On the formulation of models based on the average characteristics of a heterogeneous aquifer

1984 ◽  
pp. 344-367
Author(s):  
Hillel Rubin ◽  
Bent A. Christensen
Keyword(s):  
Heterogeneous Aquifer

Supplemental material: Transport and biogeochemical reaction of metals in a physically and chemically heterogeneous aquifer

2020 ◽  
Author(s):  
Timothy D. Scheibe ◽  
Yilin Fang ◽  
Christopher J. Murray ◽  
Eric E. Roden ◽  
Jinsong Chen ◽  
...  
Keyword(s):  
File Size ◽  
Material Transport ◽  
Heterogeneous Aquifer ◽  
Simulation Results

Geosphere, June 2006, v. 2, p. 220-235, doi: 10.1130/GES00029.1. Animation 1 - Animation of simulation results for U(VI) contamination period (22 yr) for the Geophys2 case. Upper panel—sorbed U(VI); lower panel—aqueous U(VI). All concentrations are in molar units (M). The file size is 1.4 MB.

Supplemental material: Transport and biogeochemical reaction of metals in a physically and chemically heterogeneous aquifer

2020 ◽  
Author(s):  
Timothy D. Scheibe ◽  
Yilin Fang ◽  
Christopher J. Murray ◽  
Eric E. Roden ◽  
Jinsong Chen ◽  
...  
Keyword(s):  
File Size ◽  
Material Transport ◽  
Heterogeneous Aquifer ◽  
Simulation Results

Geosphere, June 2006, v. 2, p. 220-235, doi: 10.1130/GES00029.1. Animation 1 - Animation of simulation results for U(VI) contamination period (22 yr) for the Geophys2 case. Upper panel—sorbed U(VI); lower panel—aqueous U(VI). All concentrations are in molar units (M). The file size is 1.4 MB.

A Computerized Simulation Of A Heterogeneous Aquifer Storage Formation

1968 ◽  
Author(s):  
L.N. Reed ◽  
J.H. Henderson ◽  
J.R. Dempsey ◽  
K.H. Coats
Keyword(s):  
Aquifer Storage ◽  
Heterogeneous Aquifer ◽  
Computerized Simulation

Predicting Groundwater Flow and Solute Transport in the Heterogeneous Aquifer Sandbox Using Different Parameter Estimation Methods

2021 ◽  
Author(s):  
Liqun Jiang ◽  
Ronglin Sun ◽  
Xing Liang
Keyword(s):  
Steady State ◽  
Groundwater Flow ◽  
Solute Transport ◽  
Transport Process ◽  
Homogeneous Model ◽  
Estimation Methods ◽  
Aquifer Heterogeneity ◽  
Heterogeneous Aquifer ◽  
Tracer Distribution ◽  
Parameter Estimation Methods

<p>Protection and management of groundwater resources demand high-resolution distributions of hydraulic parameters (e.g., hydraulic conductivity (K) and specific storage (Ss)) of aquifers. In the past, these parameters were obtained by traditional analytical solutions (e.g., Theis (1935) or Cooper and Jacob (1946)). However, traditional methods assume the aquifer to be homogeneous and yield the equivalent parameter, which are averages over a large volume and are insufficient for predicting groundwater flow and solute transport process (Butler & Liu, 1993). For obtaining the aquifer heterogeneity, some scholars have used kriging (e.g., Illman et al., 2010) and hydraulic tomography (HT) (e.g., Yeh & Liu, 2000; Zhu & Yeh, 2005) to describe the K distribution.</p><p>In this study, the laboratory heterogeneous aquifer sandbox is used to investigate the effect of different hydraulic parameter estimation methods on predicting groundwater flow and solute transport process. Conventional equivalent homogeneous model, kriging and HT are used to characterize the heterogeneity of sandbox aquifer. A number of the steady-state head data are collected from a series of single-hole pumping tests in the lab sandbox, and are then used to estimate the K fields of the sandbox aquifer by the steady-state inverse modeling in HT survey which was conducted using the SimSLE algorithm (Simultaneous SLE, Xiang et al., 2009), a built-in function of the software package of VSAFT2. The 40 K core samples from the sandbox aquifer are collected by the Darcy experiments, and are then used to obtain K fields through kriging which was conducted using the software package of Surfer 13. The role of prior information on improving HT survey is then discussed. The K estimates by different methods are used to predict the process of steady-state groundwater flow and solute transport, and evaluate the merits and demerits of different methods, investigate the effect of aquifer heterogeneity on groundwater flow and solute transport.</p><p>According to lab sandbox experiments results, we concluded that compared with kriging, HT can get higher precision to characterize the aquifer heterogeneity and predict the process of groundwater flow and solute transport. The 40 K fields from the K core samples are used as priori information of HT survey can promote the accuracy of K estimates. The conventional equivalent homogeneous model cannot accurately predict the process of groundwater flow and solute transport in heterogeneous aquifer. The enhancement of aquifer heterogeneity will lead to the enhancement of the spatial variability of tracer distribution and migration path, and the dominant channel directly determines the migration path and tracer distribution.</p>

Multiscale Characterization of a Heterogeneous Aquifer Using an ASR Operation

Ground Water ◽  
2006 ◽  
Vol 44 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Paul Pavelic ◽  
Peter J Dillon ◽  
Craig T Simmons
Keyword(s):  
Heterogeneous Aquifer

scholarly journals Detection of contaminant plumes released from landfills

2006 ◽  
Vol 3 (3) ◽  
pp. 819-857
Author(s):  
N. B. Yenigül ◽  
A.T. Hendsbergen ◽  
A. M. M. Elfeki ◽  
F. M. Dekking
Keyword(s):  
Simulation Model ◽  
Contaminant Transport ◽  
Analytical Model ◽  
Detection Probability ◽  
Average Concentration ◽  
Contaminant Plume ◽  
Contaminant Plumes ◽  
Heterogeneous Aquifer ◽  
Monitoring Well ◽  
Detection Probabilities

Abstract. Contaminant leaks released from landfills are a significant threat to groundwater quality. The groundwater detection monitoring systems installed in the vicinity of such facilities are vital. In this study the detection probability of a contaminant plume released from a landfill has been investigated by means of both a simulation and an analytical model for both homogeneous and heterogeneous aquifer conditions. The results of the two models are compared for homogeneous aquifer conditions to illustrate the errors that might be encountered with the simulation model. For heterogeneous aquifer conditions contaminant transport is modelled by an analytical model using effective (macro) dispersivities. The results of the analysis show that the simulation model gives the concentration values correctly over most of the plume length for homogeneous aquifer conditions, and that the detection probability of a contaminant plume at given monitoring well locations match quite well. For heterogeneous aquifer conditions the approximating analytical model based on effective (macro) dispersivities yields the average concentration distribution satisfactorily. However, it is insufficient in monitoring system design since the discrepancy between the detection probabilities of contaminant plumes at given monitoring well locations computed by the two models is significant, particularly with high dispersivity and heterogeneity.

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