Water Resource Engineering

Abstract

In this paper, two deterministic and stochastic multilateral, multi-issue, non-cooperative bargaining methodologies are proposed for urban runoff quality management. In the proposed methodologies, a calibrated Storm Water Management Model (SWMM) is used to simulate stormwater runoff quantity and quality for different urban stormwater runoff management scenarios, which have been defined considering several Low Impact Development (LID) techniques. In the deterministic methodology, the best management scenario, representing location and area of LID controls, is identified using the bargaining model. In the stochastic methodology, uncertainties of some key parameters of SWMM are analyzed using the info-gap theory. For each water quality management scenario, robustness and opportuneness criteria are determined based on utility functions of different stakeholders. Then, to find the best solution, the bargaining model is performed considering a combination of robustness and opportuneness criteria for each scenario based on utility function of each stakeholder. The results of applying the proposed methodology in the Velenjak urban watershed located in the northeastern part of Tehran, the capital city of Iran, illustrate its practical utility for conflict resolution in urban water quantity and quality management. It is shown that the solution obtained using the deterministic model cannot outperform the result of the stochastic model considering the robustness and opportuneness criteria. Therefore, it can be concluded that the stochastic model, which incorporates the main uncertainties, could provide more reliable results.

Keywords:

Bargaining theory; Info-gap theory; Low Impact Development (LID); SWMM; Uncertainty analysis; Urban runoff.

Abstract

Info-gap decision theory, a tool for evaluating severe uncertainty, can be of tremendous value for managing risk and assessing the vulnerability of water conveyance systems in areas of high seismicity. In those areas, significant consequences can result from decisions made with a profound lack of information, such as the uncertainty of seismic loading, unknown performance of pipelines and other infrastructure, unknown costs to repair seismic damage, and unknown societal impacts from downtime following an event. Info-gap is a tool for making good decisions with very little information and recognizes up front that our best projections of the future may be wrong. Thus, rather than seeking a solution that is optimal for that projection, info-gap seeks a solution that works reasonably well for all plausible conditions. In other words, info-gap seeks solutions that are robust in the face of uncertainty. EBMUD is currently using info-gap to gain insight into possible solutions for providing reliable water service to an island community within its service area. The island, containing about 75,000 customers, is particularly vulnerable to water supply disruption from earthquakes, because it has no water storage on the island and is entirely dependent on four potentially fragile water transmission mains under the waterway for its day-to-day water supply. Info-gap analysis is being used to evaluate competing tunneling strategies for supplying water to the island. Horizontal directional drilling and microtunneling alternatives are being evaluated for their ability to survive a major earthquake and for speed of repair should they be damaged by an earthquake. The analysis considers not only the expected results for each alternative but also the worst-case performance of each alternative under varying levels of uncertainty. Thus, the info-gap model is a tool for evaluating the level of reliability each mitigation buys, even in the face of significant uncertainty. This analysis is improving the quality of the planning process, because it can identify strategies that ensure minimal disruption of water supply following a major earthquake, even if the earthquake and resulting damage fail to conform to our expectations. The model results prompt discussion regarding how much mitigation is desired, or expected to be reliable enough, even in the face of great uncertainty regarding the damage. Results of the study are presented, including a discussion of how info-gap model results complemented existing tools for comparing alternative strategies and how info-gap improves our ability to quantify our tolerance for uncertainty. © 2014 American Society of Civil Engineers.

keywords

Engineering controlled terms:

Decision theory; Directional drilling; Earthquakes; Pipelines; Repair; Risk management; Structural analysis; Sustainable development; Uncertainty analysis; Water supply; Horizontal directional drilling; Info-gap decision theories; Planning process; Societal impacts; Supply disruption; Water conveyance; Water transmission mains; Worst-case performance

Engineering main heading:

Quality control

Abstract

This paper evaluates two established decision-making methods and analyzes their performance and suitability within a water resources management (WRM) problem. The methods under assessment are info-gap (IG) decision theory and robust optimization (RO). The methods have been selected primarily to investigate a contrasting local versus global method of assessing water system robustness to deep uncertainty, but also to compare a robustness model approach (IG) with a robustness algorithm approach (RO), whereby the former selects and analyzes a set of prespecified strategies and the latter uses optimization algorithms to automatically generate and evaluate solutions. The study presents a novel area-based method for IG robustness modeling and assesses the applicability of utilizing the future flows climate change projections in scenario generation for water resource adaptation planning. The methods were applied to a case study resembling the Sussex North Water Resource Zone in England, assessing their applicability at improving a risk-based WRM problem and highlighting the strengths and weaknesses of each method at selecting suitable adaptation strategies under climate change and future demand uncertainties. Pareto sets of robustness to cost are produced for both methods and highlight RO as producing the lower cost strategies for the full range of varying target robustness levels. IG produced the more expensive Pareto strategies due to its more selective and stringent robustness analysis, resulting from the more complex scenario ordering process.

Keywords

Algorithms; Climate change; Decision making; Decision theory; Optimization; Risk assessment; Uncertainty analysis; Water management; Adaptation strategies; Algorithm approaches; Climate change projections; Decision-making method; Optimization algorithms; Robustness analysis; Water resources management; Waterresource management

Abstract

This paper evaluates two established decision making methods and analyses their performance and suitability within an Integrated Water Resource Management (IWRM) problem. The methods under comparison are Info-Gap decision theory (IG) and Robust Optimisation (RO), with particular regard to two key issues: (a) a local vs global measure of water supply robustness and (b) a pre-specified vs optimisation method of generating intervention strategies. Solutions are compared with plans proposed from current industry practice especially in regard to employing a longer planning horizon. The results reveal the impact of using alternative methodologies and analysis parameters on the final intervention strategies selected.

Keywords

Water resources planning, decision making methods, climate change uncertainity, robust optimisation, info-gap decision theory

Abstract

Water resource managers are required to develop comprehensive water resources plans based on severely uncertain information of the effects of climate change on local hydrology and future socio-economic changes on localised demand. In England and Wales, current water resources planning methodologies include a headroom estimation process separate from water resource simulation modelling. This process quantifies uncertainty based on only one point of an assumed range of deviations from the expected climate and projected demand 25 years into the future. This paper utilises an integrated method based on Information-Gap decision theory to quantitatively assess the robustness of various supply side and demand side management options over a broad range of plausible futures. Findings show that beyond the uncertainty range explored with the headroom method, a preference reversal can occur, i.e. some management options that underperform at lower uncertainties, outperform at higher levels of uncertainty. This study also shows that when 50% or more of the population adopts demand side management, efficiency related measures and innovative options such as rainwater collection can perform equally well or better than some supply side options The additional use of Multi-Criteria Decision Analysis shifts the focus away from reservoir expansion options, that perform best in regards to water availability, to combined strategies that include innovative demand side management actions of rainwater collection and greywater reuse as well efficiency measures and additional regional transfers. This paper illustrates how an Information-Gap based approach can offer a comprehensive picture of potential supply/demand futures and a rich variety of information to support adaptive management of water systems under severe uncertainty.

Keywords

Climate change; Demand management; Info-Gap; Planning; Uncertainty; Water resources.

Abstract

One of the challenges in the design and selection of remediation activities for subsurface contamination is dealing with manifold uncertainties. A scientifically defensible decision process demands consideration of the uncertainties involved. A nonprobabilistic approach based on information gap (info-gap) decision theory is employed to study the robustness of alternative remediation activities. This approach incorporates both parametric and nonparametric (conceptual) uncertainty in predicting contaminant concentrations that are effected by natural processes and the remediation activities. Two remedial scenarios are explored to demonstrate the applicability of the info-gap approach to decision making related to groundwater remediation.

Keywords

Decision theory; Groundwater remediation; Uncertainty analysis

Abstract

Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and nonprobabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information-Gap Decision Theory (IGDT) to address model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real-world engineering problems related to the enumeration of possible outcomes. In the case of zero nonprobabilistic uncertainty, the method reduces to a Bayesian method. To illustrate the approach, we apply it to a site-selection decision for geologic CO2 sequestration.

Abstract

Stationarity assumptions of linked human-water systems are frequently invalid given the difficult-to-predict changes affecting such systems. In this case water planning occurs under conditions of deep or severe uncertainty, where the statistical distributions of future conditions and events are poorly known. In such situations predictive system simulation models are typically run under different scenarios to evaluate the performance of future plans under different conditions. Given that there are many possible plans and many possible futures, which simulations will lead to the best designs? Robust Decision Making (RDM) and Info-Gap Decision Theory (IGDT) provide a structured approach to planning complex systems under such uncertainty. Both RDM and IGDT make repeated use of trusted simulation models to evaluate different plans under different future conditions. Both methods seek to identify robust rather than optimal decisions, where a robust decision works satisfactorily over a broad range of possible futures. IGDT efficiently charts system performance with robustness and opportuneness plots summarising system performance for different plans under the most dire and favourable sets of future conditions. RDM samples a wider range of dire, benign and opportune futures and offers a holistic assessment of the performance of different options. RDM also identifies through ‘scenario discovery’ which combinations of uncertain future stresses lead to system vulnerabilities. In our study we apply both frameworks to a water resource system planning problem: London’s water supply system expansion in the Thames basin, UK. The methods help identify which out of 20 proposed water supply infrastructure portfolios is the most robust given severely uncertain future hydrological inflows, water demands and energy prices. Multiple criteria of system performance are considered: service reliability, storage susceptibility, capital and operating cost, energy use and environmental flows. Initially the two decision frameworks lead to different recommendations. We show the methods are complementary and can be beneficially used together to better understand results and reveal how the particulars of each method can skew results towards particular future plans.

Keywords

Info-Gap Decision Theory (IGDT); Infrastructure planning; Robust Decision Making (RDM); Uncertainty; Water resources planning

Abstract

Flood risk analysis is subject to often severe uncertainties, which can potentially undermine flood management decisions. This paper explores the use of information gap theory to analyze the sensitivity of flood management decisions to uncertainties in flood inundation models and flood frequency analysis. Information gap is a quantified nonprobabilistic theory of robustness. To analyze uncertainties in flood modeling, an energy-bounded information gap model is established and applied first to a simplified uniform channel and then to a more realistic 2-D flood model. Information gap theory is then applied to the estimation of flood discharges using regional frequency analysis. The use of an information gap model is motivated by the notion that hydrologically similar sites are clustered in the space of their L moments. The information gap model is constructed around a parametric statistical flood frequency analysis, resulting in a hybrid model of uncertainty in which natural variability is handled statistically while epistemic uncertainties are represented in the information gap model. The analysis is demonstrated for sites in the Trent catchment, United Kingdom. The analysis is extended to address ungauged catchments, which, because of the attendant uncertainties in flood frequency analysis, are particularly appropriate for information gap analysis. Finally, the information gap model of flood frequency is combined with the treatment of hydraulic model uncertainties in an example of how both sources of uncertainty can be accounted for using information gap theory in a flood risk management decision.