Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais

Detalhes bibliográficos
Autor(a) principal: Souza, Eliana de
Data de Publicação: 2013
Tipo de documento: Tese
Idioma: por
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: http://locus.ufv.br/handle/123456789/1642
Resumo: The quantitative evaluation of pedological and hydric resources is important for adoption of sustainable and efficient practices and use management of resources. Climatologic and pedological data are usually collected in a discret manner and in most of the cases, the number of sites where the information is collected is usually not enough to fuel the predictive models. To overcome the shortage and spatial discontinuity of soil and water information models have been used to estimate certain properties and attributes, from other more readily available. Predictive models employ a variety of techniques and methods for generating information in discrete and continuous format. This study aimed to generate pedotranfer fuction for estimating soil bulk density and soil water content and evaluate the spatial prediction of soil attributes and hydric balance in the Rio Doce Basin, located in the State of Minas Gerais. In Chapter 1, we evaluated the spatial prediction of soil attributes: organic carbon, clay and cation exchange capacity (CEC), with the purpose of evaluating the performance of the prediction models from soil maps generated by both the conventional and with the Multinomial Logistic Regression methods, from a set of covariates consisting of maps derived from elevation model of the terrain, satellite images and maps obtained from conventional soil mapping The geostatistical model called Regression-Kriging (RK) was evaluated for prediction of the spatial attributes. In Chapter 2, we modeled the basin water recharge process for a period of two hydrological years (09/2007 - 09/2009). Hydric balance was calculated by means of the model BALSEQ (Portuguese acronym for Daily Sequential Hydric Balance). The estimated variables from the water balance; evapotranspiration, runoff, deep infiltration and precipitation were spatially predicted with the RK model. In Chapter 3, pedotransfer functions (PTFs) were used by means of Multiple Linear Regression model to predict soil bulk density, water content in four gravimetric potentials and available water capacity. The PTFs were generated for data grouped by horizon, soil class and textural groups. Comparisons between PTFs results obtained in this study with those compiled in the literature were performed. The results of Chapter 1 evidenced the importance of the conventional soil map when developing digital soil mapping. There is a need to increase the sampling size for clay and CEC, and to evaluate the covariates of higher correlation with these two attributes, in order to improve the performance of the spatial prediction models. Organic carbon was predicted with good performance by all evaluated models, presenting the best results when using the dataset that included the soil map obtained from the conventional method of survey combined with other covariates. The estimate and spatial prediction of hydric balance performed in Chapter 2 enabled better understanding of the recharge of the unconfined aquifer in the basin and the generated scenarios can be utilized in zoning the basin for use and management of its soils and water resources. In Chapter 3, we found good predictive power of the PTFs developed with the data collected in the basin; we also observed improved performance in relation to the functions compiled from the literature. The information generated with the developed PTFs are rarely found in current literature body and are of great importance as input variables for environment modelling studies, and can be used to fuel models of spatial prediction. In general, the contribution of this study is generating soil and hidric information for the Basin that enable to carry on climate change related studies from measurements of soil carbon stock, water availability and spatial distribution of its attributes and soil classes.
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spelling Souza, Eliana dehttp://lattes.cnpq.br/9050394316046141Schaefer, Carlos Ernesto Gonçalves Reynaudhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4723204Y8Chagas, César da Silvahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4785135D2Fernandes Filho, Elpídio Ináciohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4703656Z4Santos, Gérson Rodrigues doshttp://lattes.cnpq.br/0674757734832405Carvalho Júnior, Waldir dehttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4700936P62015-03-26T12:52:51Z2014-02-182015-03-26T12:52:51Z2013-03-27SOUZA, Eliana de. Digital soil mapping and modeling of water recharge in Rio Doce Basin, State of Minas Gerais. 2013. 169 f. Tese (Doutorado em Fertilidade do solo e nutrição de plantas; Gênese, Morfologia e Classificação, Mineralogia, Química,) - Universidade Federal de Viçosa, Viçosa, 2013.http://locus.ufv.br/handle/123456789/1642The quantitative evaluation of pedological and hydric resources is important for adoption of sustainable and efficient practices and use management of resources. Climatologic and pedological data are usually collected in a discret manner and in most of the cases, the number of sites where the information is collected is usually not enough to fuel the predictive models. To overcome the shortage and spatial discontinuity of soil and water information models have been used to estimate certain properties and attributes, from other more readily available. Predictive models employ a variety of techniques and methods for generating information in discrete and continuous format. This study aimed to generate pedotranfer fuction for estimating soil bulk density and soil water content and evaluate the spatial prediction of soil attributes and hydric balance in the Rio Doce Basin, located in the State of Minas Gerais. In Chapter 1, we evaluated the spatial prediction of soil attributes: organic carbon, clay and cation exchange capacity (CEC), with the purpose of evaluating the performance of the prediction models from soil maps generated by both the conventional and with the Multinomial Logistic Regression methods, from a set of covariates consisting of maps derived from elevation model of the terrain, satellite images and maps obtained from conventional soil mapping The geostatistical model called Regression-Kriging (RK) was evaluated for prediction of the spatial attributes. In Chapter 2, we modeled the basin water recharge process for a period of two hydrological years (09/2007 - 09/2009). Hydric balance was calculated by means of the model BALSEQ (Portuguese acronym for Daily Sequential Hydric Balance). The estimated variables from the water balance; evapotranspiration, runoff, deep infiltration and precipitation were spatially predicted with the RK model. In Chapter 3, pedotransfer functions (PTFs) were used by means of Multiple Linear Regression model to predict soil bulk density, water content in four gravimetric potentials and available water capacity. The PTFs were generated for data grouped by horizon, soil class and textural groups. Comparisons between PTFs results obtained in this study with those compiled in the literature were performed. The results of Chapter 1 evidenced the importance of the conventional soil map when developing digital soil mapping. There is a need to increase the sampling size for clay and CEC, and to evaluate the covariates of higher correlation with these two attributes, in order to improve the performance of the spatial prediction models. Organic carbon was predicted with good performance by all evaluated models, presenting the best results when using the dataset that included the soil map obtained from the conventional method of survey combined with other covariates. The estimate and spatial prediction of hydric balance performed in Chapter 2 enabled better understanding of the recharge of the unconfined aquifer in the basin and the generated scenarios can be utilized in zoning the basin for use and management of its soils and water resources. In Chapter 3, we found good predictive power of the PTFs developed with the data collected in the basin; we also observed improved performance in relation to the functions compiled from the literature. The information generated with the developed PTFs are rarely found in current literature body and are of great importance as input variables for environment modelling studies, and can be used to fuel models of spatial prediction. In general, the contribution of this study is generating soil and hidric information for the Basin that enable to carry on climate change related studies from measurements of soil carbon stock, water availability and spatial distribution of its attributes and soil classes.A avaliação quantitativa de recursos pedológico e hídrico é uma medida importante para a adoção de práticas sustentáveis e eficientes de uso e manejo dos recursos. Dados climatológicos e pedológicos são, geralmente, coletados de forma pontual e, para muitas áreas o número de locais para os quais se tem informações é, quase sempre insuficiente para alimentar modelos de predição. Para suprir a escassez e a descontinuidade espacial de informações de solo e de água tem-se utilizado modelos para estimar certas propriedades e atributo, a partir de outras de mais fácil obtenção. Os modelos preditivos empregam uma variedade de técnicas e métodos para geração de informações no formato discreto e contínuo. Este trabalho teve como objetivo geral a geração de funções de pedotrasnferencia para estimar a densidade do solo e o conteúdo de água no solo e, a avaliação da predição espacial de atributos do solo e do balanço e hídrico na Bacia do Rio Doce, localizada no Estado de Minas Gerais. No Capítulo 1, avaliou-se a predição espacial dos atributos dos solos: carbono orgânico, argila e capacidade de troca catiônica (CTC). O estudo teve o objetivo de avaliar o desempenho de modelos de predição, a partir de mapas de solos gerados por método convencional e, mapa de solos gerado por Regressão Logística Multinomial. Foi utilizado um conjunto de covariáveis composto por mapas derivados de modelo de elevação do terreno, de imagens de satélite e mapas de classes dos solos do mapeamento convencional e avaliado o desempenho do método geoestatístico de Regressão-krigagem para a predição espacial. No Capítulo 2, realizou-se a modelagem da recarga hídrica na Bacia, para um período de dois anos hidrológicos (09/2007 - 09/2009). O balanço hídrico foi calculado pelo modelo de balanço hídrico sequencial diário, BALSEQ. As variáveis do balanço hídrico; evapotranspiração, escoamento superficial, infiltração profunda e precipitação, foram espacialmente preditas. No Capítulo 3, foram ajustadas funções de pedotransferência (FPTs) por Regressão Linear Múltipla para densidade do solo, conteúdo de água em quatro potenciais e, capacidade de água disponível para as plantas. As FPTs foram geradas para dados agrupados por horizonte, por classe de solo e por grupamentos texturais. Comparações entre os resultados obtidos e aqueles apresentados por FPTs compiladas na literatura foram realizadas. Os resultados do Capitulo 1 mostraram a importância do mapa de solos do mapeamento convencional para as abordagens de mapeamento digital de solos. Verificou-se a necessidade de aumentar a amostragem da argila e da CTC, e a obtenção de covariáveis com maior correlação com esses atributos para melhorar o desempenho do modelo de predição espacial. Os modelos de predição do carbono orgânico apresentaram bom desempenho com melhores resultados quando se utilizou o mapa de solos do método convencional juntamente com outras covariáveis. A estimativa e espacialização do balanço hídrico realizadas no Capítulo 2 possibilitam melhor entendimento da recarga do aquífero livre na bacia e geraram cenários que podem ser utilizados no zoneamento da bacia para uso e manejo dos solos e água. No Capítulo 3, verificou-se bom poder preditivo das FPTs desenvolvidas com os dados levantados na Bacia, e melhor desempenho dessas funções em relação àquelas compiladas da literatura. As informações geradas com as funções propostas são de disponibilidade escassa e têm grande importância como variáveis de modelos de estudos ambientais, podendo ser usadas para alimentar modelos de predição espacial. De forma geral, o trabalho contribui com a geração de informações sobre os solos e a dinâmica hídrica na Bacia que possibilitam estudos relacionados às mudanças climáticas a partir de medidas do estoque de carbono no solo, da oferta hídrica e da distribuição espacial de atributos e classes dos solos.Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorapplication/pdfporUniversidade Federal de ViçosaDoutorado em Solos e Nutrição de PlantasUFVBRFertilidade do solo e nutrição de plantas; Gênese, Morfologia e Classificação, Mineralogia, Química,GeoestatísticaMapeamento de solosRecarga hídricaGeostatisticsSoil mappingWater rechargeCNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLOMapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas GeraisDigital soil mapping and modeling of water recharge in Rio Doce Basin, State of Minas Geraisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALtexto completo.pdfapplication/pdf3199387https://locus.ufv.br//bitstream/123456789/1642/1/texto%20completo.pdf28a61d100d37beedf0a528c107fb46b1MD51TEXTtexto completo.pdf.txttexto completo.pdf.txtExtracted texttext/plain302299https://locus.ufv.br//bitstream/123456789/1642/2/texto%20completo.pdf.txtacd31545a355d286c33d92ca9181ddefMD52THUMBNAILtexto completo.pdf.jpgtexto completo.pdf.jpgIM Thumbnailimage/jpeg3528https://locus.ufv.br//bitstream/123456789/1642/3/texto%20completo.pdf.jpg407fe6638b0a2f4b875f6e3652820149MD53123456789/16422016-04-07 23:10:59.346oai:locus.ufv.br:123456789/1642Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452016-04-08T02:10:59LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.por.fl_str_mv Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
dc.title.alternative.eng.fl_str_mv Digital soil mapping and modeling of water recharge in Rio Doce Basin, State of Minas Gerais
title Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
spellingShingle Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
Souza, Eliana de
Geoestatística
Mapeamento de solos
Recarga hídrica
Geostatistics
Soil mapping
Water recharge
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO
title_short Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
title_full Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
title_fullStr Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
title_full_unstemmed Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
title_sort Mapeamento digital de solos e modelagem da recarga hídrica na Bacia do Rio Doce, Minas Gerais
author Souza, Eliana de
author_facet Souza, Eliana de
author_role author
dc.contributor.authorLattes.por.fl_str_mv http://lattes.cnpq.br/9050394316046141
dc.contributor.author.fl_str_mv Souza, Eliana de
dc.contributor.advisor-co1.fl_str_mv Schaefer, Carlos Ernesto Gonçalves Reynaud
dc.contributor.advisor-co1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4723204Y8
dc.contributor.advisor-co2.fl_str_mv Chagas, César da Silva
dc.contributor.advisor-co2Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4785135D2
dc.contributor.advisor1.fl_str_mv Fernandes Filho, Elpídio Inácio
dc.contributor.advisor1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4703656Z4
dc.contributor.referee1.fl_str_mv Santos, Gérson Rodrigues dos
dc.contributor.referee1Lattes.fl_str_mv http://lattes.cnpq.br/0674757734832405
dc.contributor.referee2.fl_str_mv Carvalho Júnior, Waldir de
dc.contributor.referee2Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4700936P6
contributor_str_mv Schaefer, Carlos Ernesto Gonçalves Reynaud
Chagas, César da Silva
Fernandes Filho, Elpídio Inácio
Santos, Gérson Rodrigues dos
Carvalho Júnior, Waldir de
dc.subject.por.fl_str_mv Geoestatística
Mapeamento de solos
Recarga hídrica
topic Geoestatística
Mapeamento de solos
Recarga hídrica
Geostatistics
Soil mapping
Water recharge
CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO
dc.subject.eng.fl_str_mv Geostatistics
Soil mapping
Water recharge
dc.subject.cnpq.fl_str_mv CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::CIENCIA DO SOLO
description The quantitative evaluation of pedological and hydric resources is important for adoption of sustainable and efficient practices and use management of resources. Climatologic and pedological data are usually collected in a discret manner and in most of the cases, the number of sites where the information is collected is usually not enough to fuel the predictive models. To overcome the shortage and spatial discontinuity of soil and water information models have been used to estimate certain properties and attributes, from other more readily available. Predictive models employ a variety of techniques and methods for generating information in discrete and continuous format. This study aimed to generate pedotranfer fuction for estimating soil bulk density and soil water content and evaluate the spatial prediction of soil attributes and hydric balance in the Rio Doce Basin, located in the State of Minas Gerais. In Chapter 1, we evaluated the spatial prediction of soil attributes: organic carbon, clay and cation exchange capacity (CEC), with the purpose of evaluating the performance of the prediction models from soil maps generated by both the conventional and with the Multinomial Logistic Regression methods, from a set of covariates consisting of maps derived from elevation model of the terrain, satellite images and maps obtained from conventional soil mapping The geostatistical model called Regression-Kriging (RK) was evaluated for prediction of the spatial attributes. In Chapter 2, we modeled the basin water recharge process for a period of two hydrological years (09/2007 - 09/2009). Hydric balance was calculated by means of the model BALSEQ (Portuguese acronym for Daily Sequential Hydric Balance). The estimated variables from the water balance; evapotranspiration, runoff, deep infiltration and precipitation were spatially predicted with the RK model. In Chapter 3, pedotransfer functions (PTFs) were used by means of Multiple Linear Regression model to predict soil bulk density, water content in four gravimetric potentials and available water capacity. The PTFs were generated for data grouped by horizon, soil class and textural groups. Comparisons between PTFs results obtained in this study with those compiled in the literature were performed. The results of Chapter 1 evidenced the importance of the conventional soil map when developing digital soil mapping. There is a need to increase the sampling size for clay and CEC, and to evaluate the covariates of higher correlation with these two attributes, in order to improve the performance of the spatial prediction models. Organic carbon was predicted with good performance by all evaluated models, presenting the best results when using the dataset that included the soil map obtained from the conventional method of survey combined with other covariates. The estimate and spatial prediction of hydric balance performed in Chapter 2 enabled better understanding of the recharge of the unconfined aquifer in the basin and the generated scenarios can be utilized in zoning the basin for use and management of its soils and water resources. In Chapter 3, we found good predictive power of the PTFs developed with the data collected in the basin; we also observed improved performance in relation to the functions compiled from the literature. The information generated with the developed PTFs are rarely found in current literature body and are of great importance as input variables for environment modelling studies, and can be used to fuel models of spatial prediction. In general, the contribution of this study is generating soil and hidric information for the Basin that enable to carry on climate change related studies from measurements of soil carbon stock, water availability and spatial distribution of its attributes and soil classes.
publishDate 2013
dc.date.issued.fl_str_mv 2013-03-27
dc.date.available.fl_str_mv 2014-02-18
2015-03-26T12:52:51Z
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dc.identifier.citation.fl_str_mv SOUZA, Eliana de. Digital soil mapping and modeling of water recharge in Rio Doce Basin, State of Minas Gerais. 2013. 169 f. Tese (Doutorado em Fertilidade do solo e nutrição de plantas; Gênese, Morfologia e Classificação, Mineralogia, Química,) - Universidade Federal de Viçosa, Viçosa, 2013.
dc.identifier.uri.fl_str_mv http://locus.ufv.br/handle/123456789/1642
identifier_str_mv SOUZA, Eliana de. Digital soil mapping and modeling of water recharge in Rio Doce Basin, State of Minas Gerais. 2013. 169 f. Tese (Doutorado em Fertilidade do solo e nutrição de plantas; Gênese, Morfologia e Classificação, Mineralogia, Química,) - Universidade Federal de Viçosa, Viçosa, 2013.
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dc.publisher.initials.fl_str_mv UFV
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dc.publisher.department.fl_str_mv Fertilidade do solo e nutrição de plantas; Gênese, Morfologia e Classificação, Mineralogia, Química,
publisher.none.fl_str_mv Universidade Federal de Viçosa
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