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

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Naranjo Fernández, Nuria and Guardiola-Albert, Carolina and Aguilera Alonso, Héctor and Serrano Hidalgo, Carolina and Montero González, Esperanza (2020) Clustering Groundwater Level Time Series of the Exploited Almonte-Marismas Aquifer in Southwest Spain. Water, 12 (4). p. 1063. ISSN 2073-4441, ESSN: 2073-4441

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Official URL: https://www.mdpi.com/2073-4441/12/4/1063



Abstract

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.


Item Type:Article
Uncontrolled Keywords:groundwater level hydrographs, k-means clustering, time series clustering, water resource management
Subjects:Sciences > Geology > Hidrology
ID Code:60505
Deposited On:14 May 2020 10:30
Last Modified:14 May 2020 18:13

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