Get started
Work

A Network-Based Clustering Method to Ensure Homogeneity in Regional Frequency Analysis of Extreme Rainfall

Year: 2024
Type: article
Abstract: The social impacts of extreme rainfall events are expected to intensify with climate change, making reliable statistical analyses essential. High quantile estimation requires substantial data; however, available records are sometimes limited. Additionally, finite data and variability across statistical models introduce uncertainties in the final estimates. This study addresses the uncertainty that arises when selecting parameters in Regional Frequency Analysis (RFA) by proposing a method to objectively identify statistically homogeneous regions. Station coordinates, elevation, annual mean rainfall, maximum annual rainfall, and l-skewness from 55 meteorological stations are selected to study annual maximum daily rainfall. These covariates are employed to investigate the interdependency of the covariates in Principal Component Analysis (PCA) as a preprocessing step in cluster analysis. Network theory, implemented through an iterative clustering process, is used in network creation where stations are linked based on the frequency of their co-occurrence in clusters. Communities are formed by maximizing the modularity index after creating a network of stations. RFA is performed in the final communities using L-moment theory to estimate regional and InSite quantiles. Quantile uncertainty is calculated through parametric bootstrapping. The application of PCA has a negligible effect on network creation in the study area. The results show that the iterative clustering approach with network theory ensures statistically created homogeneous regions, as demonstrated in Thessaly’s complex terrain for regionalisation of extreme rainfall.
Source: Water
DOI: 10.3390/w17010038
Language: English
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4405810281
FWCI: 0.67
Cites: 93
Cited by: 6
Related to: 10