Spatial variability of rainfall: Deciphering flood characteristics and model precision

Abstract

This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogenous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR’s role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results as compared to the conventional approach. For the catchments considered, lumped model demonstrated heightened performance for Class A events, while distributed models outperformed in Class B events. This study not only underscores the influence of SVR on flood dynamics but also underscores the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

Keywords:

• spatial variability index
• Flood Prediction
• calibration
• hydrological modelling

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Declaration of Interest

The authors report there are no competing interests to declare.

Data Availability Statement

Open source data was used and the source of the same are available in the manuscript in section 2.2.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02626667.2024.2371876