Evaluating hydroelectric potential in Alaknanda basin, Uttarakhand using the snowmelt runoff model (SRM)

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 consumer 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 its energy needs through fossil fuels, coal, and solid biomass, and meets approximately 80% of its energy needs (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 its total power generation (Naseem & Naseem 2021). 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 generation. 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, small and medium runoff-river hydropower plants are generally used in this region to meet the energy demand.

Hydropower is one of the most common renewable energy sources, which is economical, non-consumptive, non-radioactive, 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 patterns (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) evaluated 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 contribution 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 sometimes make the region inaccessible, especially during the monsoon season, so continuous monitoring of features such as snow depth, snow cover, and other meteorological parameters are not possible (Kuniyal et al. 2021). It is possible to overcome 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 et al. 2013), 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. 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 combined 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 (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.

The basin's meteorological parameters are crucial for modelling hydrological processes. Maximum and minimum temperatures 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).

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 for 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).

In the high-altitude regions of the Himalayas, there are only a limited number of evenly distributed and good-quality hydrometeorological 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, providing 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 (T_c). Furthermore, the critical temperature is used to classify the precipitation as snow or rain.

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). 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 snowpack. 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 constant 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. 2022). In this study, the degree day factor is taken as 0.65 cm/°C/day.

To estimate the lag time, flow velocity was calculated using the float method at four prayags, namely, Vishnuprayag, Nandaprayag, 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. 2020; 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.

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 coefficient, 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 October, 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 management, the effect of climate change in melt water runoff and optimization of resources in the IHR.

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.

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.

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.

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

The authors declare there is no conflict.