Bayesian Chance‐Constrained Hydraulic Barrier Design under Geological Structure Uncertainty

The groundwater community has widely recognized geological structure uncertainty as a major source of model structure uncertainty. Previous studies in aquifer remediation design, however, rarely discuss the impact of geological structure uncertainty. This study combines chance‐constrained (CC) programming with Bayesian model averaging (BMA) as a BMA‐CC framework to assess the impact of geological structure uncertainty in remediation design. To pursue this goal, the BMA‐CC method is compared with traditional CC programming that only considers model parameter uncertainty. The BMA‐CC method is employed to design a hydraulic barrier to protect public supply wells of the Government St. pump station from salt water intrusion in the “1500‐foot” sand and the “1700‐foot” sand of the Baton Rouge area, southeastern Louisiana. To address geological structure uncertainty, three groundwater models based on three different hydrostratigraphic architectures are developed. The results show that using traditional CC programming overestimates design reliability. The results also show that at least five additional connector wells are needed to achieve more than 90% design reliability level. The total amount of injected water from the connector wells is higher than the total pumpage of the protected public supply wells. While reducing the injection rate can be achieved by reducing the reliability level, the study finds that the hydraulic barrier design to protect the Government St. pump station may not be economically attractive.     

A Hierarchical Bayesian Model Averaging Framework for Groundwater Prediction under Uncertainty

Groundwater prediction models are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate and prioritize sources of uncertainty in a hierarchical structure and conduct BMA for concentration prediction. A BMA tree of models is developed to understand the impact of individual sources of uncertainty and uncertainty propagation to model predictions. HBMA evaluates the relative importance of different modeling propositions at each level in the BMA tree of model weights. The HBMA method is applied to chloride concentration prediction for the “1,500‐foot” sand of the Baton Rouge area, Louisiana from 2005 to 2029. The groundwater head data from 1990 to 2004 is used for model calibration. Four sources of uncertainty are considered and resulted in 180 flow and transport models for concentration prediction. The results show that prediction variances of concentration from uncertain model elements are much higher than the prediction variance from uncertain model parameters. The HBMA method is able to quantify the contributions of individual sources of uncertainty to the total uncertainty.     

Modelling study on the impact of deep building foundations on the groundwater system

Coastal areas are usually the preferred place of habitation for human beings. Anthropogenic activities such as the construction of high‐rise buildings and underground transport systems usually require extensive deep foundations and ground engineering works, which may unintentionally modify the coastal groundwater system because the construction materials of foundations are usually of low hydraulic conductivity. In this paper, the impact of these building foundations on the groundwater regime is studied using hypothetical flow and transport models. Various possible realizations of foundation distributions are generated using stochastic parameters derived from a topographical map of an actual coastal area in Hong Kong. The effective hydraulic conductivity is first calculated for different realizations and the results show that the effective hydraulic conductivity can be reduced significantly. Then a hypothetical numerical model based on FEFLOW is set up to study the change of hydraulic head, groundwater discharge, and saltwater‐fresh water interface. The groundwater level and flow are modified to various degrees, depending on the foundations percentage and the distribution pattern of the buildings. When the foundations percentage is high and the building foundations are aggregated, the hydraulic head is raised significantly and the originally one‐dimensional groundwater flow field becomes complicated. Seaward groundwater discharge will be reduced and some groundwater may become seepage through the ground surface. The transport model shows that, after foundations are added, overall the seawater and fresh groundwater interface moves landward, so extensive foundations may induce seawater intrusion. It is believed that the modification of the coastal groundwater system by building foundations may have engineering and environmental implications, such as submarine groundwater discharge, foundation corrosion, and slope stability. Copyright © 2007 John Wiley & Sons, Ltd.     

A Model-Averaging Method for Assessing Groundwater Conceptual Model Uncertainty

This study evaluates alternative groundwater models with different recharge and geologic components at the northern Yucca Flat area of the Death Valley Regional Flow System (DVRFS), USA. Recharge over the DVRFS has been estimated using five methods, and five geological interpretations are available at the northern Yucca Flat area. Combining the recharge and geological components together with additional modeling components that represent other hydrogeological conditions yields a total of 25 groundwater flow models. As all the models are plausible given available data and information, evaluating model uncertainty becomes inevitable. On the other hand, hydraulic parameters (e.g., hydraulic conductivity) are uncertain in each model, giving rise to parametric uncertainty. Propagation of the uncertainty in the models and model parameters through groundwater modeling causes predictive uncertainty in model predictions (e.g., hydraulic head and flow). Parametric uncertainty within each model is assessed using Monte Carlo simulation, and model uncertainty is evaluated using the model averaging method. Two model-averaging techniques (on the basis of information criteria and GLUE) are discussed. This study shows that contribution of model uncertainty to predictive uncertainty is significantly larger than that of parametric uncertainty. For the recharge and geological components, uncertainty in the geological interpretations has more significant effect on model predictions than uncertainty in the recharge estimates. In addition, weighted residuals vary more for the different geological models than for different recharge models. Most of the calibrated observations are not important for discriminating between the alternative models, because their weighted residuals vary only slightly from one model to another.     

Screening‐Level Sensitivity Analysis for the Design of Pump‐and‐Treat Systems

Pump‐and‐treat systems can prevent the migration of groundwater contaminants and candidate systems are typically evaluated with groundwater models. Such models should be rigorously assessed to determine predictive capabilities and numerous tools and techniques for model assessment are available. While various assessment methodologies (e.g., model calibration, uncertainty analysis, and Bayesian inference) are well‐established for groundwater modeling, this paper calls attention to an alternative assessment technique known as screening‐level sensitivity analysis (SLSA). SLSA can quickly quantify first‐order (i.e., main effects) measures of parameter influence in connection with various model outputs. Subsequent comparisons of parameter influence with respect to calibration vs. prediction outputs can suggest gaps in model structure and/or data. Thus, while SLSA has received little attention in the context of groundwater modeling and remedial system design, it can nonetheless serve as a useful and computationally efficient tool for preliminary model assessment. To illustrate the use of SLSA in the context of designing groundwater remediation systems, four SLSA techniques were applied to a hypothetical, yet realistic, pump‐and‐treat case study to determine the relative influence of six hydraulic conductivity parameters. Considered methods were: Taguchi design‐of‐experiments (TDOE); Monte Carlo statistical independence (MCSI) tests; average composite scaled sensitivities (ACSS); and elementary effects sensitivity analysis (EESA). In terms of performance, the various methods identified the same parameters as being the most influential for a given simulation output. Furthermore, results indicate that the background hydraulic conductivity is important for predicting system performance, but calibration outputs are insensitive to this parameter (K_BK). The observed insensitivity is attributed to a nonphysical specified‐head boundary condition used in the model formulation which effectively “staples” head values located within the conductivity zone. Thus, potential strategies for improving model predictive capabilities include additional data collection targeting the K_BK parameter and/or revision of model structure to reduce the influence of the specified head boundary.     

Hydraulic-Head Formulation for Density-Dependent Flow and Transport

Density-dependent flow and transport solutions for coastal saltwater intrusion investigations, analyses of fluid injection into deep brines, and studies of convective fingering and instabilities of denser fluids moving through less dense fluids typically formulate the groundwater flow equation in terms of pressure or equivalent freshwater head. A formulation of the flow equation in terms of hydraulic head is presented here as an alternative. The hydraulic-head formulation can facilitate adaptation of existing constant-density groundwater flow codes to include density-driven flow by avoiding the need to convert between freshwater head and hydraulic head within the code and by incorporating density-dependent terms as a compartmentalized “correction” to constant-density calculations already performed by the code. The hydraulic-head formulation also accommodates complexities such as unconfined groundwater flow and Newton-Raphson solution schemes more readily than the freshwater-head formulation. Simulation results are presented for four example problems solved using an implementation of the hydraulic-head formulation in MODFLOW.     

Assessing titanium dioxide nanoparticles transport models by Bayesian uncertainty analysis

With the rapid growth of nanotechnology industry, nanomaterials as an emerging pollutant are gradually released into subsurface environments and become great concerns. Simulating the transport of nanomaterials in groundwater is an important approach to investigate and predict the impact of nanomaterials on subsurface environments. Currently, a number of transport models are used to simulate this process, and the outputs of these models could be inconsistent with each other due to conceptual model uncertainty. However, the performances of different models on simulating nanoparticles transport in groundwater are rarely assessed in Bayesian framework in previous researches, and these will be the primary objective of this study. A porous media column experiment is conducted to observe the transport of Titanium Dioxide Nanoparticles (nano-TiO₂). Ten typical transport models which consider different chemical reaction processes are used to simulate the transport of nano-TiO₂, and the observed nano-TiO₂ breakthrough curves data are used to calibrate these models. For each transport model, the parameter uncertainty is evaluated using Markov Chain Monte Carlo, and the DREAM_(ZS) algorithm is used to sample parameter probability space. Moreover, the Bayesian model averaging (BMA) method is used to incorporate the conceptual model uncertainty arising from different chemical reaction based transport models. The results indicate that both two-sites and nonequilibrium sorption models can well reproduce the retention of nano-TiO₂ transport in porous media. The linear equilibrium sorption isotherm, first-order degradation, and mobile-immobile models fail to describe the nano-TiO₂ retention and transport. The BMA method could instead provide more reliable estimations of the predictive uncertainty compared to that using a single model.     

3D inverse modelling of groundwater flow at a fractured site using a stochastic continuum model with multiple statistical populations

 3D groundwater flow at the fractured site of Asp? (Sweden) is simulated. The aim was to characterise the site as adequately as possible and to provide measures on the uncertainty of the estimates. A stochastic continuum model is used to simulate both groundwater flow in the major fracture planes and in the background. However, the positions of the major fracture planes are deterministically incorporated in the model and the statistical distribution of the hydraulic conductivity is modelled by the concept of multiple statistical populations; each fracture plane is an independent statistical population. Multiple equally likely realisations are built that are conditioned to geological information on the positions of the major fracture planes, hydraulic conductivity data, steady state head data and head responses to six different interference tests. The experimental information could be reproduced closely. The results of the conditioning are analysed in terms of ensemble averaged average fracture plane conductivities, the ensemble variance of average fracture plane conductivities and the statistical distribution of the hydraulic conductivity in the fracture planes. These results are evaluated after each conditioning stage. It is found that conditioning to hydraulic head data results in an increase of the hydraulic conductivity variance while the statistical distribution of log hydraulic conductivity, initially Gaussian, becomes more skewed for many of the fracture planes in most of the realisations.     

Numerical Simulations of Seasonally Oscillated Groundwater Dynamics in Coastal Confined Aquifers

Studies investigating the effects of inland recharge on coastal groundwater dynamics were carried out typically in unconfined aquifers, with few in confined aquifers. This study focused on the groundwater dynamics in confined aquifers with seasonally sinusoidally fluctuated inland groundwater head and constant sea level by numerical simulations. It is known that the mixing zone (MZ) of saltwater wedge in response to the seasonal oscillations of inland groundwater head swings around the steady-state MZ. However, our simulation results indicate that even the most landward freshwater-saltwater interface over a year is seaward from the steady-state location when the hydraulic conductivity K is ≤10⁻⁴ m/s under certain boundary conditions with given parameter values. That is, seasonal oscillations of inland groundwater head may reduce seawater intrusion in confined coastal aquifers when K ≤ 10⁻⁴ m/s. Sensitivity analysis indicates that for aquifers of K ≤ 10⁻⁴ m/s, the larger the inland head fluctuation amplitude is, the less the seawater intrudes. This is probably due to the reason that the seawater intrusion time decreases with the increase of fluctuation amplitude when K ≤ 10⁻⁴ m/s. Numerical simulations demonstrate that seasonal inland groundwater head oscillations promote the annual averaged recirculated seawater discharge across the seaward boundary.     

Saltwater intrusion modeling in heterogeneous confined aquifers using two-relaxation-time lattice Boltzmann method

This study develops a lattice Boltzmann method (LBM) with a two-relaxation-time collision operator (LTRT) to solve saltwater intrusion problems. A directional-speed-of-sound (DSS) technique is introduced to take into account the hydraulic conductivity heterogeneity and discontinuity, as well as the velocity-dependent dispersion coefficient. The forcing terms in the LTRT model are customized in order to recover the density-dependent groundwater flow and mass transport equations. Using the LTRT with the squared DSS achieves at least second-order accuracy. The LTRT results are verified with Henry’s analytical solution as well as compared with several numerical examples and modified Henry problems that consider heterogeneous hydraulic conductivity and velocity-dependent dispersion. The numerical results show good agreement with the Henry analytical solution and with the numerical solutions obtained by other numerical methods.     

Addressing snow model uncertainty for hydrologic prediction

Streamflow forecasting methods are moving towards probabilistic approaches that quantify the uncertainty associated with the various sources of error in the forecasting process. Multi-model averaging methods which try to address modeling deficiencies by considering multiple models are gaining much popularity. We have applied the Bayesian Model Averaging method to an ensemble of twelve snow models that vary in their heat and melt algorithms, parameterization, and/or albedo estimation method. Three of the models use the temperature-based heat and melt routines of the SNOW17 snow accumulation and ablation model. Nine models use heat and melt routines that are based on a simplified energy balance approach, and are varied by using three different albedo estimation schemes. Finally, different parameter sets were identified through automatic calibration with three objective functions. All models use the snow accumulation, liquid water transport, and ground surface heat exchange processes of the SNOW17. The resulting twelve snow models were combined using Bayesian Model Averaging (BMA). The individual models, BMA predictive mean, and BMA predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best and the BMA variance was lowest at the colder sites with high winter precipitation and little mid-winter melting. An individual snow model would often outperform the BMA predictive mean. However, observed snow water equivalent (SWE) was captured within the 95% confidence intervals of the BMA variance on average 80% of the time at all sites. Results are promising that consideration of multiple snow structures would provide useful uncertainty information for probabilistic hydrologic prediction.     

Characterization of groundwater contaminant source using Bayesian method

Contaminant source identification in groundwater system is critical for remediation strategy implementation, including gathering further samples and analysis, as well as implementing and evaluating different remediation plans. Such problem is usually solved with the aid of groundwater modeling with lots of uncertainty, e.g. existing uncertainty in hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This paper presents an application of a dynamic framework coupling with a three dimensional groundwater modeling scheme in contamination source identification of groundwater. Markov Chain Monte Carlo (MCMC) is being applied to infer the possible location and magnitude of contamination source. Uncertainty existing in heterogonous hydraulic conductivity field is explicitly considered in evaluating the likelihood function. Unlike other inverse-problem approaches to provide single but maybe untrue solution, the MCMC algorithm provides probability distributions over estimated parameters. Results from this algorithm offer a probabilistic inference of the location and concentration of released contamination. The convergence analysis of MCMC reveals the effectiveness of the proposed algorithm. Further investigation to extend this study is also discussed.     

Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging

In this paper, the Genetic Algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this combined method, several SWAT models with different structures are first selected; next GA is used to calibrate each model using observed streamflow data; finally, BMA is applied to combine the ensemble predictions and provide uncertainty interval estimation. This method was tested in two contrasting basins, the Little River Experimental Basin in Georgia, USA, and the Yellow River Headwater Basin in China. The results obtained in the two case studies show that this combined method can provide deterministic predictions better than or comparable to the best calibrated model using GA. The 66.7% and 90% uncertainty intervals estimated by this method were analyzed. The differences between the percentage of coverage of observations and the corresponding expected coverage percentage are within 10% for both calibration and validation periods in these two test basins. This combined methodology provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT.     

Improvement of groundwater level prediction in sparsely gauged basins using physical laws and local geographic features as auxiliary variables

This work aims to present new modeling tools that help to better monitor and predict the groundwater level in sparsely gauged basins. The working area is the Mires basin of Mesara valley in the island of Crete (Greece). Efficient groundwater management in the basin is crucial in light of regional climate change model estimates showing a substantial risk of desertification for Crete. We propose that the prediction of the hydraulic head spatial variability in Mires basin can be improved by incorporating in the trend the distance of the prediction points from a temporary river crossing the basin and a component based on the generalized Thiem’s equation for multiple wells as well as using the flexible Spartan semivariogram family to perform Residual Kriging. Our proposal is supported by the results of cross validation analysis. Our results are applicable to other unconfined aquifers.     

Including geophysical data in ground water model inverse calibration

A nonlinear regression method is developed that can be used to estimate parameters of a ground waterflow model from a combination of observations of hydrological variables and observations of geophysical properties that are functionally related with the hydraulic conductivity. The procedure estimates: parameters characterizing the hydraulic conductivity field (e.g., zonal or pilot point values); geophysical properties that have been observed and that are functionally related with the hydraulic conductivity parameters; and a few parameters of the function that relates the hydraulic conductivity parameters with the geophysical properties (the type of function is assumed known). A fidelity factor, sigma(r)2, of a term of the minimized objective function reflects the faith one has in the validity of this functional relationship. The estimation methodology has been tested by means of synthetic models. The experimental results demonstrate that the number of estimated hydraulic conductivity parameters can be increased by adding geophysical observations to the set of hydrological observations that are traditionally used for model calibration. The improvement of the estimated hydraulic conductivity field and the simulated hydraulic head field can be significant but is dependent on the number, the locations, and the uncertainty of geophysical observations. The sensitivity of the estimation results to the value of sigma(r) is small for the studied problems except when the uncertainty of geophysical observations is high. In the latter case, a large sigma(r) value was found to be optimal to avoid that hydraulic conductivity estimates are closely tied to corresponding but highly uncertain geophysical observations.     

Prospective scenarios of the saltwater intrusion in an estuary under climate change context using Bayesian neural networks

In recent decades, saltwater intrusion over some low-lying coastal regions was deteriorated by rising sea-level and decreasing streamflow in the context of climate change. Though physically-based hydrodynamic models are the most detailed means to simulate salinity processes, they are commonly restricted by data insufficiency issues both in spatial resolution and temporal lasting. This motivates us to build a statistical model enable simulation and scenario analysis for coastal salinity change with limited observations. A Bayesian neural network (BNN) model is built hereby to simulate salinity. It offers more precise estimation compared with the conventional artificial neural network. Meanwhile, the model gives the uncertainty behaviors of the final salinity simulation which is not available for other methods. Future scenarios of salinity change are constructed and analyzed in different time periods on the basis of the validated BNN model. Results indicate that the water quality over lower Pearl River is degrading along with more significant uncertainties. Further analysis suggests that streamflow alteration has a more direct impact on salinity variations than the sea-level change does. The method allows a profound analysis of the potential influence on water quality degradation in coastal and low-lying regions in support of water management and adaptation toward global climate change.     

Groundwater modelling and marine intrusion in the semi‐arid systems (Cap‐Bon,Tunisia)

This article studies the effect of drought and pumping discharge on groundwater supplies and marine intrusion in the Korba aquifer (Cap‐Bon peninsula, Tunisia). The Groundwater Modelling System has been used to model the groundwater flow and to simulate the seawater intrusion. The calibration is based on the groundwater levels in the steady state from 1963, and in the transient state from the groundwater levels from 1963 to 2005. The main objective is to quantify the components of the groundwater mass balance and to estimate the hydraulic conductivity distribution. The impact of pumping discharge on the groundwater level evolution has been examined by two pumping scenarios P1 (no. 8420) and P2 (no. 8862) wells. The hydrodynamic modelling shows the increasing drawdowns after 14 years of pumping: 4 m in P1 well and about 5 m in P2 well below sea level. The drawdowns are accompanied by the inverse hydraulic gradient. The numerical model was used to discuss the management of the groundwater resources of Cap‐Bon. As the population continues to grow and the demand for groundwater pumping intensifies beyond the 1963 level, it can be expected that the actual extent of seawater intrusion in the future would be more severe than the model prediction. Better strategies for groundwater development and management will be necessary to protect the freshwater aquifers to the marine intrusion. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessing the uncertainties of hydrologic model selection in climate change impact studies

The uncertainties associated with atmosphere‐ocean General Circulation Models (GCMs) and hydrologic models are assessed by means of multi‐modelling and using the statistically downscaled outputs from eight GCM simulations and two emission scenarios. The statistically downscaled atmospheric forcing is used to drive four hydrologic models, three lumped and one distributed, of differing complexity: the Sacramento Soil Moisture Accounting (SAC‐SMA) model, Conceptual HYdrologic MODel (HYMOD), Thornthwaite‐Mather model (TM) and the Precipitation Runoff Modelling System (PRMS). The models are calibrated based on three objective functions to create more plausible models for the study. The hydrologic model simulations are then combined using the Bayesian Model Averaging (BMA) method according to the performance of each models in the observed period, and the total variance of the models. The study is conducted over the rainfall‐dominated Tualatin River Basin (TRB) in Oregon, USA. This study shows that the hydrologic model uncertainty is considerably smaller than GCM uncertainty, except during the dry season, suggesting that the hydrologic model selection‐combination is critical when assessing the hydrologic climate change impact. The implementation of the BMA in analysing the ensemble results is found to be useful in integrating the projected runoff estimations from different models, while enabling to assess the model structural uncertainty. Copyright © 2011 John Wiley & Sons, Ltd.     

Using semivariogram parameter uncertainty in hydrogeological applications

Geostatistical estimation (kriging) and geostatistical simulation are routinely used in ground water hydrology for optimal spatial interpolation and Monte Carlo risk assessment, respectively. Both techniques are based on a model of spatial variability (semivariogram or covariance) that generally is not known but must be inferred from the experimental data. Where the number of experimental data is small (say, several tens), as is not unusual in ground water hydrology, the model fitted to the empirical semivariogram entails considerable uncertainty. If all the practical results are based on this unique fitted model, the final results will be biased. We propose that, instead of using a unique semivariogram model, the full range of models that are inside a given confidence region should be used, and the weight that each semivariogram model has on the final result should depend on its plausibility. The first task, then, is to evaluate the uncertainty of the model, which can be efficiently done by using maximum likelihood inference. The second task is to use the range of plausible models in applications and to show the effect observed on the final results. This procedure is put forth here with kriging and simulation applications, where the uncertainty in semivariogram parameters is propagated into the final results (e.g., the prediction of ground water head). A case study using log-transmissivity data from the Vega de Granada aquifer, in southern Spain, is given to illustrate the methodology.     

A stochastic optimization framework for the restoration of an over-exploited aquifer

ABSTRACT

This study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.

Editor Z.W. Kundzewicz; Guest editor S. Weijs