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.
Abstract. We construct the Gerstenhaber bracket on Hochschild cohomology of a twisted tensor product of algebras, and, as examples, compute Gerstenhaber brackets for some quantum complete intersections arising in work of Buchweitz, Green, Madsen, and Solberg. We prove that a subalgebra of the Hochschild cohomology ring of a twisted tensor product, on which the twisting is trivial, is isomorphic, as Gerstenhaber algebras, to the tensor product of the respective subalgebras of the Hochschild cohomology rings of the factors.
Tide gauge records are commonly used as proxies to detect coastal floods and project future flood frequencies. While these proxies clearly show that sea-level rise will increase the frequency of coastal flooding, tide gauges do not account for land-based sources of coastal flooding and therefore 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 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 five months of deployment in the Town of Beaufort, North Carolina, 24 flood events were recorded. 25% of those events were driven by land-based sources – rainfall, combined with moderate high tides and reduced capacity in storm drains – and would not have been detected using tide gauge proxies. This finding suggests that tide-gauge proxies likely underestimate flood frequency in areas where the stormwater networks are at a reduced drainage capacity due to inundation by receiving waters. Our results highlight the benefits of capturing multiple drivers of coastal flooding by instrumenting stormwater networks directly. 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.
This study focuses on the development of a probabilistic rainfall generator for tropical cyclones (TCs) affecting Louisiana. We consider 12 storms making landfall along the Louisiana coast during 2002-2017 and generate ensembles of high-resolution (~5 km and 20 min) TC-rainfall fields for each storm. We develop a data-driven multiplicative model, relating observed rainfall to the rainfall obtained from a parametric TC rainfall model (Interagency Performance Evaluation Task Force Rainfall Analysis [IPET]) through the product of a deterministic and a stochastic component; the former accounts for rain-dependent biases, while the latter for the stochastic nature of the rainfall processes. As a preliminary step, we describe the overall bias of the IPET model as a function of total TC rainfall within the state and maximum wind speed at landfall. We then estimate the rain-dependent bias using a cubic spline. Finally, we characterize the random errors in terms of their probability distribution and spatial correlation. We show that the marginal distribution of the logarithm of the random errors can be described by a mixture of four Gaussian distributions, and its spatial correlation is estimated based on the nonparametric Kendall's τ. We then present a methodology to generate ensembles of random fields with the specified statistical properties. Here, the generation of probabilistic rainfall comes from the statistical modelling of the uncertainties between IPET rainfall and observations. While these results are valid for Louisiana and the IPET model, the methodology can be generalized to other parametric rainfall models and regions, and it represents a viable tool to improve our quantification of the risk associated with TC rainfall.
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