Evaluation Of Decision Making Methods For Integrated Water Resource Management Under Uncertainty

Water companies and utilities in the UK are required to produce Water Resource Management Plans (WRMPs) every five years that outline their future strategies for maintaining a secure water supply to meet anticipated demand levels. Regulatory frameworks differ around the world but in most countries similar plans are developed under the auspices of Integrated Water Resources Management (IWRM) programmes. The plans justify new demand management and water supply infrastructure needed and validate management decisions. One of the greatest problems now facing decision makers in the water industry are the increasing uncertainties in the variables used in estimating the supply and demand balance due to increased levels of climate change. WRMPs in the future will need to deliver plans that can adapt water systems to face a widening variation of possible future states; with increased consideration to uncertain water availability, resource deterioration and demand levels. This paper reviews several established decision making methods and analyses their performance and suitability within an IWRM problem. The methods include Info-Gap decision theory, Robust Optimisation, Minimax Regret, Laplace theory and Maximin theory. These methods have been designed to aid decision making under severe uncertainty but differences exist in their approach and attitude to risk. For example, the Info-Gap methodology offers solutions that provide robustness of sufficing over a wide range of uncertainty, but is highly dependent on initial parameter settings. Robust Optimisation concentrates on optimising for robustness over a set of objective functions instead of satisfying a set of constraints. Laplace, Maximin and Minimax Regret are all classical decision methods that implicitly reflect a particular attitude to risk when dealing with severe uncertainty. These methods were applied to a case study resembling the Sussex North region in England, assessing their applicability at improving the IWRM problem and highlighting the strengths and weaknesses of each method

Decision Analysis for Management of Natural Hazards

Losses from natural hazards, including geophysical and hydrometeorological hazards, have been increasing worldwide. This review focuses on the process by which scientific evidence about natural hazards is applied to support decision making. Decision analysis typically involves estimating the probability of extreme events; assessing the potential impacts of those events from a variety of perspectives; and evaluating options to plan for, mitigate, or react to events. We consider issues that affect decisions made across a range of natural hazards, summarize decision methodologies, and provide examples of applications of decision analysis to the management of natural hazards. We conclude that there is potential for further exchange of ideas and experience between natural hazard research communities on decision analysis approaches. Broader application of decision methodologies to natural hazard management and evaluation of existing decision approaches can potentially lead to more efficient allocation of scarce resources and more efficient risk management

Assessment of Fidelity to Data and Robustness to Uncertainty to Assure Credible Predictions in the Modeling of Wind Turbine Blades

In the field of wind energy, modeling and simulation techniques provide an efficient and economical alternative to experimentation for studying the behavior of wind turbines. Numerical models however are approximations of reality, thusly making it crucial to evaluate various sources of uncertainties that influence the model predictions. Credibility of a numerical model rests on the model\u27s ability to replicate existing experimental data, widely known as fidelity-to-data. This dissertation advocates that fidelity-to-data, while necessary, is insufficient to claim credibility of a numerical model. Herein, the objective is to develop numerical models that not only provide agreement to experimental data, but also remain consistent (robust) as unavoidable uncertainties are considered. The focus in this dissertation is on the development of models that are simplified yet consistent with experiments, which offer the possibility of large scale simulations for rapid prototyping and prognostics. This dissertation presents a completely integrated Verification and Validation (V&V) procedure that includes the solution and code verification, sensitivity analysis, calibration, validation, and uncertainty quantification in the development of a finite element (FE) model of the CX-100 wind turbine blade that is simplified yet consistent with experiments. This integrated V&V procedure implements a comprehensive evaluation of uncertainties, including experimental, numerical, and parametric uncertainties, to evaluate the effect of assumptions encountered in the model development process. Mesh refinement studies are performed to ensure that mesh size is chosen such that the effect of numerical uncertainty does not exceed experimental uncertainty. A main effect screening is performed to determine and eliminate the model parameters that are least sensitive to model output, reducing demands on computational resources to only calibrate parameters that significantly influence model predictions. Model calibration is performed in a two-step procedure to de-couple boundary condition effects from the material properties: first against the natural frequencies of the free-free experimental data, and second against the natural frequencies of the fixed-free experimental data. The predictive capability of the calibrated model is successfully validated by comparing model predictions against an independent dataset. Through the V&V activities, this dissertation demonstrates the development of a FE model that is simplified yet consistent with experiments to simulate the low-order vibrations of wind turbine blades. Confidence in model predictions increases when the model has been validated against experimental evidence. However, numerical models that provide excellent fidelity to data after calibration and validation exercises may run the risk of generalizing poorly to other, non-tested settings. Such issues with generalization typically occur if the model is overly complex with many uncertain calibration parameters. As a result, small perturbations in the calibrated input parameter values may result in significant variability in model predictions. Therefore, this dissertation posits that credible model predictions should simultaneously provide fidelity-to¬-data and robustness¬-to-uncertainty. This concept that relies on the trade-off between fidelity and robustness is demonstrated in the selection of a model from among a suite of models developed with varying complexity for CX-100 wind turbine blade in a configuration with added masses. The robustness to uncertainty is evaluated through info-gap decision theory (IGDT), while the fidelity to data is determined with respect to the experimentally obtained natural frequencies of the CX-100 blade. Finally, as fidelity and robustness are conflicting objectives, model calibration can result in multiple plausible solutions with comparable fidelity to data and robustness to uncertainty, raising concerns about non-uniqueness. This dissertation states that to mitigate such non-uniqueness concerns, self-consistency of model predictions must also be evaluated. This concept is demonstrated in the development of a one dimensional simplified beam model to replace the three dimensional finite element model of CX-100 wind turbine blade. The findings demonstrate that all three objectives, fidelity-to-data, robustness-to-uncertainty and self-consistency are conflicting objectives and thus, must be considered simultaneously. When all three objectives are considered during calibration it is observed that the fidelity optimal model remains both least robust and self-consistent, suggesting that robustness and self-consistency are necessary attributes to consider during model calibration

A resilience-based methodology for improved water resources adaptation planning under deep uncertainty with real world application

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI on this record.Resilience of a water resource system in terms of water supply meeting future demand under climate change and other uncertainties is a prominent issue worldwide. This paper presents an alternative methodology to the conventional engineering practice in the UK for identifying long term adaptation planning strategies in the context of resilience. More specifically, a resilience based multi-objective optimization method is proposed that identifies Pareto optimal future adaptation strategies by maximizing a water supply system’s resilience (calculated as the maximum recorded duration of a water deficit period over a given planning horizon) and minimizing total associated costs, subject to meeting target system robustness to uncertain projections (scenarios) of future supply and demand. The method is applied to a real-world case study for Bristol Water’s water resource zone and the results are compared with those derived using a more conventional engineering practice in the UK, utilizing a least-cost optimization analysis constrained to a target reliability level. The results obtained reveal that the strategy solution derived using the current practice methodology produce a less resilient system than the similar costing solutions identified using the proposed resilience driven methodology. At the same time, resilience driven strategies are only slightly less reliable suggesting that trade-off exists between the two. Further examination of intervention strategies selected shows that the conventional methodology encourages implementation of more lower cost intervention options early in the planning horizon (to achieve higher system reliability) whereas the resilience-based methodology encourages more uniform intervention options sequenced over the planning horizon (to achieve higher system resilience).This work was financially supported by the UK Engineering and Physical Sciences Research Council, HR Wallingford and The University of Exeter through the STREAM Industrial Doctorate Centre. We thank Bristol Water for allowing the use of their data and information, which is available from their publicly available water resources management plan

Identifying Trade‐Offs and Reconciling Competing Demands for Water : Integrating Agriculture Into a Robust Decision‐Making Framework

Funding Information Anglian Water Services. Grant Number: WRE Natural Environment Research Council (NERC). Grant Number: NE/L010186/1 MaRIUS. Grant Number: NE/L010186/1 Anglian Water Services (AWS)Peer reviewedPublisher PD