期刊
ENVIRONMETRICS
卷 21, 期 5, 页码 493-509出版社
WILEY
DOI: 10.1002/env.999
关键词
bootstrap; dependence; gully density; gully erosion; prediction uncertainty; random forests
Gully erosion represents one of the greatest threats to agricultural watersheds around the world and this is exacerbated with continual changes to land practices and changing climatic condition at a temporal scale Catchment based models which Include gully erosion have received increased attention in recent years due to the impact of mill), erosion on downstream water quality These types of models take gully erosion calculated via a deterministic model at a particular spatial location, along. with other measures of erosion to provide an end-of-catchment load Although seen as a major contributor to erosion in this type of modelling framework. the error surrounding the mill), erosion calculation is ignored in the sediment model We propose a methodology that investigates the error in the gully erosion model The methodology uses Random Forests to predict gully density and its associated prediction error across a catchment using environmental variables, which are then incorporated into the gully erosion model We demonstrate the approach using the Burdekin catchment in Queensland. Australia, where only a very small proportion of gullies (similar to 0.88%) have been mapped Our results cast doubt in the predictive ability of models of sediment transportation that use gully erosion where the error is estimated high Copyright (C) 2009 John Wiley & Sons, Ltd
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