Abstract
Abstract. One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R-factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression–kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have the lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June–September). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions.
Highlights
Rainfall has direct impacts on soil mobilization by processes like rapid wetting or splash and runoff effects and is, one of the main driving forces of water erosion
Through spatial–temporal mapping, it was possible to determine regions that are hardly affected by rainfall erosivity, such as Grisons and Wallis, and it was possible to determine those that are only affected in a certain months, such as the Jura Mountains
Rainfall erosivity based on high erosivity density has more severe impacts on soils, agriculture, droughts, and hazards in summer than in winter due the high impact of intense rainfalls
Summary
Rainfall has direct impacts on soil mobilization by processes like rapid wetting or splash and runoff effects and is, one of the main driving forces of water erosion. The R-factor, as one of the five soil erosion risk factors (rainfall erosivity, soil erodibility, slope steepness and length, cover management, and support practices) of the Revised Universal Soil Loss Equation (RUSLE) (Renard et al, 1997; Foster et al, 2008) expresses the impact of rainfall on soils in the form of rainfall erosivity. Besides being an important driving factor of soil erosion, the R-factor can be used to draw conclusions about soil vulnerability, flood hazards, natural hazards, or probability of droughts (Panagos et al, 2015). Schmidt et al.: Regionalization of monthly rainfall erosivity patterns in Switzerland
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