Robust Yellow River Delta Flood Management under Uncertainty

Liang Chen,Benjamin F Hobbs

doi:10.3390/w13162226

Abstract

A number of principles for evaluating water resources decisions under deep long-run uncertainty have been proposed in the literature. In this paper, we evaluate the usefulness of three widely recommended principles in the context of delta water and sedimentation management: scenario-based uncertainty definition, robustness rather than optimality as a performance measure, and modeling of adaptability, which is the flexibility to change system design or operations as conditions change in the future. This evaluation takes place in the context of an important real-world problem: flood control in the Yellow River Delta. The results give insight both on the physical function of the river system and on the effect of various approaches to modeling risk attitudes and adaptation on the long-term performance of the system. We find that the optimal decisions found under different scenarios differ significantly, while those resulting from using minimal expected cost and minmax regret metrics are similar. The results also show that adaptive multi-stage optimization has a lower expected cost than a static approach in which decisions over the entire time horizon are specified; more surprisingly, recognizing the ability to adapt means that larger, rather than smaller, first-stage investments become optimal. When faced with deep uncertainty in water resources planning, this case study demonstrates that considering scenarios, robustness, and adaptability can significantly improve decisions.

Highlights

Water resources system analysis, as originally championed by the Harvard WaterProgram [1], uses mathematical representations of the component processes and interactions of the system to improve understanding or assist in decision-making [2]
Further exploring how scenarios affect the optimal decisions for engineered avulsion, we find that a larger avulsion rule x1 and further upstream avulsion location x2 are best for smaller annual flow scenarios (S1, S2, S5 and S6)
We find that different metrics produce conflicting solution rankings, which indicates that the definition of robustness does matter in the decision-making process

Summary

Introduction

Program [1], uses mathematical representations of the component processes and interactions of the system to improve understanding or assist in decision-making [2] It integrates economics, environmental, and social objectives, risk characterization, and technical engineering analysis in order to balance the cost of the plan with the goals that clients and society want to achieve [3]. These goals include, for instance, flood mitigation, reducing water scarcity, producing hydropower, providing recreational opportunities, and minimizing harmful impacts on ecosystems and water quality [4] This kind of analysis relies heavily on the availability of information on future physical characteristics of the system, such as rates of sea level rise and frequencies of floods and low flows, as well as economic and social information such as engineering costs and land use. Another concern is that reliance on a single “best guess” scenario or even a single probability distribution for uncertainties, as required by traditional deterministic and stochastic optimization, respectively, may leave behind potentially important information and suggest choices that erode rather than enhance system resilience, not to mention public confidence in the decision process [9]

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Conclusion