Downscaled Atmospheric Forcings for Hyper-Resolution Hydrologic Modeling in High Mountain Asia

The High Mountain Asia (HMA) region is one of the greatest mountain systems in the world and is the headwater of rivers that support the ecosystem services, agriculture, energy, and livelihood of over one billion people. Developing a predictive capability for terrestrial hydrology across landscapes in HMA, with water, energy, and nutrients as the drivers of these dynamic systems, faces the challenge of scaling meter-scale processes to practical modeling scales. Hyper-resolution modeling can provide a framework for addressing science questions that we are not able to answer with coarse modeling scales. In this work, we developed downscaling techniques to study surface flux, storage, and water balance changes and investigate the causality of these changes in HMA. To this end, NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) forcing data (i.e., near-surface air temperature, pressure, humidity, wind speed, incident longwave and shortwave radiation, and precipitation) have been downscaled to 1km resolution across the HMA domain. Results are encouraging, showing that correlation coefficients between the downscaled dataset and ground observations are consistently higher and biases are consistently lower than the ones between the native resolution MERRA2 data and ground observations. The Coupled Routing and Excess STorage model soil-vegetation-atmosphere-snow (CREST-SVAS) is then forced with the downscaled surface meteorology in a two basins in Nepal to produce streamflow simulations. Hydrologic models commonly simulate and predict floods using event-based precipitation at coarser resolutions. This work seeks to improve the performance of such Earth observations-driven hydrologic predictions and explore how this scientific knowledge can effectively enhance water security in HMA.