An official website of the United States government
With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.
more »
« less
Exploring Deep Learning Methods for Short-Term Tide Gauge Water Level Predictions
Accurate and timely water level predictions are essential for effective shoreline and coastal ecosystem management. As sea levels rise, the frequency and severity of coastal inundation events are increasing, causing significant societal and economic impacts. Predicting these events with sufficient lead time is essential for decision-makers to mitigate economic losses and protect coastal communities. While machine learning methods have been developed to predict water levels at specific sites, there remains a need for more generalized models that perform well across diverse locations. This study presents a robust deep learning model for predicting water levels at multiple tide gauge locations along the Gulf of Mexico, including the open coast, embayments, and ship channels, all near major ports. The selected architecture, Seq2Seq, achieves significant improvements over the existing literature. It meets the National Oceanic and Atmospheric Administration’s (NOAA) operational criterion, with the percentage of predictions within 15 cm for lead times up to 108 h at the tide gauges of Port Isabel (92.2%) and Rockport (90.4%). These results represent a significant advancement over current models typically failing to meet NOAA’s standard beyond 48 h. This highlights the potential of deep learning models to improve water level predictions, offering crucial support for coastal management and flood mitigation.
more »
« less
- Award ID(s):
- 2019758
- PAR ID:
- 10567247
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Water
- Volume:
- 16
- Issue:
- 20
- ISSN:
- 2073-4441
- Page Range / eLocation ID:
- 2886
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Increasing exposure to coastal flood hazards will potentially induce an enormous socio‐economic toll on vulnerable communities. To accurately characterize the hazard, we must consider both natural water level variability and climate change‐induced sea‐level rise. In this study, we develop a paleo‐proxy‐based reconstruction of coastal flood events over the last 500 yr to capture natural water level variability and superimpose that reconstruction onto expected sea‐level rise to explore interannual and multidecadal variability in plausible future coastal flood risk. We first develop reconstructions of leading principal components (PCs) of sea surface temperature anomalies from 1500 CE onwards, using tree‐ring, coral, and sclerosponge chronology‐based El Niño Southern Oscillation reconstructions as predictors in a wavelet autoregression model. These reconstructions of PCs are then used in a stochastic water level emulator to develop ensemble simulations of hourly still water levels (SWLs) in the San Francisco Bay. The emulator accounts for multiple relevant processes, including monthly mean sea level (MMSL) anomalies, storm surge, and tide, all varying at different timescales. Accounting for natural variability in water levels over 1500–2000 CE increases coastal flood risk beyond that suggested by instrumental records alone. When superimposed on 0.22 m of sea‐level rise (approximately the amount experienced over the previous century), the simulations show that while high tides and large storm surges cause the smaller extreme SWLs, the larger extreme SWLs occur during concurrent high MMSL, high tides, and significant storm surges. Our findings thus highlight the need to consider natural water level variability for coastal adaptation and planning.more » « less
-
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
-
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
-
Abstract. The interaction between storm surge and concurrent precipitation is poorly understood in many coastal regions. This paper investigates the potential compound effects from these two flooding drivers along the coast of China for the first time by using the most comprehensive records of storm surge and precipitation. Statistically significant dependence between flooding drivers exists at the majority of locations that are analysed, but the strength of the correlation varies spatially and temporally and depending on how extreme events are defined. In general, we find higher dependence at the south-eastern tide gauges (TGs) (latitude < 30∘ N) compared to the northern TGs. Seasonal variations in the dependence are also evident. Overall there are more sites with significant dependence in the tropical cyclone (TC) season, especially in the summer. Accounting for past sea level rise further increases the dependence between flooding drivers, and future sea level rise will hence likely lead to an increase in the frequency of compound events. We also find notable differences in the meteorological patterns associated with events where both drivers are extreme versus events where only one driver is extreme. Events with both extreme drivers at south-eastern TG sites are caused by low-pressure systems with similar characteristics across locations, including high precipitable water content (PWC) and strong winds that generate high storm surge. Based on historical disaster damages records of Hong Kong, events with both extreme drivers account for the vast majority of damages and casualties, compared to univariate flooding events, where only one flooding driver occurred. Given the large coastal population and low capacity of drainage systems in many Chinese urban coastal areas, these findings highlight the necessity to incorporate compound flooding and its potential changes in a warming climate into risk assessments, urban planning, and the design of coastal infrastructure and flood defences.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/w16202886
