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 homogeneous (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 compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

KEYWORDS:

• spatial variability index
• flood prediction
• calibration
• hydrological modelling

Editor A. Castellarin; Associate Editor S. Huang

Editor A. Castellarin; Associate Editor S. Huang

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Open source data was used; the source is described in section 2.2 of this article. SRTM DEM (https://earthexplorer.usgs.gov/) ESRI LULC (https://livingatlas.arcgis.com/landcover/) Discharge data (https://indiawris.gov.in/wris/#/RiverMonitoring) IMD precipitation (https://www.imdpune.gov.in/lrfindex.php) FAO soil (https://data.apps.fao.org/map/catalog/srv/eng/catalog.search?id=14116#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8).

Supplementary material

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