1,721,151 research outputs found
Soil erodibility in Europe: a high-resolution dataset based on LUCAS
Full text linkThe 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 harmonized 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 t ha h ha-1 MJ-1 mm-1 with a standard deviation of 0.009 t ha h ha-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 on 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
An indicator to reflect the mitigating effect of Common Agricultural Policy on soil erosion
No full textThis study presents the updated version of the recently published LANDUM model [Land Use Policy 48, 38–50 (2015)]. LANDUM is integrated into the 100 m resolution RUSLE-based pan-European soil erosion risk modelling platform of the European Commission. It estimates the effects of local land use and management practices on the magnitude of soil erosion across each NUTS2 region of the European Union. This is done based on a spatially explicit estimation of the so-called cover-management factor of (R)USLE family models which is also known as C-factor. In this updated version, the data on soil conservation measures (i.e., reduced tillage, cover crops and plant residues) reported in the latest EU Farm Structure Survey (2016) were integrated and elaborated in LANDUM in order to estimate the changes of the C-factor in Europe between 2010 and 2016. For 2016, a C-factor of 0.2316 for the arable land of the 28 Member States of the European Union was estimated. This implies an overall decrease of C-factor of ca. -0.84 % compared to the 2010 survey. The change in C-factor from 2010 to 2016 could be an indication for the effectiveness of Common Agricultural Policy (CAP) soil conservation measures in reducing soil erosion in Europe, especially key CAP policies such as Good Agricultural and Environmental Conditions and Greening
Communicating Hydrological Hazard-Prone Areas in Italy With Geospatial Probability Maps
No full textThe recurrence of storm aggressiveness and the associated erosivity density are detrimental hydrological features for soil conservation and planning. The present work illustrates for the first time downscaled spatial pattern probabilities of erosive density to identify damaging hydrological hazard-prone areas in Italy. The hydrological hazard was estimated from the erosivity density exceeded the threshold of 3 MJ ha−1 h−1 at 219 rain gauges in Italy during the three most erosive months of the year, from August to October. To this end, a lognormal kriging (LNPK) provided a soft description of the erosivity density in terms of exceedance probabilities at a spatial resolution of 10 km, which is a way to mitigate the uncertainties associated with the spatial classification of damaging hydrological hazards. Hazard-prone areas cover 65% of the Italian territory in the month of August, followed by September and October with 50 and 30% of the territory, respectively. The geospatial probability maps elaborated with this method achieved an improved spatial forecast, which may contribute to better land-use planning and civil protection both in Italy and potentially in Europe
Reply to “The new assessment of soil loss by water erosion in Europe. Panagos P. et al., 2015 Environ. Sci. Policy 54, 438–447—A response” by Evans and Boardman [Environ. Sci. Policy 58, 11–15]
No full textThe new assessment of soil loss by water erosion in Europe (Panagos et al., 2015a) was commented by Evans and Boardman (2016), who raised not only concerns related to the spatial differences outlined by our work compared to their visual semi-qualitative assessment conducted in Britain during the late eighties, but also generally to the suitability, validity and scientific robustness of the applied modelling
approach. The objective of the pan-European assessment using the Revised Universal Soil Loss Equation (RUSLE) was not to outcompete any regional- or national-scale modelling, but to harmonize and improve our knowledge and our understanding of current soil erosion rates by water across the European Union. The focus of such a modelling project is on the differences and similarities between regions and countries beyond national borders and nationally adapted models. In order to do so, a state-of-the-art large-scale spatially distributed modelling exercise using harmonized datasets and a unified methodology to suit the pan-European scale was carried out. We reply that the semi-qualitative approach proposed by Evans and Boardman (2016) is not suitable for application at the European scale because of work force and time requirements, input data accessibility issues, accuracy of field-based estimates, subjectivity of soil loss
estimates during the aerial and terrestrial photo interpretation, impossibility of upscaling or downscaling, inadequate representation of sheet erosion processes, lack of spatial and temporal representativeness, and lack of detailed description expressing the risk level. As such, their methodology has limited applicability, with today’s financial resources it is not feasible at European or at national scale
and, most important, cannot respond to policy requests regarding scenarios of climate and land cover/use change. In contrast to Evans and Boardman (2016), we do know that RUSLE, like probably any other approach, is not able to reproduce “reality”. The latter is actually a misjudgment which has been extensively discussed 20 years ago. Modelling in general and large-scale modelling specifically can per se
not aim at an accurate prediction of point measurements, but tests our hypothesis on process understanding, relative spatial and temporal variations, scenario development and controlling factors (Oreskes et al., 1994). As such, our approach can be offered as a helpful tool to policy makers at pan-European scale. We are confident that the simple transparent structure of RUSLE as well as the discussion of the uncertainties of each modelling factor will help to supply objective guidance to policy makers.JRC.H.5 - Land Resources Managemen
New Insights into the Geography and Modelling of Wind Erosion in the European Agricultural Land. Application of a Spatially Explicit Indicator of Land Susceptibility to Wind Erosion
Full text linkThe current state of the art in erosion research does not provide answers about the ‘where’ and ‘when’ of wind erosion in European agricultural lands. Questions about the implications for the agricultural productivity remain unanswered. Tackling this research gap, the study provides a more comprehensive understanding of the spatial patterns of land susceptibility to wind erosion in European agricultural lands. The Index of Land Susceptibility to Wind Erosion (ILSWE) was introduced in a GIS environment. A harmonised input dataset ranked following a fuzzy logic technique was employed. Within the 36 European countries under investigation, moderate (17.3 million ha) and high levels (8.8 million ha) of land susceptibility to wind erosion were predicted. This corresponds to 8.0% and 4.1 % of total agricultural land, respectively.JRC.H.5 - Land Resources Managemen
Tackling soil loss across Europe
No full textA European Commission analysis indicates that soil erosion continues to outstrip soil formation across the European Union, but that the Common Agricultural Policy is narrowing the gap (P. Panagos et al. Environ. Sci. Policy 54, 438–447; 2015). The amount of soil lost to water
erosion in Europe equates to an estimated economic loss of about US20 per tonne. Between 2000 and 2010, intervention measures through the Common Agricultural Policy have reduced the rate of soil loss in the European Union by an average of 9.5% overall, and by 20% for arable lands. Continued monitoring of human-induced changes to soil every 5–10 years will be crucial for refining soil policies (D. A. Robinson Science 347, 140; 2015).JRC.H.5 - Land Resources Managemen
Rainfall erosivity in Italy: A national scale spatio-temporal assessment
No full textSoil erosion by water is a serious threat for the Mediterranean region. Raindrop impacts and consequent runoff generation are the main driving forces of this geomorphic process of soil degradation. The potential ability for rainfall to cause soil loss is expressed as rainfall erosivity, a key parameter required by most soil loss prediction models. In Italy, rainfall erosivity measurements are limited to few locations, preventing researchers from effectively assessing the geography and magnitude of soil loss across the country. The objectives of this study were to investigate the spatio-temporal distribution of rainfall erosivity in Italy and to develop a national-scale grid-based map of rainfall erosivity. Thus, annual rainfall erosivity values were measured and subsequently interpolated using a geostatistical approach. Time series of pluviographic records (10-years) with high temporal resolution (mostly 30-min) for 386 meteorological stations were analysed. Regression-kriging was used to interpolate rainfall erosivity values of the meteorological stations to an Italian rainfall erosivity map (500-m). A set of 23 environmental covariates was tested, of which seven covariates were selected based on a stepwise approach (mostly significant at the 0.01 level). The interpolation method showed a good performance for both the cross-validation dataset (R_cv^2 0.777) and the fitting dataset (R2=0.779).JRC.H.5 - Land Resources Managemen
Positive cascading effect of restoring forests
No full textRecent assessment of global tree restoration potential reports that under current climate conditions there would be room for additional 0.9 billion hectares of woodlands and forests Bastin et al. (2019). This could store 205 gigatonnes of carbon making forest restoration a viable strategy for climate change mitigation. Commenting on Bastin et al. (2019), Chazdon and Brancalion (2019) call for holistic approaches because forest restoration is a mechanism to achieve multiple goals that go beyond climate mitigation, also including biodiversity conservation, socioeconomic benefits, food security, and ecosystem services. A timely scientific debate considering the recent decision of the UN Environment Assembly in Nairobi, Kenya, to declare the coming decade 2021–2030 the UN Decade on Ecosystem Restoration.JRC.D.3 - Land Resource
A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water
Full text linkThe 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.JRC.H.5 - Land Resources Managemen
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