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Clustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain

dc.contributor.authorNaranjo Fernández, Nuria
dc.contributor.authorGuardiola-Albert, Carolina
dc.contributor.authorAguilera Alonso, Héctor
dc.contributor.authorSerrano Hidalgo, Carolina
dc.contributor.authorMontero González, Esperanza
dc.date.accessioned2023-06-16T15:18:01Z
dc.date.available2023-06-16T15:18:01Z
dc.date.issued2020
dc.description.abstractGroundwater resources are regularly the principal water supply in semiarid and arid climate areas. However, groundwater levels (GWL) in semiarid aquifers are suffering a general decrease because of anthropic exploitation of aquifers and the repercussions of climate change. Effective groundwater management strategies require a deep characterization of GWL fluctuations, in order to identify individual behaviors and triggering factors. In September 2019, the Guadalquivir River Basin Authority (CHG) declared that there was over-exploitation in three of the five groundwater bodies of the Almonte-Marismas aquifer, Southwest Spain. For that reason, it is critical to understand GWL dynamics in this aquifer before the new Spanish Water Resources Management Plans (2021–2027) are developed. The application of GWL series clustering in hydrogeology has grown over the past few years, as it is an extraordinary tool that promptly provides a GWL classification; each group can be related to different responses of a complex aquifer under any external change. In this work, GWL time series from 160 piezometers were analyzed for the period 1975 to 2016 and, after data pre-processing, 24 piezometers were selected for clustering with k-means (static) and time series (dynamic) clustering techniques. Six and seven groups (k) were chosen to apply k-means. Six characterized types of hydrodynamic behaviors were obtained with time series clustering (TSC). Number of clusters were related to diverse affections of water exploitation depending on soil uses and hydrogeological spatial distribution parameters. TSC enabled us to distinguish local areas with high hydrodynamic disturbance and to highlight a quantitative drop of GWL during the studied period.
dc.description.departmentDepto. de Geodinámica, Estratigrafía y Paleontología
dc.description.facultyFac. de Ciencias Geológicas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Economía y Competitividad (MINECO)
dc.description.sponsorshipComunidad de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/60505
dc.identifier.doi10.3390/w12041063
dc.identifier.issn2073-4441, ESSN: 2073-4441
dc.identifier.officialurlhttps://www.mdpi.com/2073-4441/12/4/1063
dc.identifier.urihttps://hdl.handle.net/20.500.14352/6250
dc.issue.number4
dc.journal.titleWater
dc.language.isoeng
dc.page.initial1063
dc.publisherMDPI
dc.relation.projectIDCLIGRO (CGL2016-77473-C3-1-R)
dc.relation.projectIDND2018/AMB-9553
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/es/
dc.subject.cdu556.3(460.354)
dc.subject.keywordgroundwater level hydrographs
dc.subject.keywordk-means clustering
dc.subject.keywordtime series clustering
dc.subject.keywordwater resource management
dc.subject.ucmHidrología
dc.subject.unesco2508 Hidrología
dc.titleClustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain
dc.typejournal article
dc.volume.number12
dspace.entity.typePublication
relation.isAuthorOfPublicatione2928ae4-1df3-472d-a525-27c92d5ffddc
relation.isAuthorOfPublication.latestForDiscoverye2928ae4-1df3-472d-a525-27c92d5ffddc

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