Tide and skew surge independence: New insights for flood risk
Storm surges are a significant hazard to coastal communities around the world, putting lives at risk and costing billions of dollars in damage. Understanding how storm surges and high tides interact is crucial for estimating extreme water levels so that we can protect coastal communities. We demonst...
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Published in | Geophysical research letters Vol. 43; no. 12; pp. 6410 - 6417 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Washington
John Wiley & Sons, Inc
28.06.2016
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Subjects | |
Online Access | Get full text |
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Summary: | Storm surges are a significant hazard to coastal communities around the world, putting lives at risk and costing billions of dollars in damage. Understanding how storm surges and high tides interact is crucial for estimating extreme water levels so that we can protect coastal communities. We demonstrate that in a tidal regime the best measure of a storm surge is the skew surge, the difference between the observed and predicted high water within a tidal cycle. Based on tide gauge records spanning decades from the UK, U.S., Netherlands, and Ireland we show that the magnitude of high water exerts no influence on the size of the most extreme skew surges. This is the first systematic proof that any storm surge can occur on any tide, which is essential for understanding worst‐case scenarios. The lack of surge generation dependency on water depth emphasizes the dominant natural variability of weather systems in an observation‐based analysis. Weak seasonal relationships between skew surges and high waters were identified at a minority of locations where long‐period changes to the tidal cycle interact with the storm season. Our results allow advances to be made in methods for estimating the joint probabilities of storm surges and tides.
Key Points
Skew surge is the best metric of storm surge in a tidal regime
Any skew surge can coincide with any tide
Where seasonal relationships exist, they should be included in risk predictions |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0094-8276 1944-8007 |
DOI: | 10.1002/2016GL069522 |