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.
Highlights
Understanding the hydrological response of catchments is crucial for various issues related to water resources management
As the temperature-based cluster and rainfall-based cluster are identified using the Fuzzy C-means (FCM) algorithm, it is inferred that the FCM algorithm has a better performance in grouping objects with similar characteristics and separating objects that are dissimilar in terms of the same characteristics
This study attempts to improve the hydrological model performance by using time-varying parameters to represent the variation of the hydrological response
Summary
Understanding the hydrological response of catchments is crucial for various issues related to water resources management. The accuracy of hydrological models is affected by multiple factors. The error associated with the observed data is one of these factors. The model’s representation of the hydrological process (or model structure) affects the model performance. Magnusson et al ( ) demonstrated the usefulness of a multimodel framework for identifying appropriate model structures according to data availability, properties of interest and computational
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