This study presents the first mapping of soil erosion risk modelling based on the Revised Universal Soil Loss Equation (RUSLE) at a sub-annual (monthly) temporal resolution and national scale (100 m spatial resolution). The monthly maps show highest water erosion rates on Swiss grasslands in August (1.25 t ha–1 month–1). In summer, the mean monthly soil loss by water erosion is 48 times higher than the mean soil loss in winter. Considering the annual average fraction of green vegetation cover of 54%, the predicted soil erosion rate for the Swiss national grassland area would add up to a total eroded soil mass of 5.26 Mt yr–1. The RUSLE application with an intact 100% vegetation cover would largely reduce the soil loss to an average annual rate of 0.14 t ha–1 year–1. These findings clearly highlight the importance to consider and maintain the current status of the vegetation cover for soil erosion prediction and soil conservation, respectively.

This study presents the first mapping of soil erosion risk modelling based on the Revised Universal Soil Loss Equation (RUSLE) at a sub-annual (monthly) temporal resolution and national scale (100 m spatial resolution). The monthly maps show highest water erosion rates on Swiss grasslands in August (1.25 t ha month). In summer, the mean monthly soil loss by water erosion is 48 times higher than the mean soil loss in winter. Considering the annual average fraction of green vegetation cover of 54%, the predicted soil erosion rate for the Swiss national grassland area would add up to a total eroded soil mass of 5.26 Mt yr. The RUSLE application with an intact 100% vegetation cover would largely reduce the soil loss to an average annual rate of 0.14 t ha year. These findings clearly highlight the importance to consider and maintain the current status of the vegetation cover for soil erosion prediction and soil conservation, respectively. ARTICLE HISTORY Received 13 November 2018 Accepted 19 February 2019

Soil 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.

The decrease in vegetation cover is one of the main triggering factors for soil erosion of grasslands. Within the Revised Universal Soil Loss Equation (RUSLE), a model commonly used to describe soil erosion, the vegetation cover for grassland is expressed in the cover and management factor (C-factor). The site-specific C-factor is a combination of the relative erosion susceptibility of a particular plant development stage (here expressed as soil loss ratio SLR) and the corresponding rainfall pattern (here expressed as R-factor ratio). Thus, for grasslands the fraction of green vegetation cover (FGVC) determines the SLRs. Although Switzerland is a country dominated by grassland with high percentages of mountainous regions and evidence for high erosion rates of grassland exists, soil erosion risk modeling of grasslands and especially of mountainous grasslands in Switzerland is restricted to a few studies. Here, we present a spatio-temporal approach to assess the dynamics of the C-factor for Swiss grasslands and to identify erosion prone regions and seasons simultaneously. We combine different satellite data, aerial data, and derivative products like Climate Change Initiative (CCI) Land Cover, Swissimage false-color infrared (Swissimage FCIR), PROBA-V Fraction of green Vegetation Cover (FCover300m), and MODIS Vegetation Indices 16-Day L3 Global (MOD13Q1) for the FGVC mapping of grasslands. In the spatial mapping, the FGVC is extracted from Swissimage FCIR (spat. res. 2 m) by linear spectral unmixing (LSU). The spatially derived results are then fused with the 10-day deviations of temporal FGVC derived by FCover300m. Following the original RUSLE approach, the combined FGVC are transformed to SLRs and weighted with high spatiotemporal resolved ratios of R-factors to result in spatio-temporal C-factors for Swiss grasslands. The annual average C-factor of all Swiss grasslands is 0.012. Seasonal and regional patterns (low C in winter, high C in summer, dependency on elevation) are recognizable in the spatio-temporal mapping approach. They are mainly explicable by the R-factor distribution within a year. Knowledge about the spatio-temporal dynamic of erosion triggering factors is of high interest for agronomists who can introduce areal and time specific selective erosion control measures and thereby reduce the direct costs of mitigation as well as erosion measures.

So far, neither a grassland map, temporal analysis of the conversion of permanent grassland (PG) to other land uses nor the differentiation of permanent and temporal grassland exists for Switzerland. For the first time in Switzerland, we present a Swiss national grassland map for the year 2015 capturing the extent of both, permanent and temporal grasslands (here called grasslands) by intersecting the information of three datasets. We blended the high temporal resolution Climate Change Initiate (CCI) Land Cover of 2015 (processed by the European Space Agency (ESA)), with the high spatial resolution Swiss topographical landscape model "SwissTLM3D" and the landscape model "vector25" both provided by Swisstopo. The final data presents the spatial patterns and the national extent of Swiss grasslands. Furthermore, the recently published (April 2017) CCI Land Cover dataset allow extracting the extent of grasslands for 24 years (1992-2015) with a coarse spatial resolution of 300 m. We used the time series data of the grassland extent to produce annual PG maps from 1996 to 2015. That data enables the identification of the development of grassland extent over two decades. The Swiss national grassland map is used for investigating the spatio-temporal patterns of the soil erosion risk of Swiss grasslands (see Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland, doi:10.1016/j.rse.2018.04.008 (Schmidt el al., 2018)). (C) 2018 The Authors. Published by Elsevier lnc. This is an open access article under the CC BY license

Abstract. One major controlling factor of water erosion is rainfall erosivity, which is quantified by the kinetic energy of a rainfall event and its maximum 30-min intensity. 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 dynamic rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a characteristic regional and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modelling approach to assess simultaneously spatial and monthly pattern 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-minute temporal resolution to calculate long-term monthly mean R-factors. Stepwise regression and Leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal pattern of the R-factor for each month across Switzerland. The monthly R-factor is mapped by its specific regression equation and the ordinary kriging interpolation of its residuals (Regression-Kriging). As covariates, a variety of precipitation indicator data has been included like 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 highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June to September). Highest erosion risk can be expected for July where not only rainfall erosivity but also erosivity density is high. Additionally 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.

While it needs yet to be assessed whether or not wind erosion in Western Saxony is a major point of concern regarding land degradation and fertility, it has already been recognized that considerable off-site effects of wind erosion in the adjacent regions of Saxony-Anhalt and Brandenburg are connected to the spread of herbicides, pesticides and dust. So far, no wind erosion assessment for Western Saxony, Germany, exists. The wind erosion model previously applied for Germany (DIN standard 19706) is considering neither changes in wind direction over time nor influences of field size. This study aims to provide a first assessment of wind erosion for Western Saxony by extending the existing DIN model to a multidirectional model on soil loss by wind (SoLoWind) with new controlling factors (changing wind directions, soil cover, mean field length and mean protection zone) combined by fuzzy logic. SoLoWind is used for a local off-site effect evaluation in combination with high-resolution wind speed and wind direction data at a section of the highway A72. The model attributes 3.6% of the arable fields in Western Saxony to the very-high-wind erosion risk class. A relationship between larger fields (greater than 116 ha) and higher proportions (51.7%) of very-high-wind erosion risk can be observed. Sections of the highway A72 might be under high risk according to the modelled off-site effects of wind erosion. The presented applications showed the potential of SoLoWind to support and consult management for protection measures on a regional scale. (C) 2016 The Authors. Land Degradation and Development published by John Wiley & Sons, Ltd.

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.