A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water

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.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

The 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

FAO calls for actions to reduce global soil erosion

Letter

A New Assessment of Soil Loss Due to Wind Erosion in European Agricultural Soils Using a Quantitative Spatially Distributed Modelling Approach

Field measurements and observations have shown that wind erosion is a threat for numerous arable lands in the European Union (EU). Wind erosion affects both the semi-arid areas of the Mediterranean region as well as the temperate climate areas of the northern European countries. Yet, there is still a lack of knowledge, which limits the understanding about where, when and how heavily wind erosion is affecting European arable lands. Currently, the challenge is to integrate the insights gained by recent pan-European assessments, local measurements, observations and field-scale model exercises into a new generation of regional-scale wind erosion models. This is an important step to make the complex matter of wind erosion dynamics more tangible for decision-makers and to support further research on a field-scale level. A geographic information system version of the Revised Wind Erosion Equation was developed to (i) move a step forward into the large-scale wind erosion modelling; (ii) evaluate the soil loss potential due to wind erosion in the arable land of the EU; and (iii) provide a tool useful to support field-based observations of wind erosion. The model was designed to predict the daily soil loss potential at a ca. 1 km2 spatial resolution. The average annual soil loss predicted by geographic information system Revised Wind Erosion Equation in the EU arable land totalled 0·53 Mg ha−1 y−1, with the second quantile and the fourth quantile equal to 0·3 and 1·9 Mg ha−1 y−1, respectively. The cross-validation shows a high consistency with local measurements reported in literature

A Preliminary Assessment Using Satellite Remote Sensing

The upper ranges of the northern Andes are characterized by unique Neotropical, high altitude ecosystems known as paramos. These tundra-like grasslands are widely recognized by the scientific community for their biodiversity and their important ecosystem services for the local human population. Despite their remoteness, limited accessibility for humans and waterlogged soils, paramos are highly flammable ecosystems. They are constantly under the influence of seasonal biomass burning mostly caused by humans. Nevertheless, little is known about the spatial extent of these fires, their regime and the resulting ecological impacts. This paper presents a thorough mapping and analysis of the fires in one of the world’s largest paramo, namely the “Complejo de Páramos” of Cruz Verde-Sumapaz in the Eastern mountain range of the Andes (Colombia). Landsat TM/ETM+ and MODIS imagery from 2001 to 2013 was used to map and analyze the spatial distribution of fires and their intra- and inter-annual variability. Moreover, a logistic regression model analysis was undertaken to test the hypothesis that the dynamics of the paramo fires can be related to human pressures. The resulting map shows that the burned paramo areas account for 57,179.8 hectares, of which 50% (28,604.3 hectares) are located within the Sumapaz National Park. The findings show that the fire season mainly occurs from January to March. The accuracy assessment carried out using a confusion matrix based on 20 reference burned areas shows values of 90.1% (producer accuracy) for the mapped burned areas with a Kappa Index of Agreement (KIA) of 0.746. The results of the logistic regression model suggest a significant predictive relevance of the variables road distance (0.55 ROC (receiver operating characteristic)) and slope gradient (0.53 ROC), indicating that the higher the probability of fire occurrence, the smaller the distance to the road and the higher the probability of more gentle slopes. The paper sheds light on fires in the Colombian paramos and provides a solid basis for further investigation of the impacts on the natural ecosystem functions and biodiversity. View Full-Tex

The implications of fire management in the Andean paramo: A preliminary assessment using satellite remote sensing

The upper ranges of the northern Andes are characterized by unique Neotropical, high altitude ecosystems known as paramos. These tundra-like grasslands are widely recognized by the scientific community for their biodiversity and their important ecosystem services for the local human population. Despite their remoteness, limited accessibility for humans and waterlogged soils, paramos are highly flammable ecosystems. They are constantly under the influence of seasonal biomass burning mostly caused by humans. Nevertheless, little is known about the spatial extent of these fires, their regime and the resulting ecological impacts. This paper presents a thorough mapping and analysis of the fires in one of the world’s largest paramo, namely, the ‘Complejo de Páramos’ of Cruz Verde – Sumapaz in the Eastern mountain range of the Andes (Colombia). Landsat TM/ETM+ and MODIS imagery from 2001 to 2013 were used to map and analyse the spatial distribution of fires and their intra- and inter-annual variability. Moreover, a logistic regression model analysis was undertaken to test the hypothesis that the dynamics of the paramo fires can be related to human pressures. The resulting map shows that the burned paramo areas account for 57,179.8 hectares of which 50% (28,604.3 hectares) are located within the Sumapaz National Park. The findings show that the fire season mainly occurs from January to March. The accuracy assessment carried out using a confusion matrix based on 20 reference burned areas shows values of 90.1% (producer accuracy) for the mapped burned areas with a Kappa Index of Agreement (KIA) of 0.746. The results of the logistic regression model suggest a significant predictive relevance of the variables road distance (0.55 ROC (Receiver Operating Characteristic)) and slope gradient (0.53 ROC), indicating that the higher the probability of fire occurrence the smaller the distance to the road and the higher the probability of more gentle slopes. The paper sheds light on fires in the Colombian paramos and provides a solid basis for further investigation of the impacts on the natural ecosystem functions and biodiversity.JRC.H.5-Land Resources Managemen

Soil Erosion map of Europe based on high resolution input datasets

Modelling soil erosion in European Union is of major importance for agro-environmental policies. Soil erosion estimates are important inputs for the Common Agricultural Policy (CAP) and the implementation of the Soil Thematic Strategy. Using the findings of a recent pan-European data collection through the EIONET network, it was concluded that most Member States are applying the empirical Revised Universal Soil Loss Equation (RUSLE) for the modelling soil erosion at National level. This model was chosen for the pan-European soil erosion risk assessment and it is based on 6 input factors.JRC.H.5-Land Resources Managemen

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

Effect of Good Agricultural and Environmental Conditions on erosion and soil organic carbon balance: A national case study

Since, the Common Agricultural Policies (CAP) reform in 2003, many efforts have been made at the European level to promote a more environmentally friendly agriculture. In order to oblige farmers to manage their land sustainably, the GAEC (Good Agricultural and Environmental Conditions) were introduced as part of the Cross Compliance mechanism. Among the standards indicated, the protection of soils against erosion and the maintenance of soil organic matter and soil structure were two pillars to protect and enhance the soil quality and functions. While Member States should specifically define the most appropriate management practices and verify their application, there is a substantial lack of knowledge about the effects of this policy on erosion prevention and soil organic carbon (SOC) change. In order to fill this gap, we coupled a high resolution erosion model based on Revised Universal Soil Loss Equation (RUSLE) with the CENTURY biogeochemical model, with the aim to incorporate the lateral carbon fluxes occurring with the sediment transportation. Three scenarios were simulated on the whole extent of arable land in Italy: (i) a baseline without the GAEC implementation; (ii) a current scenario considering a set of management related to GAEC and the corresponding area of application derived from land use and agricultural management statistics and (iii) a technical potential where GAEC standards are applied to the entire surface. The results show a 10.8% decrease, from 8.33 Mg ha −1 year −1 to 7.43 Mg ha −1 year −1 , in soil loss potential due to the adoption of the GAEC conservation practices. The technical potential scenario shows a 50.1% decrease in the soil loss potential (soil loss 4.1 Mg ha −1 year −1 ). The GAEC application resulted in overall SOC gains, with different rates depending on the hectares covered and the agroecosystem conditions. About 17% of the SOC change was attributable to avoided SOC transport by sediment erosion in the current scenario, while a potential gain up to 23.3 Mt of C by 2020 is predicted under the full GAEC application. These estimates provide a useful starting point to help the decision-makers in both ex-ante and ex-post policy evaluation while, scientifically, the way forward relies on linking biogeochemical and geomorphological processes occurring at landscape level and scaling those up to continental and global scales

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

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha −1 h −1 yr −1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April–September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models