The availability of remotely sensed (RS) actual evapotranspiration (ET) provides a possibility for improving runoff prediction in ungauged basins. To develop a general practical method to improve runoff prediction by directly incorporating RS-ET into rainfall-runoff (RR) models, two modeling schemes are proposed: (i) using RS-ET as direct input; and (ii) using RS-ET as partial direct input. The principle is to use RS-ET in cases where the runoff prediction can be improved. The two schemes are compared in over 200 basins using three RR models (Xinanjiang model, SIMHYD, and GR4J) and RS-ET inverted from AVHRR, and the modeling results in ungauged basins are assessed using the spatial proximity method. Results show that: (i) it is beneficial to incorporate RS-ET into the Xinanjiang model for over 85% of the basins, but this is not the case for SIMHYD and GR4J models; (ii) further model improvements can be obtained by using RS-ET as partial direct input, and are achieved in 91.1%, 59.0%, and 53.2% of the basins for Xinanjiang, SIMHYD, and GR4J, respectively; and (iii) incorporation of RS-ET is more applicable for Xinanjiang while less so for GR4J, and the efficacy is superior for basins that are relatively arid and were originally poorly simulated. Overall, using RS-ET as partial direct input is recommended.

Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a nonstationary model through the Bayesian weighted averaging of two lumped conceptual rainfall–runoff (RR) models (the Xinanjiang and GR4J model) with time-varying weights; and (ii) detecting the temporal variation in the optimized Bayesian weights under climate change conditions. By combining the BMA method with period partition and time sliding windows, the efficacy of adopting time-varying model structures is investigated over three basins located in the U.S. and Australia. The results show that (i) the nonstationary ensemble-averaged model with time-varying weights surpasses both individual models and the ensemble-averaged model with time-invariant weights, improving $NSE[\sqrt{Q}]$ from 0.04 to 0.15; (ii) the optimized weights of Xinanjiang model increase and that of GR4J declines with larger precipitation, and vice versa; (iii) the change in the optimized weights is proportional to that of precipitation under monotonic climate change, as otherwise the mechanism changes significantly. Overall, it is recommended to adopt nonstationary structures in hydrological modeling.