Regionalization of monthly rainfall erosivity patterns in Switzerland

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 (I 30 ). 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 Rfactors. 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 (JuneSeptember). 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.

Object‐oriented soil erosion modelling: A possible paradigm shift from potential to actual risk assessments in agricultural environments

“…Although the fitting of the Gaussian process regression model scored R 2 values around 0.4 (Figure 4b), the leave-one-out cross-validation score remained fixed at around 0.1 (Figure 4c). The final set of covariates comprised covariate factors for the diurnal anisotropic heating derived from the DTM, topographical (RUSLE slope length LS-factor and slope gradient), and climatic (monthly values of rainfall erosivity (Schmidt, Alewell, Panagos, & Meusburger, 2016) and total rainfall).…”

Rainfall Erosivity: An Overview of Methodologies and Applications

“…Some of the studies performed with the interpolation techniques of kriging and inverse distance weighting also presented monthly or seasonal rainfall erosivity maps as well as annual average erosivity maps (Lu and Yu, 2002; Shamshad et al, 2008; Sadeghi et al, 2011, 2017; Klik et al, 2015). Interpolation including the use of covariates represented approximately 20% of the observations, mostly realized through regression kriging (Meusburger et al, 2012; Borrelli et al, 2016; Schmidt et al, 2016), Gaussian process regression (Panagos et al, 2015, 2017b), generalized additive model (Panagos et al, 2016a; Laceby et al, 2016), and Cubist regression trees (Ballabio et al, 2017).…”

“…In Greece, Panagos et al (2016b) spatially interpolated monthly rainfall erosivity values (30‐min data for 80 stations covering about 30 yr) through a generalized additive model with average monthly rainfall, elevation, longitude, and latitude as the covariates. Schmidt et al (2016) calculated at‐site rainfall erosivity for 87 stations with 10‐min rainfall data across Switzerland and generated 12 monthly rainfall erosivity maps with high spatial resolution for Switzerland based on a stepwise generalized linear regression method and high spatial resolution of precipitation and topography information as covariates. Ballabio et al (2017) investigated the monthly variation of rainfall erosivity in Europe based on 1568 rainfall stations with high‐resolution rainfall data in the Rainfall Erosivity Database at European Scale (REDES).…”

Temperature signal in suspended sediment export from an Alpine catchment

“…Rainfall erosivity, expressed by the R-factor of the Revised Soil Loss Equation (Wischmeier and Smith, 1978;Brown and Foster, 1987), computed on the basis of 10 min resolution precipitation data, was recently analysed for Switzerland. Although the upper Rhône Basin together with the eastern part of Switzerland was found to have relatively low rainfall erosivity (low Rfactor) compared to the rest of the country, due to a lower frequency of thunderstorms and convective events (Schmidt et al, 2016), there is evidence of an increasing trend for the R-factor from May to October during the last 22 years (1989Meusburger et al, 2012). This suggests that the increase in effective rainfall on snow-free surfaces may have contributed to suspended sediment concentration rise, through a combination of reduced snow-cover fraction, increased rainfall-snowfall ratio, and possible increases in rainfall intensity on a sub-daily scale.…”