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The aim of this paper is to assess the correlation of groundwater level changes (or groundwater level anomalies (GWLA)) obtained from direct measurements in wells with groundwater storage anomalies (GWSA) calculated using Gravity Recovery and Climate Experiment (GRACE) products and Global Land Data Assimilation Systems (GLDAS) models across different climate zones, from temperate Poland to Arctic Sweden. We recognize that such validation studies are needed to increase the understanding of the spatio-temporal limits of remote sensing model applicability, not least in data-scarce sub-Arctic and Arctic environments where processes are complex due to the impacts of snow and (perma) frost. Results for temperate climates in Poland and southern Sweden show that, whereas one of the models (JPL_NOAH_GWSA) failed due to water balance term overestimation, the other model (CSR_CLM_GWSA) produced excellent results of monthly groundwater dynamics when compared with the observations in 387 groundwater wells in the region during 2003–2022 (cross-correlation coefficient of 0.8). However, for the sub-Arctic and Arctic northern Sweden, the model suitable for other regions failed to reproduce typical northern groundwater regimes (of the region's 85 wells), where winter levels decrease due to the blocking effect of ground frost on groundwater recharge. This suggests, more generally, that conventional methods for deriving GWSA and its seasonality ceases to be reliable in the presence of considerably infiltration-blocking ground frost and permafrost (whereas snow storage modules perform well), which hence need further attention in future research. Regarding long-term groundwater level trends, remote sensing results for southern Sweden show increasing levels, in contrast with observed unchanged to decreasing (~10 mm/a) levels, which may not necessarily be due to errors in the remote sensing model but may rather emphasize impacts of anthropogenic pressures, which are higher near the observation wells that are often located in eskers used for water supply. For sub-Arctic and Arctic Sweden, the (relatively uncertain) trend of the remote sensing results nevertheless agrees reasonably well with the groundwater well observations that show increasing groundwater levels of up to ~14 mm/a, which, e.g., is consistent with reported trends of large Siberian river basins. [ABSTRACT FROM AUTHOR]

Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling. [ABSTRACT FROM AUTHOR]

Immense liquefaction damage was observed in the 2011 off the Pacific coast of Tohoku Earthquake. It was reported that, in Chiba Prefecture, Japan, the main shock oozed muddy water from the sandy ground and the aftershock which occurred 29 min after the main shock intensified the water spouting; thus, the aftershock expanded the liquefaction damage in the sandy ground. For comprehending such a phenomenon, using a soil–water–air coupled elastoplastic finite deformation analysis code, a rise in groundwater level induced by main shock is demonstrated, which may increase the potential of liquefaction damage during the aftershock. The authors wish to emphasize that these results cannot be obtained without soil–water–air coupled elastoplastic finite deformation analysis. This is because the rise in groundwater level is caused by the negative dilatancy behavior (plastic volume compression) of the saturated soil layer which supplies water to the upper unsaturated soil layer, and it is necessary to precisely calculate the settlement of ground and the amount of water drainage/absorption to investigate the groundwater level rise. This study provides insight into the mechanism of ground liquefaction during a series of earthquakes. [ABSTRACT FROM AUTHOR]

In recent times, groundwater has emerged as an alternative drinking water source for populations in Ghana's gold mining communities due to high pollution levels in surface water resources caused by artisanal miners. This study presents the findings on different radioactivity levels in groundwater, their sources, the characteristics of aquifers in the study area, and the impact of radionuclides on human health as a factor of water quality. The mean activity concentrations of radionuclides in groundwater samples were 0.1 ± 0.04 Bq/L for Ra-226, 0.36 ± 0.05 Bq/L for Ra-228, and 1.41 ± 0.10 Bq/L for K-40. The activity concentration of 228Ra exceeded the World Health Organization (WHO) guideline level of 0.1 Bq/L. The elevated levels of Ra-228 in groundwater were found to be of natural origin, emanating from the aquifer rocks in the study area. Groundwater resources near mining zones showed higher radioactivity levels. The corresponding committed effective dose from consuming such groundwater was estimated to be higher than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) recommended value of 0.3 mSv/y for children and infants, indicating potential health risks. [ABSTRACT FROM AUTHOR]