Improvements in Sub-Catchment Fractional Snowpack and Snowmelt Parameterizations and Hydrologic Modeling for Climate Change Assessments in the Western Himalayas

Abstract

The present work proposes to improve estimates of snowpack and snowmelt and their assessment in the steep Himalayan ranges at the sub-catchment scale. Temporal variability of streamflow and the associated distribution of accumulated snow in catchments with glacier presence in the Himalayas illustrates how changes in snowpack and snowmelt can affect the water supply for local water management. The primary objective of this study is to assess the role of elevation, temperature lapse rate (TLR), and precipitation lapse rate (PLR) in the computation of snowpack (or snowfall) and snowmelt in sub-catchments of the Satluj River basin. Modeling of snowpack and snowmelt was constructed using the Soil Water Assessment Tool (SWAT) in both historical (1991–2008) and near-time scenarios (2011–2030) by implementing real-time hydrometeorological, snow-hydrological parameters, and Global Circulation Model (GCM) datasets. The modeled snowmelt-induced streamflow showed a good agreement with the observed streamflow (~60%), calibrated and validated at three gauges. A Sequential Uncertainty Parameter Fitting (SUFI2) method (SUFI2) resulted that the curve number (CN2) was found to be significantly sensitive during calibration. The snowmelt hydrological parameters such as snowmelt factor maximum (SMFMX) and snow coverage (SNO50COV) significantly affected objective functions, such as R2 and NSE, during the model optimization. For the validation of snowpack and snowmelt, the results have been contrasted with previous studies and found comparable. The computed snowpack and snowmelt were found highly variable over the Himalayan sub-catchments, as also reported by previous researchers. The magnitude of snowpack change consistently decreases across all the sub-catchments of the Satluj river catchment (varying between 4% and 42%). The highest percentage of changes in the snowpack was observed over high-elevation sub-catchments.

Modeling Water Quantity and Quality Nonlinearities for Watershed Adaptability to Hydroclimate Extremes in Agricultural Landscapes

“…ydrology 2022, 9, x FOR PEER REVIEW 13 of 2 also indicates the reliability of the streamflow simulations. Calibration efficiency indice were similar to efforts using monthly rain gauge-based data for SWAT [24,28,78] and ra dar-based simulations [22,23,26,79,80]; however, the validation efficiency indices wer higher than those for calibration. NSE and RSR metrics for calibration were 0.58 and 0.65 respectively; for validation, the values were 0.92 and 0.28, respectively.…”

Glacier changes and their impacts on the melt runoff in high and moderate elevation ranges of Himalayan Upper Ganges Basin

“…For the computation of snow cover, snowmelt and glacier melt, SPHY works based on the degree‐day approach using temperature index model (Terink et al, 2015). Many studies related to cryospheric applications have shown the applicability of temperature index model using degree‐day factor (DDF) in the modelling of snowmelt and glacier melt across world that follows an empirical relationship between melt rate and air temperature (Singh, Jain, & Goyal, 2021; Singh & Muñoz‐Arriola, 2021). In SPHY model, the snow–glacier induced and other Q components namely, glacier Q , snow Q , rain Q , base Q and total Q are computed at each grid scale and their contribution can be accounted at subbasin scale or at a desired outlet, separately.…”

“…In Himalaya, along with glaciers, snow is equally important in the context of natural hazards, such as avalanche mitigation, flood predictions, and so forth (Bolch et al, 2012). In few studies, climate variations have shown a strong influence on the seasonal distribution of snow cover, which has negatively impacted the snowmelt runoff and glacier mass balances (Singh, Jain, & Goyal, 2021; Singh & Muñoz‐Arriola, 2021). Based on various topographical and weather parameters such as elevation level and temperature variations, the Himalayan climatology can be distributed into three zones namely, upper zones, middle and lower (Sood et al, 2020).…”

“…A few studies have shown a clear sign of an increase in temperature in the Ganga river basin (Lutz et al, 2019; Mittal et al, 2014). Due to climate change, the temperature lapse rate and precipitation gradient have found varied and they have shown significant sensitivity to snow cover and glacier mass balance variabilities in different parts of the Himalaya (Singh & Goyal, 2016; Singh & Muñoz‐Arriola, 2021). Recent climate change‐based studies revealed an increase in liquid precipitation and demonstrated their impacts on glacier melting (Ragettli et al, 2016; Singh & Muñoz‐Arriola, 2021) and therefore, it is necessary to understand the impact of increasing liquid precipitation over glacier retreat.…”