Exploration on hydrological model calibration by considering the hydro-meteorological variability

The hydrological response is changeable for catchments with hydro-meteorological variations, which is neglected by the traditional calibration approach through using time-invariant parameters. This study aims to reproduce the variation of the hydrological response by allowing parameters to vary over clusters with hydro-meteorological similarities. The Fuzzy C-means algorithm is used to partition 1-month periods into temperature-based and rainfall-based clusters. 1-month periods are also classified based on seasons and random numbers for comparison. This study is carried out in three catchments in the southwest of UK, with the use of the IHACRES rainfall-runoff model. Results show when using time-varying parameters to account for the variation of the hydrological process, it is important to identify the key factors that cause the change of the hydrological response, and the selection of the time-varying parameters should correspond to the identified key factors. In the study sites, temperature plays a more important role in controlling the change of the hydrological response than rainfall. It is found the number of clusters has an effect on model performance, model performances for calibration period become better with the increase of cluster number; however, the increase of model complexity leads to poor predictive capabilities of the model due to overfitting. It is of great importance to select the appropriate number of clusters to achieve a balance between model complexity and model performance.