Effects of coal mining and tunnel excavation on groundwater flow system in karst areas by modeling: A case study in Zhongliang Mountain, Chongqing, Southwest China
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Abstract: A karst groundwater system ranks among the most sensitive and vulnerable types of groundwater systems. Coal mining and tunnel excavation can greatly change the natural hydrogeological flow system, groundwater-dependent vegetation, soil, as well as hydrology of surface water systems. Abandoned coal mine caves and proposed highway tunnels may have significant influences on groundwater systems. This study employs MODFLOW, a 3D finite-difference groundwater model software, to simulate the groundwater system's response to coal mining and tunnel excavation impact in Zhongliang Mountain, Chongqing, from 1948 to 2035. The results show a regional decline in groundwater levels within the study area following mining and tunnel construction. The groundwater flow system in the study area evolves from the Jialing River groundwater flow system to encompass the Jialing River, Moxinpo highway tunnel, Moxinpo, and the Liujiagou coal mine cave groundwater flow systems between 1948 and 2025. With the completion of tunnel construction, the groundwater level at the top of the tunnel is gradually restored to the water level in the natural state. The model also predicts groundwater level variations between 2025 and 2035. The groundwater level will rise further initially, however, it may take about 10 years for the system to stabilize and reach a new equilibrium. In light of these findings, it is advised that changes in groundwater flow systems caused by tunnel construction should be modeled prior to the practical construction. This approach is crucial for evaluating potential engineering and environmental implications.
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Key words:
- Human activities /
- Aquifer system /
- Evolution of groundwater system /
- Numerical modeling
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Figure 10. D-D' cross-section (Fig. 1) view of modeled hydraulic heads and flow paths in the four scenarios
Notes: a. Scenario of natural state; b. Scenario of coal mining; c. Scenario of tunnel excavation combined with coal mine cave; d. Scenario of the built tunnel and coal mine cave
Table 1. Division and distribution of aquifer system in the study area
Aquifer system Aquifer group Groundwater types Main distribution GNW aquifer system Upper Triassic Xujiahe Formation (T3xj) Clastic rock pore-fracture water Niujiao Temple-Dayakou-Wenjiapo area Middle Triassic Leikoupo Formation (T2l) Carbonate fractured cave water Daqiao Bend-Old Linpo-Tea Bend area Lower Triassic Jialingjiang Formation (T1j) Gandongzi-Tianfu Town-Zongqiaogou area GC aquifer system Lower Triassic Feixianguan Formation (T1f 1-3) Clastic rock with carbonate fractured cave water Shizigou-Datiangou-Wulipo area Upper Permian Changxing Formation (P2c) Upper Permian Longtan Formation (P2l) GSE aquifer system Upper Triassic Xujiahe Formation (T3xj) Clastic rock pore-fracture water Tiefeng Temple-Tanjiagou-Lishu Village Middle Triassic Leikoupo Formation (T2l) Carbonate fractured cave water Dabaozhai-Confucius Temple-belt Lower Triassic Jialingjiang Formation (T1j) Uki Village-Houba-Da Uki area Table 2. List of four modeling scenarios
Number Scenarios Recharge boundary Drainage boundary State Simulation-period MXP-1 Scenario of natural state Rainfall 1. Jialing River
2. Subterranean streamSteady 1948 MXP-2 Scenario of coal mining Rainfall 1. Jialing River
2. Subterranean stream
3. Moxinpo and Liujiagou coal miningTransient 1948–2020 MXP-3 scenario of tunnel excavation and coal mine cave Rainfall 1. Jialing River
2. Subterranean stream
3. Moxinpo and Liujiagou coal mine cave
4. Moxinpo highway tunnel excavationTransient 2020–2025 MXP-4 Scenario of tunnel construction and coal mine cave Rainfall 1. Jialing River
2. Subterranean stream
3. Moxinpo and Liujiagou coal mine cave
4. Moxinpo highway tunnel builtTransient 2025–2035 Table 3. Hydraulic conductivity values of each strata
Strata Kx /m/d Ky /m/d Kz /m/d Ss Sy Parameter source T1f 2 0.010 0.020 0.010 5E-6 0.001 Pumping test results and modeled parameters
(Zhao et al. 2015; Li, 2017)T1f 3 0.082 0.163 0.082 5E-6 0.001 T1f 4 0.0069 0.0138 0.0069 7E-6 0.005 T1j 0.119 0.238 0.119 8E-5 0.03 T2l 0.010 0.020 0.010 8E-5 0.02 T3xj 0.036 0.072 0.036 5E-6 0.005 P2-3 0.010 0.020 0.010 8E-5 0.03 J1-2 0.0069 0.0138 0.0069 0.0069 7E-6 Fault 0.5 1 1 5E-4 0.08 -
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