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© 2023 The Authors Journal of Water and Climate Change Vol 00 No 0, 1 doi: 10.2166/wcc.2023.341

Evaluating hydroelectric potential in Alaknanda basin, Uttarakhand using the snowmelt

runoff model (SRM)

Kuldeep Singh Rautelaa, Dilip Kumar a, *, Bandaru Goutham Rajeev Gandhia, Ajay Kumara, Amit Kumar Dubeyb
and Bhishm Singh Khatia
a
Civil Engineering Department, G.B. Pant Institute of Engineering and Technology, Gurdhauri, Pauri (Garhwal), Uttarakhand 246194, India
b
Space Applications Centre, ISRO, Ahmedabad, Gujarat 380015, India
*Corresponding author. E-mail: jhadilip27@gmail.com

DK, 0000-0003-4945-2625

ABSTRACT

The remote sensing and GIS tools provide reliable information for assessing the available water. In this study, the basin is divided into 12
elevation zones and temperature and precipitation are extrapolated within these zones. The MODIS (Terra and Aqua) cloud-free images
have been used for mapping snow cover area (SCA) and it was found that the SCA will vary from 12 to 72% during the simulation period.
The model simulation period is divided into calibration (2003–2015) and validation (2016–2019). The three hydrological and statistical indices
have been used to judge the model efficiency. It was found that the efficiency parameters are much beyond the acceptable range for both
periods. In this study, the snowmelt’s contribution increases till zone 8; after this, the snowmelt contribution decreases, and the snow
accumulation increases. The simulation of daily streamflow and generated hydroelectric power (HEP) are compared with the measured
values, and both tracked the observed pattern very precisely. The findings of the present study will be implemented on the other ungauged
basins and could help us to identify the potential sites for HEP with the help of RS and GIS tools.

Key words: GIS, HEP, IHR, MODIS, SCA, snowmelt contribution

HIGHLIGHTS

• First of its kind relating hydropower potential with snow melt.

• First of its kind for high altitude river.

INTRODUCTION
India’s energy policy emphasizes increasing energy production and reducing energy poverty particularly green energy such as
hydropower, solar energy, and wind energy. India is the third biggest energy consummator in the world after the USA and
China, and as of 2017, India was self-sufficient in energy to the tune of 63% (IEA 2017). As of now, India fulfils their
energy need through fossil fuels, coal, and solid biomass, and they meet approximately 80% of the energy need (IEA
2021). The pollution issue has been a pressing concern in India for a long time. In recent years, the nation’s capital has
been plagued by hazardous air quality, and numerous other cities across the country are experiencing the same issue
(TERI 2021). Foraying into sustainable energy systems has become highly important for the country after a recent study
deemed Indian coal plants to be the ‘unhealthiest’ in the world. To overcome these, the Indian government has focused
on producing renewable energy. As a result, it became the third largest producer with 38%, i.e., 136 GW of 373 GW of
energy capacity installed in 2020 (Koundal 2020; Ernst & Young 2021). By 2030, India plans to produce 50% of its electricity
from non-fossil fuel sources under the Paris Agreement’s Intended Nationally Determined Contributions (TOI 2022). A target
was set by the Central Electricity Authority (CEA) in 2018 for non-fossil fuel electricity to represent 50% of the total by 2030.
India has also established a target for renewable energy production to reach 175 GW by 2022 and 500 GW by 2030
(ET 2021). Hydropower energy will play a significant role in achieving this goal because India has tremendous hydroelectric
power (HEP) potential and ranks 5th in the global HEP generation with a total installed capacity of 45,699 MW, i.e., 12% of

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and
redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

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its total power generation (Khan et al. 2020). The Indian Himalaya Region (IHR) consists of most perennial rivers with a
significant availability of water throughout the year, having several ideal sites with a considerable head for hydropower gen-
eration. Also, IHR is an eco-sensitive zone regarding natural hazards, so the Government of India (GoI) restricted large
reservoirs in this zone. To overcome this, runoff-river hydropower plants are generally used in this region.
Hydropower is one of the most common renewable energy sources, which is economical, non-consumptive, non-radio-
active, non-pollutive, and environment friendly (Bhadra et al. 2015). In the IHR, permafrost dominates, and snow cover,
glaciers, and associated meltwater runoff through narrow valleys. Most of the perennial rivers and their tributaries are fed
by more than 10,000 glaciers (Raina & Srivastava 2008; Rautela et al. 2022a). Glacier surfaces will eventually be covered
by ice, resulting in more significant runoff. Meltwater is more readily available for the ablation process as the glacier melts
(Milner et al. 2017). The runoff rate of meltwater decreases when air temperatures drop, and fresh snow falls on higher
reaches following the end of the ablation process (Arora & Malhotra 2020). In the Himalayan basins, snow and glacier
runoff significantly impact the flow of streams (Rautela et al. 2020, 2022a). As a result, the pace of glacial melting depends
on prevailing conditions (Dobhal et al. 2021). Structural changes, including exposition trends, influence melt and runoff pat-
terns (Pohl et al. 2017). In most of the studies in IHR, seasonally varying runoff components have been measured in rivers,
but their contributions have not been quantified (Nazeer et al. 2022). When planning and managing Himalayan water
resources, it is essential to calculate runoff from snow and glacier melt in Himalayan Rivers.
Various past studies have been conducted in the different river basins of the IHR to find out the contribution of snowmelt
using several process-based hydrological models, which are either semi-distributed or distributed in nature (Dahri et al. 2011).
The hydrological models help in the quantification of streamflow and its associated components. A study by Singh et al.
(1997) on the Chenab River shows it receives 49% of its annual flow from snow and glacier melt water using a water balance
approach. Similarly, according to Singh & Jain (2002), snow and glacial contributions to the Satluj River at Bhakra were 59%
of the total river flow at the Bhakra Dam. Similarly, Soni et al. (2015) assessed the snowmelt contribution of the Mahakali
(Sarda) river up to the Tanakpur barrage using the WinSRM model. They found approximately 16.5% snowmelt contribution
to the total streamflow. Jain et al. (2010) developed SNOW-MOD with GCMs to determine the role of climate change on the
snow and glacier melt. However, Jain et al. (2017) used SWAT to simulate the hydrological response of a large river basin in
Uttarakhand. They found a significant contribution of snowmelt up to 20% of the total streamflow. Gaddam et al. (2018) eval-
uated the contribution of snow and glacier melt of the Baspa River basin under a sparse hydrometeorological data condition.
They found the snow and glacier melt contribute 81 and 7% in the total streamflow, respectively. However, Tanmoyee &
Abdul (2015) use the variable infiltration capacity (VIC) model to assess the climate change impact on the snowmelt contri-
bution up to Rudraprayag on a monthly time scale. These hydrological models with a GIS platform provide a robust solution
for assessing snowmelt runoff in the small to large river basins in the IHR with the desired accuracy. However, as the climate
warms, the interannual changes and trends in the snow and glacier melt contribution to the streamflow in the spring season
will be a key concern for the vulnerability of water resources.
The availability of hydrometeorological data in the high-altitude regions of the Himalayas is always a key concern (Sofi
et al. 2021). The region consists of higher variations in the climate, rough topography, and poor communication, which some-
times make the region inaccessible, especially during the monsoon season, so continuous monitoring of the features such as
snow depth, snow cover, and other meteorological parameters are not possible (Kuniyal et al. 2021). It is possible to over-
come this problem using remote sensing (RS) and geographic information system (GIS) tools, which provide new
opportunities for investigating snow cover (Verdin 2012; Yang et al. 2016; Kumar et al. 2023), terrain studies (Reddy &
Sarkar 2012), mountain hazard planning (Rengers et al. 1992; Rai et al. 2014), and watershed management (Rautela et al.
2022b, 2022c). It is difficult to estimate the snow cover in mountainous basins due to harsh climate, complex access, and
poor communication facilities. Currently, comprehensive information about the Himalayan region’s natural resources is
available due to modern earth resources and satellite monitoring (Dhari et al. 2010). The hydrological data set developed
by Asokan et al. (2020) combines satellite imagery and computer analysis. Databases with difficult terrain can be enriched
with biophysical and socioeconomic information using GIS. Through these devices, multispectral spatial data can be com-
bined and presented in an understandable format, such as a map, rather than using traditional methodologies. In IHR,
conventional snow cover monitoring methods are difficult to use due to snow cover (Sood et al. 2020). For this reason,
RS and GIS tools may be better tools (Yang et al. 2016) for measuring snow cover extent and properties. In this study, we
attempted to simulate the daily streamflow for the Alaknanda basin using the snowmelt runoff model (SRM). The present
study could provide a preliminary database for planning hydropower projects to generate green energy.

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MATERIAL AND METHODS

Study area
The Alaknanda River system (Figure 1) is a significant tributary of the Ganges that emerges near the confluence and receives
its feed from the Satopath and Bhagirathi Kharak glaciers in Uttarakhand, India. The Alaknanda River passes through three
districts of Uttarakhand: Chamoli, Rudraprayag, and Pauri before joining the Bhagirathi at Devprayag and draining an area of
11,063.68 sq. km. In terms of culture, the river is crucial, and its major tributaries (Vishnuprayag, Nandprayag, Karnaprayag,
Rudraprayag, and Devprayag) meet at their confluence (Rautela et al. 2022c). As the Alaknanda River runs downstream, the
principal tributaries include the Saraswati (meets at Mana), western Dhauliganga (meets at Vishnuprayag), Nandkini (meets
at Nandprayag), Pinder (meets at Karnaprayag), and Mandakini (meets at Rudraprayag). This terrain has been sculpted by
powerful neotectonics and significant rainfall, resulting in high relief, steep slopes, and a dense drainage network (Chopra
et al. 2012). Furthermore, the basin’s rugged geography creates a variety of microclimates, with temperatures fluctuating sea-
sonally and regionally. 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 the Alaknanda River basin (Panwar et al. 2017). The monsoon, which accounts for 80% of
India’s yearly rainfall, causes torrential rains throughout the Indian summer (Kumar et al. 2010). As a result of the excessive
rainfall and small valleys, the Alaknanda basin frequently sees cloud bursts, flash floods, and riverine flooding. Among the
tributaries that contribute to the river’s flow are the western Dhauliganga, Nandakini, Pinder, and Mandakini. Snowmelt
and glacier melt, as well as seasonal rainfall, all contribute to these persistent rivers (Rautela et al. 2022d). From an economic
standpoint, the Alaknanda basin has a significant quantity of hydroelectric potential. According to the South Asian Network
on Dams, Rivers, and Public (SANDRP), 37 hydroelectric dams on the Alaknanda River and its tributaries are now oper-
ational, proposed, or under construction.

Hydrometeorological data
The basin’s meteorological parameters are crucial for modelling hydrological processes. Maximum and minimum tempera-
tures were taken from NASA POWER (0.5°
0.5°), and daily rainfall was derived from IMD (0.25°
0.25°) high-
resolution gridded data at a daily time step (Table 1; Rautela et al. 2022c). The Central Water Commission (CWC) gauging
station installed at Devprayag provided daily streamflow data for the study area for the years 2003 through 2019 (Table 1).

Spatial data
The process-based hydrological models require digital elevation model (DEM) as their primary input to delineate basin
boundaries, generate stream networks, classification of elevation zones, mean elevation, identification of slope and aspect,
etc. In the present study, ASTER-DEM of spatial resolution 30 m has been used to and classification of elevation zones,
area, and mean elevation. Collection-6 (C6) of the daily MODIS (Terra and Aqua) products for the years 2003–2019 on 8-
day interval. To be comparable to the improved MODIS (Terra and Aqua) products reclassified into three classes: (1) the
values 40–100 are snow class and reclassified to 200; (2) value 250 is cloud and reclassified to 50; and (3) the remaining
values are classified as no snow (25) (Muhammad & Thapa 2020, 2021).

Extrapolation of meteorological data

In the high-altitude regions of the Himalayas, there are only a limited number of evenly distributed and good-quality hydro-
meteorological stations available for data collection. Unfortunately, these stations cannot be utilized effectively due to various
challenges such as inaccessibility, adverse climatic conditions, and management issues. In this study, grids of IMD were used,
and extrapolation of the data was done with reference to these grids in each elevation. In the SRM, the temperature data for
each elevation zone are extracted using the lapse rate, which quantifies the temperature change with elevation. Six stations at
different elevations collect the data, and the lapse rate is used to estimate values for zones without direct measurements, pro-
viding input for simulating hydrological processes in the SRM. Furthermore, in temperature index-based SRMs, the air
temperature is the index parameter and is used for the identification of critical temperature (Tc). Furthermore, the critical
temperature is used to classify the precipitation as snow or rain.

Snowmelt runoff model (SRM)

Modelling of streamflow was based on the conceptual, deterministic, and degree-day hydrological model that simulates and
forecasts the daily streamflow of the basin resulting from snow and rainfall (Figure 2). SRM is a temperature index model

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Figure 1 | Location map of the study area.

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Table 1 | Input data and its sources of the SRM model

Resolution
S. No. Data type Source (Spatial/Temporal) Description and source

1. Topography USGS earth explorer 30 m Advanced Spaceborne Thermal Emission and Reflection
Radiometer-Digital Elevation Model (ASTER-DEM)
(https://www.earthdata.nasa.gov/)
2. MODIS Data PANGAEA: Data Publisher for 500 m Snow cover area analysis (https://doi.pangaea.de/10.
Earth & Environmental 1594/PANGAEA.918198)
Science
4. Rainfall IMD gridded data Daily Average rainfall for 0.25°
0.25° grid (https://www.
imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_
Download.html)
5. Temperature NASA Prediction of Worldwide Daily Maximum, Minimum, and mean Temperature 0.5°
0.5°
Energy Resources (POWER) (https://power.larc.nasa.gov/)
6. Hydrological Central Water Commission Daily Streamflow data obtained at the gauging station
(CWC)
7. Plant Generation Alaknanda HEP Daily Power produced by the turbine

which was developed by Martinec (1975) and Martinec et al. (2008). The model is applicable to the basin area ranging from
0.76 to 91,744 sq. km and elevation ranges from 0 to 8,840 m where snowmelt is a major contributor and can be able to simu-
late the impact of climate change on the seasonal snow cover and streamflow (Immerzeel et al. 2010). This model was chosen
for this study to evaluate the snowmelt process because it only requires a small amount of input data and can be applied to
many different geographical regions.

Figure 2 | Flowchart of SRM and hydropower assessment.

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Initially, the ArcGIS 10.4 is used to divide the elevation zones and the mean elevation zones (Table 1). Further, the snow
cover area (SCA) in each elevation zone has been extracted using the MODIS imaginary. The gaps in between the SCA in
each elevation zone are filled by the linear interpolation. The input parameters such as temperature, precipitation, and
SCA, the SRM simulates the streamflow as follows (Martinec et al. 2008):


10, 000
Qnþ1 ¼ [CSn a(Tn þ DTn )Sn þ CRn Pn ]
A

(1  knþ1 ) þ Qn knþ1 (1)
86, 400

knþ1 ¼ xQyn (2)

where Q is the streamflow (in m3/s) at day n þ 1, CS and CR are the runoff coefficient for snow and rain for each zone, α is the
degree day factor (in cm/°C/day), T þ ΔT is the degree days (°C), P is the precipitation (in cm), S is the snow cover fraction, A
is the basin area (in km2), and k is the recession coefficient. After subtracting all abstractions from runoff with the streamflow
on the nth day (Qn), the daily average streamflow on the n þ 1th day is calculated by adding snowmelt and precipitation that
contribute to runoff. The Qn is a product of α, T þ ΔT, and S. Further, CS and A are multiplied with previous day products to
compute the percentage contributing to the runoff. Similarly, on the other hand, CR and A determine the precipitation con-
tribution to runoff. The recession coefficient (k) is one of the important parameters in the SRM model as it describes the slope
of the falling limb of the hydrograph.

Model performance criteria

The streamflow of the Alaknanda River has been simulated for 17 years (2003–2019) which is further divided into calibration
(2003–2015) and validation (2016–2019) periods. To judge the model efficiency, hydrological and statistical indices such as
the coefficient of determination (R 2) (Equation (3)), Nash–Sutcliffe efficiency (NSE) (Equation (4)), and volume difference
(Dv) (Equation (5)) are used. The model is considered satisfactory if R 2 . 0.55, NSE . 0.5, and Dv , 10% (Moriasi et al.
2007; Khajuria et al. 2022; Rautela et al. 2022c):

 2
P
t
(Qmi  Qm )(Qsi  Qs )
i¼0
R2 ¼ (3)
P
t P
t
(Qmi  Qm ) (Qsi  Qs )
i¼0 i¼0

P
t
(Qmi  Qs )2
i¼0
NSE ¼ 1  (4)
Pt 2
(Qmi  Qm )
i¼0

Vm  Vs
Dv ¼
100 (5)
Vm

where Qm and Qs are the mean measured and simulated streamflow during the period, Qmi and Qsi are the measured and
simulated streamflow in the ith day (cum/s), respectively, and n is the number of data points, Vm and Vs are the measured
and simulated annual runoff volume.

Assessment of HEP
The modelled streamflow is further used for the assessment of HEP at the Alaknanda Hydro-Electric Power Plant, Srinagar
and co-related with the measure HEP. The HEP at the dam site has been computed based on the AHEC (2008):

P ¼ 1, 000
(hgQH) (6)

where P is the power produced (in MW), h is the turbine efficiency, which is taken as 0.8, g is the acceleration due to gravity
(in m/s2), Q is the streamflow (in m3/s), and H is the gross hydraulic head (in meters).

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RESULTS AND DISCUSSION

Estimation of input parameters
The area and elevation ranges of the study area were estimated using DEM. The study area is divided into 12 elevation zones
(Table 2; Figure 3). Further, this area and elevation are used to calculate the hypsometric mean elevation of the basin which is
used in the extrapolation in the temperature (Figure 4). The total SCA of the basin varies from 18 to 72% of the total basin
area (Figure 5). In the Alaknanda basin, the accumulation and ablation periods start from November to March and March to
October, respectively (Figure 6). The SCA is maximum in the month of February and minimum in the month of September.
The average SCA in the Alaknanda basin shows a decreasing trend, i.e., ranges from 14 to 78% in the year 2003 to 11 to 53%
in the year 2019. The anthropogenic impacts in the Alaknanda River basin and the combined effect of global warming and
climate change are the main possible reasons behind the ablation of snow. Similarly, the gradual decrease in the precipitation
in this area is from 2003 to 2019.
The runoff coefficient (c) accounts for the losses, which are the difference between the available water volume and the out-
flow 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). In fact, a comparison of historical precipitation and runoff ratios provides a starting
point for the runoff coefficient values. However, these ratios are not always easily obtained in view of the precipitation
gauge catch deficit, which particularly affects snowfall and inadequate precipitation data from Himalayan regions. At the
beginning of the ablation period, the initial losses are very small because they are limited to only evaporation from the snow-
pack. After that, when some soil becomes exposed and vegetation grows, more losses must be expected due to ET and
interception. Towards the end of the ablation period, the channel flow from the reaming SCA and glaciers may prevail in
basins, leading to a decrease in losses and an increase in runoff coefficient. The runoff coefficient is different in snow and
rainfall. The runoff coefficient for snow and rainfall of the basin ranges from 0.04 to 0.75 and 0.05 to 0.8, respectively.
The degree day factor (α) converts the number of degree days into the daily snowmelt depth. The degree day factor is a con-
stant which varies from 0.55 to 0.75 cm/°C/day. Based on the literatures and historical data of 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. 2023). In this study, the degree day factor is taken as 0.65 cm/°C/day.
In the basins where historical temperature data is available, the temperature lapse rate (TLR) (γ) can be predetermined
easily; otherwise, it must be estimated using the analogy from the other basins which have similar meteorological charac-
teristics. The value of TLR generally varies from 6 to 7 °C/km, but usually, it is taken as 6.5 °C/km. The lapse rate is used in
the temperature adjustment and distribution of zonal temperature in the SRM. In the SRM, the critical temperature (Tc)
determines whether the precipitation is snow or rain. The precipitation immediately contributes to the runoff if T . Tc,
whereas it delays if T , Tc. The SRM automatically keeps the fresh snow in storage until it is melted on subsequent

Table 2 | Zone-wise area and mean elevation

Zone Elevation range (m) Area (sq. km) Mean elevation (m)

1 Less than 1,200 728.122 953.11

2 1,200–1,800 1,463.24 1,513.75
3 1,800–2,400 1,563.77 2,095.36
4 2,400–3,000 1,239.97 2,683.09
5 3,000–3,600 979.01 3,293.03
6 3,600–4,200 969.131 3,909.55
7 4,200–4,800 1,375.73 4,518.19
8 4,800–5,400 1,564.57 5,098.22
9 5,400–6,000 959.618 5,640.95
10 6,000–6,600 186.733 6,225.07
11 6,600–7,200 30.9284 6,789.25
12 Greater than 7,200 2.63 7,399.82

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Figure 3 | Distribution of elevation zones over the Alaknanda basin.

Figure 4 | Area–elevation curve.

warm days. In the present study, the Tc is taken as 2 °C (Vinze & Azam 2023). When precipitation is determined to be rain
in snow-fed basins, it can be treated in two ways. In the initial situation, it is assumed that rain falling on the snowpack
early in the snowmelt season is retained by the snow, which is usually dry and deep. Runoff generated by rainfall is added
to the snowmelt runoff only from the snow-free area. The ratio of non-SCA reduces the rainfall depth to the zonal area. The
recession coefficient (k) is analyzed using historical data of streamflow of the current day and previous day on a log scale is
plotted. The lower envelope line of all points is considered to indicate the k values (Figure 7). Based on that the x and y
values are calculated by solving the equation knþ1 ¼ xQn y. In the present study, the values of x and y are estimated as 1.13
and 0.235, respectively.
To estimate the lag time, flow velocity was calculated using the float method at four prayags, namely, Vishnuprayag, Nan-
daprayag, Karnapyrayag, and Devprayag by various field surveys during lean and high flow periods. The average surface
velocity of the river has been estimated as 2.70, 1.80, 1.75, and 1.45 m/s at Vishnuprayag, Nandaprayag, Karnapyrayag,
and Devprayag, respectively, since the surface velocity decreases towards the banks and bed of the stream (Bisht et al.
2021; Rautela et al. 2022a). A correction factor of 0.8 has been applied to convert the surface velocity into average velocity
(Rautela et al. 2022a) and it is estimated as 1.52 m/s. The total length of the river is 195 km. So, the lag time of the river is
estimated as 1.4 days. In the SRM, the calibrated value of lag time is estimated as 33 h.

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Figure 5 | Monthly average snow cover area of the Alaknanda basin.

Simulation of snowmelt runoff

The simulation streamflow of snow and glacier-fed rivers is essential for determining the amount of water generated by the col-
lection of rainfall and melting snow and ice in the catchment (Thakur et al. 2017). According to Rautela et al. (2020, 2022a), the
streamflow is affected by climatic and meteorological factors in a specific region. In the present study, the streamflow of 17 years
has been simulated and the average zone-wise component of the snow and rainfall has been estimated. Table 3 shows the year-
wise model efficiency parameters during the calibration and validation periods, respectively. The indices such as R 2, NSE, and
Dv of the calibration will range from 0.71 to 0.86%, 0.7 to 0.83%, and 0.47 to 7.53%, respectively. During the calibration
period, the simulated flow tracked the measured flow with captured the peak-flow very precisely with minimal error

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Figure 6 | Zone-wise distribution of monthly average SCA in the Alaknanda basin.

Figure 7 | Graph showing the regression and lower envelope line made by the present and previous-day streamflow in log scale.

(Figure 8(a)–8(d)). The main reason for this higher accuracy is that WinSRM simulates the streamflow only for one year. So, the
calibration will be done for each year. Similarly, during the validation period, indices such as R 2, NSE, and Dv attain higher
values as compared to the calibration period (Table 3). This might be due to the data being acquired upstream of the reservoir
from AHEP, Srinagar, during the validation period. This study shows a significant variation in the peak-flow before and after
reservoir construction. The average monthly zone-wise contribution of rain and snowmelt is shown in Figure 9. The findings
show that zone 1 has a negligible contribution of snowmelt compared to the rainfall. However, as the zone altitude increases,
the contribution of snowmelt’s contribution increases in the runoff. However, in zones 8 and 9, there is a contribution of fresh

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Table 3 | Model evaluation parameters for calibration and validation periods

Year R2 NSE Volume difference

Calibration period
2003 0.76 0.74 1.31
2004 0.86 0.83 1.88
2005 0.79 0.78 3.34
2006 0.78 0.72 0.47
2007 0.8 0.76 2.86
2008 0.73 0.72 3.31
2009 0.71 0.7 1.36
2010 0.82 0.76 2.91
2011 0.79 0.77 7.53
2012 0.78 0.76 1.8
2013 0.8 0.72 0.66
2014 0.79 0.76 2.85
2015 0.76 0.75 6.63
Validation period
2016 0.92 0.85 0.71
2017 0.88 0.82 1.23
2018 0.9 0.84 1.15
2019 0.73 0.71 2.12

Figure 8 | Streamflow pattern in between measured and simulated streamflow for (a) calibration and (b) validation periods and correlation
for (c) calibration and (d) validation periods.

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Figure 9 | Snow and rainfall contributions for (a) Zone 1, (b) Zone 2, (c) Zone 3, (d) Zone 4, (e) Zone 5, (f) Zone 6, (g) Zone 7, (h) Zone 8,
(i) Zone 9, and (j) Zone 10.

snow in the runoff, while in zone 10, there is negligibly less contribution of rain in the runoff. Higher elevation zones have a
larger fresh snowmelt contribution due to colder temperatures, increased snowfall, delayed snowmelt timing, and the accumu-
lation of deep and dense snowpack (Stewart 2009). In zones 11 and 12, there is no contribution from snowmelt and rainfall in
the runoff because in this zone significantly less rainfall is observed and due to very low temperature and fresh snow accumu-
lates and forms dense ice, which forms glaciers and provide a freshwater throughout the year in the downstream regions. In the
higher elevation zones, the melting starts from the end of May, while in the lower elevations, the contribution of snow is just
after the fresh snowfall. In this study, the contribution of rainfall in the runoff is up to 4,800 m, increasing the climate
change scenarios (Chettri et al. 2020). The shift in the tree line, shift of forests towards snow-dominated regions, and higher
rainfall intensity are the possible regions of the generation of runoff from rainfall in those regions.

Hydropower assessment
In this study, the data were acquired from AHEP, Srinagar, for validation. The capacity of the generation of electricity of
AHEP is 330 MW. Still, the available water is not present throughout the year for the required HEP generation (330
MW), and a variation in the HEP is observed. The correlation between the actual HEP and the modelled HEP was 0.75
from June 2017 to December 2019. However, some sharp falls were also observed in the graph during the high-flow
season due to mechanical errors in the turbines or flushing of the sediments from the reservoir (Figure 10(a)). We also inves-
tigate the cumulative simulated HEP in comparison to the HEP generated by the power plant at the AHEP, Srinagar station
(Figure 10(b)). However, it’s notable that the modeled HEP tends to overestimate the actual plant-produced HEP. This dis-
crepancy could possibly be attributed to factors such as the various operational and maintenance requirements of the
turbines, which the model does not consider. Despite this discrepancy, the model’s performance still yields satisfactory
results. The comparison between simulated and actual HEP highlights an observation about the complexities involved in
accurately modeling real-world systems. The model, while capable of capturing certain aspects of the HEP generation pro-
cess, may miss out on intricate operational details that play a role in determining the actual energy output. This
underscores the importance of considering not only the theoretical aspects but also the practical implementation and main-
tenance factors when assessing the performance of such models. In the headwater basins of the Himalayan River, there are
various potential sites available for the generation of HEP. The rivers which are perennial in nature, snow, and glacier melt
provide the minimum available water throughout the year. In addition, certain villages situated at high altitudes in the Indian

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Figure 10 | (a) Daily simulated HEP and plant-produced HEP and (b) accumulated simulated HEP and plant-produced HEP from AHEP,
Srinagar.

Himalayan Region (IHR) continue to experience a lack of electricity access. This ongoing study aims to offer a potential sol-
ution for evaluating meltwater resources, which could be harnessed for HEP generation.

CONCLUSION
The present study shows a SRM was effectively applied to a snow-fed basin in the IHR using the RS-derived products. Many
elevation zones improve the distribution of input parameters, increase the model’s effectiveness, and simulate the streamflow
pattern very precisely. Also, the cloud-free MODIS (Terra and Aqua) image will easily distinguish between the snow and
snow-free area. In the present study, the most sensitive input variable for the calibration of snowmelt runoff is runoff coeffi-
cient, SCA and recession coefficients because they directly influence the dynamics of the snowmelt process and its
subsequent contribution to the overall streamflow. The snowmelt contribution in the streamflow increases from April to Octo-
ber, and higher flow in the streamflow during the monsoons when rainfall runoff contribution is significant in the river. The
model efficiency parameters during the calibration and validation periods are much higher than the acceptable range, and the
model’s high flow and low flows are very accurately simulated by the model. The modeled HEP in the present study is nearer
to the produced HEP. In the snow-fed basins of IHR, melt water is available throughout the year. Based on the present study,
the model will provide a better solution for assessing available water for identifying potential micro- to mini-HEP with the RS
and GIS-derived products. Also, the major findings of the study could provide baseline information on water resource man-
agement, the effect of climate change in melt water runoff and optimization of resources in the IHR.

ACKNOWLEDGEMENTS
The authors sincerely thank the Director, Govind Ballabh Pant Institute of Engineering and Technology, Pauri (Garhwal),
Uttarakhand, for providing facilities. The research work was conducted as a part of the Research project titled ‘Assessment
of flood vulnerability in upstream catchments of Himalayan River basin’ funded by the ISRO (SARITA), under the aegis of the
Department of Space, Govt. of India, New Delhi is thankfully acknowledged.

AUTHOR CONTRIBUTIONS
Conceptualization: K.S.R., D.K.; Methodology: K.S.R., D.K.; Formal analysis and investigation: K.S.R.; Writing – original
draft preparation: K.S.R.; Writing – review and editing: K.S.R., D.K.; Supervision: D.K., B.G.R.G., A.K., A.K.D., B.S.K.

FUNDING
The Indian Space Research Organization (ISRO) – SAtellite-based RIver hydrological Techniques and Application (SARITA)
Program, Government of India provided financial support for the current study. The opinions expressed herein are those of
the authors and do not necessarily reflect the views of the study sponsors.

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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.

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First received 10 June 2023; accepted in revised form 20 September 2023. Available online 6 October 2023

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