[Paper Review] Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map

Claudio Bosco, Daniele de Rigo+2 more|arXiv (Cornell University)|Jan 1, 2014

Soil erosion and sediment transport20 references3 citations

TL;DR

This paper presents a reproducible, Pan-European soil erosion map using a modified RUSLE model with publicly available datasets and open-source software. By applying a novel climatic ensemble model based on relative-distance similarity to merge 7 empirical rainfall erosivity equations, it produces a spatially consistent, validated R-factor map that enhances transparency and reusability in environmental modeling across Europe.

ABSTRACT

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. <strong>Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map</strong>. <em>Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”</em>, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847 <strong><br></strong> <strong><br></strong> <strong>Towards the reproducibility in soil erosion modeling:</strong><br><strong>a new Pan-European soil erosion map</strong> <strong><br></strong> Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ <br><br> ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,<br>Via E. Fermi 2749, I-21027 Ispra (VA), Italy<br>² Politecnico di Milano, Dipartimento di Elettronica e Informazione,<br>Via Ponzio 34/5, I-20133 Milano, Italy <br> Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research. <strong><br></strong> <strong><br></strong> <strong>References</strong> <br>[1] Rusco, E., Montanarella, L., Bosco, C., 2008. Soil erosion: a main threats to the soils in Europe. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 37-45 [2] Casagrandi, R. and Guariso, G., 2009. Impact of ICT in Environmental Sciences: A citation analysis 1990-2007. Environmental Modelling &amp; Software 24 (7), 865-871. DOI:10.1016/j.envsoft.2008.11.013 [3] Stallman, R. M., 2005. Free community science and the free development of science. PLoS Med 2 (2), e47+. DOI:10.1371/journal.pmed.0020047 [4] Waldrop, M. M., 2008. Science 2.0. 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In prep. [29] Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility. In prep. [30] R Development Core Team, 2005. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [31] Stallman, R. M., 2009. Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52 (6), 31–33. DOI:10.1145/1516046.1516058 [32] de Rigo, D. 2011. Multi-dimensional weighted median: the module "wmedian" of the Mastrave modelling library. Mastrave project technical report. http://mastrave.org/doc/mtv_m/wmedian [33] Shakesby, R. A., 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105 (3-4), 71-100. DOI:10.1016/j.earscirev.2011.01.001 [34] Zuazo, V. H., Pleguezuelo, C. R., 2009. Soil-Erosion and runoff prevention by plant covers: A review. 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Motivation & Objective

• To address the lack of harmonized soil erosion assessment methodologies across Europe.
• To improve reproducibility in soil erosion modeling by using public datasets and free software.
• To develop a transparent, modular computational framework for environmental modeling.
• To estimate the rainfall erosivity factor (R) across Europe using a climatic ensemble model based on empirical equations.
• To enable future integration of the model into climate change impact assessments.

Proposed method

• Applied a revised RUSLE model with a stoniness correction factor for improved accuracy.
• Used public datasets: E-OBS (precipitation), SGDBE (soil properties), SRTM (elevation), CORINE (land cover).
• Implemented the R factor calculation using GNU R and GNU Octave for reproducibility.
• Developed a climatic ensemble model by merging 7 empirical erosivity equations using relative-distance similarity (RDS) across 26 climatic indicators.
• Applied weighted median aggregation of empirical models based on their similarity to target regions.
• Designed a lightweight, semantically constrained software architecture for modular, reusable environmental modeling.

Experimental results

Research questions

• RQ1 How can soil erosion modeling be made reproducible across Europe using only public data and free software?
• RQ2 What is the most effective way to extend the geographical validity of empirical rainfall erosivity equations?
• RQ3 How can climatic similarity be quantified to enable robust spatial extrapolation of erosivity factors?
• RQ4 Can a reproducible, open-source framework improve the consistency and transparency of Pan-European soil erosion mapping?
• RQ5 How can such a model be adapted for future climate change impact assessments?

Key findings

• The climatic ensemble model successfully extended the validity of 7 empirical erosivity equations beyond their original geographic domains using relative-distance similarity.
• The R factor map was generated with consistent, reproducible methods using only public datasets and open-source tools (GNU R, MATLAB for validation).
• The model achieved a high degree of transparency and modularity through semantic array programming and self-documenting code.
• A trustability map was generated to qualitatively assess the reliability of the ensemble R-factor estimates based on similarity metrics.
• The framework is designed for future reuse in modeling land use, vegetation, and climate change impacts on soil erosion.
• The final soil erosion map identifies high-risk areas across Europe, particularly in the Mediterranean and alpine regions, based on integrated RUSLE factors.