Effects of the Japanese 2016 Kumamoto Earthquake on Nitrate Content in Groundwater Supply

Self-organizing map improves understanding on the hydrochemical processes in aquifer systems

The holistic understanding of hydrochemical features is a crucial task for management and protection of water resources. However, it is challenging for a complex region, where multiple factors can cause hydrochemical changes in studied catchment. We collected 208 groundwater samples from such region in Kumamoto, southern Japan to explicitly characterize these processes by applying machine learning technique. The analyzed groundwater chemistry data like major cations and anions were fed to the self-organizing map (SOM) and the results were compared with classical classification approaches like Stiff diagram, standalone cluster analysis and score plots of principal component analysis (PCA). The SOM with integrated application of clustering divided the data into 11 clusters in this complex region. We confirmed that the results provide much greater details for the associated hydrochemical and contamination processes than the traditional approaches, which show quite good correspondence with the recent high resolution hydrological simulation model and aspects from geochemical modeling. However, the careful application of the SOM is necessary for obtaining accurate results. This study tested different normalization approaches for selecting the best SOM map and found that the topographic error (TE) was more important over the quantization error (QE). For instance, the lower QE obtained from min-max and log normalizations showed problems after clustering the SOM map, since the QE did not confirm the topological preservation. In contrast, the lowest TE obtained from Z-transformation data showed better spatial matching of the clusters with relevant hydrochemical characteristics. The results from this study clearly demonstrated that the SOM is a helpful approach for explicit understanding of the hydrochemical processes on reginal scale that may capably facilitate better groundwater resource management.

Aquifer heterogeneity controls to quality monitoring network performance for the protection of groundwater production wells

A groundwater monitoring network surrounding a pumping well (such as a public water supply) allows for early contaminant detection and mitigation where possible contaminant source locations are often unknown. This numerical study investigates how the contaminant detection probability of a hypothetical sentinel-well monitoring network consisting of one to four monitoring wells is affected by aquifer spatial heterogeneity and dispersion characteristics, where the contaminant source location is randomized. This is achieved through a stochastic framework using a Monte Carlo approach. A single production well is considered that results in converging non-uniform flow close to the well. Optimal network arrangements are obtained by maximizing a weighted risk function that considers true and false positive detection rates, sampling frequency, early detection, and contaminant travel time uncertainty. Aquifer dispersivity is found to be the dominant parameter for the quantification of network performance. For the range of parameters considered, a single monitoring well screening the full aquifer thickness is expected to correctly and timely identify at least 12% of all incidents resulting in contaminants reaching the production well. This proportion increases to a global maximum of 96% for a network consisting of four wells and very dispersive transport conditions. Irrespective of network size and sampling frequency, more dispersive transport conditions result in higher detection rates. Increasing aquifer heterogeneity and decreasing aquifer spatial continuity also lead to higher detection rates, though these effects are diminished for networks of 3 or more wells. Statistical anisotropy has no effect on the network performance. Earlier detection, which is critical for remedial action and supply safety, comes with a significant cost in terms of detection rate, and should be carefully considered when a monitoring network is being designed.

Estimation of Groundwater Recharge in Kumamoto Area, Japan in 2016 by Mapping Land Cover Using GIS Data and SPOT 6/7 Satellite Images

Agricultural fields, grasslands, and forests are very important areas for groundwater recharge. However, these types of land cover in the Kumamoto area, Japan, were damaged by the Kumamoto earthquake and heavy rains in 2016. In this region, where groundwater provides almost 100% of the domestic water supply for a population of about 1 million, quantitative evaluation of changes in groundwater recharge due to land cover changes induced by natural disasters is important for the sustainable use of groundwater in the future. The objective of this study was to create a land cover map and estimate the groundwater recharge in 2016. Geographic information system (GIS) data and SPOT 6/7 satellite images were used to classify the Kumamoto area into nine categories. The maximum likelihood classifier of supervised classification was applied in ENVI 5.6. Eventually, the map was cleaned up with a 21 × 21 kernel filter, which is larger than the common size of 3 × 3. The created land cover map showed good performance of the larger filter size and sufficient validity, with overall accuracy of 91.7% and a kappa coefficient of 0.88. The estimated total groundwater recharge amount reached 757.56 million m3. However, if areas of paddy field, grassland, and forest had not been reduced due to the natural disasters, it is estimated that the total groundwater recharge amount would have been 759.86 million m3, meaning a decrease of 2.30 million m3 in total. The decrease of 2.13 million m3 in the paddy fields is temporary, because the paddy fields and irrigation channels have been improved and the recharge amount will recover. On the other hand, since the topsoil on the landslide scars will not recover easily in natural conditions, it is expected to take at least 100 years for the groundwater recharge to return to its original state. The recharge amount was estimated to decrease by 0.17 million m3 due to landslides. This amount is quite small compared to the total recharge amount. However, since the reduced recharge amount accounts for the annual water consumption for 1362 people, and 12.1% of the recharge decrease of 1.41 million m3 each year to fiscal year 2024 is expected by municipalities, we conclude that efforts should be made to compensate for the reduced amount due to the disasters.