Compounding factors for extreme flooding around Galveston Bay during Hurricane Harvey

Huang, Wei; Ye, Fei; Zhang, Y. Joseph; Park, Kyeong; Du, Jiabi; Moghimi, Saeed; Myers, Edward; Pe’eri, Shachak; Calzada, Jaime Roset; Yu, Hailing; Nunez, Karinna; Liu, Zhuo

doi:10.1016/j.ocemod.2020.101735

Cited by 54 publications

(61 citation statements)

References 26 publications

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“…Even though different setups (2D, 3D barotropic, and 3D baroclinic) were tuned to their best possible skills, the 3D baroclinic setup was shown to better capture the total elevation during the post-storm adjustment phase. In addition, during the ongoing effort to operationalize the model, we found that including 3D processes greatly simplified the bottom friction parameterization at some coastal locations (e.g., NOAA Station 8447930 at Woods Hole, MA; Huang et al, 2021b). A 3D model can also produce relevant 3D variables (e.g., 3D velocity and tracer concentration) that are important for safe navigation and ecosystem health.…”

Section: Introductionmentioning

confidence: 98%

A cross-scale study for compound flooding processes during Hurricane Florence

Huang

Zhang

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. We study the compound flooding processes that occurred in Hurricane Florence (2018), which was accompanied by heavy precipitation, using a 3D creek-to-ocean hydrodynamic model. We examine the important role played by barrier islands in the observed compound surges in the coastal watershed. Locally very high resolution is used in some watershed areas in order to resolve small features that turn out to be critical for capturing the observed high water marks locally. The wave effects are found to be significant near barrier islands and have contributed to some observed over-toppings and breaches. Results from sensitivity tests applying each of the three major forcing factors (oceanic, fluvial, and pluvial) separately are succinctly summarized in a “dominance map” that highlights significant compound effects in most of the affected coastal watersheds, estuaries, and back bays behind the barrier islands. Operational forecasts based on the current model are being set up at NOAA to help coastal resource and emergency managers with disaster planning and mitigation efforts.

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Section: Introductionmentioning

confidence: 98%

A cross-scale study for compound flooding processes during Hurricane Florence

Huang

Zhang

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…Riverine and coastal floods characterized by the simultaneous or successive occurrence of high sea levels and high river flows can be life threatening and cause infrastructures damage (Nadal et al, 2010;Ganguli et al, 2020;Khanam et al, 2021). This type of flooding was remarkable during hurricane Harvey in Houston-Galveston Bay (Valle-Levinson et al, 2020;Huang et al, 2021b). For example, around 600 million people in coastal regions may be exposed to this type of compound flood by 2,100 (Kulp and Strauss, 2019).…”

Section: Coastal and Riverine Floodsmentioning

confidence: 99%

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

et al. 2021

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Compound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.

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“…The OPENCoastS evolution to complete coastal physics was made possible by the comprehensive representation of physical processes available in the SCHISM modeling suite (Zhang et al, 2016; SCHISM has been extensively tested against ocean/coastal benchmarks (Chen et al, 2013;Lynett et al, 2017) and applied to several regional seas, embayments and estuaries worldwide in the fields of general circulation, tsunami, storm-surge and compound inundation, wave-current interaction, water quality, coastal ecology, and morphodynamics (e.g., Guérin et al, 2016;Rodrigues and Fortunato, 2017;Fortunato et al, 2017b;Allen et al, 2018;Li et al, 2018;Du et al, 2020;Wang et al, 2020;Lavaud et al, 2020;Huang et al, 2021). SCHISM is also the hydrodynamic engine of several forecast systems besides OPENCoastS (Stanev et al, 2016;Fortunato et al, 2017a;Chiu et al, 2018;Fernandez-Montblanc et al, 2019).…”

Section: Current Application Of Schism Modeling Suite In Opencoastsmentioning

confidence: 99%