资源环境科技发展态势分析平台

One major challenge in large scale modeling is the estimation of spatially consistent distributed parameters, that are parameters with a clear functional relationship to climate and landscape characteristics. We present a newly developed PArameter Set Shuffling (PASS) approach, which is able to provide such regionally consistent parameter sets. The PASS method does not require any a priori assumption on the relationship between model parameters and catchment descriptors. It instead derives these relationships from observed patterns of calibrated parameters and available catchment descriptors. We tested the PASS approach to derive parameters of a conceptual hydrological model applied to 263 German catchments. The resulting median model efficiencies for training and test catchments are, respectively, 0.74 and 0.72, similar to those obtained by other modeling approaches, which use regional calibration. In this study, a combination of catchment descriptors that clearly controls model parameters is not found. In fact, we show that various regional functional relationships between catchment descriptors and model parameters result in similarly good model performances. Moreover, catchment descriptors used for parameter prediction can be replaced in the parameter prediction, without any decrease in model performance. Our results suggest that by using conventional catchment descriptors based on averages, only the amount of information that is also retained in the existing correlations among climatic and catchment indicators is exploited. Development of a new generation of hydrologically meaningful catchment and climate descriptors is required to further improve our capability of forecasting hydrological dynamics of interest by means of large scale models and regionalization approaches.