Precipitation variability results from atmospheric circulation and complex site-specific bio-geoclimatic characteristics; therefore, climatic variables are expected to be correlated in a scale-dependent way. This paper studies the influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. For this purpose, the mutual benefits of an integrated geographic information system (GIS) and a geostatistics approach was used for spatial precipitation interpolation from rainfall observations measured at 51 climatic stations in a mountainous region of southern Italy (Benevento province). As no single method is optimal for all regions, it is important to compare the results obtained using alternative methods applied to the same data set. Therefore, besides ordinary kriging examination, two auxiliary variables were added for ordinary co-kriging of annual and seasonal precipitation: terrain elevation data and a topographic index. Cross-validation indicated that the ordinary kriging yielded the largest prediction errors. The smallest prediction errors were produced by a multivariate geostatistical method. However, the results favour the ordinary co-kriging with inclusion of information on the topographic index. The application of co-kriging is particularly justified in areas where there are nearby stations and where landform is very complex. We conclude that ordinary co-kriging is a very flexible and robust interpolation method because it may take into account several properties (soft and hard data) of the landscape. Copyright © 2005 Royal Meteorological Society

The computation of the erosion index (EI), which is basic to the determination of the rainfall-runoff erosivity factor R of the Revised Universal Soil Loss Equation (RUSLE), is tedious and time-consuming and requires a continuous record of rainfall intensity. In this study, a power equation(r² = 0.867) involving annual erosion index (EI_30-annual) in the Mediterranean part of Italy is obtained. Data from 12 raingauge stations are used to derive and then test a regional relationship for estimating the erosion index from only three rainfall parameters. Erosivity rainfall data derived from 5 additional stations are used for validation and critical examination. The empirical procedures give results which compare satisfactorily with relationships calibrated elsewhere. Keywords: erosion index, rainfall, erosivity, Revised Universal Soil Loss Equation

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.