2010
DOI: 10.1029/2009wr007950
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Sensitivity analysis of distributed erosion models: Framework

Abstract: [1] We introduce the (P, R, p) procedure for analysis of distributed erosion models, evaluating separate sensitivities to input fluxes (precipitations P), to the propensity of soil to surface flow (runoff conditions R), and to specific erosion properties (descriptive parameters p). For genericity and easier comparisons between models, superparameters of equivalent slope and equivalent erodibility are assembled from innate descriptive parameters: parameterization is reduced to four coded integers that are argum… Show more

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Cited by 11 publications
(16 citation statements)
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“…Furthermore, sensitivity analyses performed by Cheviron et al . () revealed that the most sensitive parameters of MHYDAS‐Erosion were K s , N rill , K r and A s . The evaluation metrics used to assess model calibration are introduced in Table .…”
Section: Methodsmentioning
confidence: 97%
“…Furthermore, sensitivity analyses performed by Cheviron et al . () revealed that the most sensitive parameters of MHYDAS‐Erosion were K s , N rill , K r and A s . The evaluation metrics used to assess model calibration are introduced in Table .…”
Section: Methodsmentioning
confidence: 97%
“…These values may be calculated using the DEM if available, or they can be informed by field observations. Sensitivity analyses performed by Cheviron et al (2010) identified the most sensitive parameters for MHYDAS‐Erosion: ks, N rill , K r and A s .…”
Section: Model Descriptionmentioning
confidence: 99%
“…These values may be calculated using the DEM if available, or they can be informed by field observations. Sensitivity analyses performed by Cheviron et al (2010) identified the most sensitive parameters for MHYDAS-Erosion: ks, N rill , K r and A s .…”
Section: Model Input Parametersmentioning
confidence: 99%