Accurate estimation of missing daily precipitation data remains a difficult task. A wide variety of methods exists for infilling missing values, but the percentage of gaps is one of the main factors limiting their applicability. The present study compares three techniques for filling in large amounts of missing daily precipitation data: spatio-temporal kriging (STK), multiple imputation by chained equations through predictive mean matching (PMM), and the random forest (RF) machine learning algorithm. To our knowledge, this is the first time that extreme missingness (>90%) has been considered. Different percentages of missing data and missing patterns are tested in a large dataset drawn from 112 rain gauges in the period 1975–2017. The results show that both STK and RF can handle extreme missingness, while PMM requires larger observed sample sizes. STK is the most robust method, suitable for chronological missing patterns. RF is efficient under random missing patterns. Model evaluation is usually based on performance and error measures. However, this study outlines the risk of just relying on these measures without checking for consistency. The RF algorithm overestimated daily precipitation outside the validation period in some cases due to the overdetection of rainy days under time-dependent missing patterns.
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 Don ˜ana National Park concerning groundwater recharge behavior over a long period . 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.
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.
In large-scale regional models, used for the management of underground resources, it is quite common to find that relationships between the regional aquifer and small wetlands are not included. These models do not consider this connection because of the small amount of water involved, but they should consider the potential for significant ecological impacts if the groundwater resources in the ecosystems associated with these wetlands are mismanaged. The main objective of this work is to investigate the possibilities offered by MODFLOW LGR-V2 to represent (at small scale) the Santa Olalla pond, located in the Doñana Natural Park (South of Spain), and its relationship with the Almonte-Marismas regional aquifer. As a secondary objective, we propose to investigate the advantages and disadvantages that DRAIN, RIVER and LAKE MODFLOW packages offer within the MODFLOW LGR-V2 discretizations. The drain boundary condition with a coarse discretization implemented through ModelMuse allows the most adequate performance of the groundwater levels in the environment of the pond. However, when using lake boundary condition, the use of the MODFLOW LGR-V2 version is particularly useful. The present work also gives some guidelines to employ these packages with the MODFLOW graphical user’s interface, ModelMuse 4.2.
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