Comparative sensitivity analysis of four distributed erosion models - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Water Resources Research Année : 2011

Comparative sensitivity analysis of four distributed erosion models

(1, 2) , (2) , (1) , (3) , (2) , (1) , (4) , (2)


Using a previously defined framework, we performed a comparative sensitivity analysis of four very different distributed erosion models (MHYDAS, STREAM, PESERA, and MESALES). We investigated their sensitivities to input fluxes, hydrological submodels, and specific erosion parameters gathered into equivalent slope and equivalent erodibility for each model, thus allowing explicit comparisons between models. Tests involved multiple combinations of rain intensities and runoff conditions for selected screenings of the equivalent parameter space, resorting to one-at-a-time displacements and Latin hypercube samples. Sensitivity to spatial distributions of erosion parameters was calculated as a normalized index of numerical spread of soil loss results, obtained at the outlet of a nine-cell virtual catchment endowed with a fixed flow pattern. Spatially homogeneous or distributed parameterizations yielded responses of comparable magnitudes. Equivalent erodibility was often the key parameter, while sensitivity trends depended on input fluxes and the propensity of soils for runoff, affecting continuous and discrete models in clearly dissimilar ways.
Fichier principal
Vignette du fichier
Cheviron-Water_Resources_Research-2011_1.pdf (2.05 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-00560466 , version 1 (29-05-2020)


Paternité - Pas d'utilisation commerciale - CC BY 4.0



Bruno Cheviron, Yves Le Bissonnais, Jean-François Desprats, Alain Couturier, Silvio José Gumière, et al.. Comparative sensitivity analysis of four distributed erosion models. Water Resources Research, 2011, 47, pp.W01510. ⟨10.1029/2010WR009158⟩. ⟨hal-00560466⟩
93 Consultations
132 Téléchargements



Gmail Facebook Twitter LinkedIn More