 0.01 0.16 −0.02 
PRP −0.87 −0.95 −1.01 −1.17 −0.73 −0.81 −1.03 −1.76 −0.89 −0.99 −0.82 −1.04 
SB4 0.8 0.58 0.58 0.59 0.45 0.62 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r −0.13 0.11 0.04 −0.01 −0.1 0.12 0.11 −0.02 −0.05 0.07 0.03 0.14 
PRP −1.16 −0.82 −0.93 −1.02 −1.22 −0.8 −0.87 −1.24 −1.06 −0.92 −0.96 −0.67 
SB5 0.79 0.57 0.57 0.57 0.42 0.61 0.85 0.08 0.91 0.86 0.9 0.44 
Partial r 0.13 0.04 −0.01 −0.11 0.13 0.13 −0.03 −0.07 0.12 0.18 −0.02 
PRP −0.84 −0.94 −1.01 −1.2 −0.68 −0.78 −1.04 −1.84 −0.87 −1 −0.8 −1.05 
SB6 0.8 0.58 0.58 0.59 0.45 0.62 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r 0.12 0.03 −0.01 −0.11 0.15 0.13 −0.02 −0.06 0.08 0.05 0.16 −0.04 
PRP −0.85 −0.95 −1.01 −1.19 −0.68 −0.79 −1.03 −1.68 −0.91 −0.95 −0.83 −1.1 
SB7 0.76 0.54 0.55 0.56 0.42 0.59 0.81 0.06 0.86 0.82 0.85 0.41 
Partial r 0.12 0.04 −0.01 −0.11 0.14 0.13 −0.03 −0.07 0.11 0.01 0.17 −0.03 
PRP −0.84 −0.94 −1.01 −1.2 −0.67 −0.77 −1.03 −2.11 −0.87 −0.98 −0.8 −1.06 
Variables Stations Statistics hfls pr prc rhs rhsmax rhsmin rlds sfcwind tas tasmax tasmin vas 
Precipitation SB1 0.38 0.52 0.32 0.29 0.21 0.31 0.41 0.11 0.62 0.41 0.42 0.24 
Partial r 0.03 0.01 0.01 −0.03 0.01 0.04 0.01 0.01 −0.02 0.04 0.04 
PRP −0.91 −0.97 −0.97 −1.1 −0.95 −0.88 −0.98 −0.95 −1.04 −0.91 −0.9 −1 
SB2 0.37 0.31 0.32 0.28 0.2 0.3 0.4 0.1 0.42 0.4 0.42 0.24 
Partial r 0.03 0.01 0.01 −0.02 0.01 0.03 0.01 0.01 −0.02 0.03 0.05 
PRP −0.93 −0.97 −0.97 −1.09 −0.96 −0.89 −0.99 −0.95 −1.04 −0.93 −0.88 −0.99 
SB3 0.37 0.31 0.31 0.27 0.19 0.3 0.4 0.1 0.53 0.4 0.42 0.24 
Partial r 0.03 0.02 0.01 −0.02 0.03 0.01 −0.02 0.03 0.05 
PRP −0.91 −0.95 −0.97 −1.09 −0.98 −0.89 −0.98 −0.98 −1.03 −0.94 −0.88 −1 
SB4 0.38 0.32 0.32 0.28 0.2 0.31 0.41 0.1 0.43 0.41 0.43 0.63 
Partial r 0.03 0.01 0.01 −0.02 0.01 0.03 0.01 0.01 −0.02 0.03 0.05 
PRP −0.92 −0.96 −0.97 −1.08 −0.96 −0.89 −0.99 −0.95 −1.04 −0.94 −0.89 −1 
SB5 0.4 0.34 0.34 0.31 0.21 0.33 0.44 0.11 0.44 0.42 0.45 0.27 
Partial r 0.04 0.02 0.01 −0.02 0.04 −0.01 0.02 0.05 0.01 
PRP −0.89 −0.95 −0.96 −1.08 −0.99 −0.88 −0.99 −1.03 −1.01 −0.96 −0.89 −0.95 
SB6 0.38 0.32 0.65 0.28 0.2 0.31 0.41 0.1 0.43 0.41 0.43 0.24 
Partial r 0.03 0.01 0.01 −0.03 0.01 0.03 0.01 0.01 −0.02 0.03 0.05 0.01 
PRP −0.92 −0.96 −0.98 −1.09 −0.95 −0.89 −0.97 −0.95 −1.04 −0.93 −0.89 −0.95 
SB7 0.38 0.56 0.32 0.28 0.19 0.3 0.41 0.1 0.43 0.41 0.43 0.24 
Partial r 0.02 0.01 0.01 −0.02 0.01 0.03 −0.01 0.02 0.05 0.01 
PRP −0.94 −0.98 −0.96 −1.07 −0.96 −0.91 −1 −0.97 −1.01 −0.96 −0.88 −0.95 
Max temperature SB1 0.73 0.51 0.52 0.5 0.39 0.54 0.77 0.1 0.85 0.82 0.83 0.39 
Partial r −0.04 −0.06 0.05 −0.07 0.15 0.07 −0.07 0.02 0.13 −0.03 0.06 −0.05 
PRP −1.05 −1.12 −0.9 −1.15 −0.63 −0.88 −1.1 −0.8 −0.85 −1.04 −0.93 −1.12 
SB2 0.73 0.51 0.52 0.5 0.38 0.53 0.77 0.07 0.86 0.83 0.84 0.39 
Partial r −0.03 −0.05 0.05 −0.08 0.14 0.06 −0.07 0.15 −0.06 0.08 −0.03 
PRP −1.03 −1.1 −0.91 −1.16 −0.64 −0.89 −1.09 −0.95 −0.83 −1.07 −0.91 −1.07 
SB3 0.73 0.5 0.51 0.49 0.36 0.52 0.77 0.08 0.86 0.83 0.83 0.39 
Partial r −0.02 −0.05 0.04 −0.08 0.13 0.06 −0.07 −0.01 0.16 −0.07 0.08 −0.02 
PRP −1.02 −1.09 −0.91 −1.17 −0.65 −0.89 −1.09 −1.1 −0.82 −1.09 −0.9 −1.05 
SB4 0.74 0.51 0.52 0.5 0.38 0.53 0.77 0.08 0.86 0.83 0.83 0.38 
Partial r −0.03 −0.06 0.05 −0.08 0.14 0.06 −0.08 0.01 0.15 −0.06 0.07 −0.03 
PRP −1.04 −1.11 −0.91 −1.15 −0.64 −0.88 −1.1 −0.91 −0.83 −1.07 −0.91 −1.09 
SB5 0.72 0.5 0.51 0.49 0.35 0.52 0.77 0.08 0.86 0.82 0.83 0.39 
Partial r −0.02 −0.05 0.05 −0.08 0.12 0.06 −0.07 −0.01 0.16 −0.08 0.09 −0.02 
PRP −1.02 −1.09 −0.91 −1.16 −0.65 −0.89 −1.1 −1.14 −0.81 −1.09 −0.9 −1.04 
SB6 0.73 0.51 0.52 0.5 0.38 0.53 0.77 0.08 0.85 0.83 0.83 0.38 
Partial r −0.03 −0.05 0.05 −0.07 0.14 0.06 −0.07 0.01 0.14 −0.05 0.07 −0.03 
PRP −1.04 −1.1 −0.91 −1.15 −0.64 −0.89 −1.1 −0.88 −0.83 −1.06 −0.91 −1.09 
SB7 0.66 0.45 0.46 0.44 0.33 0.47 0.69 0.05 0.78 0.75 0.75 0.33 
Partial r −0.02 −0.05 0.05 −0.08 0.13 0.06 −0.07 −0.01 0.16 −0.07 0.08 −0.02 
PRP −1.03 −1.1 −0.9 −1.17 −0.62 −0.88 −1.1 −1.13 −0.8 −1.09 −0.89 −1.05 
Min temperature SB1 0.81 0.58 0.58 0.59 0.45 0.62 0.85 0.11 0.91 0.87 0.9 0.44 
Partial r 0.1 0.02 −0.09 0.13 0.12 −0.02 −0.05 0.04 0.07 0.13 −0.05 
PRP −0.88 −0.96 −1.01 −1.16 −0.72 −0.81 −1.02 −1.42 −0.95 −0.92 −0.86 −1.11 
SB2 0.8 0.58 0.58 0.58 0.44 0.62 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r 0.1 0.03 −0.1 0.12 0.12 −0.02 −0.06 0.08 0.03 0.15 −0.03 
PRP −0.87 −0.95 −1.01 −1.17 −0.73 −0.81 −1.02 −1.68 −0.92 −0.97 −0.84 −1.07 
SB3 0.79 0.57 0.57 0.58 0.42 0.61 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r 0.11 0.03 −0.1 0.11 0.12 −0.02 −0.06 0.1 0.01 0.16 −0.02 
PRP −0.87 −0.95 −1.01 −1.17 −0.73 −0.81 −1.03 −1.76 −0.89 −0.99 −0.82 −1.04 
SB4 0.8 0.58 0.58 0.59 0.45 0.62 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r −0.13 0.11 0.04 −0.01 −0.1 0.12 0.11 −0.02 −0.05 0.07 0.03 0.14 
PRP −1.16 −0.82 −0.93 −1.02 −1.22 −0.8 −0.87 −1.24 −1.06 −0.92 −0.96 −0.67 
SB5 0.79 0.57 0.57 0.57 0.42 0.61 0.85 0.08 0.91 0.86 0.9 0.44 
Partial r 0.13 0.04 −0.01 −0.11 0.13 0.13 −0.03 −0.07 0.12 0.18 −0.02 
PRP −0.84 −0.94 −1.01 −1.2 −0.68 −0.78 −1.04 −1.84 −0.87 −1 −0.8 −1.05 
SB6 0.8 0.58 0.58 0.59 0.45 0.62 0.85 0.08 0.91 0.87 0.9 0.44 
Partial r 0.12 0.03 −0.01 −0.11 0.15 0.13 −0.02 −0.06 0.08 0.05 0.16 −0.04 
PRP −0.85 −0.95 −1.01 −1.19 −0.68 −0.79 −1.03 −1.68 −0.91 −0.95 −0.83 −1.1 
SB7 0.76 0.54 0.55 0.56 0.42 0.59 0.81 0.06 0.86 0.82 0.85 0.41 
Partial r 0.12 0.04 −0.01 −0.11 0.14 0.13 −0.03 −0.07 0.11 0.01 0.17 −0.03 
PRP −0.84 −0.94 −1.01 −1.2 −0.67 −0.77 −1.03 −2.11 −0.87 −0.98 −0.8 −1.06 

Note: the underlined values are found to be significant for the process.

Table 3

Statistics of objective functions used in predictor's selection for RCM

Variables Stations Statistics huss pr psl rsds tas tasmax tasmin uas vas 
Precipitation SB1 0.34 0.02 −0.04 0.20 0.30 0.30 0.31 −0.10 0.19 
Partial r 0.16 0.01 −0.03 0.03 −0.02 0.01 0.00 0.00 −0.02 
PRP −0.53 −0.38 −0.17 −0.86 −1.08 −0.96 −1.00 −1.00 −1.10 
SB2 0.33 0.03 −0.04 0.21 0.29 0.29 0.29 −0.09 0.16 
Partial r 0.15 0.02 −0.04 0.02 −0.01 0.01 −0.01 −0.01 −0.02 
PRP −0.54 −0.41 0.06 −0.89 −1.03 −0.98 −1.05 −0.86 −1.11 
SB3 0.34 0.03 −0.04 0.22 0.30 0.30 0.29 −0.09 0.16 
Partial r 0.16 0.01 −0.03 0.03 0.00 0.00 −0.02 −0.01 −0.02 
PRP −0.53 −0.48 −0.09 −0.88 −1.01 −1.01 −1.05 −0.85 −1.13 
SB4 0.33 0.02 −0.04 0.21 0.29 0.29 0.29 −0.10 0.18 
Partial r 0.17 0.02 −0.03 0.04 −0.01 0.00 −0.02 0.00 −0.03 
PRP −0.49 −0.05 −0.08 −0.83 −1.03 −1.01 −1.05 −1.01 −1.15 
SB5 0.41 0.00 −0.03 0.28 0.37 0.37 0.37 −0.10 0.23 
Partial r 0.19 0.00 −0.02 0.05 0.00 −0.03 −0.01 0.00 −0.04 
PRP −0.55 −2.00 −0.31 −0.83 −0.99 −1.07 −1.02 −0.96 −1.16 
SB6 0.54 −0.02 0.00 0.40 0.48 0.48 −0.01 −0.15 0.24 
Partial r 0.31 0.02 0.00 0.07 −0.04 −0.01 0.00 0.01 −0.05 
PRP −0.44 −2.06 −1.50 −0.82 −1.09 −1.02 −0.50 −1.06 −1.22 
SB7 0.34 0.02 −0.03 0.24 0.30 0.31 0.30 −0.14 0.17 
Partial r 0.14 0.03 −0.03 0.04 −0.02 0.02 −0.01 −0.01 −0.01 
PRP −0.59 0.47 −0.09 −0.84 −1.06 −0.94 −1.04 −0.93 −1.06 
Max temperature SB1 0.83 −0.07 −0.02 0.72 0.85 0.84 −0.01 −0.19 0.41 
Partial r 0.22 0.06 0.00 0.26 0.23 −0.15 0.00 −0.14 −0.18 
PRP −0.73 −1.81 −1.20 −0.64 −0.73 −1.18 −1.00 −0.27 −1.45 
SB2 0.83 −0.07 −0.02 0.72 0.84 0.83 0.00 −0.19 0.34 
Partial r 0.23 −0.02 −0.01 0.08 0.21 −0.08 0.00 −0.15 −0.12 
PRP −0.73 −0.79 −0.40 −0.89 −0.75 −1.09 −3.00 −0.20 −1.37 
SB3 0.82 −0.08 −0.02 0.67 0.83 0.82 0.00 −0.19 0.33 
Partial r 0.28 −0.03 0.00 0.05 0.21 −0.07 0.00 −0.16 −0.12 
PRP −0.66 −0.63 −1.06 −0.93 −0.74 −1.09 −2.00 −0.16 −1.36 
SB4 0.84 −0.08 −0.02 0.72 0.85 0.84 0.00 −0.20 0.39 
Partial r 0.24 0.03 −0.01 0.16 0.23 −0.12 0.00 −0.14 −0.18 
PRP −0.71 −1.37 −0.64 −0.78 −0.73 −1.14 −3.00 −0.33 −1.45 
SB5 0.83 −0.09 −0.02 0.69 0.84 0.83 0.00 −0.19 0.39 
Partial r 0.26 −0.01 −0.01 0.07 0.23 −0.10 0.00 −0.15 −0.15 
PRP −0.69 −0.89 −0.75 −0.91 −0.73 −1.11 −2.00 −0.22 −1.39 
SB6 0.83 −0.08 −0.02 0.71 0.85 0.84 0.00 −0.24 0.36 
Partial r 0.24 0.02 0.00 0.13 0.22 −0.10 0.01 −0.14 −0.16 
PRP −0.71 −1.25 −0.80 −0.82 −0.74 −1.12 −3.50 −0.44 −1.43 
SB7 0.77 −0.08 −0.04 0.68 0.79 0.79 0.00 −0.27 0.29 
Partial r 0.15 0.02 −0.03 0.09 0.16 −0.05 0.01 −0.10 −0.11 
PRP −0.80 −1.24 −0.14 −0.87 −0.80 −1.06 5.00 −0.65 −1.37 
Min temperature SB1 0.91 −0.08 −0.01 0.76 0.91 0.90 −0.01 −0.20 0.46 
Partial r 0.45 0.02 0.04 0.33 0.23 −0.16 −0.01 −0.16 −0.19 
PRP −0.50 −1.21 −8.60 −0.57 −0.75 −1.18 −0.44 −0.19 −1.41 
SB2 0.90 −0.08 −0.01 0.74 0.89 0.88 −0.01 −0.19 0.37 
Partial r 0.49 −0.06 0.03 0.14 0.20 −0.08 0.00 −0.16 −0.13 
PRP −0.46 −0.31 −4.38 −0.82 −0.78 −1.09 −0.71 −0.15 −1.35 
SB3 0.90 −0.08 −0.01 0.73 0.88 0.88 −0.01 −0.19 0.36 
Partial r 0.49 −0.07 0.03 0.11 0.20 −0.07 0.00 −0.17 −0.12 
PRP −0.46 −0.13 −6.00 −0.85 −0.77 −1.08 −0.57 −0.09 −1.33 
SB4 0.91 −0.09 −0.01 0.77 0.91 0.90 −0.01 −0.21 0.44 
Partial r 0.48 −0.01 0.03 0.23 0.22 −0.13 0.00 −0.15 −0.18 
PRP −0.47 −0.86 −3.50 −0.70 −0.76 −1.14 −0.86 −0.27 −1.41 
SB5 0.91 −0.10 −0.01 0.74 0.89 0.88 −0.01 −0.19 0.43 
Partial r 0.47 −0.06 0.03 0.13 0.22 −0.11 0.00 −0.17 −0.16 
PRP −0.48 −0.43 −3.50 −0.83 −0.75 −1.12 −0.71 −0.14 −1.36 
SB6 0.91 −0.10 −0.01 0.77 0.90 0.90 −0.01 −0.25 0.41 
Partial r 0.46 −0.02 0.02 0.21 0.21 −0.11 0.00 −0.15 −0.16 
PRP −0.49 −0.75 −3.09 −0.73 −0.76 −1.13 −1.00 −0.39 −1.39 
SB7 0.87 −0.10 −0.02 0.76 0.87 0.87 0.00 −0.30 0.34 
Partial r 0.34 −0.02 0.00 0.16 0.15 −0.06 0.01 −0.10 −0.10 
PRP −0.61 −0.85 −0.86 −0.79 −0.82 −1.07 −3.67 −0.65 −1.30 
Variables Stations Statistics huss pr psl rsds tas tasmax tasmin uas vas 
Precipitation SB1 0.34 0.02 −0.04 0.20 0.30 0.30 0.31 −0.10 0.19 
Partial r 0.16 0.01 −0.03 0.03 −0.02 0.01 0.00 0.00 −0.02 
PRP −0.53 −0.38 −0.17 −0.86 −1.08 −0.96 −1.00 −1.00 −1.10 
SB2 0.33 0.03 −0.04 0.21 0.29 0.29 0.29 −0.09 0.16 
Partial r 0.15 0.02 −0.04 0.02 −0.01 0.01 −0.01 −0.01 −0.02 
PRP −0.54 −0.41 0.06 −0.89 −1.03 −0.98 −1.05 −0.86 −1.11 
SB3 0.34 0.03 −0.04 0.22 0.30 0.30 0.29 −0.09 0.16 
Partial r 0.16 0.01 −0.03 0.03 0.00 0.00 −0.02 −0.01 −0.02 
PRP −0.53 −0.48 −0.09 −0.88 −1.01 −1.01 −1.05 −0.85 −1.13 
SB4 0.33 0.02 −0.04 0.21 0.29 0.29 0.29 −0.10 0.18 
Partial r 0.17 0.02 −0.03 0.04 −0.01 0.00 −0.02 0.00 −0.03 
PRP −0.49 −0.05 −0.08 −0.83 −1.03 −1.01 −1.05 −1.01 −1.15 
SB5 0.41 0.00 −0.03 0.28 0.37 0.37 0.37 −0.10 0.23 
Partial r 0.19 0.00 −0.02 0.05 0.00 −0.03 −0.01 0.00 −0.04 
PRP −0.55 −2.00 −0.31 −0.83 −0.99 −1.07 −1.02 −0.96 −1.16 
SB6 0.54 −0.02 0.00 0.40 0.48 0.48 −0.01 −0.15 0.24 
Partial r 0.31 0.02 0.00 0.07 −0.04 −0.01 0.00 0.01 −0.05 
PRP −0.44 −2.06 −1.50 −0.82 −1.09 −1.02 −0.50 −1.06 −1.22 
SB7 0.34 0.02 −0.03 0.24 0.30 0.31 0.30 −0.14 0.17 
Partial r 0.14 0.03 −0.03 0.04 −0.02 0.02 −0.01 −0.01 −0.01 
PRP −0.59 0.47 −0.09 −0.84 −1.06 −0.94 −1.04 −0.93 −1.06 
Max temperature SB1 0.83 −0.07 −0.02 0.72 0.85 0.84 −0.01 −0.19 0.41 
Partial r 0.22 0.06 0.00 0.26 0.23 −0.15 0.00 −0.14 −0.18 
PRP −0.73 −1.81 −1.20 −0.64 −0.73 −1.18 −1.00 −0.27 −1.45 
SB2 0.83 −0.07 −0.02 0.72 0.84 0.83 0.00 −0.19 0.34 
Partial r 0.23 −0.02 −0.01 0.08 0.21 −0.08 0.00 −0.15 −0.12 
PRP −0.73 −0.79 −0.40 −0.89 −0.75 −1.09 −3.00 −0.20 −1.37 
SB3 0.82 −0.08 −0.02 0.67 0.83 0.82 0.00 −0.19 0.33 
Partial r 0.28 −0.03 0.00 0.05 0.21 −0.07 0.00 −0.16 −0.12 
PRP −0.66 −0.63 −1.06 −0.93 −0.74 −1.09 −2.00 −0.16 −1.36 
SB4 0.84 −0.08 −0.02 0.72 0.85 0.84 0.00 −0.20 0.39 
Partial r 0.24 0.03 −0.01 0.16 0.23 −0.12 0.00 −0.14 −0.18 
PRP −0.71 −1.37 −0.64 −0.78 −0.73 −1.14 −3.00 −0.33 −1.45 
SB5 0.83 −0.09 −0.02 0.69 0.84 0.83 0.00 −0.19 0.39 
Partial r 0.26 −0.01 −0.01 0.07 0.23 −0.10 0.00 −0.15 −0.15 
PRP −0.69 −0.89 −0.75 −0.91 −0.73 −1.11 −2.00 −0.22 −1.39 
SB6 0.83 −0.08 −0.02 0.71 0.85 0.84 0.00 −0.24 0.36 
Partial r 0.24 0.02 0.00 0.13 0.22 −0.10 0.01 −0.14 −0.16 
PRP −0.71 −1.25 −0.80 −0.82 −0.74 −1.12 −3.50 −0.44 −1.43 
SB7 0.77 −0.08 −0.04 0.68 0.79 0.79 0.00 −0.27 0.29 
Partial r 0.15 0.02 −0.03 0.09 0.16 −0.05 0.01 −0.10 −0.11 
PRP −0.80 −1.24 −0.14 −0.87 −0.80 −1.06 5.00 −0.65 −1.37 
Min temperature SB1 0.91 −0.08 −0.01 0.76 0.91 0.90 −0.01 −0.20 0.46 
Partial r 0.45 0.02 0.04 0.33 0.23 −0.16 −0.01 −0.16 −0.19 
PRP −0.50 −1.21 −8.60 −0.57 −0.75 −1.18 −0.44 −0.19 −1.41 
SB2 0.90 −0.08 −0.01 0.74 0.89 0.88 −0.01 −0.19 0.37 
Partial r 0.49 −0.06 0.03 0.14 0.20 −0.08 0.00 −0.16 −0.13 
PRP −0.46 −0.31 −4.38 −0.82 −0.78 −1.09 −0.71 −0.15 −1.35 
SB3 0.90 −0.08 −0.01 0.73 0.88 0.88 −0.01 −0.19 0.36 
Partial r 0.49 −0.07 0.03 0.11 0.20 −0.07 0.00 −0.17 −0.12 
PRP −0.46 −0.13 −6.00 −0.85 −0.77 −1.08 −0.57 −0.09 −1.33 
SB4 0.91 −0.09 −0.01 0.77 0.91 0.90 −0.01 −0.21 0.44 
Partial r 0.48 −0.01 0.03 0.23 0.22 −0.13 0.00 −0.15 −0.18 
PRP −0.47 −0.86 −3.50 −0.70 −0.76 −1.14 −0.86 −0.27 −1.41 
SB5 0.91 −0.10 −0.01 0.74 0.89 0.88 −0.01 −0.19 0.43 
Partial r 0.47 −0.06 0.03 0.13 0.22 −0.11 0.00 −0.17 −0.16 
PRP −0.48 −0.43 −3.50 −0.83 −0.75 −1.12 −0.71 −0.14 −1.36 
SB6 0.91 −0.10 −0.01 0.77 0.90 0.90 −0.01 −0.25 0.41 
Partial r 0.46 −0.02 0.02 0.21 0.21 −0.11 0.00 −0.15 −0.16 
PRP −0.49 −0.75 −3.09 −0.73 −0.76 −1.13 −1.00 −0.39 −1.39 
SB7 0.87 −0.10 −0.02 0.76 0.87 0.87 0.00 −0.30 0.34 
Partial r 0.34 −0.02 0.00 0.16 0.15 −0.06 0.01 −0.10 −0.10 
PRP −0.61 −0.85 −0.86 −0.79 −0.82 −1.07 −3.67 −0.65 −1.30 

Note: the bold values are found to be significant for the process.

For future scenario generation, the combined calibration/validation of SDSM was performed using observed daily minimum and maximum temperature, and precipitation datasets. For the model calibration and parameterization, SDSM was developed on monthly sub-models (Wilby et al. 2002). The model performance was evaluated using multiple evaluation methods such as coefficient of determination (R2), root mean square error (RMSE), mean bias and standard deviation.

Bias correction of the downscaled datasets

The GCM and RCM model datasets generally contain bias in their time series values (Taylor et al. 2012). Therefore, it may lead to significant inaccuracies in impact assessments. For the removal of bias, a bias correction methodology as proposed by Mahmood & Babel (2012) was used in this study. Equations (1) and (2) were used for bias correction of temperature and precipitation, respectively: 
formula
(1)
 
formula
(2)
where is the de-biased/corrected temperature time series and is the de-biased/corrected precipitation time series. SCEN stands for the downscaled scenarios (2006–2060) and CONT stands for downscaled data for the present period (1981–2005). represent the daily time series of precipitation and temperature, respectively, generated by SDSM for future periods. and represent the long-term monthly values for temperature and precipitation, respectively, for the control period simulated by SDSM. and stand for the long-term monthly observed values for temperature and precipitation, respectively. The bar over P and T represents the long-term average.

Analysis methods

MK test

A non-parametric significance test such as MK (Kendall 1975; Mann 1975) was used for the trend analysis of the meteorological variables. The MK test quantifies the magnitude of change in the time series datasets (Subash & Sikka 2014). The significance level to determine significant increase or decrease was defined (α) = 0.05. As per significance level (α), the Z-statistic values larger than +1.96 or lower than −1.96 designate the significantly increasing (positive) or decreasing (negative) trends, respectively. In this study, trend analysis was performed on monthly time series of precipitation and temperature.

SWAT model description, setup and adjustment of curve number

A SWAT model is a deterministic, semi-distributed, and continuous time step hydrological model, which is capable of simulating and forecasting various water balance components on a daily, monthly, and annual basis (Arnold et al. 1998). SWAT is fully capable of simulating and forecasting surface water parameters, snowmelt hydrology parameters, groundwater parameters, and water quality parameters at the sub-catchment scale (Arnold et al. 1998; Jain et al. 2010; Neitsch et al. 2011). SWAT uses curve number (CN) method for generating surface runoff (Neitsch et al. 2011). However, the CNs were modified by Mishra et al. (2014) to avoid extreme peaks and to reduce uncertainties that generally occur in high hilly catchments. A similar method was used in this study as per the fractional slopes at each hydrological response unit (HRU) level (Mishra et al. 2014). For the CN modification, a modified CN equation, initially adopted by Mishra et al. (2014) was used. CNs were modified externally through the management database file and were added to the main database. Then, SWAT-derived CNs as per the respective LULC class were adjusted using a modified CN equation (Equation (6)). The initial CN equation is given in Equation (3) (Neitsch et al. 2011): 
formula
(3)
where, represents total surface runoff (mm), represents daily rainfall (mm), represents the initial abstraction (mm), S is a retention parameter as defined in Equation (4): 
formula
(4)
In Equation (4), the CN stands for the curve number for the given day. is generally approximated as 0.2S and Equation (4) can be written as Equation (5): 
formula
(5)
As per Equation (3), runoff will occur if . The slopes are generated for each HRU and then a new modified CN was calculated for each HRU as per Equation (6) (Mishra et al. 2014): 
formula
(6)
where, represents the antecedent moisture condition (AMC) II CN. is the fractional slope for each HRU. Here, the value of varies from 0.14 to 1.4 (Mishra et al. 2014). Likewise, CN2 stands for AMC II CN.

For the SWAT setup, weather generator parameters daily precipitation, daily minimum and maximum temperature, daily humidity, daily wind speed, and daily net solar radiation were used. The physical and topographical parameters LULC, soil parameters, and DEM were processed to generate the drainage and physiographical characteristics of the catchment.

SUFI2 parameterization and sensitivity analysis

The SUFI2 algorithm is incorporated in the SWATCUP tool (Abbaspour et al. 2007). The parameter-based sensitivity analysis actually helps to recognize the significance of a particular parameter in calibration, and whether the process is influenced by the parameter values if it changes. A global sensitivity analysis approach, using two sensitivity evaluation functions, P value test and statistical t-test, was used to rank the highly sensitive and non-sensitive parameters. In SUFI2, an iterative process updates the old coefficient parameters into new coefficients during the calibration process to achieve the final estimates using the stratified Latin hypercube sampling method (Abbaspour 2011). The SUFI2 algorithm basically assumes a large parameter uncertainty range (or physically meaningful range that can occur due to the used data inputs such as LULC, precipitation, temperature, etc.) to make sure that the observed data lie in the 95% prediction uncertainty (95PPU) band for the first iteration and then reduces this uncertainty band in a step-wise manner while monitoring the p-factor and r-factor. The 95PPU (e.g., upper and lower uncertainty ranges) can be calculated between 2.5% and 97.5% levels of the cumulative distribution of an output variable obtained through Latin hypercube sampling. It disallows 5% of the very bad simulations (Abbaspour 2011). Theoretically, the value of p-factor ranges between 0 and 100% and r-factor ranges between 0 and infinity. The value of p-factor = 1 and r-factor = 0 corresponds to measured data equivalent to observed data.

The coefficient of determination (R2) and Nash–Sutcliffe equation (NSE) (Nash & Sutcliffe 1970) were used as the model optimization objective functions, Equations (7) and (8), respectively (Abbaspour 2011): 
formula
(7)
 
formula
(8)
where, Q represents the discharge, m and s stand for measured and simulated, respectively, and bar stands for average. i represents the ith measured or simulated data.

RESULTS AND DISCUSSION

Evaluation of RCM and GCMs with rain gauge station data

The predictor selection was carried out separately according to the methodology discussed. Tables 24 present a comparison between the predictors selected for GCMs and RCM. For example, for precipitation and SB1 in GCM, tas has the highest value of R (=0.62), hence, tas was chosen as the FSSP (Table 2). Similarly, the FSSP were chosen for other variables and SBs. The selection of other predictors was carried out based on the values of partial r and PRP. For example, for precipitation and SB1 in GCM, rhs, tas, and vas were selected as the 2nd, 3rd, and 4th predictors, respectively, based on the values of PRP and partial r (Table 2). A similar procedure, as discussed in the Methodology, was used for selection of predictors for different variables, SBs, and models (i.e., GCM and RCM). Tables 24 also highlight the differences in the suitable predictors for both model types. For example, in the case of GCM, tas, tasmin, vas, rhs, tasmax, and pr were the most widely selected predictors for precipitation, whereas for RCM, huss, psl, and rsds were the most widely selected (Su et al. 2016). Singh & Goyal (2016) highlighted the scope of similar GCM predictors over the eastern Himalayan catchment and Mahmood & Babel (2012) also revealed the same over the western Himalayan-based Jhelum River catchment. Similarly, GCMs and RCM also showed little dissimilarity in suitable variables for maximum and minimum temperatures. For maximum temperature, tas was the FSSP for most of the stations in the case of RCM and for all stations in the case of GCM. For minimum temperature, again, tas was the first suitable predictor for all stations except SB5 in the case of GCM. On the other hand, like precipitation, for minimum temperature also, huss was the most used FSSP for RCM. Shivam et al. (2017) worked on the Subansiri River basin and also applied the same approach to find out the best suitable super predictors.

Table 4

Selection of final best suitable predictor details

Predictand SBs GCM predictors
 
RegCM4 RCM predictors
 
FSSP Second SP Third SP Fourth SP FSSP Second SP Third SP Fourth SP 
Precipitation SB1 tas rhs tas vas huss psl rsds NA 
SB2 tasmin rhs tas rlds huss psl NA NA 
SB3 tas rhs tasmax vas huss psl NA NA 
SB4 vas rhs tas vas huss psl rsds NA 
SB5 tas rhs sfcwind tasmax huss rsds vas NA 
SB6 prc rhs tas prc huss rsds tas vas 
SB7 pr rhs tas rlds huss rsds psl NA 
Max temperature SB1 tas rhs vas pr tasmax huss rsds uas 
SB2 tas rhs pr rlds tas huss uas NA 
SB3 tas rhs sfcwind pr tas huss pr uas 
SB4 tas rhs pr rlds tas huss psl uas 
SB5 tas rhs sfcwind rlds tas huss rsds uas 
SB6 tas rhs rlds pr tas huss rsds uas 
SB7 tas rhs sfcwind rlds tas huss pasl uas 
Min temperature SB1 tas sfcwind rhs vas tas tasmin huss NA 
SB2 tas sfcwind rhs vas huss pr uas NA 
SB3 tas sfcwind rhs vas huss pr uas NA 
SB4 tas sfcwind rhsmax hfls huss rsds tas uas 
SB5 tasmax sfcwind rhs vas huss pr tas uas 
SB6 tas sfcwind rhs vas huss pr tas uas 
SB7 tas sfcwind rhs vas tas huss uas rsds 
Predictand SBs GCM predictors
 
RegCM4 RCM predictors
 
FSSP Second SP Third SP Fourth SP FSSP Second SP Third SP Fourth SP 
Precipitation SB1 tas rhs tas vas huss psl rsds NA 
SB2 tasmin rhs tas rlds huss psl NA NA 
SB3 tas rhs tasmax vas huss psl NA NA 
SB4 vas rhs tas vas huss psl rsds NA 
SB5 tas rhs sfcwind tasmax huss rsds vas NA 
SB6 prc rhs tas prc huss rsds tas vas 
SB7 pr rhs tas rlds huss rsds psl NA 
Max temperature SB1 tas rhs vas pr tasmax huss rsds uas 
SB2 tas rhs pr rlds tas huss uas NA 
SB3 tas rhs sfcwind pr tas huss pr uas 
SB4 tas rhs pr rlds tas huss psl uas 
SB5 tas rhs sfcwind rlds tas huss rsds uas 
SB6 tas rhs rlds pr tas huss rsds uas 
SB7 tas rhs sfcwind rlds tas huss pasl uas 
Min temperature SB1 tas sfcwind rhs vas tas tasmin huss NA 
SB2 tas sfcwind rhs vas huss pr uas NA 
SB3 tas sfcwind rhs vas huss pr uas NA 
SB4 tas sfcwind rhsmax hfls huss rsds tas uas 
SB5 tasmax sfcwind rhs vas huss pr tas uas 
SB6 tas sfcwind rhs vas huss pr tas uas 
SB7 tas sfcwind rhs vas tas huss uas rsds 

Developing the downscaled results from GCMs and RCMs

The performance of GCMs and RCM for calibration and validation periods (within historical time series duration) was evaluated and compared using three statistics, R, R2, and RMSE, for all three downscaled variables, as shown in Table 5. For precipitation, RCM showed higher R and R2 values compared to GCM for all the stations, which indicated a better correlation between downscaled and observed precipitation records. However, RMSE for RCM was higher compared to GCM, which indicated the presence of larger error in the case of RCM (Table 5). Based on RMSE, it followed that GCM downscaled precipitation was more accurate compared to RCM downscaled precipitation. For maximum temperature, RCM showed higher R and R2 compared to GCM. Considering RMSE, it can be seen that RCM performed better at four out of seven stations, which showed that the RCM was more accurate than GCM. Similar to the previous two variables, RCM was able to follow the trend for minimum temperature and showed better R and R2. RCM showed less RMSE than GCM for all stations except SB7, which showed that RCM was better than GCMs for minimum temperature also.

Table 5

Comparison of GCM and RCM after calibration/validation (bias corrected)

Variables Statistical functions SB1
 
SB2
 
SB3
 
SB4
 
SB5
 
SB6
 
SB7
 
GCM RCM GCM RCM GCM RCM GCM RCM GCM RCM GCM RCM GCM RCM 
Precipitation 0.84 0.89 0.84 0.88 0.85 0.88 0.83 0.89 0.89 0.91 0.86 0.89 0.84 0.89 
R2 0.71 0.78 0.70 0.78 0.72 0.78 0.71 0.78 0.79 0.83 0.73 0.79 0.72 0.78 
RMSE (mm) 32.36 87.00 45.92 86.00 15.54 86.59 16.46 83.93 40.58 83.91 21.21 92.51 28.76 86.59 
Max temperature 0.92 0.96 0.91 0.97 0.96 0.97 0.90 0.98 0.91 0.98 0.92 0.97 0.84 0.92 
R2 0.82 0.91 0.84 0.95 0.92 0.95 0.82 0.95 0.84 0.95 0.84 0.95 0.72 0.94 
RMSE (°C) 0.85 1.57 1.13 1.10 0.59 1.12 1.29 1.06 1.13 1.08 1.14 1.08 0.81 1.84 
Min Temperature 0.93 0.98 0.97 0.99 0.97 0.99 0.97 0.99 0.94 0.99 0.94 0.99 0.92 0.95 
R2 0.93 0.96 0.93 0.97 0.94 0.97 0.93 0.97 0.93 0.97 0.93 0.97 0.84 0.90 
RMSE (°C) 1.48 1.45 1.27 1.21 1.24 1.21 1.27 1.20 1.28 1.14 1.30 1.20 1.23 2.18 
Variables Statistical functions SB1
 
SB2
 
SB3
 
SB4
 
SB5
 
SB6
 
SB7
 
GCM RCM GCM RCM GCM RCM GCM RCM GCM RCM GCM RCM GCM RCM 
Precipitation 0.84 0.89 0.84 0.88 0.85 0.88 0.83 0.89 0.89 0.91 0.86 0.89 0.84 0.89 
R2 0.71 0.78 0.70 0.78 0.72 0.78 0.71 0.78 0.79 0.83 0.73 0.79 0.72 0.78 
RMSE (mm) 32.36 87.00 45.92 86.00 15.54 86.59 16.46 83.93 40.58 83.91 21.21 92.51 28.76 86.59 
Max temperature 0.92 0.96 0.91 0.97 0.96 0.97 0.90 0.98 0.91 0.98 0.92 0.97 0.84 0.92 
R2 0.82 0.91 0.84 0.95 0.92 0.95 0.82 0.95 0.84 0.95 0.84 0.95 0.72 0.94 
RMSE (°C) 0.85 1.57 1.13 1.10 0.59 1.12 1.29 1.06 1.13 1.08 1.14 1.08 0.81 1.84 
Min Temperature 0.93 0.98 0.97 0.99 0.97 0.99 0.97 0.99 0.94 0.99 0.94 0.99 0.92 0.95 
R2 0.93 0.96 0.93 0.97 0.94 0.97 0.93 0.97 0.93 0.97 0.93 0.97 0.84 0.90 
RMSE (°C) 1.48 1.45 1.27 1.21 1.24 1.21 1.27 1.20 1.28 1.14 1.30 1.20 1.23 2.18 

Validation of downscaled results with observation

Figure 2(a) shows the annual average maximum and minimum temperature trends for the observed period (1981–2005). A significant increase in observed minimum temperature can be seen in Figure 2(a). Figure 2(b) and 2(c) show the projected scenarios (2006–2060) for maximum and minimum temperatures, respectively, for three GCMs and RCM. An increasing trend in both minimum and maximum temperatures can be observed for all models. The RCP-based projected scenarios of minimum and maximum temperatures show a consistent increase in temperature (2006–2060). Comparing both the models, the RegCM4 projection showed higher increase than GCMs. Shivam et al. (2017) and Su et al. (2016) also used the CMIP5 GCMs and observed a consistent increase in temperature over the Himalaya. To find and quantify the trends present in the sub-catchment (SB)-wise projected scenarios of precipitation, maximum and minimum temperatures, the MK test was performed. Figure 3 shows the MK-based significant and non-significant trend analyses and their comparisons for projected maximum and minimum temperatures, and precipitation utilizing RegCM4 (RCP 4.5), CM3 (RCP 4.5), ESM-2M (RCP 4.5), and CM2P1 (RCP 4.5) models. The magnitude of (e.g., increasing or decreasing) the trends for significant trends was assessed by MK test statistic Z (as explained in the Methodology section). For maximum temperature (Figure 3(a)), an increasing trend was found for all the sub-catchments and for most of the months with a few exceptions like January, June, and July where a decreasing trend was also observed. Similar trends were observed by Shivam et al. (2017) over the eastern Himalayan catchment. In January, a decreasing trend was found for all sub-catchments except the SB7 where a significant increasing trend was found. A significant decreasing trend was found for the months of June and July using RegCM4 RCM, whereas different GCMs showed mixed trends (increasing as well as decreasing). For the rest of the months, a clear significant increasing trend was found. However, their magnitude of change varied for different SBs and months. In the case of maximum temperature, SB1 and SB7 showed maximum changes compared to other SBs, which illustrated that these two portions of the catchment would be highly affected by climate change. Similar to maximum temperature, in the case of minimum temperature (Figure 3(b)), an increasing trend was obtained for all months and for most of the SBs. For post-monsoon months (July–November), the CM3 model showed a very significant increase in the minimum temperature. The Z-statistics for other models, for monsoon months, were less compared to the CM3 model. For SB1 (most elevated station), in the month of July, a very significant decreasing trend was observed using CM2P1 and RegCM4 models, with Z-statistics less than −5.00, whereas for the same case the other two models showed a significant increasing trend. Similar to maximum temperature, the minimum temperature also showed maximum change for SB1 and SB7 compared to other SBs. The overall trends showed significant warming signatures over the hilly terrains of the Teesta River catchment as projected by GCM and RCM datasets. Figure 3(c) shows the MK trend analysis for precipitation scenarios (2006–2060). The Indian subcontinent gets precipitation mainly in monsoon, which generally happens during the months of June to September (Singh & Goyal 2016). All four models showed an increasing trend for precipitation in pre-monsoon months (March–May), whereas a decreasing trend was observed for monsoon months (June, July, and August). Similar findings were also observed by Shivam et al. (2017) over the Brahmaputra region. Projections from all models were found to be highly variable from SB1 to SB7. The uppermost sub-catchment (SB7) corresponded with very high elevation zones and was thus always covered with snow and glaciers, while SB1 belongs to moderate elevation ranges and does not receive snow during summer. Therefore, the climatology of the SB7 significantly differs from that of other catchments. Several previous studies also highlighted similar observations over the high hilly terrains of Himalayas (Collins et al. 2013).

Figure 2

Observed and projected scenarios of average annual minimum and maximum temperature at the Teesta catchment: (a) variation in minimum and maximum temperature for the years 1980 to 2005 and variations in (b) maximum and (c) minimum temperature during the years 2006 to 2060 as per GCMs and RCM.

Figure 2

Observed and projected scenarios of average annual minimum and maximum temperature at the Teesta catchment: (a) variation in minimum and maximum temperature for the years 1980 to 2005 and variations in (b) maximum and (c) minimum temperature during the years 2006 to 2060 as per GCMs and RCM.

Figure 3

MK-based significant and non-significant trend analyses and their comparison as per the downscaled (a) maximum temperature, (b) minimum temperature, and (c) precipitation for the year (2006–2060) utilizing RegCM4, CM3, ESM-2M, and CM2P1 models. (continued.)

Figure 3

MK-based significant and non-significant trend analyses and their comparison as per the downscaled (a) maximum temperature, (b) minimum temperature, and (c) precipitation for the year (2006–2060) utilizing RegCM4, CM3, ESM-2M, and CM2P1 models. (continued.)

Simulation of past hydrological parameters from SWAT using downscaled inputs

Validation of SWAT results with observation from CWC data

As discussed in the methodology, SWATCUP was used for calibration and sensitivity analysis of SWAT model parameters. The sensitivity of the streamflow and snowmelt-related parameters was computed using 15 parameters, curve number (CN2), baseflow (ALPHA_BF), groundwater delayed flow (GW_DELAY), minimum flow for groundwater (GWQMN), soil evaporation compensation factor (ESCO), snowfall temperature (SFTMP), soil water content (SOL_AWC), soil hydraulic conductivity (SOL_K), Manning roughness for main channel (V__CH_N2), effective hydraulic conductivity in main channel (V__CH_K2), bank erosion (V__ALPHA_BNK), snow cover maximum (SNOCOVMX), snow cover minimum (SNOCOVMN), snow cover at 50% snow coverage (SNO50COV), and precipitation lapse rate (PLR) in this case, to overcome the model uncertainty issues in the model calibration. The parameterization and uncertainty analysis was performed using R2 and NSE objective functions. The best optimized coefficients and parameter values were opted for after running a number of iterations. The most sensitive parameters were found to be curve number (CN2), groundwater delay (GW_DELAY), maximum snow water content (SNOCOVMX), and fraction of snow volume (SNO50COV) (Singh et al. 2015). These parameters showed a significant response in optimizing the best coefficient values to project the future scenarios of streamflow, snowfall, and snowmelt. A total of three iterations were performed and in each iteration, 500 simulations were carried out to get the optimal coefficient and parameter values.

The r-factor and p-factor are computed as 0.48 and 0.30 for calibration, and 0.54 and 0.45 for validation, respectively, at Lachung station, whereas the values of p-factor and r-factor were recorded as 0.58 and 0.14 for calibration, and 0.55 and 0.45 for calibration and validation, respectively, at Chungthang station. The uncertainty analysis outcomes show that the simulated data were mostly falling within the uncertainty band (upper 95PPU). As per Zhang et al. (2014) and Singh & Goyal (2017), the above results are acceptable as compared to their studies. The r-factor shows reasonable uncertainty level in simulated discharge. The coefficient of determination (R2) was 0.52 and 0.60 for calibration and validation, respectively, at Lachung station, whereas the values of R2 were 0.62 and 0.61 for calibration and validation, respectively, at Chungthang station. NSE was found to be 0.38 and 0.53 for calibration and validation, respectively, at Lachung station, whereas NSE was 0.46 and 0.77 for calibration and validation, respectively, at Chungthang station. The model performance was found to be better at Chungthang, as indicated by the values of R2 and NSE. The observed discharge and modeled discharge were recorded as 800 m3/sec during monsoon time and 300–400 m3/sec during the summer season.

Construction of future hydrological parameters using SWAT

The projected scenarios of streamflow and precipitation at basin level were computed and compared as per the GCM and RCM scenarios (Figure 4(a) and 4(b)). A calibrated SWAT model was used to simulate the projected water balance components based on the downscaled precipitation and temperature (i.e., GCM and RCM) as input to the model. Here, it is assumed that up to 2060 there will be no significant changes in LULC and soil except for climatic factors, as the upper portion of the Teesta catchment has a very small population and is mostly covered with snow and glaciers. Figure 4(a) and 4(b) show the basin-level variations in precipitation and water yield scenarios, respectively, from the year 2008 to 2060 (the first two years 2006–2007 were taken as the warm-up period for the SWAT model). Figure 4(a) shows a significant increase in precipitation from the year 2008 to 2060. ESM2M predicted less precipitation compared to other models. Overall, an increasing trend was found for all models. Figure 4(b) shows a basin-level comparison of projected water yield scale, simulated using the SWAT model, under multiple GCMs and RegCM4 projected scenarios from the year 2008 to 2060. All scenarios showed a consistent significant increase in water yield from the year 2008 to 2060. Among all the GCMs and RCM, RegCM4-based projected scenarios showed a maximum increase in water yield.

Figure 4

Basin level comparison of (a) precipitation scenarios and (b) water yield from SWAT for the years 2008 to 2060 for different climate models. The first two years (2006–2007) were used as the warm-up period for the SWAT model.

Figure 4

Basin level comparison of (a) precipitation scenarios and (b) water yield from SWAT for the years 2008 to 2060 for different climate models. The first two years (2006–2007) were used as the warm-up period for the SWAT model.

Further, the projected scenarios of streamflow at both the gauges (i.e., Lachung and Chungthang) were plotted from the year 2008 to 2060 to highlight the increasing trend in streamflow (Figure 5(a) and 5(b)). All the models showed a significant increase in streamflow at both the gauge locations. The average annual streamflow varied from 20 m3/s to 45 m3/s and from 100 m3/s to 200 m3/s for Lachung and Chungthang, respectively. The CM3 and CM2P1 models showed a higher increase compared to ESM-2M and RegCM4. However, a sudden increasing trend in streamflow was observed for all the models after 2040. The RegCM4 and ESM-2M models showed an almost similar increase in streamflow amount in the future time domain.

Figure 5

Comparison of SWAT model predicted streamflow at (a) Lachung gauge and (b) Chungthang gauge for the years 2008 to 2060 as per the RegCM4, CM3, ESM-2M, and CM2P1 climate models.

Figure 5

Comparison of SWAT model predicted streamflow at (a) Lachung gauge and (b) Chungthang gauge for the years 2008 to 2060 as per the RegCM4, CM3, ESM-2M, and CM2P1 climate models.

CONCLUSION

This study demonstrated a comprehensive research work related to climate change and water balance components over a snow glacier-induced Himalayan catchment. In this study, the three latest CMIP5 GCMs and one RCM were used to highlight and compare the projected scenarios through statistical evaluation methods. This study showed the comparative responses of the high resolution RCM and moderate resolution GCM datasets in the assessment of the main water balance components. The RegGCM4 model performed well for temperature downscaling; whereas, in the case of precipitation, there was not much difference between GCMs and RCM. The overall performance of the RegCM4 model was found to be comparatively superior to GCMs as it showed higher accuracy for the statistical evaluation. The SUFI2-based model parameterization and uncertainty analysis provided great scope for improving modeling performances, especially in the case of future forecasting. The projected scenarios showed a significant increase in precipitation, water yield, and streamflow at each sub-catchment and both the gauge locations. The RegCM4-based projections of the minimum and maximum temperature clearly highlighted the extremity of temperature increments over the snow glacier-induced Himalayan catchment.

ACKNOWLEDGEMENTS

This present research work has been carried out under the Department of Science and Technology (DST), Government of India, Research Project No. SB/DGH-66/2013 and is awarded to Dr Manish Kumar Goyal. The financial support from DST is gratefully acknowledged. We also thank Geophysical Fluid Dynamics Laboratory (GFDL), New Jersey, USA, Earth Explorer NASA, Central Water Commission (CWC), New Delhi, India, the Indian Meteorological Department (IMD) Pune, India, and Indian Institute of Tropical Meteorology (IITM), Pune, India for providing the required datasets for the completion of this research work.

REFERENCES

REFERENCES
Abbaspour
K. C.
2011
SWAT-CUP4: SWAT Calibration and Uncertainty Programs – A User Manual
.
Swiss Federal Institute of Aquatic Science and Technology
,
Eawag, Daubendorf
,
Switzerland
.
Abbaspour
K. C.
,
Yang
J.
,
Maximov
I.
,
Siber
R.
,
Bogner
K.
,
Mieleitner
J.
,
Zobrist
J.
&
Srinivasan
R.
2007
Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT
.
Journal of Hydrology
333
,
413
430
.
Akhter
J.
,
Das
L.
&
Deb
A.
2016
CMIP5 ensemble-based spatial rainfall projection over homogeneous zones of India
.
Climate Dynamics
1
32
http://dx.doi.org/10.1016/j.atmosres.2016.07.013
.
Arnold
J.
,
Srinivasan
R.
,
Muttiah
R. S.
&
Williams
J. R.
1998
Large area hydrologic modeling and assessment – part I: model development
.
Journal of American Water Resource Association
34
(
1
),
73
89
.
Collins
D. N.
,
Davenport
J. L.
&
Stoffel
M.
2013
Climatic variation and runoff from partially-glacierized Himalayan tributary basins of the Ganges
.
Science of the Total Environment
468
,
S48
S59
.
Das
L.
,
Annan
J. D.
,
Hargreaves
J. C.
&
Emori
S.
2012
Improvements over three generations of climate model simulations for eastern India
.
Climate Research
51
(
3
),
201
216
.
Das
L.
,
Meher
J. K.
&
Dutta
M.
2016
Construction of rainfall change scenarios over the Chilka Lagoon in India
.
Atmospheric Research
182
,
36
45
.
Field
C. B.
,
Barros
V. R.
,
Mach
K.
&
Mastrandrea
M.
2014
Climate Change 2014: Impacts, Adaptation, and Vulnerability
.
Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
,
Cambridge
,
UK
and
New York
,
USA
.
Gao
X.
,
Shi
Y.
,
Zhang
D.
,
Wu
J.
,
Giorgi
F.
,
Ji
Z.
&
Wang
Y.
2012
Uncertainties in monsoon precipitation projections over China: results from two high-resolution RCM simulations
.
Climate Research
52
(
2
),
213
226
.
Gardner
A. S.
,
Sharp
M. J.
,
Koerner
R. M.
,
Labine
C.
,
Boon
S.
,
Marshall
S. J.
&
Lewis
D.
2009
Near-surface temperature lapse rates over Arctic glaciers and their implications for temperature downscaling
.
Journal of Climate
22
(
16
),
4281
4298
.
IPCC
2013
The Physical Science Basis
.
Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
.
Cambridge
,
UK
and
New York
,
USA
.
Jain
S. K.
,
Tyagi
J.
&
Singh
V.
2010
Simulation of runoff and sediment yield for a Himalayan watershed using SWAT model
.
Journal of Water Resource and Protection
2
(
3
),
267
281
.
Kendall
M. G.
1975
Rank Correlation Methods
.
Griffin
,
London
,
UK
,
202
pp.
Kharin
V. V.
,
Zwiers
F. W.
,
Zhang
X.
&
Wehner
M.
2013
Changes in temperature and precipitation extremes in the CMIP5 ensemble
.
Climatic Change
119
(
2
),
345
357
.
Lee
J. W.
,
Hong
S. Y.
,
Chang
E. C.
,
Suh
M. S.
&
Kang
H. S.
2014
Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP
.
Climate Dynamics
42
(
3–4
),
733
747
.
Mann
H. B.
1975
Nonparametric tests against trend
.
Econometrica
13
,
245
259
.
Mishra
S. K.
,
Chaudhary
A.
,
Shrestha
R. K.
,
Pandey
A.
&
Lal
M.
2014
Experimental verification of the effect of slope and land use on SCS runoff curve number
.
Water Resources Management
28
(
11
),
3407
3416
.
Neitsch
S. L.
,
Arnold
J. G.
,
Kiniri
J. R.
&
Williams
J. R.
2011
Soil and Water Assessment Tool: Theoretical Documentation Version 9
.
Texas Water Resource Institute of Technical report No. 406
,
Texas A&M University
,
College Station, TX
,
USA
.
Oh
S. G.
,
Park
J. H.
,
Lee
S. H.
&
Suh
M. S.
2014
Assessment of the RegCM4 over East Asia and future precipitation change adapted to the RCP scenarios
.
Journal of Geophysical Research: Atmospheres
119
(
6
),
2913
2927
.
Park
J. H.
,
Oh
S. G.
&
Suh
M. S.
2013
Impacts of boundary conditions on the precipitation simulation of RegCM4 in the CORDEX East Asia domain
.
Journal of Geophysical Research: Atmospheres
118
(
4
),
1652
1667
.
Shivam
,
,
Goyal
M. K.
&
Sarma
A. K.
2017
Analysis of the change in temperature trends in subansiri river basin for RCP scenarios using CMIP5 datasets
.
Theoretical and Applied Climatology
129
,
1175
1187
.
Singh
V.
,
Goyal
M. K.
&
Chu
X.
2015
Multicriteria evaluation approach for assessing parametric uncertainty during extreme peak and low flow conditions over snow glaciated and inland catchments
.
Journal of Hydrologic Engineering
21
(
1
),
04015044-1
.
Su
B.
,
Huang
J.
,
Gemmer
M.
,
Jian
D.
,
Tao
H.
,
Jiang
T.
&
Zhao
C.
2016
Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin
.
Atmospheric Research
,
178
,
138
149
.
Subash
N.
&
Sikka
A. K.
2014
Trend analysis of rainfall and temperature and its relationship over India
.
Theoretical and Applied Climatology
117
(
3–4
),
449
462
.
Taylor
K. E.
,
Stouffer
R. J.
&
Meehl
G. A.
2012
An overview of CMIP5 and the experiment design
.
Bulletin of the American Meteorological Society
93
,
485
498
.
Wilby
R. L.
,
Dawson
C. W.
&
Barrow
E. M.
2002
SDSM – a decision support tool for the assessment of regional climate change impacts
.
Environmental Modelling & Software
17
,
147
159
.
Xue-Jie
G.
,
Mei-Li
W.
&
Giorgi
F.
2013
Climate change over China in the 21st century as simulated by BCC_CSM1. 1-RegCM4. 0
.
Atmospheric and Oceanic Science Letters
6
(
5
),
381
386
.