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1. Data From the Drain: A Sensor Framework That Captures Multiple Drivers of Chronic Coastal Floods

   https://doi.org/10.1029/2022WR032392  

   Gold, Adam; Anarde, Katherine; Grimley, Lauren; Neve, Ryan; Srebnik, Emma Rudy; Thelen, Thomas; Whipple, Anthony; Hino, Miyuki (April 2023, Water Resources Research)

   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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2. Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks

   https://doi.org/10.3390/w11051098  

   Bowes, Benjamin D.; Sadler, Jeffrey M.; Morsy, Mohamed M.; Behl, Madhur; Goodall, Jonathan L. (May 2019, Water)

   Many coastal cities are facing frequent flooding from storm events that are made worse by sea level rise and climate change. The groundwater table level in these low relief coastal cities is an important, but often overlooked, factor in the recurrent flooding these locations face. Infiltration of stormwater and water intrusion due to tidal forcing can cause already shallow groundwater tables to quickly rise toward the land surface. This decreases available storage which increases runoff, stormwater system loads, and flooding. Groundwater table forecasts, which could help inform the modeling and management of coastal flooding, are generally unavailable. This study explores two machine learning models, Long Short-term Memory (LSTM) networks and Recurrent Neural Networks (RNN), to model and forecast groundwater table response to storm events in the flood prone coastal city of Norfolk, Virginia. To determine the effect of training data type on model accuracy, two types of datasets (i) the continuous time series and (ii) a dataset of only storm events, created from observed groundwater table, rainfall, and sea level data from 2010–2018 are used to train and test the models. Additionally, a real-time groundwater table forecasting scenario was carried out to compare the models’ abilities to predict groundwater table levels given forecast rainfall and sea level as input data. When modeling the groundwater table with observed data, LSTM networks were found to have more predictive skill than RNNs (root mean squared error (RMSE) of 0.09 m versus 0.14 m, respectively). The real-time forecast scenario showed that models trained only on storm event data outperformed models trained on the continuous time series data (RMSE of 0.07 m versus 0.66 m, respectively) and that LSTM outperformed RNN models. Because models trained with the continuous time series data had much higher RMSE values, they were not suitable for predicting the groundwater table in the real-time scenario when using forecast input data. These results demonstrate the first use of LSTM networks to create hourly forecasts of groundwater table in a coastal city and show they are well suited for creating operational forecasts in real-time. As groundwater table levels increase due to sea level rise, forecasts of groundwater table will become an increasingly valuable part of coastal flood modeling and management. 

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3. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

   https://doi.org/10.3390/hydrology8030104  

   Mejia Manrique, Said A.; Harmsen, Eric W.; Khanbilvardi, Reza M.; González, Jorge E. (September 2021, Hydrology)

   null (Ed.)

   Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events. 

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4. Stakeholder Driven Sensor Deployments to Characterize Chronic Coastal Flooding in Key West Florida

   https://doi.org/10.1029/2023EF003631  

   O’Donnell, K L; Tomiczek, T; Higgins, A; Munoz, S; Scyphers, S (July 2024, Earth's Future)

   Abstract A changing climate and growing coastal populations exacerbate the outcomes of environmental hazards. Large‐scale flooding and acute disasters have been extensively studied through historic and current data. Chronic coastal flooding is less well understood and poses a substantial threat to future coastal populations. This paper presents a novel technique to record chronic coastal flooding using inexpensive accelerometers. This technique was tested in Key West, FL, USA using storm drains to deploy HOBO pendant G data loggers. The accuracy and feasibility of the method was tested through four deployments performed by a team of local stakeholders and researchers between July 2019–November 2021 resulting in 22 sensors successfully recording data, with 15 of these sensors recording flooding. Sensors captured an average of 13.58 inundation events, an average of 12.07% of the deployment time. Measured flooding events coincided with local National Oceanic and Atmospheric Administration (NOAA) water level measurements of high tides. Multiple efforts to predict coastal flooding were compared. Sensors recorded flooding even when NOAA water levels did not exceed the elevation or flooding thresholds set by the National Weather Service (NWS), indicating that NOAA water levels alone were not sufficient in predicting flooding. Access to an effective and inexpensive sensor, such as the one tested here, for measuring flood events can increase opportunities to measure chronic flood hazards and assess local vulnerabilities with stakeholder participation. The ease of use and successful recording of loggers can give communities an increased capacity to make data‐informed decisions surrounding sea level rise adaptation. 

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5. Patterns and processes of beach and foredune geomorphic change along a Great Lakes shoreline: Insights from a year-long drone mapping study along Lake Michigan

   https://doi.org/10.34237/1008926  

   Theuerkauf, Ethan; Mattheus, C. Robin; Braun, Katherine; Bueno, Jenny (May 2021, Shore & Beach)

   null (Ed.)

   Coastal storms are an important driver of geomorphic change along Great Lakes shorelines. While there is abundant anecdotal evidence for storm impacts in the region, only a handful of studies over the last few decades have quantified them and addressed system morphodynamics. Annual to seasonal lake-level fluctuations and declining winter-ice covers also influence coastal response to storms, yet relationships between hydrodynamics and geomorphology are poorly constrained. Given this, the Great Lakes region lags behind marine coasts in terms of predictive modeling of future coastal change, which is a necessary tool for proactive coastal management. To help close this gap, we conducted a year-long study at a sandy beach-dune system along the western shore of Lake Michigan, evaluating storm impacts under conditions of extremely high water level and absent shorefast ice. Drone-derived beach and dune topography data were used to link geomorphic changes to specific environmental conditions. High water levels throughout the year of study facilitated erosion during relatively minor wave events, enhancing the vulnerability of the system to a large storm in January 2020. This event occurred with no shorefast ice present and anomalously high winter water levels, resulting in widespread erosion and overwash. This resulted in 20% of the total accretion and 66% of the erosion documented at the site over the entire year. Our study highlights the importance of both antecedent and present conditions in determining Great Lakes shoreline vulnerability to storm impacts. 

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