Using Hydrological Models and Remote Sensing to Characterize Snow Pack Behaviour in High Mountain Environments

• DOI: 10.20944/preprints202311.0645.v1
• Publication Date: 2023-11-10T08:45:17Z
• Authors: Jamal Hassan Ougahi; John Sibbald Rowan
• Keywords: Snowpack; SWAT model; Flood forecasting; Hydrological modelling

Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, melt contributions to floods and subsequent availability of resources downstream. Accurately estimating SWE understanding its spatial temporal variations presents considerable challenge, particularly within mountainous regions complex terrain limited observational data. Seeking enhance performance widely used Soil Water Assessment Tool (SWAT), we report new approach characterising snowpack behaviour incorporating both modelled remotely sensed derived calibration We focus on Chenab River Basin (CRB) headwater catchment Indus Basin, globally significant terms human inhabitants intensifying flood risk due climate change. conducted thorough assessment five satellite-derived reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, CPC UPP. This reveals levels uncertainty global products when compared reference data from observed stations as well resulting simulated streamflow SWAT model. Subsequently, expanded scope model encompass simulation SWE. was achieved information products, manually adjusting parameters R-SWAT for main basin at sub-basin scales. Integrating into process, alongside data, substantially enhanced modelling accuracy simulate conventional auto-calibration single-variable approaches reliant solely results improvement predictions some extent catchments dominated snow. research highlights potential remote sensing parameterisation absence in-situ high-altitude environments. An improved is vital predicting responses spanning hazards populous downstream especially face