An official website of the United States government
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.
more »
« less
Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks
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.
more »
« less
- Award ID(s):
- 1735587
- PAR ID:
- 10112275
- Date Published:
- Journal Name:
- Water
- Volume:
- 11
- Issue:
- 5
- ISSN:
- 2073-4441
- Page Range / eLocation ID:
- 1098
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Rising groundwater tables due to sea level rise (SLR) pose a critical but understudied threat to low‐lying coastal regions. This study uses field observations and dynamic modeling to investigate drivers of groundwater variability and to project flooding risks from emergent groundwater in Imperial Beach, California. Hourly groundwater table data from four monitoring wells (2021–2024) reveal distinct aquifer behaviors across soil types. In transmissive coastal sandy soils, groundwater levels are dominated by ocean tides, with secondary contributions from non‐tidal sea level variability and seasonal recharge. In this setting, we calibrated an empirical groundwater model to observations, and forced the model with regional SLR scenarios. We project that groundwater emergence along the low‐lying coastal road will begin by the 2060s under intermediate SLR trajectories, and escalate to near‐daily flooding by 2100. Over 20% of San Diego County's coastline shares similar transmissive sandy geology and thus similar flooding risk. Results underscore the urgency of integrating groundwater hazards into coastal resilience planning, as current adaptation strategies in Imperial Beach—focused on surface flooding—are insufficient to address infrastructure vulnerabilities from below. This study provides a transferable framework for assessing groundwater‐driven flooding in transmissive coastal aquifers, where SLR‐induced groundwater rise threatens critical infrastructure decades before permanent inundation.more » « less
-
ABSTRACT Sea level rise and storm surges affect coastal forests along low‐lying shorelines. Salinization and flooding kill trees and favour the encroachment of salt‐tolerant marsh vegetation. The hydrology of this ecological transition is complex and requires a multidisciplinary approach. Sea level rise (press) and storms (pulses) act on different timescales, affecting the forest vegetation in different ways. Salinization can occur either by vertical infiltration during flooding or from the aquifer driven by tides and sea level rise. Here, we detail the ecohydrological processes acting in the critical zone of retreating coastal forests. An increase in sea level has a three‐pronged effect on flooding and salinization: It raises the maximum elevation of storm surges, shifts the freshwater‐saltwater interface inland, and elevates the water table, leading to surface flooding from below. Trees can modify their root systems and local soil hydrology to better withstand salinization. Hydrological stress from intermittent storm surges inhibits tree growth, as evidenced by tree ring analysis. Tree rings also reveal a lag between the time when tree growth significantly slows and when the tree ultimately dies. Tree dieback reduces transpiration, retaining more water in the soil and creating conditions more favourable for flooding. Sedimentation from storm waters combined to organic matter decomposition can change the landscape, affecting flooding and runoff. Our results indicate that only a multidisciplinary approach can fully capture the ecohydrology of retreating forests in a period of accelerated sea level rise.more » « less
-
null (Ed.)Abstract Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tidal boundary and two retention ponds with controllable valves. RL's performance is compared to the passive system, a model predictive control (MPC) strategy, and a rule-based control strategy (RBC). RL learns to proactively manage pond levels using current and forecast conditions and reduced flooding by 32% over the passive system. Compared to the MPC approach using a physics-based model and genetic algorithm, RL achieved nearly the same flood reduction, just 3% less than MPC, with a significant 88× speedup in runtime. Compared to RBC, RL was able to quickly learn similar control strategies and reduced flooding by an additional 19%. This research demonstrates that RL can effectively control a simple system and offers a computationally efficient method that could scale to RTC of more complex stormwater systems.more » « less
-
Climate-driven sea-level rise is increasing the frequency of coastal flooding worldwide, exacerbated locally by factors like land subsidence from groundwater and resource extraction. However, a process rarely considered in future sea-level rise scenarios is sudden (over minutes) land subsidence associated with great (>M8) earthquakes, which can exceed 1 m. Along the Washington, Oregon, and northern California coasts, the next great Cascadia subduction zone earthquake could cause up to 2 m of sudden coastal subsidence, dramatically raising sea level, expanding floodplains, and increasing the flood risk to local communities. Here, we quantify the potential expansion of the 1 % floodplain (i.e., the area with an annual flood risk of 1%) under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios at 24 Cascadia estuaries. If a great earthquake occurred today, floodplains could expand by 90 km² (low), 160 km² (medium), or 300 km² (high subsidence), more than doubling the flooding exposure of residents, structures, and roads under the high subsidence scenario. By 2100, when climate-driven sea-level rise will compound the hazard, a great earthquake could expand floodplains by 170 km² (low), 240 km² (medium), or 370 km² (high subsidence), more than tripling the flooding exposure of residents, structures, and roads under the high subsidence scenario compared to the 2023 floodplain. Our findings can support decision makers and coastal communities along the Cascadia subduction zone as they prepare for compound hazards from earthquake-cycle and climate-driven sea-level rise, and provide critical insights for tectonically active coastlines globally.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/w11051098
