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.

As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.

Growth in human population and demand for wealth creates ever-increasing pressure on global soils, leading to soil losses and degradation worldwide. Critical Zone science studies the impact linkages between these pressures, the resulting environmental state of soils, and potential interventions to protect soil and reverse degradation. New research on soil processes is being driven by the scientific hypothesis that soil processes can be described along a life cycle of soil development. This begins with formation of new soil from parent material, development of the soil profile, and potential loss of the developed soil functions and the soil itself under overly intensive anthropogenic land use, thus closing the cycle. Four Critical Zone Observatories in Europe have been selected focusing research at sites that represent key stages along the hypothetical soil life cycle; incipient soil formation, productive use of soil for farming and forestry, and decline of soil due to longstanding intensive agriculture. Initial results from the research show that soil develops important biogeochemical properties on the time scale of decades and that soil carbon and the development of favourable soil structure takes place over similar time scales. A new mathematical model of soil aggregate formation and degradation predicts that set-aside land at the most degraded site studied can develop substantially improved soil structure with the accumulation of soil carbon over a period of several years. Further results demonstrate the rapid dynamics of soil carbon; how quickly it can be lost, and also demonstrate how data from the CZOs can be used to determine parameter values for models at catchment scale. A structure for a new integrated Critical Zone model is proposed that combines process descriptions of carbon and nutrient flows, a simplified description of the soil food web, and reactive transport; all coupled with a dynamic model for soil structure and soil aggregation. This approach is proposed as a methodology to analyse data along the soil life cycle and test how soil processes and rates vary within, and between, the CZOs representing different life cycle stages. In addition, frameworks are discussed that will help to communicate the results of this science into a more policy relevant format using ecosystem service approaches.