High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalised extreme value distributions

TL;DR

This paper develops a regional frequency analysis approach using clustering to estimate high return levels of daily precipitation across Europe from ERA-5 data, enabling detailed spatial mapping for hydrological planning.

Contribution

It introduces a step-by-step clustering-based RFA method that simplifies modeling by using only a spatially varying scale parameter, improving efficiency and accuracy.

Findings

Sparse regional models perform well against more complex models.

The approach produces detailed return level maps for Europe.

Method is computationally efficient and practical for large datasets.

Abstract

Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25 x 0.25 grid (about 27 x 27 km). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity.