A simple validated GIS expert system to map relative soil vulnerability and patterns of erosion during the muddy floods of 2000-2001 on the South Downs Sussex, UK.
Faulkner, Hazel P., Boardman, John and Ruiz, Jose-Luis (2010) A simple validated GIS expert system to map relative soil vulnerability and patterns of erosion during the muddy floods of 2000-2001 on the South Downs Sussex, UK. Land Degradation and Development, 21 (4). pp. 310-321. ISSN 1085-3278
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The soils of the South Downs in East Sussex, England (UK), are dominated by loessic silt ( > 70 per cent) and are prone to crusting. Continuing erosion of these soils means that they are thin, typically less than 25 cm thick and are becoming stonier, more droughty and less easier to work. Rates of erosion are relatively low but during extreme events, soils are vulnerable and on- and off-site erosion is a current and long-term risk. Property damage due to muddy flooding is of particular concern. Due to a long history of research interest, a rich database exists on the erosional history of an area of approximately 75 km2 of these thin, calcareous South Downs soils. In particular, during the winter of 2000-2001, Hortonian overland flow was common on certain crop types. Consequent sheet, rill and gully erosion was intense.The gullies and rills formed by runoff during these winter events were mapped in detail. In this paper, a method to estimate soil vulnerability to erosion is described and illustrated. Then, to validate the predictive efficacy of the algorithm used, the actual mapped distribution of rills and gullies following the winter events of 2001 on a particularly badly-affected site are compared with predictions from our soil erosion vulnerability model. Methods for adjusting the land-cover weightings to optimise the map fit are outlined.In a further survey of the utility of the map, it was discovered that farmers' recollections of events provided additional verification. Thus, one implication of our research is that erosion models can be validated by inviting farmers to comment on their efficacy to predict known histories.
|Research Areas:||Science & Technology > Environmental Science|
|Citations on ISI Web of Science:||0|
|Deposited On:||14 Apr 2011 08:07|
|Last Modified:||06 Feb 2013 11:52|
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