Groundwater Simulations and Uncertainty Analysis Using MODFLOW and Geostatistical Approach with Conditioning Multi-Aquifer Spatial Covariance

Lin, Yu‐Pin; Chen, Yu Wen; Chang, Liang-Cheng; Yeh, Ming Sheng; Huang, Guan‐Hua; Petway, Joy R.

doi:10.3390/w9030164

Cited by 13 publications

(6 citation statements)

References 46 publications

(104 reference statements)

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“…A stochastic simulation has to consist of a sufficient number of realizations to explore the complete uncertainty space of a parameter, and those realizations may be used to propagate such uncertainty into subsequent results (e.g., numerically simulated groundwater levels) by a Monte Carlo approach. Despite these advantages, the application of such stochastic simulation approaches has so far been very limited in the context of spatially variable hydraulic transmissivity [11] or other hydrogeological parameters [1,12] in karst aquifers.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region

Gonçalves,

Klammler,

Bastos Leal

2024

Water

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Aquifer properties, such as hydraulic transmissivity T and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with the quantification of uncertainties. Moreover, direct measurements of T are typically scarce, while those of specific capacity Sc are more frequent. In this study, we establish the linear regression relationship between the logarithms of T and Sc measured in 51 wells in a semi-arid karst region in Northeastern Brazil. This relationship is used to estimate empirical values logTemp based on measurements of logSc at 269 wells. LogTemp values are found to be normally distributed with an isotropic variogram of a significant nugget effect (attributed to local-scale karst features) and approximately 10 km range (attributed to larger-scale gradual changes in karst feature density). Ordinary kriging cross-validation indicates an optimum number of 25 neighboring wells for interpolation, which is used in a conditional sequential Gaussian simulation (SGSIM) to generate 500 realizations of logTemp with respective maps of standard deviations and probabilities of (not) exceeding threshold values. High-transmissivity areas mostly coincide with karstified river valleys, while low-transmissivity areas occur toward the edges where aquifer thickness decreases. The resulting transmissivity maps are relevant for optimizing regional water management strategies, which includes stochastic approaches where transmissivity realizations can be used to parameterize multiple runs of numerical groundwater models.

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Section: Introductionmentioning

confidence: 99%

Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region

Gonçalves,

Klammler,

Bastos Leal

2024

Water

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“…We will address the objective of this research by building a 3D numerical groundwater flow model linked to a particle tracking routine. Numerical models provide a convenient method for determining groundwater flow paths and estimating velocities (Weaver, 1991;Lin et al, 2017). This study encompasses transition from unconfined to confined aquifer conditions, presence of a regional groundwater divide ( Figure 5) and a fault.…”

Section: Oregon Anticlinementioning

confidence: 99%

“…However, stochastic modeling is an approach that helps to reduce the uncertainty. Stochastic analysis allows for a quantitative evaluation of the effects of variability and thereby provides a means of addressing uncertainty in the resultant head and flow that is caused by uncertainty in the model parameters (Lin et al, 2017). With the stochastic approach, a set of probable models were generated in GMS using MODFLOW.…”

Section: Stochastic Modelingmentioning

confidence: 99%

Capture Zone Analysis of a Wellfield to Assess Contaminant Transport

Oware¹

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Model Gaps and Recommendation CHAPTER VI: CONCLUSIONS REFERENCES groundwater. Due to the importance of those two isotopes in groundwater, Ra concentrations in groundwater are usually reported with respect to the combined concentration of those two isotopes, 226 Ra and 228 Ra with half-lives of 1600 years and 5.75 years, respectively (Kelly, 2008). Ra concentrations in groundwater differ because of differences in chemical properties of the parent isotopes at the head of the decay series, either 232 Th, 235 U, or 238 U (Stackelberg, 2018). The Thorium isotopes 228 Th 230 Th, and 232 Th, which are said to be insoluble in groundwater, are the immediate radioactive sources for 226 Ra, 228 Ra, and 224 Ra respectively (Stackelberg, 2018). 224 Ra has a half-life of 3.6 days and its concentration declines rapidly after about one half-life, whiles 223 Ra occurs infrequently in the environment at high concentrations (Stackelberg, 2018), hence both are not reported for combined Ra levels in drinking waters. Ra has been identified as the most potentially hazardous naturally occurring radionuclide likely to occur in drinking water (Szabo et al., 2012). Radium in drinking water increases cancer risk, primarily bone and sinus cancers (Szabo et al., 2012). The risk incurred from alpha particle emission depends on the way the body metabolizes the ingested radionuclide (Szabo et al., 2012). The Maximum Contaminant Level (MCL) for combined Ra is 5 picocuries per liter (pCi/L), which is set by the United States Environmental Protection Agency (USEPA) (Stackelberg, 2017). Radium in Northern Illinois The Cambrian-Ordovician aquifer system underlies area of 476,560 km 2 extending across parts of Midwestern states (Stackelberg, 2018). Within the northern part of Illinois, the Cambrian-Ordovician aquifer system is used as an important source of domestic and public water supplies. Concentrations of naturally occurring radioactive isotopes 226 Ra and 228 Ra in excess of the USEPA standard for drinking water has been detected in water from deep bedrock aquifers used for public water supply (Kay, 1999). One of the distinct stratigraphic difference between areas having higher concentration of Ra recorded in drinking water supplies and areas not having higher concentration is the presence of the Maquoketa

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“…Specifically, it is a metaheuristic method based on the analogy with the physical annealing process, approximating global optimization in a large search space. Lin et al (2017) adopted simulated annealing simulation for obtaining limited sets of realizations of hydraulic conductivity of multiple aquifers and spatial correlations among aquifers to simulate realizations of hydraulic heads and quantify their uncertainty in the Pingtung Plain, Taiwan. More specifically, simulated annealing was used to generate large sets of natural logarithm hydraulic conductivity realizations in multiple aquifers based on spatial correlations among them.…”

Section: Hydrogeological Modelingmentioning

confidence: 99%

Computationally effient geostatistical simulation for uncertainty propagation in models with spatially distributed parameters

Liodakis¹,

Λιοδάκης²

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Uncertainty is endemic in geospatial data due to the imperfect means of recording, processing, and representing spatial information. Propagating geospatial model inputs inherent uncertainty to uncertainty in model predictions is a critical requirement in each model's impact assessment and risk-conscious policy decision-making. It is still extremely difficult, however, to perform in practice uncertainty analysis of model outputs, particularly in complex spatially distributed environmental models, partially due to computational constraints.In the field of groundwater hydrology, the "stochastic revolution" has produced an enormous number of theoretical publications and greatly influenced our perspective on uncertainty and heterogeneity; it has had relatively little impact, however, on practical modeling. Monte Carlo simulation using simple random (SR) sampling from a multivariate distribution is one of the most widely used family of methods for uncertainty propagation in hydrogeological flow and transport model predictions, the other being analytical propagation.Real-life hydrogeological problems however, consist of complex and non-linear three dimensional groundwater models with millions of nodes and irregular boundary conditions. The number of Monte-Carlo runs required in these cases, depends on the number of uncertain parameters and on the relative accuracy required for the distribution of model predictions. In the context of sensitivity studies, inverse modelling or Monte-Carlo analyses, the ensuing computational burden is usually overwhelming and computationally impractical. These tough computational constrains have to be relaxed and removed before meaningful stochastic groundwater modeling applications are possible.A computationally efficient alternative to classical Monte Carlo simulation based on SR sampling is Latin hypercube (LH) sampling, a form of stratified random sampling. The latter yields a more representative distribution of model outputs (in terms of smaller sampling variability of their statistics) for the same number of input simulated realizations. The ability to generate unbiased LH realizations becomes critical in a spatial context, where random variables are geo-referenced and exhibit spatial correlation, to ensure unbiased outputs of complex models. On this regard, this dissertation offers a detailed analysis of LH sampling and compares it with SR sampling in a hydrogeological context. Additionally, two alternative stratified sampling methods, here named stratified likelihood (SL) sampling and minimum energy (ME) sampling, are examined (proposed in a spatial context) and their efficiency is further compared to SR and LH in a hydrogeological context; also accounting for the uncertainty related to the particular model at hand via a two step sampling method. All three stratified sampling methods (accounting for model sensitivity in the second case study) were found in this work to be more efficient than simple random sampling.Additionally, this thesis proposes a novel method for the expansion of the application domain of LH sampling to very large regular grids which is the common case in environmental (hydrogeological or not) models. More specifically, a novel combination of Stein's Latin Hypercube sampling with a Monte Carlo simulation method applicable over high discretization domains is proposed, and its performance is further validated in 2D and 3D hydrogeological problems of flow and transport in a mid-heterogeneous porous media, both consisting of about $1$ million nodes. Last, an additional novel extension of the proposed LH sampling on large grids is adopted for conditional high discretized problems. In this case too, the performance of the proposed approach is evaluated in a 3D hydrogeological model of flow and transport. Results indicate that both extensions (conditional and not) of LH sampling on large grids facilitate efficient uncertainty propagation with fewer model runs due to more representative model inputs. Overall, it could be argued that all the proposed methodological approaches could reduce the time and computer resources required to perform uncertainty analysis in hydrogeological flow and transport problems. Additionally, since it is the first time that stratified sampling is performed over high discretization domains, it could be argued that the proposed extensions of LH sampling on large grids could be considered a milestone for future uncertainty analysis efforts. Moreover, all the proposed stratified methods could contribute to a wider application of uncertainty analysis endeavors in a Monte Carlo framework for any spatially distributed impact assessment study.

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Groundwater Simulations and Uncertainty Analysis Using MODFLOW and Geostatistical Approach with Conditioning Multi-Aquifer Spatial Covariance

Cited by 13 publications

References 46 publications

Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region

Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region

Capture Zone Analysis of a Wellfield to Assess Contaminant Transport

Computationally effient geostatistical simulation for uncertainty propagation in models with spatially distributed parameters

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