Sensitivity of water reallocation performance assessments to water use data

[EN] The lack of detailed and reliable data on the estimates of water use has been a key limitation in informing sustainable, equitable and efficient water reallocations in the agricultural sector. Conventional water use data have been commonly obtained from surveys or agronomic models, which have l...

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Autores: Sánchez Daniel, Ángel, Garrido Rubio, Jesús, Molina Medina, Antonio Jesús, Gil García, Laura, Sapino, Francesco, González-Piqueras, José, Pérez Blanco, Carlos Dionisio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/169816
Acceso en línea:http://hdl.handle.net/10366/169816
Access Level:acceso abierto
Palabra clave:Remote sensing
Positive mathematical programming
Buyback program
Crop water use
Water scarcity
5308 Economía General
5401.01 Distribución de Recursos Naturales
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spelling Sensitivity of water reallocation performance assessments to water use dataSánchez Daniel, ÁngelGarrido Rubio, JesúsMolina Medina, Antonio JesúsGil García, LauraSapino, FrancescoGonzález-Piqueras, JoséPérez Blanco, Carlos DionisioRemote sensingPositive mathematical programmingBuyback programCrop water useWater scarcity5308 Economía General5401.01 Distribución de Recursos Naturales[EN] The lack of detailed and reliable data on the estimates of water use has been a key limitation in informing sustainable, equitable and efficient water reallocations in the agricultural sector. Conventional water use data have been commonly obtained from surveys or agronomic models, which have limitations on accurately reflecting the actual water use. This paper integrates cutting-edge satellite-based water use data with an ensemble of four Calibrated Mathematical Programming Models (CMPM) (one Positive Multi-Attribute Utility Programming model, one Weighted Goal Programming model, and two Positive Mathematical Programming models) to assess and compare the performance of water reallocations under satellite-based versus conventional water use estimates. We apply these methods to the water-stressed Mancha Oriental Aquifer (MOA) in central Spain, where we simulate the impacts of a hypothetical temporary water reacquisition policy in 2017, the last dry year in record. We find that water use estimates obtained with conventional approaches (which range between 4916 m3/ha and 4510m3/ha, on average) are 13–24 % lower than satellite-based estimates (5577 m3/ha on average) during the dry year. Moreover, the water reacquisition simulation using the CMPM ensemble shows that the reserve prices (25–66 % higher) and buyback costs (26–67 % higher) derived from conventional water use data approaches are consistently and significantly higher than those derived from satellite-based water use estimates for all the elements of the ensemble, suggesting that a policy informed with satellite-based data could significantly reduce the costs of the reallocation.The research leading to these results has been developed with the support of the Ministry of Science and Innovation's IRENE Project (Integrated socioeconomic and environmental modelling using remote sensing data for the management of unauthorized water abstractions), the PRIMA Foundation's TALANOA-WATER Project (Talanoa Water Dialogue for Transformational Adaptation to Water Scarcity under Climate Change), the EU Horizon Europe's TRANSCEND Project (Transformational and Robust Adaptation to Water Scarcity and Climate Change under Deep Uncertainty) and the EU Horizon Europe's REXUS Project (Managing Resilient Nexus Systems Through Participatory Systems Dynamics Modelling).Elsevier202620262024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10366/169816reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1698162026-06-07T06:28:51Z
dc.title.none.fl_str_mv Sensitivity of water reallocation performance assessments to water use data
title Sensitivity of water reallocation performance assessments to water use data
spellingShingle Sensitivity of water reallocation performance assessments to water use data
Sánchez Daniel, Ángel
Remote sensing
Positive mathematical programming
Buyback program
Crop water use
Water scarcity
5308 Economía General
5401.01 Distribución de Recursos Naturales
title_short Sensitivity of water reallocation performance assessments to water use data
title_full Sensitivity of water reallocation performance assessments to water use data
title_fullStr Sensitivity of water reallocation performance assessments to water use data
title_full_unstemmed Sensitivity of water reallocation performance assessments to water use data
title_sort Sensitivity of water reallocation performance assessments to water use data
dc.creator.none.fl_str_mv Sánchez Daniel, Ángel
Garrido Rubio, Jesús
Molina Medina, Antonio Jesús
Gil García, Laura
Sapino, Francesco
González-Piqueras, José
Pérez Blanco, Carlos Dionisio
author Sánchez Daniel, Ángel
author_facet Sánchez Daniel, Ángel
Garrido Rubio, Jesús
Molina Medina, Antonio Jesús
Gil García, Laura
Sapino, Francesco
González-Piqueras, José
Pérez Blanco, Carlos Dionisio
author_role author
author2 Garrido Rubio, Jesús
Molina Medina, Antonio Jesús
Gil García, Laura
Sapino, Francesco
González-Piqueras, José
Pérez Blanco, Carlos Dionisio
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Remote sensing
Positive mathematical programming
Buyback program
Crop water use
Water scarcity
5308 Economía General
5401.01 Distribución de Recursos Naturales
topic Remote sensing
Positive mathematical programming
Buyback program
Crop water use
Water scarcity
5308 Economía General
5401.01 Distribución de Recursos Naturales
description [EN] The lack of detailed and reliable data on the estimates of water use has been a key limitation in informing sustainable, equitable and efficient water reallocations in the agricultural sector. Conventional water use data have been commonly obtained from surveys or agronomic models, which have limitations on accurately reflecting the actual water use. This paper integrates cutting-edge satellite-based water use data with an ensemble of four Calibrated Mathematical Programming Models (CMPM) (one Positive Multi-Attribute Utility Programming model, one Weighted Goal Programming model, and two Positive Mathematical Programming models) to assess and compare the performance of water reallocations under satellite-based versus conventional water use estimates. We apply these methods to the water-stressed Mancha Oriental Aquifer (MOA) in central Spain, where we simulate the impacts of a hypothetical temporary water reacquisition policy in 2017, the last dry year in record. We find that water use estimates obtained with conventional approaches (which range between 4916 m3/ha and 4510m3/ha, on average) are 13–24 % lower than satellite-based estimates (5577 m3/ha on average) during the dry year. Moreover, the water reacquisition simulation using the CMPM ensemble shows that the reserve prices (25–66 % higher) and buyback costs (26–67 % higher) derived from conventional water use data approaches are consistently and significantly higher than those derived from satellite-based water use estimates for all the elements of the ensemble, suggesting that a policy informed with satellite-based data could significantly reduce the costs of the reallocation.
publishDate 2024
dc.date.none.fl_str_mv 2024
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/169816
url http://hdl.handle.net/10366/169816
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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