Annual and Monthly Reconstruction of Historical R-Factor from REDES and ERA5-Land

Soil erosion by water remains a major environmental threat in Europe, with rainfall erosivity (R-factor) acting as a key driver of its magnitude and spatiotemporal variability, and with indications that erosion pressures may intensify under future climatic conditions (Panagos et al., 2021). At the European scale, event-based rainfall erosivity has been robustly characterized through the REDES database, resulting in a mean multi-annual R-factor map (Panagos et al., 2015) and monthly climatological R-factor maps derived from REDES and WorldClim data (Ballabio et al., 2017), together providing a consistent spatial framework for erosion assessments. Building on these established frameworks, we extend these products by reconstructing temporally resolved annual and monthly rainfall erosivity for Europe from 1950 to present, combining the spatial patterns of REDES with the temporal variability captured by ERA5-Land hourly precipitation.

ERA5-Land hourly precipitation is used to identify erosive rainfall events at each grid cell using standard criteria (Renard et al., 1997). Rainfall erosivity (EI₆₀) is calculated for each erosive event, and monthly erosivity is obtained by aggregating event contributions and applying month-specific conversion factors to express erosivity on a 30-min basis (Panagos et al., 2016), ensuring consistency with established formulas and enabling comparable monthly and seasonal analyses across heterogeneous recording intervals. Annual and monthly reconstruction is performed by scaling the REDES multi-annual and monthly climatological erosivity maps using ERA5-derived annual and monthly anomalies, therefore preserving the spatial patterns of REDES while transferring ERA5-captured interannual and intra-annual variability. The workflow produces gridded annual and monthly ERA5 erosivity and reconstructed erosivity maps accompanied by statistics and extreme-event diagnostics.

Results indicate that most years and months exhibit scaling ratios close to unity across large parts of Europe, while a limited fraction of pixels shows substantial anomalies and are subjected to targeted hotspot analysis. By extending rainfall erosivity reconstruction beyond the reference period to the present and transforming established multi-annual and monthly climatological R-factor products into fully time-resolved annual and monthly maps, we enable a consistent assessment of recent variability and extremes fully aligned with European erosivity mapping, providing a robust basis for erosion modeling, risk assessment, and climate-impact studies.

Acknowledgement: K.K, F.M., P.B, were funded by the European Union Horizon Europe Project Soil O-LIVE (Grant No. 101091255). P.S. was funded by the European Union Horizon Europe Project AI4SoilHealth (Grant No. 101086179).

References:

Ballabio, C., Borrelli, P., Spinoni, J., Meusburger, K., Michaelides, S., Beguería, S., ... & Panagos, P. (2017). Mapping monthly rainfall erosivity in Europe. Science of the Total Environment, 579, 1298-1315.

Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., ... & Alewell, C. (2015). Rainfall erosivity in Europe. Science of the Total Environment, 511, 801-814.

Panagos, P., Borrelli, P., Spinoni, J., Ballabio, C., Meusburger, K., Beguería, S., ... & Alewell, C. (2016). Monthly rainfall erosivity: conversion factors for different time resolutions and regional assessments. Water, 8(4), 119.

Renard, K.G., et al., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE) (Agricultural Handbook 703). US Department of Agriculture, Washington, DC, p. 404.