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.

While warming temperatures are known to increase atmospheric moisture capacity and heavy precipitation frequency; as yet there is little evidence for corresponding increases in floods. This study comprehensively examines global changes in multidimensional flood behaviors (magnitude, frequency, and duration), and aims to identify the possible mechanisms behind the heavy precipitation and flood change dichotomy. Our global assessment shows that floods become more frequent but not larger. Regionally, consistent changes can be observed among multidimensional flood behaviors, i.e., floods becoming larger in magnitude, more frequent, and longer in duration in some regions (e.g., North Europe), while smaller, less frequent, and shorter in other regions (e.g., South Australia). Attribution analysis indicates that spatial patterns of global flood trends are primarily controlled by shifts in atmospheric circulation patterns, terrestrial water storage changes, and temperature increases. The dams are crucial for reducing the floods, with the greatest impacts on flood magnitude, followed by flood frequency and duration. Catchment characteristics (i.e., vegetation coverage, irrigation, and urbanization) regulate the response of flood changes to changing environments.

Evaluation and quantification of possible sources of uncertainty and their influence on water resource planning and extreme management is very important for risk modeling and extreme hydrological management. The main objective of this research work is to combine statistical climate ensembles, multiple parameter sets for three conceptual hydrological model structure and five flood frequency distribution models to investigate the interplay among the associated uncertainty in flood and low flow modelling. Uncertainty in the modeling of extreme high flow frequency mainly comes from the quality of the input data, while in the modeling of low flow frequency, the main contributor to the total uncertainty is from model parameterization. This result is also confirmed by using the Analysis Of Variance Analysis (ANOVA) that considers additional information about the interaction impact of the main factors. The total uncertainty of QT90 (extreme peak flow quantile at 90-year return period) quantile shows the interaction of input data and extreme frequency models has significant influence on the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10-year return period) estimation, the hydrological models and hydrological parameters have significant impact on the total uncertainty. This implies that the four factors and their interactions may cause significant risk in water resource management and flood and drought risk management, and neglecting of these four factors and their interaction in disaster risk management, water resource planning and evaluation of environmental impact assessment is not feasible and may lead to big risk.