Citation
Gold, Adam; Anarde, Katherine; Grimley, Lauren; Neve, Ryan; Srebnik, Emma Rudy; Thelen, Thomas; Whipple, Anthony; & Hino, Miyuki (2023). Data from the Drain: A Sensor Framework that Captures Multiple Drivers of Chronic Coastal Floods. Water Resources Research, 59(4), e2022WR032392.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.URL
https://doi.org/10.1029/2022WR032392Reference Type
Journal ArticleYear Published
2023Journal Title
Water Resources ResearchAuthor(s)
Gold, AdamAnarde, Katherine
Grimley, Lauren
Neve, Ryan
Srebnik, Emma Rudy
Thelen, Thomas
Whipple, Anthony
Hino, Miyuki