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Prof. Dr. Wenrui Huang
Civil and Environmental Engineering, Florida State University, Tallahassee, FL, USA

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Research Keywords & Expertise

0 Sediment Transport
0 Waves
0 Navigation channel
0 Typhoon damrey
0 Changjiang estuary

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Original paper
Published: 26 January 2021 in Natural Hazards
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Storm surge induced by hurricane is a major threat to the Gulf Coasts of the United States. A numerical modeling study was conducted to simulate the storm surge during Hurricane Michael, a category 5 hurricane that landed on the Florida Panhandle in 2018. A high-resolution model mesh was used in the ADCIRC hydrodynamic model to simulate storm surge and tides during the hurricane. Two parametric wind models, Holland 1980 model and Holland 2010 model, have been evaluated for their effects on the accuracy of storm surge modeling by comparing simulated and observed maximum water levels along the coast. The wind model parameters are determined by observed hurricane wind and pressure data. Results indicate that both Holland 1980 and Holland 2010 wind models produce reasonable accuracy in predicting maximum water level in Mexico Beach, with errors between 1 and 3.7%. Comparing to the observed peak water level of 4.74 m in Mexico Beach, Holland 1980 wind model with radius of 64-knot wind speed for parameter estimation results in the lowest error of 1%. For a given wind model, the wind profiles are also affected by the wind data used for parameter estimation. Away from hurricane eye wall, using radius of 64-knot wind speed for parameter estimation generally produces weaker wind than those using radius of 34-knot wind speed for parameter estimation. Comparing model simulated storm tides with 17 water marks observed along the coast, Holland 2010 wind model using radius of 34-knot wind speed for parameter estimation leads to the minimum mean absolute error. The results will provide a good reference for researchers to improve storm surge modeling. The validated model can be used to support coastal hazard mitigation planning.

ACS Style

Linoj Vijayan; Wenrui Huang; Kai Yin; Eren Ozguven; Simone Burns; Mahyar Ghorbanzadeh. Evaluation of parametric wind models for more accurate modeling of storm surge: a case study of Hurricane Michael. Natural Hazards 2021, 106, 2003 -2024.

AMA Style

Linoj Vijayan, Wenrui Huang, Kai Yin, Eren Ozguven, Simone Burns, Mahyar Ghorbanzadeh. Evaluation of parametric wind models for more accurate modeling of storm surge: a case study of Hurricane Michael. Natural Hazards. 2021; 106 (3):2003-2024.

Chicago/Turabian Style

Linoj Vijayan; Wenrui Huang; Kai Yin; Eren Ozguven; Simone Burns; Mahyar Ghorbanzadeh. 2021. "Evaluation of parametric wind models for more accurate modeling of storm surge: a case study of Hurricane Michael." Natural Hazards 106, no. 3: 2003-2024.

Journal article
Published: 24 February 2020 in Estuarine, Coastal and Shelf Science
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ACS Style

Qi Shen; Wenrui Huang; Yuanyang Wan; Fengfeng Gu; Dingman Qi. Observation of the sediment trapping during flood season in the deep-water navigational channel of the Changjiang Estuary, China. Estuarine, Coastal and Shelf Science 2020, 237, 1 .

AMA Style

Qi Shen, Wenrui Huang, Yuanyang Wan, Fengfeng Gu, Dingman Qi. Observation of the sediment trapping during flood season in the deep-water navigational channel of the Changjiang Estuary, China. Estuarine, Coastal and Shelf Science. 2020; 237 ():1.

Chicago/Turabian Style

Qi Shen; Wenrui Huang; Yuanyang Wan; Fengfeng Gu; Dingman Qi. 2020. "Observation of the sediment trapping during flood season in the deep-water navigational channel of the Changjiang Estuary, China." Estuarine, Coastal and Shelf Science 237, no. : 1.

Journal article
Published: 02 November 2019 in International Journal of Disaster Risk Reduction
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Shelters serve as critical facilities where people gather during and after hurricanes. A basic requirement of ‘operation’ for a shelter is being functional and available to help. However, what happens when shelters themselves are damaged and unable to serve those people? An open question with key policy implications is: How can we identify the most critical shelter(s) as a part of broader emergency evacuation operations and how can we respond to an interdiction? This paper develops a two-step modeling framework consisting of enhanced r-interdiction median models (RIM) to identify the most significant shelter(s) and revised p-median models to identify shelters to repurpose during such an interdiction where shelters are rendered off-line. Proposed models are applied on a Southeast Florida case study with respect to scenarios based on varying hurricane strength and shelter demand. Findings indicate that models are susceptible to travel cost variation based on the demand-weighted objectives, and that shelter selections vary due to different demand scenarios created which focus on different population segments. As hurricane strength increases, critical shelter identification is driven by flooding and storm surge risks. These findings can inform efforts to harden those critical shelters so that they can better serve populations in need.

ACS Style

Onur Alisan; Mahyar Ghorbanzadeh; Mehmet Ulak; Ayberk Kocatepe; Eren Erman Ozguven; Mark Horner; Wenrui Huang. Extending interdiction and median models to identify critical hurricane shelters. International Journal of Disaster Risk Reduction 2019, 43, 101380 .

AMA Style

Onur Alisan, Mahyar Ghorbanzadeh, Mehmet Ulak, Ayberk Kocatepe, Eren Erman Ozguven, Mark Horner, Wenrui Huang. Extending interdiction and median models to identify critical hurricane shelters. International Journal of Disaster Risk Reduction. 2019; 43 ():101380.

Chicago/Turabian Style

Onur Alisan; Mahyar Ghorbanzadeh; Mehmet Ulak; Ayberk Kocatepe; Eren Erman Ozguven; Mark Horner; Wenrui Huang. 2019. "Extending interdiction and median models to identify critical hurricane shelters." International Journal of Disaster Risk Reduction 43, no. : 101380.

Journal article
Published: 19 June 2019 in Geosciences
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The southern New England coast of the United States is particularly vulnerable to land-falling hurricanes because of its east-west orientation. The impact of two major hurricanes on the city of Providence (Rhode Island, USA) during the middle decades of the 20th century spurred the construction of the Fox Point Hurricane Barrier (FPHB) to protect the city from storm surge flooding. Although the Rhode Island/Narragansett Bay area has not experienced a major hurricane for several decades, increased coastal development along with potentially increased hurricane activity associated with climate change motivates an assessment of the impacts of a major hurricane on the region. The ocean/estuary response to an extreme hurricane is simulated using a high-resolution implementation of the ADvanced CIRCulation (ADCIRC) model coupled to the Precipitation-Runoff Modeling System (PRMS). The storm surge response in ADCIRC is first verified with a simulation of a historical hurricane that made landfall in southern New England. The storm surge and the hydrological models are then forced with winds and rainfall from a hypothetical hurricane dubbed “Rhody”, which has many of the characteristics of historical storms that have impacted the region. Rhody makes landfall just west of Narragansett Bay, and after passing north of the Bay, executes a loop to the east and the south before making a second landfall. Results are presented for three versions of Rhody, varying in the maximum wind speed at landfall. The storm surge resulting from the strongest Rhody version (weak Saffir–Simpson category five) during the first landfall exceeds 7 m in height in Providence at the north end of the Bay. This exceeds the height of the FPHB, resulting in flooding in Providence. A simulation including river inflow computed from the runoff model indicates that if the Barrier remains closed and its pumps fail (for example, because of a power outage or equipment failure), severe flooding occurs north of the FPHB due to impoundment of the river inflow. These results show that northern Narragansett Bay could be particularly vulnerable to both storm surge and rainfall-driven flooding, especially if the FPHB suffers a power outage. They also demonstrate that, for wind-driven storm surge alone under present sea level conditions, the FPHB will protect Providence for hurricanes less intense than category five.

ACS Style

David S. Ullman; Isaac Ginis; Wenrui Huang; Catherine Nowakowski; Xuanyu Chen; Peter Stempel. Assessing the Multiple Impacts of Extreme Hurricanes in Southern New England, USA. Geosciences 2019, 9, 265 .

AMA Style

David S. Ullman, Isaac Ginis, Wenrui Huang, Catherine Nowakowski, Xuanyu Chen, Peter Stempel. Assessing the Multiple Impacts of Extreme Hurricanes in Southern New England, USA. Geosciences. 2019; 9 (6):265.

Chicago/Turabian Style

David S. Ullman; Isaac Ginis; Wenrui Huang; Catherine Nowakowski; Xuanyu Chen; Peter Stempel. 2019. "Assessing the Multiple Impacts of Extreme Hurricanes in Southern New England, USA." Geosciences 9, no. 6: 265.

Journal article
Published: 01 November 2018 in Journal of Waterway, Port, Coastal, and Ocean Engineering
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A typhoon is one of the major factors that often cause sediment transport and bed erosion in estuarine navigational channels. A 12.5-m deepwater navigational channel (DNC) is located in the north passage of the Changjiang Estuary. Because it acts as the entrance navigation waterway of the Changjiang River, it is important to investigate the impact of typhoons on sediment suspension and transport in the navigation channel. In this study, a previously calibrated hydrodynamic and sediment transport model [shallow-water equation model (SWEM)] was integrated with a storm wind model [weather research and forecasting (WRF)] and a wave model [simulating waves nearshore (SWAN)] to investigate the effect of Typhoon Damrey on the navigation channel in 2012. The typhoon produced a weak storm surge but significant wave heights in the Changjiang Estuary. By comparing bottom shear stress induced by current and wave, numerical modeling results indicated that the increase of sediment concentration in the navigation channel during Typhoon Damrey was mainly caused by sediment transport fluxed into the channel from shallow-water areas outside the channel, where wave-induced bottom shear stress during Typhoon Damrey caused sediment resuspension. The high sediment flux overtopping from the south dike into the channel was the important sediment source for the navigation channel. During the passage of Typhoon Damrey, there was a convergence area of sediment flux between the upstream seaward sediment transport and the lateral transport of sediment flux overtopping from the south dike at the middle-lower reach of the north passage. Near the outlet of the north passage, the near-bottom residual transport of sediment was in the upstream direction against the seaward sediment transport from the river. The convergences of sediment flux produced the high-turbidity maximum zone at the lower reach of the north passage, where in situ bathymetric surveys within the DNC before and after Typhoon Damrey showed the sediment deposition area in the channel.

ACS Style

Qi Shen; Wenrui Huang; Dingman Qi. Integrated Modeling of Typhoon Damrey’s Effects on Sediment Resuspension and Transport in the North Passage of Changjiang Estuary, China. Journal of Waterway, Port, Coastal, and Ocean Engineering 2018, 144, 04018015 .

AMA Style

Qi Shen, Wenrui Huang, Dingman Qi. Integrated Modeling of Typhoon Damrey’s Effects on Sediment Resuspension and Transport in the North Passage of Changjiang Estuary, China. Journal of Waterway, Port, Coastal, and Ocean Engineering. 2018; 144 (6):04018015.

Chicago/Turabian Style

Qi Shen; Wenrui Huang; Dingman Qi. 2018. "Integrated Modeling of Typhoon Damrey’s Effects on Sediment Resuspension and Transport in the North Passage of Changjiang Estuary, China." Journal of Waterway, Port, Coastal, and Ocean Engineering 144, no. 6: 04018015.

Original article
Published: 22 March 2018 in Neural Computing and Applications
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Estuarine organisms have varying tolerances and respond differently to salinity. Bottom-dwelling species such as oysters tolerate some change in salinity, but salinity outside an acceptable range will negatively affect their abundance as well as their survival within this sensitive ecosystem. Salinity in the Apalachicola Bay is heavily influenced by freshwater inflow discharged from the Apalachicola River. In this study, artificial neural network (ANN) was applied to correlate the monthly salinity variations at an oyster reef in Apalachicola Bay to the river inflow and wind. Parameters in the ANN were trained until the simulated salinity data correlated well with the observations from 2005 to 2007. Once the model is trained and optimized, the ANN structure is verified comparing the simulated data to the second dataset from 2008–2010. Four neural network training algorithms, including gradient decent, scaled conjugate gradient, quasi-Newton, and Levenberg–Marquardt, have been evaluated. The scaled conjugate gradient algorithm was selected for this study because it provides the best correlation with the value of 0.85. The verified ANN model was applied to investigate the potential impacts of freshwater reductions from upstream river on the salinity in the oyster reef. By comparing the resulting salinity from ANN model simulations to the optimal salinity range for oyster growth, the impacts of freshwater reduction scenarios on oyster growth can be examined.

ACS Style

Duc Le; Wenrui Huang; Elijah Johnson. Neural network modeling of monthly salinity variations in oyster reef in Apalachicola Bay in response to freshwater inflow and winds. Neural Computing and Applications 2018, 31, 6249 -6259.

AMA Style

Duc Le, Wenrui Huang, Elijah Johnson. Neural network modeling of monthly salinity variations in oyster reef in Apalachicola Bay in response to freshwater inflow and winds. Neural Computing and Applications. 2018; 31 (10):6249-6259.

Chicago/Turabian Style

Duc Le; Wenrui Huang; Elijah Johnson. 2018. "Neural network modeling of monthly salinity variations in oyster reef in Apalachicola Bay in response to freshwater inflow and winds." Neural Computing and Applications 31, no. 10: 6249-6259.

Original paper
Published: 15 November 2017 in Natural Hazards
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Reliable predictions of storm runoff from rainfall and snowmelt are important for flood hazard mitigation and resilience. In this study, the HEC-HMS and PRMS hydrological models have been applied to simulate storm runoff in Taunton River Basin for the storm event in 2010 when maximum rainfall intensity reached approximate 5 in/day in March, and the snowfall reached about 11 inches in December. Model parameters were calibrated, and model performance was evaluated by comparing model-simulated daily stream flow with observations. For the rainstorm period during March–April, results indicate that both HEC-HMS and PRMS provide very good predictions of rainfall runoff with the correlation values above 0.95, and PRMS produces lower root-mean-square errors than those from HEC-HMS. Over the 12-month period including the snowfall in December, the time series of flow by PRMS match better with observations than those from the HEC-HMS. The 12-month overall correlation values for HEC-HMS and PRMS are 0.91 and 0.97 at Bridgewater Station, and 0.89 and 0.97 at Threemile Station, respectively. For an extreme storm scenario of the maximum historical 36.7-inch snowfall in early February in combination with the rainstorm in March–April of 2010, model simulations indicate that the flow would substantially increase by about 50% or more. Comparisons between HEC-HMS and RPMS models have been analyzed to provide references for storm runoff modeling.

ACS Style

Fei Teng; Wenrui Huang; Isaac Ginis. Hydrological modeling of storm runoff and snowmelt in Taunton River Basin by applications of HEC-HMS and PRMS models. Natural Hazards 2017, 91, 179 -199.

AMA Style

Fei Teng, Wenrui Huang, Isaac Ginis. Hydrological modeling of storm runoff and snowmelt in Taunton River Basin by applications of HEC-HMS and PRMS models. Natural Hazards. 2017; 91 (1):179-199.

Chicago/Turabian Style

Fei Teng; Wenrui Huang; Isaac Ginis. 2017. "Hydrological modeling of storm runoff and snowmelt in Taunton River Basin by applications of HEC-HMS and PRMS models." Natural Hazards 91, no. 1: 179-199.