Ougahi, J.H.; Rowan, J.S. Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains. Remote Sens.2024, 16, 264.
Ougahi, J.H.; Rowan, J.S. Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains. Remote Sens. 2024, 16, 264.
Ougahi, J.H.; Rowan, J.S. Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains. Remote Sens.2024, 16, 264.
Ougahi, J.H.; Rowan, J.S. Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains. Remote Sens. 2024, 16, 264.
Abstract
Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, snow melt contributions to floods and the subsequent availability of water resources downstream. Accurately estimating SWE and understanding its spatial and temporal variations presents a considerable challenge, particularly within mountainous regions characterized by complex terrain and limited observational data. Seeking to enhance the performance of the widely used Soil and Water Assessment Tool (SWAT), we report a new approach characterising snowpack behaviour incorporating both modelled and remotely sensed derived SWE calibration data. We focus on the Chenab River Basin (CRB) a headwater catchment of the Indus Basin, globally significant in terms of human inhabitants and intensifying flood risk due to climate change. We conducted a thorough assessment of five satellite-derived and reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, and CPC UPP. This reveals significant levels of uncertainty in global precipitation products when compared to reference data from observed stations as well as in the resulting simulated streamflow from the SWAT model. Subsequently, we expanded the scope of the SWAT model to encompass the spatial and temporal simulation of SWE. This was achieved by incorporating information from remotely sensed and modelled SWE products, manually adjusting snow parameters in R-SWAT for both the main basin and at sub-basin scales. Integrating SWE from reference snow products into the calibration process, alongside streamflow data, substantially enhanced modelling accuracy to simulate SWE compared to the conventional auto-calibration and single-variable approaches reliant solely on streamflow data. This approach results in considerable improvement in SWE predictions and to some extent in streamflow simulation in catchments dominated by snow. This research highlights the potential of remote sensing and modelled SWE parameterisation in the absence of in-situ snowpack data in high-altitude environments. An improved understanding of SWE behaviour is vital for predicting hydrological responses spanning hazards to water resources in the populous downstream regions of the Indus Basin, especially in the face of climate change.
Keywords
SWAT; snow water equivalent; global precipitation products; multi-variable calibration; remote sensing
Subject
Environmental and Earth Sciences, Geography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
