The role of coastal wetlands in reducing back bay flooding: New Jersey Back Bays case study

2020 ◽  
Author(s):  
Gregory Slusarczyk ◽  
Mary Cialone
Keyword(s):  
New Jersey ◽  
Flood Risk ◽  
Coastal Wetlands ◽  
Numerical Models ◽  
Atlantic Coast ◽  
Water Levels ◽  
Army Corps ◽  
Priority List ◽  
Us Army ◽  
Bay Area

<p>This paper will provide an analysis of the numerical modeled water levels in the vicinity of New Jersey Back Bays (NJBB) coastal wetlands in response to wave and surge forcing. The main focus of the analysis is to evaluate the contribution of the wetlands to reduce storm and flood risk, resist and recover from storms, and mitigate for degradation of the NJBB shorelines.  In order to provide information that addresses these needs, the US Army Corps of Engineers (USACE) Engineer Research and Development Center (ERDC) evaluated a set of “high” ranked Engineering with Nature (EWN)/ Natural and Nature Based Features (NNBF) measures through an application of the predictive numerical models ADvanced CIRCulation (ADCIRC) and STeady-state spectral WAVE (STWAVE) coupled via the Coastal Storm Modeling System (CSTORM-MS).</p><p>The ERDC modeling team developed a priority list of wetland configurations to evaluate, grouped into four categories: 1) Base Option designed to determine the maximum feasible benefits from a subset of NNBF measures, 2) Option 1 designed to determine how the benefits scale with NNBF size, 3) Option 2 designed to determine how the current marsh extent contributes to flood risk, 4) Option 3 designed to determine the interaction of waves with proposed NNBF measures predominantly in the Barnegat Bay area.</p><p>The above configurations were subject to wind forcing composed of a statistically-selected subset of synthetic tropical cyclones that were part of North Atlantic Coast Comprehensive Study (NACCS) storm suite. An analysis of the effectiveness of the wetland configurations was performed with respect to the following criteria: maximum surge envelopes, water level time series, and characteristics of tropical storm forcing conditions.</p>

Parameterization of Coastal Wetlands and Their Role in Back Bay Hydrodynamics

2020 ◽  
Author(s):  
Mary Cialone ◽  
Gregory Slusarczyk
Keyword(s):  
New Jersey ◽  
Flood Risk ◽  
Coastal Wetlands ◽  
Management Practices ◽  
Coastal Wetland ◽  
The United States ◽  
Hurricane Sandy ◽  
Water Levels ◽  
Depth Analysis

<p>This paper will provide an evaluation of the role of coastal wetlands in flood risk mediation by performing hydrodynamic modeling of storm surge in back bays that include various configurations of wetland features. Wetland parameters varied in the research study include the elevation, shape, volume, and vegetation type (represented by the Manning’s friction coefficient) to identify the role of wetlands in reducing back bay flooding.   This information can be used to determine best future management practices for dredged material placement that will serve to maintain and restore wetlands in light of environmental pressures such as climate change, subsidence, storm-induced erosion, boat wakes, and other factors influencing coastal wetland dynamics.</p><p>Following Hurricane Sandy in 2012, the United States (U.S.) Congress authorized the large scale North Atlantic Coast Comprehensive Study (NACCS) to address the present and future flood risk to this region. Part of that study was an in-depth numerical modeling and statistical analysis using the ADvanced CIRCulation (ADCIRC) and STeady-state spectral WAVE (STWAVE) models and the Joint Probability with Optimal Sampling (JPM-OS) statistical technique. Following the NACCS, the New Jersey back bays were identified as a high-risk area requiring further in-depth analysis of the effectiveness of surge barriers and coastal wetlands to reduce water levels in the back bays during storms. This paper will discuss the analysis of a set of coastal wetland configurations in the New Jersey back bay region simulated with a set of 10 synthetic storm suite selected from the NACCS study.   Analysis of maximum surge envelopes, water level time series, and characteristics of tropical storm forcing conditions were used to evaluate and compare the effectiveness of the wetland configurations.</p>

scholarly journals FRF WAVE TEST BED AND BATHYMETRY INVERSION

2017 ◽  
pp. 22 ◽  
Author(s):  
Jane McKee Smith ◽  
Spicer Bak ◽  
Tyler Hesser ◽  
Mary A. Bryant ◽  
Chris Massey
Keyword(s):  
Numerical Models ◽  
Field Research ◽  
Wave Model ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Test Bed ◽  
Inversion Algorithm ◽  
Us Army ◽  
Spectral Wave Model ◽  
Limited Application

An automated Coastal Model Test Bed has been built for the US Army Corps of Engineers Field Research Facility to evaluate coastal numerical models. In October of 2015, the test bed was expanded during a multi-investigator experiment, called BathyDuck, to evaluate two bathymetry sources: traditional survey data and bathymetry generated through the cBathy inversion algorithm using Argus video measurements. Comparisons were made between simulations using the spectral wave model STWAVE with half-hourly cBathy bathymetry and the more temporally sparse surveyed bathymetry. The simulation results using cBathy bathymetry were relatively close to those using the surveyed bathymetry. The largest differences were at the shallowest gauges within 250 m of the coast, where wave model normalized root-mean-square was approximately twice are large using the cBathy bathymetry. The nearshore errors using the cBathy input were greatest during events with wave height greater than 2 m. For this limited application, the Argus cBathy algorithm proved to be a suitable bathymetry input for nearshore wave modeling. cBathy bathymetry was easily incorporated into the modeling test bed and had the advantage of being updated on approximately the same temporal scale as the other model input conditions. cBathy has great potential for modeling applications where traditional surveys are sparse (seasonal or yearly).

Flood Risk Management: US Army Corps of Engineers and Layperson Perceptions

Risk Analysis ◽  
2012 ◽  
Vol 32 (8) ◽  
pp. 1349-1368 ◽  
Author(s):  
Matthew Wood ◽  
Daniel Kovacs ◽  
Ann Bostrom ◽  
Todd Bridges ◽  
Igor Linkov
Keyword(s):  
Risk Management ◽  
Flood Risk ◽  
Flood Risk Management ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Corps Of Engineers ◽  
Us Army

The Importance of Coastal Observations to Activities of the U.S. Army Corps of Engineers

2010 ◽  
Vol 44 (6) ◽  
pp. 42-53
Author(s):  
William Birkemeier ◽  
Linda Lillycrop ◽  
Robert Jensen ◽  
Charley Chesnutt
Keyword(s):  
Disaster Preparedness ◽  
Numerical Models ◽  
Wave Climate ◽  
Water Levels ◽  
Coastal Protection ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Corps Of Engineers ◽  
Coastal Mapping ◽  
The U.S

AbstractThe U.S. Army Corps of Engineers (Corps) is a project-oriented agency with multiple national missions under its Civil Works program including navigation, hydropower, flood risk management, ecosystem restoration, water supply, regulatory authority for wetlands and U.S. waters, recreation, and disaster preparedness and response. The Corps ocean and coastal activities revolve around the design, construction, and maintenance of specific projects such as channel dredging, coastal protection, beach nourishment, and harbor construction, all requiring research, modeling, and observations. Several Corps activities contribute ocean observations to the Integrated Ocean Observing System (IOOS®) and have requirements for existing or planned IOOS observations. Collected observations include long-term coastal wave climate, water levels, and coastal mapping data information. These provide project-specific and regional data that are used to develop and verify numerical models which are extensively used in project design and to evaluate project costs, benefits, and associated risk. An overview of the Corps coastal activities, data collection, and modeling programs is provided along with information regarding how IOOS coastal and ocean data are being used by the Corps.

scholarly journals Managing High Water Levels in Florida’s Largest Lake: Lake Okeechobee

EDIS ◽  
2018 ◽  
Vol 2018 (2) ◽  
Author(s):  
Karl Havens
Keyword(s):  
High Water ◽  
Water Levels ◽  
Army Corps ◽  
Lake Okeechobee ◽  
Army Corps Of Engineers ◽  
Corps Of Engineers ◽  
Us Army ◽  
Trade Offs ◽  
The Us ◽  
History Of

This 7-page fact sheet written by Karl E. Havens and published by the Florida Sea Grant College Program, UF/IFAS Extension, provides a history of Lake Okeechobee regulation schedules and an overview of the risks, constraints, and trade-offs that the US Army Corps of Engineers must consider when deciding to release flood water from the lake. http://edis.ifas.ufl.edu/sg154

Using Rapid Repeat SAR Interferometry to improve Hydrodynamic Models of flood propagation in Coastal Wetlands 

2021 ◽  
Author(s):  
Sergio Fagherazzi ◽  
Xiaohe Zhang ◽  
Cathleen Jones ◽  
Talib Oliver-Cabrera ◽  
Marc Simard
Keyword(s):  
Water Level ◽  
Spatial Resolution ◽  
Coastal Wetlands ◽  
High Spatial Resolution ◽  
Numerical Models ◽  
Water Levels ◽  
Sar Interferometry ◽  
Model Parameters ◽  
Water Level Change ◽  
Level Change

<p>The propagation of tides and riverine floodwater in coastal wetlands is controlled by subtle topographic differences and a thick vegetation canopy. A precise quantification of fluxes of water, sediments and nutrients is crucial to determine the resilience and vulnerability of coastal wetlands to sea level rise. High-resolution numerical models have been used in recent years to simulate fluxes across wetlands. However, these models are based on sparse field data that can lead to unreliable results. Here, we utilize high spatial-resolution, rapid repeat interferometric data from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) to provide a synoptic measurement of sub-canopy water-level change resulting from tide propagation into wetlands.  These data are used to constrain crucial model parameters and improve the performance and realism of simulations of the Wax Lake wetlands in coastal Louisiana (USA). A sensitivity analysis shows that the boundary condition of river discharge should be calibrated first, followed by iterative correction of terrain elevation. The calibration of bed friction becomes important only with the boundary and topography calibrated. With the model parameters calibrated, the overall Nash-Sutcliffe model efficiency for water-level change increases from 0.15 to 0.53 with the RMSE reduced by 26%. More importantly, constraining model simulations with UAVSAR observations drives iterative modifications of the original Digital Terrain Model. In areas with dense wetland grasses, the LiDAR signal is unable to reach the soil surface, but the L-band UAVSAR instrument detects changes in water levels that can be used to infer the true ground elevation. The high spatial resolution and repeat-acquisition frequency (minutes to hours) observations provided by UAVSAR represent a groundbreaking opportunity for a deeper understanding of the complex hydrodynamics of coastal wetlands.</p>

scholarly journals HAWAII HURRICANE WAVE AND SURGE MODELING AND FAST FORECASTING

2012 ◽  
Vol 1 (33) ◽  
pp. 8 ◽  
Author(s):  
Jane McKee Smith ◽  
Andrew B. Kennedy ◽  
Joannes J. Westerink ◽  
Alexandros A. Taflanidis ◽  
Kwok Fai Cheung
Keyword(s):  
High Resolution ◽  
Surrogate Model ◽  
Numerical Models ◽  
Equation Model ◽  
Army Corps ◽  
High Fidelity ◽  
Army Corps Of Engineers ◽  
Wave Runup ◽  
Us Army ◽  
High Fidelity Simulations

The US Army Corps of Engineers’ Surge and Wave Island Modeling Studies developed a fast forecasting system for hurricane waves and inundation in Hawaii. The system is based on coupled high-resolution, high-fidelity simulations of waves and surge applying the SWAN and ADCIRC numerical models on a 2D finite-element grid. Additionally, wave runup is simulated on high-resolution cross-shore transects using the Boussinesq-equation model BOUSS-1D. Approximately 1500 storms were simulated to cover the range of hurricane parameters of landfall location, track angle at landfall, central pressure, forward speed, and radius of maximum winds expected to impact Hawaii. To create a forecast system that is fast and robust, a moving least-squares response surface surrogate model was developed based on the high-fidelity model results. The surrogate model is approximately seven orders of magnitude faster than the high-fidelity simulations. The efficiency of the surrogate model allows both deterministic and probabilistic simulations in seconds to minutes on a personal computer.

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