Abstract
AbstractSoil erodibility, commonly expressed as the K‐factor in USLE‐type erosion models, is a crucial parameter for determining soil loss rates. However, a national soil erodibility map based on measured soil properties did so far not exist for Switzerland. As an EU non‐member state, Switzerland was not included in previous soil mapping programs such as the Land Use/Cover Area frame Survey (LUCAS). However, in 2015 Switzerland joined the LUCAS soil sampling program and extended the topsoil sampling to mountainous regions higher 1500 m asl for the first time in Europe. Based on this soil property dataset we developed a K‐factor map for Switzerland to close the gap in soil erodibility mapping in Central Europe. The K‐factor calculation is based on a nomograph that relates soil erodibility to data of soil texture, organic matter content, soil structure, and permeability. We used 160 Swiss LUCAS topsoil samples below 1500 m asl and added in an additional campaign 39 samples above 1500 m asl. In order to allow for a smooth interpolation in context of the neighboring regions, additional 1638 LUCAS samples of adjacent countries were considered. Point calculations of K‐factors were spatially interpolated by Cubist Regression and Multilevel B‐Splines. Environmental features (vegetation index, reflectance data, terrain, and location features) that explain the spatial distribution of soil erodibility were included as covariates. The Cubist Regression approach performed well with an RMSE of 0.0048 t ha h ha−1 MJ−1 mm−1. Mean soil erodibility for Switzerland was calculated as 0.0327 t ha h ha−1 MJ−1 mm−1 with a standard deviation of 0.0044 t ha h ha−1 MJ−1 mm−1. The incorporation of stone cover reduces soil erodibility by 8.2%. The proposed Swiss erodibility map based on measured soil data including mountain soils was compared to an extrapolated map without measured soil data, the latter overestimating erodibility in mountain regions (by 6.3%) and underestimating in valleys (by 2.5%). The K‐factor map is of high relevance not only for the soil erosion risk of Switzerland with a particular emphasis on the mountainous regions but also has an intrinsic value of its own for specific land use decisions, soil and land suitability and soil protection.
Highlights
The productive capacity of the soil is the most important resource for human food supply (Morgan, 2006; Borrelli et al, 2017)
We modeled the spatial distribution of soil erodibility for Switzerland with Cubist Regression and Multilevel B-Splines under consideration of environmental covariates
A comparison of the K-factors interpolated with 199 measured Land Use/Cover Area frame Survey (LUCAS) topsoil samples in Switzerland and extrapolated values based only on soil samples of the neighboring countries < 1500m asl of previous LUCAS campaigns not considering Switzerland, resulted in surprisingly consistent average values, but indicated considerable spatial deviations mostly at high elevations and in Alpine valleys
Summary
The productive capacity of the soil is the most important resource for human food supply (Morgan, 2006; Borrelli et al, 2017). The susceptibility of a soil to erode is commonly called soil erodibility It is assessed as the K-factor in the Universal Soil Loss Equation (USLE; Wischmeier and Smith, 1965) and its revised versions (RUSLE; Renard et al, 1997) which compute soil erosion by a multiplication of the rainfall erosivity R, cover and management C, slope length and steepness LS, and support practices P (Wischmeier and Smith, 1978). The K-factor is the average annual soil loss (A) per rainfall erosivity unit (R) measured for the standard conditions of the unit plot (Wischmeier and Smith, 1978): K
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