Análise de incertezas do balanço hídrico climatológico espacializado
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/15101 |
Resumo: | The spacialized Climatic Water Balance (CWB) is a model that simulates the availability of water in the soil, for plants, in a space distributed manner. It is important to understand the possible uncertainties of this spatialization, so the opportunities of its use in the public policies can be discussed, as well as the advantages of its use, its limitations, having a satisfying result in its use and that its optimization may be allowed. Searching to know the uncertainties in the CWB's spatial distribution, this thesis aims to evaluate such uncertainties due to: 1) the spatialization methods and 2) the density of information used for the spatialization. To address the matter of the uncertainties regarding the spatialization methods, two methods were analyzed. The first one calculates the CWB punctually at stations and then spatializes these values by interpolation (Calculation-Interpolation principle, CI), while the second method is to interpolate first the CWB's variables (precipitation and evapotranspiration) and then calculates it for each pixel (Interpolation-Calculation principle, IC). In addition, the influence of the interpolators were also analyzed. To analyze the uncertainties relating to the density of information, the strategy of comparing the differences arising from the results of the precipitation and the evapotranspiration interpolations and the calculation of the spacialized CWB was used, deleting stations and analyzing the error generated by this decrease of information density. This analysis was first done with the suppression of one station and then, by removing two, three and so on, until the remaining of 3 stations, referring to the minimum number of points required to perform interpolation. To make possible the spatialization of the CWB in a distributed way, a tool in the PythonTM programming language, using the package ArcPy® was created to perform the calculations of the CWB. The study area of this work was the plain area of Veneto, Region of Italy. The results showed that although the analyzes have indicated a trend of smaller uncertainties in the IC method in relation to the CI, these differences were not statistically significant at the 5% level. It was also observed that the CI method brings more uncertainties to the spatialization, particularly when there is water deficit in the CWB and/or ground recharge, by smoothing such balance values between stations, not properly representing the CWB in these areas. The uncertainty analysis performed in this study was also able to show which months can carry greater uncertainty into their spatializations, both P, ETo and the CWB and that the high variability of precipitation carries uncertainties in their spatial distribution. The spatial representation of the CWB showed, for this study, to have greater uncertainty at the beginning of the dry season, when starts the reservoir drawdown, or the beginning of the rains that cause the filling of the reservoir in the soil. The estimated uncertainties to the stations reduction from 15 to 3, ranged from 3 to 27% for precipitation, from 1 to 36% for ETo and 1 to 88% for CWB, considering 16 stations as the truth reference. As the IC method allows the CWB spatialization with different scenarios of Available Water Capacity (AWC), which is not feasible with the CI method, since CI considers only the AWC at the station's place, the use of the IC method was more suitable to represent the CWB at smaller scales (larger areas). This possibility provides more options for the application of the spatialized CWB in public policies, allowing the generation of crops scenarios in a more detailed and dynamic way than the CI method, besides the possibility of its adequacy to the reality of each soil type. |
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2018-12-13T20:44:46Z2018-12-13T20:44:46Z2016-08-05http://repositorio.ufsm.br/handle/1/15101The spacialized Climatic Water Balance (CWB) is a model that simulates the availability of water in the soil, for plants, in a space distributed manner. It is important to understand the possible uncertainties of this spatialization, so the opportunities of its use in the public policies can be discussed, as well as the advantages of its use, its limitations, having a satisfying result in its use and that its optimization may be allowed. Searching to know the uncertainties in the CWB's spatial distribution, this thesis aims to evaluate such uncertainties due to: 1) the spatialization methods and 2) the density of information used for the spatialization. To address the matter of the uncertainties regarding the spatialization methods, two methods were analyzed. The first one calculates the CWB punctually at stations and then spatializes these values by interpolation (Calculation-Interpolation principle, CI), while the second method is to interpolate first the CWB's variables (precipitation and evapotranspiration) and then calculates it for each pixel (Interpolation-Calculation principle, IC). In addition, the influence of the interpolators were also analyzed. To analyze the uncertainties relating to the density of information, the strategy of comparing the differences arising from the results of the precipitation and the evapotranspiration interpolations and the calculation of the spacialized CWB was used, deleting stations and analyzing the error generated by this decrease of information density. This analysis was first done with the suppression of one station and then, by removing two, three and so on, until the remaining of 3 stations, referring to the minimum number of points required to perform interpolation. To make possible the spatialization of the CWB in a distributed way, a tool in the PythonTM programming language, using the package ArcPy® was created to perform the calculations of the CWB. The study area of this work was the plain area of Veneto, Region of Italy. The results showed that although the analyzes have indicated a trend of smaller uncertainties in the IC method in relation to the CI, these differences were not statistically significant at the 5% level. It was also observed that the CI method brings more uncertainties to the spatialization, particularly when there is water deficit in the CWB and/or ground recharge, by smoothing such balance values between stations, not properly representing the CWB in these areas. The uncertainty analysis performed in this study was also able to show which months can carry greater uncertainty into their spatializations, both P, ETo and the CWB and that the high variability of precipitation carries uncertainties in their spatial distribution. The spatial representation of the CWB showed, for this study, to have greater uncertainty at the beginning of the dry season, when starts the reservoir drawdown, or the beginning of the rains that cause the filling of the reservoir in the soil. The estimated uncertainties to the stations reduction from 15 to 3, ranged from 3 to 27% for precipitation, from 1 to 36% for ETo and 1 to 88% for CWB, considering 16 stations as the truth reference. As the IC method allows the CWB spatialization with different scenarios of Available Water Capacity (AWC), which is not feasible with the CI method, since CI considers only the AWC at the station's place, the use of the IC method was more suitable to represent the CWB at smaller scales (larger areas). This possibility provides more options for the application of the spatialized CWB in public policies, allowing the generation of crops scenarios in a more detailed and dynamic way than the CI method, besides the possibility of its adequacy to the reality of each soil type.O Balanço Hídrico Climatológico (BHC) espacializado é um modelo que simula a variação da disponibilidade de água no solo, para as plantas, de forma distribuída no espaço. É importante compreender as possíveis incertezas desta espacialização, para que sejam discutidas suas possibilidades de emprego em políticas públicas, as vantagens de sua utilização, bem como as suas limitações, para se ter um resultado satisfatório em seu uso e possibilite sua otimização. Procurando conhecer as incertezas na espacialização do BHC, objetiva-se nesta tese avaliar tais incertezas em decorrência de: 1) os métodos de espacialização e 2) a densidade de informação utilizada para a espacialização. Para analisar a questão das incertezas referentes aos métodos de espacialização, foram analisados dois métodos. O primeiro consiste em calcular o BHC pontualmente nas estações e depois espacializar esses valores, por interpolação (princípio Cálculo-Interpolação, CI), enquanto o segundo método consiste em interpolar primeiro as variáveis do BHC (precipitação e evapotranspiração) e, depois, calculá-lo para cada pixel (princípio Interpolação-Cálculo, IC). Complementarmente, foram analisadas também a influência dos interpoladores. Para analisar as incertezas referentes à densidade das informações utilizou-se a estratégia de comparar as diferenças geradas nos resultados das interpolações da precipitação, da evapotranspiração e do cálculo do BHC espacializado, suprimindo estações e analisando o erro gerado por esse decréscimo de densidade de informação. Essa análise foi feita, primeiro com a supressão de uma estação e depois, suprimindo duas, três e assim por diante, até que restassem um mínimo de 3 estações, referente ao número mínimo de pontos necessários para realização de interpolação. Para possibilitar a espacialização do BHC de forma distribuída, desenvolveu-se uma ferramenta, em linguagem de programação PythonTM, utilizando do package ArcPy® para realizar os cálculos do BHC. A área de estudo desse trabalho foi a planície de Vêneto, Região da Itália. Os resultados mostraram que embora as análises tenham indicado uma tendência de menores incertezas com o método IC em relação ao CI, essas diferenças não apresentaram significância estatística, ao nível de 5%. Observou-se também que o método CI traz maiores incertezas à espacialização em especial quando existe déficit hídrico no BHC e/ou recarga do solo, por suavizar tais valores de balanço entre estações, não representando propriamente o BHC nestas áreas. A análise de incertezas realizada neste trabalho também conseguiu demonstrar quais meses podem carregar maiores incertezas em suas espacializações, tanto para P, ETo e o BHC e que a alta variabilidade da precipitação carrega incertezas na sua espacialização. A representação espacial do BHC demonstrou, para este estudo, maiores incertezas no começo de épocas de estiagem, quando começa o deplecionamento do armazenamento, ou começo das precipitações que provocam o enchimento do reservatório no solo. As incertezas estimadas para a redução de estações de 15 para 3, variaram de 3 a 27% para precipitação, de 1 a 36% para ETo e de 1 a 88% para o BHC, considerando 16 estações como a verdade de referência. Como o método IC permite a espacialização do BHC com diferentes cenários de Capacidade de Água Disponível (CAD), o que não é viável com o método CI, dado que CI considera apenas a CAD no local da estação, a utilização do método IC se mostrou mais adequado para representar o BHC em escalas menores (áreas maiores). Tal possibilidade proporciona mais opções de aplicação da espacialização do BHC nas políticas públicas, possibilitando a geração de cenários de culturas de forma mais detalhada e dinâmica que o método CI, além de adequar-se a realidade de cada tipo de solo.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências RuraisPrograma de Pós-Graduação em Engenharia FlorestalUFSMBrasilRecursos Florestais e Engenharia FlorestalAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessIncertezasAnálise de incertezasDisponibilidade hídricaDéficit hídricoExcesso hídricoBalanço hídrico climatológico espacializadoBHCPythonThornthwaiteUncertaintiesUncertainties analysisWater availabilityWater deficitWater surplusSpatilized climatic water balanceCWBCNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTALAnálise de incertezas do balanço hídrico climatológico espacializadoUncertainty analysis of the spatialized climatic water balanceinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisCruz, Jussara Cabralhttp://lattes.cnpq.br/3525141443261254Lima, Jorge Enoch Furquim Werneckhttp://lattes.cnpq.br/7781318574289200Trevisan, Mario Luizhttp://lattes.cnpq.br/5664274438056008Petit, GiaiPiccilli, Daniel Gustavo Allasiahttp://lattes.cnpq.br/4778398527369949Carvalho Neto, Romário Moraes500200000003600500500c1603398-1815-4ff4-b6aa-273091cb7f371744fadd-b616-4bf9-9134-4a2ff7022e534e9cacdf-9d83-408e-b157-579284fb21e243b14ab8-628d-4753-81f2-5c9bc9f348b26bd6f048-e91a-455c-a216-bc6cc0ceb1dae4bbaeee-3ded-43e8-a9d1-c28e6cbf73cdreponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Análise de incertezas do balanço hídrico climatológico espacializado |
dc.title.alternative.eng.fl_str_mv |
Uncertainty analysis of the spatialized climatic water balance |
title |
Análise de incertezas do balanço hídrico climatológico espacializado |
spellingShingle |
Análise de incertezas do balanço hídrico climatológico espacializado Carvalho Neto, Romário Moraes Incertezas Análise de incertezas Disponibilidade hídrica Déficit hídrico Excesso hídrico Balanço hídrico climatológico espacializado BHC Python Thornthwaite Uncertainties Uncertainties analysis Water availability Water deficit Water surplus Spatilized climatic water balance CWB CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
title_short |
Análise de incertezas do balanço hídrico climatológico espacializado |
title_full |
Análise de incertezas do balanço hídrico climatológico espacializado |
title_fullStr |
Análise de incertezas do balanço hídrico climatológico espacializado |
title_full_unstemmed |
Análise de incertezas do balanço hídrico climatológico espacializado |
title_sort |
Análise de incertezas do balanço hídrico climatológico espacializado |
author |
Carvalho Neto, Romário Moraes |
author_facet |
Carvalho Neto, Romário Moraes |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Cruz, Jussara Cabral |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3525141443261254 |
dc.contributor.referee1.fl_str_mv |
Lima, Jorge Enoch Furquim Werneck |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/7781318574289200 |
dc.contributor.referee2.fl_str_mv |
Trevisan, Mario Luiz |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/5664274438056008 |
dc.contributor.referee3.fl_str_mv |
Petit, Giai |
dc.contributor.referee4.fl_str_mv |
Piccilli, Daniel Gustavo Allasia |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4778398527369949 |
dc.contributor.author.fl_str_mv |
Carvalho Neto, Romário Moraes |
contributor_str_mv |
Cruz, Jussara Cabral Lima, Jorge Enoch Furquim Werneck Trevisan, Mario Luiz Petit, Giai Piccilli, Daniel Gustavo Allasia |
dc.subject.por.fl_str_mv |
Incertezas Análise de incertezas Disponibilidade hídrica Déficit hídrico Excesso hídrico Balanço hídrico climatológico espacializado BHC Python Thornthwaite |
topic |
Incertezas Análise de incertezas Disponibilidade hídrica Déficit hídrico Excesso hídrico Balanço hídrico climatológico espacializado BHC Python Thornthwaite Uncertainties Uncertainties analysis Water availability Water deficit Water surplus Spatilized climatic water balance CWB CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
dc.subject.eng.fl_str_mv |
Uncertainties Uncertainties analysis Water availability Water deficit Water surplus Spatilized climatic water balance CWB |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
description |
The spacialized Climatic Water Balance (CWB) is a model that simulates the availability of water in the soil, for plants, in a space distributed manner. It is important to understand the possible uncertainties of this spatialization, so the opportunities of its use in the public policies can be discussed, as well as the advantages of its use, its limitations, having a satisfying result in its use and that its optimization may be allowed. Searching to know the uncertainties in the CWB's spatial distribution, this thesis aims to evaluate such uncertainties due to: 1) the spatialization methods and 2) the density of information used for the spatialization. To address the matter of the uncertainties regarding the spatialization methods, two methods were analyzed. The first one calculates the CWB punctually at stations and then spatializes these values by interpolation (Calculation-Interpolation principle, CI), while the second method is to interpolate first the CWB's variables (precipitation and evapotranspiration) and then calculates it for each pixel (Interpolation-Calculation principle, IC). In addition, the influence of the interpolators were also analyzed. To analyze the uncertainties relating to the density of information, the strategy of comparing the differences arising from the results of the precipitation and the evapotranspiration interpolations and the calculation of the spacialized CWB was used, deleting stations and analyzing the error generated by this decrease of information density. This analysis was first done with the suppression of one station and then, by removing two, three and so on, until the remaining of 3 stations, referring to the minimum number of points required to perform interpolation. To make possible the spatialization of the CWB in a distributed way, a tool in the PythonTM programming language, using the package ArcPy® was created to perform the calculations of the CWB. The study area of this work was the plain area of Veneto, Region of Italy. The results showed that although the analyzes have indicated a trend of smaller uncertainties in the IC method in relation to the CI, these differences were not statistically significant at the 5% level. It was also observed that the CI method brings more uncertainties to the spatialization, particularly when there is water deficit in the CWB and/or ground recharge, by smoothing such balance values between stations, not properly representing the CWB in these areas. The uncertainty analysis performed in this study was also able to show which months can carry greater uncertainty into their spatializations, both P, ETo and the CWB and that the high variability of precipitation carries uncertainties in their spatial distribution. The spatial representation of the CWB showed, for this study, to have greater uncertainty at the beginning of the dry season, when starts the reservoir drawdown, or the beginning of the rains that cause the filling of the reservoir in the soil. The estimated uncertainties to the stations reduction from 15 to 3, ranged from 3 to 27% for precipitation, from 1 to 36% for ETo and 1 to 88% for CWB, considering 16 stations as the truth reference. As the IC method allows the CWB spatialization with different scenarios of Available Water Capacity (AWC), which is not feasible with the CI method, since CI considers only the AWC at the station's place, the use of the IC method was more suitable to represent the CWB at smaller scales (larger areas). This possibility provides more options for the application of the spatialized CWB in public policies, allowing the generation of crops scenarios in a more detailed and dynamic way than the CI method, besides the possibility of its adequacy to the reality of each soil type. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016-08-05 |
dc.date.accessioned.fl_str_mv |
2018-12-13T20:44:46Z |
dc.date.available.fl_str_mv |
2018-12-13T20:44:46Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/15101 |
url |
http://repositorio.ufsm.br/handle/1/15101 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
500200000003 |
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600 500 500 |
dc.relation.authority.fl_str_mv |
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dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Florestal |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Recursos Florestais e Engenharia Florestal |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
collection |
Biblioteca Digital de Teses e Dissertações do UFSM |
bitstream.url.fl_str_mv |
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MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
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1801485289608183808 |