Detecting groundwater level changes related to the 2016 Kumamoto Earthquake.

This study presented the first attempt to detect precursory changes in groundwater level before the 2016 Kumamoto Earthquake. This detection was achieved by accurately determining the relationship between long-term groundwater level fluctuation and crustal deformation over 16 years through analysis of groundwater level time-series data acquired at 17 sites within the study area. Here, we show that the observed groundwater levels were lower than the modelled levels in aquifers composed of porous strata (Togawa lava and part of the pre-Aso volcanic rocks), and that there were larger differences until 2014, which diminished until the occurrence of the Kumamoto Earthquake. The initial reduction in the modelled groundwater level and the latter recovery were most likely caused by crustal strain relaxation associated with the large 2011 earthquake off the Pacific coast of Tohoku (Mw 9.0) and the strain accumulation prior to the 2016 Kumamoto Earthquake.

Evaluating the effectiveness of a geostatistical approach with groundwater flow modeling for three-dimensional estimation of a contaminant plume.

When assessing the risk from an underground environment that is contaminated by radioactive nuclides and hazardous chemicals and planning for remediation, the contaminant plume distribution and the associated uncertainty from measured data should be estimated accurately. While the release history of the contaminant plume may be unknown, the extent of the plume caused by a known source and the associated uncertainty can be calculated inversely from the concentration data using a geostatistical method that accounts for the temporal correlation of its release history and groundwater flow modeling. However, the preceding geostatistical approaches have three drawbacks: (1) no applications of the three-dimensional plume estimation using concentration data from multiple depths in real situations, (2) no constraints for the estimation of the plume distribution, which can yield negative concentration and large uncertainties, and (3) few applications to actual cases with multiple contaminants. To address these problems, the non-negativity constraint using Gibbs sampling was incorporated into the geostatistical method with groundwater flow modeling for contaminant plume estimation. This method was then tested on groundwater contamination in the Gloucester landfill in Ontario, Canada, using three-dimensional contaminant transport model and concentration data from multiple depths. The method was applied to three water soluble organic contaminants: 1,4-dioxane, tetrahydrofuran, and diethyl ether. The effectiveness of the proposed method was verified by the general agreement of the calculated plume distributions of the three contaminants with concentration data from 66 points in 1982 (linear correlation coefficient of about 0.7). In particular, the reproduced peak of 1,4-dioxane corresponding to the large disposal in 1978 was more accurate than the result of preceding minimum relative entropy-based studies. The same peak also appeared in the tetrahydrofuran and diethyl ether distributions approximately within the range of the retardation factor derived from the fraction of organic carbon.

Spatial variations of tritium concentrations in groundwater collected in the southern coastal region of Fukushima, Japan, after the nuclear accident.

Spatial variations in tritium concentrations in groundwater were identified in the southern part of the coastal region in Fukushima Prefecture, Japan. Higher tritium concentrations were measured at wells near the Fukushima Daiichi Nuclear Power Station (F1NPS). Mean tritium concentrations in precipitation in the 5 weeks after the F1NPS accident were estimated to be 433 and 139 TU at a distance of 25 and 50 km, respectively, from the F1NPS. The elevations of tritium concentrations in groundwater were calculated using a simple mixing model of the precipitation and groundwater. By assuming that these precipitation was mixed into groundwater with a background tritium concentration in a hypothetical well, concentrations of 13 and 7 TU at distances of 25 and 50 km from the F1NPS, respectively, were obtained. The calculated concentrations are consistent with those measured at the studied wells. Therefore, the spatial variation in tritium concentrations in groundwater was probably caused by precipitation with high tritium concentrations as a result of the F1NPS accident. However, the highest estimated tritium concentrations in precipitation for the study site were much lower than the WHO limits for drinking water, and the concentrations decreased to almost background level at the wells by mixing with groundwater.