Approaches to optimal aquifer management and intelligent control in a multiresolutional decision support system

453

359

[1] Uncertainty analysis of models has received increasing attention over the last two decades in water resources research. However, a significant part of the community is still reluctant to embrace the estimation of uncertainty in hydrological and hydraulic modeling. In this paper, we summarize and explore seven common arguments: uncertainty analysis is not necessary given physically realistic models; uncertainty analysis cannot be used in hydrological and hydraulic hypothesis testing; uncertainty (probability) distributions cannot be understood by policy makers and the public; uncertainty analysis cannot be incorporated into the decision-making process; uncertainty analysis is too subjective; uncertainty analysis is too difficult to perform; uncertainty does not really matter in making the final decision. We will argue that none of the arguments against uncertainty analysis rehearsed are, in the end, tenable. Moreover, we suggest that one reason why the application of uncertainty analysis is not normal and expected part of modeling practice is that mature guidance on methods and applications does not exist. The paper concludes with suggesting that a Code of Practice is needed as a way of formalizing such guidance.Citation: Pappenberger, F., and K. J. Beven (2006), Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302,

Section: Uncertainty Analysis Cannot Be Incorporated Into the Decisiomentioning

confidence: 99%

Ignorance is bliss: Or seven reasons not to use uncertainty analysis

Pappenberger

Beven

2006

453

359

“…Heuristic solvers have also proven to be robust in tackling problems involving objective function discontinuities. Their performance is less sensitive to the nature of decision variables, type of constraints and noise, as has been reported in relation to several groundwater management formulations [ Rogers and Dowla , 1994; Guan and Aral , 1999; Smalley et al , 2000; Orr and Meystel , 2005].…”

Section: Introductionmentioning

confidence: 89%

Optimization of concentration control by evolution strategies: Formulation, application, and assessment of remedial solutions

Bayer

Finkel

2007

[1] The uniqueness and mathematical complexity of typical groundwater remediation or control problems involving numerical models necessitate appropriate solvers that find optimal solutions reliably and within reasonable computational time. The aim of this paper is to introduce an innovative evolutionary algorithm, the evolution strategies with covariance matrix adaptation and rank m update (CMA-ES), used as an external solver in combination with groundwater transport models. A broad range of hypothetical pumpand-treat design problems is set up to derive recommendations for a robust CMA-ES configuration. One-and five-well cases are distinguished, for which both extraction rates and well positions have to be optimized in order to partly capture a contaminant plume. Intrinsic natural attenuation of the contaminant that emanates from a continuous source is considered. During this study, it is revealed that when pumping rates are minimized, well positions should be focused close to the source. In contrast, when contaminant mass extraction is minimized, positions distant from the source close to the plume fringes prove to be preferable.Citation: Bayer, P., and M. Finkel (2007), Optimization of concentration control by evolution strategies: Formulation, application, and assessment of remedial solutions, Water Resour.

“…Works that discuss the categories and techniques for coupling simulation with optimization methods used to guide aquifer management may be found in Gorelick [], Yeh [], Wagner [], Ahlfeld and Mulligan [], Mayer et al . [], Orr and Meystel [] , Nicklow et al . [] , Peralta [] , Reed et al .…”

Section: Introductionmentioning

confidence: 99%

Global change and the groundwater management challenge

Gorelick

Chen

2015

314

142

With rivers in critical regions already exploited to capacity throughout the world and groundwater overdraft as well as large-scale contamination occurring in many areas, we have entered an era in which multiple simultaneous stresses will drive water management. Increasingly, groundwater resources are taking a more prominent role in providing freshwater supplies. We discuss the competing fresh groundwater needs for human consumption, food production, energy, and the environment, as well as physical hazards, and conflicts due to transboundary overexploitation. During the past 50 years, groundwater management modeling has focused on combining simulation with optimization methods to inspect important problems ranging from contaminant remediation to agricultural irrigation management. The compound challenges now faced by water planners require a new generation of aquifer management models that address the broad impacts of global change on aquifer storage and depletion trajectory management, land subsidence, groundwater-dependent ecosystems, seawater intrusion, anthropogenic and geogenic contamination, supply vulnerability, and long-term sustainability. The scope of research efforts is only beginning to address complex interactions using multiagent system models that are not readily formulated as optimization problems and that consider a suite of human behavioral responses.