Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling

Autores
Scherger, Leonardo Ezequiel; Valdes Avellan Javier; Lexow, Claudio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Aim of study: To investigate the monitoring strategies that let us to build effective models able to best estimate water contents, θ and pressure heads, h with the least amount of data. Area of study: Field data was acquired in an experimental plot at Bahía Blanca (Argentina). Material and methods: Field data of θ(t), h(t) for six soil depth were used to optimize the SHP (θr, θs, α, n and Ks) by inverse modeling with HYDRUS 1D. Several scenarios of available data from θ(t) and h(t) were considered: (1) six monitoring depths (6-MD); (2) five monitoring depths (5-MD); (3) four monitoring depths (4-MD). Model accuracy was assessed by comparing the measured and predicted θ and h for each monitoring strategy. Additionally, field measured SHP with independent methods were compared to inversely optimized SHP. Main results: The best fit between predicted and observed θ and h was achieved with the 6-MD strategy. Nevertheless, deterioration of statistics EF and rRMSE in the 5-MD or 4-MD schemes were lower than 10%, depending on the location of the missing data. The observation points that had less importance in parameter prediction corresponded to the intermediate vadose zone and to the deeper layers. The proposed strategies presented a better performance than field measured SHP to reproduce soil water retention curves for each layer of the soil profile. Research highlights: By reducing the number of vertical observations in the profile without harming the final SHP estimation, the resources needed in data monitoring strategies can be greatly enhanced.
Fil: Scherger, Leonardo Ezequiel. Universidad Nacional del Sur. Departamento de Geología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
Fil: Valdes Avellan Javier. Universidad de Alicante; España
Fil: Lexow, Claudio. Universidad Nacional del Sur. Departamento de Geología; Argentina
Materia
HYDRUS
SOIL MONITORING STRATEGY
VADOSE ZONE
WATER FLUX
WATER MANAGEMENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/215783

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network_name_str CONICET Digital (CONICET)
spelling Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modelingScherger, Leonardo EzequielValdes Avellan JavierLexow, ClaudioHYDRUSSOIL MONITORING STRATEGYVADOSE ZONEWATER FLUXWATER MANAGEMENThttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Aim of study: To investigate the monitoring strategies that let us to build effective models able to best estimate water contents, θ and pressure heads, h with the least amount of data. Area of study: Field data was acquired in an experimental plot at Bahía Blanca (Argentina). Material and methods: Field data of θ(t), h(t) for six soil depth were used to optimize the SHP (θr, θs, α, n and Ks) by inverse modeling with HYDRUS 1D. Several scenarios of available data from θ(t) and h(t) were considered: (1) six monitoring depths (6-MD); (2) five monitoring depths (5-MD); (3) four monitoring depths (4-MD). Model accuracy was assessed by comparing the measured and predicted θ and h for each monitoring strategy. Additionally, field measured SHP with independent methods were compared to inversely optimized SHP. Main results: The best fit between predicted and observed θ and h was achieved with the 6-MD strategy. Nevertheless, deterioration of statistics EF and rRMSE in the 5-MD or 4-MD schemes were lower than 10%, depending on the location of the missing data. The observation points that had less importance in parameter prediction corresponded to the intermediate vadose zone and to the deeper layers. The proposed strategies presented a better performance than field measured SHP to reproduce soil water retention curves for each layer of the soil profile. Research highlights: By reducing the number of vertical observations in the profile without harming the final SHP estimation, the resources needed in data monitoring strategies can be greatly enhanced.Fil: Scherger, Leonardo Ezequiel. Universidad Nacional del Sur. Departamento de Geología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Valdes Avellan Javier. Universidad de Alicante; EspañaFil: Lexow, Claudio. Universidad Nacional del Sur. Departamento de Geología; ArgentinaSpanish National Institute for Agriculture and Food Research and Technology2022-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/215783Scherger, Leonardo Ezequiel; Valdes Avellan Javier; Lexow, Claudio; Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling; Spanish National Institute for Agriculture and Food Research and Technology; Spanish Journal Of Agricultural Research; 20; 2; 7-2022; 1-151695-971XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://revistas.inia.es/index.php/sjar/article/view/18861info:eu-repo/semantics/altIdentifier/doi/10.5424/sjar/2022202-18861info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:55:08Zoai:ri.conicet.gov.ar:11336/215783instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:55:09.118CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
title Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
spellingShingle Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
Scherger, Leonardo Ezequiel
HYDRUS
SOIL MONITORING STRATEGY
VADOSE ZONE
WATER FLUX
WATER MANAGEMENT
title_short Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
title_full Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
title_fullStr Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
title_full_unstemmed Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
title_sort Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling
dc.creator.none.fl_str_mv Scherger, Leonardo Ezequiel
Valdes Avellan Javier
Lexow, Claudio
author Scherger, Leonardo Ezequiel
author_facet Scherger, Leonardo Ezequiel
Valdes Avellan Javier
Lexow, Claudio
author_role author
author2 Valdes Avellan Javier
Lexow, Claudio
author2_role author
author
dc.subject.none.fl_str_mv HYDRUS
SOIL MONITORING STRATEGY
VADOSE ZONE
WATER FLUX
WATER MANAGEMENT
topic HYDRUS
SOIL MONITORING STRATEGY
VADOSE ZONE
WATER FLUX
WATER MANAGEMENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Aim of study: To investigate the monitoring strategies that let us to build effective models able to best estimate water contents, θ and pressure heads, h with the least amount of data. Area of study: Field data was acquired in an experimental plot at Bahía Blanca (Argentina). Material and methods: Field data of θ(t), h(t) for six soil depth were used to optimize the SHP (θr, θs, α, n and Ks) by inverse modeling with HYDRUS 1D. Several scenarios of available data from θ(t) and h(t) were considered: (1) six monitoring depths (6-MD); (2) five monitoring depths (5-MD); (3) four monitoring depths (4-MD). Model accuracy was assessed by comparing the measured and predicted θ and h for each monitoring strategy. Additionally, field measured SHP with independent methods were compared to inversely optimized SHP. Main results: The best fit between predicted and observed θ and h was achieved with the 6-MD strategy. Nevertheless, deterioration of statistics EF and rRMSE in the 5-MD or 4-MD schemes were lower than 10%, depending on the location of the missing data. The observation points that had less importance in parameter prediction corresponded to the intermediate vadose zone and to the deeper layers. The proposed strategies presented a better performance than field measured SHP to reproduce soil water retention curves for each layer of the soil profile. Research highlights: By reducing the number of vertical observations in the profile without harming the final SHP estimation, the resources needed in data monitoring strategies can be greatly enhanced.
Fil: Scherger, Leonardo Ezequiel. Universidad Nacional del Sur. Departamento de Geología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
Fil: Valdes Avellan Javier. Universidad de Alicante; España
Fil: Lexow, Claudio. Universidad Nacional del Sur. Departamento de Geología; Argentina
description Aim of study: To investigate the monitoring strategies that let us to build effective models able to best estimate water contents, θ and pressure heads, h with the least amount of data. Area of study: Field data was acquired in an experimental plot at Bahía Blanca (Argentina). Material and methods: Field data of θ(t), h(t) for six soil depth were used to optimize the SHP (θr, θs, α, n and Ks) by inverse modeling with HYDRUS 1D. Several scenarios of available data from θ(t) and h(t) were considered: (1) six monitoring depths (6-MD); (2) five monitoring depths (5-MD); (3) four monitoring depths (4-MD). Model accuracy was assessed by comparing the measured and predicted θ and h for each monitoring strategy. Additionally, field measured SHP with independent methods were compared to inversely optimized SHP. Main results: The best fit between predicted and observed θ and h was achieved with the 6-MD strategy. Nevertheless, deterioration of statistics EF and rRMSE in the 5-MD or 4-MD schemes were lower than 10%, depending on the location of the missing data. The observation points that had less importance in parameter prediction corresponded to the intermediate vadose zone and to the deeper layers. The proposed strategies presented a better performance than field measured SHP to reproduce soil water retention curves for each layer of the soil profile. Research highlights: By reducing the number of vertical observations in the profile without harming the final SHP estimation, the resources needed in data monitoring strategies can be greatly enhanced.
publishDate 2022
dc.date.none.fl_str_mv 2022-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/215783
Scherger, Leonardo Ezequiel; Valdes Avellan Javier; Lexow, Claudio; Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling; Spanish National Institute for Agriculture and Food Research and Technology; Spanish Journal Of Agricultural Research; 20; 2; 7-2022; 1-15
1695-971X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/215783
identifier_str_mv Scherger, Leonardo Ezequiel; Valdes Avellan Javier; Lexow, Claudio; Identifying optimal monitoring strategies to predict soil hydraulic characteristics and water contents by inverse modeling; Spanish National Institute for Agriculture and Food Research and Technology; Spanish Journal Of Agricultural Research; 20; 2; 7-2022; 1-15
1695-971X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://revistas.inia.es/index.php/sjar/article/view/18861
info:eu-repo/semantics/altIdentifier/doi/10.5424/sjar/2022202-18861
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Spanish National Institute for Agriculture and Food Research and Technology
publisher.none.fl_str_mv Spanish National Institute for Agriculture and Food Research and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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