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Climate change impact assessment on mountain snow hydrology by water and energy budget-based distributed hydrological model

Bhatti, Asif M., Koike, Toshio, Shrestha, Maheswor
Journal of hydrology 2016 v.543 pp. 523-541
General Circulation Models, basins, climate, energy, global warming, hydrologic models, moderate resolution imaging spectroradiometer, rain, rivers, runoff, snow, snowmelt, snowpack, spatial variation, temporal variation, watersheds, Japan
A water and energy budget-based distributed hydrological model with improved snow physics (WEB-DHM-S) was applied to elucidate the impact of climate change on mountain snow hydrology in the Shubuto River basin, Hokkaido, Japan. The simulated spatial distribution of snow cover was evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2) product, which revealed the model’s capability for capturing the spatiotemporal variations in snow cover within the study area. Four Atmosphere Ocean General Circulation Models (AOGCMs) were selected and the SRESA1B emission scenario of the Intergovernmental Panel on Climate Change was used to describe climate predictions in the basin. All AOGCMs predict a future decrease in snowmelt contribution to total discharge 11–22% and an average decrease in SWE of 36%, with a shift in peak SWE by 4–14 days. The shift in runoff regime is broadly consistent between the AOGCMs with snowmelt-induced peak discharge expected to occur on average about two weeks earlier in the future hydrological year. The warming climate will drive a shift in runoff regime from a combined rainfall- and snowmelt-driven regime to one with a reduced contribution from snowmelt. The results of the study revealed that the model could be successfully applicable on the basin scale to simulate river discharge and snow processes and to investigate the effect of climate change on hydrological processes. This research contributes to improve the understanding of basin hydrological responses and the pace of change associated with climate variability.