Estimating the effects of spatial variability of infiltration on the output of a distributed runoff and soil erosion model using Monte Carlo methods

Roo, A. P. J. De; Hazelhoff, L.; Heuvelink, G.B.M.

doi:10.1002/hyp.3360060202

Cited by 39 publications

(15 citation statements)

References 30 publications

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“…Lenhart et al (2002) applied two different approaches to sensitivity analysis on the same model (SWAT). Sensitivity analysis was conducted for the hydrological and soil erosion model LISEM (the Limburg soil erosion model) by De Roo et al (1992). Mendicino (1999) used sensitivity analysis on different GISbased methodologies to estimate the Length-Slope factor in order to determine which of these is more reliable for spatial erosion risk assessment.…”

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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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“…De Roo et al (1992), on the other hand, have examined the eect of varying the spatial pattern in one parameter used in the ANSWERS model. They observed that ANSWERS is very sensitive to in®ltration rates, and that in their study area in®ltration had very low levels of spatial autocorrelation (nugget variance).…”

Section: Applicationmentioning

confidence: 99%

“…Thus, Rewerts and Engel (1991) showed that it was sensitive to the algorithm for calculating slope from a DEM, while Wu et al (1993) showed that ANSWERS provides a more consistent prediction of conditions than AgNPS or CREAMS. Brown et al (1993) have examined the eect of spatial resolution of the database, and De Roo et al (1989Roo et al ( , 1992 have shown that the model is extremely sensitive to in®ltration, both in terms of its absolute amount and its spatial pattern.…”

Section: Spatial Sensitivity Analysis Of Answers the Modelmentioning

confidence: 99%

The Sensitivity of Two Distributed Non-Point Source Pollution Models to the Spatial Arrangement of the Landscape

Fisher

Abrahart

Herbinger³

1997

Hydrol. Process.

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Distributed hydrological models are becoming increasingly complex with respect to spatial phenomena, and with the widespread availability of spatial data from GIS, this trend is likely to increase. In all such models the spatial arrangement of phenomena, such as soil properties and land-use categories is fundamental, and so the arrangement should have an in¯uence on the model output. Testing for this in¯uence we term spatial sensitivity analysis. Here, we report on the spatial sensitivity of two widely used models, AgNPS (agricultural non-point source pollution model) and ANSWERS (areal nonpoint source watershed environment response simulation). The input spatial data were subjected to spatially random mixing to varying degrees, such that the organized landscape became disorganized. The chemical discharge from AgNPS, and the sediment and water discharge from ANSWERS, are examined. In both cases most outputs exhibited little or no sensitivity to the spatial distribution of most input data. Only in®ltration-related inputs produced large variations, but these changes were not in the sense that might have been predicted. Although the analytical methods used require further re®nement, there must now be some doubt as to the validity of the models, and whether they repay their computational complexity. Furthermore, it is felt that spatial sensitivity analysis should become a fundamental part of the veri®cation of all such models.

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“…Poiani and Bedford (1995) recently presented a cursory review of GE-based NPS pollution models emphasizing surface applications. Numerous hydrologic-water quality models of runoff and soil erosion have been used with a GIS to determine surface sources of NPS pollutants from watersheds (Pelletier, 1985;Potter et al, 1986;Oslin et al, 1988;Sivertun et al, 1988;DeRoo et al, 1989DeRoo et al, , 1992Rudra et al, 1991;Bhaskar et al, 1992;Drayton et al, 1992;Joao & Walsh, 1992;Tim et al, 1992;Walker et al, 1992;Wolfe, 1992;He et al, 1993;Heidtke & Auer, 1993;Levine et al, 1993;Mitchell et al, 1993;Warwick & Haness, 1994) agricultural areas (Hopkins & Clausen, 1985;Gilliland & Baxter-Potter, 1987;Hession & Shanholtz, 1988Panuska & Moore, 1991;Hamlett et al, 1992;Lee & White, 1992;Geleta et al, 1994;Tim & Jolly, 1994) and urban areas (Smith & Brilly, 1992;Smith, 1993;Ventura & Kim, 1993). In addition, several groundwater models have been coupled to a GIS to simulate water flow and/or NPS pol-lutants in aquifers (Kernodle & Philip, 1989;Baker & Panciera, 1990;Hinaman, 1993;Roaza et al, 1993;El-Kadi et al, 1994;Darling & Hubbard, 1994).…”

Section: Gis-based Models For Nps Pollution Estimationmentioning

confidence: 99%

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Corwin

2015

SSSA Special Publications

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In recent years, worldwide attention has shifted from point source to non-point source (NPS) pollutants, particularly with regard to the pollution of surface and subsurface sources of drinking water. This is due to the widespread occurrence and potential chronic health effects of NPS pollutants. The ubiquitous nature of NPS pollutants poses a complex technical problem. The area1 extent of their contamination increases the complexity and sheer volume of data required for assessment far beyond that of typical point source pollutants. The spatial nature of the NPS pollution problem necessitates the use of a geographic information system (GIS) to manipulate, retrieve, and display the large volumes of spatial data. This chapter provides an overview of the components (i.e., spatial variability, scale dependency, parameter-data estimation and measurement, uncertainty analysis, and others) required to successfully model NPS pollutants with GIS and a review of recent applications of GIS to the modeling of non-point source pollutants in the vadose zone with deterministic solute transport models. The compatibility, strengths, and weaknesses of coupling a GIS to deterministic one-dimensional transport models are discussed. BACKGROUND IN NON-POINT SOURCE POLLUTANTS

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Estimating the effects of spatial variability of infiltration on the output of a distributed runoff and soil erosion model using Monte Carlo methods

Cited by 39 publications

References 30 publications

Erosion Potential Method (Gavrilović method) sensitivity analysis

Erosion Potential Method (Gavrilović method) sensitivity analysis

The Sensitivity of Two Distributed Non-Point Source Pollution Models to the Spatial Arrangement of the Landscape

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

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