Dynamic assessment of rainfall erosivity in Europe: evaluation of EURADCLIM ground-radar data

Abstract. Heavy rainfall is the main driver of water-induced soil erosion, necessitating accurate spatial and temporal predictions of rainfall erosivity to predict the soil erosion response. This study evaluates the ground radar-based EUropean RADar CLIMatology (EURADCLIM) precipitation grids to quantify rainfall erosivity across European countries. Compared to Global Rainfall Erosivity Database (GloREDa) gauge-based interpolations, EURADCLIM overpredicts rainfall erosivity, principally due to residual artefacts in some regions which inflate the instantaneous rainfall rates. Overprediction is most pronounced in European regions with lower radar antenna coverage and complex topography, whereas flatter regions with lower erosivity and better radar coverage are better predicted spatially but with a tendency towards underprediction. Disagreement attributes to the input radar quality in EURADCLIM (derived from OPERA) and to a lesser extent the uncertainty in GloREDa due to its limited gauge records in some regions. Event (EI30) time series analysis showed reasonably good performance (Kling-Gupta Efficiency (KGE) > 0.4) in 50 % of the evaluated gauge locations, although significant overprediction by EURADCLIM was evident in the upper quantiles in some countries. To account for the propagation of these remaining single-hour rainfall artefacts, which have a large impact on the temporally-aggregated R-factor, applying a 80 mm h−1 threshold to limit the maximum I30 value (i.e., less than 0.1 % of GloREDa events exceed this threshold) during the calculation of rainfall erosivity significantly improves the performance of the EURADCLIM dataset at annual, monthly and event time scale. Following adjustment, EURADCLIM best agrees with GloREDa across Europe in July and August, while bigger differences were observed in June and winter in general. Annually, the spatially aggregated rainfall erosivity per country had a percent bias below 10 %. While applying simple I30 thresholds is promising, radar artefacts remain significant in areas with lower quality rainfall retrievals. In the absence of spatiotemporally continuous, high-quality ground-radar retrievals across Europe, we show the value of ensemble R-factor layers of EURADCLIM with three other rainfall erosivity grids (e.g., satellite retrievals) and discuss the possibility of ground radar to offer unique spatial detail in such ensembles.

Authors

Institutions

Publication Details

Journal
Hydrology and earth system sciences
Published
2025-10-20
DOI
https://doi.org/10.5194/hess-29-5299-2025
Primary Topic
Soil erosion and sediment transport
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dynamic assessment of rainfall erosivity in Europe: evaluation of EURADCLIM ground-radar data

Francis Matthews, Nejc Bezak, Panos Panagos, Pasquale Borrelli
Hydrology and earth system sciences
Soil erosion and sediment transport
article

Dynamic assessment of rainfall erosivity in Europe: evaluation of EURADCLIM ground-radar data

Francis Matthews, Nejc Bezak, Panos Panagos, Pasquale Borrelli
article en

Abstract

Abstract. Heavy rainfall is the main driver of water-induced soil erosion, necessitating accurate spatial and temporal predictions of rainfall erosivity to predict the soil erosion response. This study evaluates the ground radar-based EUropean RADar CLIMatology (EURADCLIM) precipitation grids to quantify rainfall erosivity across European countries. Compared to Global Rainfall Erosivity Database (GloREDa) gauge-based interpolations, EURADCLIM overpredicts rainfall erosivity, principally due to residual artefacts in some regions which inflate the instantaneous rainfall rates. Overprediction is most pronounced in European regions with lower radar antenna coverage and complex topography, whereas flatter regions with lower erosivity and better radar coverage are better predicted spatially but with a tendency towards underprediction. Disagreement attributes to the input radar quality in EURADCLIM (derived from OPERA) and to a lesser extent the uncertainty in GloREDa due to its limited gauge records in some regions. Event (EI30) time series analysis showed reasonably good performance (Kling-Gupta Efficiency (KGE) > 0.4) in 50 % of the evaluated gauge locations, although significant overprediction by EURADCLIM was evident in the upper quantiles in some countries. To account for the propagation of these remaining single-hour rainfall artefacts, which have a large impact on the temporally-aggregated R-factor, applying a 80 mm h−1 threshold to limit the maximum I30 value (i.e., less than 0.1 % of GloREDa events exceed this threshold) during the calculation of rainfall erosivity significantly improves the performance of the EURADCLIM dataset at annual, monthly and event time scale. Following adjustment, EURADCLIM best agrees with GloREDa across Europe in July and August, while bigger differences were observed in June and winter in general. Annually, the spatially aggregated rainfall erosivity per country had a percent bias below 10 %. While applying simple I30 thresholds is promising, radar artefacts remain significant in areas with lower quality rainfall retrievals. In the absence of spatiotemporally continuous, high-quality ground-radar retrievals across Europe, we show the value of ensemble R-factor layers of EURADCLIM with three other rainfall erosivity grids (e.g., satellite retrievals) and discuss the possibility of ground radar to offer unique spatial detail in such ensembles.

Hydrology and earth system sciencesVol. 29(20)
Roma Tre University (IT), University of Ljubljana (SI), University of Basel (CH), Joint Research Centre (IT), KU Leuven (BE)
Openalex Percentile: Top 62%
Soil erosion and sediment transport
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.