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.

The analysis of the spatiotemporal characteristics of groundwater level variation is a prerequisite for evaluating groundwater potential or underpinning aquifer sustainability development in hydrogeological engineering practices. This study explores the dominant influencing factors that control groundwater dynamics and develops an estimation of groundwater level fluctuation (GWLF) potential in the complex aquifer systems of mountainous areas. Eight natural environmental factors, including slope, drainage density, land use, lithology, hydraulic conductivity, porosity, groundwater depth, and regolith thickness, have been selected as influencing factors, and the feature scores for different factors associated with GWLF potential were given with the expert scoring method. The weighting coefficients of individual influencing factors for wet/dry seasons were determined using the pseudo-inverse method based on the groundwater level data of 18 well stations observed from 1 November 2011 to 31 October 2019 in Taiwan mountainous areas. The results show that the weighting coefficients of these factors in controlling GWLF potential are variable and affected by seasonal and annual rainfall conditions. Based on the determined weighting coefficients, the spatial distribution of GWLF potential can be effectively produced. Finally, the simulated GWLF potential results were verified by comparing the observed data. The verification result demonstrates that the developed model can predict the spatial GWLF distribution based on the groundwater level data from a few wells.