Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 20, 4359-4373, 2016
https://doi.org/10.5194/hess-20-4359-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
26 Oct 2016
Regionalization of monthly rainfall erosivity patterns in Switzerland
Simon Schmidt1, Christine Alewell1, Panos Panagos2, and Katrin Meusburger1 1Environmental Geosciences, University of Basel, Bernoullistrasse 30, 4056 Basel, Switzerland
2European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, 21027 Ispra, Italy
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.



Citation: Schmidt, S., Alewell, C., Panagos, P., and Meusburger, K.: Regionalization of monthly rainfall erosivity patterns in Switzerland, Hydrol. Earth Syst. Sci., 20, 4359-4373, https://doi.org/10.5194/hess-20-4359-2016, 2016.
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Short summary
We present novel research on the seasonal dynamics of the impact of rainfall (R-factor) on the mobilization of topsoil as soil erosion by water for Switzerland. A modeling approach was chosen that enables the dynamical mapping of the R-factor. Based on the maps and modeling results, we could investigate the spatial and temporal distribution of that factor, which is high for Switzerland. With these results, agronomists can introduce selective erosion control measures.
We present novel research on the seasonal dynamics of the impact of rainfall (R-factor) on the...
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