Predicting runoff risks by digital soil mapping

Vista sencilla de ítem
dc.creatorSilva, Mayesse Aparecida da
dc.creatorNaves Silva, Marx Leandro
dc.creatorRay Owens, Phillip
dc.creatorCuri, Nilton
dc.creatorHoffmann Oliveira, Anna
dc.creatorMoreira Candido, Bernardo
dc.date2016
dc.date2016-10-25T18:32:40Z
dc.date2016-10-25T18:32:40Z
dc.date.accessioned2026-06-27T14:35:30Z
dc.descriptionDigital soil mapping (DSM) permits continuous mapping soil types and properties through raster formats considering variation within soil class, in contrast to the traditional mapping that only considers spatial variation of soils at the boundaries of delineated polygons. The objective of this study was to compare the performance of SoLIM (Soil Land Inference Model) for two sets of environmental variables on digital mapping of saturated hydraulic conductivity and solum depth (A + B horizons) and to apply the best model on runoff risk evaluation. The study was done in the Posses watershed, MG, Brazil, and SoLIM was applied for the following sets of co-variables: 1) terrain attributes (AT): slope, plan curvature, elevation and topographic wetness index. 2) Geomorphons and terrain attributes (GEOM): slope, plan curvature, elevation and topographic wetness index combined with geomorphons. The most precise methodology was applied to predict runoff areas risk through the Wetness Index based on contribution area, solum depth, and saturated hydraulic conductivity. GEOM was the best set of co-variables for both properties, so this was the DSM model used to predict the runoff risk. The runoff risk showed that the critical months are from November to March. The new way to classify the landscape to use on DSM was demonstrated to be an efficient tool with which to model process that occurs on watersheds and can be used to forecast the runoff risk.
dc.identifierhttps://hdl.handle.net/10568/77398
dc.identifier.urihttp://hdl.handle.net/123456789/82593
dc.languageen
dc.publisherFapUNIFESP
dc.rightsOpen Access
dc.sourceDa Silva, Mayesse; Silva, Marx; Owens, Phillip; Curi, Nilton; Oliveira, Anna; Candido, Bernardo. 2016. Predicting runoff risks by digital soil mapping . Revista Brasileira de Ciência do solo. 40:e0150353.
dc.subjectsimulation models
dc.subjectsoil
dc.subjecterosion
dc.subjectland use
dc.subjectsoil properties
dc.subjectmodelos de simulación
dc.subjectsuelo
dc.subjecterosión
dc.subjectutilización de la tierra
dc.titlePredicting runoff risks by digital soil mapping
dc.typeJournal Article

Archivos

Colecciones

Centro Internacional de Agricultura Tropical (CIAT)