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Total basin discharge plays an important role in the hydrological cycle for the regions. Estimation of total basin discharge cannot only help us manage water resources well but also provide a better understanding about water resources variability and hydrologic cycle. In this study, the Gravity Recovery and Climate Experiment (GRACE) data combined with precipitation and evapotranspiration (ET) is used to present first estimate of total basin discharge (~60.2 km3/year) based on water balance method over the entire Yarlung Tsangpo River basin during 2003–2014. The estimated basin discharge shows a good correlation with observed runoff (NSE = 0.70; NRMSE = 0.37; BIAS = −6.1%). An artificial neural network (ANN) model is also proposed to build the relationship between terrestrial water storage anomalies (TWSAs) with the other hydrological data available (e.g., precipitation, temperature, ET, and soil moisture storage) during 2003–2010 and then applied to hindcasting TWSA before 2003 in order to generate a longer record of TWSA. The results show that ANN‐generated TWSA using soil moisture storage and ET matches best with GRACE‐derived TWSA during 2003–2010, showing a correlation coefficient (r) of 0.89 and a normalized root‐mean‐square error (NRMSE) of 0.40, which indicate that the ANN model is an effective way to generate TWSA beyond the GRACE data record. The comparison between total basin discharge using ANN‐generated TWSA with the observed runoff data during 1998–2010 indicates a significant consistency with NSE = 0.66, NRMSE = 0.41, and BIAS = −4.9%. Our findings illustrate that GRACE data and the ANN model can be jointly and effectively used to assess the total basin discharge in the large‐scale basins with limited hydrological data.

Abstract. In recent year, floods becomes a serious issue in the Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide an early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River (YZR) basin, China, has not been investigated. This study adopts the variable infiltration capacity (VIC) model to forecast the annual maximum floods and annual first floods in the YZR based on precipitation and the maximum and minimum temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). N simulations are proposed to account for parameter uncertainty in VIC. Results show that when trade-offs between multiple objectives are significant, N simulations are recommended for better simulation and forecasting. This is why better results are obtained for the Nugesha and Yangcun stations. Our ensemble flood forecasting system can skillfully predict the maximum floods with a lead time of more than 10 d and can predict about 7 d ahead for meltwater-related components. The accuracy of forecasts for the first floods is inferior, with a lead time of only 5 d. The base-flow components for the first floods are insensitive to lead time, except at the Nuxia station, whilst for the maximum floods an obvious deterioration in performance with lead time can be recognized. The meltwater-induced surface runoff is the most poorly captured component by the forecast system, and the well-predicted rainfall-related components are the major contributor to good performance. The performance in 7 d accumulated flood volumes is better than the peak flows.

This study presents an approach that integrates remote sensing evapotranspiration into multi-objective calibration (i.e., runoff and evapotranspiration) of a fully distributed hydrological model, namely a distributed hydrology–soil–vegetation model (DHSVM). Because of the lack of a calibration module in the DHSVM, a multi-objective calibration module using ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII) and based on parallel computing of a Linux cluster for the DHSVM (εP-DHSVM) is developed. The module with DHSVM is applied to a humid river basin located in the mid-west of Zhejiang Province, east China. The results show that runoff is simulated well in single objective calibration, whereas evapotranspiration is not. By considering more variables in multi-objective calibration, DHSVM provides more reasonable simulation for both runoff (NS: 0.74% and PBIAS: 10.5%) and evapotranspiration (NS: 0.76% and PBIAS: 8.6%) and great reduction of equifinality, which illustrates the effect of remote sensing evapotranspiration integration in the calibration of hydrological models.