Person:
Montero González, Esperanza

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First Name
Esperanza
Last Name
Montero González
URI
https://hdl.handle.net/20.500.14352/75248
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Geológicas
Department
Geodinámica, Estratigrafía y Paleontología
Area
Geodinámica Externa
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UCM identifierORCIDScopus Author IDWeb of Science ResearcherIDDialnet IDGoogle Scholar ID

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20202
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Author: Aguilera Alonso, Héctor × Subject: 556.3(460.354) ×

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Now showing 1 - 2 of 2
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    Clustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain
    (Water, 2020) Naranjo Fernández, Nuria; Guardiola-Albert, Carolina; Aguilera Alonso, Héctor; Serrano Hidalgo, Carolina; Montero González, Esperanza
    Groundwater 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.
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    Relevance of spatio-temporal rainfall variability regarding groundwater management challenges under global change: case study in Doñana (SW Spain)
    (Stochastic Environmental Research and Risk Assessment, 2020) Naranjo Fernández, Nuria; Guardiola-Albert, Carolina; Aguilera Alonso, Héctor; Serrano Hidalgo, C.; Rodríguez Rodríguez, M.; Fernández Ayuso, A.; Ruiz Bermudo, F.; Montero González, Esperanza
    Rainfall is the major contribution for groundwater recharge in arid and semiarid climates, therefore a key factor in water resources estimation. This work presents the results of an in-depth study in Doñana National Park concerning groundwater recharge behavior over a long period (1975–2016). The spatio-temporal kriging algorithm was used as a supportive tool to improve the reconstruction of the spatio-temporal rainfall variability. One of the main findings was that monthly recharge estimations range between 21 and 91% of the maximum rainfall, being overestimated in areas that also demonstrate spatial heterogeneity in rainfall distribution. In the light of these results, for water management purposes in the Mediterranean area, rainfall spatio-temporal scale is a critical aspect and it must be taken into account in groundwater reservoir allocation. Moreover, it is highlighted that local studies of rainfall and recharge, in an area of high ecological fragility, are essential to developing management strategies that prevent climate change effects and guarantee optimal conditions for groundwater resources in the future.