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

Abstract

Abstract 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 hydrological responses by allowing parameters to vary over clusters with hydro-meteorological similarities. The Fuzzy C-means algorithm is used to partition one-month periods into temperature-based and rainfall-based clusters. One-month periods are also classified based on seasons and random numbers for comparison. This study is carried out in three catchments in the UK, using the IHACRES rainfall-runoff model. Results show when using time-varying parameters to account for the variation of hydrological processes, it is important to identify the key factors that cause the change of hydrological responses, 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 hydrological responses than rainfall. It is found that 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 due to overfitting. It is important to select the appropriate number of clusters to achieve a balance between model complexity and model performance.