资源环境科技发展态势分析平台

Conceptual rainfall-runoff (CRR) models are commonly used to assess the potential impact of climate change on water resources systems. However, they are often characterized by poorer performance when used to simulate a different climate compared to that of the calibration period. This is generally referred to as low model robustness, and these issues have been thoroughly explored using historical data. However, the implications of robustness are unknown for a changing climate where models may have to operate under conditions that lie beyond existing observations. This study extends these ideas to evaluate the "potential robustness" of different CRR models in the context of a changing climate. To achieve this aim, we combine a generalized split-sample test framework with a stochastic weather generator. This allows us to assess the variabilities in runoff predictions obtained from using different calibration periods within each CRR model. We tested the potential robustness on three catchments with contrasting hydroclimatic conditions. We observed a consistent higher potential robustness in all models under drier conditions at all catchments. The three catchments illustrate contrasting patterns in the relative potential robustness of the three CRR models, which are related to both the structures of the CRR models and the unique catchment characteristics, highlighting the need of case-specific assessment. This study illustrates a transferable empirical testing strategy to understanding variabilities in CRR model predictions. This approach can improve our knowledge of model behavior and thus informs the suitability of alternative models to simulate catchments hydrology under a changing climate.