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.

Recent decades have seen a change in the runoff characteristics of the Suntar River in Eastern Siberia. This study attempts to attribute these changed hydrological conditions through parameterizing a hydrological model based on historical short-term observations conducted in 1957-1959 at the Suntar-Khayata research station. The Hydrograph model is applied as it has the advantage of using observed physical properties of landscapes as its parameters. The developed parametrization of the goltsy landscape (rocky-talus) is verified by comparison of the results of simulations of variable states of snow and frozen ground with observations carried out in 1957-1959. Continuous simulations of streamflow on a daily time step are conducted for the period 1957-2012 in the Suntar River (area 7680 km2, altitude 828-2794 m) with mean and median values of Nash-Sutcliff criteria reaching 0.58 and 0.67 respectively. The results of simulations have shown that the largest component of runoff (about 70%) is produced in the high-altitude area which comprises only 44% of the Suntar River basin area. The simulated streamflow reproduces the patterns of recently observed changes, including the increase in low flows, suggesting that the increase in the proportion of liquid precipitation in autumn due to air temperature rise is an important factor in driving streamflow changes in the region. The data presented are unique for the vast mountainous parts of North-Eastern Eurasia which play an important role in global climate system. The results indicate that parameterizing a hydrological model based on observations rather than blind calibration allows the model to be used in studying the response of river basins to climate change with greater confidence.

The study investigates the possibility to parameterize a hydrological model for remote high-altitude permafrost basin based on the data of historical short-term observations conducted in 1957-1959 at the Suntar-Khayata research station (Eastern Siberia) and simulate the changes of runoff observed in recent decades in the region. The Hydrograph model is applied as it has the advantage of using observed physical properties of landscapes as its parameters. The developed parametrization of the goltsy landscape is verified by the results of simulations of variable states of snow and frozen ground. Continuous simulations of streamflow with daily time step are conducted for the period of 1957-2012 at the Suntar River basin (area 7680 km2, altitude 828-2794 m) with average and median values of Nash-Sutcliff criteria reaching 0.58 and 0.67 respectively. The results of simulations have shown that the largest part of runoff (about 70%) is formed in the high-altitude area which takes only 44% of the Suntar River basin area. Simulated series of streamflow reproduce the patterns of recently observed changes, including the increase of low flow, by magnitude of trends and their change period, suggesting that the increase of the increase of liquid precipitation share in autumn months due to air temperature rise can be important factor of streamflow changes in the region. The data presented in the paper are unique for the vast mountainous parts of North-Eastern Eurasia which play important role in general climate circulation. The results indicate that if the assessment of hydrological model parameters is based on observation data instead of calibration, the models can be used in the tasks of studying the response of river basins to climate change with more confidence.