Alpine snow is crucial for the water cycle, as the runoff and environment of arid and semi-arid regions rely entirely on these glaciers. Mountain-fed rivers provides the water for domestic, agricultural irrigation, hydroelectric power, and other uses. Due to climate variability, river catchment flows may shift, causing floods and droughts that will aggravate the economy. This study estimated the SCA using Google Earth Engine (GEE) for the Larji River basin, situated in Himachal Pradesh, from the year 2001 to 2021. The snow cover area (SCA) varies from 0.7–22% in the basin. The present study highlights the effectiveness of cloud computing for assessing trends in snow-cover regions in the basin. Moderate Resolution Imaging Spectroradiometer (MODIS) snow product (MOD10A1 V6 Snow Cover Daily Global 500m product) is utilized for SCA calculation. This study simulates runoff from the Larji river, using Snow-melt Runoff Model (SRM) and Soil Water Assessment Tool (SWAT) for years (2019-2021). Furthermore, the Coefficient of determination (R2) Coefficient of corelation (r), Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) were used to evaluate the efficacy of models. This study will help the policymakers and stakeholders to carry out the water resource management practices in the study area.

  • Google Earth Engine-based snow cover area analysis.

  • Trends analysis of the Snow Cover Area (SCA) using R studio.

  • Development of Snow Melt Runoff Model (SRM) and Soil Water Assessment Tool (SWAT).

  • R studio-based statistical performance metrics (r, NSE, R2 and KGE) for model's efficacy.

The cryosphere is the part of the Earth's surface where water is found in the form of ice, glaciers, and permafrost. In the winter, snow covers nearly half of the land surface. The snow area distribution is generally different between the two hemispheres. The upper hemisphere has massive permanent snow cover, whereas the lower hemisphere has transient and annual snow. Snow covers an essential climate system component, and is sensitive to climate change. Snow is a valuable natural resource in the Himalayan region, used for various purposes, including hydropower generation, agricultural water supply, and home consumption. Snow, on the other hand, is crucial in natural hazards such as avalanche mitigation, flood forecasting, and so on (Bühler et al. 2015). The northern hemisphere has already faced the implications of climate change as the snow-covered area (SCA) is decreasing significantly. Various studies extensively examined the correlation between in situ temperature, radiation, and wind speed in the Himalayan region. Maskey et al. (2011) used MODIS satellite data from 2000 to 2008 and findings indicate a negative correlation with SCA. A similar study in the Hindu Kush region using MODIS snow data has shown that an increase in temperature and decrease in rainfall have contributed to more snowmelt (Moazzam et al. 2023).

Some of the studies in the Indian subcontinent have shown the changing climate's implications for SCA. (Banerjee et al. 2021) conducted a similar investigation in the Uttarakhand region to examine the changes in SCA and climatic variables based on terrain attributes by utilizing the cloud computing technique Google Earth Engine (GEE), CHIRPS rainfall data, and MODIS satellite data and the correlation between rainfall and SCA was found to be 0.78, from the years 2000 to 2020, there has been a decline in SCA, which can be attributed to the increasing temperature trend further resulting in increased melting.

An inverse relation of SCA with temperature and direct relation with precipitation was found in the Chenab River Basin for the years 2001–2017 (Dharpure et al. 2020). Ban et al. (2021) used the MOD10A1 and, MOD11 A2 data for SCA and temperature to carry out he thorough study in the Yarlung Zangbo River basin in China, pearson correlation coefficient with temperature shows a good relationship with SCA as compared to precipitation (CHIRPS). Guo et al. (2022) examined the spatiotemporal variability of the SCA and climate variable in the Yrlung Tsangpo Basin in Brahmaputra River with the help of MODIS Satellite data, during winter season correlation between SCA and climatic variables like Precipitation and temperature but in spring and autumn SCA is significantly related with the temp and precipitation. Accuracy assessment and trend analysis of the of MODIS derived snow data in the Satluj Basin of the western Himalayas with the help of Mann–Kendall (MK) and Sen's slope methods were performed by Mir et al. (2015), linear regression of snowfall and SCA shows a great relation of nearly 0.95. Negative trends were observed for the SCA whereas increasing trends were observed for the Tmax and Tmin resulting in the reduction of the SCA . The implication of increased temperature may alter the hydrological and ecological balance of the Satluj River Basin (Maskey et al. 2011).

There is a lack of stations in the alpine regions for monitoring of snow because of inhospitable terrain, owing to the importance of snow dynamics it is necessary to understand the trends and snowmelt contribution to the adjacent lowlands (Saloranta et al. 2019). Some of the researchers have used the GEE for SCA estimation and glacial studies (Jain et al. 2021; Zhang et al. 2021; Wangchuk et al. 2022).

According to the various Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC 2014), climate change will have a significant impact on economic growth, and the population will be susceptible as a result of commonly encountered problems such as increasing sea level rise, atmospheric disasters, and extreme weather events like storms, floods, and droughts. Melting Snow and ice absorbs more radiation and leaves less to the atmosphere which also contributes to the increase of the temperature. As these Himalayan basins are hydrologically very significant and owing to the importance of the Snow Dynamics more comprehensive understanding of its response to climate change is needed. Snowmelt models are often categorized as either temperature index (TI) models or energy balance (EB) models. The TI methodology considers temperature as the main factor influencing snowmelt, resulting in the very small amount of data required for the simulations (Ohmura 2001). In the TI model, the rate of ablation is determined by adding up the positive air temperatures within a specific time span (Hock 2003). The degree day factor is a parameter that establishes the relationship between daily snowmelt rates and air temperature and is adjusted in such a way that the agreement between air temperature and snowmelt is optimized. TI models have been successfully utilized by various researchers worldwide for the estimation of snowmelt but these types of models may not work effectively where the temperature is not effectively predicting how the process of snow melting happens due to the exchange of energy (Garen & Marks 2005).

Whereas EB models are mechanistic/process-based models of snowmelt that simulate the exchange of energy between the earth, snow, and atmosphere. These systems rely on the fundamental principles of radiative, sensible, latent, and convective heat transfer, and therefore, they do not need to be calibrated (Marks et al. 1999). EB models require extensive data inputs, although some researchers worldwide contend that the underlying physical processes of these models result in a more precise estimation of snowmelt (Kumar et al. 2013). One of the major challenges in utilizing the EB-based models is the possible scarcity of data, particularly in geographically isolated mountainous areas (Day 2009).

The TI, Snowmelt Runoff Model (SRM) were used with the help of MODIS 10A1, precipitation and temperature data in Zayandeh Rud River Basin by (Javadinejad et al. 2020) with different changing climate scenarios, indicates an increase in temperature leads to a decrease in precipitation and SCA on the study area which further aggravates the snowmelt runoff. A similar study was carried out by Khajuria et al. (2022) in parts of the Beas River basin using the SRM model for the years 2015–2018 and showed the efficacy of the model. Although both TI and EB models require the remotely sensed SCA, typically, the input consists of a Snow Depletion Curve (SDC) that is developed for either the whole research basin or specific elevation zones within the study basin (Homan et al. 2011). As an illustration, the SRM necessitates daily estimations of SCA for each elevation zone of approximately 500 m (Martinec et al. 1983).

Various Hydrological models, i.e. lumped, semi-distributed and distributed models have also been found very helpful for hydrological simulation of any river basin. Soil and Water Assessment Tool (SWAT) is a semi-distributed model which works on the Hydrological Response Unit (HRU) scale and requires a large amount of data for simulation. Chekole et al. 2024 did a comparative assessment of HEC-HMS and SWAT mode in the Ghumra basin in Ethiopia, results show that SWAT outperforms the HEC-HMS model. Similarly, Prajapati et al. 2024 simulated the Sunkoshi River Basin in Nepal using the SWAT model to generate spatially distributed rainfall–runoff and sub-basin-wise water balance components using various performance indicators, which has shown the SWAT as a successful tool for hydrological simulations. A similar study was carried out by Anand et al. 2024 in the data-scarce Manipur River basin in India with the help of SWAT and used coefficient of determination and Kling Gupta Efficiency (KGE) for validation of the simulated results. Raaj et al. 2024 used a similar approach with the help of the SWAT model for peak flow estimation in the Beas River, western Himalayas.

This study has the potential to give a preliminary database for the development of hydroelectric projects downstream. Additionally, it has the potential to provide baseline information for the management of land and water resources in the region. The following objectives have been accomplished for the Larji River basin:

  • 1.

    To calculate the SCA using GEE and analysis of the seasonal variation of SCA in the Larji river basin of western Himalaya.

  • 2.

    To carry out the trends analysis of the SCA using R studio on a seasonal and annual basis.

  • 3.

    To develop the SRM and SWAT model for estimation of runoff in the Larji river basin.

  • 4.

    The research used statistical performance metrics such as r, NSE, R2 and KGE to evaluate the hydrological model's efficacy.

Study area

In this study, Larji Basin has been chosen for the research and is located within the well-known Himachal Pradesh (India) (Figure 1). Its elevation ranges from 938 to 5,241 m from amsl. The delineated watershed of 680 km² (260 sq. miles) with an outlet near coordinates: 31.72° N, and 77.22° E. The Larji River is tributary to the Beas River. A recent study by Kant et al. 2023 using geomorphological parameters revealed the vulnerability of the Beas Basin and its tributaries to floods. Because of this huge relief in the elevation and season-wise temperature winter snowline is experienced above 1,500 m amsl and in summer it goes up to around 4,500 m. The weather in this particular region is chilly and dry. The temperature ranges from 15.8 to 15.6 °C. from January to June. Temperatures range from 21.1 to 0.7 °C. from July through December (average for the years 1985–2007). The average total annual precipitation is 1,363 mm (53.7 in) (est.hp.gov.in/sites/default/files/PDF/Climate_Change_Vulnerability_Kullu_HP.pdf). In general, the summers are moderate, but the winters are especially harsh because most of the region is covered in snow. Further, the elevation, slope and aspect map has been derived using ArcGIS 10.2 with the help of SRTM DEM 30 m resolution shown in Figure 2.
Figure 1

Study area showing the Larji Basin.

Figure 1

Study area showing the Larji Basin.

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Figure 2

Terrain attributes (slope, elevation, aspect, and hypsometry of the basin).

Figure 2

Terrain attributes (slope, elevation, aspect, and hypsometry of the basin).

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Data used

The discharge data for the Larji basin was acquired from the State Data Centre (SDC) Mandi for the three seasons spanning from 2019 to 2021. The temperature and precipitation data were acquired from the India Meteorological Department (IMD) for the same time duration. DEM with a 1 arc second resolution (30 m) from the Shuttle Radar Topography Mission (SRTM) was used (http://earthexplorer.usgs.gov/). The NASA NSIDC DAAC at CIRES provided further data on the extent of snow cover and was retrieved via the Earth Engine Snippet – ee.ImageCollection(‘MODIS/061/MOD10A1’). The MOD10A1 V6 Snow Cover Daily Global 500 m dataset comprises information on snow cover, snow albedo, fractional snow cover, and quality assessment (QA) data. The snow cover data are derived from a snow mapping method that utilizes a Normalized Difference Snow Index (NDSI) and additional criteria tests. Satellite images have been in use for so long and these satellite images have many advantages and disadvantages. Studies from the recent past (Hall & Riggs 2007; Pu et al. 2007; Liang et al. 2008; Wang et al. 2009) have revealed high efficacy (≥90% under clear sky conditions and at snow depths of ≥4 cm) of MOD10A1 data, in comparison to in situ observations. Tekeli et al. (2005) compared the SCA from MODIS satellite data with ground observations and found that the MODIS assessed the SCA proficiently even in rugged and undulating terrains like the Himalayas. Land use land cover (LULC) maps have been used from LANDSAT (usgs) and soil data from FAO (Food and Agriculture org) for the setting of the hydrological model.

Google Earth Engine

The conventional method of calculating the snow area uses the Normalized Difference Snow Index (NDSI) approach, applied to detect snow and ice, as well as differentiate snow and ice from the majority of cloud cover. For non-forest areas, a general threshold of NDSI > 0.4 is taken into account in order to differentiate snow or no snow. Certain phenomena, such as atmospheric or radiometric, might cause distorted pictures of the earth's surface during collection. As a result, removing these impacts before additional processing, such as categorization, change detection, or NDSI calculation, is strongly recommended. SCA calculation in ArcGIS is time-consuming and various correction measures have to be applied before using the satellite image. So, we have the alternative to this conventional method in the form of API as GEE. In this shape file of the study area has to be ingested into the portal, then with the help of Javascript SCA can be calculated, without any need for satellite image correction as shown in the (Figure 3). The complete methodology is shown in the (Figure 4). For calculating the area of snow in the Larji basin, MOD10A1 satellite images have been used for the calculation of SCA with the help of a Java API script in GEE. GEE is an advanced tool with Javascript APA which provides a whole library of satellite data and it provides powerful tools for spatial analysis of remote sensing data, including the ability to perform image classification, object detection, and spatial modeling, which can be used for a wide range of applications, from land cover mapping to disaster monitoring. In this study, we have used GEE for SCA extraction using Java API.
Figure 3

Procedure of GEE.

Figure 3

Procedure of GEE.

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Figure 4

Methodology for extracting the SCA using ArcGIS and GEE.

Figure 4

Methodology for extracting the SCA using ArcGIS and GEE.

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Trends analysis

R studio (4.3.2) with package (mmky) has been used to carry out the trends analysis of SCA for 2001–2021. MK test is a non-parametric test which detects monotonic increasing or decreasing trends. However, the results of the test may contain errors if autocorrelation exists in the data series. To avoid this problem, a pre-whitening procedure is performed to remove the autocorrelation in the time series. If the autocorrelation is statistically significant and the coefficient of autocorrelation is r1 then the MK test is applied to the “pre-whitened” series obtained as

MK Statistics:
formula
(1)

The MK test was used for the analysis of trends for the whole time period (Mann 1945). Where x1, x2, x3…xn are the data points of the series. The null hypothesis, H0, states there is no monotonic trend, and ts is tested against one of three possible alternative hypotheses, Hα:

  • I.

    There is an upward trend (monotonic).

  • II.

    There is a downward trend (monotonic).

  • III.

    There is either a (I) or (II).

Sen's Slope Estimator is used to identify the magnitude of trend in a time series can be determined using a non-parametric method known as Sen's slope estimator (Sen 1968). Sen's slope can be calculated as follows.
formula
(2)
for i = 1,2,3……,..N

where . and . are data values of Ti gives the Sen's slope estimator with positive value as increasing and negative value as decreasing trends.

Soil and Water Assessment Tool

SWAT model is a semi-distributed hydrological model which calculates the hydrology of the basin on the HRU scale using the water balance equation as shown by Equation (2). For a further explanation of the model components, refer to the SWAT Theoretical Documentation by Neitsch et al. (2011). SWAT's hydrologic components encompass surface runoff, infiltration, evapotranspiration, deep seepage, consumptive usage via pumping, shallow aquifer input to streamflow (baseflow), and recharge from surface water seepage (Chekole et al. 2024).
formula
(3)
The SWAT model requires hydro-metrological data, i.e. digital elevation map (DEM), soil map, land use map, and climate data, for simulating the basin. Surface runoff in the Soil Water and Assessment Tool is calculated with the help of the SCS curve number method on the basis of land use, soil type and other prevalent atmospheric conditions. The workflow of the hydrological model used in the study is given in Figure 5.
Figure 5

Showing the methodology of the hydrological models (SWAT/SRM).

Figure 5

Showing the methodology of the hydrological models (SWAT/SRM).

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Snowmelt Runoff Model

The winSRM model is a TI hydrological model that is deterministic, semi-distributed, and operates on a daily time step. It has demonstrated efficacy in replicating the runoff and snowmelt mechanisms in basins of diverse magnitudes, spanning from 0.76 to 917,444 km2 (Martinec et al. 2008). The calculation for the discharge of snowmelt, denoted as Q and measured in cubic meters per second (m3/sec), is as follows:
formula
(4)
where ‘n’ is the day number, ‘i’ is the index for each elevation zone, and ‘f’ is an adaptation factor (cm km2 day–1 to m3 s–1). Snowmelt and rainfall contributions are calculated separately for each elevation zone (area of ‘A’), and require the following input variables and parameters: T (°C day–1), the number of degree-days, the temperature-lapse rate adjustment ‘ΔT (°C)’ the precipitation P contributing to runoff (cm), the fraction of snow-covered area ‘S’ (SCA), MODIS snow product (MOD10A1 V6 Snow Cover Daily Global 500 m product) is utilized for SCA calculation, the degree day factor ‘a’ (cm °C–1 day–1), and the runoff coefficients for snow and rain (and ), which represent the difference between the available water volume and the outflow from the basin. Parameters to be calibrated in SRM are given in Table 1.
Table 1

Calibration parameters for the SRM model

VariablesRange
Lapse rate (°C/100 m) 0.55–0.65 
Degree day factor (cm °C−1 d−10.32–0.79 
Critical temperature; TCRIT (°C) 0.0–2.0 
Time lag (h) 4.0–18.0 
Runoff coefficient for snow,  0.42–0.80 
Runoff coefficient for rain,  0.40–0.75 
Rainfall contributing area, RCA 0 .1–0.02 
 1.16–1.16 
 0.031–0.037 
VariablesRange
Lapse rate (°C/100 m) 0.55–0.65 
Degree day factor (cm °C−1 d−10.32–0.79 
Critical temperature; TCRIT (°C) 0.0–2.0 
Time lag (h) 4.0–18.0 
Runoff coefficient for snow,  0.42–0.80 
Runoff coefficient for rain,  0.40–0.75 
Rainfall contributing area, RCA 0 .1–0.02 
 1.16–1.16 
 0.031–0.037 

Since the precipitation data offered for the high-altitude Himalayas catchments are not of very good quality the use of SRM has shown the sensitivity to the daily snow cover and temperature input. The SRM model establishes a correlation between rising air temperature and decreasing SCAs in order to quantify the contribution of snowmelt to streamflow. This function necessitates daily assessments of the ratio of snow coverage in each elevation zone, encompassing the snow cover beneath the canopy. Furthermore, with the advent of satellite data (MOD10A1 in this study), the model has been upgraded over a period of time for its applications to large basins in remote and rugged terrain such as the Himalayas (Dey & Rango 1989).

Calculation of the SCAs using GEE and analysis of the seasonal variation of SCA in the Larji River basin

Various researchers have used a similar approach for the estimation of SCA using GEE worldwide. Goodarzi et al. (2022) carried out a study in the Achijai-Iran for snow phenology followed by the trends analysis using MK and Sen's slope estimator. Zhang et al. (2021) simulated the Tarim basin using a similar approach using GEE and MODIS satellite data, it was found that during the summer months, the categorization of multiple snowfall and snowmelt events leads to intermittent snow cover in the basin. In this study, GEE has been used for snow area calculation to obtain monthly SCA from the year 2001 to 2021 as shown in Table 2. Seasonal and annual variation of the SCA over the Larji Basin is studied thoroughly. In this study, MODIS 10A1v6 satellite data has been used for SCA estimation. GEE provides a vast variety of functions to work in a single library without wasting time on the pre-processing of the images.

Table 2

SCA results extracted from GEE from 2001 to 2021 in km2

YearJanFebMarAprMayJunJulAugSepOctNovDec
2001 69.73 63.18 75.38 48.84 39.10 16.43 13.85 18.19 10.37 13.16 51.24 73.55 
2002 128.24 110.68 137.11 54.34 40.95 17.34 11.65 10.83 8.97 28.26 33.59 29.49 
2003 53.37 98.18 67.95 63.31 31.39 19.40 12.10 23.02 11.85 13.83 33.13 61.52 
2004 82.42 151.74 74.60 35.10 56.31 15.55 11.08 11.17 11.87 60.40 49.25 44.02 
2005 103.15 71.79 92.26 62.39 31.54 33.79 28.70 18.86 17.11 22.52 23.94 24.39 
2006 73.11 65.00 71.93 55.69 34.32 16.92 24.40 13.73 15.37 21.78 45.00 84.34 
2007 73.73 67.12 146.27 72.11 27.97 28.40 8.49 15.92 9.31 33.35 26.18 81.83 
2008 72.23 145.55 85.55 62.25 28.26 15.84 15.39 13.78 24.16 34.46 42.87 39.97 
2009 60.59 58.40 57.75 47.99 34.14 26.98 13.01 12.61 29.05 18.78 35.71 34.87 
2010 82.24 93.64 80.21 42.82 33.84 25.08 9.63 13.41 12.25 41.35 55.47 33.31 
2011 130.60 70.50 86.15 53.78 44.70 27.83 23.56 16.91 10.25 18.14 21.14 35.07 
2012 128.65 110.20 92.15 29.62 51.19 24.67 14.77 13.46 23.14 24.28 26.13 71.55 
2013 113.79 128.05 103.49 49.26 48.33 35.04 12.96 9.05 7.71 16.80 57.96 70.83 
2014 101.73 124.62 87.33 92.12 44.39 41.41 24.84 16.86 12.17 20.81 21.63 115.37 
2015 114.73 88.06 117.82 71.56 56.16 28.56 23.17 15.68 12.38 24.81 36.24 67.37 
2016 67.10 121.58 62.34 47.41 34.22 18.18 23.62 6.57 15.10 18.39 7.89 54.60 
2017 88.48 81.80 82.32 48.06 21.28 25.36 18.43 5.24 9.10 14.62 34.33 44.73 
2018 73.88 57.36 75.04 45.66 37.56 15.51 15.52 8.89 9.92 24.76 76.86 61.51 
2019 101.67 98.93 102.89 78.48 36.88 25.94 19.96 24.50 23.21 26.71 30.87 103.24 
2020 128.81 108.44 76.67 47.99 39.02 32.65 22.67 26.91 17.88 9.30 52.93 70.49 
2021 82.73 100.11 76.44 58.08 31.64 28.95 17.27 15.34 12.27 32.70 41.44 61.37 
YearJanFebMarAprMayJunJulAugSepOctNovDec
2001 69.73 63.18 75.38 48.84 39.10 16.43 13.85 18.19 10.37 13.16 51.24 73.55 
2002 128.24 110.68 137.11 54.34 40.95 17.34 11.65 10.83 8.97 28.26 33.59 29.49 
2003 53.37 98.18 67.95 63.31 31.39 19.40 12.10 23.02 11.85 13.83 33.13 61.52 
2004 82.42 151.74 74.60 35.10 56.31 15.55 11.08 11.17 11.87 60.40 49.25 44.02 
2005 103.15 71.79 92.26 62.39 31.54 33.79 28.70 18.86 17.11 22.52 23.94 24.39 
2006 73.11 65.00 71.93 55.69 34.32 16.92 24.40 13.73 15.37 21.78 45.00 84.34 
2007 73.73 67.12 146.27 72.11 27.97 28.40 8.49 15.92 9.31 33.35 26.18 81.83 
2008 72.23 145.55 85.55 62.25 28.26 15.84 15.39 13.78 24.16 34.46 42.87 39.97 
2009 60.59 58.40 57.75 47.99 34.14 26.98 13.01 12.61 29.05 18.78 35.71 34.87 
2010 82.24 93.64 80.21 42.82 33.84 25.08 9.63 13.41 12.25 41.35 55.47 33.31 
2011 130.60 70.50 86.15 53.78 44.70 27.83 23.56 16.91 10.25 18.14 21.14 35.07 
2012 128.65 110.20 92.15 29.62 51.19 24.67 14.77 13.46 23.14 24.28 26.13 71.55 
2013 113.79 128.05 103.49 49.26 48.33 35.04 12.96 9.05 7.71 16.80 57.96 70.83 
2014 101.73 124.62 87.33 92.12 44.39 41.41 24.84 16.86 12.17 20.81 21.63 115.37 
2015 114.73 88.06 117.82 71.56 56.16 28.56 23.17 15.68 12.38 24.81 36.24 67.37 
2016 67.10 121.58 62.34 47.41 34.22 18.18 23.62 6.57 15.10 18.39 7.89 54.60 
2017 88.48 81.80 82.32 48.06 21.28 25.36 18.43 5.24 9.10 14.62 34.33 44.73 
2018 73.88 57.36 75.04 45.66 37.56 15.51 15.52 8.89 9.92 24.76 76.86 61.51 
2019 101.67 98.93 102.89 78.48 36.88 25.94 19.96 24.50 23.21 26.71 30.87 103.24 
2020 128.81 108.44 76.67 47.99 39.02 32.65 22.67 26.91 17.88 9.30 52.93 70.49 
2021 82.73 100.11 76.44 58.08 31.64 28.95 17.27 15.34 12.27 32.70 41.44 61.37 

The images from the GEE library can be exported into Google Drive by just giving simple commands, following are the some of sample images imported from the GEE which are hardly in Kilobyte, which is also an advantage of using the GEE for satellite image processing instead of the ArcGIS. SCA when calculated for the years 2001–2021, it was seen that the trend of SCA in December January and February months is having the maximum SCA and June July and August have the minimum snow area. During the study period (2001–2021) in February month, snow has been accumulated and is maximum since the start of the winter season and similarly in August month it is decreasing as it falls just after the summer season. The annual variation of SCA has been shown by the Figure 6,. Further, it was seen that during the monsoon season SCA was minimum and in the winter season it was maximum. During the analysis of results obtained using GEE, it was found that for the year 2009 SCA was minimum (35.8 km2) and for the year 2014 (58.6 km2) highest during the study period (2001–2021) as shown in the Figure 6. Feb 2004 had the maximum snow cover in the basin (151 km2) covering 22% of the basin, whereas in the month of Aug 2017 basin experienced a minimum SCA of 5.2 km2 covering 0.7% of the entire basin area for the study duration (2001–2021). Results have shown a similar trend of SCA (max in Dec Jan Feb and minimum in Jun Jul Aug) as a study done by the Munawar et al. (2023) in the Jhelum River basin in the western Himalayan region.
Figure 6

SCA variation with respect to time (axis title).

Figure 6

SCA variation with respect to time (axis title).

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Analysis of seasonal variation of SCA in the basin

Further, the seasonal variations have also been carried for four different seasons, i.e. winter (Dec, Jan, Feb), pre-monsoon (March, April, May), monsoon (June, July, Aug), post-monsoon (Sept, Oct, Nov) as shown Figures 710 for each season. In northern India, there are basically four seasons namely winter, summer, pre-monsoon, and post-monsoon. SCAs extracted using GEE have been plotted on the basis of these seasons to check their variabilities throughout the time period. During the analysis of the seasonal (winter) snow cover it was found that Feb month (2004) has experienced the max snow cover (151 km2) for the study duration (2001–2021) since it's the peak of the winter season and Dec 2005 has the minimum snow area during the season (24.3 km2) as shown by the Figure 7.
Figure 7

SCA variation for the winter season (2001–2021).

Figure 7

SCA variation for the winter season (2001–2021).

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Figure 8

SCA variation for the pre-monsoon season (2001–2021).

Figure 8

SCA variation for the pre-monsoon season (2001–2021).

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Figure 9

SCA variation for the monsoon season (2001–2021).

Figure 9

SCA variation for the monsoon season (2001–2021).

Close modal
Figure 10

SCA variation for the post-monsoon season (2001–2021).

Figure 10

SCA variation for the post-monsoon season (2001–2021).

Close modal

During the analysis of SCA for the Pre-monsoon season (March Apr May), May month has shown the min SCA (May 2017 as 21.2 km2) followed by Apr and Mar as max (146 km2 for the year 2007 March) as shown by Figure 8.

During the analysis of SCA for the monsoon season (Jun Jul Aug), August month has shown the min SCA (May 2017 as 5.27 km2) followed by July and June as max (41.4 km2 for the year 2014 June) as shown by Figure 9. A similar approach was used to compute the SCA in the post-monsoon season, during the analysis of SCA for the post-monsoon season (Sept Oct Nov), September month has shown the min SCA (May 2013 as 7.71 km2) followed by Oct and Nov as max (76.8 km2 for the year 2018 Nov) as shown by Figure 10.

Trends analysis of the SCA using R studio on seasonal and annual basis

A non-parametric trends test has been used in this study; many researchers have emphasized the non-parametric trends tests instead of parametric for climate-related data as this does not necessarily need normally distributed data sets. In this study, we have used MK's test for trends analysis and Sen's slope for the detection of the magnitude of trends analysis using R software (4.3.2) with package (mmky). Results obtained using GEE in this study show no trend as shown in Table 3. Results from the trends analysis test have shown that except for the pre-monsoon season, all the season has increasing trends in SCA. The negative trends in the pre-monsoon season may be due to the increasing of the temperature from March to May. This is a clear indication of the changing climate as the IPCC has mentioned in their various reports that the Himalayan region is the most vulnerable to climate change (IPCC 2014). Annually there are increasing trends with the magnitude of 0.28 km2 per year as given in Table 3. Similar trends were found in the Tons River basin, a major tributary of Yamuna River by Thakur et al. (2023) authors have detected a larger variation in the higher elevations. This study's trend analysis results corroborate to a great extent with the study carried out in the Hindu Kush Himalayan region by Conyers & Roy (2023).

Table 3

Trends test for the SCA on seasonal and annual basis at 5% significance level

SeasonKendell τSen's slope (magnitude of trend)
Winter 0.22 0.78 
Pre-monsoon −0.019 −0.063 
Monsoon 0.257 0.239 
Post-monsoon 0.019 0.073 
Annual 0.161 0.282 
SeasonKendell τSen's slope (magnitude of trend)
Winter 0.22 0.78 
Pre-monsoon −0.019 −0.063 
Monsoon 0.257 0.239 
Post-monsoon 0.019 0.073 
Annual 0.161 0.282 

Development of the SWAT and SRM model for calculation of runoff estimation

SWAT and SRM models frequently assume spatial homogeneity within a sub-basin or watershed, suggesting that soil characteristics, land use, and climatic conditions are homogenous throughout the area but may not accurately capture the whole variability in the watershed. Both models are TI models and typically, require calibration before they can be used in applications. This calibration (manual calibration) is done using observed and simulated data. The dataset spanning three consecutive years was divided into two sections: one was used for calibration reasons (2019, 2020), while the other set of one-year data was utilized for the validation period (2021) for both models. Following the successful completion of model calibration, it is imperative to validate the model before utilizing it in the application. In this case, a one-year dataset from 2021 was utilized for the validation process as given by Khajuria et al. (2022). The parameters of the calibrated model were used to achieve the same outcome. The computed hydrograph exhibits an acceptable correlation with the observed data throughout all years as shown in Table 4 and Figure 11. For the analysis four different types of statistical indicators, i.e. Coefficient of Determination (R2), Correlation Coefficient (r), Nash-Sutcliffe Efficiency (NSE) and KGE (Gupta et al. 2009) were used with the help of HydroGOF package in R studio (4.3.2), all these indicators have given values in the acceptable limit as shown in Table 4. These results indicate SWAT model outperforms the SRM (snowmelt model) over the selected period (2019–2021). From the overall analysis, we can conclude that the SWAT model is giving better results as compared to the SRM model as shown by Figure 11.
Table 4

Calibration and validation results of both models (2019–2021)

YearSWAT
SRM
rR2KGENSErR2KGENSE
2019 0.92 0.54 0.52 0.54 0.84 0.67 0.68 0.67 
2020 0.76 0.57 0.61 0.57 0.70 0.53 0.62 0.53 
2021 0.90 0.77 0.70 0.77 0.88 0.71 0.65 0.71 
YearSWAT
SRM
rR2KGENSErR2KGENSE
2019 0.92 0.54 0.52 0.54 0.84 0.67 0.68 0.67 
2020 0.76 0.57 0.61 0.57 0.70 0.53 0.62 0.53 
2021 0.90 0.77 0.70 0.77 0.88 0.71 0.65 0.71 
Figure 11

Runoff modeling results using SRM and SWAT for years 2019, 2020, and 2021.

Figure 11

Runoff modeling results using SRM and SWAT for years 2019, 2020, and 2021.

Close modal

The SRM computer software evaluates the model by comparing the estimated hydrograph with the observed one (Martinec et al. 2008; Tahir et al. 2011). The various performance indicators used in this study are given as follows in Equations (5)–(8).

Coefficient of Determination:
formula
(5)
where Qobs = measured daily discharge, Qcal = simulated daily discharge, = average measured discharge is the predicted value from the statistical model inferred from the observed values. R2 value shows that the measured and simulated (calculated) values coincide perfectly and plot as 1:1.
formula
(6)
where n = sample size; X = observed data,Y = simulated data
formula
(7)
where Qobs is the observed and Qcal is the simulated discharge; is the mean observed discharge.
formula
(8)
where r is the Pearson correlation (PC) coefficient between calculated and observed values.

β is the bias ratio of the standard deviation of simulated to the standard deviation of observed streamflow. γ is the variability ratio of the mean of simulated to the mean of observed streamflow.

The runoff coefficient (c) accounts for the losses, which are the difference between the available water volume and the outflow from the basin. For a long period of time, it should correspond to the ratio of measured runoff to the measured precipitation (Bhadra et al. 2015). Based on the literature and historical data on temperature and snowmelt to determine the ratio of snowmelt per degree day, which is then used as the degree day factor for future simulations (Khajuria et al. 2022). In this study, the degree day factor is taken as 0.65 cm/°C/day. Based on the limited data set, the study demonstrates that SRM, a snowmelt runoff model that is based on temperature indices, is more applicable to the study basin.

Accurate simulations and projections SCA and snowmelt runoff play a crucial role in addressing water resources management challenges related to irrigation, recreation, flood control, and hydroelectric generation. In recent years, there have been significant advancements in remote sensing, enabling the optimization of the input data for hydrologic models. The current research estimates SCA variation concerning time with the help of the GEE. Season-wise SCA variation is also examined, showing maximum in the winter and minimum in the pre-monsoon season. Overall, the maximum SCA was found in February and the Minimum in August. Non-parametric trends test using Mann Kendall and Sen's slope test in R studio (4.3.2) showed increasing trends (5% confidence level) for winter, monsoon and post-monsoon season, whereas pre-monsoon season has decreased trends and an overall increasing trend in the annual SCA from the year 2001 to the year 2021 were found.

Furthermore, this study simulates runoff from the Larji River, the major tributary to the Beas River, using SRM (Snowmelt Runoff Modeling) and SWAT. While comparing the results SWAT model R2 (0.54–0.77) and SRM model showed R2 of (0.53–0.71) while KGE ranging from 0.52 to 0.70 in SWAT and 0.62–0.68 in SRM and NSE for SWAT 0.54–0.77 whereas for SRM 0.53–0.71. Overall analysis showed SWAT model performed better based on the performance indicators. The projected runoff indicates reduced flow during the late summer and early fall periods. Without a significant reservoir, there will be a severe water shortage during these seasons. Effective resource management is crucial for minimizing the impact of climate change on the local hydrological system. The report proposes the establishment of sufficient reservoirs to store excess water during the peak season. It will guarantee water accessibility during low irrigation and power production demand. The sustainable ecological and socio-economic growth of the Larji Basin necessitates the enhancement of the existing infrastructure for surface water management.

C.K. conceptualized the study, designed the research methodology, conducted experiments, collected and analyzed data, and drafted the initial manuscript. R.S.M. provided substantial guidance and expertise in refining the study design, offering critical insights during data analysis, and contributing to the interpretation of findings. She also played a pivotal role in revising the manuscript, providing intellectual feedback, and approving the final version for submission.

No funding was received for this study.

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

The authors declare there is no conflict.

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