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Abstract High tide floods (HTFs) are minor, shallow flooding events whose frequency has increased due to relative sea‐level rise (SLR) and secular changes in tides. Here we isolate and examine the role of historical landscape change (geomorphology, land cover) and SLR on tides and HTF frequency in an urbanized lagoonal estuary: Jamaica Bay, New York. The approach involves data archeology, historical (1870s) map digitization, as well as numerical modeling of the bay. Numerical simulations indicate that a century of landscape alterations (e.g., inlet deepening and widening, channel deepening, and wetland reclamation) increased the mean tidal range at the head of the bay by about 20%. The observed historical shift from the attenuation to amplification of semidiurnal tides is primarily associated with reduced tidal damping at the inlet and increased tidal reflection. The 18% decrease in surface area exerts a minor influence. A 1‐year (2020) water level simulation is used to evaluate the effects of both SLR and altered morphology on the annual number of HTFs. Results show that of 15 “minor flood” events in 2020, only one would have occurred without SLR and two without landscape changes since the 1870s. Spectral and transfer function analyses of water level reveal frequency‐dependent fingerprints of landscape change, with a significant decrease in damping for high‐frequency surges and tides (6–18 hr time scale). By contrast, SLR produced only minor effects on frequency‐dependent amplification. Nonetheless, the geomorphic influence on the dynamical response significantly increases the vulnerability of the system to SLR, particularly high‐tide flooding.
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Data From the Drain: A Sensor Framework That Captures Multiple Drivers of Chronic Coastal Floods
Abstract Tide gauge water levels are commonly used as a proxy for flood incidence on land. These proxies are useful for projecting how sea‐level rise (SLR) will increase the frequency of coastal flooding. However, tide gauges do not account for land‐based sources of coastal flooding and therefore flood thresholds and the proxies derived from them likely underestimate the current and future frequency of coastal flooding. Here we present a new sensor framework for measuring the incidence of coastal floods that captures both subterranean and land‐based contributions to flooding. The low‐cost, open‐source sensor framework consists of a storm drain water level sensor, roadway camera, and wireless gateway that transmit data in real‐time. During 5 months of deployment in the Town of Beaufort, North Carolina, 24 flood events were recorded. Twenty‐five percent of those events were driven by land‐based sources—rainfall, combined with moderate high tides and reduced capacity in storm drains. Consequently, we find that flood frequency is higher than that suggested by proxies that rely exclusively on tide gauge water levels for determining flood incidence. This finding likely extends to other locations where stormwater networks are at a reduced drainage capacity due to SLR. Our results highlight the benefits of instrumenting stormwater networks directly to capture multiple drivers of coastal flooding. More accurate estimates of the frequency and drivers of floods in low‐lying coastal communities can enable the development of more effective long‐term adaptation strategies.
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- Award ID(s):
- 2215195
- PAR ID:
- 10405399
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 59
- Issue:
- 4
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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