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The quantitative assessment of geothermal water resources is important to the exploitation and utilization of geothermal resources. In the geothermal water systems the density of groundwater changes with the temperature, therefore the variations in hydraulic heads and temperatures are very complicated. A three-dimensional density-dependent model coupling the groundwater flow and heat transport is established and used to simulate the geothermal water flow in the karst aquifers in eastern Weibei, Shaanxi Province, China. The multilayered karst aquifer system in the study area is cut by some major faults which control the regional groundwater flow. In order to calibrate and simulate the effect of the major faults, each fault is discretized as a belt of elements with special hydrological parameters in the numerical model. The groundwater dating data are used to be integrated with the groundwater flow pattern and calibrate the model. Simulation results show that the calculated hydraulic heads and temperature fit with the observed data well.
The quantitative assessment of geothermal water resources is important to the exploitation and utilization of geothermal resources. In the geothermal water systems the density of groundwater changes with the temperature, therefore the variations in hydraulic heads and temperatures are very complicated. A three-dimensional density- dependent model coupling the groundwater flow and heat transport is established and used to simulate the geothermal water flow in the karst aquifers in eastern Weibei, Shaanxi Province, China. The multilayered karst aquifer system in the study area is cut by some major faults which control the In order to calibrate and simulate the effect of the major faults, each fault is discretized as a belt of elements with special hydrological parameters in the numerical model. The groundwater dating data are used to be integrated with the groundwater flow pattern and calibrate the model. Simulation results show that the calculated hydraulic hea ds and temperature fit with the observed data well.