Geostatistical based optimization of groundwater monitoring well network design

Monitoring groundwater quality in economically important and other aquifers is carried out regularly as part of regulatory processes for water and other resource development. Many water quality parameters are measured as part of baseline monitoring around mining and onshore gas resource development regions to develop improved understanding of the hydrogeological system as well as to inform managerial decisions to assess and manage contamination risks and health hazards. Water quality distribution in an aquifer is most often inferred from point measurements from limited number of bores drilled at arbitrary locations. Estimating the distribution of water quality parameters in the aquifer based on these point measurements is often a challenging task and results in high uncertainty in the estimates due to limited data availability. Minimizing uncertainty can be achieved by drilling more bores to collect water quality data and several approaches are available to identify optimal bore hole locations to minimize estimation uncertainty. However, optimization of borehole locations is difficult when multiple water quality parameters are of interest and have different spatial distributions in the aquifer. In this study we use geostatistical kriging to interpolate a large number of groundwater quality parameters. Then we integrate these predicted values and use the Differential Evolution algorithm to determine optimal locations for bores that would simultaneously reduce spatial prediction uncertainty of all parameters. The method is applied for designing a groundwater monitoring network in the Namoi region of Australia for monitoring groundwater quality in an economically important aquifer of the Great Artesian Basin. Optimal locations for 10 new monitoring bores are identified using this approach

Monitoring feed-back based multi-objective management of saltwater intrusion in coastal aquifers using linked simulation-optimization

This work dealt with new methodologies based on simulation and optimization for the management of saltwater intrusion into coastal aquifers. The developed methodology can be useful to optimize the pumping from coastal regions for beneficial use and at the same time limit the salinization of the fresh groundwater regions

Integrated multi-objective management of saltwater intrusion in coastal aquifers using coupled simulation-optimisation and monitoring feedback information

Coastal aquifers are aquifers which are hydraulically connected to the sea. They are important sources of groundwater which are often over-exploited due to the high density of population existing near the coasts. Coastal aquifers are susceptible to seawater intrusion caused by over-exploitation or other factors like sea level rise due to climate change. Carefully planned groundwater extraction and monitoring strategies are required for the optimal and sustainable use of coastal aquifers. This study develops methodologies for multi-objective optimal groundwater extraction strategies using simulation and optimisation techniques. Two conflicting objectives of management, viz, maximising the total beneficial pumping and minimising the total barrier well pumping are considered in this work. This study also develops optimal monitoring network designs for evaluating the compliance of the implemented strategies with the prescribed ones and illustrates the sequential modification of the prescribed strategies based on the feedback information from the compliance monitoring network. \ud \ud A coupled simulation-optimisation framework is proposed and developed as the basic tool for deriving optimal groundwater management strategies. A three dimensional density-dependent flow and transport simulation model FEMWATER is used to simulate the coastal aquifer responses to groundwater extraction, in terms of the saltwater intrusion levels. A large number of such simulations is performed to generate the concentration levels resulting from different combinations of pumping from the beneficial and barrier well pumping locations. This pumping-salinity dataset is used as input-output patterns to train and test surrogate models based on modular neural networks (MNN) and genetic programming (GP). Properly trained and tested surrogate models are coupled to the multi-objective genetic algorithm. The optimisation algorithm iteratively searches for the optimal groundwater extraction strategies in a number of generations and in each step, the surrogate models are run to evaluate the salinity levels resulting from the candidate pumping strategies considered. The Pareto-optimal set of solutions is evolved after a number of such generations. It is observed from the obtained results that both surrogate modelling approaches identified similar Pareto-optimal front of solutions for the coastal aquifer management problem. However, the genetic programming based surrogate modelling approach is found to have specific advantages when used in the simulation-optimisation framework. \ud \ud One of the main concerns regarding surrogate modelling based simulation-optimisation is the non-reliability issues associated with the optimal solutions resulting from the approximation involved and predictive uncertainty of the surrogate models. In this study a methodology is developed for obtaining reliable solutions to coastal aquifer management by overcoming the predictive uncertainty of the surrogate models. In this approach, an ensemble of surrogate models is developed to predict the aquifer responses to pumping. Bootstrap samples of pumping-salinity patterns are used to train and test different surrogate models using genetic programming. The number of surrogate models in the ensemble is determined by an uncertainty criterion. All the surrogate models in the ensemble are independently coupled to the multi-objective genetic algorithm, and a multiple-realisation optimisation approach is utilised to derive reliable optimal pumping strategies for coastal aquifer management. Reliability of optimal solutions is defined in terms of the percentage of the surrogate models, for which the imposed constraints are satisfied in deriving the pumping solutions. From these results, it is observed that optimal solutions with increased levels of reliability can be obtained using this proposed approach. The ensemble surrogate based methodology is further extended to address coastal aquifer management under parameter uncertainty. Uncertainty in the values of hydraulic conductivity and annual aquifer recharge are considered. The realisations of hydraulic conductivity and aquifer recharge are sampled from their respective distributions using Latin hypercube sampling. Bootstrap samples of pumping-salinity patterns generated using the numerical simulation model over different realisations of the uncertain parameters are used to train and test different surrogate models in the ensemble. Thus, surrogate models in the ensemble have different predictive capabilities in different regions of the parameter-decision space. All the surrogate models are then coupled with the multi-objective genetic algorithm, and multiple-realisation optimisation is performed incorporating the reliability criterion. This approach results in the robust optimisation of the groundwater management strategies under parameter uncertainty. On validating the derived optimal solutions with the numerical simulation model for different realisations of the uncertain parameters, it was observed that these solutions are robust for the range of values of the uncertain parameters considered. For performance evaluation, the methodology is applied to an existing well field in a realistic coastal aquifer system in the Lower Burdekin in Australia. \ud \ud Compliance monitoring is an essential component of any groundwater management project. A methodology is developed for the design of compliance monitoring networks in this study. The network is necessary to monitor the compliance of the actual field level implementation with the simulated results. The design is performed subject to the imposed constraint of budgetary limitation, implemented as the maximum permissible number of monitoring wells. Subject to this constraint, the design methodology incorporates two goals within a single objective, viz, to place the monitoring wells where there is maximum uncertainty and to reduce the redundancy in monitored information by minimising the coefficient of correlation between the monitored locations. This objective of monitoring network design is compared against the widely used objective of uncertainty maximisation and the advantages are illustrated. The use of compliance monitoring information to sequentially update the coastal aquifer management strategies is illustrated by simulation experiments. A deviation from the prescribed optimal strategy, at any stage during the field implementation, may result in undesirable effects like increased levels of salinity. Based on the compliance monitoring information, the pumping strategies for the subsequent stages of management are modified to compensate for these ill effects. The results of the simulation experiments conducted for the Lower Burdekin aquifer illustrate that sequential updating of the management strategies based on compliance information helps to better achieve the objectives of management.\ud \ud A coupled simulation-optimisation framework using trained and tested surrogate models based on genetic programming are shown to be computationally efficient tools for developing optimal extraction strategies for coastal aquifer management. The newly developed ensemble surrogate modelling with multiple realisation optimisation has potential applications in deriving reliable and robust strategies for coastal aquifer management under parameter uncertainty. The developed simulation-optimisation methodology for developing optimal pumping strategies, together with the designed compliance monitoring network and sequential updating of the strategies constitute an integrated approach for the management and monitoring of coastal aquifer systems

Multi-objective management models for optimal and sustainable use of coastal aquifers

Determining the sustainable rates of groundwater extraction from coastal aquifers is a challenging groundwater\ud management problem. Overexploitation of coastal aquifers owing to ever-increasing demands results in the saltwater intrusion and eventual contamination of these valuable sources of freshwater. Saltwater intrusion is a slow process and it is very difficult, if not impossible, to remediate saltwater intruded aquifers. Hence carefully planned strategies of management are required for the sustainable use of coastal aquifers. In this study, a linked simulationoptimization model is developed for the management of coastal aquifers. The potential applicability of the management model is illustrated by applying the model to a small coastal aquifer study area to determine the\ud optimal pumping rates at different locations and times.\ud Salinity intrusion management models are used to prescribe management strategies for the sustainable use of coastal aquifers by controlling salt water intrusion. Developing an optimal management model involves integrating a groundwater flow and transport simulation model within an optimization framework. Flow and transport equations for salinity intrusion are coupled together by the density variation occurring during the mixing process, requiring simultaneous solution of both the equations. The numerical model for the density dependent flow and transport simulation would be computationally expensive, especially when used in a simulation-optimization framework. In this study, trained and tested surrogate models based on Genetic Programming are used as approximators for the numerical simulation model for simulating flow and transport process in the aquifer. A threedimensional, density dependent flow and transport simulation model FEMWATER is used to simulate the aquifer processes. The input-output patterns generated using the simulation model is then used to train and test the Genetic Programming based surrogate models. The surrogate models are linked to an optimal decision model to evolve multi-objective optimal management strategies for the aquifer. The use of trained and tested surrogate models\ud ensure that the evolved optimal strategies are based on the physical processes in the aquifer, while substantially\ud reducing the computational burden involved in directly linking the numerical simulation model. Two objectives of\ud management are considered in this study. The first objective is to maximize the total beneficial pumping of fresh water from the coastal aquifer. The second objective is to minimize the pumping from a set of barrier wells near the coast which pump out saltwater to hydraulically control saltwater intrusion. The two objectives are conflicting to\ud each other; hence a Multi-Objective Genetic Algorithm is used to solve the optimization model. The management model provides a Pareto-optimal set of solutions specifying the optimal rates of pumping. The developed management strategy ensures that the salinity levels are maintained below the permissible maximum salt concentrations for the intended use of the withdrawn water. The obtained optimal solutions are also validated by simulating the aquifer responses corresponding to the optimal solution using the numerical simulation model

Estimation of groundwater storage loss for the Indian Ganga Basin using multiple lines of evidence

Abstract We used remote sensing data, field observations and numerical groundwater modelling to investigate long-term groundwater storage losses in the regional aquifer of the Ganga Basin in India. This comprised trend analysis for groundwater level observations from 2851 monitoring bores, groundwater storage anomaly estimation using GRACE and Global Land Data Assimilation System (GLDAS) data sets and numerical modelling of long-term groundwater storage changes underpinned by over 50,000 groundwater level observations and uncertainty analysis. Three analyses based on different methods consistently informed that groundwater storage in the aquifer is declining at a significant rate. Groundwater level trend indicated storage loss in the range − 1.1 to − 3.3 cm year−1 (median − 2.6 cm year−1) while the modelling and GRACE storage anomaly methods indicated the storage loss in the range of − 2.1 to − 4.5 cm year−1 (median − 3.2 cm year−1) and − 1.0 to − 4.2 cm year−1 (median − 1.7 cm year−1) respectively. Probabilistic modelling analysis also indicated that the average groundwater storage is declining in all the major basin states, the highest declining trend being in the western states of Rajasthan, Haryana and Delhi. While smaller compared to the western states, average groundwater storage in states further towards east—Uttar Pradesh, Bihar and West Bengal within the basin are also declining. Time series of storage anomalies obtained from the three methods showed similar trends. Probabilistic storage analysis using the numerical model vetted by observed trend analysis and GRACE data provides the opportunity for predictive analysis of storage changes for future climate and other scenarios

Groundwater balance and long-term storage trends in the regional Indo-Gangetic aquifer in northwest Bangladesh

Study Region: The study region is the northwest region of Bangladesh Study Focus: The study focuses on developing an improved understanding of groundwater balance and long-term groundwater storage trends in the districts of northwest Bangladesh. We used MODFLOW-2005 to construct two groundwater models of northwest Bangladesh, which differed in the conceptual representation of groundwater recharge to investigate groundwater balance and storage trends. One approach was based on estimating gross recharge and the other on estimating net recharge by ignoring direct use of groundwater by vegetation. The two models were calibrated by fitting to observed groundwater levels, with a probabilistic method using PEST-IES, resulting in 500 realisations of comparable goodness of fit for each model. New Hydrological Insights for the Region: The two modelling approaches provided plausible range of groundwater storage decline in the northwest region with long-term average between − 1.1 mm/y and − 5.3 mm/y. In the southern parts of the region groundwater storage is declining with highest rates of − 12.5 mm/y, − 10.6 mm/y and − 8.9 mm/y respectively in Nawabganj, Naogaon and Rajshahi respectively. Groundwater use appears to be unsustainable in this area. The northern part receives greater rainfall, and groundwater storage is declining less. Furthermore, the recharge rates are less than the potential rates. In this part of the region, some further development of groundwater use may be sustainable