To give soils and soil degradation, which are among the most crucial threats to ecosystem stability, social and political visibility, small and large scale modelling and mapping of soil erosion is ...inevitable. The most widely used approaches during an 80year history of erosion modelling are Universal Soil Loss Equation (USLE)-type based algorithms which have been applied in 109 countries. Addressing soil erosion by water (excluding gully erosion and land sliding), we start this review with a statistical evaluation of nearly 2,000 publications). We discuss model developments which use USLE-type equations as basis or side modules, but we also address recent development of the single USLE parameters (R, K, LS, C, P). Importance, aim and limitations of model validation as well as a comparison of USLE-type models with other erosion assessment tools are discussed. Model comparisons demonstrate that the application of process-based physical models (e.g., WEPP or PESERA) does not necessarily result in lower uncertainties compared to more simple structured empirical models such as USLE-type algorithms. We identified four key areas for future research: (i) overcoming the principally different nature of modelled (gross) versus measured (net) erosion rates, in coupling on-site erosion risk to runoff patterns, and depositional regime, (ii) using the recent increase in spatial resolution of remote sensing data to develop process based models for large scale applications, (iii) strengthen and extend measurement and monitoring programs to build up validation data sets, and (iv) rigorous uncertainty assessment and the application of objective evaluation criteria to soil erosion modelling.
Most common author keywords used in USLE/RUSLE papers of the last 40 years excluding geographical terms. Size of keywords represents frequency of use. Display omitted
•Soil erosion modelling is crucially needed for soil monitoring and mapping.•Critical debate on USLE models requires discussion of model concepts and suitability.•USLE type modelling strives from purely empirical to more process based concepts.•Model comparisons illustrate that there is not one superior model.•Model concepts need to be chosen dependent on data availability and project needs.
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 500m) 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 0.032thahha−1MJ−1mm−1 with a standard deviation of 0.009thahha−1MJ−1mm−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.
•Soil erodibility in Europe is estimated at 0.032thahha−1MJ−1mm−1.•Stoniness has an important impact in Mediterranean countries.•High resolution (500m grid cell) dataset of K-factor is available for modelling.•Coarse fragments, permeability and soil structure were considered in K-factor.•K-factor map has very good correspondence with regional data in literature studies.
•LANDUM is a new model for estimating the C-factor in soil erosion modelling in Europe.•C-factor in arable lands combines crop composition and management practices.•Reduced tillage is the most ...efficient compared to plant residues and winter crop.•The 3 management practices reduce C-factor by 19.1% in European arable lands.•Fraction of vegetation cover is taken into account in C-factor for non-arable lands.
Land use and management influence the magnitude of soil loss. Among the different soil erosion risk factors, the cover-management factor (C-factor) is the one that policy makers and farmers can most readily influence in order to help reduce soil loss rates. The present study proposes a methodology for estimating the C-factor in the European Union (EU), using pan-European datasets (such as CORINE Land Cover), biophysical attributes derived from remote sensing, and statistical data on agricultural crops and practices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data on management practices such as conservation tillage, plant residues and winter crop cover. The C-factor in non-arable lands was estimated by weighting the range of literature values found according to fractional vegetation cover, which was estimated based on the remote sensing dataset Fcover. The mean C-factor in the EU is estimated to be 0.1043, with an extremely high variability; forests have the lowest mean C-factor (0.00116), and arable lands and sparsely vegetated areas the highest (0.233 and 0.2651, respectively). Conservation management practices (reduced/no tillage, use of cover crops and plant residues) reduce the C-factor by on average 19.1% in arable lands.
The methodology is designed to be a tool for policy makers to assess the effect of future land use and crop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arable land expansion) and the effect of policies (such as the Common Agricultural Policy and the push to grow more renewable energy crops) can potentially be quantified with the proposed model. The C-factor data and the statistical input data used are available from the European Soil Data Centre.
•RUSLE2015 model estimates soil loss at 100 m resolution based on best available data.•The mean soil loss rate in European Union is estimated to 2.46 t/ha annually.•Policy interventions (CAP) reduced ...overall soil loss by 9.5% during last decade.•12.7% of European arable lands have soil loss >5 t/ha annually requiring protection.•Among all land uses, arable and sparse vegetation have the highest soil loss rates.
Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European Commission's Soil Thematic Strategy has identified soil erosion as a relevant issue for the European Union, and has proposed an approach to monitor soil erosion. This paper presents the application of a modified version of the Revised Universal Soil Loss Equation (RUSLE) model (RUSLE2015) to estimate soil loss in Europe for the reference year 2010, within which the input factors (Rainfall erosivity, Soil erodibility, Cover-Management, Topography, Support practices) are modelled with the most recently available pan-European datasets. While RUSLE has been used before in Europe, RUSLE2015 improves the quality of estimation by introducing updated (2010), high-resolution (100m), peer-reviewed input layers. The mean soil loss rate in the European Union's erosion-prone lands (agricultural, forests and semi-natural areas) was found to be 2.46 t ha−1 yr−1, resulting in a total soil loss of 970 Mt annually.
A major benefit of RUSLE2015 is that it can incorporate the effects of policy scenarios based on land-use changes and support practices. The impact of the Good Agricultural and Environmental Condition (GAEC) requirements of the Common Agricultural Policy (CAP) and the EU's guidelines for soil protection can be grouped under land management (reduced/no till, plant residues, cover crops) and support practices (contour farming, maintenance of stone walls and grass margins). The policy interventions (GAEC, Soil Thematic Strategy) over the past decade have reduced the soil loss rate by 9.5% on average in Europe, and by 20% for arable lands. Special attention is given to the 4 million ha of croplands which currently have unsustainable soil loss rates of more than 5 t ha−1 yr−1, and to which policy measures should be targeted.
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
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.
The Universal Soil Loss Equation (USLE) model is the most frequently used model for soil erosion risk estimation. Among the six input layers, the combined slope length and slope angle (LS-factor) has ...the greatest influence on soil loss at the European scale. The S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length. The combined LS-factor describes the effect of topography on soil erosion. The European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil erosion assessment to achieve a better understanding of the spatial and temporal patterns of soil erosion in Europe. The LS-calculation was performed using the original equation proposed by Desmet and Govers (1996) and implemented using the System for Automated Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and contributes to a precise estimation of flow accumulation. The LS-factor dataset was calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole European Union, resulting in an improved delineation of areas at risk of soil erosion as compared to lower-resolution datasets. This combined approach of using GIS software tools with high-resolution DEMs has been successfully applied in regional assessments in the past, and is now being applied for first time at the European scale.
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
h
yr
, 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.
•Sensitivity analysis of LWF-Brook90 revealed a distinct parameter importance ranking•Bayesian calibration indicated high water use for a poplar short rotation forest•The LWF-Brook90 SVAT model ...excellently captures temporal variations of soil moisture•The LWFBrook90R package facilitates complex statistical analysis and parallelization•The modelling case study with code examples demonstrates the utility of the R package
Soil vegetation atmosphere transport (SVAT) models are important for the quantification of water fluxes, soil water availability, drought stress and their uncertainties under climate change. We present LWFBrook90R, an enhanced implementation of the well-established, process-based SVAT model LWF-Brook90 for the R environment for statistical computing. The package provides new functions and sub-models for model parameterization, and facilitates parallel computing, sensitivity analysis and inverse calibration of the model. A case study comprising i) basic forward water balance simulations for temperate grassland vegetation, deciduous and evergreen forest, ii) a parallelized sensitivity analysis, and iii) Bayesian calibrations based on soil water storage observed in a poplar (Populus nigra × P. maximowiczii) Short Rotation Forest (SRF) demonstrates the utility of the R package. The sensitivity analysis revealed parameters affecting plant-available soil water storage capacity and the vegetation's timing and level of water demand to be most important for the annual course of simulated soil water storage, with seasonal and interannual differences in parameter importance rankings. The subsequent calibration yielded a very high agreement between daily simulated and observed soil water storage (0-200 cm soil depth) for the calibration and validation datasets, with Nash-Sutcliffe efficiencies of 0.97 and 0.95, respectively. The final model predicted high though realistic rates of annual evapotranspiration (2011: 844 ± 3.8 mm y-1, 2012: 733 ± 4.5 mm y-1) for the poplar SRF, regularly exceeding grass reference evaporation (ET0) by 20-47% during the months of the growing season. However, basing calibrations solely on observed soil water storage probably resulted in biased partitioning of evapotranspiration towards interception losses. The integration of the LWF-Brook90 hydrological model into R with its wide variety of extensions was successfully tested and may provide efficient, reliable and reproducible water balance predictions by facilitating complex statistical analyses and large-scale applications of the model.
Forests provide essential ecosystem services that range from the production of timber to the mitigation of natural hazards. Rapid environmental changes, such as climate warming or the intensification ...of disturbance regimes, threaten forests and endanger forest ecosystem services. In light of these challenges, it is essential to understand forests' demographic processes of regeneration, growth, and mortality and their relationship with environmental conditions. Specifically, understanding the regeneration process in present‐day forests is crucial since it lays the foundation for the structure of future forests and their tree species composition. We used Swiss National Forest Inventory (NFI) data covering vast bio‐geographic gradients over four decades to achieve this understanding. Trees that reached a diameter at breast height of 12 cm between two consecutive NFI campaigns were used to determine regeneration and were referred to as ingrowth. Employing three independent statistical models, we investigated the number, species, and diameter of these ingrowth trees. The models were subsequently implemented into a forest simulator to project the development of Swiss forests until the mid‐21st century. The simulation results showed an ingrowth decrease and a shift in its species composition, marked by a significant reduction in Norway spruce Picea abies and concurrent increases in broadleaves. Nevertheless, the pace of this change towards climatically better adapted species composition is relatively slow and is likely to slow down even further as ingrowth declines in the future, in contrast to the fast‐changing climatic conditions. Hence, support through adaptive planting strategies should be tested in case ingrowth does not ensure the resilience of forests in the future. We conclude that since the regeneration of forests is becoming increasingly challenging, the current level at which ecosystem services are provided might not be ensured in the coming decades.
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The slope length and slope steepness factor (LS-factor) is one of five factors of the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) describing the influence of ...topography on soil erosion risk. The LS-factor was originally developed for slopes less than 50% inclination and has not been tested for steeper slopes. To overcome this limitation, we adapted both factors slope length L and slope steepness S for conditions experimentally observed at Swiss alpine grasslands. For the new L-factor (Lalpine), a maximal flow path threshold, corresponding to 100 m, was implemented to take into account short runoff flow paths and rapid infiltration that has been observed in our experiments. For the S-factor, a fitted quadratic polynomial function (Salpine) has been established, compiling the most extensive empirical studies. As a model evaluation, uncertainty intervals are presented for this modified S-factor. We observed that uncertainty increases with slope gradient. In summary, the proposed modification of the LS-factor to alpine environments enables an improved prediction of soil erosion risk including steep slopes.
•Empirical experiments (rainfall simulation, sediment measurements) were conducted on Swiss alpine grasslands to assess the maximal flow length and slope steepness factor (S-factor).•Flow accumulation is limited to a maximal flow threshold (100 m) at which overland runoff is realistic in alpine grassland.•Slope steepness factor is modified by a fitted S-factor equation from existing empirical S-factor functions.
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