Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.

The aim of this paper is to analyse the differences in the long-term regimes of extreme precipitation and floods across the Alpine–Carpathian range using seasonality indices and atmospheric circulation patterns to understand the main flood-producing processes. This is supported by cluster analyses to identify areas of similar flood processes, both in terms of precipitation forcing and catchment processes. The results allow to isolate regions of similar flood generation processes including southerly versus westerly circulation patterns, effects of soil moisture seasonality due to evaporation and effects of soil moisture seasonality due to snow melt. In many regions of the Alpine–Carpathian range, there is a distinct shift in flood generating processes with flood magnitude as evidenced by a shift from summer to autumn floods. It is argued that the synoptic approach proposed here is valuable in both flood analysis and flood estimation.

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha-1h-1) compared to winter (87MJmmha-1h-1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

The Carpathians are the longest mountain range in Europe and a geographic barrier between Central Europe, Eastern Europe, and the Balkans. To investigate the climate of the area, the CARPATCLIM project members collected, quality-checked, homogenized, harmonized, and interpolated daily data for 16 meteorological variables and many derived indicators related to the period 1961–2010. The principal outcome of the project is the Climate Atlas of the Carpathian Region, hosted on a dedicated website (www.carpatclim-eu.org) and made of high-resolution daily grids (0.1° × 0.1°) of all variables and indicators at different time steps. In this article, we analyze the spatial and temporal variability of 10 variables: minimum, mean, and maximum temperature, daily temperature range, precipitation, cloud cover, relative sunshine duration, relative humidity, surface air pressure, and wind speed at 2 m. For each variable, we present the gridded climatologies for the period 1961–2010 and discuss the linear trends both on an annual and seasonal basis. Temperature was found to increase in every season, in particular in the last three decades, confirming the trends occurring in Europe; wind speed decreased in every season; cloud cover and relative humidity decreased in spring, summer, and winter, and increased in autumn, while relative sunshine duration behaved in the opposite way; precipitation and surface air pressure showed no significant trend, though they increased slightly on an annual basis. We also discuss the correlation between the variables and we highlight that in the Carpathian Region positive and negative sunshine duration anomalies are highly correlated to the corresponding temperature anomalies during the global dimming (1960s and 1970s) and brightening (1990s and 2000s) periods.

State-of-the-art climate models project a substantial decline in precipitation for the Mediterranean region in the future¹. Supporting this notion, several studies based on observed precipitation data spanning recent decades have suggested a decrease in Mediterranean precipitation^2,3,4, with some attributing a large fraction of this change to anthropogenic influences^3,5. Conversely, certain researchers have underlined that Mediterranean precipitation exhibits considerable spatiotemporal variability driven by atmospheric circulation patterns^6,7 maintaining stationarity over the long term^8,9. These conflicting perspectives underscore the need for a comprehensive assessment of precipitation changes in this region, given the profound social, economic and environmental implications. Here we show that Mediterranean precipitation has largely remained stationary from 1871 to 2020, albeit with significant multi-decadal and interannual variability. This conclusion is based on the most comprehensive dataset available for the region, encompassing over 23,000 stations across 27 countries. While trends can be identified for some periods and subregions, our findings attribute these trends primarily to atmospheric dynamics, which would be mostly linked to internal variability. Furthermore, our assessment reconciles the observed precipitation trends with Coupled Model Intercomparison Project Phase 6 model simulations, neither of which indicate a prevailing past precipitation trend in the region. The implications of our results extend to environmental, agricultural and water resources planning in one of the world’s prominent climate change hotspots¹⁰.