Predicting Runoff risks by digital soil mapping
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/32773 |
Resumo: | Digital 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. |
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Predicting Runoff risks by digital soil mappingGeomorphonsTerrain attributesSaturated hydraulic conductivitySolum depthGeomorfosAtributos do terrenoCondutividade hidráulica saturadaProfundidade do soloDigital 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.Sociedade Brasileira de Ciência do Solo2019-02-15T09:28:56Z2019-02-15T09:28:56Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSILVA, M. A. da et al. Predicting Runoff risks by digital soil mapping. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 40, p. 1-13, 2016.http://repositorio.ufla.br/jspui/handle/1/32773Revista Brasileira de Ciência do Soloreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessSilva, Mayesse Aparecida daSilva, Marx Leandro NavesOwens, Phillip RayCuri, NiltonOliveira, Anna HoffmannCandido, Bernardo Moreirapor2019-02-15T09:28:57Zoai:localhost:1/32773Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2019-02-15T09:28:57Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Predicting Runoff risks by digital soil mapping |
title |
Predicting Runoff risks by digital soil mapping |
spellingShingle |
Predicting Runoff risks by digital soil mapping Silva, Mayesse Aparecida da Geomorphons Terrain attributes Saturated hydraulic conductivity Solum depth Geomorfos Atributos do terreno Condutividade hidráulica saturada Profundidade do solo |
title_short |
Predicting Runoff risks by digital soil mapping |
title_full |
Predicting Runoff risks by digital soil mapping |
title_fullStr |
Predicting Runoff risks by digital soil mapping |
title_full_unstemmed |
Predicting Runoff risks by digital soil mapping |
title_sort |
Predicting Runoff risks by digital soil mapping |
author |
Silva, Mayesse Aparecida da |
author_facet |
Silva, Mayesse Aparecida da Silva, Marx Leandro Naves Owens, Phillip Ray Curi, Nilton Oliveira, Anna Hoffmann Candido, Bernardo Moreira |
author_role |
author |
author2 |
Silva, Marx Leandro Naves Owens, Phillip Ray Curi, Nilton Oliveira, Anna Hoffmann Candido, Bernardo Moreira |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Silva, Mayesse Aparecida da Silva, Marx Leandro Naves Owens, Phillip Ray Curi, Nilton Oliveira, Anna Hoffmann Candido, Bernardo Moreira |
dc.subject.por.fl_str_mv |
Geomorphons Terrain attributes Saturated hydraulic conductivity Solum depth Geomorfos Atributos do terreno Condutividade hidráulica saturada Profundidade do solo |
topic |
Geomorphons Terrain attributes Saturated hydraulic conductivity Solum depth Geomorfos Atributos do terreno Condutividade hidráulica saturada Profundidade do solo |
description |
Digital 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. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2019-02-15T09:28:56Z 2019-02-15T09:28:56Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
SILVA, M. A. da et al. Predicting Runoff risks by digital soil mapping. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 40, p. 1-13, 2016. http://repositorio.ufla.br/jspui/handle/1/32773 |
identifier_str_mv |
SILVA, M. A. da et al. Predicting Runoff risks by digital soil mapping. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 40, p. 1-13, 2016. |
url |
http://repositorio.ufla.br/jspui/handle/1/32773 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
dc.source.none.fl_str_mv |
Revista Brasileira de Ciência do Solo reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1784549971991724032 |