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.

Tropical cyclones (TCs) generate substantial damage raising concerns about how climate change may amplify their impacts. However, linking changes in TC characteristics (wind, precipitation) to shifts in flood hazards and exposure, particularly due to the interaction of multiple drivers, is challenging. In this study, we use highly resolved physics-based models to investigate how flooding from three recent TCs in North and South Carolina would change under 4 degrees Celsius of warming. Runoff processes are the largest contributor to the total flood extent in both the present and future. However, the relative contribution of compound processes increases in the future, expanding upriver and beyond the floodplain where runoff processes previously occurred in isolation. The total area exposed to compound flooding increases by 119% and flood depths in these areas increase by 0.8 m highlighting the importance of simulating compound processes in TC flood exposure assessment.

Estimates of flood inundation generated by runoff and coastal flood processes during tropical cyclones (TCs) are needed to better understand how exposure varies inland and at the coast. While reduced‐complexity flood models have been previously shown to efficiently simulate TC flood processes across large regions, a lack of detailed validation studies of these models, which are being applied globally, has led to uncertainty about the quality of the predictions of inundation depth and extent and how this translates to exposure. In this study, we complete a comprehensive validation of a hydrodynamic model (SFINCS) for simulating pluvial, fluvial, and coastal flooding. We hindcast Hurricane Florence (2018) flooding in North and South Carolina, USA using high‐resolution meteorologic data and coastal water level output from an ocean recirculation model (ADCIRC). Modeled water levels are compared to traditional validation datasets (e.g., water level gages, high water marks) as well as property‐level records of insured flood damage to draw conclusions about the model's performance. SFINCS shows skill in simulating runoff and coastal processes of TC flooding (peak error of 0.11 m with an RMSE of 0.92 m) at large scales with minimal computational requirements and limited calibration. We use the validated model to attribute flood extent and building exposure to flood processes (e.g., runoff, coastal, compound) during Hurricane Florence. The results highlight the critical role runoff processes have in TC flood exposure and support the need for broader implementation of models capable of realistically representing the compound effects resulting from coastal and runoff processes.

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.

The authors predicted streamflow in an urban–rural watershed using a nested regional–local modeling approach for the community of Manchester, Iowa, which is downstream of a largely rural watershed. The nested model coupled the hillslope-link model (HLM), used to simulate the upstream rural basins, and XPSWMM, which was used to simulate the more complex rainfall–runoff dynamics and surface and subsurface drainage in the urban areas, making it capable of producing flood maps at the street level. By integrating these models built for different purposes, we enabled fast and accurate simulation of hydrological processes in the rural basins while also modeling the flows in an urban environment. Using the model, we investigated how the spatial and temporal resolution of radar rainfall inputs can affect the modeled streamflow. We used a combination of three radar rainfall products to capture the uncertainty of rainfall estimation in the model results. Our nested model was able to simulate the hydrographs and timing and duration above the threshold known to result in nuisance flooding in Manchester. The spatiotemporal resolution the radar rainfall input to the model impacted the streamflow outputs of the regional, local, and nested models differently depending on the storm event.

We consider deformations of quantum exterior algebras extended by finite groups. Among these deformations are a class of algebras which we call truncated quantum Drinfeld Hecke algebras in view of their relation to classical Drinfeld Hecke algebras. We give the necessary and sufficient conditions for which these algebras occur, using Bergman’s Diamond Lemma. We compute the relevant Hochschild cohomology to make explicit the connection between Hochschild cohomology and truncated quantum Drinfeld Hecke algebras. To demonstrate the variance of the allowed algebras, we compute both classical type examples and demonstrate an example that does not arise as a factor algebra of a quantum Drinfeld Hecke algebra.