Estimating the Effects of Water-Induced Shallow Landslides on Soil Erosion. Bosco, C. and Sander, G. 7(2):910137+.
Estimating the Effects of Water-Induced Shallow Landslides on Soil Erosion [link]Paper  doi  abstract   bibtex   
Rainfall-induced landslides and soil erosion are part of a complex system of multiple interacting processes, and both are capable of significantly affecting sediment budgets. These sediment mass movements also have the potential to significantly impact on a broad network of ecosystems, in terms of their health, functionality and the services they provide. To support the integrated assessment of these processes it is necessary to develop reliable modelling architectures. This paper proposes a semi-quantitative integrated methodology for a robust assessment of soil erosion rates in data-poor regions affected by landslide activity. It combines heuristic, empirical and probabilistic approaches. This proposed methodology is based on the geospatial semantic array programming paradigm and has been implemented on a catchment scale methodology using Geographic Information Systems (GIS) spatial analysis tools and GNU Octave. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. In order to improve computational reproducibility, the geospatial data transformations implemented in Esri ArcGis are made available in the free software GRASS GIS. The proposed modelling architecture is flexible enough for future transdisciplinary scenario analysis to be more easily designed. In particular, the architecture might contribute as a novel component to simplify future integrated analyses of the potential impact of wildfires or vegetation types and distributions on sediment transport from water-induced landslides and erosion.
@article{boscoEstimatingEffectsWaterinduced2014,
  title = {Estimating the Effects of Water-Induced Shallow Landslides on Soil Erosion},
  author = {Bosco, Claudio and Sander, Graham},
  date = {2014-11},
  journaltitle = {IEEE Earthzine},
  volume = {7},
  pages = {910137+},
  doi = {10.1101/011965},
  url = {https://doi.org/10.1101/011965},
  abstract = {Rainfall-induced landslides and soil erosion are part of a complex system of multiple interacting processes, and both are capable of significantly affecting sediment budgets. These sediment mass movements also have the potential to significantly impact on a broad network of ecosystems, in terms of their health, functionality and the services they provide. To support the integrated assessment of these processes it is necessary to develop reliable modelling architectures. This paper proposes a semi-quantitative integrated methodology for a robust assessment of soil erosion rates in data-poor regions affected by landslide activity. It combines heuristic, empirical and probabilistic approaches. This proposed methodology is based on the geospatial semantic array programming paradigm and has been implemented on a catchment scale methodology using Geographic Information Systems (GIS) spatial analysis tools and GNU Octave. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. In order to improve computational reproducibility, the geospatial data transformations implemented in Esri ArcGis are made available in the free software GRASS GIS. The proposed modelling architecture is flexible enough for future transdisciplinary scenario analysis to be more easily designed. In particular, the architecture might contribute as a novel component to simplify future integrated analyses of the potential impact of wildfires or vegetation types and distributions on sediment transport from water-induced landslides and erosion.},
  archivePrefix = {arXiv},
  eprint = {1501.05739},
  eprinttype = {arxiv},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13455081,environmental-modelling,geospatial-semantic-array-programming,integrated-modelling,integration-techniques,land-cover,landslides,monte-carlo,risk-assessment,semantic-array-programming,semap,soil-erosion,soil-resources,vegetation},
  number = {2}
}
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