Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged or poorly gauged catchments, a challenging area of research in hydrology over the last several decades. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed-evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments of Yalong River basin, China. To this end, seven RS data calibration schemes are explored, compared to traditional calibration against observed runoff and traditional regionalization using spatial proximity. Our results show that using bias-corrected remotely sensed AET (bias-corrected PML-AET data) for constraining model calibration performs much better than using the non bias-corrected remotely sensed AET data (non bias-corrected AET obtained from PML model estimate). Using the bias-corrected PML-AET data in a gridded way is much better than that in a lumped way, and outperforms the traditional regionalization approach especially at upstream and large catchments. Combining the bias-corrected PML-AET and GRACE water storage data performs similarly to using the bias-corrected PML-AET data only. This study demonstrates that and there is great potential to use RS-AET based data for calibrating hydrological models in order to predict runoff in data sparse regions with complex terrain conditions.

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored, and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias-corrected remotely sensed AET (bias-corrected PML-AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (non-bias-corrected AET obtained from PML model estimate). Using the bias-corrected PML-AET data in a gridded way is much better than using lumped data, and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias-corrected PML-AET and GRACE water storage data performs similarly to using the bias-corrected PML-AET data only. This study demonstrates that there is great potential in using bias-corrected RS-AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions.

Drought risk assessment can identify high-risk areas and bridge the gap between impacts and adaptation. However, very few dynamic drought risk assessments and projections have been performed worldwide at high spatial resolution (e.g., 0.5{degree sign} × 0.5{degree sign}) under different greenhouse gas emission scenarios. Here, future global drought risk is projected combing three components (i.e., hazard, exposure, and vulnerability) during 2021-2100 under combined scenarios of Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. This study first investigates dynamic drought risks and exposed population and GDP across the six continents (Antarctica is not examined due to data availability). The results show that high-risk regions mainly concentrate in southeastern China, India, Western Europe, eastern United States, and western and eastern Africa. Drought risk will further strengthen in the future under four scenarios, with the highest under SSP5-8.5 and the lowest under SSP3-7.0. Populations exposed to high drought risk for Asia and Africa are much more than other continents. Among four SSP-RCPs, populations exposed to high risk are the largest under SSP3-7.0 for Africa, Asia, and South America, while under SSP5-8.5 for Australia, Europe, and North America. GDP exposed to high drought risk is the largest for Asia among the six continents and the largest under SSP5-8.5 among the SSP-RCPs. The most significant increases in population and GDP under high drought risk both occur in Africa. This study provides a scientific basis for effective adaptation measures to enhance drought resilience in potential high-risk areas.