Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

Land Cover and Soil Erodibility within the e-RUSLE Model

<p>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. doi: 10.6084/m9.figshare.856670</p> <p>Version: DRAFT 0.1.2<br>This is a preliminary version. The views expressed are those of the authors and may not be regarded as stating an official position of mentioned organisations.</p> <p> </p> <p><strong>Land Cover and Soil Erodibility within the e-RUSLE Model</strong></p> <p> </p> <p>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</p> <p> </p> <p>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></p> <p> </p

Visual Validation of the e-RUSLE Model Applied at the Pan-European Scale

<p>Bosco, C., de Rigo, D., Dewitte, O., 2014. <strong>Visual Validation of the e-RUSLE Model Applied at the Pan-European Scale</strong>. <em>Scientific Topics Focus 1</em>, MRI-11a13. Notes Transdiscipl. Model. Env., Maieutike Research Initiative. doi: 10.6084/m9.figshare.844627</p> <p><br>Version: DRAFT 0.4.2<br>This is a preliminary version. The views expressed are those of the authors and may not be regarded as stating an official position of mentioned organisations.</p> <p> </p> <p> </p> <p><strong>Visual Validation of the e-RUSLE Model Applied at the Pan-European Scale</strong></p> <p>Claudio Bosco ¹ ² ⁴, Daniele de Rigo ² ³ ⁴ and Olivier Dewitte ⁵</p> <p>1 Loughborough University, Department of Civil and Building Engineering,<br>Loughborough, United Kingdom</p> <p>2 Joint Research Centre of the European Commission,<br>Institute for Environment and Sustainability, Ispra, Italy</p> <p>3 Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, Italy</p> <p>4 Maieutike Research Initiative, Milano, Italy</p> <p>5 Royal Museum for Central Africa, Department of Earth Sciences, Tervuren, Belgium</p> <p> </p> <p> </p> <p>Validating soil erosion estimates at regional or continental scale is still extremely challenging. The common procedures are not technically and financially applicable for large spatial extents, despite this some options are still applicable.</p> <p>For validating the European map of soil erosion by water calculated using the approach proposed in Bosco et al. [1] we applied alternative qualitative methods based on visual evaluation. The 1 km 2 map was validated through a visual and categorical comparison between modelled and observed soil erosion.</p> <p>A procedure employing high-resolution Google Earth images and pictures as validation data is here shown. The resolution of the images, rapidly increased during the last years, allows for a visual qualitative estimation of local soil erosion rates.</p> <p>A cluster of 3x3 K m 2 around 85 selected points was analysed by the authors. The results corroborate the map obtained applying the e-RUSLE model. The 63% of a random sample of 732 grid cells are accurate, 83% at least moderately accurate with a bootstrap p ≤ 0.05).</p> <p>For each of the 85 clusters, the complete details of the validation also containing the comments of the evaluators and the geo-location of the analysed areas have been reported.</p> <p> </p> <p> <br><strong>References</strong></p> <p><br>[1] Bosco, C., de Rigo, D., Poesen, J., Dewitte, O., Panagos, P., 2014. Modelling Soil Erosion<br>at European Scale: Towards Harmonization and Reproducibility. Nat. Hazards Earth<br>Syst. Sci. Discuss.</p> <p>[2] Merritt, W.S., Letcher, R.A., Jakeman, A.J., 2003. A review of erosion and sediment<br>transport models. Environmental Modelling and Software 18 (8), 761-799.</p> <p>[3] Aksoy, H., Kavvas, M. L., 2005. A review of hillslope and watershed scale erosion and<br>sediment transport models. Catena 64 (2), 247-271.</p> <p>[4] Jetten, V., de Roo, A., Favis-Mortlock, D., 1999. 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Interactive comment (reply to Anonymous Referee 3) on Modelling soil erosion at European scale: towards harmonization and reproducibility - by Bosco et al

<p>Cite as:</p> <p>Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P., 2014. Interactive comment (reply to Anonymous Referee 3) on Modelling soil erosion at European scale: towards harmonization and reproducibility - by Bosco et al. Nat. Hazards Earth Syst. Sci. Discuss. 2, C1786-C1795.</p> <p> </p> <p><strong>Interactive comment (reply to Anonymous Referee 3) on Modelling soil erosion at European scale: towards harmonization and reproducibility - by Bosco et al<br></strong></p> <p>Claudio Bosco, Daniele de Rigo, Olivier Dewitte, Jean Poesen, Panos Panagos</p> <p> </p> <p>Throughout the public discussion of our article Bosco et al. (Nat. Hazards Earth Syst. Sci. Discuss., 2, 2639-2680, 2014), the Anonymous Referee 3 provided (Nat. Hazards Earth Syst. Sci. Discuss., 2, C1592-C1594, 2014) a variety of insights. This work presents our replies to them.</p

Interactive comment (reply to Dino Torri) on Modelling soil erosion at European scale: towards harmonization and reproducibility - by Bosco et al

<p>Cite as:</p> <p>Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P., 2014. Interactive comment (reply to Dino Torri) on Modelling soil erosion at European scale: towards harmonization and reproducibility - by Bosco et al. Nat. Hazards Earth Syst. Sci. Discuss. 2, C671-C688.</p> <p> </p> <p><strong>Interactive comment (reply to Dino Torri) on Modelling soil erosion at European scale: towards harmonization and reproducibility - by Bosco et al</strong></p> <p> Claudio Bosco, Daniele de Rigo, Olivier Dewitte, Jean Poesen, Panos Panagos</p> <p> </p> <p>During the public discussion of our article Bosco et al. (Nat. Hazards Earth Syst. Sci. Discuss., 2, 2639-2680, 2014), D. Torri provided numerous insights (Nat. Hazards Earth Syst. Sci. Discuss. 2, C528-C532, 2014). This work offers our replies to them.</p

Fuzzy gain scheduled load frequency controller in presence of grid code frequency responsive wind power penetration

Additional file 1. An extended description of methodology on Bayesian modelling

MOESM4 of Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys

Additional file 4: Table S3. Comparison of absolute bias data for Kenya and Rwanda where the spatial random sample represented 20% (Sample 1), 30% (Sample 2) and 40% (Sample 3) hold out set of the clusters. Absolute bias is the difference between the mean from simple random sampling and that generated after MCMC 50,000 iteration in the different cluster-sample

MOESM5 of Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys

Additional file 5: Figure S1. Convergence diagnostic: Example of trace plots extracted for malaria prevalence survey parameters in the 2010 Senegal DHS for the monitored parameters over the duration of model run

Hierarchical mixed effects logistic regression model odds ratios of MNH outcomes among female DHS respondents in five East African countries (N = 25,325).

<p>Hierarchical mixed effects logistic regression model odds ratios of MNH outcomes among female DHS respondents in five East African countries (N = 25,325).</p

Births adjusted probability maps representing the probability of obtaining MNH care for a given birth at the administrative II unit.

<p><b>a)</b> Delivery with a skilled birth attendant (SBA) present, <b>b)</b> Four or more antenatal care (ANC) visits at time of delivery, and <b>c)</b> Postnatal care (PNC) received within 48 hours of delivery.</p