Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis
Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/16/6/865 |
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| author | Matthew G. Montgomery Miles B. Yaw John S. Schwartz |
| author_facet | Matthew G. Montgomery Miles B. Yaw John S. Schwartz |
| author_sort | Matthew G. Montgomery |
| collection | DOAJ |
| description | Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 10<sup>2</sup> to 5.68 × 10<sup>3</sup>) than estimates that assume coincident failures are the result of independent hydrologic events. |
| first_indexed | 2024-04-24T17:45:29Z |
| format | Article |
| id | doaj.art-d4f8ad6637014c0c83a0c62611c058ea |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-24T17:45:29Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-d4f8ad6637014c0c83a0c62611c058ea2024-03-27T14:08:23ZengMDPI AGWater2073-44412024-03-0116686510.3390/w16060865Joint Failure Probability of Dams Based on Probabilistic Flood Hazard AnalysisMatthew G. Montgomery0Miles B. Yaw1John S. Schwartz2Tennessee Valley Authority, Knoxville, TN 37902, USATennessee Valley Authority, Knoxville, TN 37902, USADepartment of Civil and Environmental Engineering, Tickle College of Engineering, University of Tennessee Knoxville, Knoxville, TN 37916, USAProbabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 10<sup>2</sup> to 5.68 × 10<sup>3</sup>) than estimates that assume coincident failures are the result of independent hydrologic events.https://www.mdpi.com/2073-4441/16/6/865stochastic hydrologyregulated riversflood analysisreservoir managementclimate change |
| spellingShingle | Matthew G. Montgomery Miles B. Yaw John S. Schwartz Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis Water stochastic hydrology regulated rivers flood analysis reservoir management climate change |
| title | Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis |
| title_full | Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis |
| title_fullStr | Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis |
| title_full_unstemmed | Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis |
| title_short | Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis |
| title_sort | joint failure probability of dams based on probabilistic flood hazard analysis |
| topic | stochastic hydrology regulated rivers flood analysis reservoir management climate change |
| url | https://www.mdpi.com/2073-4441/16/6/865 |
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