Katrin Meusburger

Rainfall erosivity in Europe

Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 min. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 min using linear regression functions. Precipitation time series ranged from a minimum of 5 years to a maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression (GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha(-1) h(-1) yr(-1), with the highest values (>1000 MJ mm ha(-1) h(-1) yr(-1)) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha(-1) h(-1) yr(-1)) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also the highest in Mediterranean regions which implies high risk for erosive events and floods.

Soil erodibility in Europe: A high-resolution dataset based on LUCAS

The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 thahha(-1)MJ(-1)mm(-1) with a standard deviation of 0.009 thahha(-1)MJ(-1)mm(-1). The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed.

Assessing soil erosion in Europe based on data collected through a European network

Abstract The European Commission Directorate-General for the Environment (DG Environment) and the European Environmental Agency (EEA) have identified soil organic matter conservation and mitigation of soil loss by erosion as priorities for the collection of policy-relevant soil data at the European scale. In order to support European Union (EU) soil management policies, soil quality indicators are required that can be applied using harmonized data for the EU Member States. In 2010, the European Soil Data Centre (ESDAC) of the European Commission conducted a project to collect data on soil erosion from national institutions in Europe, using the European Environment Information and Observation Network for soil (EIONET-SOIL). The aim of this paper is to present a selection of the results obtained for soil erosion from the participating countries. The data collected were compared with estimates of soil loss using the Pan-European Soil Erosion Risk Assessment (PESERA) model, and aggregated soil erosion data from pan-European experimental plot studies. The comparison focuses on eight countries for which complete soil erosion data have been received. Overall, the mean values of soil loss reported by the national institutes (EIONET-SOIL) are larger than the PESERA estimates, with the main differences being for sloping land (> 2°) and for the land cover type forest and heterogeneous agricultural [land cover types according to CORINE (“coordination of information on the environment”) Land Cover 2006].

Suitability of 239+240Pu and 137Cs as tracers for soil erosion assessment in mountain grasslands.

Anthropogenic radionuclides have been distributed globally due to nuclear weapons testing, nuclear accidents, nuclear weapons fabrication, and nuclear fuel reprocessing. While the negative consequences of this radioactive contamination are self-evident, the ubiquitous fallout radionuclides (FRNs) distribution form the basis for the use as tracers in ecological studies, namely for soil erosion assessment. Soil erosion is a major threat to mountain ecosystems worldwide. We compare the suitability of the anthropogenic FRNs, 137Cs and 239+240Pu as soil erosion tracers in two alpine valleys of Switzerland (Urseren Valley, Canton Uri, Central Swiss Alps and Val Piora, Ticino, Southern Alps). We sampled reference and potentially erosive sites in transects along both valleys. 137Cs measurements of soil samples were performed with a Li-drifted Germanium detector and 239+240Pu with ICP-MS. Our data indicates a heterogeneous deposition of the 137Cs, since most of the fallout origins from the Chernobyl April/May 1986 accident, when large parts of the European Alps were still snow-covered. In contrast, 239+240Pu fallout originated mainly from 1950s to 1960s atmospheric nuclear weapons tests, resulting in a more homogenous distribution and thus seems to be a more suitable tracer in mountainous grasslands. Soil erosion assessment using 239+240Pu as a tracer pointed to a huge dynamic and high heterogeneity of erosive processes (between sedimentation of 1.9 and 7 t ha(-1) yr(-1) and erosion of 0.2-16.4 t ha(-1) yr(-1) in the Urseren Valley and sedimentation of 0.4-20.3 t ha(-1) yr(-1) and erosion of 0.1-16.4 t ha(-1) yr(-1) at Val Piora). Our study represents a novel and successful application of 239+240Pu as a tracer of soil erosion in a mountain environment.

An assessment of the global impact of 21st century land use change on soil erosion

Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, worldwide socio-economic conditions. Here we present an unprecedentedly high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data. We challenge the previous annual soil erosion reference values as our estimate, of 35.9 Pg yr−1 of soil eroded in 2012, is at least two times lower. Moreover, we estimate the spatial and temporal effects of land use change between 2001 and 2012 and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The greatest increases are predicted to occur in Sub-Saharan Africa, South America and Southeast Asia. The least developed economies have been found to experience the highest estimates of soil erosion rates.Human activity and related land use change are the primary cause of soil erosion. Here, the authors show the impacts of 21st century global land use change on soil erosion based on an unprecedentedly high resolution global model that provides insights into the mitigating effects of conservation agriculture.

Software, data and modelling news: Soil erodibility estimation using LUCAS point survey data of Europe

Modelling soil erosion is mostly hampered by low data availability, particularly of soil parameters. One key parameter for soil erosion modelling is the soil erodibility, expressed as the K-factor in the commonly used soil erosion model USLE (Universal Soil Loss Equation). The K-factor is related to crucial soil factors triggering erosion (organic matter content, soil texture, soil structure, permeability). We calculated soil erodibility using measured soil data, collected during the 2009 LUCAS (Land Use and Cover Area frame Survey) soil survey campaign across the member states of the European Union. The soil erodibility dataset overcomes the problems of limited data availability for K-factor assessment and presents a high quality resource for modellers who aim at soil erosion estimation on local/regional, national or European scale.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Mapping monthly rainfall erosivity in Europe

Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale (REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315 MJ mm ha− 1 h− 1) compared to winter (87 MJ mm ha− 1 h− 1). The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets

Graphical abstract

Application of in-situ measurement to determine 137Cs in the Swiss Alps.

Establishment of (137)Cs inventories is often used to gain information on soil stability. The latter is crucial in mountain systems, where ecosystem stability is tightly connected to soil stability. In-situ measurements of (137)Cs in steep alpine environments are scarce. Most studies have been carried out in arable lands and with Germanium (Ge) detectors. Sodium Iodide (NaI) detector system is an inexpensive and easy to handle field instrument, but its validity on steep alpine environments has not been tested yet. In this study, a comparison of laboratory measurements with GeLi detector and in-situ measurements with NaI detector of (137)Cs gamma soil radiation has been done in an alpine catchment with high (137)Cs concentration (Urseren Valley, Switzerland). The aim of this study was to calibrate the in-situ NaI detector system for application on steep alpine slopes. Replicate samples from an altitudinal transect through the Urseren Valley, measured in the laboratory with a GeLi detector, showed a large variability in (137)Cs activities at a meter scale. This small-scale heterogeneity determined with the GeLi detector is smoothed out by uncollimated in-situ measurements with the NaI detector, which provides integrated estimates of (137)Cs within the field of view (3.1 m(2)) of each measurement. There was no dependency of (137)Cs on pH, clay content and carbon content, but a close relationship was determined between measured (137)Cs activities and soil moisture. Thus, in-situ data must be corrected for soil moisture. Close correlation (R(2) = 0.86, p < 0.0001) was found for (137)Cs activities (in Bq kg(-1)) estimated with in-situ (NaI detector) and laboratory (GeLi detector) methods. We thus concluded that the NaI detector system is a suitable tool for in-situ measurements in alpine environments. This paper describes the calibration of the NaI detector system for field application under elevated (137)Cs activities originating from Chernobyl fallout.