The hydrologic function and behavior of the Houzhai underground river basin, Guizhou Province, southwestern China

Chen

et al. 2010

Water Resour Manage

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Karstic water systems are usually characterized by high heterogeneity and lack boreholes and points of observation. Thus, a study of the karstic water system using lumped parameters may help distinguish the degree of the flow structure, the influence of any wide conduits, the controlling effect of stored reserves, and other hydrologic factors. A modified Darcian model derived from Criss and Winston (Geophys Res Lett 30 (6): [1314][1315][1316][1317] 2003) was developed in this study and used to model spring discharges in a highly karstified water system in Houzhai, Guizhou Province, China. The modified model successfully reproduced the discharge responses of the Houzhai karstic water system by using two lumped parameters. After analyzing 28 storm events, the authors obtained the parameter values which reflect the lag time to the rainfall input signal and verified the parameter values by spectral analysis results. The sensitivity analysis of the two lumped parameters indicated that the immediate rainfall event is the dominating factor influencing the discharge response.

Section: Geology and Hydrogeology Of The Study Areamentioning

confidence: 99%

Rainfall-Driven Spring Hydrograph Modeling in a Karstic Water System, Southwestern China

Chen

et al. 2010

Water Resour Manage

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“…1). The basin is approximately 81 km 2 (Liu et al 2009). More than 95% of the outcrop of the Houzhai karstic water system is the Middle Triassic Guanling Formation (T 2g ), consisting mainly of dolomite, limestone, argillaceous limestone, and argillaceous dolomite interbedded with shale.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for a karst aquifer with unknown thickness using the genetic algorithm method

Chen

et al. 2010

Environ Earth Sci

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The thickness of an aquifer (B) is a critical hydrogeologic parameter in understanding groundwater system, especially in a karst aquifer where the high heterogeneity in this parameter brings more uncertainty to the evaluation of groundwater resources. In this study, the genetic algorithm (GA) method was used to estimate B, as well as five other parameters, including K r (horizontal hydraulic conductivity), K z (vertical hydraulic conductivity), S (storativity), S y (specific yield), and a (delayed coefficient) of a karst aquifer based on a pumping test. The search bounds of the six parameters for the GA method were obtained from numerous hypothetic numerical tests. The feasibility of the GA method for parameter estimation was verified by the results obtained from the gradient method in terms of a pumping test conducted in the Platte River valley in Nebraska. Then, this approach was used to estimate the hydraulic parameters in a karst aquifer with unknown aquifer thickness based on a pumping recovery test conducted in the Houzhai karst basin, southwestern China. The results show that the K r , K z , and the anisotropy ratio of K r to K z are strongly affected by B. The differences between the estimated parameter values at two observation wells indicate the existence of high heterogeneity in the karst aquifer. The parameter B has the highest absolute normalized sensitivity among these variables, implying that the estimated B may be more reliable as compared to other parameters. This study demonstrates that the GA method is an alternative method of parameter estimation with the unknown aquifer thickness.

“…It is necessary to understand the hydrodynamic characteristics of karst-spring groundwater systems for the continued availability of water resources (Liu et al 2010), and it is important to model groundwater dynamics for effective resources management (Bakker et al 2007). Various methods have been used in groundwater flow modeling.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of groundwater level in Jinci Spring Basin (China) based on artificial neural network modeling

Yin³

et al. 2012

Hydrogeol J

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Jinci Spring in Shanxi, north China, is a major local water source. It dried up in April 1994 due to groundwater overexploitation. The groundwater system is complex, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are statistical techniques to study parameter nonlinear relationships of groundwater systems. However, ANN models offer little explanatory insight into the mechanisms of prediction models. Sensitivity analysis can overcome this shortcoming. In this study, a back-propagation neural network model was built based on the relationship between groundwater level and its sensitivity factors in Jinci Spring Basin; these sensitivity factors included precipitation, river seepage, mining drainage, groundwater withdrawals and lateral discharge to the associated Quaternary aquifer. All the sensitivity factors were analyzed with Garson's algorithm based on the connection weights of the neural network model. The concept of "sensitivity range" was proposed to describe the value range of the input variables to which the output variables are most sensitive. The sensitivity ranges were analyzed by a local sensitivity approach. The results showed that coal mining drainage is the most sensitive anthropogenic factor, having a large effect on groundwater level of the Jinci Spring Basin.