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Handbook of Erosion Modelling

Mark Nearing (Hrsg.), R. P. C. Morgan (Hrsg.)

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Naturwissenschaften, Medizin, Informatik, Technik / Geologie


The movement of sediment and associated pollutants over thelandscape and into water bodies is of increasing concern withrespect to pollution control, prevention of muddy floods andenvironmental protection. In addition, the loss of soil on site hasimplications for declining agricultural productivity, loss ofbiodiversity and decreased amenity and landscape value. The fate ofsediment and the conservation of soil are important issues for landmanagers and decision-makers. In developing appropriate policiesand solutions, managers and researchers are making greater use oferosion models to characterise the processes of erosion and theirinteraction with the landscape. A study of erosion requires one to think in terms ofmicroseconds to understand the mechanics of impact of a singleraindrop on a soil surface, while landscapes form over periods ofthousands of years. These processes operate on scales ofmillimetres for single raindrops to mega-metres for continents.Erosion modelling thus covers quite a lot of ground. This bookintroduces the conceptual and mathematical frameworks used toformulate models of soil erosion and uses case studies to show howmodels are applied to a variety of purposes at a range of spatialand temporal scales. The aim is to provide land managers and otherswith the tools required to select a model appropriate to the typeand scale of erosion problem, to show what users can expect interms of accuracy of model predictions and to provide anappreciation of both the advantages and limitations of models.Problems covered include those arising from agriculture, theconstruction industry, pollution and climatic change and range inscale from farms to small and large catchments. The book will alsobe useful to students and research scientists as an up-to-datereview of the state-of-art of erosion modelling and, through aknowledge of how models are used in practice, in highlighting thegaps in knowledge that need to be filled in order to develop evenbetter models.

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