Soil erosion modelling using ‘answers’ and geographical information systems

Roo, A. P. J. De; Hazelhoff, L.; Burrough, P.A.

doi:10.1002/esp.3290140608

Cited by 84 publications

(13 citation statements)

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“…Numerous studies (e.g. Jetten et al 1999Jetten et al , 2003Tucker & Whipple 2002;Tucker 2004;Van Griensven et al 2006) applied sensitivity analysis on various erosion models such as MPSIAC (Behnam & Parehkar 2011), CREAM (Lane & Ferriera 1982), EUROSEM (Veihe & Quinton 2000), WEPP (Nearing et al 1990), PSEM-2D (Nord & Esteves 2005), USLE (Tattari & Bärlund 2001;Liu & Liu 2010), GUEST (Misra & Rose 1996), ANSWERS (De Roo et al 1989), etc. Furthermore, White and Chaubey (2005) used sensitivity analysis to identify the parameters that most influence predicted flow, sediment and nutrient outcomes for the Soil and Water Assessment Tool (SWAT) model.…”

mentioning

confidence: 99%

Erosion Potential Method (Gavrilović method) sensitivity analysis

Dragičević¹,

Karleuša²,

Ožanić³

2017

Soil Water Res.

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Dragičević N., Karleuša B., Ožanić N. (2017): Erosion Potential Method (Gavrilović method) sensitivity analysis. Soil & Water Res., 12: 51−59.In recent decades, various methods for erosion intensity and sediment production assessment have been developed. The necessity for better model performance has led to the more frequent application of the method sensitivity and uncertainty assessments in order to decrease errors that arise from the model concept and its main assumptions. The analysis presented in this paper refers to the application of the Gavrilović method (Erosion Potential Method), an empirical and semi-quantitative method that can estimate the amount of sediment production and sediment transport as well as the erosion intensity and indicate the areas potentially threatened by erosion. The emphasis in this paper is given upon the method sensitivity analysis that has not previously been conducted for the Gavrilović method. The sensitivity analysis was conducted for fourteen different parameters included in the method, all in relation to different model outputs. Each parameter was perceived and discussed individually in relation to its effect upon the method outputs, and ranked into categories depending on their influence on one or more model outputs. The objective of the analysis was to explore the constraints of the Gavrilović method and the method response to changes deriving from the each individual parameter in an attempt to provide a better understanding of the method, the weight and the contribution of each parameter in the overall method. The parameters that could potentially be used in future research, for method modification and calibration in areas with different catchment characteristics (e.g. climate, geological, etc.) were identified. The most sensitive model parameters resulting from conducted sensitivity analysis for the Gavrilović method are also those considered to be significant in the scientific literature on erosion. The Gavrilović method sensitivity analysis has been done on a case study for the Dubracina catchment area, Croatia.

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mentioning

confidence: 99%

Erosion Potential Method (Gavrilović method) sensitivity analysis

Dragičević¹,

Karleuša²,

Ožanić³

2017

Soil Water Res.

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“…In LISEM the Manning equation is used to calculate flow velocities. Sensitivity analysis has shown that model results are very sensitive to the value of Manning's n (De Roo et al, 1989;Jetten et al, 1998). It is also questionable whether the Manning equation, which was developed for open channel flow, can be applied to shallow overland flow, where flow depths may be less than the average roughness height (Takken and Govers, 2000).…”

Section: Hydraulic Resistance Of Interrill Flowmentioning

confidence: 99%

The influence of both process descriptions and runoff patterns on predictions from a spatially distributed soil erosion model

Takken

Govers

Jetten

et al. 2005

Earth Surf Processes Landf

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Spatially distributed, process-based erosion models have been built to predict both erosion and deposition rates and patterns. However, predictions by these models often do not meet our expectations. There are several possible reasons for this, which can be subdivided into three groups: (i) process descriptions within the models may be incomplete or incorrect, leading to structural errors (model error); (ii) parameterization is difficult leading to a high degree of uncertainty regarding model inputs (input error); and (iii) the spatial implementation of the model (the spatial discretization and the routing methods used) may be inadequate. Hitherto, most studies of erosion model performance have focused on input parameter uncertainty: the effects of process description and spatial implementation of the model have often been neglected. In this study an attempt is made to compare some aspects of the effects of process description, parameter uncertainty and model implementation on model performance. A series of model runs was carried out to evaluate to what extent the predictions of the spatially distributed erosion model LISEM are affected by the description of the hydraulic resistance, the parameter values used to characterize hydraulic resistance as well as the routing algorithm used to generate the runoff pattern.The process description used for overland flow resistance and velocity has a clear effect on the erosion and deposition rates predicted by the model. However, relatively minor variations in a single parameter value (hydraulic roughness) appear to affect model output more strongly than variations in the process description itself. The use of different routing algorithms leads to notably different erosion and deposition patterns, but total erosion and deposition rates are much less affected.

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“…These also vary considerably in terms of their purposes and both spatial and temporal scales involved, as well as their conceptual bases (De Vente and Poesen, 2005). They are usually classified by the way erosion is either physically evaluated or not, and accordingly, into empirical models (Table I) and process‐based models (De Roo et al , 1989; Almorox et al , 1994; Favis‐Mortlock et al , 1996; Mitasova and Mitas, 1998; Jetten et al , 2003).…”

Section: Introductionmentioning

confidence: 99%

Assessment of soil erosion in karst regions of Havana, Cuba

Febles-González¹,

Vega-Carreño²,

Tolón‐Becerra³

et al. 2011

Land Degrad Dev

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Only recently have erosion models begun to be used in research work in Cuba, specifically the USLE and the thematic cartography of factors in a GIS framework without using a specific model. It therefore becomes necessary to include simulation models for karst regions that make possible an integral assessment of the specific types of soil erosion in those environments and take into consideration the effects of climate change in soil management systems. Morphometric analysis of karst doline absorption forms in regions of La Habana Province in 1986, 1997, and 2009 allowed the characterisation and application of the Morgan Morgan Finney (MMF) conceptual empirical erosion model in the Country for the first time. The results showed previously unreported losses of 12·3–13·7 t of soil ha −1 y−1, which surpasses the permissible erosion threshold. Furthermore, it clearly shows the unsustainable trend of Red Ferralitic and Ferrasol Rhodic (World Reference Base) soils use. The model applied considered the effects of extreme rainfall events associated with climate change in recent years. The results found have led to strategies for coping with future climate change in each scenario and have made it possible to evaluate the consequences. Copyright © 2011 John Wiley & Sons, Ltd.

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Soil erosion modelling using ‘answers’ and geographical information systems

Cited by 84 publications

References 1 publication

Erosion Potential Method (Gavrilović method) sensitivity analysis

Erosion Potential Method (Gavrilović method) sensitivity analysis

The influence of both process descriptions and runoff patterns on predictions from a spatially distributed soil erosion model

Assessment of soil erosion in karst regions of Havana, Cuba

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