Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data

Zhu, Lin; Gong, Huili; Chen, Yongqin David; Li, Xiaojuan; Chen, Xiang; Cui, Yijiao

doi:10.1038/srep22224

Cited by 16 publications

(16 citation statements)

References 44 publications

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“…Spatio-temporal dynamics of soil moisture content affects evapotranspiration, deep drainage and local recharge processes. Electrical resistivity is inversely related to soil moisture content and this relationship is largely controlled by soil physico-chemical properties such as texture, particle size and geometry of pores (void distribution and form), and pore fillings 14 , 22 , 28 . The relationship is therefore soil-specific and follows linear or non-linear relationships that can include second order polynomial, power, exponential and logarithmic relationships 28 .…”

Section: Discussionmentioning

confidence: 99%

“…The vadose zone in water-limited regions is usually thick 19 – 21 and deep percolation of water and storage in the unsaturated zone is sensitive to the depth of the rooting system 20 . Alteration in water use patterns and rooting architecture 22 associated with changes in vegetation functional type, such as a transition between grassland and woodlands, is likely to affect the deep percolation dynamics and local recharge processes 17 , 23 , 24 . A global synthesis of groundwater recharge showed that grasslands produce higher recharge compared to woodlands 25 .…”

Section: Introductionmentioning

confidence: 99%

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Vegetation Controls on the Spatio-Temporal Heterogeneity of Deep Moisture in the Unsaturated Zone: A Hydrogeophysical Evaluation

Acharya

Halihan

Zou

et al. 2017

Sci Rep

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Information on the spatio-temporal variability of soil moisture in the vadose zone is important to assess groundwater recharge and solute transport in unconsolidated substrate as influenced by biological processes. Time-lapse electrical resistivity imaging (ERI) was used to monitor soil moisture dynamics to a depth of 9 m in a grassland, a grassland encroached by a juniper species (eastern redcedar, Juniperus virginiana), a juniper woodland and an oak forest in the south-central Great Plains, Oklahoma, USA. A site-specific relationship between moisture content and electrical conductivity data was developed for the soil zone, and a perched water zone was monitored at two of the sites. Results showed that (a) change in soil moisture content was linearly correlated to change in electric conductivity in the soil zone; (b) vegetation cover type induced differences in vertical bulk electrical resistivity (ER) profiles and influenced the temporal evolution of soil moisture profiles; and (c) juniper encroachment lowered the water level in the perched groundwater aquifer. Our results suggest land use and vegetation cover type, as opposed to rock properties, controls deep water drainage for the vegetation transition zone. Methods used to measure hydrogeophysical changes, such as ERI, can be used for broader understanding of geological, physical, and biological processes and their links in Earth's critical zones.Understanding the existence and magnitude of deep drainage and water flowpaths in the vadose zone under contrasting vegetation types is critical to manage groundwater quantity and quality. Soil water content can be determined by using moisture probes or dielectric sensors, cosmic ray neutrons, gamma ray attenuation, distributed temperature sensing, and/or by using remote sensing methods 1 . However, these methods fail to provide adequate information on deep water content due to limits of coring depth 2 and calibration techniques 1 , especially where competent rock is shallow. Electrical resistivity imaging (ERI), a non-intrusive technique, has been used since the 1830's 3 to characterize and monitor water distribution, contaminant plumes, contaminations and remediation, fluid transport, groundwater flow and reactions, subsurface heterogeneity and anisotropy, to map soil texture and to monitor geo-hazards 4-8 , but it's use in monitoring vadose zone soil moisture dynamics and groundwater recharge is still limited 2, 9, 10 .Electrical resistivity imaging is a geophysical technique which uses surface electrodes for measurement and acquisition of apparent resistivity data 7,9,11,12 . Electrical resistivity (ER) data are influenced by soil particle size, form and distribution of voids, soil water content, fluid properties, and temperature 13,14 . Temporal changes in resistivity are derived by collecting apparent resistivity from same location at different time intervals 13 . The apparent resistivity data are converted to true resistivity using an inversion model and developed into two-dimensional and three-dimensional i...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vegetation Controls on the Spatio-Temporal Heterogeneity of Deep Moisture in the Unsaturated Zone: A Hydrogeophysical Evaluation

Acharya

Halihan

Zou

et al. 2017

Sci Rep

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“…which is widely accepted to derive the hydraulic conductivity from grain size and porosity (Soupious et al, 2007;Utom et al, 2013;Khalil and Santos, 2013;Zhu et al, 2016b). In Eq.…”

Section: Hydraulic Conductivity Estimates From Geophysical Acquisitionsmentioning

confidence: 99%

“…Geophysical data (such as surface electric resistivity and various logging data) are relatively inexpensive and can provide considerable information for characterizing subsurface heterogeneous properties (Hubbard et al, 2001;Yeh et al, 2002;Dai et al, 2004a;Morin, 2006;Sikandar et al, 2010;Bevington et al, 2016). Electric resistivity data have been proven useful to derive sediment porosity distributions (Niwas and Singhal, 1985;Niwas et al, 2011;Niwas and Celik, 2012;Zhu et al, 2016b). Zhu et al (2016b) simulated the spatial distributions of hydraulic conductivity by combining the interpolated resistivity on the basis of vertical electrical soundings (VESs) and the stochastic simulated facies through the empirical equation, in which the hydraulic conductivity was converted from the porosity data calculated from resistivity measurements and the grain size.…”

Section: Introductionmentioning

confidence: 99%

“…Electric resistivity data have been proven useful to derive sediment porosity distributions (Niwas and Singhal, 1985;Niwas et al, 2011;Niwas and Celik, 2012;Zhu et al, 2016b). Zhu et al (2016b) simulated the spatial distributions of hydraulic conductivity by combining the interpolated resistivity on the basis of vertical electrical soundings (VESs) and the stochastic simulated facies through the empirical equation, in which the hydraulic conductivity was converted from the porosity data calculated from resistivity measurements and the grain size.…”

Section: Introductionmentioning

confidence: 99%

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Modeling 3-D permeability distribution in alluvial fans using facies architecture and geophysical acquisitions

Zhu

Gong

Dai

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Alluvial fans are highly heterogeneous in hydraulic properties due to complex depositional processes, which make it difficult to characterize the spatial distribution of the hydraulic conductivity (K). An original methodology is developed to identify the spatial statistical parameters (mean, variance, correlation range) of the hydraulic conductivity in a three-dimensional (3-D) setting by using geological and geophysical data. More specifically, a large number of inexpensive vertical electric soundings are integrated with a facies model developed from borehole lithologic data to simulate the log 10 (K) continuous distributions in multiplezone heterogeneous alluvial megafans. The Chaobai River alluvial fan in the Beijing Plain, China, is used as an example to test the proposed approach. Due to the non-stationary property of the K distribution in the alluvial fan, a multiplezone parameterization approach is applied to analyze the conductivity statistical properties of different hydrofacies in the various zones. The composite variance in each zone is computed to describe the evolution of the conductivity along the flow direction. Consistently with the scales of the sedimentary transport energy, the results show that conductivity variances of fine sand, medium-coarse sand, and gravel decrease from the upper (zone 1) to the lower (zone 3) portion along the flow direction. In zone 1, sediments were moved by higher-energy flooding, which induces poor sorting and larger conductivity variances. The composite variance confirms this feature with statistically different facies from zone 1 to zone 3. The results of this study provide insights to improve our understanding on conductivity heterogeneity and a method for characterizing the spatial distribution of K in alluvial fans.

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Estimation of anisotropic hydraulic conductivity using geophysical data in a coastal aquifer of Karnataka, India

Nagaraj

Subbarayappa

Jean-Michel

et al. 2021

Hydrological Processes

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Estimation of hydraulic parameters is essential to understand the interaction between groundwater flow and seawater intrusion. Though several studies have addressed hydraulic parameter estimation, based on pumping tests as well as geophysical methods, not many studies have addressed the problem with clayey formations being present. In this study, a methodology is proposed to estimate anisotropic hydraulic conductivity and porosity values for the coastal aquifer with unconsolidated formations. For this purpose, the one-dimensional resistivity of the aquifer and the groundwater conductivity data are used to estimate porosity at discrete points.The hydraulic conductivity values are estimated by its mutual dependence with porosity and petrophysical parameters. From these estimated values, the bilinear relationship between hydraulic conductivity and aquifer resistivity is established based on the clay content of the sampled formation. The methodology is applied on a coastal aquifer along with the coastal Karnataka, India, which has significant clayey formations embedded in unconsolidated rock. The estimation of hydraulic conductivity values from the established correlations has a correlation coefficient of 0.83 with pumping test data, indicating good reliability of the methodology. The established correlations also enable the estimation of horizontal hydraulic conductivity on twodimensional resistivity sections, which was not addressed by earlier studies. The inventive approach of using the established bilinear correlations at one-dimensional to two-dimensional resistivity sections is verified by the comparison method. The horizontal hydraulic conductivity agrees with previous findings from inverse modelling. Additionally, this study provides critical insights into the estimation of vertical hydraulic conductivity and an equation is formulated which relates vertical hydraulic conductivity with horizontal. Based on the approach presented, the anisotropic hydraulic conductivity of any type aquifer with embedded clayey formations can be estimated. The anisotropic hydraulic conductivity has the potential to be used as an important input to the groundwater models.

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Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data

Cited by 16 publications

References 44 publications

Vegetation Controls on the Spatio-Temporal Heterogeneity of Deep Moisture in the Unsaturated Zone: A Hydrogeophysical Evaluation

Vegetation Controls on the Spatio-Temporal Heterogeneity of Deep Moisture in the Unsaturated Zone: A Hydrogeophysical Evaluation

Modeling 3-D permeability distribution in alluvial fans using facies architecture and geophysical acquisitions

Estimation of anisotropic hydraulic conductivity using geophysical data in a coastal aquifer of Karnataka, India

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