Needs for Portfolio Risk Assessment of Aging Dams in the United States
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 3
Abstract
Dam failure can cause extraordinary flooding. Water and wastewater treatment plants, electricity generation facilities, bridges, highways, population centers, and other dams often lie downstream of a dam. If poorly maintained, aging dams can be overtopped by extreme rainfall events, and long-term loss of critical infrastructure services for many communities and regions may result. Such events are now more likely as the climate changes. How should we prioritize dams that need immediate attention and retrofit or remove them? Losses from a dam failure could exceed the infrastructure’s replacement costs, through cascading effects in multiple sectors such as electricity, transportation, water supply, and environmental services. The existing dam hazard classification frameworks in the US and many other countries do not formally characterize “hazard hotspots” considering these impacts. Given that there are over 90,000 dams at different stages of repair, maintenance, and budgetary constraints, a systematic approach is needed to rank their potential failure hazard and allocate resources for risk mitigation. In this paper, we synthesize an approach for preliminary ranking of the priority areas of concern. The approach is scalable over many regions for rapid assessment of magnitude and exposure of a dam failure. The estimation of the consequences of a dam failure, including its financial losses, as well as affected critical infrastructures and population, is explored using publicly available dam break and consequence tools as well as national infrastructure data sets.
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Data Availability Statement
No data, models, or code were generated or used during the study.
Acknowledgments
This work was partially funded by the Global Risk Institute. None of the authors have conflicts of interest. This research is based upon work supported in part by the Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) that is funded by the National Science Foundation (NSF) under award No. 2118329. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 149 • Issue 3 • March 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 7, 2021
Accepted: Sep 14, 2022
Published online: Dec 19, 2022
Published in print: Mar 1, 2023
Discussion open until: May 19, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bridge engineering
- Bridges
- Bridges (by type)
- Dam failures
- Disaster risk management
- Disasters and hazards
- Electric power
- Energy engineering
- Energy sources (by type)
- Engineering fundamentals
- Environmental engineering
- Failure analysis
- Failures (by type)
- Highway bridges
- Hydro power
- Man-made disasters
- Renewable energy
- Structural engineering
- Wastewater treatment plants
- Water treatment
- Water treatment plants
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Cited by
- Mohammad Amin Hariri-Ardebili, Fernando Salazar, Farhad Pourkamali-Anaraki, Guido Mazzà, Juan Mata, Soft Computing and Machine Learning in Dam Engineering, Water, 10.3390/w15050917, 15, 5, (917), (2023).