Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/2318-0331.231820170115 http://hdl.handle.net/11449/164422 |
Resumo: | Spatial data became increasingly utilized in many scientific fields due to the accessibility of monitoring data from different sources. In the case of hydrological mapping, measurements of external environmental conditions, such as soil, climate, vegetation, are often available in addition to the measurements of water characteristics. An integrated modelling approach capable to incorporate multiple input data sets that may have heterogeneous geometries and other error characteristics can be achieved using geostatistical techniques. In this study, different physical hydric properties of soils extensively sampled and topography were used as auxiliary information for making optimal, point-level inferences of water table depths in forest areas. We used data from 48 wells in the Bauru Aquifer System in the Santa Barbara Ecological Station (EEcSB), in the municipality of Aguas de Santa Barbara in Sao Paulo State, Brazil. Using the resistance of soil to penetration and topography as auxiliary variables helped reduce prediction errors. With the generated maps, it was possible to estimate the volumes of water recovered from the water table in two periods during the monitoring period. These values showed that 30% of the recovered volume would be sufficient for a three-month supply of water for a population of 30,000 inhabitants. Therefore, this raises the possibility of using areas such as the EEcSB as strategic supplies in artificial recharging management. |
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Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volumeData fusionGroundwater managementGeostatisticsBauru Aquifer SystemGroundwater rechargeSpatial data became increasingly utilized in many scientific fields due to the accessibility of monitoring data from different sources. In the case of hydrological mapping, measurements of external environmental conditions, such as soil, climate, vegetation, are often available in addition to the measurements of water characteristics. An integrated modelling approach capable to incorporate multiple input data sets that may have heterogeneous geometries and other error characteristics can be achieved using geostatistical techniques. In this study, different physical hydric properties of soils extensively sampled and topography were used as auxiliary information for making optimal, point-level inferences of water table depths in forest areas. We used data from 48 wells in the Bauru Aquifer System in the Santa Barbara Ecological Station (EEcSB), in the municipality of Aguas de Santa Barbara in Sao Paulo State, Brazil. Using the resistance of soil to penetration and topography as auxiliary variables helped reduce prediction errors. With the generated maps, it was possible to estimate the volumes of water recovered from the water table in two periods during the monitoring period. These values showed that 30% of the recovered volume would be sufficient for a three-month supply of water for a population of 30,000 inhabitants. Therefore, this raises the possibility of using areas such as the EEcSB as strategic supplies in artificial recharging management.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Paulista, Programa Posgrad Agron, Fac Ciencias Agron, Botucatu, SP, BrazilUniv Estadual Paulista, Fac Ciencias & Engn Tupa, Tupa, SP, BrazilUniv Estadual Paulista, Programa Posgrad Agron, Fac Ciencias Agron, Botucatu, SP, BrazilUniv Estadual Paulista, Fac Ciencias & Engn Tupa, Tupa, SP, BrazilFAPESP: 2014/04524-7FAPESP: 2016/09737-4FAPESP: 2015/05171-3Assoc Brasileira Recursos Hidricos-abrhUniversidade Estadual Paulista (Unesp)Santarosa, Lucas Vituri [UNESP]Manzione, Rodrigo Lilla [UNESP]2018-11-26T17:54:29Z2018-11-26T17:54:29Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article13application/pdfhttp://dx.doi.org/10.1590/2318-0331.231820170115Rbrh-revista Brasileira De Recursos Hidricos. Porte Alegre: Assoc Brasileira Recursos Hidricos-abrh, v. 23, 13 p., 2017.1414-381Xhttp://hdl.handle.net/11449/16442210.1590/2318-0331.231820170115S2318-03312018000100222WOS:000438515500023S2318-03312018000100222.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRbrh-revista Brasileira De Recursos Hidricosinfo:eu-repo/semantics/openAccess2024-06-10T14:49:04Zoai:repositorio.unesp.br:11449/164422Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:27:04.699601Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
title |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
spellingShingle |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume Santarosa, Lucas Vituri [UNESP] Data fusion Groundwater management Geostatistics Bauru Aquifer System Groundwater recharge |
title_short |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
title_full |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
title_fullStr |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
title_full_unstemmed |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
title_sort |
Soil variables as auxiliary information in spatial prediction of shallow water table levels for estimating recovered water volume |
author |
Santarosa, Lucas Vituri [UNESP] |
author_facet |
Santarosa, Lucas Vituri [UNESP] Manzione, Rodrigo Lilla [UNESP] |
author_role |
author |
author2 |
Manzione, Rodrigo Lilla [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Santarosa, Lucas Vituri [UNESP] Manzione, Rodrigo Lilla [UNESP] |
dc.subject.por.fl_str_mv |
Data fusion Groundwater management Geostatistics Bauru Aquifer System Groundwater recharge |
topic |
Data fusion Groundwater management Geostatistics Bauru Aquifer System Groundwater recharge |
description |
Spatial data became increasingly utilized in many scientific fields due to the accessibility of monitoring data from different sources. In the case of hydrological mapping, measurements of external environmental conditions, such as soil, climate, vegetation, are often available in addition to the measurements of water characteristics. An integrated modelling approach capable to incorporate multiple input data sets that may have heterogeneous geometries and other error characteristics can be achieved using geostatistical techniques. In this study, different physical hydric properties of soils extensively sampled and topography were used as auxiliary information for making optimal, point-level inferences of water table depths in forest areas. We used data from 48 wells in the Bauru Aquifer System in the Santa Barbara Ecological Station (EEcSB), in the municipality of Aguas de Santa Barbara in Sao Paulo State, Brazil. Using the resistance of soil to penetration and topography as auxiliary variables helped reduce prediction errors. With the generated maps, it was possible to estimate the volumes of water recovered from the water table in two periods during the monitoring period. These values showed that 30% of the recovered volume would be sufficient for a three-month supply of water for a population of 30,000 inhabitants. Therefore, this raises the possibility of using areas such as the EEcSB as strategic supplies in artificial recharging management. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-11-26T17:54:29Z 2018-11-26T17:54:29Z |
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 |
http://dx.doi.org/10.1590/2318-0331.231820170115 Rbrh-revista Brasileira De Recursos Hidricos. Porte Alegre: Assoc Brasileira Recursos Hidricos-abrh, v. 23, 13 p., 2017. 1414-381X http://hdl.handle.net/11449/164422 10.1590/2318-0331.231820170115 S2318-03312018000100222 WOS:000438515500023 S2318-03312018000100222.pdf |
url |
http://dx.doi.org/10.1590/2318-0331.231820170115 http://hdl.handle.net/11449/164422 |
identifier_str_mv |
Rbrh-revista Brasileira De Recursos Hidricos. Porte Alegre: Assoc Brasileira Recursos Hidricos-abrh, v. 23, 13 p., 2017. 1414-381X 10.1590/2318-0331.231820170115 S2318-03312018000100222 WOS:000438515500023 S2318-03312018000100222.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Rbrh-revista Brasileira De Recursos Hidricos |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
13 application/pdf |
dc.publisher.none.fl_str_mv |
Assoc Brasileira Recursos Hidricos-abrh |
publisher.none.fl_str_mv |
Assoc Brasileira Recursos Hidricos-abrh |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128654096465920 |