There is an increasing need for accurate groundwater-level (GWL) prediction to support effective seasonal water management. It is desirable for forecasting tools to be not only accurate but also accessible for decision-makers. We test the Prophet forecasting procedure, an open-source code released by Facebook, to address these challenges. It is based on an additive model considering non-periodic changes and periodic components in a Bayesian framework with easily-interpretable parameters. Predictions of daily GWL data in an area affected by pumping near a tourist complex in the Ramsar wetland area of Doñana (Spain) are compared to other forecasting methods. Prophet outperforms most methods in predicting GWL making it a fast and flexible forecasting tool for hydrologists and water managers. Furthermore, it allows gaining insight into the influence of each component of the forecast separately, helping to assess the hydrodynamic response to external drivers such as groundwater pumping.

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.

Mathematical groundwater modelling with homogeneous permeability zones has been used for decades to manage water resources in the Almonte-Marismas aquifer (southwest Spain). This is a highly heterogeneous detrital aquifer which supports valuable ecological systems in the Doñana National Park. The present study demonstrates that it is possible to better characterize this heterogeneity by numerical discretization of the geophysical and lithological data available. We identified six hydrofacies whose spatial characteristics were quantified with indicator variogram modelling. Sequential Indicator Simulation then made it possible to construct a 3D geological model. Finally, this detailed model was included in MODFLOW through the Model Muse interface. This final process is still a challenge due to the difficulty of downscaling to a handy numerical modelling scale. New piezometric surfaces and water budgets were obtained. The classical model with zones and the model with 3D simulation were compared to confirm that, for management purposes, the effort of improving the geological heterogeneities is worthwhile. This paper also highlights the relevance of including subsurface heterogeneities within a real groundwater management model in the present global change scenario.