An official website of the United States government
Abstract This study investigates the sensitivity of the Calcasieu Lake estuarine region to channel deepening in southwest Louisiana in the USA. We test the hypothesis that the depth increase in a navigational channel in an estuarine region results in the amplification of the inland penetration of storm surge, thereby increasing the flood vulnerability of the region. We run numerical experiments using the Delft3D modeling suite (validated with observational data) with different historic channel depth scenarios. Model results show that channel deepening facilitates increased water movement into the lake–estuary system during a storm surge event. The inland peak water level increases by 37% in the presence of the deepest channel. Moreover, the peak volumetric flow rate increases by 291.6% along the navigational channel. Furthermore, the tidal prism and the volume of surge prism passing through the channel inlet increase by 487% and 153.3%, respectively. In our study, the presence of the deepest channel results in extra 56.72 km2of flooded area (approximately 12% increase) which is an indication that channel deepening over the years has rendered the region more vulnerable to hurricane-induced flooding. The study also analyzes the impact of channel deepening on storm surge in estuaries under different future sea-level rise (SLR) scenarios. Simulations suggest that even the most conservative scenario of SLR will cause an approximately 51% increase in flooded area in the presence of the deepest ship channel, thereby suggesting that rising sea level will cause increased surge penetration and increased flood risk.
more »
« less
Assessing compound flooding potential with multivariate statistical models in a complex estuarine system under data constraints
Abstract Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Here, we use statistical methods to assess compounding effects from storm surge and multiple riverine discharges in Sabine Lake, TX. We employ several trivariate statistical models, including vine‐copulas and a conditional extreme value model, to examine the sensitivity of results to the choice of data pre‐processing steps, statistical model setup, and outliers. We define a response function that represents water levels resulting from the interaction between discharge and surge processes inside Sabine Lake and explore how it is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. Under data constraints, this highlights the need for testing various statistical modelling approaches, while the choice of an optimal approach remains subjective.
more »
« less
- Award ID(s):
- 1929382
- PAR ID:
- 10447969
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of Flood Risk Management
- Volume:
- 14
- Issue:
- 4
- ISSN:
- 1753-318X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Coastal environments around the globe are subject to anthropogenic stresses due to dense coastal populations. The response of development activities on dynamic estuarine ecosystems, influenced by tidal forces, freshwater flows, salinity variations, and intricate coastal land morphology, is often uncertain. This case study evaluates how connectivity and coastal geomorphology influence flow patterns by modeling the effects of a proposed hydraulic reconnection project on water movement between the Manchester Waterway, a coastal residential community, and Charlotte Harbor, a large open water estuary in the Gulf of Mexico. An unstructured grid, 2D model was developed utilizing Delft3D Flexible Mesh to simulate estuary hydrodynamics under proposed conditions for four different weather conditions, including recorded 2021–2022 weather, future sea level rise, an extreme weather event, and a combination of extreme weather and sea level rise. Simulated flow results for proposed conditions were compared to present day flow patterns for analysis of the predicted changes in water levels and velocity magnitudes in the waterway. The results show that increased connectivity between the Manchester Waterway and Charlotte Harbor is expected to increase tidal amplitudes largely due to a lowering of minimum water levels in the waterway. During storm events, water elevations are predicted to drop to lower elevations following peak storm surge due to proposed conditions, which may provide flooding relief. Model simulation results will aid hydraulic reconnection and guide a more comprehensive ecological restoration plan. This case study will also improve understanding of the major influencing forces in intricate estuarine environments and how ecosystems may respond to land development, sea level rise, and increasing magnitude and frequency of tropical storms.more » « less
-
Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.more » « less
-
Abstract Compound flooding, characterized by the co‐occurrence of multiple flood mechanisms, is a major threat to coastlines across the globe. Tropical cyclones (TCs) are responsible for many compound floods due to their storm surge and intense rainfall. Previous efforts to quantify compound flood hazard have typically adopted statistical approaches that may be unable to fully capture spatio‐temporal dynamics between rainfall‐runoff and storm surge, which ultimately impact total water levels. In contrast, we pose a physics‐driven approach that utilizes a large set of realistic TC events and a simplified physics‐based rainfall model and simulates each event within a hydrodynamic model framework. We apply our approach to investigate TC flooding in the Cape Fear River, NC. We find TC approach angle, forward speed, and intensity are relevant for compound flood potential, but rainfall rate and time lag between the centroid of rainfall and peak storm tide are the strongest predictors of compounding magnitude. Neglecting rainfall underestimates 100‐year flood depths across 28% of the floodplain, and taking the maximum of each hazard modeled separately still underestimates 16% of the floodplain. We find the main stem of the river is surge‐dominated, upstream portions of small streams and pluvial areas are rainfall dominated, but midstream portions of streams are compounding zones, and areas close to the coastline are surge dominated for lower return periods but compounding zones for high return periods (100 years). Our method links joint rainfall‐surge occurrence to actual flood impacts and demonstrates how compound flooding is distributed across coastal catchments.more » « less
-
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
