Groundwater contamination source estimation (GCSE) involves an inverse process to match time-series monitoring data in sparse observation wells. It is commonly accompanied by a search task in high-dimensional space and huge computational burden brought about by massive callings of the simulation model. Particle filters can provide accurate estimation for a high-dimensional search task in source estimation, but the process suffers from particle degradation and huge computational load brought about by repeatedly solving the transport simulation model. To tackle the particle degradation, an iterative ensemble smoother was introduced to provide a proper proposal distribution, improving the search efficiency of the traditional particle filter. Moreover, to relieve the computational burden, a deep residual neural network was proposed to perform the surrogate task for the highly nonlinear and long-running-time original simulation model. In general, a refined particle filter with a deep-learning-method surrogate was proposed as an inverse framework for GCSE, which was evaluated by estimation tasks for a point-source contamination case and an areal-source contamination case, respectively, under different levels of observation errors. The results indicated that the deep-residual-neural-network surrogate model achieved the performance R² of 0.993 and 0.995, respectively for point-source and aerial-source contamination, to substitute the simulation models with a swift invoking process. Furthermore, the iterative ensemble smoother evidently improved the estimation efficiency of the particle filter. The proposed inverse framework can provide reliable and stable estimation of the groundwater contamination source and aquifer hydraulic conductivity.

The low-resolution characteristic of Gravity Recovery and Climate Experiment (GRACE) satellite data greatly limits their application in many fields at regional or local scales. Aiming to overcome this limitation, the partial least squares regression (PLSR) model is firstly utilized to assess the importance of some independent variables that are commonly employed in GRACE downscaling research. Three kinds of downscaling models are chosen to improve the resolution of GRACE-based water storage estimates from 1 to 0.25°, namely: multivariable linear regression, random forest (RF), and NoahV2.1. Results indicate that terrestrial water storage anomalies are more closely related to four independent variables in the Haihe River Basin, China: these variables are evapotranspiration, land surface temperature, air temperature, and soil moisture. With respect to the spatial distribution, the downscaled results based on the NoahV2.1 and RF models can effectively capture the subgrid heterogeneity while preserving the water storage characteristics at the original scale. By verifying the downscaled results with measured groundwater levels, it can be observed that the correlation coefficient between the RF-based downscaled groundwater storage anomalies (GWSA) and in-situ measurements is increased by 20.55% (Beijing), 9.13% (Tianjin), and 10.48% (Hebei) relative to the downscaled results based on the NoahV2.1 model. The cross wavelet transform illustrates that the meteorological factors have a strong influence on the GWSA series in the Haihe River Basin with an approximately 12-month signal during 2003–2016. This study can provide high-resolution GWSA datasets for water resources management and also provide a reference for the selection of dominant independent variables.

Microbes live throughout the soil profile. Microbial communities in subsurface horizons are impacted by a saltwater–freshwater transition zone formed by seawater intrusion (SWI) in coastal regions. The main purpose of this study is to explore the changes in microbial communities within the soil profile because of SWI. The study characterizes the depth-dependent distributions of bacterial and archaeal communities through high-throughput sequencing of 16S rRNA gene amplicons by collecting surface soil and deep core samples at nine soil depths in Longkou City, China. The results showed that although microbial communities were considerably impacted by SWI in both horizontal and vertical domains, the extent of these effects was variable. The soil depth strongly influenced the microbial communities, and the microbial diversity and community structure were significantly different (p < 0.05) at various depths. Compared with SWI, soil depth was a greater influencing factor for microbial diversity and community structure. Furthermore, soil microbial community structure was closely related to the environmental conditions, among which the most significant environmental factors were soil depth, pH, organic carbon, and total nitrogen.

Domestic supply wells meet much of the world’s potable water demand. These wells tend to fail as regional groundwater levels decline from intensive agricultural groundwater use, especially during drought when additional pumping occurs. This work examines approaches for addressing impacts on domestic wells in much of the San Joaquin Valley in California, USA, where groundwater management is now required. Mitigation actions and their costs are considered to allow continued well operations as groundwater levels decline to target levels specified in groundwater management plans. The estimated total mitigation cost for groundwater-level declines to the planned management targets ranges from $42 to $96 million depending upon well retirement age. If groundwater levels decline further to defined limits below the management targets allowed during drought, costs increase by $78 to $153 million. There will likely be competition for specialized labor to implement the mitigation actions since agricultural wells will also be affected. Unless current groundwater management plans become more stringent and specify shallower groundwater depth targets, proactive mitigation should be considered for the most vulnerable areas to prevent impacts from growing beyond the capacity for timely mitigation and to avoid widespread failure of rural domestic water supplies. The cost of mitigation for impacted wells is estimated to be less than 2% of the benefit to agriculture from being allowed to pump groundwater in excess of management targets during a multiyear drought.

Increasing population growth and global climatic changes threaten water security in semiarid regions such as Northern Ghana. The Tamnean Plutonic Suite aquifer is the main source of water supply for the inhabitants of the Tamne River basin, which is a transboundary subbasin of the White Volta Basin, Ghana. The basin is a flood-prone area where flooding occurs every rainy season, but there is water scarcity during the dry season, mainly due to poor groundwater resources planning. It is expected that the population will increase in the next 10 years, implying a greater water demand. A steady-state and transient groundwater flow model has been developed to understand the hydrogeological conditions and assess the feasibility of managed aquifer recharge (MAR) in the area. A single granitic aquifer formation was delineated from the three-dimensional lithology modelling. The calibrated aquifer recharge through precipitation is very low due to high evapotranspiration and low rainfall. A MAR injection scenario was tested using the available treated floodwater that is registered during the rainy season in the area. The results show the total volume of water injected at the end of the 4-month study period is 11,000 m³/day (approximately 1.3 × 10⁶ m³), which significantly increases aquifer storage and groundwater levels. The volume of water recovered at the end of 8 months (1.4 × 10⁶ m³) is enough for domestic and irrigation purposes during the dry season. In general, MAR is feasible in augmenting the water levels in the area when combined with controllable irrigation and domestic withdrawals.

Assessment of the level of activity of advective transport through faults and fractures is essential for guiding the geological disposal of radioactive waste. In this study, the advective flow (active, inactive) of meteoric water through fractures is assessed by comparing stable isotopes (δD and δ¹⁸O) between fracture and pore waters obtained from four boreholes in marine deposits in the Horonobe area, Japan. At 27–83-m depth in one borehole and 28–250 m in another, the isotopic compositions of pore and fracture water reflect mixing with meteoric water, with stronger meteoric-water signatures being observed in the fracture water than in pore water of the rock matrix. At greater depths in these boreholes and at all sampling depths in the other two studied boreholes, the isotopic compositions of fracture and pore waters are comparable. These results suggest that the advective flow of meteoric water is active at shallow depths where fossil seawater is highly diluted in the two boreholes. This interpretation is compatible with the occurrence of present or paleo meteoric waters and tritium, whereby present meteoric water and tritium are limited to those depths in the two boreholes. This difference in the level of activity of advective flow is probably because of the glacial–interglacial difference in hydraulic gradients resulting from sea-level change. Although fractures are hydraulically connected to the surface through the sedimentary rock, advective flow through them is inferred to remain inactive so long as sea level does not fall substantially.

The significant increase in urbanization has resulted in greater use of the subsurface in urban planning and, therefore, increased interaction between groundwater and underground infrastructure. Numerical models are the primary tool adopted to manage the resulting problems; however, their construction is time- and cost-consuming. Groundwater-level time-series analysis can be a complementary method, as this data-driven approach does not require an extensive understanding of the geological and boundary conditions, even if providing insights into the hydrogeologic behaviour. Thus, a data-driven approach was adopted to analyse groundwater time-series of the shallow aquifer, occupied by several underground structures, beneath Milan city (Northern Italy). Statistical (Mann-Kendall and Sen’s slope estimator, autocorrelation and cross-correlation, hierarchical cluster analysis) and geospatial techniques were used to detect the potential variables influencing the groundwater levels of 95 monitoring wells, covering the period 2005–2019. A general rising trend of the water table was identified, with local hydrogeologic differences in the western and southernmost areas. Based on time-series analysis results, four management areas have been identified. These areas could act as future geographic units with specific groundwater management strategies. In particular, subsurface public car parks can be classified with respect to groundwater flooding as (1) not submerged, (2) possibly critical, or (3) submerged at different groundwater conditions. According to these outcomes, targeted guidelines for constructing new car parks have been elaborated for each management area. The methodology proved to be efficient in improving the urban conceptual model and helping stakeholders design the planned underground development, considering groundwater aspects.