The precise identification of basin characteristics and climate factors that plays a significant role in determining water and sediment yield is of paramount importance. However, due to the enormous complexity associated with the hydrologic cycle, it is usually challenging to extricate the influence of individual parameters on the combined water and sediment yield responses. To accomplish this, a combined hydrological modelling and statistical approach was adopted in this study. The Soil and Water Assessment Tool (SWAT) model was adopted to simulate different components of the watershed and the results were utilized in Boosted Regression Trees (BRTs) to analyze the contribution of different parameters to water and sediment yield at spatio-temporal and seasonal scales in the upstream Teesta River basin. The outcomes of the analysis showed that precipitation and baseflow play a crucial role in regulating the water yield at all spatio-temporal scales. On the other hand, precipitation alone has a key role in determining the sediment yield, especially at the daily (49.30%) and monthly (21.14%) temporal scales. The relative contribution of the remaining parameters at a yearly temporal scale, and small and intermediate spatial scales showed relatively close results with an exception at the large basin scale (precipitation alone by 35.88%). The average contribution of actual evapotranspiration was found to be less on both water and sediment yield prediction in all spatio-temporal scales considered. The analysis also revealed that the precipitation, baseflow, and minimum temperature play a key role in regulating the water and sediment yield in both monsoon and non-monsoon seasons, whereas the contribution of maximum temperature and snowmelt was found less during monsoon and non-monsoon seasons. The outcomes of this study may assist policymakers and water managers in rational water management goals as well as in coping with soil degradation issues.

  • A hydro-statistical approach to access the significance of basin and hydro-climatic factors on the water and sediment yield.

  • Precipitation and baseflow are the key contributors to the water yield.

  • Precipitation regulates sediment yield mechanisms at all scales.

  • Negligible role of evapotranspiration in determining water and sediment yield at all scales.

  • Weather and vegetation indices vary inversely to water and sediment yield at all scales.

Graphical Abstract

Graphical Abstract
Graphical Abstract

The availability of freshwater is essential for the survival of humans and for maintenance of ecosystems. The mountainous regions which cover about 24% of landmass (Kapos et al. 2000) have enormous amounts of freshwater resources as they receive more precipitation than low lying areas, suffer less evapotranspiration, and hold huge supplies of water in the form of snow and ice (Somers & McKenzie 2020). Because of this, they are also known as ‘water tower’. The different components of surface and sub-surface runoff resulting from precipitation, snowmelt, and groundwater provide significant water resources to neighbouring areas which habitually includes arid and semi-arid areas. The mountainous areas play a significant role in providing water reserves in low lying areas (Viviroli et al. 2007) by regulating the snowmelt process for seasonal flow by storing and releasing water during dry and wet seasons (Painter et al. 2009; Armstrong et al. 2019). A unit millimetre of snowpack may produce 1–1.5 mm of water. It is reported that the dependency on mountainous water resources is increasing (Viviroli et al. 2020), the population of which is projected to increase to 1.4 billion in the 2050s. The distribution of population density is closely associated to the existence of a large water source which habitually originates from mountains (Meybeck et al. 2001) and the management of runoff is one way to lessen stress on existing freshwater resources (Vashisht & Ranjan 2020). The total flow contribution from mountains at global scale is estimated to be about 32% (Meybeck et al. 2001) and 95% at a regional scale (Liniger et al. 1998).

Globally, the estimated 1.386 billion cubic kilometres of total water on Earth covers about 71% of Earth's surface, and consists of 97.5% of ocean salt water, and only 2.5% available as freshwater (Gleick & Palaniappan 2010). Putting an emphasis on freshwater, about 69% is in the form of solid such as ice, snow cover, and glaciers. The remaining 31% of water comprises the freshwater lakes and groundwater components (Cavazza & Paglaria 2009). The surface freshwater is easily accessible; however, the sub-surface water, namely groundwater, requires external energy to extract especially in plain areas. In hilly areas, the components of sub-surface water can flow out as in the form of baseflow, orifice springs, and seepage springs. It has been well recognized that the surface and sub-surface water components of the watersheds are driven by climate conditions and various aspects of the basins (Sun et al. 2019). The water yield (WY), which implies the total volume of water that comes out of the hydrological response unit (HRU) and joining at some points from networks of streams in the stipulated time period (Arnold et al. 2011), is of great significance because it provides water reserve support to the ecological unit and for individual life. The water shortages and their increasing demand scenarios associated with increasing populations in hilly regions have brought negative aspects to long-term sustainable water resources management (Ranjan & Kumar Pandey 2020).

An attempt at WY services as well as impacts of multi-dimensional aspects over spatio-temporal scales employing different methodologies have been explored in the past. Sun et al. (2019) analyzed the basin and climate factor contributions to the WY using the coupled hydrological model SWAT and statistical tool Boosted Regression Trees (BRTs) at spatio-temporal scales; the WY and its dominant factors including land cover, precipitation, the Normalized Difference Vegetation Index (NDVI) by the Seasonal Water Yield Model (Lu et al. 2020) at spatio-temporal scales; long-term groundwater recharge studies using the partially-distributed water balance model WetSpass and investigated the influencing factors by the correlation technique (Zomlot et al. 2015). Other relationships between the WY and parameters like potential evapotranspiration (PET) (Wang et al. 2011), glacier, and snow melt (Anand et al. 2018; Li et al. 2021) have also been reported.

The sediment yield (SY), on the other hand, is the total quantity of sediment from a unit area detached from the basin by the action of flowing water all through a definite time period, signifying anthropogenic activities happening within the basin. The estimation of the SY is of utmost importance as it provides substrates for aquatic plants and animals, assessment of the reservoir sedimentation process that adversely affects the operational life of the hydro-electric dam, and recreational purposes. Soil erosion poses a serious threat to land degradation and agricultural land productivity (De Luis et al. 2010). The Himalayan range suffers erosion due to its undulating topographical features, slope, and improper management of watersheds (Chinnasamy & Sood 2020). Therefore, the prioritization of the basin as per erosion and SY is essential for executing soil and water conservation goals of the watersheds (Singh et al. 2019). Many studies have reported that precipitation (De Luis et al. 2010), snowmelt (Lana-Renault et al. 2011), precipitation and temperature (Hirschberg et al. 2021), land use land cover (LULC) change, and slope (Sok et al. 2020) play a vital role in determining the soil erosion and SY process.

Consequently, it is obvious from the vast review of the literature that the WY and SY vary with different spatio-temporal scales. Thus, a reasonable assessment of the WY is necessary to grasp the complex inter-relationships between different basin and climate parameters and for developing better management plans. In this research, a coupled hydrological (SWAT model) and statistical approach (BRTs) is adopted to study the influence of different parameters considered in WY and SY prediction. The SWAT model has been adopted to simulate WY (Shawul et al. 2013; Adeogun et al. 2014; Abeysingha et al. 2015; Jain et al. 2017) and SY (Duan et al. 2009; Chandra et al. 2014; Liu & Jiang 2019; Kuti & Ewemoje 2021) in different mountainous regions over the world. The BRTs model is adopted in multi-disciplinary field of studies like ecological modelling (De'ath 2007; Elith et al. 2008; Franklin 2010), and in various hydrological applications like quantifying the influence of basin and climate characteristics on the WY (Sun et al. 2019); mapping of groundwater potential zones (Naghibi et al. 2016, 2018), anthropogenic change impacts on aquatic life (Hale et al. 2014); defining non-linear relationships between nutrients concentration and basin variables (Golden et al. 2016), because of its superiority in capturing dynamic correlation between complex dependent and independent variables. BRTs use the combined statistical regression trees and the machine learning techniques to rank the variables and thereby enhance the predictions (Elith et al. 2008). Despite the superiority of BRTs in hydrological applications, they are still not often used.

Although various models and techniques are available to explore the WY and SY studies, limited studies have been conducted upon the contributions of various factors at different spatial and temporal scales especially in the context of the Indian Himalayan regions as they are one of the largest suppliers of freshwater (Bandyopadhyay & Gyawali 1994). In a previous study by Sun et al. (2019), only basin characteristics and climate parameters were considered to explore the WY. In this study, a comprehensive investigation was considered to understand the influence of different aspects, inclusive of hydrological components including snowmelt, groundwater flow, actual evapotranspiration, and the vegetation indices influence on the WY and SY. It could serve to enhance the knowledge of underlying processes governing watersheds, especially soil and water managements goals, prioritizing the areas which need special soil and water management aspects.

Study area

The Teesta River originates from Tso Lhamo Lake, which is fed by Pauhunri glacier, Zemu Glacier, and Gurudongmar lake with an elevation of about 5,280 m amsl in the north district of Sikkim. It flows towards the south through valleys in the Sikkim Himalaya. It is fed by various rivulets on the way arising in Thangu, Yumthang, and Kangchenjunga Mountain ranges. The Teesta River is deemed one of the precious natural reserves of Sikkim owing to its richness in biodiversity, providing livelihoods for the residents along its entire length and potential in hydro-electric power generation. However, the proposed study area is limited to the upstream Teesta River basin lying between 27.38 °N to 28.12 °N latitude and 88.13 °E to 88.88 °E longitude with an elevation ranging from 730 to 8,375 m, having a total area of about 3,862.26 km2 (see Figure 1).
Figure 1

(a) Study area showing drainage networks, elevation range, and sub-basins. (b) Slope map of the study area.

Figure 1

(a) Study area showing drainage networks, elevation range, and sub-basins. (b) Slope map of the study area.

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Figure 2

Monthly variations of precipitation, maximum, and minimum temperature in study area. Note that precipitation consists of nine stations and temperature with single station near the basin outlet.

Figure 2

Monthly variations of precipitation, maximum, and minimum temperature in study area. Note that precipitation consists of nine stations and temperature with single station near the basin outlet.

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Hydro-meteorological data for the SWAT model

The SWAT model requires comprehensive data about weather, LULC, soil properties, topographical aspects, and various land management practices adhered to the basin. All these records were collected from different sources and are presented in Table 1 and Figure 2.

Table 1

Descriptions of data used in SWAT modelling

SlDate typeData periodDescription
1. Meteorological data  IMD-gridded dataset 
 (i) Precipitation 2000–2020 
 (ii) Maximum and minimum temperature 2000–2020 
2. Hydrological data  Central Water Commission, Kolkata 
 (i) Streamflow 2009–2013 
 (ii) Sediment yield 2009–2013 
3. Digital elevation model  Shuttle Radar Topography Mission (1 arc-second) 
4. LULC 2010 Landsat 5 
5. Soil map 2000 National Bureau of Soil Survey and Land Use Planning, Kolkata (Scale 1:250,000) 
SlDate typeData periodDescription
1. Meteorological data  IMD-gridded dataset 
 (i) Precipitation 2000–2020 
 (ii) Maximum and minimum temperature 2000–2020 
2. Hydrological data  Central Water Commission, Kolkata 
 (i) Streamflow 2009–2013 
 (ii) Sediment yield 2009–2013 
3. Digital elevation model  Shuttle Radar Topography Mission (1 arc-second) 
4. LULC 2010 Landsat 5 
5. Soil map 2000 National Bureau of Soil Survey and Land Use Planning, Kolkata (Scale 1:250,000) 

The meteorological data required to run the SWAT model are precipitation, maximum–minimum temperature, relative humidity (RH), solar radiation, and wind speed. The precipitation (Pai et al. 2014) and maximum–minimum temperature (Srivastava et al. 2009) data used in this model were acquired from the gridded dataset of India Meteorological Department (IMD), Pune. The weather generator of IMD-gridded dataset processed into SWAT format (https://swat.tamu.edu/data/india-dataset/) has been used to generate the missing data for RH, solar radiation, and wind speed. The discharge (two stations) and sediment data (one station) for a common period starting from 2009 to 2013 have been acquired from the Central Water Commission, Kolkata for calibration and validation of the model.

Digital elevation model

The Shuttle Radar Topography Mission global 1 arc-second (∼30 m resolution) digital elevation model (DEM) was obtained from www.earthexplorer.usgs.gov to delineate the basin boundary, preparation of drainage network, stream length, channel widths, slope, and elevation aspects of the basin. The DEM was converted to Universal Transverse Mercator zone 45 °N. The topography of the study area is characterized by steep slopes and high relief. The slope was categorized into the following five classes, namely (0–20), (20–40), (40–60), (60–80), and >80 covering 16, 20.42, 21.97, 17.65, and 23.97%, respectively.

LULC map

The Landsat 4-5 image acquired on December 2010 has been processed to prepare the LULC map of the research area. The LULC map has been prepared using the ERDAS Imagine 2014 software by the hybrid method, involving unsupervised classification followed by supervised classification. The ground truth data have been obtained using the historical Google Earth Pro images. The statistics of final LULC maps showed good results having Kappa coefficient and overall accuracy of 0.86 and 88.68%, respectively. The classified land-use map (30 m resolution) consists of seven land-use classes, namely agricultural land (0.45%), build-up land (0.12%), barren land 17.42%), grassland (14.78%), water bodies (0.61%), evergreen forest (24.06%), and snow cover/glaciers (42.54%) as shown in Figure 3(a).
Figure 3

(a) LULC map and (b) soil map of the study area.

Figure 3

(a) LULC map and (b) soil map of the study area.

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Soil map

The soil map acquired from the National Bureau of Soil Survey and Land Use Planning, Kolkata, for the year 2000 with a scale of 1:250,000 was digitized using the ArcMap 10.2.2 software. Six different types of soil based on the Food and Agriculture Organization classification have been identified (see Table 2). Additionally, two complementary categories comprising glaciers with rocky mountains covering snow and glaciers throughout the year and water bodies with inclusion of lakes and rivers were also identified as shown in Figure 3(b).

Table 2

Hydrological and physical properties of soil types in study area

SlSoil typeArea (km2)Basin area (%)TextureSaturated hydraulic conductivity (mm/h)Available water holding capacity (mm water/ mm soil)Hydrologic Soil Group
Bd32-2bc-3662 108.74 2.86 Loam 61.87 0.118 
Be84-2a-3685 53.97 1.42 Loam 8.31 0.175 
Bh25-2bc-3024 1,094.49 28.76 Loam 56.36 0.072 
GLACIER-6998 907.55 23.85 Unweathered bedrock – – 
Hh11-2bc-3711 71.62 1.88 Clay loam 2.88 0.16 
Hl38-2bc-5889 39.12 1.03 Loam 5.27 0.144 
I-Bh-U-c-3717 1,523.75 40.04 Loam 33.91 0.064 
WATER-6997 6.39 0.17 Water – – 
SlSoil typeArea (km2)Basin area (%)TextureSaturated hydraulic conductivity (mm/h)Available water holding capacity (mm water/ mm soil)Hydrologic Soil Group
Bd32-2bc-3662 108.74 2.86 Loam 61.87 0.118 
Be84-2a-3685 53.97 1.42 Loam 8.31 0.175 
Bh25-2bc-3024 1,094.49 28.76 Loam 56.36 0.072 
GLACIER-6998 907.55 23.85 Unweathered bedrock – – 
Hh11-2bc-3711 71.62 1.88 Clay loam 2.88 0.16 
Hl38-2bc-5889 39.12 1.03 Loam 5.27 0.144 
I-Bh-U-c-3717 1,523.75 40.04 Loam 33.91 0.064 
WATER-6997 6.39 0.17 Water – – 

Hydrological modelling of the upstream Teesta River basin using the SWAT model

SWAT is a physically-based, partial-distributed, continuous-time, basin scale hydrological model developed by the United States Department of Agriculture, Agricultural Research Service to simulate management decisions on agricultural compounds, water, and sediment in watersheds (Arnold et al. 1998). The SWAT model has been a remarkably adaptable tool that has been used extensively in different areas of the world to estimate the consequence of land-use management practices and climate change on WY and SY in small to large multifaceted watersheds with different combinations of land-use, soil, and management conditions, over a period of time (Ayivi & Jha 2018; Kuti & Ewemoje 2021). The main driving factors in every process in the SWAT model is the water balance, as a consequence of plant growth and the activities of water, sediments, nutrients, and pesticides within the basin (Arnold et al. 2011). The SWAT model initially allocates the whole basin into several sub-basins based on the threshold area, which is further segmented into HRUs comprised of single LULC, soil, and slope properties (Neitsch et al. 2005). The water balance calculation in SWAT is based on the following equation (Neitsch et al. 2005):
(1)
where is the water content of soil (mm), is the water content of soil initially (mm), t is the time (in days), is the total precipitation on day x (mm), is the total surface–runoff on day x (mm), is the actual evapotranspiration on day x (mm), is the quantity of water towards the vadose zone from soil profile on day x (mm), and is the total groundwater discharge on day x (mm).
The surface runoff is calculated by the following SCS curve number approach:
(2)
where is the daily rainfall depth (mm); is the initial abstractions (mm); and is the retention parameter (mm). The retention parameter is calculated using the following equation
(3)
where CN is the daily curve number. The Ia in Equation (2) is generally replaced by .
WY is one of the key factors used for evaluating sustainable water resources management of the watershed. It is the collective quantity of water draining the HRUs and joining the principal reach during the specified time period (Arnold et al. 2011). It is calculated based on the following equation:
(4)
where is the water yield (mm), is the surface runoff (mm), is the groundwater flow (mm), is the lateral flow (mm), and is the transmission loss (mm).

The hydrological simulations of the basin comprise two major components, namely land and water. The land component simulates water, nutrients, pesticides, and sediment resulting from the surface runoff towards the main channel. Similarly, the water components predict these through the movement of water from the networks of the channel in the watershed (Neitsch et al. 2011).

The SWAT uses the Modified Universal Soil Loss Equation (MUSLE) model to calculate erosion from individual HRU. The delivery ratio is not essential as the R-factor in Universal Soil Loss Equation (USLE) is replaced by runoff intensity which improves the SY estimation which permits the equation to be applicable to each storm event. The MUSLE equation (Williams 1975) is mathematically expressed by:
(5)
where is the sediment yield (tons per day); and are the coefficients; is the peak discharge (cumec); is the HRU area (ha); is the USLE soil erodibility factor (0.013 metric-ton-m2-h/m3-metric-ton-cm); , , are dimensionless factors relating to crop cover, soil management, and topography of the USLE equation, is the coarse fragment factor.
The sediment movement in the channel is governed by two concurrent actions, deposition, and degradation. Based on the conveyance capacity and sediment concentration in the river channel, deposition or degradation of sediment occurs. The description about the model can be accessed from the SWAT theoretical documentation (Neitsch et al. 2011). The flow chart showing the methodology implemented in this study is shown in Figure 4.
Figure 4

Methodology adopted for the WY and SY using the SWAT model.

Figure 4

Methodology adopted for the WY and SY using the SWAT model.

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SWAT model setup

In this study, Arc SWAT 2012 extension was used in the ArcMap 10.2.2 software to set up the model. The watershed characteristics were created based on the 30 m spectral resolution of the DEM. All other input raster components were resampled to same spatial resolution using the resampling tool in ArcMap. A threshold area of 30 km2 was defined for the generation of stream networks. The topographical components, stream and channel networks were also derived from the DEM. A total of 21 monitoring points were defined manually based on the observed discharge gauging stations for ease of model calibration and validation. Furthermore, the sub-basins were divided into 299 HRUs with land-use, soils, and slope thresholds of 20, 10, and 15%, respectively. The water balance mechanisms processed at the HRU stage are collected at the corresponding sub-basin which is further directed to the basin outlet (Grusson et al. 2015). For computation of water balance components, runoff was estimated using the curve number method, PET by the Penman–Monteith method, and channel routing by the variable storage method. The daily surface runoff was estimated by the SCS-CN method based on daily observed precipitation data. The curve number value for the particular day was estimated as per the hydrologic group of soil and the mean antecedent soil moisture condition. For the estimation of PET, the input data used are observed precipitation, maximum–minimum temperature; and the generated RH, solar radiation, and wind speed data from IMD-gridded weather generators. The sub-surface flow components were estimated using the variable storage method.

Model calibration, validation, and sensitivity analysis

Before applying the SWAT model outputs for planning or decision-making, the model was calibrated and validated against observed discharge data using the Sequential Uncertainty Fitting (SUFI-2) algorithm embedded in SWAT-CUP 2012. It consists of the following steps: (i) dividing the period of availability of observed data into calibration and validation periods; (ii) running the SWAT model at different values for the model parameters range during the calibration period, until the fit of the simulated output to observed is good; and (iii) applying the model with calibrated parameters during the validation period and validating the modelled output against observed data. The model was evaluated for its performance based on coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent BIAS (PBIAS). The model is considered appropriate when R2>0.65, NSE>0.65, and PBIAS=±15 for the flow and R2>0.50, NSE>0.50, and PBIAS=±20 for the SY. The model was calibrated from 2009 to 2011 and validated from 2012 to 2013 (see Figure 5). The statistics obtained from model calibration and validation show good adherence with the desired criteria as shown in Table 3.
Table 3

Performance of SWAT model in calibration and validation of streamflow and sediment yield

Daily time step
Calibration
Validation
NSE
R2
PBIAS
NSE
R2
PBIAS
StationQsrQsyQsrQsyQsrQsyQsrQsyQsrQsyQsrQsy
Chungthang 0.67 – 0.71 – −4.7 – 0.68 – 0.73 – −6.6 – 
Sangkalang 0.70 0.55 0.76 0.56 6.1 −8.6 0.66 0.60 0.68 0.62 13.0 −12.7 
Monthly time step 
QsrQsyQsrQsyQsrQsyQsrQsyQsrQsyQsrQsy
Chungthang 0.81 – 0.82 – 7.9 – 0.83 – 0.88 – −5.3 – 
Sangkalang 0.79 0.70 0.84 0.73 14.1 −8.2 0.72 0.71 0.74 0.76 13.5 −12.6 
Daily time step
Calibration
Validation
NSE
R2
PBIAS
NSE
R2
PBIAS
StationQsrQsyQsrQsyQsrQsyQsrQsyQsrQsyQsrQsy
Chungthang 0.67 – 0.71 – −4.7 – 0.68 – 0.73 – −6.6 – 
Sangkalang 0.70 0.55 0.76 0.56 6.1 −8.6 0.66 0.60 0.68 0.62 13.0 −12.7 
Monthly time step 
QsrQsyQsrQsyQsrQsyQsrQsyQsrQsyQsrQsy
Chungthang 0.81 – 0.82 – 7.9 – 0.83 – 0.88 – −5.3 – 
Sangkalang 0.79 0.70 0.84 0.73 14.1 −8.2 0.72 0.71 0.74 0.76 13.5 −12.6 

Qsr, runoff; Qsy, sediment yield.

Figure 5

Calibration and validation of streamflow for (a) Chungthang and (b) Sangkalang gauging stations. (c) Calibration and validation of sediment yield at the Sangkalang gauging station.

Figure 5

Calibration and validation of streamflow for (a) Chungthang and (b) Sangkalang gauging stations. (c) Calibration and validation of sediment yield at the Sangkalang gauging station.

Close modal

Sensitivity analysis was carried out to govern the effect of chosen parameters on calculating the WY and SY. SWAT Calibration Uncertainty Programmes embedded with SUFI-2 were employed to carry out calibration and sensitivity analysis in this study.

The SUFI-2 optimization algorithm is widely used to calibrate the SWAT model using a Bayesian system as it required a lower number of model runs with good prediction capability (Yang et al. 2008). Generally, the sensitivity analysis is carried out in two ways: local sensitivity or one-at-a-time (OAT) analysis (Abbaspour et al. 2017) or global sensitivity or all-at-a-time (AAT) analysis. In OAT analysis, while keeping all other parameters constant and changing one parameter at a time, the effect on output is accessed simultaneously. While in AAT analysis, all parameters are changed simultaneously to access the effect of individual parameters on the model output. Based on the analysis, the sensitive parameters were identified, and reduction of model parameters was achieved with further improving the objective function. The sensitive parameters were further used to calibrate the SWAT model for prediction of the WY and SY (see Tables 4 and 5).

Table 4

SWAT parameters, range, and calibrated values for streamflow

ParametersMin.Max.Calibrated valueSensitivity rankt-statp-valueDescription
Management parameters       
r__CN2 −0.4 0.4 −0.01300 −3.495 0.001 Antecedent moisture condition-II SCS runoff curve number 
Soil parameters               
r__SOL_AWC −0.20 0.20 −0.04160 13 0.361 0.719 Soil available water capacity 
r__SOL_K −0.40 0.40 0.30057 17 0.092 0.927 Saturated hydraulic conductivity 
HRUs parameters             
r__HRU_SLP 0.6 0.40825 2.198 0.031 Average slope steepness 
v__CANMX 20 8.86925 1.957 0.054 Maximum canopy storage 
v__EPCO 0.56157 14 −0.252 0.802 Plant uptake compensation factor 
Groundwater parameters             
v__GW_SPYLD 0.4 0.38634 11 −0.760 0.449 Shallow aquifer specific yield 
v__GWQMN 400 1.88539 18 −0.072 0.943 Shallow aquifer's threshold depth for return flow to occur 
v__GW_DELAY 50 29.03028 10 1.189 0.238 Groundwater delay time 
v__ALPHA_BF 0.43385 −5.209 0.000 Baseflow alpha factor 
Main channel parameters             
v__CH_N2 0.01 0.3 0.28865 2.439 0.017 Mannings's ‘n’ for main channel 
v__CH_K2 50 250 239.47588 1.607 0.112 Effective hydraulic conductivity of main channel 
Sub-basin parameters             
v__TLAPS −8 −5.21234 −8.010 0.000 Temperature lapse rate 
v__PLAPS −40 200 185.97263 12 −0.611 0.543 Precipitation lapse rate 
Basin parameters             
v__SFTMP −2.5 2.5 −0.20370 3.644 0.000 Snowfall temperature 
v__SMFMN 2.21313 15 0.154 0.878 Minimum snowmelt occurring during winter season 
v__SMFMX 4.06792 −1.528 0.130 Maximum snowmelt occurring during summer season 
v__ESCO 0.83547 16 −0.135 0.893 Soil evaporation compensation factor 
ParametersMin.Max.Calibrated valueSensitivity rankt-statp-valueDescription
Management parameters       
r__CN2 −0.4 0.4 −0.01300 −3.495 0.001 Antecedent moisture condition-II SCS runoff curve number 
Soil parameters               
r__SOL_AWC −0.20 0.20 −0.04160 13 0.361 0.719 Soil available water capacity 
r__SOL_K −0.40 0.40 0.30057 17 0.092 0.927 Saturated hydraulic conductivity 
HRUs parameters             
r__HRU_SLP 0.6 0.40825 2.198 0.031 Average slope steepness 
v__CANMX 20 8.86925 1.957 0.054 Maximum canopy storage 
v__EPCO 0.56157 14 −0.252 0.802 Plant uptake compensation factor 
Groundwater parameters             
v__GW_SPYLD 0.4 0.38634 11 −0.760 0.449 Shallow aquifer specific yield 
v__GWQMN 400 1.88539 18 −0.072 0.943 Shallow aquifer's threshold depth for return flow to occur 
v__GW_DELAY 50 29.03028 10 1.189 0.238 Groundwater delay time 
v__ALPHA_BF 0.43385 −5.209 0.000 Baseflow alpha factor 
Main channel parameters             
v__CH_N2 0.01 0.3 0.28865 2.439 0.017 Mannings's ‘n’ for main channel 
v__CH_K2 50 250 239.47588 1.607 0.112 Effective hydraulic conductivity of main channel 
Sub-basin parameters             
v__TLAPS −8 −5.21234 −8.010 0.000 Temperature lapse rate 
v__PLAPS −40 200 185.97263 12 −0.611 0.543 Precipitation lapse rate 
Basin parameters             
v__SFTMP −2.5 2.5 −0.20370 3.644 0.000 Snowfall temperature 
v__SMFMN 2.21313 15 0.154 0.878 Minimum snowmelt occurring during winter season 
v__SMFMX 4.06792 −1.528 0.130 Maximum snowmelt occurring during summer season 
v__ESCO 0.83547 16 −0.135 0.893 Soil evaporation compensation factor 

Min., minimum value; Max., maximum value; r, multiplicative operator; v, replace operator.

Table 5

SWAT parameters, range, and calibrated values for sediment yield

ParametersMin.Max.Calibrated valueSensitivity rankt-statp-valueDescription
Management parameters 
r__CN2 −0.4 0.4 −0.0919 9.324 0.000 Antecedent moisture condition-II SCS runoff curve number 
v__USLE_P 0.3 0.8 0.5984 5.201 0.000 USLE support practice factor 
v__USLE_C 0.5 0.2778 −0.005 0.227 USLE cover management factor 
Soil parameters 
r__SOL_AWC −0.20 0.20 0.0829 1.356 0.881 Soil available water capacity 
r__SOL_K −0.40 0.40 −0.0959 −2.978 0.003 Saturated hydraulic conductivity 
r__SOL_Z −0.4 0.4 −0.3286 11 −0.105 0.946 Soil surface to bottom layer depth 
r__SOL_ZMX −0.4 0.4 0.0109 10 1.008 0.920 Soil profile maximum rooting depth 
r__USLE_K −0.4 0.4 0.1539 1.895 0.061 USLE soil erodibility factor 
HRUs parameters 
r__OV_N −0.2 0.2 −0.0087 12 −0.062 0.996 Average slope steepness 
Main channel parameters 
v__CH_S2 −0.4 0.4 −0.1363 −1.355 0.178 Mannings's ‘n’ for main channel 
Sub-basin parameters 
v__TLAPS −8 −7.5006 −6.617 0.000 Temperature lapse rate 
Basin parameters 
v__SPEXP 1.5 1.0600 −13.720 0.000 Exponential factor for calculating sediment re-entrained in channel 
ParametersMin.Max.Calibrated valueSensitivity rankt-statp-valueDescription
Management parameters 
r__CN2 −0.4 0.4 −0.0919 9.324 0.000 Antecedent moisture condition-II SCS runoff curve number 
v__USLE_P 0.3 0.8 0.5984 5.201 0.000 USLE support practice factor 
v__USLE_C 0.5 0.2778 −0.005 0.227 USLE cover management factor 
Soil parameters 
r__SOL_AWC −0.20 0.20 0.0829 1.356 0.881 Soil available water capacity 
r__SOL_K −0.40 0.40 −0.0959 −2.978 0.003 Saturated hydraulic conductivity 
r__SOL_Z −0.4 0.4 −0.3286 11 −0.105 0.946 Soil surface to bottom layer depth 
r__SOL_ZMX −0.4 0.4 0.0109 10 1.008 0.920 Soil profile maximum rooting depth 
r__USLE_K −0.4 0.4 0.1539 1.895 0.061 USLE soil erodibility factor 
HRUs parameters 
r__OV_N −0.2 0.2 −0.0087 12 −0.062 0.996 Average slope steepness 
Main channel parameters 
v__CH_S2 −0.4 0.4 −0.1363 −1.355 0.178 Mannings's ‘n’ for main channel 
Sub-basin parameters 
v__TLAPS −8 −7.5006 −6.617 0.000 Temperature lapse rate 
Basin parameters 
v__SPEXP 1.5 1.0600 −13.720 0.000 Exponential factor for calculating sediment re-entrained in channel 

Min., minimum value; Max., maximum value; r, multiplicative operator; v, replace operator.

BRTs for statistical analysis

BRTs are capable of scrutinizing the high non-linearity between dependent and independent variables. Nowadays, due to its superiority in separating complex and highly interdependent variables, the BRTs model has been employed in various hydrological studies (Sun et al. 2019) to detect the importance of each environmental factor in predicting the WY.

The BRTs approach proposed by Friedman (2002), deals with response variables y and predictor variables of vector x that are connected through a coupled probability distribution P(x, y). Utilizing the training sample of known values of x and corresponding values of y, the purpose is to find an approximation F(x) to a function F*(x) that minimizes the expected value of loss function
(6)
The boosting estimates F*(x) by an ‘additive’ expansion in the form of
(7)
where are functions of x with parameters a={a1, a2,…}. The parameters and the expansion coefficients are jointly fit to the training data by forward stage-wise manner. The boosting solves loss function initially by fitting the function h(x; a) by least squares to the current pseudo residuals. The following equation depicts the residuals from a given stage of the tree building.
(8)
Based on given the coefficient is estimated based on the following equation,
(9)
The BRTs perform with a base learner h(x;a) of an L terminal node regression tree. The regression tree divides the feature space into L separate regions and envisages a discrete constant value at each iteration m.
(10)
The tree in Equation (5) predicts constant value within each region , Equation (9) reduces to location estimate based on the criterion
(11)
Finally, the current approximation is individually updated in all the corresponding regions
(12)

The model calculates the significance of each independent variable over recursive binary splitting techniques which improved the discrepancies among variables. A forward stage-by-stage technique reserves already-built trees at each stage and adds new trees by reweighing the residuals from earlier trees. As a result, the BRT models generate thousands of trees, from which a mean variable importance estimate is extracted across all trees (De'ath 2007; Elith et al. 2008). In order to run BRTs, five key parameters needed to be defined: (1) probability distribution function (PDF) of response variable, (2) learning rate, (3) tree complexity, (4) bag fraction, and (5) cross-validation folds (Leathwick et al. 2006; Elith et al. 2008). The estimated parameters of the selected PDF as the response variables were assumed to be Gaussian distributed. As suggested by Elith et al. (2008), BRTs with the learning rate 0.005, bag fraction 0.5, tree complexity 1, and the cross-validation fold, which is the number of times the cross-validation was carried out to get the optimum number of trees to be assembled for the final model was set to 10 folds, and is considered in this study. The analysis of BRTs was carried out using dismo package (Hijmans et al. 2020) in R software (R Core Team 2021).

To access the effect of spatial scales on the WY and its independent variables, the whole watershed was divided into three different spatial scales. They are (i) small watersheds having 45 small sub-watersheds with a drainage area extending from 23.65 to 174.12 km2; (ii) intermediate watersheds having 21 medium sized sub-watersheds with drainage area extending from 111.35 to 258.51 km2; and the (iii) large watersheds covering whole watersheds having a drainage area of 3862.26 km2. The representative figures for small, intermediate, and large basins are illustrated in Figure 6. Additionally, the calibrated SWAT model was daily simulated for the period of 21 years starting from the 2000s. The simulated output data were then utilized in order to account for temporal scales at daily, monthly, and yearly timescales.
Figure 6

Spatial map showing small, intermediate, and large basins considered in this study.

Figure 6

Spatial map showing small, intermediate, and large basins considered in this study.

Close modal

In this study, to analyze the significance of different variables on predicting WY, few hydro-climatic and basin factors including precipitation, maximum and minimum temperature, snowmelt, actual evapotranspiration (ETa), baseflow, NDVI, Enhanced Vegetation Index (EVI) have been considered. These variables have been revealed to have an influence on WY and SY predictions. The daily NDVI and EVI dataset of the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform with an average resolution of about 463.31 m were obtained from the Google Earth Engine platform. The NDVI value is generated from surface reflectance composites in Near-Infrared and red bands as (NIR–red band)/(NIR+red band), which ranges from −1.0 to 1.0. EVI, on the other hand, is superior to the NDVI as it also incorporates visible blue band in depicting the vegetation index, which allows the aerosol-scattering effects to be compensated.

Comparative contribution of variables to the WY at spatio-temporal scales

The findings of statistical analysis for spatial scales (large, intermediate, and small basins), temporal scales (yearly, monthly, and daily scales), and seasonal scales (monsoon and non-monsoon) are summarized and presented. In the Indian context, about 75% of the total rainfall occurs during monsoon months starting from June to September (Kulkarni et al. 2020). At a small basin scale, out of the eight independent variables considered, precipitation has shown a greater significance on daily scales which contributes an average of about 30.14%, which is then followed by baseflow, minimum temperature, maximum temperature, and snowmelt by about 21.40, 13.75, 11.64, and 9.97%, respectively. The remaining three parameters including NDVI, ET, and EVI summed up to a 13.09% contribution to WY, in which EVI has the least importance of 3.76%. At monthly and yearly time scales, the sequence pattern of the significance of the first three predictors was the same starting from baseflow, precipitation, and minimum temperature with a contribution of 19.59, 17.28, and 14.43% at a monthly scale and 16.41, 16.30, and 13.03% at a yearly scale. It is then followed by maximum temperature (11.93%), EVI (10.24%), snowmelt (9.86%), ET (8.42%), and NDVI (8.24%) at monthly scale. Furthermore, on a yearly scale, it is followed by snowmelt (12.77%), EVI (11.70%), NDVI (10.57%), ET (9.70%), and maximum temperature (9.53%), respectively. It is worth mentioning here that the contribution of maximum temperature to WY on a daily and monthly scale were higher as compared to the annual time scale in a small basin. In contrast, the first three predictors alone when combined contribute to about 65.29, 51.31, and 45.71% to WY in all three spatial scales. It was observed that the significance of climatic factors including precipitation, maximum and minimum temperature decreases from daily to yearly timescales, but the condition is the opposite in the case of vegetation indices (NDVI and EVI). The graphical representation of each parameter contributing to WY at daily timescales is illustrated in Figure 7.
Figure 7

(a)–(h) Percent contributions of parameters to the WY at 45 small sub-basins. Note: Tmin: minimum temperature (°C), Tmax: maximum temperature (°C), ETa: actual evapotranspiration.

Figure 7

(a)–(h) Percent contributions of parameters to the WY at 45 small sub-basins. Note: Tmin: minimum temperature (°C), Tmax: maximum temperature (°C), ETa: actual evapotranspiration.

Close modal
The contribution of these parameters was further analyzed for monsoon (June–September) and non-monsoon (October–May) seasons (Jhajharia et al. 2009). At the small basin scale, during the monsoon season, precipitation and baseflow equally contribute to WY by about 16.55%, which is followed by minimum temperature, EVI, NDVI, ETa, snowmelt, and maximum temperature by 15.12, 11.45, 10.60, 10.14, 10.06, and 9.54%, respectively. During the non-monsoon period, the contribution of baseflow (14.95%) to WY surpasses the precipitation (13.30%), followed by EVI, NDVI, maximum temperature, minimum temperature, ETa, and snowmelt by 13.19, 13.05, 11.97, 11.75, 11.12, and 10.67%, respectively (see Figure 8). It is worth mentioning that the contribution of climatic factors in monsoon season (41.20%) was more as compared to the non-monsoon season (37.02%). However, the case was the opposite when vegetation index was considered (see Figure 9).
Figure 8

Relative contributions of parameters to the WY at intermediate basins during monsoon and non-monsoon periods.

Figure 8

Relative contributions of parameters to the WY at intermediate basins during monsoon and non-monsoon periods.

Close modal
At the intermediate basin, the sequence of contribution to WY of all the parameters at daily scales is the same as that of small basins. However, to be specific, precipitation, baseflow, and minimum temperature alone contribute to about 63.06% to total WY. It is then followed by maximum temperature, snowmelt, NDVI, EVI, and ET by 12.93, 11.66, 5.13, 3.81, and 3.41%, respectively. Here, the combined effect of climatic factors including precipitation, and maximum and minimum temperatures contribute more than half of the total WY by about 55.19%. At monthly scales, the baseflow alone contributes 19.96%, followed by minimum temperature, precipitation, and maximum temperature with 18.35, 17.15, and 14.28%, respectively. The snowmelt contributes about 10.13% and the vegetation indices including NDVI and EVI both contribute about 14.29% and ET with the least importance of about 5.83% to WY. At annual scales, it is worth mentioning that the EVI contributes more to WY than any other predictors followed by precipitation, baseflow, NDVI, snowmelt, ET, maximum temperature, and minimum temperature. However, the average contribution ranges from the highest of 14.14% by EVI to the lowest of 10.25% by minimum temperature. It is worth mentioning that the contributions of climatic factors gradually decrease from daily to yearly timescales and that of vegetation indices abruptly increase from daily to yearly time scales. The graphical depiction of each parameter contributing to WY at intermediate basins is shown in Figure 10.
Figure 9

Relative contributions of parameters to the WY at small basins during monsoon and non-monsoon periods. Note: M indicates monsoon; NM indicates non-monsoon season; BF indicates baseflow; Prc indicates precipitation; and Snow indicates snowmelt.

Figure 9

Relative contributions of parameters to the WY at small basins during monsoon and non-monsoon periods. Note: M indicates monsoon; NM indicates non-monsoon season; BF indicates baseflow; Prc indicates precipitation; and Snow indicates snowmelt.

Close modal
At the intermediate basin during the monsoon season, precipitation and baseflow contribute about one-third of the total WY, followed by minimum temperature, EVI, snowmelt, NDVI, ETa, and maximum temperature by 14.79, 10.98, 10.91, 10.65, 10.31, and 9.46%, respectively. In the non-monsoon season, baseflow contributes more to WY by about 15.81% followed by precipitation (14.31%), and least by snowmelt (10.32%). The contribution to WY by the remaining parameters ranges from 11.26 to 12.98%. The climatic factor contributes about 40.75% in monsoon and 36.85% during the non-monsoon period. Similarly, the vegetation index contributes about 21.63% during monsoon and increases to 25.55% during the non-monsoon period.
Figure 10

(a–h) Relative contributions of parameters to the WY at intermediate basins.

Figure 10

(a–h) Relative contributions of parameters to the WY at intermediate basins.

Close modal
At large basin scales, precipitation and groundwater were dominant and together contribute about 47.10, 33.48, and 35.97% at daily, monthly, and yearly temporal scales. Further, EVI has the least significance on a daily scale with 1.39%, NDVI with 5.70% on monthly, and maximum temperature with 7.65% on yearly time scales. The combined climate variables including precipitation, maximum and minimum temperature contribute about 53.10% on daily, 42.35% on monthly, and 38% on yearly time scales. On the other hand, an increasing contribution of vegetation indices including NDVI and EVI from about 5.12% on daily, 18.97% on monthly, and 23.96% on yearly time scales. The bar graph showing the contribution of variables to WY at yearly timescales is shown in Figure 11.
Figure 11

Relative contributions of parameters to the WY at large basins.

Figure 11

Relative contributions of parameters to the WY at large basins.

Close modal

The BRTs analysis of the monsoon season at a large basin scale revealed that precipitation, baseflow, and minimum temperature contribute about half to total WY with precipitation being the highest contributor (16.50%). On the other hand, the contribution of ETa was the least among all parameters with 7.90%. During the non-monsoon season, baseflow (15.19%) and precipitation (14.68%) contribute more to WY. The contribution of snowmelt and maximum temperature were equally observed least among all the parameters by about 10.23%. The climatic factors contribute about 41.38% during monsoon and 35.31% during the non-monsoon season to WY. On the other hand, about 20.98% of total WY was contributed by vegetation index during monsoon and 27.31% during non-monsoon season.

Contribution of variables to SY at spatio-temporal scales

The outcomes BRTs statistical analysis in SY are analyzed at spatial and temporal scales and are presented. At small basin scales, the climate factors were dominant with an overall contribution of 69.58% starting from precipitation, maximum and minimum temperature. The vegetation factor contributes about 11.41% and baseflow with the least significance of about 3.07%. At the monthly timescale, the ranking pattern of the first three predictors was the same as that of the daily time scale with an overall contribution of 48.46%. However, the contribution made by vegetation factors increases slightly by 18.06% but was found less significant at monthly timescales. At an annual timescale, precipitation (16.99%) again contributes more to WY than any other parameters. However, the individual contribution of other parameters considered in descending order, by baseflow, EVI, minimum temperature, NDVI, and ET ranges from a maximum of 15.55% to a minimum of 10.51%, respectively. Contemplating the vegetative and climatic factors on a yearly from a daily and monthly timescale, an increase in the contribution of vegetative factors (25.62%) and a decrease in climatic factors (41.04%) were observed in the small basins (see Figure 12).
Figure 12

(a–h) Relative contributions of parameters to the SY at small basins.

Figure 12

(a–h) Relative contributions of parameters to the SY at small basins.

Close modal
Figure 13

Relative contributions of parameters to the SY at small basins during monsoon and non-monsoon periods.

Figure 13

Relative contributions of parameters to the SY at small basins during monsoon and non-monsoon periods.

Close modal
Figure 14

(a–h) Relative contributions of parameters to the SY at intermediate basins.

Figure 14

(a–h) Relative contributions of parameters to the SY at intermediate basins.

Close modal
Figure 15

Relative contributions of parameters to the SY at intermediate basins during monsoon and non-monsoon periods.

Figure 15

Relative contributions of parameters to the SY at intermediate basins during monsoon and non-monsoon periods.

Close modal

The contribution of precipitation to SY at the small basin scale was highest amongst all the parameters by 17.24%, followed by baseflow, minimum temperature, and EVI by 15.33, 14.13, and 12.23%, respectively. The least contributor to SY was snowmelt with 7.19% and the remaining three parameters contributes from 11.09 to 11.59%, respectively. In the non-monsoon season, ETa contributes more to SY by 13.67%, though the relative contribution of minimum temperature, snowmelt, and baseflow is very close to ETa. The precipitation contribution to SY was relatively low at 12.94% and the NDVI with the least by 10.53%, respectively. The contribution of both climate factor and vegetative index decreases from monsoon to non-monsoon season by 42.96–38.53% and 23.32–21.36%, respectively (see figure 13).

In the intermediate basin, the climate factors were dominant with an overall contribution of about 67.62%. The snowmelt and baseflow together contribute about 22.17%, followed by a vegetative factor (7.17%) and ET with the least significance of 3.04% only. The sequence of significant parameters was same at both monthly and annual scales. However, precipitation contributes more at monthly scale (21.48%) as compared to the annual scale by 16.69%. Baseflow and snowmelt contribute slightly more at annual scale (26.76%) when compared to the monthly scale (23.37). However, the snowmelt contribution approximately remains the same in both time scales. Taking the climatic and vegetative factors into account, the former accounts for 48.69% (monthly) and 39.25% (yearly) only, and the latter accounts for 18.03% (monthly); and 24.02% (annual) which follows the same order of increasing (vegetative factors) and decreasing (climate) over the small basin scale. The ET was found to be less significant at about 10% approximately in both temporal scales. The graphical representation of the relative contribution of each parameter to SY is shown in Figure 14.

In the monsoon season, at the intermediate basin scale, precipitation contributes more to SY by 16.39% and snowmelt contributes least by 9.86%, respectively. On the other hand, during the non-monsoon season, the contribution of baseflow (13.77%) and precipitation (13.53) were highest to SY. The role of snowmelt and minimum temperature were felt to be low with a contribution of about 11.13% and 11.48%, respectively. In the intermediate basin scale, the contribution of climatic factors decreases from monsoon to non-monsoon season by 42.10–37.10%, and the vegetation index increases from 21.80 to 24.90% to SY (see Figure 15).

In the large basins, precipitation solely accounts for 73.73% at daily time scale and the rest by remaining parameters. It is worth mentioning here that the climatic parameters alone contribute about 85.6% to SY followed by baseflow and snowmelt of about 10.40%. On the other hand, the contribution of ET was found to be less by about 0.50%, respectively. The parameters at daily and monthly timescale follow the same ranking pattern. Precipitation influences SY more at daily scale, but at monthly scale, the contribution of precipitation and baseflow was approximately the same by 17%. The influence of climatic factors on SY was about 44.53% and that of vegetative factors 20.35%. The significance of ET increases further at monthly timescale to 7.70% when compared to daily timescale. At yearly timescale, the contribution of each parameter remained slightly uniform within a range of 10% (maximum temperature) to 17.03% (precipitation). The influence of climatic factor reduces to about 39.83% but the vegetation factor remained almost the same as that on a monthly scale. The bar chart representing the contribution of parameters to SY is represented in Figure 16.
Figure 16

Relative contributions of parameters to the SY at the large basin.

Figure 16

Relative contributions of parameters to the SY at the large basin.

Close modal
Figure 17

Relative contributions of parameters to the SY at the large basin during monsoon and non-monsoon periods.

Figure 17

Relative contributions of parameters to the SY at the large basin during monsoon and non-monsoon periods.

Close modal

The contribution of precipitation to SY at large basin scale during monsoon season was highest (15.75%) with the lowest contributors being maximum temperature (10.44%) and NDVI (10.49%). On the other hand, during the non-monsoon season, precipitation was the highest contributor to SY with a slight increase in contrast to monsoon season by about 1.16%. The least contributor to SY during non-monsoon was found to be same parameters with the same value as in the case of monsoon season. The overall contribution of climate and vegetation index decreases from monsoon to non-monsoon season from 40.07 to 38.33% and 22.88 to 21.34%, respectively (see Figure 17).

The statistical analysis report of BRTs demonstrates that the precipitation and baseflow were two of the principal factors that influenced WY on all spatio-temporal scales. Precipitation is one of the important factors that governs WY as previously reported by Jiang et al. (2016); Sun et al. (2019) and Lu et al. (2020) is used by policymakers, especially in water resources management and goals in conducting apportioning of water resources. The baseflow is one of the key components of the groundwater system, which provides sub-surface flow and other delayed sources like snowmelt into the stream, which is the second most important factor contributing to WY. Its importance is attributed to the improvement of water management plans, particularly for drought situations, assessment of small to medium water supplies, water quality (Santhi et al. 2008), water supply, generation of hydro-electric power, and recreational purposes (McMahon & Mein 1986). The significance of baseflow was felt next to precipitation in all spatial and temporal scales. The third key variable to WY, temperature, plays a crucial role in monitoring the water cycle of the watershed by altering the different components. The importance of minimum temperature was perceived as slightly higher than that of snowmelt and maximum temperature to WY. This may be attributed to the fact that minimum temperature helps to generate more snow and glaciers (in cold regions), and maximum temperature drives the snowmelt process. Thereby, storing water in the form of snow and glaciers when the temperature is minimum and melts in the course of a rise in temperature. The contribution of vegetation indices was comparatively less at daily timescale (5.12–8.89%) and gradually increases at monthly (18.29–18.98%) and yearly (22.27–27.22%) timescales. The contribution of ET was less than WY in all spatio-temporal scales. The average ETa values at daily, monthly, and annual scales are much less than precipitation values. A small change in precipitation and ETa will cause disproportionate changes in runoff and water storage within the basin (Rungee et al. 2021). For example, the average annual precipitation is about 215 mm with an SD of ∼593, and ETa value was 21 mm with an SD of ∼17. The large deviation of precipitation obviously depicts the significant role played by precipitation in overall runoff variations in the basin as compared to ETa. Moreover, as per our analysis, almost 60% of the total basin area is predominantly covered by bare land and snow/glaciers throughout the year and the temperature remained well below 0 °C especially in the mid- to northern part of the basin. In the southern part of the basin, the average temperature within the basin seldom exceeds 21 °C, which could limit the process of ETa. Therefore, the low degree of variations in ETa data series results in a low coefficient of variation, which resulted in low dependency by runoff, and then the SY. Further, the regression analysis between ETa with WY and SY revealed the coefficient of variation value ranges from 0.04 to 0.06 for both WY and SY. This may be one of the important reasons for an insignificant contribution of ETa to both WY and SY.

In contrast to monsoon and non-monsoon seasons, on average, baseflow, precipitation, and minimum temperature were the key parameters governing half of the total WY in monsoon season. On average, the overall contribution of precipitation, baseflow, and vegetation index was about 55.78% of the total WY during the non-monsoon season. However, the average contribution of maximum temperature, ETa, and NDVI were the lowest (<9%) during monsoon season and in the case of non-monsoon, snowmelt contributes less to WY. Putting an emphasis on climate and vegetation indices, the contribution of climate factors decreases from monsoon to non-monsoon seasons from 41.10 to 36.89%. On the other hand, the contribution of the vegetation index increases by 4.82% from 21.60% during the monsoon season, respectively.

A change in SY at the outlet to a maximum extent indicates the anthropogenic activities linked to the watershed. An analysis of BRTs established that precipitation is the key factor that has a greater influence on SY in all spatio-temporal scales. As reported by Jiang et al. (2016) and Zhang et al. (2022), precipitation is one of the principal factors affecting the degree of soil erosion. The contribution of maximum and minimum temperatures tends to be more emphasized at daily and monthly scales than yearly scales. Subsequently, the importance of vegetation indices firmly remained low to SY in all the spatio-temporal scales. This may be attributed to the fact that where precipitation increases erosion, vegetation inhibits the erosion process. The contribution of ETa to SY constantly remained low to negligible, in contrast to other predictors at all spatio-temporal scales. The relation between ETa and SY is similar to the relation between ETa and WY. Based on our analysis, a very high correlation exists between SY and WY (correlation coefficient is equal to 0.93 at the monthly timescale). This is one of the important reasons for the lower contribution of ETa to SY as well. This may be attributed to the fact that the correlation coefficient between ETa and SY was found to be closer to zero (0.04–0.06) in all spatio-temporal scales in the study area.

As analogous to WY, the average contribution of precipitation, baseflow, and minimum temperature tends to be more during the monsoon season than SY at all spatial scales. However, during the non-monsoon season, only precipitation and baseflow were found to be dominant parameters contributing to SY. The maximum temperature during monsoon and snowmelt during non-monsoon were found to be the lowest contributors to SY. It is worth mentioning that the contribution of climate factors during monsoon to non-monsoon season was reduced by 3.37% and that of vegetation index increased by 2.19%, respectively. This clearly demonstrates the influence of climate and vegetation parameters on SY.

In this study, by combining the SWAT with BRTs, the relationships between dependent (WY and SY) and independent variables at multiple spatio-temporal scales are quantitated and evaluated. Our investigations undoubtedly discover that the response of WY and SY to hydro-climatic and basin parameters is physically flexible and essentially relies on the scale.

Evidently, the impact of hydro-climatic and basin parameters on WY and SY is basin-specific, making it impractical to characterize the quantitative relationships employing a standard acceptable rule. In this circumstance, we argue that it is inappropriate to discuss how different parameters affect the WY and SY without addressing the precise site and time scale. It is therefore suggested that any extrapolation of WY and SY relationships from one basin to another must be done with caution (Berg et al. 2016). Compared to coarser scales, simulations at daily scale are more detailed in their representation of hydrological processes. The opportunity to analyze and observe the relative contributions of parameters that might be misinterpreted at coarser resolutions is provided by the daily scale. It makes sense to advise researchers looking at changes in WY and SY to give priority to performing their research at smaller temporal scales.

The analysis was undertaken at different spatial (small, intermediate, and large basins), temporal (daily, monthly, and yearly), and seasonal (monsoon and non-monsoon) scales. Based on the results obtained from the BRTs model, the following major outcomes have been identified:

  • Out of the eight parameters considered, precipitation and baseflow to a larger extent contributes more to WY at all spatio-temporal scales except for (monthly and yearly scale) at the intermediate basin, baseflow, and minimum temperature at monthly timescale; precipitation and actual evapotranspiration at yearly temporal scale. On the other hand, the contribution of ETa was consistently low to WY at all spatio-temporal scales considered.

  • Similarly, the contribution of precipitation was much higher than any other parameters to SY at all spatio-temporal scales. Although the ranking of precipitation is higher in all spatio-temporal scales, the relative contributions of all parameters at yearly timescale are close to each other. The importance of ETa was similar to that of WY.

  • The seasonal analysis of eight parameters to WY and SY revealed that precipitation, baseflow, and minimum temperature contribute about 40–49% of the total WY, and 42–47% of the total SY at all spatial scales. The average contribution of maximum temperature was relatively low during monsoon and snowmelt during the non-monsoon season. The relative significance of the remaining parameters to WY and SY differs among monsoon and non-monsoon seasons.

  • At the small basin scale, weather parameters including precipitation, maximum and minimum temperature account for 55.53, 43.65, and 38.86% to WY at daily, monthly, and yearly timescales. Similarly, the same parameters account for 69.58, 48.46, and 38.73% to SY at the same order of temporal scales. At the intermediate basin, they contribute about 55.19, 49.79, and 35.08% to WY, and 67.62, 48.69, and 45.76% to SY at daily, monthly, and yearly temporal scales. In view of the large basin scale, their contributions were about 53.10, 42.35, and 38% to WY; and 85.96, 44.53, and 39.83% to SY at daily, monthly, and yearly temporal scales.

  • The overall influence of weather parameters gradually decreases from daily, monthly, and yearly at all spatial scales for both WY and SY. On the other hand, the contributions of vegetation indices were more pronounced to yearly scale than monthly and daily scale at all spatial scales for both WY and SY. In fact, gradually increasing importance was observed from daily, monthly to yearly timescales in all spatial scales. The significance of baseflow to WY was felt to be higher (next to precipitation) to WY at all spatial-temporal scales, but in the case of SY, was suppressed by other predictors except at yearly timescales. The relative significance of snowmelt to WY and SY differs among spatial and temporal scales. In contrast, the significance was more pronounced to SY than WY.

P.T.L. acquired and interpreted the data, and analyzed and drafted the manuscript; P.K.P. helped in study conception and design; V.P. edited and critically revised the manuscript. All authors of this paper have directly participated in the writing, editing, planning, execution, and analysis of this study.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Abbaspour
K. C.
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