Groundwater Management Optimization and Saltwater Intrusion Mitigation under Uncertainty

Groundwater is valuable to supply fresh water to the public, industries, agriculture, etc. However, excessive pumping has caused groundwater storage degradation, water quality deterioration and saltwater intrusion problems. Reliable groundwater flow and solute transport modeling is needed for sustainable groundwater management and aquifer remediation design. However, challenges exist because of highly complex subsurface environments, computationally intensive groundwater models as well as inevitable uncertainties. The first research goal is to explore conjunctive use of feasible hydraulic control approaches for groundwater management and aquifer remediation. Water budget analysis is conducted to understand how groundwater withdrawals affect water levels. A mixed integer multi-objective optimization model is constructed to derive optimal freshwater pumping strategies and investigate how to promote the optimality through regulating pumping locations. A solute transport model for the Baton Rouge multi-aquifer system is developed to assess saltwater encroachment under current condition. Potential saltwater scavenging approach is proposed to mitigate the salinization issue in the Baton Rouge area. The second research goal aims to develop robust surrogate-assisted simulation-optimization modeling methods for saltwater intrusion mitigation. Machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a complex high-fidelity solute transport model for predicting saltwater intrusion. Two different methods including Bayesian model averaging and Bayesian set pair analysis are used to construct ensemble surrogates and quantify model prediction uncertainties. Besides. different optimization models that incorporate multiple ensemble surrogates are formulated to obtain optimal saltwater scavenging strategies. Chance-constrained programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. The results show that conjunctive use of hydraulic control approaches would be effective to mitigate saltwater intrusion but needs decades. Machine learning based ensemble surrogates can build accurate models with high computing efficiency, and hence save great efforts in groundwater remediation design. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach

Bayesian model averaging on hydraulic conductivity estimation and groundwater head prediction

Characterization of aquifer heterogeneity is inherently difficult because of the insufficiency of data, the inflexibility of parameterization methods, and non-uniqueness of parameterization methods. Groundwater predictions are greatly affected by multiple interpretations of aquifer properties and the uncertainties of model parameters. This study introduces a Bayesian model averaging (BMA) method along with multiple generalized parameterization (GP) methods to identify hydraulic conductivity and along with multiple simulation models to predict groundwater head and quantify the prediction uncertainty. Two major issues about BMA are discussed. The first problem is with using Occam’s window in usual BMA applications. Occam’s window only accepts models in a very narrow range, tending to single out the best method and discard other good methods. A variance window is proposed to replace Occam’s window to cope with this problem. The second problem is with using the Kashyap information criterion (KIC) in the approximation of posterior model probabilities, which tends to prefer highly uncertain model by considering the Fisher information matrix. The Bayesian information criterion (BIC) is recommended because it is able to avoid controversial results and it is computationally efficient. Numerical examples are designed to test the Bayesian model averaging method on hydraulic conductivity identification and groundwater head prediction. The proposed methodologies are then applied to the hydraulic conductivity identification of the Alamitos Gap area, and the hydraulic conductivity estimation and groundwater head prediction of the “1,500-foot” sand in East Baton Rouge Parish, Louisiana. The results show that the GP method provides great flexibility in parameterization with small conditional variance. The use of the variance window is necessary to avoid a dominant model when many models perform equally well. Compared to KIC, BIC is able to give an unbiased posterior model probability. It is also concluded that the uncertainty increases by including multiple models under the BMA framework, but risks are reduced by avoiding overconfidence in the solution from one model

Characterization and uncertainty analysis of siliciclastic aquifer-fault system

The complex siliciclastic aquifer system underneath the Baton Rouge area, Louisiana, USA, is fluvial in origin. The east-west trending Baton Rouge fault and Denham Springs-Scotlandville fault cut across East Baton Rouge Parish and play an important role in groundwater flow and aquifer salinization. To better understand the salinization underneath Baton Rouge, it is imperative to study the hydrofacies architecture and the groundwater flow field of the Baton Rogue aquifer-fault system. This is done through developing multiple detailed hydrofacies architecture models and multiple groundwater flow models of the aquifer-fault system, representing various uncertain model propositions. The hydrofacies architecture models focus on the Miocene-Pliocene depth interval that consists of the “1,200-foot” sand, “1,500-foot” sand, “1,700-foot” sand and the “2,000-foot” sand, as these aquifer units are classified and named by their approximate depth below ground level. The groundwater flow models focus only on the “2,000-foot” sand. The study reveals the complexity of the Baton Rouge aquifer-fault system where the sand deposition is non-uniform, different sand units are interconnected, the sand unit displacement on the faults is significant, and the spatial distribution of flow pathways through the faults is sporadic. The identified locations of flow pathways through the Baton Rouge fault provide useful information on possible windows for saltwater intrusion from the south. From the results we learn that the “1,200-foot” sand, “1,500-foot” sand and the “1,700-foot” sand should not be modeled separately since they are very well connected near the Baton Rouge fault, while the “2,000-foot” sand between the two faults is a separate unit. Results suggest that at the “2,000-foot” sand the Denham Springs-Scotlandville fault has much lower permeability in comparison to the Baton Rouge fault, and that the Baton Rouge fault plays an important role in the aquifer salinization

Multi-Scale Assessment of Prediction Uncertainty in Coupled Reactive Transport Models Conducted at the Florida State University

This report summarizes the research activities in the Florida State University for quantifying parametric and model uncertainty in groundwater reactive transport modeling. Mathematical and computational research was conducted to investigate the following five questions: (1) How does uncertainty behave and affect groundwater reactive transport models? (2) What cause the uncertainty in groundwater reactive transport modeling? (3) How to quantify parametric uncertainty of groundwater reactive transport modeling? (4) How to quantify model uncertainty of groundwater reactive transport modeling? and (5) How to reduce predictive uncertainty by collecting data of maximum value of information or data-worth? The questions were addressed using Interdisciplinary methods, including computational statistics, Bayesian uncertainty analysis, and groundwater modeling. Both synthetic and real-world data were used to evaluate and demonstrate the developed methods. The research results revealed special challenges to uncertainty quantification for groundwater reactive transport models. For example, competitive reactions and substitution effects of reactions also cause parametric uncertainty. Model uncertainty is more important than parametric uncertainty, and model averaging methods are a vital tool to improve model predictions. Bayesian methods are more accurate than regression methods for uncertainty quantification. However, when Bayesian uncertainty analysis is computationally impractical, uncertainty analysis using regression methods still provides insights into uncertainty analysis. The research results of this study are useful to science-informed decision-making and uncertainty reduction by collecting data of more value of information

Climate Change and Atlantic salmon (Salmo salar): Changes in Flow and Freshwater Habitat in the Burrishoole Catchment

Climate change is anticipated to impact the flow regime of riverine systems with resultant consequences for the freshwater habitat of Atlantic salmon (Salmo salar) and the long-term sustainability of their population numbers. The Burrishoole catchment, a relatively small but productive salmon catchment (~90 km2) located on Ireland’s west coast, is used as a case study to investigate this. A series of high resolution climate scenarios were employed to examine potential changes in the climate and hydrology of this catchment. The climate scenarios used represent different combinations of greenhouse gas emission scenarios, driving GCMs and statistical/dynamical downscaling models; in addition, three different rainfall-runoff models (HBV, HYSIM and TOPMODEL) were employed – integrating across both structural and parameter uncertainty. By considering multiple model pathways this study attempts to sample across the uncertainties encountered at each stage in the process of translating prescribed anthropogenic forcings into local scale responses in the catchment system. The hydrological projections were examined in the context of the habitat and flow requirements of Atlantic salmon at key stages in their life-cycle (e.g. spawning, migration). Model projections suggest that the catchment is likely to become warmer, with wetter winters and drier summers occurring. The results of the hydrological modelling suggest that this will be accompanied by an increase in the seasonality of its flow regime - manifest in an increase in low (Q95) summer and high (Q05) winter flows. If realised, these changes are likely to impact salmon through a reduction in the availability of preferred habitat, a loss in connectivity across the catchment system and a disruption to the evolved synchrony between the occurrence of optimal in-stream conditions and the time at which certain life history events occur. Each of these factors is likely to impact the processes of migration, reproduction and recruitment - each of which is critical for the long-term viability of healthy, self-sustaining wild stocks in the catchment. Based on the projected flow data it is likely that the carrying capacity and productivity of the catchment may be reduced. In addition, by affecting those life stages which are already subject to significant mortality losses (e.g. fry emergence, smolt migration), changes in climate may result in population collapse - particularly if successive year-classes are affected. The results of the hydrological modelling highlight the sensitivity of smaller spatey catchments to changes in climate. Given that the Burrishoole system is typical of many catchment systems found along Ireland’s western seaboard, the results highlight a vulnerability to climate change which is present more generally across the region

Climate Change and Atlantic salmon (Salmo salar): Changes in Flow and Freshwater Habitat in the Burrishoole Catchment

Climate change is anticipated to impact the flow regime of riverine systems with resultant consequences for the freshwater habitat of Atlantic salmon (Salmo salar) and the long-term sustainability of their population numbers. The Burrishoole catchment, a relatively small but productive salmon catchment (~90 km2) located on Ireland’s west coast, is used as a case study to investigate this. A series of high resolution climate scenarios were employed to examine potential changes in the climate and hydrology of this catchment. The climate scenarios used represent different combinations of greenhouse gas emission scenarios, driving GCMs and statistical/dynamical downscaling models; in addition, three different rainfall-runoff models (HBV, HYSIM and TOPMODEL) were employed – integrating across both structural and parameter uncertainty. By considering multiple model pathways this study attempts to sample across the uncertainties encountered at each stage in the process of translating prescribed anthropogenic forcings into local scale responses in the catchment system. The hydrological projections were examined in the context of the habitat and flow requirements of Atlantic salmon at key stages in their life-cycle (e.g. spawning, migration). Model projections suggest that the catchment is likely to become warmer, with wetter winters and drier summers occurring. The results of the hydrological modelling suggest that this will be accompanied by an increase in the seasonality of its flow regime - manifest in an increase in low (Q95) summer and high (Q05) winter flows. If realised, these changes are likely to impact salmon through a reduction in the availability of preferred habitat, a loss in connectivity across the catchment system and a disruption to the evolved synchrony between the occurrence of optimal in-stream conditions and the time at which certain life history events occur. Each of these factors is likely to impact the processes of migration, reproduction and recruitment - each of which is critical for the long-term viability of healthy, self-sustaining wild stocks in the catchment. Based on the projected flow data it is likely that the carrying capacity and productivity of the catchment may be reduced. In addition, by affecting those life stages which are already subject to significant mortality losses (e.g. fry emergence, smolt migration), changes in climate may result in population collapse - particularly if successive year-classes are affected. The results of the hydrological modelling highlight the sensitivity of smaller spatey catchments to changes in climate. Given that the Burrishoole system is typical of many catchment systems found along Ireland’s western seaboard, the results highlight a vulnerability to climate change which is present more generally across the region

The Utility of Using Multiple Conceptual Models for the Design of Groundwater Remediation Systems

The design of pump and treat systems for groundwater remediation is often aided by numerical groundwater modelling. Model predictions are uncertain, with this uncertainty resulting from unknown parameter values, model structure and future system forcings. Researchers have begun to suggest that uncertainty in groundwater model predictions is largely dominated by structural/conceptual model uncertainty and that multiple conceptual models be developed in order to characterize this uncertainty. As regulatory bodies begin to endorse the more expensive multiple conceptual model approach, it is useful to assess whether a multiple model approach provides a signi cant improvement over a conventional single model approach for pump and treat system design, supplemented with a factor of safety. To investigate this question, a case study located in Tacoma, Washington which was provided by Conestoga-Rovers & Associates (CRA) was used. Twelve conceptual models were developed to represent conceptual model uncertainty at the Tacoma, Washington site and a pump and treat system was optimally designed for each conceptual model. Each design was tested across all 12 conceptual models with no factor of safety applied, and a factor of safety of 1.5 and 2 applied. Adding a factor of safety of 1.5 decreased the risk of containment failure to 15 percent, compared to 21 percent with no factor of safety. Increasing the factor of safety from 1.5 to 2 further reduced the risk of containment failure to 9 percent, indicating that the application of a factor of safety reduces the risk of design failure at a cost directly proportional to the value of the factor of safety. To provide a relatively independent estimate of a factor of safety approach a single "best" model developed by CRA was compared against the multiple model approach. With a factor of safety of 1.5 or greater, adequate capture was demonstrated across all 12 conceptual models. This demonstrated that in this case using the single \best" model developed by CRA with a factor of safety would have been a reasonable surrogate for a multiple model approach. This is of practical importance to engineers as it demonstrates that the a conventional single model approach may be su cient. However, it is essential that the model used is a good model. Furthermore, a multiple model approach will likely be an excessive burden in cases such as pump and treat system design, where the cost of failure is low as the system can be adjusted during operation to respond to new data. This may not be the case for remedial systems with high capital costs such as permeable reactive barriers, which cannot be easily adjusted

High-resolution boreal winter precipitation projections over tropical America from CMIP5 models

Climate change projections for boreal winter precipitation in Tropical America has been addressed by statistical downscaling (SD) using the principal component regression with sea-level pressure (SLP) as the predictor variable. The SD model developed from the reanalysis of SLP and gridded precipitation GPCC data, has been applied to SLP outputs from 20 CGMS of CMIP5, both from the present climate (1971-2000) and for the future (2071-2100) under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The SD model shows a suitable performance over large regions, presenting a strong bias only in small areas characterized by very dry climate conditions or poor data coverage. The difference in percentage between the projected SD precipitation and the simulated SD precipitation for present climate, ranges from moderate to intense changes in rainfall (positive or negative, depending on the region and the SD GCM model considered), as the radiative forcing increases from the RCP2.6 to RCP8.5. The disparity in the GCMs outputs seems to be the major source of uncertainty in the projected changes, while the scenario considered appears less decisive. Mexico and eastern Brazil are the areas showing the most coherent decreases between SD GCMs, while northwestern and southeastern South America show consistently significant increases. This coherence is corroborated by the results of the ensemble mean which projects positive changes from 10ºN towards the south, with exceptions such as eastern Brazil, northern Chile and some smaller areas, such as the center of Colombia, while projected negative changes are the majority found in the northernmost part.Departamento Física Aplicada, Facultad de Ciencias, Universidad de Granad

High-resolution boreal winter precipitation projections over Tropical America from CMIP5 models

Climate change projections for boreal winter precipitation in Tropical America has beenaddressed by statistical downscaling (SD) using the principal component regression with sea-level pressure (SLP) as the predictor variable. The SD model developed from the reanalysis of SLP and gridded precipitation GPCC data, has been applied to SLP outputs from 20 CGMS of CMIP5, both from the present climate (1971-2000) and for the future (2071-2100) under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The SD model shows a suitable performance over large regions, presenting a strong bias only in small areas characterized by very dry climate conditions or poor data coverage. The difference in percentage between the projected SD precipitation and the simulated SD precipitation for present climate, ranges from moderate to intense changes in rainfall (positive or negative, depending on the region and the SD GCM model considered), as the radiative forcing increases from the RCP2.6 to RCP8.5. The disparity in the GCMs outputs seems to be the major source of uncertainty in the projected changes, while the scenario considered appears less decisive. Mexico and eastern Brazil are the areas showing the most coherent decreases between SD GCMs, while northwestern and southeastern South America show consistently significant increases. This coherence is corroborated by the results of the ensemble mean which projects positive changes from 10N towards the south, with exceptions such as eastern Brazil, northern Chile and some smaller areas, such as the center of Colombia, while projected negative changes are the majority found in the northernmost part

Critical Review of the Literature on Marine Mammal Population Modelling

A comprehensive literature review and modeling effort have been conducted in order to determine which vital rates are most important to determining the growth and sustainability of marine mammal populations. Also addressed are the impacts of life-history, ecological, and genetic variation on vital rates and population sustainability and how much each vital parameter can change before a change in population trend would be expected. Additionally, the influence of ecological energetics and foraging strategies on vital rates and their limits of sustainable change are examined, and the nature of how an increase in sound in the marine environment might influence marine mammal behavior, and thus life functions, vital rates and population sustainability is explored. An analysis of the elasticity and sensitivity of marine mammal population models suggests that: 1) Most whale populations appear to be most sensitive to changes in adult female survival and least sensitive to calf survival. 2) Most whale populations appear to be secondarily sensitive to changes in juvenile survival and growth. 3) Most whale populations, with the exception of North Atlantic right whales (Eubalaena glacialis), appear to be insensitive to changes in fecundity at any age. 4) Adult female whales may be sensitive to changes in foraging success that limit their ability to acquire sufficient body stores of energy to sustain gestation, parturition, and lactation. 5) These results are similar to those arising from studies of non-mammalian marine predators as well as terrestrial vertebrates with similar life history characteristics. A risk assessment of the potential impacts of ocean noise on marine mammal populations based on modeling marine mammal populations suggests that: 1) Any increase in anthropogenic noise in the marine environment that reduces adult female survival, for whatever reason, is to be avoided, 2) It may be impossible to detect the impact of a change in a population vital rate on population growth because such a change may be less than the confidence interval around the estimates of the rate of growth of most marine mammal populations. 3) Sensitivity and elasticity analyses of marine mammal population models predict linear changes in marine mammal population growth rates caused by linear changes in vital rates, and do not indicate thresholds within which vital rates can change without altering population growth rates. Future research efforts should focus on the following: 1) The relationship between noise in the marine environment and adult female and juvenile survival. 2) To increase the precision and decrease the uncertainty of marine mammal population and vital rate estimates. 3) Improving the concept of potential biological removal (PBR) to reflect cumulative mortality impacts and to incorporate the effects of noise. 4) Increasing knowledge of marine mammal activity budgets seasonally and in different parts of their habitats. 5) To more fully elucidate the roles of marine mammals in their ecosystems, and their importance as sentinels of ecosystem health. 6) To exhaustively utilize existing data and models because of the cost and difficulty of gathering more data