Variability in Groundwater Flow and Chemistry in the Houzhai Karst Basin, Guizhou Province, China

Barna, Joshua; Fryar, Alan E.; Cao, Lixin; Peng, Tao; Zhu, Chen

doi:10.2113/eeg-2306

Cited by 5 publications

(1 citation statement)

References 31 publications

(69 reference statements)

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“…Although groundwater is buried in the ground, it has catchment areas and water flow systems just like rivers and lakes on the ground surface [3][4][5][6][7]. The change trends and laws of groundwater level and flow are important basis for the scientific planning and reasonable management of groundwater; and by controlling its trends, we can avoid problems such as the appearance of regional depression cones caused by over-exploitation [8][9][10][11]. Therefore, extracting the basic characteristics of the groundwater flows and comprehensively considering the tridimensional flow space, the complex streamline combination, and the concurrent flow direction are especially important for constructing highprecision and high-reliability groundwater model.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Flow Calculation Model and Neural Network Prediction Based on Groundwater Modeling System

Bo¹,

Cheng²,

Sun³

2020

I2M

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Due to its merits of good water quality, pollution-free, and wide range, groundwater has become an important source for the water supply of industry, agriculture and urban domestic living in China. The change trends and laws of groundwater level and flow are important basis for the scientific planning and reasonable management of groundwater. In order to obtain a more accurate 3D groundwater flow model, first, this paper constructed a Groundwater Flow Calculation (GFC) model based on the Groundwater Modeling System (GMS), and analyzed the periodic groundwater recharge, the tridimensional flow space, the complex streamline combination, and the concurrent flow direction; then, the paper gave the preprocessing process of the calculated parameter data of groundwater flow, and built a grayscale-embedded BP neural network to predict the groundwater flow. At last, the paper verified the effectiveness of the model with experimental results. This study provided a reference for the flow prediction of other similar fields and the research on groundwater evaluation.

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Section: Introductionmentioning

confidence: 99%

Groundwater Flow Calculation Model and Neural Network Prediction Based on Groundwater Modeling System

Bo¹,

Cheng²,

Sun³

2020

I2M

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The effect of reservoir expansion from underground karst cave to surface reservoir on water quality in southwestern China

Wang

Yue

et al. 2022

Environ Sci Pollut Res

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A catchment scale GPR application potential zonation: case study from SW China

Gao,

Hussain,

Cao

et al. 2023

Environ Earth Sci

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The epikarst together with its soil stocks (subcutaneous structure), resulted from the dissolution and weathering of soft rocks, are crucial to the fact that they may contribute to the canopy growth and can signi cantly in uence the ecological restoration and organic carbon sequestration. For the delineation of these ecological signi cant karst features, ground penetrating radar (GPR) seemed to be a promising technique because of its noninvasive, cost-prohibited and lesser labor-intensive operations. However, the landscape heterogeneity, connection between surface morphology and underground environments and high vegetative endemism making karst as a complicated environment for any geophysical application. Same is the case with the GPR applicability in SW Chinese catchment as it is affected by numerous features such as epikarst slope, peak-cluster depression, tree trunks and roots, precipitation and moisture contents as well as proximity to high voltage power lines. Considering these factors, the present study analyzes the GPR data acquired at the sites representing each of these aforementioned features. The analysis includes calculation of GPR attributes as average energy, coherence and total energy together with the forward calculations wherever required. Tilt signals from surrounding hills mix with the tilt signals from subsurface inclined interfaces in a GPR image. The information of soil-rock distribution above epikarst in the slope is di cult to obtain completely for GPR. The interpretation of epikarst bottom boundary faces two possibilities considering the affects of moisture. The affects of tree trunks and roots and strong electromagnetic elds of high voltages lines make the GPR data interpretation about subsurface soil-rock structure high di cult. The soil moisture greater than ~ 30% makes GPR inapplicability. These site-speci c ndings are used for the generalized GPR application potential zonation in the typical SW Chinese catchment (the central Guizhou plateau). The ndings of the present study may prove as a reconnaissance and an application paradigm for the future GPR utilities in complex karst characterization especially, in SW China as well as the areas having similar karstic conditions.

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Variability in Groundwater Flow and Chemistry in the Houzhai Karst Basin, Guizhou Province, China

Cited by 5 publications

References 31 publications

Groundwater Flow Calculation Model and Neural Network Prediction Based on Groundwater Modeling System

Groundwater Flow Calculation Model and Neural Network Prediction Based on Groundwater Modeling System

The effect of reservoir expansion from underground karst cave to surface reservoir on water quality in southwestern China

A catchment scale GPR application potential zonation: case study from SW China

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