It is not possible, using numerical methods, to model groundwater flow and transport in the fractured crystalline rock of northeastern Brazil. As an alternative, the usefulness of self-organizing map (SOM), k-means clustering, and Davies-Bouldin techniques to conceptualize the hydrogeology was evaluated. Also estimated was the well yield and groundwater quality across the Juá region. This process relies on relations in the underlying multivariate density function associated with a sparse local set of hydrogeologic (electrical conductivity, geology, temperature, and well yield) and a complete regional set of airborne geophysical (electromagnetic, magnetic, and radiometric) and satellite spectrometric measurements. Resampling of the regional well yield and electrical conductivity estimates provides sufficient resolution to construct variograms for stochastic modeling of the hydrogeologic variables. The combination of these stochastic maps provides a way to identify potential drilling targets for future groundwater development. The data-driven estimation approach, when applied to available airborne electromagnetic and water-well hydrogeologic measurements, provides a low-cost alternative to numerical groundwater flow modeling. In addition to fractured rock environments, the alternative modeling framework can provide spatial parameter estimates and associated variograms for constraints to improve the traditional calibration of equivalent groundwater-porous-media models.

Sustainable development and management of groundwater resources require application of scientific principles and modern techniques. An integrated approach is implemented using remote sensing and geographic information system (GIS)-based multi-criteria evaluation to identify promising areas for groundwater exploration in Raya Valley, northern Ethiopia. The thematic layers considered are lithology, lineament density, geomorphology, slope, drainage density, rainfall and land use/cover. The corresponding normalized rates for the classes in a layer and weights for thematic layers are computed using Saaty’s analytical hierarchy process. Based on the computed rates and weights, aggregating the thematic maps is done using a weighted linear combination method to obtain a groundwater potential (GP) map. The GP map is verified by overlay analysis with observed borehole yield data. Map-removal and single-parameter sensitivity analyses are used to examine the effects of removing any of the thematic layers on the GP map and to compute effective weights, respectively. About 770 km²(28 % of the study area) is designated as ‘very good’ GP. ‘Good’, ‘moderate’ and ‘poor’ GP areas cover 630 km²(23 %), 600 km²(22 %) and 690 km²(25 %), respectively; the area with ‘very poor’ GP covers 55 km²(2 %). Verification of the GP map against observed borehole yield data shows 74 % agreement, which is fairly satisfactory. The sensitivity analyses reveal the GP map is most sensitive to lithology with a mean variation index of 6.5 %, and lithology is the most effective thematic layer in GP mapping with mean effective weight of 52 %.

The Gravity Recovery and Climate Experiment (GRACE) satellite mission is aimed at assessment of groundwater storage under different terrestrial conditions. The main objective of the presented study is to highlight the significance of aquifer complexity to improve the performance of GRACE in monitoring groundwater. Vidarbha region of Maharashtra, central India, was selected as the study area for analysis, since the region comprises a simple aquifer system in the western region and a complex aquifer system in the eastern region. Groundwater-level-trend analyses of the different aquifer systems and spatial and temporal variation of the terrestrial water storage anomaly were studied to understand the groundwater scenario. GRACE and its field application involve selecting four pixels from the GRACE output with different aquifer systems, where each GRACE pixel encompasses 50–90 monitoring wells. Groundwater storage anomalies (GWSA) are derived for each pixel for the period 2002 to 2015 using the Release 05 (RL05) monthly GRACE gravity models and the Global Land Data Assimilation System (GLDAS) land-surface models (GWSAGRACE) as well as the actual field data (GWSAAcₜᵤₐₗ). Correlation analysis between GWSAGRACE and GWSAAcₜᵤₐₗ was performed using linear regression. The Pearson and Spearman methods show that the performance of GRACE is good in the region with simple aquifers; however, performance is poorer in the region with multiple aquifer systems. The study highlights the importance of incorporating the sensitivity of GRACE in estimation of groundwater storage in complex aquifer systems in future studies.

The North China Plain (NCP) has been suffering from groundwater storage (GWS) depletion and land subsidence for a long period. This paper collects data on GWS changes and land subsidence from in situ groundwater-level measurements, literature, and satellite observations to provide an overview of the evolution of the aquifer system during 1971–2015 with a focus on the sub-regional variations. It is found that the GWS showed a prolonged declining rate of −17.8 ± 0.1 mm/yr during 1971–2015, with a negative correlation to groundwater abstraction before year ~2000 and a positive correlation after ~2000. Statistical correlations between subsidence rate and the GWS anomaly (GWSA), groundwater abstraction, and annual precipitation show that the land subsidence in three sub-regions (Beijing, Tianjin, and Hebei) represents different temporal variations due to varying driver factors. Continuous drought caused intensive GWS depletion (−76.1 ± 6.5 mm/yr) and land subsidence in Beijing during 1999–2012. Negative correlations between total groundwater abstraction and land subsidence exhibited after the 1980s indicate that it may be questionable to infer subsidence from regional abstraction data. Instead, the GWSA generally provides a reliable correlation with subsidence. This study highlights the spatio-temporal variabilities of GWS depletion and land subsidence in the NCP under natural and anthropogenic impacts, and the importance of GWS changes for understanding land subsidence development.

The Gravity Recovery and Climate Experiment (GRACE) twin satellites introduced a new opportunity to monitor changes in groundwater level. However, the performance of the GRACE-derived Liquid Water Equivalent Thickness (GRACE-LWET) in estimating groundwater-level changes at a local scale requires evaluation. Thus, the main aim of this study is to evaluate the performance of the GRACE-derived estimation in monitoring groundwater-level changes in Iran, which is experiencing decreasing trends and subsequent impacts. Another aim is to investigate the time lag between the water levels derived from the GRACE estimation and direct measurements. Four regions in Iran were studied between the years 2002 and 2016. To evaluate the results of GRACE-LWET, groundwater levels in 144 piezometric wells were measured monthly. The changes of the earth’s mass due to surface-water changes were assessed using four datasets of the Global Land Data Assimilation System. Furthermore, the statistical trend of the groundwater-level changes acquired from the GRACE estimations and observational data was investigated using the Mann-Kendall test and Sen’s slope estimator at a significance level of 0.05. The results showed that the best performance of the GRACE estimations was acquired when considering a 2-month time lag. In this case, the average correlation coefficient of the GRACE estimations against the observational data for the entire study region was 0.57. Moreover, the GRACE-LWET showed a significant decreasing trend for the whole study area using both considered tests. Hence, GRACE-derived estimation of groundwater-level changes can be used in regions with insufficient observational well data with an acceptable accuracy.

The determination of space–time variation in groundwater accumulation in Colombia’s Eastern Llanos foreland basin from 2003 to 2014 was done using terrestrial water storage (TWS) anomalies identified in two versions of the Gravity Recovery and Climate Experiment (GRACE) data—from the Global Data Center for Space Research (CSR) at the University of Texas at Austin (USA) and from the Institute of Geodesy at the Graz University of Technology (ITSG, Austria)—and also soil moisture storage (SMS) data from the Global Land Data Assimilation System (GLDAS). These data were compared to changes in groundwater storage obtained using the water-budget equation, calculated based on recorded data from hydrometeorological stations. This study confirmed the viability of using satellite information to understand and monitor temporal variation in groundwater recharge in the study area. Temporal variations in TWS, SMS, and groundwater level were shown to correspond to regional rain and drought periods, which are sensitive to climate phenomena such as El Niño and La Niña. Comparing changes in TWS and groundwater level to changes in infiltration and recharge revealed correlation coefficients of 0.56 and 0.98 with CSR data and 0.71 and 0.86 with ITSG data, respectively.

Coal mining in northwestern China is an important industry. For the traditional monitoring of water resources in coal-rich regions, a single monitoring well or remote-sensing image is often used to obtain the groundwater level or water body area. The process is restricted by the spatial distribution of monitoring wells and the quality of remote sensing images. The regions of Ordos, Northern Shaanxi (including Yan’an and Yulin cities), herein collectively referred to as OYY, and Shanxi (SX) were studied. Here, groundwater storage anomalies (GWSA) were derived using the gravity recovery and climate experiment (GRACE) satellite data and WaterGAP global hydrology model, and the change trend of groundwater storage (GWS) was explored. Using time series analysis and grey slope relational analysis, the potential driving factors of regional GWSA were derived and considered independent variables. In combination with GWSA, the quantitative relationship between the variables was established by partial least squares regression. Results showed that: (1) the decreasing rate of GWS in OYY and SX reached –0.65 and –1.16 cm/year, respectively, from 2003 to 2014; (2) the main driving factors leading to the reduction of GWS included coal-mining water consumption for OYY and water consumption by coal mining and agricultural irrigation for SX, and the weights of water consumption by coal mining and agricultural irrigation for SX were both 50%. Therefore, GRACE satellite data show good application in groundwater monitoring of coal-mining concentrated areas, providing an important basis for the formulation of water resource management measures.