Abstract
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.
HIGHLIGHTS
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
LIST OF ABBREVIATIONS
- 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
INTRODUCTION
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.
STUDY AREA
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.
MATERIALS AND METHODS
Data
Spatial data
(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.
(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.
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.
Sources of the dataset used in this study
S.No. . | Data type . | Source . | Spatio-temporal resolution . | Description . |
---|---|---|---|---|
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 type . | Source . | Spatio-temporal resolution . | Description . |
---|---|---|---|---|
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






Elevation bands
Modelling of catchment hydrology
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





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).
RESULT AND DISCUSSION
Different sub-watersheds of the Alaknanda river along the mainstream.
Water balance components after the initial simulation of the SWAT model.
Calibration and validation of the hydrological simulation
Description of the parameters with fitted values for daily and monthly simulated streamflow
S. No. . | Parameter with qualifier . | Minimum range . | Maximum range . | Fitted value (daily) . | Fitted value (monthly) . |
---|---|---|---|---|---|
1 | V__PLAPS.sub | 1,000 | 2,000 | 1,468.75 | 1,739.47 |
2 | V__TLAPS.sub | −7 | −6 | −6.09 | −6.25 |
3 | V__SFTMP.bsn | −5 | 5 | 4.06 | 2.29 |
4 | V__SMTMP.bsn | −5 | 5 | 3.44 | 2.71 |
5 | V__SMFMX.bsn | 1 | 10 | 2.41 | 1.31 |
6 | V__SMFMN.bsn | 1 | 10 | 6.91 | 1.54 |
7 | R__TIMP.bsn | −1 | 1 | −0.19 | −0.47 |
8 | R__SNO50COV.bsn | 0 | 0.25 | 0.07 | 0.16 |
9 | V__SNOCOVMX.bsn | 0 | 200 | 156.25 | 60.53 |
10 | R__CN2.mgt | −0.3 | 0.3 | 0.08 | 0.10 |
11 | R__SOL_AWC().sol | 0 | 0.2 | 0.13 | 0.18 |
12 | R__ESCO.hru | 0.05 | 0.1 | 0.06 | 0.10 |
13 | R__ALPHA_BF.gw | 0 | 0.25 | 0.20 | 0.19 |
14 | R__RCHRG_DP.gw | 0 | 0 | 0.00 | 0.00 |
15 | V__GWQMN.gw | 0 | 20 | 9.38 | 19.00 |
16 | V__GW_DELAY.gw | 10 | 50 | 11.25 | 28.00 |
17 | V__REVAPMN.gw | 0 | 100 | 96.88 | 96.58 |
18 | V__GW_REVAP.gw | 0 | 0 | 0.00 | 0.00 |
19 | V__CH_K2.rte | 5 | 10 | 7.34 | 6.43 |
S. No. . | Parameter with qualifier . | Minimum range . | Maximum range . | Fitted value (daily) . | Fitted value (monthly) . |
---|---|---|---|---|---|
1 | V__PLAPS.sub | 1,000 | 2,000 | 1,468.75 | 1,739.47 |
2 | V__TLAPS.sub | −7 | −6 | −6.09 | −6.25 |
3 | V__SFTMP.bsn | −5 | 5 | 4.06 | 2.29 |
4 | V__SMTMP.bsn | −5 | 5 | 3.44 | 2.71 |
5 | V__SMFMX.bsn | 1 | 10 | 2.41 | 1.31 |
6 | V__SMFMN.bsn | 1 | 10 | 6.91 | 1.54 |
7 | R__TIMP.bsn | −1 | 1 | −0.19 | −0.47 |
8 | R__SNO50COV.bsn | 0 | 0.25 | 0.07 | 0.16 |
9 | V__SNOCOVMX.bsn | 0 | 200 | 156.25 | 60.53 |
10 | R__CN2.mgt | −0.3 | 0.3 | 0.08 | 0.10 |
11 | R__SOL_AWC().sol | 0 | 0.2 | 0.13 | 0.18 |
12 | R__ESCO.hru | 0.05 | 0.1 | 0.06 | 0.10 |
13 | R__ALPHA_BF.gw | 0 | 0.25 | 0.20 | 0.19 |
14 | R__RCHRG_DP.gw | 0 | 0 | 0.00 | 0.00 |
15 | V__GWQMN.gw | 0 | 20 | 9.38 | 19.00 |
16 | V__GW_DELAY.gw | 10 | 50 | 11.25 | 28.00 |
17 | V__REVAPMN.gw | 0 | 100 | 96.88 | 96.58 |
18 | V__GW_REVAP.gw | 0 | 0 | 0.00 | 0.00 |
19 | V__CH_K2.rte | 5 | 10 | 7.34 | 6.43 |
Model performance evaluation for daily and monthly simulation
Performance evaluation parameters . | R2 . | NSE . | bR2 . | RSR . | KGE . | PBIAS . | p-factor . | r-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 parameters . | R2 . | NSE . | bR2 . | RSR . | KGE . | PBIAS . | p-factor . | r-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 |
(a) Calibration and (b) validation of streamflow on the daily time step.
(a) Calibration and (b) validation of streamflow on monthly time step.
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.
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.
Sensitivity analysis
Sensitive parameters used in the simulation of hydrological response of ARB.
Water balance components of the basin
Water balance components for Alaknanda basin
Components . | Initial simulation . | Final 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) | 8 | 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 |
Components . | Initial simulation . | Final 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) | 8 | 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 |
Water balance components for (a) initial simulation (b) final simulation.
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.
CONCLUSIONS
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.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.