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North China (NC) is experiencing significant groundwater depletion. We used GRACE and GRACE-FO RL06 Level-2 data with Mascon data from April 2002 to July 2022. We fused these two types of data through the generalized three-cornered hat method and further combined them with hydrological models, precipitation, in situ groundwater-level, and groundwater extraction (GWE) data to determine and verify temporal and spatial variations in groundwater storage (GWS) in NC. We quantitatively assessed groundwater sustainability by constructing a groundwater index in NC. We further explored the dynamic cyclic process of groundwater change and quantified the impact of the South-to-North Water Transfer Project (SNWTP) on GWS change in NC. The overall GWS shows a decreasing trend. The GRACE/GRACE-FO-derived GWS change results are consistent with those shown by the in situ groundwater-level data from the monitoring well. Groundwater in NC is in various states of unsustainability throughout the period 2002 to 2022. The SNWTP affected the water use structure to some extent in NC. This study elucidates the latest spatial–temporal variations in GWS, especially in the groundwater sustainability assessment and quantitative description of the effects of the SNWTP on changes in GWS in NC. The results may provide a reference for groundwater resource management.

Groundwater modeling in data-scarce regions faces significant challenges due to the lack of comprehensive, high-quality data, impacting model accuracy. This systematic review of Scopus-indexed papers identifies various approaches to address these challenges, including coupled hydrological-groundwater models, machine learning techniques, distributed hydrological models, water balance models, 3D groundwater flow modeling, geostatistical techniques, remote sensing-based approaches, isotope-based methods, global model downscaling, and integrated modeling approaches. Each methodology offers unique advantages for groundwater assessment and management in data-poor environments, often combining multiple data sources and modeling techniques to overcome limitations. However, these approaches face common challenges related to data quality, scale transferability, and the representation of complex hydrogeological processes. This review emphasizes the importance of adapting methodologies to specific regional contexts and data availability. It underscores the value of combining multiple data sources and modeling techniques to provide robust estimates for sustainable groundwater management. The choice of method ultimately depends on the specific objectives, scale of the study, and available data in the region of interest. Future research should focus on improving the integration of diverse data sources, enhancing the representation of complex hydrogeological processes in simplified models, and developing robust uncertainty quantification methods tailored for data-scarce conditions.

Using publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002–2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km 3 during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Iran’s groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Iran’s land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Iran’s planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening country’s water, food, socio-economic security.

Quality control of large-scale monitoring networks requires the use of automatic procedures to detect potential outliers in an unambiguous and reproducible manner. This paper describes a methodology that combines existing statistical methods to accommodate for the specific characteristics of measurement data obtained from groundwater quality monitoring networks: the measurement series show a large variety of dynamics and often comprise few (< 25) measurements, the measurement data are not normally distributed, measurement series may contain several outliers, there may be trends in the series, and/or some measurements may be below detection limits. Furthermore, the detection limits may vary in time. The methodology for outlier detection described in this paper uses robust regression on order statistics (ROS) to deal with measured values below the detection limit. In addition, a biweight location estimator is applied to filter out any temporal trends from the series. The subsequent outlier detection is done in z-score space. Tuning parameters are used to attune the robustness and accuracy to the given dataset and the user requirements. The method has been applied to data from the Dutch national groundwater quality monitoring network, which consists of approximately 350 monitoring wells. It proved to work well in general, detecting outliers at the top and bottom of the regular measurement range and around the detection limit. Given the diversity exhibited by measurement series, it is to be expected that the method does not give 100% satisfactory results. Measured values identified by the method as potential outliers will therefore always need to be further assessed on the basis of expert knowledge, consistency with other measurement data and/or additional research.

Hydrogeology of the lower sector of the Basento and Cavone river basins (southern Italy), has been little studied so far, although this area plays a strategic role being an agricultural area, for the production of wheat and organic olive oil, and represents a historical economic pole, with the presence of an industrial area and several extraction sites of natural gas. In this paper, starting from official Italian geological maps, a hydrogeological conceptual model and geodatabase were developed through hydrogeological characterization of lithological formations, analysis of topographic cartography and satellite images, as well as an extensive hydrogeological survey. The results are represented by a hydrogeological map at the 1:50,000 scale, hydrostratigraphic cross-sections, groundwater flow schemes, and a groundwater database. These are valuable tools for knowledge and may be used as a reference for future hydrogeological studies, as well as planning and decision-making in groundwater management.

GWML2 is an international standard for the online exchange of groundwater data that addresses the problem of data heterogeneity. This problem makes groundwater data hard to find and use because the data are diversely structured and fragmented into numerous data silos. Overcoming data heterogeneity requires a common data format; however, until the development of GWML2, an appropriate international standard has been lacking. GWML2 represents key hydrogeological entities such as aquifers and water wells, as well as related measurements and groundwater flows. It is developed and tested by an international consortium of groundwater data providers from North America, Europe, and Australasia, and facilitates many forms of data exchange, information representation, and the development of online web portals and tools.

Large, confined aquifer systems play a vital role in sustaining human settlements and industries in many regions. Understanding the sustainability of these water resources requires the evaluation of groundwater storage change. Direct in‐situ observation of groundwater storage is limited by the distribution and availability of groundwater level and aquifer storativity data. Here, we use and compare two auxiliary methods, applied at basin and sub‐basin scales, to assess groundwater storage changes in the Great Artesian Basin (GAB), one of the World's largest confined aquifer systems. The first, the groundwater budget, derives storage change as the residual of fluxes in and out of the GAB, assuming they are all accounted for and accurately estimated. The second uses time‐variable gravity data from GRACE satellites to estimate temporal changes in groundwater mass, assuming that all other components of the terrestrial water mass change detected by GRACE are correctly subtracted. Despite the depletion observed during the 20th century, groundwater storage is mostly stable during 2002–2022. An increase in storage is detected in the Surat sub‐basin, a major recharge area. This increase is attributed to an over‐representation of large recharge events during the study period and/or storage recovery following rehabilitation of free‐flowing bores. The approach consisting in disaggregating GRACE data assumes that water storage changes in confined aquifers is dominated by changes in the GAB, and as such, it may overestimate the increase in the GAB by incorrectly attributing the increase occurring in overlying aquifers to the GAB. In contrast, the recharge estimates used in the groundwater budgets do not account for flood recharge and might underestimate storage increase in the GAB. Plain Language Summary Monitoring groundwater storage in large, confined aquifers is often impossible as it requires large groundwater level and lithological data sets that are often unavailable. However, monitoring is crucial for assessing and managing the sustainability of this resource and manage it appropriately. This study uses and compares two auxiliary methods, applied at basin and sub‐basin scales, to assess groundwater storage changes in the Great Artesian Basin (GAB), one of the World's largest confined aquifer systems. The groundwater budget approach estimates water storage changes by adding up the amounts of groundwater that goes in and out of the aquifer system. The satellite gravimetry approach uses the temporal changes of Earth's gravity field to infer changes in groundwater mass. Both methods agree that, despite the depletion observed during the 20th century, groundwater storage in the GAB was mostly stable during 2002–2022. An increase in groundwater storage is detected near major recharge areas. It is attributed to an over‐representation of large recharge events during the study period and/or groundwater storage recovery following capping of free‐flowing bores. Key Points GRACE and groundwater budgets agree that water storage in the Great Artesian Basin was stable for the period 2002–2022 Increased storage in the Surat sub‐basin is attributed to bore rehabilitation and/or increased recharge during the study period Within the Surat sub‐basin, increased storage may be overestimated by GRACE and/or underestimated by the groundwater budgets

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km2 with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection.

The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 ± 0.1 cm/a (or 14 ± 0.4 km 3 /a) for Jan 2005–Dec 2010, consistent with the GWD rate (2.8 cm/a or 12.3 km 3 /a) from groundwater-level monitoring data. Published studies (e.g., 4 ± 1 cm/a or 18 ± 4.4 km 3 /a) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.

Groundwater management requires a systematic approach since it is crucial to the long-term viability of livelihoods and regional economies all over the world. There is insufficient groundwater management and difficulties in storage plans as a result of increased population, fast urbanisation, and climate change, as well as unpredictability in rainfall frequency and intensity. Groundwater exploration using remote sensing (RS) data and geographic information system (GIS) has become a breakthrough in groundwater research, assisting in the assessment, monitoring, and conservation of groundwater resources. The study region is the Mand catchment of the Mahanadi basin, covering 5332.07 km 2 and is located between 21°42′15.525″N and 23°4′19.746″N latitude and 82°50′54.503″E and 83°36′1.295″E longitude in Chhattisgarh, India. The research comprises the generation of thematic maps, delineation of groundwater potential zones and the recommendation of structures for efficiently and successfully recharging groundwater utilising RS and GIS. Groundwater Potential Zones (GPZs) were identified with nine thematic layers using RS, GIS, and the Multi-Criteria Decision Analysis (MCDA) method. Satty's Analytic Hierarchy Process (AHP) was used to rank the nine parameters that were chosen. The generated GPZs map indicated regions with very low, low to medium, medium to high, and very high groundwater potential encompassing 962.44 km 2 , 2019.92 km 2 , 969.19 km 2 , and 1380.42 km 2 of the study region, respectively. The GPZs map was found to be very accurate when compared with the groundwater fluctuation map, and it is used to manage groundwater resources in the Mand catchment. The runoff of the study area can be accommodated by the computing subsurface storage capacity, which will raise groundwater levels in the low and low to medium GPZs. According to the study results, various groundwater recharge structures such as farm ponds, check dams and percolation tanks were suggested in appropriate locations of the Mand catchment to boost groundwater conditions and meet the shortage of water resources in agriculture and domestic use. This study demonstrates that the integration of GIS can provide an efficient and effective platform for convergent analysis of various data sets for groundwater management and planning.