This study presents the stochastic Monte Carlo simulation (MCS) to assess the uncertainty of flow and conservative transport in 3-D discrete fracture networks (DFNs). The MCS modeling workflow involves a number of developed modules, including a DFN generator, a DFN mesh generator, and a finite element model for solving steady-state flow and conservative transport in 3-D DFN realizations. The verification of the transport model relies on the comparison of transport solutions obtained from HYDROGEOCHEM model and an analytical model. Based on 500 DFN realizations in the MCS, the study assesses the effects of fracture intensities on the variation of equivalent hydraulic conductivity and the exhibited behaviors of concentration breakthrough curves (BTCs) in fractured networks. Results of the MCS show high variations in head and Darcy velocity near the specified head boundaries. There is no clear stationary region obtained for the head variance. However, the transition zones of nonstationarity for x-direction Darcy velocity is obvious, and the length of the transition zone is found to be close to the value of the mean fracture diameter for the DFN realizations. The MCS for DFN transport indicates that a small sampling volume in DFNs can lead to relatively high values of mean BTCs and BTC variations.

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.

The Choushui River groundwater basin (CRGB) in Yunlin County, Taiwan, is a significant groundwater source for the western part of the region. However, increasing groundwater demand and human activities have triggered a potential crisis due to overexploitation. Therefore, groundwater potential zone (GWPZ) maps are crucial for mapping groundwater resources and water resource management. This study employs the normalized index–overlay method and fuzzy extended analytical hierarchy process (FE-AHP) to map GWPZs cost-effectively. The methodology objectively incorporates weightings from various thematic layers by normalizing and correlating parameters with observed groundwater availability (GA). Site-specific observations, including aquifer thickness, depth to the groundwater level, and porosity, inform GA calculations. Seven comprehensive layers derived from remote sensing (RS) data are processed to obtain weightings and ratings for the groundwater potential index (GWPI) in the CRGB. Selected parameters are categorized into hydrological processes, human interventions, geological, and surface profiles. Hydrological processes include precipitation, modified normalized difference water index (MNDWI), and drainage density. Human interventions consist of the enhanced vegetation index (EVI) and normalized difference building index (NDBI). Surface profiles encompass the terrain ruggedness index (TRI) and slope, enhancing the study’s multi-criteria approach. The observed GA validates the GWPZ accuracy, classifying zones into five categories. According to the GWPI of FE-AHP, about 59.56% of the CRGB area can be categorized as “moderate” to “very good” potential groundwater recharge zones. Pearson’s correlation coefficient between GWPI and GA, based on FE-AHP, outperforms the conventional AHP. This RS-based approach efficiently evaluates GA in aquifers with limited wells, highlighting crucial zones in CRGB’s proximal-fan and southeastern mid-fan for informed groundwater management strategies.

The scarcity of groundwater and precipitation stations has limited accurate assessments of basin-scale groundwater systems. This study proposes a workflow that integrates satellite and on-site observations to improve the spatial and temporal resolution of the groundwater level and enable recharge estimations for the Choushui River groundwater basin (CRGB) in Western Taiwan. The workflow involves multiple data processing steps, including analysis of correlation, evaluation of residuals, and geostatistical interpolation based on kriging methods. The observed groundwater levels and recharge are then the basis to assess spatial-temporal interactions between groundwater and recharge in the CRGB from 2006 to 2015. Results of correlation analyses show the high correlation between the groundwater level and the land surface elevation in the study area. However, the multicollinearity problem exists for the additional precipitation data added in the correlation analyses. The correlation coefficient, root mean square error, and normalized root mean square parameters indicate that the Regression Kriging (RK) performs better the groundwater variations than the Ordinary Kriging (OK) dose. The data-driven approach estimates an annual groundwater recharge of approximately 1.40 billion tons, representing 37% of the yearly precipitation. The correlation between groundwater levels and groundwater recharge exhibits low or negative correlation zones in the groundwater basin. These zones might have resulted from multipurpose pumping activities and the river and drainage networks in the area. The event-based precipitation and groundwater level have shown strong recharge behavior in the low-land area of the basin. Artificial weir operations at the high-land mountain pass might considerably influence the groundwater and surface water interactions.

The landforms and human activities play important roles in quantifying surface water and groundwater interactions (SGIs) for water resources management. The study uses the groundwater and surface water flow (GSFLOW) model to quantify the dynamics of SGIs in the Pingtung Plain groundwater basin (PPGB) in southern Taiwan. Specifically, the study uses a physical-based numerical model to quantify the spatial and seasonal variations of water cycles influenced by complex fluvial landform conditions and human activities. Results of the model calibrations show good agreement with the data obtained from the available groundwater monitoring network and the selected stream stations. The basin-scale water budgets show highly nonuniform precipitation in the study area, and over 80% annual precipitation is from wet seasons in the PPGB. With high permeable surficial deposits in the PPGB, the year-averaged surface runoff and infiltration are approximately 57% and 40% of the total precipitation. The fluvial landforms with the high slope in the PPGB lead to 70% of annual surface runoff that becomes the streamflow, and the interflow dominates water interactions near streambeds. Results show that the interflow rate in the wet seasons is 200% more than that in the dry seasons. The net groundwater discharge to the streams is relatively small as compared to the interflow. Only 10% of the river flow is from the net groundwater discharge. In the PPGB, The pumping-induced variations of groundwater levels are insignificant as compared with the factor of the natural landforms. Because of the relatively small area of the proposed artificial lake, the contribution of the artificial lake on the local water budgets is insignificant, indicating the low impact of the artificial recharge lake on the surface water environment.