Interest in both environmental impact and potential beneficial uses of produced water (PW) has increased with growth in unconventional oil and gas production, especially in semi-arid regions, e.g. the Permian Basin, the most productive tight-oil region in the USA. Characterization of PW compositional variability is needed to evaluate environmental impact, treatment, and reuse potential. Geochemical variability of PW from Guadalupian (Middle Permian) to Ordovician formations was statistically and geostatistically evaluated in the western half of the Permian Basin (Delaware Basin, Central Basin Platform, and Northwest Shelf) using the US Geological Survey’s Produced Waters Geochemical Database and the New Mexico Water and Infrastructure Data System. Mean total dissolved solids (TDS) of PW increased with depth in the Delaware Basin and Central Basin Platform to the Delaware and Wolfcamp formations (Guadalupian age). Mean TDS decreased with further increases in depth. In contrast, the mean salinity of PW was significantly higher within the shallow, younger formations (largest mean TDS in the Artesia Formation); TDS decreased with depth below Guadalupian age formations in the Northwest Shelf. Kriged contour maps of TDS and major ions illustrated spatial variability across the three geo-structural regions as a function of depth. The occurrence of meteoric waters in upper and deeper formations across the three regions was significant, and was attributed to Laramide Orogeny and Basin and Range extension uplifting and tilting effects and recent water flooding. These results quantify PW composition variability, and suggest that upon treatment, PW would support some uses such as onsite reuse and mining.

A groundwater flow model of the Alpine valley aquifer in the Aosta Plain (NW Italy) showed that well pumping can induce river streamflow depletions as a function of well location. Analysis of the water budget showed that ∼80% of the water pumped during 2 years by a selected well in the downstream area comes from the baseflow of the main river discharge. Alluvial aquifers hosted in Alpine valleys fall within a particular hydrogeological context where groundwater/surface-water relationships change from upstream to downstream as well as seasonally. A transient groundwater model using MODFLOW2005 and the Streamflow-Routing (SFR2) Package is here presented, aimed at investigating water exchanges between the main regional river (Dora Baltea River, a left-hand tributary of the Po River), its tributaries and the underlying shallow aquifer, which is affected by seasonal oscillations. The three-dimensional distribution of the hydraulic conductivity of the aquifer was obtained by means of a specific coding system within the database TANGRAM. Both head and flux targets were used to perform the model calibration using PEST. Results showed that the fluctuations of the water table play an important role in groundwater/surface-water interconnections. In upstream areas, groundwater is recharged by water leaking through the riverbed and the well abstraction component of the water budget changes as a function of the hydraulic conditions of the aquifer. In downstream areas, groundwater is drained by the river and most of the water pumped by wells comes from the base flow component of the river discharge.

Isotope hydrological studies carried out since 1987 in Haiti have proven to be effective in improving conceptual models of the principal aquifers in this Caribbean island nation. However, many of these studies have been limited and are mostly unpublished. It is, therefore, important to share the available datasets with scientists and water managers to support further studies and guide decision-making. Eleven datasets were compiled in a unique database for continued advancement and application. This dataset now contains 248 δ¹⁸O and δ²H results from water samples collected at 190 locations throughout the country. δ¹⁸O and δ²H values range, respectively, from –3.8 to –8.95‰ and –67.4 to –16.8‰ for precipitation (n = 5), 0.9 to 2.76‰ and 8.8 to 18.4‰ for lakes (n = 4), –2.61 to 4.98‰ and –26.63 to 28.2‰ for rivers (n = 11), –5.11 to –2.6‰ and –29.8 to –6.9‰ for springs (n = 82) excluding hot springs, and –5.1 to –1.37‰ and –29.4 to –2.9‰ for boreholes (n = 142). The overall presentation of the available data is complemented with an overview of the studies in which stable isotope data acquisition was performed. The global dataset is presented under three primary contexts: (1) elaboration of a local isotope meteoric water line of equation δ²H = 7.2δ¹⁸O + 8.8 supported with data predominately from the Ouest jurisdictional department, (2) spatial variability analysis of the dataset, and (3) definition of the altitude gradient (per 100 m) using freshwater springs of the Plaine du Cul-de-Sac/Massif de la Selle area of –6.67‰ for δ²H and –0.09‰ for δ¹⁸O.

This study aims to assess the sensitivity of river level estimations to the stream–aquifer exchanges within a hydrogeological model of the Upper Rhine alluvial aquifer (France/Germany), characterized as a large shallow aquifer with numerous hydropower dams. Two specific points are addressed: errors associated with digital elevation models (DEMs) and errors associated with the estimation of river level. The fine-resolution raw Shuttle Radar Topographic Mission dataset is used to assess the impact of the DEM uncertainties. Specific corrections are used to overcome these uncertainties: a simple moving average is applied to the topography along the rivers and additional data are used along the Rhine River to account for the numerous dams. Then, the impact of the river-level temporal variations is assessed through two different methods based on observed rating curves and on the Manning formula. Results are evaluated against observation data from 37 river-level points located over the aquifer, 190 piezometers, and a spatial database of wetlands. DEM uncertainties affect the spatial variability of the stream–aquifer exchanges by inducing strong noise and unrealistic peaks. The corrected DEM reduces the biases between observations and simulations by 22 and 51% for the river levels and the river discharges, respectively. It also improves the agreement between simulated groundwater overflows and observed wetlands. Introducing river-level time variability increases the stream–aquifer exchange range and reduces the piezometric head variability. These results confirm the need to better assess river levels in regional hydrogeological modeling, especially for applications in which stream–aquifer exchanges are important.

Amidst changing climates, understanding the world’s water resources is of increasing importance. In Ontario, Canada, low water conditions are currently assessed using only precipitation and watershed-based stream gauges by the Conservation Authorities in Ontario and the Ministry of Natural Resources and Forestry. Regional groundwater-storage changes in Ontario are not currently measured using satellite data by research institutes. In this study, contributions from the Gravity Recovery and Climate Experiment (GRACE) data are compared to a hydrogeological database covering southern Ontario from 2003 to 2013, to determine the suitability of GRACE total water storage estimates for monitoring groundwater storage in this location. Terrestrial water storage data from GRACE were used to determine monthly groundwater storage (GWS) anomaly values. GWS values were also determined by multiplying groundwater-level elevations (from the Provincial Groundwater Monitoring Network wells) by specific yield. Comparisons of GRACE-derived GWS to well-based GWS data determined that GRACE is sufficiently sensitive to obtain a meaningful signal in southern Ontario. Results show that GWS values produced by GRACE are useful for identifying regional changes in groundwater storage in areas with limited available hydrogeological characterization data. Results also indicate that GRACE may have an ability to forecast changes in groundwater storage, which will become useful when monitoring climate shifts in the near future.

Groundwater of the Tafilalet oasis system (TOS) is an important water resource in the lower Ziz and Rheris valleys of arid southeastern Morocco. The unconfined aquifer is exploited for domestic consumption and irrigation. A groundwater flow model was developed to assess the impact of climatic variations and development, including the construction of hydraulic structures, on the hydrodynamic behavior of the aquifer. Numerical simulations were performed by implementing a spatial database within a geographic information system and using the Arc Hydro Groundwater tool with the code MODFLOW-2000. The results of steady-state and transient simulations between 1960 and 2011 show that the water table is at equilibrium between recharge, which is mainly by surface-water infiltration, and discharge by evapotranspiration. After the commissioning of the Hassan Addakhil dam in 1971, hydraulic heads became more sensitive to annual variations than to seasonal variations. Heads are also influenced by recurrent droughts and the highest water-level changes are recorded in irrigated areas. The model provides a way of managing groundwater resources in the TOS. It can be used as a tool to predict the impact of different management plans for the protection of groundwater against overexploitation and deterioration of water quality.

An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor’s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09 %. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.