The assessment of high recharge areas using DO indicators and recharge potential analysis: a case study of Taiwan’s Pingtung plain

Tsai, Jung‐Fa; Chen, Yu Wen; Chang, Liang-Cheng; Chen, Wen Fu; Chiang, C. J.; Chen, You Cheng

doi:10.1007/s00477-014-0941-0

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…[12][13][14][15][16][17] and those that influence infiltration (soil, land use, slope, geology, lithology, hydraulic conductivity, etc.) [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Identification of the Dominant Factors in Groundwater Recharge Process, Using Multivariate Statistical Approaches in a Semi-Arid Region

et al. 2021

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Identifying contributing factors of potential recharge zones is essential for sustainable groundwater resources management in arid regions. In this study, a data matrix with 66 observations of climatic, hydrogeological, morphological, and land use variables was analyzed. The dominant factors in groundwater recharge process and potential recharge zones were evaluated using K-means clustering, principal component analysis (PCA), and geostatistical analysis. The study highlights the importance of multivariate methods coupled with geospatial analysis to identify the main factors contributing to recharge processes and delineate potential groundwater recharge areas. Potential recharge zones were defined into cluster 1 and cluster 3; these were classified as low potential for recharge. Cluster 2 was classified with high potential for groundwater recharge. Cluster 1 is located on a flat land surface with nearby faults and it is mostly composed of ignimbrites and volcanic rocks of low hydraulic conductivity (K). Cluster 2 is located on a flat lowland agricultural area, and it is mainly composed of alluvium that contributes to a higher hydraulic conductivity. Cluster 3 is located on steep slopes with nearby faults and is formed of rhyolite and ignimbrite with interbedded layers of volcanic rocks of low hydraulic conductivity. PCA disclosed that groundwater recharge processes are controlled by geology, K, temperature, precipitation, potential evapotranspiration (PET), humidity, and land use. Infiltration processes are restricted by low hydraulic conductivity, as well as ignimbrites and volcanic rocks of low porosity. This study demonstrates that given the climatic and geological conditions found in the Sierra de San Miguelito Volcanic Complex (SSMVC), this region is not working optimally as a water recharge zone towards the deep aquifer of the San Luis Potosí Valley (SLPV). This methodology will be useful for water resource managers to develop strategies to identify and define priority recharge areas with greater certainty.

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“…[12][13][14][15][16][17] and those that influence infiltration (soil, land use, slope, geology, lithology, hydraulic conductivity, etc.) [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Identification of the Dominant Factors in Groundwater Recharge Process, Using Multivariate Statistical Approaches in a Semi-Arid Region

et al. 2021

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“…GPR is a great practical choice given its spatial coverage, resolution, and efficiency. Based on the assumptions that GPR traveltimes are closely related to dielectric permittivity distribution in the vadose zone, and that the dielectric permittivity is mainly determined by the soil moisture (Lunt et al 2005), tomographic GPR data can be used to infer soil moisture (Moysey et al 2003;Tsai et al 2015). Tomographic GPR can provide centimeters to meters spatial resolution (Vereecken et al 2008), sub-daily temporal resolution, and meanwhile is minimally invasive to the study site.…”

Section: Introductionmentioning

confidence: 99%

Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework

Bao

Hou

Ray

et al. 2018

Stoch Environ Res Risk Assess

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In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a lowdimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)-Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.

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Discrimination of water quality monitoring sites in River Vouga using a mixed-effect state space model

Costa

Monteiro

2015

Stoch Environ Res Risk Assess

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The assessment of high recharge areas using DO indicators and recharge potential analysis: a case study of Taiwan’s Pingtung plain

Cited by 4 publications

References 20 publications

Identification of the Dominant Factors in Groundwater Recharge Process, Using Multivariate Statistical Approaches in a Semi-Arid Region

Identification of the Dominant Factors in Groundwater Recharge Process, Using Multivariate Statistical Approaches in a Semi-Arid Region

Soil moisture estimation using tomographic ground penetrating radar in a MCMC–Bayesian framework

Discrimination of water quality monitoring sites in River Vouga using a mixed-effect state space model

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