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 (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 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.

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Abstract.We have studied the performances of (a) a two-way coupled atmosphere-ocean modeling system and (b) one-way coupled ocean model (forced by the atmosphere model), as compared to the available in situ measurements during and after a strong Adriatic bora wind event in February 2012, which led to extreme air-sea interactions. The simulations span the period between January and March 2012. The models used were ALADIN (Aire Limitée Adaptation dynamique Développement InterNational) (4.4 km resolution) on the atmosphere side and an Adriatic setup of Princeton ocean model (POM) (1 • /30 × 1 • /30 angular resolution) on the ocean side. The atmosphere-ocean coupling was implemented using the OASIS3-MCT model coupling toolkit. Two-way coupling ocean feedback to the atmosphere is limited to sea surface temperature. We have compared modeled atmosphere-ocean fluxes and sea temperatures from both setups to platform and CTD (conductivity, temperature, and depth) measurements from three locations in the northern Adriatic. We present objective verification of 2 m atmosphere temperature forecasts using mean bias and standard deviation of errors scores from 23 meteorological stations in the eastern part of Italy. We show that turbulent fluxes from both setups differ up to 20 % during the bora but not significantly before and after the event. When compared to observations, two-way coupling ocean temperatures exhibit a 4 times lower root mean square error (RMSE) than those from one-way coupled system. Two-way coupling improves sensible heat fluxes at all stations but does not improve latent heat loss. The spatial average of the two-way coupled atmosphere component is up to 0.3 • C colder than the one-way coupled setup, which is an improvement for prognostic lead times up to 20 h. Daily spatial average of the standard deviation of air temperature errors shows 0.15 • C improvement in the case of coupled system compared to the uncoupled. Coupled and uncoupled circulations in the northern Adriatic are predominantly wind-driven and show no significant mesoscale differences.

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