1. Introduction
Tropical cyclones (TCs), associated with extreme wind, rainfall and
storm surge, are responsible for significant property damage and loss of
life in coastal areas (Smith and Katz, 2013). Often, TC-induced storm
surge together with astronomical high tide have been suggested as the
primary causes of episodic flooding in low-lying coastal regions. Recent
flood catastrophes illustrate that coastal cities, as the agglomeration
of population and assets, are particularly vulnerable to storm tide
flooding. For example, the unprecedented flooding of New Orleans due to
Hurricane Katrina (2005) caused hundreds of fatalities and more than
$40 billion in economic losses, making it the worst natural disaster in
U.S. history (Kates et al., 2006). Hurricane Sandy (2012) resulted in
the deaths of tens of people due to drowning and at least $20 billion
in flood-related damages in New York City (Blake et al., 2013). Coastal
flooding is expected to be more frequent and devastating, owing to rapid
urbanization, sea level rise (SLR) and TC intensification in a warming
climate (Emanuel, 2013; Woodruff et al., 2013; Hallegatte et al., 2013;
Lin and Shullman 2017; Vitousek et al., 2017; Marsooli et al. 2019;
Knutson et al., 2020). Hence, it is essential to quantify TC-induced
flood hazards at city or regional scales.
To ensure an accurate assessment of TC-induced flood hazards for coastal
cities, a large number of TC events are required particularly for
addressing extreme flood hazards induced by low-probability storms. Due
to the lack of sufficient measurements over a long period, statistical
methods have been commonly used to generate large samples of synthetic
TCs for a specific site (e.g., a city) of interest (Batts et al., 1980;
Georgiou et al., 1983). The basic idea of these models is that
probability distributions of key TC parameters (e.g., central pressure
and radius of maximum wind) are fitted to the historical record, and
then a Monte Carlo approach is applied to sample from these
distributions. To overcome the limitation of local data, Vickery et al.
(2000, 2009) applied the basin-wide data to develop a probabilistic
track model with TC intensity along the track estimated based on storm
persistent and environmental variables such as the sea surface
temperature. Furthermore, Emanuel et al. (2008), Lee et al. (2018), and
Jing and Lin (2020) developed more advanced probabilistic track models
by incorporating more environmental and oceanic variables that are
essential to storm development. The advent of synthetic TC simulation
has enabled probabilistic TC hazard assessment for coastal cities.
Recently, synthetic TC simulation has been increasingly used to drive
storm tide simulation and assess flood hazards at the city scale. Lin et
al. (2010, 2012, 2016) applied the statistical-deterministic model
developed by Emanuel et al. (2008) to produce large numbers of synthetic
storms for New York City (NYC), and then they used two hydrodynamic
models: the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model
(Jelesnianski et al., 1992) and the Advanced Circulation (ADCIRC) model
(Luettich et al., 1992; Westerink et al., 1994) with a high resolution
mesh (up to 10 m around NYC) to simulate storm surges and storm tides
induced by synthetic TCs. They estimated the probabilistic distribution
of storm tides along NYC coast and found a heavy tail for very low
occurrence probability events. Furthermore, the simulated surge heights
along the coast from Lin et al. (2012) were interpolated and applied to
terrain elevation for determining the flood inundation (extent and
depth) and associated probabilities for NYC (Aerts et al., 2013).
However, the simple nearest neighbor extrapolation method used in this
analysis does not consider the dynamic nature of flood routing and may
cause biases in the final inundation maps (Barnard et al., 2019). More
recently, Yin et al. (2016) described a new method for coastal flood
simulation by coupling ADCIRC and a simplified 2D flood inundation model
(FloodMap) and applied the analysis to NYC for the case of Hurricane
Sandy. The coupled model demonstrated efficiency and improved prediction
over ADCIRC-only simulations for estimating storm-induced flooding in
coastal areas.
In this study, an integrated statistical-hydrodynamic approach is
proposed by running the statistical-deterministic hurricane model,
ADCIRC, and FloodMap in sequence, to derive probabilistic flood hazard
maps and to investigate possible worst-case flood scenarios for coastal
cities under the current climate conditions as well as projected future
SLR scenarios. To our knowledge, this study is the first that couples
probabilistic hurricane model, storm surge model, inundation model,
coastal protection data, and SLR projections to quantify the inundation
hazards (probabilities). The proposed approach is applied to analyze the
coastal flood hazard for the city of Shanghai, which is within the
Northwest Pacific Basin and is highly exposed to typhoon-induced coastal
flooding. For instance, Typhoon Winnie (1997) caused storm tides up to
nearly 7 meters above Wusong Datum, the highest recorded in Shanghai’s
history. Meanwhile, Shanghai is protected by high-standard flood
defense, which needs to be taken into account when estimating the
inundation risk and effects of SLR. The remainder of this paper is
organized as follows. Section 2 describes materials and methods,
including synthetic TC dataset, storm tide simulation, flood probability
analysis, and coastal inundation modeling. Section 3 presents the
results and discusses the key findings. Section 4 provides the
conclusions and offers suggestions for further research.