A large population depends on water resources generated due to runoff from Himalayan River basins. They provide enough water for drinking, domestic, industrial, and irrigation. Also, these rivers have a high hydropower potential. A lack of in-depth studies has made it difficult to understand how these rivers respond hydrologically to climate change (CC) and, thus, impact the environment. In this paper, Alaknanda River Basin (ARB) has been modelled using the Soil and Water Assessment Tool (SWAT) to understand the hydrological response and assess its water balance components. The result shows that the basin's water yield and evapotranspiration (ET) vary from 58–63% and 34–39% of total precipitation. The average annual contribution of snowmelt to the total riverine flow will range from 20–24% throughout the simulation period. SFTMP, TLAPS, SMTMP, CN2, SMFMX, and GW_DELAY is found to be most sensitive at the significance level of less than 0.05, showing the contribution of the snowmelt is significant in streamflow, while delay in the groundwater will affect the contribution of surface runoff and groundwater in the streamflow. Based on the results, it is highly recommended that the spatial and temporal hydro-meteorological should be investigated in-depth to find out the actual water potential of the basin.

  • A methodology has been proposed to obtain the streamflow pattern of a high-altitude river.

  • The model can also derive the snowmelt contribution to the total streamflow.

  • The SWAT model is applied to the snow domination basin of the Himalayan region of Uttarakhand. The evaluation of the results shows that the model can obtain streamflow fluctuation.

Graphical Abstract

Graphical Abstract
Graphical Abstract
ARB

Alaknanda River Basin

SWAT

Soil and Water Assessment Tool

ET

evapotranspiration

SFTMP

snowfall temperature

TLAPS

temperature lapse rate

PLAPS

precipitation lapse rate

CN

curve number

SMTMP

snow melt base temperature

SMFMX

maximum melt rate for snow during the year (occurs on summer solstice)

SMFMN

minimum melt rate for snow during the year (occurs on winter solstice)

TIMP

snow pack temperature lag factor

SNOCOVMX

minimum snow water content that corresponds to 100% snow cover

SNO50COV

snow water equivalent that corresponds to 50% snow cover

SOL_AWC

available water capacity of the soil layer

ESCO

soil evaporation compensation factor

ALPHA_BF

base-flow recession coefficient

RCHRG_DP

deep aquifer percolation fraction

GWMQN

threshold depth of water in the shallow aquifer required for return flow to occur (mm)

GW_DELAY

groundwater delay

REVAPMN

threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm)

GW_REVAP

groundwater ‘revap’ coefficient

CH_K2

effective hydraulic conductivity in main channel alluvium

CWC

Central Water Commission

TRMM

Tropical Rainfall Measurement Mission

IMD

Indian Meteorological Department

ASTER DEM

Advanced Spaceborne Thermal Emission and Reflection Radiometer Digital Elevation Model

CUP

Calibration and Uncertainty Programs

IHR

Indian Himalayan Region

The mountains covered with snow and glaciers are the early indicators of climate change (CC) (Kuniyal et al. 2021). The glaciers in the Himalayas are one of the largest glacier-mountain systems in the world outside the polar region, with a length of 2,400 km and a width of 150–400 km (Reilly et al. 1996; Hasnain 2002; Bahuguna et al. 2014; Rautela et al. 2022a). There are approximately 9,500 glaciers in the Indian Himalayan Region (IHR), covering a land area of about 40,000 km2 (Sangewar & Shukla 2009). In the region of the Indian Himalayas, most rivers, streams, springs, and lakes are fed by the significant contribution from the melting of glaciers and snow, and the basins of these hold a special place in the high mountain ecosystems (Srivastava 2007; Scott et al. 2019; Rautela et al. 2022a). In the upstream catchments of the major rivers, such as Ganga, Indus, Brahmaputra, and so on, the glacier and snow melt contribute most of the headwater (NRC 2012). It is usually found that snow is temporarily stored in high mountains, and melted water is released into rivers later in the summer. As glacier and snow runoff is necessary for major Himalayan river systems to remain perennial, rainfall volume made up in the monsoon season is responsible for the high flow levels of these rivers (Tayal 2019). The snow accumulation in this region will start from November to March, while this snow's ablation will occur from April to September (Bisht et al. 2020; Rautela et al. 2020). From April to June, snowmelt runoff in the mountainous basins is a more dominant streamflow component, and it accounts for a significant portion of streamflow from July to September (Jain et al. 2010). The snowmelt runoff contributes 5% of streamflow as compared to the streamflow generated by the rainfall-runoff in the country (Schaner et al. 2012; Raina & Srivastava 2014). This shows that snowmelt runoff is a good distributer of freshwater to the downstream regions throughout the year (Ramanathan 2011; Rautela et al. 2022a). The process of snowmelt and snow accumulation is largely affected due to CC and global warming. These processes will create a disturbance in the hydrological cycle of upstream river basins of the Himalayan region due to the impact on precipitation patterns and temperature (Gebre & Ludwig 2014; Kaini et al. 2021). The spatial and temporal variations in the precipitation and temperature pattern will significantly affect the downstream regions in terms of water availability and associated water-related disasters (Kaini et al. 2020). The average annual inflows of various snow and glacier-fed rivers will increase with respect to CC since 2050, and consequently, the flow will decrease after 2050, and these perennial rivers will become seasonal rivers (IPCC 2018; ICIMOD 2020; Kaini et al. 2020). The changes in the hydrological regime of these rivers will directly or indirectly affect the 1/5th of world's populations those depends on these rivers (ICIMOD 2020).

In mountainous environments, hydro-meteorological conditions are highly variable over long periods and require physically realistic and computationally efficient either distributed or semi-distributed modelling (Liston & Elder 2006). To understand the hydrological characteristics of the mountainous basins, it is most common to describe the snowmelt distribution in the basin, thus allowing for each watershed's topography to be accounted for (Li et al. 2013, 2015, 2016). The various hydrological models have been developed to simulate the hydrological response subject to both solid and liquid precipitation, such as Mike Zero (NAM and SHE), and the public-domain models, such as HBV, Xinanjiang Model, HEC-HMS, SRM, and SWAT, and so on. The SWAT model was used very commonly in water resources (Bergstrom 1992; Zhao & Liu 1995; Neitsch et al. 2011). The benefit of utilizing the public-domain models is that these are freely available and simple to share model arrangements. SWAT provides a user-friendly interface for model setup in a GIS framework. Also, the SWAT model offers a more extensive user base and a detailed user manual to the users for processing multiple processes.

SWAT is a continuous, semi-distributed model which has been used to simulate different hydrological responses using process-based equations for daily, monthly, and yearly time series (Nasiri et al. 2020; Rautela et al. 2022b). SWAT is applied to the catchment with an area of a couple of square kilometers to a thousand square kilometers (Spruill et al. 2000; Zhang et al. 2008). Several studies show the applications of SWAT for the modelling of snowmelt (Panhalkar 2014; Gupta et al. 2018; Kumar & Bhattacharjya 2020), Rainfall–Runoff (Tripathi et al. 1999a; Shawul et al. 2013; Addis et al. 2016; Himanshu et al. 2017), sediment transport (Tripathi et al. 1999b; Srivastava et al. 2020), and to estimate the hydropower of a river-based in the results of SWAT (Pandey et al. 2015; Tamm et al. 2016). Many other model frameworks, such as energy budget with distributed approach, have also been used to model snow-fed catchments. Still, this modelling requires larger input datasets that are sometimes unavailable for Himalayan catchments. It is crucial to model the hydrological characteristics of Himalayan river basins for various reasons (Kumar & Bhattacharjya 2021). These rivers provide water to nearly 2 billion people (Prakash 2020). These rivers have a high hydropower potential due to their perennial nature and steep terrain, but developing that potential requires a solid knowledge of hydrologic response mechanisms (Pandey et al. 2015; Ghosh 2018). Moreover, these basins are prone to water-caused disasters like flash floods (Shrestha & Bajracharya 2013). Hydrological data are scarce in the region due to its complicated topography and worst climatic conditions.

Moreover, land-use and land-cover (LULC) changes could significantly impact the amount of snow and ice accumulated, melting, and the hydrological response of these river basins (Miller et al. 2012). For the long-term planning of water resources, it is necessary to study the effects of global warming on snow and glacier melt (NRC 2012). The hydrology of these rivers has not been well studied, despite being common and highly important to human existence. In the present study, an attempt is made to fill this void. The specific objective of the study is to simulate the long-term hydrological response of the Alaknanda River Basin (ARB) and its effects on the basin's water balance components on two different time scales, daily and monthly, using SWAT. Since the river Alaknanda will be largely affected by CC and anthropogenic impacts. The findings of the present study will provide a piece of important information about the contribution of the various hydrological components to the generation of the streamflow.

The Alaknanda river system (Figure 1) is the significant upstream of the river Ganges that arises at the confluence and is fed by Satopath and Bhagirathi Kharak glaciers in the Uttarakhand state of India. The Alaknanda river travels a distance of 195 km through the Chamoli, Rudraprayag, and Pauri districts of Uttarakhand and after that, confluences in the Bhagirathi river and forms the Ganges and the river system drains an area of 11,063.68 km2. The river also plays a significant role from the cultural point of view, and at the confluence of the major tributaries of the river, the Panchprayags (Vishnuprayag, Nandprayag, Karnaprayag, Rudraprayag, and Devprayag) is located. The main tributaries of the Alaknanda river are Saraswati (meets at Mana), western Dhauliganga (meets at Vishnuprayag), Nandakini (meets at Nandprayag), Pinder (meets at Karnaprayag), and Mandakini (meets at Rudraprayag). The combination of intense neotectonic activities and extreme rainfall has shaped the geomorphology of the Alaknanda basin into steep slopes, high relief, and a high drainage density (Chopra et al. 2012; Rautela et al. 2022c). Mountainous terrain makes the basin subject to microclimates, and temperatures vary seasonally and spatially (from river valleys to higher altitude regions). In ARB, Tungnath has the lowest average daily temperature of 0.5 °C in January and the highest average daily temperature of 30 °C in June in Srinagar (Panwar et al. 2017). The monsoon brings more than 80% of the annual rainfall to India during the summer months of June to September (Kumar et al. 2010).
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

Consequently, the Alaknanda basin often witnesses cloud bursts, flash floods, and landslides due to heavy rainfall and narrow valleys. The tributaries contribute a high streamflow of water to the river, including the western Dhauliganga, Nandakini, Pinder, and Mandakini. Snowmelt, glacier melt, and seasonal rainfall are the main contributors to the perennial flows in these tributaries. According to Strahler (1964) classification, the Alaknanda River makes a dendritic drainage pattern with an order of 6th, and the average slope of the basin is approximately 30°C (Figure 1). In terms of LULC, water bodies, forest, grass, agricultural land, residential area, barren land, and permanent snow covers an area of 0.41, 65, 2, 0.80, 1.70, 17.40, and 11%, respectively. Alaknanda basin has substantial hydropower potential from an economic perspective. Using the Alaknanda River and its tributaries as a renewable energy source, the SNDRP (2021) notes 37 hydropower dams are operating, under construction, or planned.

Data

Spatial data

Digital Elevation Model (DEM) is the essential input parameter for each hydrological model. The DEM stores information about the geographic grid of a particular area, where each pixel of the grid describes a specific location with its elevation (Figure 2(a)). In ArcSWAT, DEM is used to delineate the catchment boundary, sub-catchments boundary, stream network generation, and identification of the catchment slope. In this study, the ASTER DEM for resolution 30 m has been used. Also, the DEM is further used to classify the study area based on the elevation (Figure 2(b)). The LULC data for the catchment area have been prepared using Sentinal-2 imagery of 10 m resolution on Erdass imagine 2014 (Figure 2(c)). The study area is further divided into 10 land-use classes using supervised classification. Soil data sets were acquired from Harmonized World Soil Database (HSWDS) – Food and Agriculture (FAO), and the classification of the soil was done according to the universal soil classification (Figure 2(d)). The catchment slope was classified into five categories such as 0–3, 3–10, 10–15, 15–30, and 30–9,999 m (Figure 2(e)).
Figure 2

(a) Digital elevation model, (b) elevation zones, (c) LULC classification, (d) soil classification, (e) slope classification, and (f) average yearly precipitation (in mm) received by the Alaknanda river basin.

Figure 2

(a) Digital elevation model, (b) elevation zones, (c) LULC classification, (d) soil classification, (e) slope classification, and (f) average yearly precipitation (in mm) received by the Alaknanda river basin.

Close modal

Meteorological data

The meteorological parameters of the catchment are the most important dataset for modelling the hydrological processes. The meteorological parameters such as solar radiation (S), relative humidity (RH), wind speed (W), and max–min temperature (Tmax and Tmin) were taken on a daily time scale from MERRA-2 (Table 1), whereas the precipitation data (Figure 2(f)) will be acquired from TRMM and IMD gridded weather data.

Table 1

Sources of the dataset used in this study

S.No.Data typeSourceSpatio-temporal resolutionDescription
1. Topography USGS earth data 30 m ASTER DEM 
2. Land-use land cover USGS earth explorer- Sentinel-2A satellite imaginary 10 m Land-use classification 
3. Soils Harmonized World Soil Database (HSWDS) – Food and Agriculture (FAO) – Soil Classification 
4. Meteorological MERRA-2 Daily Relative Humidity, Solar radiation, Wind speed, Max & Min Temperature 
5. Meteorological TRMM Daily Precipitation 
6. Hydrological Central Water Commission (CWC) Daily Streamflow data obtained at the gauging station 
Monthly 
S.No.Data typeSourceSpatio-temporal resolutionDescription
1. Topography USGS earth data 30 m ASTER DEM 
2. Land-use land cover USGS earth explorer- Sentinel-2A satellite imaginary 10 m Land-use classification 
3. Soils Harmonized World Soil Database (HSWDS) – Food and Agriculture (FAO) – Soil Classification 
4. Meteorological MERRA-2 Daily Relative Humidity, Solar radiation, Wind speed, Max & Min Temperature 
5. Meteorological TRMM Daily Precipitation 
6. Hydrological Central Water Commission (CWC) Daily Streamflow data obtained at the gauging station 
Monthly 

Hydrological data

The hydrological data of Alaknanda river at the outlet of the basin was acquired for a period of 1982–2016 on both a daily and monthly period from the Central Water Commission (CWC) (Table 1). The gauging station was installed at the Devprayag before the confluence of the Alaknanda and Bhagirathi. These measured data were used to correlate with the simulated streamflow, and the model was calibrated and validated based on it.

Model setup

Snowmelt modelling

The SWAT model estimates snow accumulation and melting using a temperature-index approach. The melt received by snow at the outlet of the basin is calculated based on the difference between the maximum and threshold temperature of the snowpack. During the calculation of infiltration and runoff, snowmelt is combined with rainfall. SWAT does not specifically address snowmelt processes in frozen soils, but a provision is made for adjusting infiltration and estimating runoff when soils are frozen (Neitsch et al. 2011). Despite this limitation, SWAT is still thought to be an appropriately integrated approach for dealing with a wide range of problems. Many existing models are unable to simulate both snowmelt and rainfall–runoff processes together. According to the temperature-index model, the temperature significantly affects snowmelt (Hock 2003). The computation of the snowmelt is as follows (Equation (1)):
(1)
where is the melt factor (mm H2O-day), is the amount of snowmelt (in mm of H2O), is the maximum air temperature (°C) on an ith day (mm of water), and SMTMP (°C) is allowable snowmelt base temperature.
The infiltration and runoff rates are computed using precipitation and snowmelt. In addition, precipitation is classified according to the average air temperature as a threshold value. In the hydrological response unit (HRU), if the average daily temperature falls below the SFTMP (also called the critical temperature), the precipitation is considered solid precipitation and it is added to the snowpack. The process of snowmelt and sublimation causes depletion of the snowpack and the mass balance for the HRUs is given in the following equation.
(2)
where SNOi is the water content of snowpack, Ps is the snow precipitation equivalent to water, snow sublimation, and is the snowmelt equivalent to water (mm of water).
The area depletion curve is accounted for the HRUs by taking the variable snow coverage as shown in Equation (3) (Anderson 1976).
(3)
where is the fraction of HRU area covered by snow, SNOi is the water content of the snowpack on an ith day, SNOCOVMX is the minimum snow water content that corresponds to 100% snow cover (mm H2O), and cov1 and cov2 are coefficients that control the shape of the curve.

Elevation bands

The elevation and temperature determine the snowpack and snowmelt caused by changes in the orographic variation of precipitation and temperature. Several studies have found that elevation plays a significant role in determining temperature and precipitation variations (Zhang et al. 2008). A modified snowfall–snowmelt routine for mountainous terrain was introduced into SWAT by Fontaine et al. (2002). Due to this modification, each sub-basin can be partitioned into 10 elevation bands, and snowpack and snowmelt can be simulated based on elevation. To adjust for temperature and precipitation, the following factors were used:
(4)
(5)
where P and PB are the measured precipitation at stations and mean precipitation in the elevation band (mm), respectively, T and TB are the measured temperature at stations and mean temperature (°C) in the elevation band respectively, Z and ZB are the elevations and mean elevation of the band (m), dP/dZ and dT/dZ are the precipitation and temperature lapse rate in mm/km and °C/km, respectively.

Modelling of catchment hydrology

The most important inputs to the SWAT model are topography, vegetation, soil properties, and land management practices (Figure 3). An exponential function of the soil depth and water content is used in SWAT to determine actual soil-water evaporation. Runoff is computed using the modified Soil Conservation Service (SCS) CN method. Also, the runoff calculation takes into account both canopy infiltration and the amount of snow on the ground. To ensure that soil–water processes, such as evaporation, infiltration, lateral flow, plant uptake, and percolation to lower layers are supported, numerous layers are present in the soil profile. A downward flow occurs when the field capacity of a soil layer exceeds, and the layer below is not saturated. Recharge of the shallow aquifer occurs through percolation from the soil profile. In parallel with the percolation rate, the lateral subsurface flow is calculated. A shallow aquifer storage component is routed to a stream to simulate the contribution of groundwater to total streamflow. The runoff is routed through the channel network using either the variable storage routing technique or the Muskingum routing technique (Neitsch et al. 2011).
Figure 3

Flow chart of the methodology for simulation of streamflow.

Figure 3

Flow chart of the methodology for simulation of streamflow.

Close modal
A main possible technique for assessing water yield, nutrient, and sediment circulation is to simulate the hydrologic cycle, which incorporates the overall water circulation within the catchment (Equation (6)).
(6)
where SWt is the Soil humidity/final water content, SW is the base humidity/initial soil–water content, Rv is the rainfall volume, Qs is the surface runoff, Wseep is the seepage of water in the underlying soil layer, ET is the Evapotranspiration, Qlat is the amount of lateral flow, Qgw is the amount of return flow on an ith day (mm water), and t is time in days.

Model simulation

The model simulation was done using the ArcSWAT 2012 interface using 35 years of streamflow data in two different time scales, namely daily and monthly have been used for this study.

The number of years to skip (NYSKIP) also called the model warm up period has been taken as 4 years (1982–1985).

Model calibration and validation

Sequential Uncertainty Fitting (SUFI-2) algorithm has been adopted for calibration and validation of simulated streamflow with the measured streamflow using SWAT-CUP 2012 for daily and monthly time step (Abbaspour et al. 2015). SWAT-CUP is a computerized program used to calibrate, validate, and analyse sensitivity for various hydrological processes simulated by SWAT. SWAT-CUP program uses various algorithms such as sequential uncertainty fitting (SUFI), particle swarm optimization (PSO), generalized likelihood uncertainty estimation (GLUE), Parasol, and Markov chain Monte Carlo (MCMC) to improve the model performance by changing the model parameters systematically. Different parameters such as soil parameters, groundwater, basin, HRU, and channel roughness parameters have been introduced to improve the model performance. The calibration period was set from 1986 to 2007 for 22 years. Furthermore, optimized values of these improved parameters are used for the validation of the streamflow data. The validation period was kept to be nine years from 2008 to 2016. The assessment of strength of the simulated streamflow was measured by the p-factor and r-factor. When the p-factor approaches unity and the r-factor approaches zero, the model is considered an ideal hydrological model (Abbaspour et al. 2007). For the streamflow simulation, a value of p-factor greater than 0.7 is adequate (Abbaspour et al. 2015). Also, the model performance was measured by the coefficient of determination (R2) (Equation (7)), Nash–Sutcliffe efficiency (NSE) (Equation (8)), modified coefficient of determination (bR2) (Equation (9)), ratio of the standard deviation of observations to root mean square error (RSR) (Equation (10)), King–Gupta efficiency (KGE) (Equation (11)), and percentage of bias (PBIAS) (Equation (12)). The simulated streamflow was optimized by setting Nash–Sutcliffe efficiency (NSE) as an objective function.
(7)
(8)
(9)
(10)
(11)
(12)
where   are the mean measured and simulated streamflow during the period, and is the measured and simulated streamflow in the ith day (cum/s), respectively, and n is the number of data points, R is the correlation coefficient, b is the coefficient of regression, r is the linear regression coefficient between observed and simulated streamflow, and , where σm and σs are the standard deviations of the observed and simulated data, respectively, and μm and μs are the mean of observed and simulated data, respectively. The model is considered satisfactory if R2 > 0.55, NSE >0.5, bR2 ≥ 0.4, RSR ≤ 0.7, KGE ≥ 0.5, and PBIAS ≤± 0.25 (Moriasi et al. 2007; Akhavan et al. 2010; Muleta 2012; Abbaspour et al. 2015; Mehdi et al. 2015; Kouchi et al. 2017).

Sensitivity analysis

The SWAT is a continuous complex semi-distributed model that requires a large number of parameters. In the calibration of streamflow, sensitivity analysis plays an important role in finding the suitable parameters (Imani et al. 2019). Sensitivity analysis is a technique used to identify parameters which have a significant influence on model performance (Holvoet et al. 2005). Local sensitivity analysis (LSA) uses the one-at-a-time (OAT) methodology, which analyses the impact of a single parameter at a time, while keeping the others unchanged (Abbaspour et al. 2017). Whereas, using multiple regression analysis, the Latin hypercube-generated parameters were regressed on the goal function values so that the global sensitivity (GS) of model parameters could be estimated (Arnold et al. 2012). A GS analysis was performed to evaluate the sensitivity of the calibrated model parameters. To assess the importance of each calibrated parameter, the statistical t-test and p-value have been conducted. Sensitivity is considered to be higher for larger t-values. If the p-value approaches zero, the model is considered significant (Abbaspour et al. 2015).

The basin of the Alaknanda river is sub-divided into five sub-basins, namely, Alaknanda Main Basin (AMB), Western Dhauliganga Basin (WDB), Nandakini Basin (NB), Pinder Basin (PB), and Mandakini Basin (MB) in the direction of the main river (Figure 4). The model divides the land-use land cover into 10 sub-classes (Figure 2(c)). In addition, 0.42, 33.99, 0.92, 0.76, 32.98, 1.67, 17.39, 10.52, 1.34, and 0.01% of the catchment area is covered by water, evergreen forests, grassland, agricultural land, mixed forest with small shrubs, built-up area, barren land, permanent snow and ice, pastureland and deciduous forest, respectively. The watershed is divided into three sub-classes of soils. About 61.63% of the total catchment area is covered by soil type I-Bh-U-C-3717 (Loam) (Soil hydrologic group C) followed by 23.63% area is covered by soil type Bd29-3c-3661 (clay_loam) (Soil hydrologic group C) (clay_loam) and 15.24% area is covered by soil type Glacier-6998 (UWB) (Soil hydrologic group D) (Figure 2(d)). The soils are categorized in group C and D which allows very less infiltration and are responsible for the quick generation of surface runoff. In the last step, the model divides the catchment slope into the 5 classes as 0–3, 3–10, 10–15, 15–30, and 30–9,999 m with the help of DEM. About 47.96% of the total area is covered by the slope range 60–9,999 m followed by the 35.95% area is covered by a range 30–60 m (Figure 2(e)) which shows the catchment has very steep slopes. A total of 122 nos. of HRU has been created by the unique combinations of 5 land-use classes, 3 soil classes, and 5 slope classes for the 5 sub-watersheds of the Alaknanda River. Four land-use classes such as WATR (water), INDN (grassland), AGRR (Agricultural land) and FRSD (Deciduous forests) have been neglected because their value is below the threshold limit.
Figure 4

Different sub-watersheds of the Alaknanda river along the mainstream.

Figure 4

Different sub-watersheds of the Alaknanda river along the mainstream.

Close modal
The components of the hydrological cycle obtained after the simulation are shown in Figure 5. The precipitation received by the catchment is very high due to the circulation of the South-West monsoon. The Potential Evapotranspiration (PET) is also quite high because of the high vegetative cover over the catchment area. The average CN for the catchment was found to be 77.72 (Figure 5). The estimated surface runoff for the catchment was 160 mm. The streamflow/precipitation ratio (runoff–rainfall ratio) was 0.62, which is also satisfactory for this region. The surface runoff of the catchment is high due to the high precipitation values received by the catchment. All the water balance ratios are under the permissible limits and are shown in Figure 5.
Figure 5

Water balance components after the initial simulation of the SWAT model.

Figure 5

Water balance components after the initial simulation of the SWAT model.

Close modal

Calibration and validation of the hydrological simulation

The model calibration was done by using the thumb rule. According to the thumb rule the number of simulations depends on the number of parameters used in the calibration (Abbaspour et al. 2015). A minimum of 100 simulations was used for each parameter during the calibration phase of the model. The number of iterations depends on the model evaluation parameters. If the model satisfies the range of statistical indices the calibration process was stopped. The model evaluation parameters such as R2, NSE, bR2, RSR, KGE and PBIAS for the initial calibration were found to be 0.20, −0.20, 0.08, 1.10, 0.25, 71.7 and 0.48, 0.26, 0.22, 0.86, 0.33 54.9, respectively, for daily and monthly time step (Table 3). The result of initial calibration indicates there is a very high inconsistency between the observed and simulated streamflow. The SUFI algorithm uses five elevation bands and 19 parameters to improve consistency, high flow, low flows, and model efficiency. Using the thumb rule for the iterations, the optimum values of the calibrated model parameters have been adjusted. The range and the optimum fitted values of the model parameters are shown in Table 2. The Precipitation lapse rate (PLAPS) was calibrated within 1,000–2,000 mm, and it was fitted at 1,468.50 and 1,739.47 mm, respectively, for the daily and monthly time steps. The temperature lapse rate (TLAPS) was calibrated within −7 to −6 °C and fitted at −6.25 °C for daily and monthly time steps (Table 2). These two parameters are used to adjust the precipitation and temperature rates according to the elevation; as a result, snowmelt contribution in the streamflow is improved. The Snowfall temperature (SFTMP) was calibrated within −5 to 5 °C and fitted at 4.06, and 2.29 °C, Snowmelt base temperature (SMTMP) was calibrated within −5 to 5 and fitted at 3.48 and 2.71 °C, maximum melt factor (SMFMX) was calibrated within 1–10 and fitted at 2.40 and 1.31, minimum melt factor (SMFMN) was calibrated with and 1–10 and fitted at 6.91 and 1.54, and snowpack temperature lag factor (TIMP) was calibrated within −1 to 1 and fitted at −0.19 and −0.47, respectively, for daily and monthly time step (Table 2). These five parameters have also improved the snowmelt contribution in the streamflow. The minimum snow water content that corresponds to 100% snow cover (SNOCOVMAX) was calibrated within 0–200 mm and fitted at 156 and 60.53 mm, and the fraction of snow volume represented by SNOCOMX corresponds to 50% snow cover (SNO50COV) was calibrated within 0–0.25 and fitted at 0.07 and 0.16, respectively, for daily and monthly time step (Table 2). These two parameters show there is significant snowfall in the basin. The curve number CN2 value was calibrated within −0.3 to 0.3, and it was fitted at 0.09 and 0.10. The available water capacity of the soil layer (SOL_AWC) was calibrated within 0–0.2 mm water/mm soil. It was fitted at 0.13 and 0.18 mm water/mm soil, and the soil evaporation compensation factor (ESCO) was calibrated within 0.05–0.2. It was fitted at 0.06 and 0.10, respectively, for daily and monthly time steps (Table 2). These three parameters indicate the peak surface runoff and streamflow were improved due to a small increase in the CN2 and higher water uptake demand from the lower soil layer. The threshold water level in a shallow aquifer for base flow (GWQMN) was calibrated within 0–20 mm. It was fitted at 9.37 and 19 mm, the groundwater ‘revap’ coefficient (GE_REVAP) was set to be zero 0.0, and the percolation of the surface runoff to the deep aquifer (REVAPM) was calibrated within 0–100 mm. It was fitted at 96.87 and 96.58 mm. Re-evaporation through ground water (GW_REVAP) was set to be zero, delay in the ground water (GW_DELAY) was calibrated within 10–50 days and fitted at 11 and 28 days, respectively, for daily and monthly time step and base-flow recession coefficient (ALPHA_BF) was calibrated within 0–0.25. It was fitted at 0.19, respectively, for both daily and monthly time steps (Table 2). The calibrated groundwater parameters indicate that a 19 mm water level is required for base flow in the shallow aquifer while 96 mm is the threshold depth of water in the shallow aquifer percolate to the deep aquifer, and the maximum amount of groundwater will transfer to the overlaying saturated zone from the shallow aquifer whereas (Singh & Saravanan 2020), the very less response of groundwater flow to changes in recharge because of the presence of impermeable rocks present in the Himalayan region. The effective hydraulic conductivity of the channel (CH_K2) was calibrated within 5–10 mm/h, and it was fitted at 7.34 and 6.43 mm/h, respectively, for daily and monthly time steps (Table 2) and shows there is a very less rate of surface water from the main channel. After calibration, the model evaluation parameters such as R2, NSE, bR2, RSR, KGE, and PBIAS were found to be 0.60, 0.55, 0.46, 0.68, 0.75, 13.2 and 0.75, 0.74, 0.64, 0.51, 0.86, 10.50, respectively, and also p-factor and r-factor for the calibration period were found to be 0.79, 1.21 and 0.79, 1.30, respectively, for daily and monthly time step (Table 3). Due to these, the peak flow, as well as base flow, has been improved (Figures 6(a) and 7(a)) also the correction of the observed streamflow and simulated streamflow is improved (Figure 8(a,b)). Further, the calibrated parameters were used to validation of the streamflow for the period 2008–2016. The R2, NSE, bR2, RSR, KGE, and PBIAS were found to be 0.65, 0.59, 0.50, 0.56, 0.79, 7.5 and 0.82, 0.78, 0.64, 0.51, 0.86, 9.25, respectively, for daily and monthly time step and also the p-factor and r-factor for the validation period was found to be 0.73, 1.30 and 0.87, 1.35, respectively, for daily and monthly time step (Table 3). In the validation, some peaks of the streamflow hydrograph were not properly captured by the model, whereas the model captured the lower peaks due to the lower range of CN2 (Figures 6(b) and 7(b)), and the results show the observed and simulated discharge shows the good correlation for the validation period (Figure 8(c) and 8(d)). The efficiency parameters are also improved for calibration as well as the validation period.
Table 2

Description of the parameters with fitted values for daily and monthly simulated streamflow

S. No.Parameter with qualifierMinimum rangeMaximum rangeFitted value (daily)Fitted value (monthly)
V__PLAPS.sub 1,000 2,000 1,468.75 1,739.47 
V__TLAPS.sub −7 −6 −6.09 −6.25 
V__SFTMP.bsn −5 4.06 2.29 
V__SMTMP.bsn −5 3.44 2.71 
V__SMFMX.bsn 10 2.41 1.31 
V__SMFMN.bsn 10 6.91 1.54 
R__TIMP.bsn −1 −0.19 −0.47 
R__SNO50COV.bsn 0.25 0.07 0.16 
V__SNOCOVMX.bsn 200 156.25 60.53 
10 R__CN2.mgt −0.3 0.3 0.08 0.10 
11 R__SOL_AWC().sol 0.2 0.13 0.18 
12 R__ESCO.hru 0.05 0.1 0.06 0.10 
13 R__ALPHA_BF.gw 0.25 0.20 0.19 
14 R__RCHRG_DP.gw 0.00 0.00 
15 V__GWQMN.gw 20 9.38 19.00 
16 V__GW_DELAY.gw 10 50 11.25 28.00 
17 V__REVAPMN.gw 100 96.88 96.58 
18 V__GW_REVAP.gw 0.00 0.00 
19 V__CH_K2.rte 10 7.34 6.43 
S. No.Parameter with qualifierMinimum rangeMaximum rangeFitted value (daily)Fitted value (monthly)
V__PLAPS.sub 1,000 2,000 1,468.75 1,739.47 
V__TLAPS.sub −7 −6 −6.09 −6.25 
V__SFTMP.bsn −5 4.06 2.29 
V__SMTMP.bsn −5 3.44 2.71 
V__SMFMX.bsn 10 2.41 1.31 
V__SMFMN.bsn 10 6.91 1.54 
R__TIMP.bsn −1 −0.19 −0.47 
R__SNO50COV.bsn 0.25 0.07 0.16 
V__SNOCOVMX.bsn 200 156.25 60.53 
10 R__CN2.mgt −0.3 0.3 0.08 0.10 
11 R__SOL_AWC().sol 0.2 0.13 0.18 
12 R__ESCO.hru 0.05 0.1 0.06 0.10 
13 R__ALPHA_BF.gw 0.25 0.20 0.19 
14 R__RCHRG_DP.gw 0.00 0.00 
15 V__GWQMN.gw 20 9.38 19.00 
16 V__GW_DELAY.gw 10 50 11.25 28.00 
17 V__REVAPMN.gw 100 96.88 96.58 
18 V__GW_REVAP.gw 0.00 0.00 
19 V__CH_K2.rte 10 7.34 6.43 
Table 3

Model performance evaluation for daily and monthly simulation

Performance evaluation parametersR2NSEbR2RSRKGEPBIASp-factorr-factor
Daily Initial calibration 0.20 −0.20 0.08 1.10 0.25 71.7 – – 
Calibration 0.60 0.54 0.46 0.68 0.75 13.2 0.79 1.21 
Validation 0.65 0.59 0.50 0.56 0.79 7.5 0.73 1.30 
Monthly Initial calibration 0.48 0.26 0.22 0.86 0.33 54.9 – – 
Calibration 0.75 0.74 0.64 0.51 0.86 10.50 0.79 1.30 
Validation 0.82 0.78 0.64 0.48 0.82 9.25 0.87 1.35 
Performance evaluation parametersR2NSEbR2RSRKGEPBIASp-factorr-factor
Daily Initial calibration 0.20 −0.20 0.08 1.10 0.25 71.7 – – 
Calibration 0.60 0.54 0.46 0.68 0.75 13.2 0.79 1.21 
Validation 0.65 0.59 0.50 0.56 0.79 7.5 0.73 1.30 
Monthly Initial calibration 0.48 0.26 0.22 0.86 0.33 54.9 – – 
Calibration 0.75 0.74 0.64 0.51 0.86 10.50 0.79 1.30 
Validation 0.82 0.78 0.64 0.48 0.82 9.25 0.87 1.35 
Figure 6

(a) Calibration and (b) validation of streamflow on the daily time step.

Figure 6

(a) Calibration and (b) validation of streamflow on the daily time step.

Close modal
Figure 7

(a) Calibration and (b) validation of streamflow on monthly time step.

Figure 7

(a) Calibration and (b) validation of streamflow on monthly time step.

Close modal
Figure 8

Correlation between measured and simulated streamflow (a) for calibration on daily time step, (b) validation on daily time step, (c) calibration on monthly time step, and (d) validation on monthly time step.

Figure 8

Correlation between measured and simulated streamflow (a) for calibration on daily time step, (b) validation on daily time step, (c) calibration on monthly time step, and (d) validation on monthly time step.

Close modal

Sensitivity analysis

For simulated streamflow, the calibrated parameters were used for the sensitivity analysis, and the GS analysis was used for the monthly and daily periods. A higher t-value and lesser p-value indicate the most sensitivity at a 0.05 significance level (Abbaspour et al. 2017). The calibrated SWAT parameters have been used for the sensitivity analysis, and apart from that, six parameters such as SFTMP, TLAPS, SMTMP, CN2, SMFMX, and GW_DELAY are found most sensitive at a significance level less than 0.05 during the daily and monthly simulated streamflow, respectively (Figure 9). Out of six sensitive parameters, four parameters are snow parameters, and the result shows the contribution of the snowmelt is significant in streamflow while the CN2 and delay in the groundwater will affect the contribution of surface runoff and groundwater in the streamflow.
Figure 9

Sensitive parameters used in the simulation of hydrological response of ARB.

Figure 9

Sensitive parameters used in the simulation of hydrological response of ARB.

Close modal

Water balance components of the basin

Due to the high elevation range in the Alaknanda basin, the snowmelt is found as a predominant factor in the streamflow. Results of this study show the snowmelt's contribution in the total streamflow ranges between 20 and 24% (Table 4), whereas the contribution of rainfall is also high in the streamflow, which also ranges between 10 and 36% (Table 4). The loss of water due to ET will range from 34 to 39% of the total precipitation received by the catchment (Figure 10(a) and 10(b)). The groundwater contribution of the basin is also good due to the contribution of springs to the streamflow. These springs are recharged during the monsoons and provide an adequate amount of water to the streamflow. A very little amount of water percolates into the deep aquifer due to the presence of hard strata. The components of the water yield are shown in Figure 10(a) and 10(b). It also shows there is a heavy loss of water through ET. An adequate amount of runoff is found at the basin outlet due to the combined effect of rainfall, snowmelt, and groundwater flow. Also, recharge of shallow aquifers in the basin due to precipitation ranges from 15 to 17%, 2% of water is re-evaporated through groundwater, and only 1% of water is used to recharge the deep aquifers (Figure 10(a) and 10(b)).
Table 4

Water balance components for Alaknanda basin

ComponentsInitial simulationFinal simulation
Precipitation 786.3 689.3 
Snow fall 74.61 70.57 
Snow melt 74.44 70.49 
Sublimation 0.32 0.29 
Surface runoff 160 121.95 
Lateral soil 194.73 169.33 
Groundwater (shallow aquifer) 135.78 106.31 
Groundwater (deep aquifer) 6.38 
Re-evaporation through Shallow aquifer 15.71 16.14 
Deep aquifer recharge 7.97 6.44 
Total aquifer recharge 159.42 128.82 
Total water yield 498.52 403.98 
Percolation out of soil 159.35 128.78 
ET 272.9 270 
PET 786 807 
All units are in mm 
ComponentsInitial simulationFinal simulation
Precipitation 786.3 689.3 
Snow fall 74.61 70.57 
Snow melt 74.44 70.49 
Sublimation 0.32 0.29 
Surface runoff 160 121.95 
Lateral soil 194.73 169.33 
Groundwater (shallow aquifer) 135.78 106.31 
Groundwater (deep aquifer) 6.38 
Re-evaporation through Shallow aquifer 15.71 16.14 
Deep aquifer recharge 7.97 6.44 
Total aquifer recharge 159.42 128.82 
Total water yield 498.52 403.98 
Percolation out of soil 159.35 128.78 
ET 272.9 270 
PET 786 807 
All units are in mm 
Figure 10

Water balance components for (a) initial simulation (b) final simulation.

Figure 10

Water balance components for (a) initial simulation (b) final simulation.

Close modal

Singh & Jain (2002) conducted a modelling analysis for the Satluj basin, using the SNOWMOD snowmelt runoff model to cover the Satluj basin up to the Bhakra dam site, downstream of Rampur Site. They discovered that the average annual contributions of snowmelt and rainfall to streamflow are around 68 and 32%, respectively. Singh & Jain (2002) determined that the contribution from snow and glacier melt about 59% of the yearly flow and the contribution from rainfall is around 41% in another study of this basin at the Bhakra Dam site (downstream of Rampur). According to Khajuria et al. (2022), a significant portion of streamflow is produced throughout the summer and monsoon seasons, with a contribution from snowmelt ranging from 10 to 45%. Kumar et al. (2021) assess the CC impact on snowmelt runoff of the Alaknanda river using SRM. They discovered a 20% and 2°C rise in the precipitation and temperature would result in a 37 and 53% rise in the streamflow. Based on previous researches, the Alaknanda region experiences the lowest snow cover percentage during the summer months, while February sees the highest snow cover area percentage of the entire year, at about 77%. Changes in snow cover and snow depth are common events that have a significant impact on snowmelt flow. As the snow cover begins to go, the snow's depth also decreases, the volume of melting rises, and there is an increase in runoff from snowmelt. Snow cover starts to thaw after February and persists through the end of November, which can be a major factor in increasing Snow melt Runoff at this time of year. The Snowmelt runoff starts to grow from June, peaking in July to August, and decreases until the end of December. However, the snowmelt contribution up to the Devprayag location is equivalent to previous researchers' estimates in the current study (Singh & Jain 2002, 2003; Jain et al. 2010; Shukla et al. 2019).

Sustainable strategies for land and water resource management

Numerous studies from the last four decades have indicated the stress on land and water resource of the mountains under increasing population coupled with changing land use, and CC. Development and management of water resources continue to be central to the fight for sustainable growth, economic development, and reduced poverty. Results presented in this study illustrate the significant contribution of snowmelt in the streamflow along with the preponderance of groundwater delay that shall affect the inputs from surface runoff and groundwater towards streamflow in the Alaknanda basin. This commends to very favourable condition(s) for artificial recharge of groundwater via site suitable Groundwater Augmentation Measures (GAMs). A groundwater recharge potentiality map for the Area of Interest (AoI), considering most of the influencing parameters like slope, soil, geology, lineaments, LULC, etc., can be prepared following RS and GIS techniques. This shall aid in the identification of sites of various recharge capabilities within the AoI. Based on the developed map, attempt towards delineating apposite GAMs like the use of recharge pits and trenches in areas of high recharge potentiality, masonry and live check dams for the region of moderate potentiality, and seeding of grasses, stone bunds, plantation of broad-leaf hardwood, etc., over the regions of low potentiality which are usually accompanied by excessive slopes and relatively impervious stratum, can be suggested. However, the downstream regions of Himalayan rivers largely depend on these rivers in terms of irrigation, and due to CC the irrigation patterns and cropping intensity of these rivers are largely affected (Suhardiman et al. 2018; Kaini et al. 2020). On the other hand, various socioeconomic factors such as household size, occupation, landholding, and food sufficiently from land affect crop intensity (Kaini et al. 2020). The Indian state of Uttarakhand's government has acknowledged the need to use the potential for irrigation and hydropower fully. The aforesaid work, therefore, not only aligns with the ethics of field applicability and practicality towards conserving groundwater and surface water resources in mountains and fluvial valleys but also offers baseline data of seasonal water availability for water resource planners and policymakers in the formulation of sustainable land and water management strategies, thus, securing future water sustainability under changing land use and climate.

This study has attempted a long-term hydrological simulation to understand the hydrological response of a Himalayan River viz, before the confluence of Alaknanda River at Devprayag. The model evaluation parameters for both calibration and validation would be considered as good for both periods. In general, the hydrograph shape was reproduced satisfactorily, except for some peaks and recession limbs that were difficult to reproduce. Therefore, a basin situated in the Himalayas can thus be considered to have a good water balance model through SWAT, which allows modelling of streamflow hydrographs and other components in a basin. The water yield of the basin is found to be 40%, ET ranges between 29 and 33%, of the total precipitation received by the basin. The contribution of the snowmelt in the total streamflow ranges between 20 and 24%, whereas the contribution of rainfall is also high in the streamflow, which also ranges between 10 and 36%. In the lower Alaknanda basin, interflow has a significant contribution to the streamflow. However, it is necessary to supplement these results with more detailed hydrologic modelling of additional river basins to study their response mechanisms. The importance of upgrading spatial, soil, and hydro-meteorological databases and monitoring precipitation (rain and snow) and other climatic variables at different elevations should be given greater consideration for the distributed hydrological modelling. Furthermore, isotope analysis can be performed and compared with hydrologic models to separate the components of runoff. This study provides the baseline data for identifying the flood peaks and should be used to develop a model for flash floods in the different sub-basins of the ARB with the availability of detailed hydro-meteorological data sets.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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