DOI: 10.6084/m9.figshare.856670
title: Land Cover and Soil Erodibility within the e-RUSLE Model

description: Bosco, C., de Rigo, D., 2013. <strong>Land Cover and Soil Erodibility within the e-RUSLE Model</strong>. <em>Scientific Topics Focus 1</em>, MRI-11b13. Notes Transdiscipl. Model. Env., Maieutike Research Initiative. https://doi.org/10.6084/m9.figshare.856670<br>PDF: http://purl.org/mtv/STF/pdf/1293888<br> Version: DRAFT 0.1.2<br>This is a preliminary version. Any future version will be accessible from the same DOI code (https://doi.org/10.6084/m9.figshare.856670). The views expressed are those of the authors and may not be regarded as stating an official position of mentioned organisations. <strong>Land Cover and Soil Erodibility within the e-RUSLE Model</strong> Claudio Bosco ¹ ² ⁴ and Daniele de Rigo ² ³ ⁴<br><br>1 Loughborough University, Department of Civil and Building Engineering,<br>Loughborough, United Kingdom<br><br>2 Joint Research Centre of the European Commission,<br>Institute for Environment and Sustainability, Ispra, Italy<br><br>3 Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, Italy<br><br>4 Maieutike Research Initiative, Milano, Italy Soil is a valuable, non-renewable resource that offers a multitude of ecosystems goods and services. At geological time-scales there is a balance between erosion and soil formation, but in many areas of the world today there is an imbalance with respect to soil loss and its subsequent deposition, principally caused by anthropogenic activity and climate change. Detailed methods regarding soil erosion dynamics are growingly available at local and catchment scale. The integrated assessment of natural hazards often require wider scales to be considered. At these scales, computational modelling need to deal with multiple sources of uncertainty. This complex modelling activity is required in order to assess appropriate management options (Integrated Natural Resources Modelling and Management, INRMM). <br><br>There is usually a discrepancy between the spatial scale at which the process is studied and formulated, the scale at which information is available (e.g. a generalized value for land use or land cover unit), and the scale at which policy-makers or managers need to make decisions (watersheds, regions or wider scale). Modelling strategies typical of the local scale may not be suitable for large scale analysis. At regional or wider scale, the predictive power of existing models is still limited. Empirical approaches based on regressions may offer a way for reducing the modelling uncertainty (mainly reducing the input parameters uncertainty), partially explaining why regression-based models often better predict soil erosion than physically based models. <br><br>The present report shows the way followed for calculating the cover-management and the soil erodibility factor in applying the e-RUSLE soil erosion model at pan-European scale. The e-RUSLE is a model that estimates soil loss due to sheet and rill erosion extending a well-established empirical model. The extended model is based on the Revised Universal Soil Loss Equation (RUSLE). e-RUSLE is an array-based, semantically enhanced modification of the RUSLE exploiting the multiplicity intrinsic in large scale complex and uncertain problems. Its architecture takes advantage from the semantic array programming (SemAP) paradigm. <br><br>The model considers seven main factors controlling soil erosion: the erosivity of the eroding agents (water), the erodibility of the soil, the slope steepness and the slope length of the land, the land cover, the stoniness and the human practices designed to control erosion. In an effort for increasing the reproducibility in soil erosion modelling and for obtaining the most complete and homogeneous pan-European coverage, our attention was mainly focused in using only publicly available datasets and free scientific software for applying the e-RUSLE model and its factors. Implementing the e-RUSLE model we also introduced an innovative SemAP ensemble model, based on climatic similarity, for estimating rain erosivity from multiple available empirical relationships. Further researches are ongoing for applying the same technique to the cover-management factor, in order to obtain a more homogeneous pan-European cover. <br><br>