Investigating the Impact of Seawater Intrusion on the Operation Cost of Groundwater Supply in Island Aquifers

Managing fragile island freshwater resources requires identifying pumping strategies that trade off the financial cost of groundwater supply against controlling the seawater intrusion (SWI) associated with aquifer pumping. In this work, these tradeoffs are investigated through a sensitivity analysis conducted in the context of an optimization formulation of the groundwater management problem, which aims at minimizing the groundwater supply operation cost associated with groundwater pumping and desalination treatment, subject to constraints on SWI control, as quantified by the water table drawdown over the well (∆s), the reduction in freshwater volume (∆FV) in the aquifer, or the salt mass increase (∆SM) in the aquifer. This study focuses on a simplified two-dimensional model of the San Salvador Island aquifer (Bahamas). Pumping strategies are characterized by the distance of the pumping system from the shoreline (WL), the abstraction screen depth (D) and overall pumping rate (Q), constituting the decision variables of the optimization problem. We investigate the impacts of pumping strategies on the operation cost, ∆s, ∆FV and ∆SM. Findings indicate increasing D or decreasing WL reduces ∆s, ∆FV and ∆SM, thus preserving the aquifer hydrogeologic stability, but also leads to extracting saltier groundwater, thus increasing the water treatment requirements, which have a strong impact on the overall groundwater supply cost. From a financial perspective, groundwater abstraction near the island center and at shallow depths seems the most convenient strategy. However, the analysis of the optimization constraints reveals that strategies where the pumping system approaches the island center tend to cause more severe SWI, highlighting the need to trade off groundwater supply cost against SWI control

AGU hydrology days 2010

2010 annual AGU hydrology days was held at Colorado State University on March 22 - March 24, 2010.Includes bibliographical references

AGU hydrology days 2010

2010 annual AGU hydrology days was held at Colorado State University on March 22 - March 24, 2010.Includes bibliographical references.Groundwater models, often used to enhance understanding of hydrologic and chemical processes in local or regional aquifers, are often hindered by inadequate representation of the parameters which characterize these processes. Furthermore, attempts to estimate these parameters are usually limited to studies employing simplified aquifer representations. In this study we present preliminary results of using a data assimilation algorithm, the Ensemble Smoother, to provide enhanced estimates of aquifer hydraulic conductivity within a fully-coupled, surface-subsurface flow modeling framework through assimilation of water table elevation measurements. Based on the Kalman Filter methodology, the algorithm uses residuals between forecasted model results and assimilated measurement data, together with the covariance of model results, to correct model results throughout the model domain. Parameter estimation is achieved by incorporating spatially variable hydraulic conductivity values into the algorithm, thereby allowing the correlation between water table values and hydraulic conductivity to correct the hydraulic conductivity fields. The applicability of the Ensemble Smoother scheme is demonstrated via a synthetic three-dimensional catchment system incorporating variably-saturated subsurface flow, overland flow, and channel flow. Results indicate that assimilating water table measurements provides an improved estimate of the hydraulic conductivity fields

AGU hydrology days 2011

2011 annual AGU hydrology days was held at Colorado State University on March 21 - March 23, 2011.Includes bibliographical references.Numerical models capable of simulating solute reactive transport in groundwater systems are often used as tools to assess the state of contaminated aquifer systems. Accurately simulating the fate and transport of solutes, however, is often hindered by a lack of information regarding the chemical reactions parameters that govern the fate of the solute. Furthermore, field and laboratory methods used to determine these parameters are often labor- and resource-intensive, and often cannot be translated to numerical models due to differences in scale, especially for largescale aquifer systems. In this study, we employ a steady-state Ensemble Kalman Filter (EnKF), a data assimilation algorithm, to provide improved estimates of a spatially-variable first-order rate constant λ through assimilation of solute concentration C measurement data into reactive transport simulation results. The numerical model establishes correlation between λ and the calculated C values throughout the model domain. This correlation, along with model results and measured C values from a reference field, are used by the EnKF to correct model-calculated C values as well as λ in adjacent locations in the model domain. The methodology is applied in a steady-state, synthetic aquifer system in which a contaminant is leached to the saturated zone and undergoes advection, dispersion, and first-order decay in the aquifer system. Uncertainty regarding the hydraulic conductivity of the aquifer is also included. Results from all simulations show that the filter scheme is successful in conditioning the λ ensemble to a reference λ field

AGU hydrology days 2012

2012 annual AGU hydrology days was held at Colorado State University on March 21 - March 23, 2012.Includes bibliographical references.A long history of using of fossil fuels has resulted in increased atmospheric CO₂ concentrations. This anthropogenic process has influenced global climate change. One possible way to retard this increasing atmospheric concentration is to geologically sequester CO₂ emissions. Successfully implementation of this technology requires a full understand of associated risks and detailed resource optimization. It is important to choose the appropriate level of complexity when selecting the type of simulation model to apply to this problem. Many risk assessment and optimization tools require large numbers of realizations. In most cases, using a full scale numerical CO₂ leakage model for this process becomes computationally prohibitive. Therefore, faster CO₂ leakage estimations are needed. An excellent semi-analytical multi-phase CO₂ leakage algorithm has been developed by Celia et al. (2011). In the following work, three possible accuracy improvements to this algorithm are proposed and explored. Differences between leakage rates and pressure distributions are compared between existing and modified methods. Improvement suggestions are then made from these observations

AGU hydrology days 2013

2013 annual AGU hydrology days was held at Colorado State University on March 25 - March 27, 2013.Includes bibliographical references.Geological sequestration has been identified as having potential to reduce increasing atmospheric concentrations of carbon dioxide (CO₂). However, a global impact will only be achieved if this technology is implemented on a massive scale. This work presents a methodology for finding optimal operational schemes for potential sequestration sites having uncertain physical parameters. This tool uses a semi-analytical algorithm to estimate leakage rather than a calibrated numerical model enabling application to potential sites having vastly different domain characteristics. A genetic algorithm is used to heuristically determine non-dominated solutions between the following competing objectives: 1) minimize project cost, 2) minimize risk, and 3) maximize mass of CO₂ sequestered. Parallel processing and archiving are employed to reduce computational cost. This framework has been developed into an application (COSMOS: CO₂ sequestration simulation and multi-objective optimization software) to visually display domain characteristics, pressure pulse and CO₂ plume propagation during simulation, and pareto-optimal tradeoff solutions. Due to the large set of assumptions made by the semi-analytical CO₂ leakage algorithm, this framework may only be used for initial site planning and characterization. Once full developed, this tool has the potential for initial screening and ranking of large sets of potential geological sequestration sites

AGU hydrology days 2011

2011 annual AGU hydrology days was held at Colorado State University on March 21 - March 23, 2011.Includes bibliographical references.Increased greenhouse gas emissions, resulting from our heavy dependence upon fossil fuels, have been found to be directly related to global warming. Global warming may lead to adverse conditions, such as the melting of polar ice caps, raised ocean levels, as well as altered weather patterns producing higher intensity hurricanes and storms. While technological advances, public education, and enacting policy changes are excellent long-term solutions to this problem, carbon sequestration (CS) in addition to other short term solutions may provide a bridge to a sustainable future. Unfortunately, leakage of sequestrated CO₂ may contaminate air and water resources as well as adversely affect plant and animal life. These risks must be fully understood and minimized before implementation. A preliminary decision support system (DSS) has been constructed to optimize CS at a given number of potential injection sites, with the goal of minimizing the total cost of CO₂ leakage while meeting a specified sequestered mass target. This DSS uses a graphic user interface (GUI) and employs CSUDP, a generalized dynamic programming software, as an optimization driver. A semi-analytical leakage model was integrated into CSUDP's objective function to estimate leakage costs. Based upon work by Nordbotten et al. (2009), this model quantifies the mass of CO₂ leakage through weak areas, such as abandoned oil wells, of the caprock overlying the injected aquifer. The resulting DSS uses a wide range of geological, economical, and infrastructural parameters to output optimal CO₂ injection rates and injection durations for each site

Stochastic poromechanical modeling of anthropogenic land subsidence

A key issue in poromechanical modeling, e.g. for predicting anthropogenic land subsidence due to fluid withdrawal, is the evaluation and use of representative mechanical properties for the deforming porous medium at a regional scale. One such property is the vertical uniaxial rock compressibility cM which can be obtained through either laboratory oedometer tests or in situ measurements, and typically exhibits quite a marked scattering. This paper addresses the influence of the cM uncertainty on the predicted land settlement using a stochastic simulation approach where cM is regarded as a random variable and a large number of equally likely cM realizations are generated and implemented into a poroelastic finite element model. A compressibility law, characterized by a log-normal distribution with depth-dependent mean, constant variance and exponential covariance, is assumed. The Monte Carlo simulation provides a set of responses which can be analyzed statistically. The results from a number of numerical experiments show how the cM variance and covariance affect the reliability of the simulated land subsidence and provide a quantitative evaluation of the intrinsic uncertainty of the model prediction. © 2005 Elsevier Ltd. All rights reserved