Uncertainty in future climate change challenges water infrastructure development decisions. Flexible infrastructure development, in which infrastructure is proactively designed to be changed in the future, can reduce the risk of overbuilding unnecessary infrastructure while maintaining reliable water supply. Flexible strategies assume that water planners will learn over time, updating future climate projections and using that new information to change plans. Previous work has developed methods to incorporate learning using climate observations into flexible planning but has not quantified the impact of different amounts of learning on the effectiveness of flexible planning. In this work, we develop a framework to assess how differences in the amount of learning about climate uncertainty affect the value of flexible water infrastructure planning. In the first part of our framework, we design climate scenarios with different amounts of learning using an exploratory Bayesian modeling approach. Then, we quantify the impacts of learning on flexibility using simulated costs and infrastructure decisions. We demonstrate this framework on a stylized case study of the Mwache Dam near Mombasa, Kenya. Flexible planning is more effective in avoiding over‐ or underbuilding under high‐learning scenarios, especially in avoiding overbuilding in wet climates. This framework provides insight on the climate conditions and learning scenarios that make flexible infrastructure most valuable.
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Learning and flexibility for water supply infrastructure planning under groundwater resource uncertainty
Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (<1%). This striking result may be explained by the slow-evolving nature of groundwater decline, which provides time for planners to react, in contrast to more sudden risks such as flooding where tradeoffs between cost and reliability risk are heightened. This Bayesian approach shows promise for many civil infrastructure domains by providing a method to quantify learning in environmental modeling and assess the effectiveness of adaptive planning.
more » « less- NSF-PAR ID:
- 10303202
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 14
- Issue:
- 11
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 114022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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