Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s40899-020-00469-6 http://hdl.handle.net/11449/209629 |
Resumo: | Hydrological modeling is still a challenge for better management of water resources since most of the established models are based on point data. The advent, improvement and popularization of remote sensing has brought new perspectives to modelers, allowing access to reliable and representative data over vast areas. However, several tools are still under-explored and actually used in the water resources planning and decision-making process, especially groundwater, which is a hidden resource. The objective of this work was to contribute to groundwater dynamics comprehension assessing the suitability of using remote sensing data in the water-budget equation for estimating groundwater recharge (GWR) and its impact at water table depths (WTD) in a representative Guarani Aquifer System (GAS) outcrop area. The GAS is the largest transboundary groundwater reservoir in South America, yet recharge in the GAS outcrop zones is one of the least known hydrological variables. The remotely sensed WTD model was adapted from the Water Table Fluctuation (WTF) method. We used Google Earth Engine to extract time series of precipitation, evapotranspiration, and surface runoff from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) dataset for the Angatuba Ecological Station (EEcA), Sao Paulo State, Brazil, over 2014-2017 period. GWR and WTD were modeled in eight groundwater monitoring wells. Bias analysis of precipitation data from FLDAS were perfomed using rain gauge data (2000-2018). Two GWR scenarios (S1 and S2) were assessed as well as the impact of the specific yield values (Sy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{\mathrm{y}}$$\end{document}) in the model outputs. In C1 GWR ranged from 10.8% to 19.69% of rain gauge, although in C2 GWR ranged from 0.9 to 13%. The WTD model showed RMSE values ranging from 0.36 to 1.12 m, showing better results in the shallow wells than the deeper ones. These results are useful for future studies on assessing groundwater recharge in the GAS outcrop zones. This remotely sensed approach can be reproduced in regions where data are scarce or nonexistent. |
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Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computingFLDASTime seriesWater budgetGoogle Earth EngineHydrological modeling is still a challenge for better management of water resources since most of the established models are based on point data. The advent, improvement and popularization of remote sensing has brought new perspectives to modelers, allowing access to reliable and representative data over vast areas. However, several tools are still under-explored and actually used in the water resources planning and decision-making process, especially groundwater, which is a hidden resource. The objective of this work was to contribute to groundwater dynamics comprehension assessing the suitability of using remote sensing data in the water-budget equation for estimating groundwater recharge (GWR) and its impact at water table depths (WTD) in a representative Guarani Aquifer System (GAS) outcrop area. The GAS is the largest transboundary groundwater reservoir in South America, yet recharge in the GAS outcrop zones is one of the least known hydrological variables. The remotely sensed WTD model was adapted from the Water Table Fluctuation (WTF) method. We used Google Earth Engine to extract time series of precipitation, evapotranspiration, and surface runoff from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) dataset for the Angatuba Ecological Station (EEcA), Sao Paulo State, Brazil, over 2014-2017 period. GWR and WTD were modeled in eight groundwater monitoring wells. Bias analysis of precipitation data from FLDAS were perfomed using rain gauge data (2000-2018). Two GWR scenarios (S1 and S2) were assessed as well as the impact of the specific yield values (Sy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{\mathrm{y}}$$\end{document}) in the model outputs. In C1 GWR ranged from 10.8% to 19.69% of rain gauge, although in C2 GWR ranged from 0.9 to 13%. The WTD model showed RMSE values ranging from 0.36 to 1.12 m, showing better results in the shallow wells than the deeper ones. These results are useful for future studies on assessing groundwater recharge in the GAS outcrop zones. This remotely sensed approach can be reproduced in regions where data are scarce or nonexistent.FEHI-DRO (Sao Paulo State Water Resources Fund) projectConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Estadual Paulista, Agron Sci Fac, Rua Jose Barbosa de Barros 1780, BR-18610307 Botucatu, SP, BrazilUniv Estadual Paulista, Sch Sci & Engn, Rua Domingos da Costa Lopes 780, BR-17602496 Tupa, SP, BrazilUniv Estadual Paulista, Agron Sci Fac, Rua Jose Barbosa de Barros 1780, BR-18610307 Botucatu, SP, BrazilUniv Estadual Paulista, Sch Sci & Engn, Rua Domingos da Costa Lopes 780, BR-17602496 Tupa, SP, BrazilFEHI-DRO (Sao Paulo State Water Resources Fund) project: 2012-ALPA-244SpringerUniversidade Estadual Paulista (Unesp)Jandreice Magnoni, Pedro Henrique [UNESP]Ferreira Silva, Cesar de Oliveira [UNESP]Manzione, Rodrigo Lilla [UNESP]2021-06-25T12:24:20Z2021-06-25T12:24:20Z2020-11-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16http://dx.doi.org/10.1007/s40899-020-00469-6Sustainable Water Resources Management. Cham: Springer International Publishing Ag, v. 6, n. 6, 16 p., 2020.2363-5037http://hdl.handle.net/11449/20962910.1007/s40899-020-00469-6WOS:000587119200001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSustainable Water Resources Managementinfo:eu-repo/semantics/openAccess2024-06-10T14:49:16Zoai:repositorio.unesp.br:11449/209629Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:15:46.806130Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
title |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
spellingShingle |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing Jandreice Magnoni, Pedro Henrique [UNESP] FLDAS Time series Water budget Google Earth Engine |
title_short |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
title_full |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
title_fullStr |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
title_full_unstemmed |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
title_sort |
Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing |
author |
Jandreice Magnoni, Pedro Henrique [UNESP] |
author_facet |
Jandreice Magnoni, Pedro Henrique [UNESP] Ferreira Silva, Cesar de Oliveira [UNESP] Manzione, Rodrigo Lilla [UNESP] |
author_role |
author |
author2 |
Ferreira Silva, Cesar de Oliveira [UNESP] Manzione, Rodrigo Lilla [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Jandreice Magnoni, Pedro Henrique [UNESP] Ferreira Silva, Cesar de Oliveira [UNESP] Manzione, Rodrigo Lilla [UNESP] |
dc.subject.por.fl_str_mv |
FLDAS Time series Water budget Google Earth Engine |
topic |
FLDAS Time series Water budget Google Earth Engine |
description |
Hydrological modeling is still a challenge for better management of water resources since most of the established models are based on point data. The advent, improvement and popularization of remote sensing has brought new perspectives to modelers, allowing access to reliable and representative data over vast areas. However, several tools are still under-explored and actually used in the water resources planning and decision-making process, especially groundwater, which is a hidden resource. The objective of this work was to contribute to groundwater dynamics comprehension assessing the suitability of using remote sensing data in the water-budget equation for estimating groundwater recharge (GWR) and its impact at water table depths (WTD) in a representative Guarani Aquifer System (GAS) outcrop area. The GAS is the largest transboundary groundwater reservoir in South America, yet recharge in the GAS outcrop zones is one of the least known hydrological variables. The remotely sensed WTD model was adapted from the Water Table Fluctuation (WTF) method. We used Google Earth Engine to extract time series of precipitation, evapotranspiration, and surface runoff from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) dataset for the Angatuba Ecological Station (EEcA), Sao Paulo State, Brazil, over 2014-2017 period. GWR and WTD were modeled in eight groundwater monitoring wells. Bias analysis of precipitation data from FLDAS were perfomed using rain gauge data (2000-2018). Two GWR scenarios (S1 and S2) were assessed as well as the impact of the specific yield values (Sy\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{\mathrm{y}}$$\end{document}) in the model outputs. In C1 GWR ranged from 10.8% to 19.69% of rain gauge, although in C2 GWR ranged from 0.9 to 13%. The WTD model showed RMSE values ranging from 0.36 to 1.12 m, showing better results in the shallow wells than the deeper ones. These results are useful for future studies on assessing groundwater recharge in the GAS outcrop zones. This remotely sensed approach can be reproduced in regions where data are scarce or nonexistent. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-06 2021-06-25T12:24:20Z 2021-06-25T12:24:20Z |
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/10.1007/s40899-020-00469-6 Sustainable Water Resources Management. Cham: Springer International Publishing Ag, v. 6, n. 6, 16 p., 2020. 2363-5037 http://hdl.handle.net/11449/209629 10.1007/s40899-020-00469-6 WOS:000587119200001 |
url |
http://dx.doi.org/10.1007/s40899-020-00469-6 http://hdl.handle.net/11449/209629 |
identifier_str_mv |
Sustainable Water Resources Management. Cham: Springer International Publishing Ag, v. 6, n. 6, 16 p., 2020. 2363-5037 10.1007/s40899-020-00469-6 WOS:000587119200001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sustainable Water Resources Management |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
16 |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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_ |
1808128912619732992 |