2.3. Storm Tide Simulation
We use the ADvanced CIRCulation model (ADCIRC) to simulate storm tides
(i.e., the total water level including storm surge and astronomical
tide). ADCIRC, originally developed by Luettich et al. (1992) and
Westerink et al. (1994), is a well-established open-source hydrodynamic
model applicable to free surface circulation and transport problems. The
model adopts the hydrostatic pressure assumption to numerically solve
the governing equations in space and time using, respectively, the
finite-element and finite-difference numerical methods. ADCIRC solves
the 2-D or 3-D momentum equations to calculate velocity fields. For
storm surge modeling in the coastal ocean, the 2-D depth-integrated
model is usually preferred to the 3-D model because it is
computationally cheaper and largely captures the dominant processes that
control the storm-induced water level (Resio and Westerink, 2008). In
the present study, we use the 2-D depth-integrated version of ADCIRC.
Due to the extremely large computational resource that would be required
to simulate wind waves associated with the large number of synthetic
storms, we do not apply wave simulations here. Thus we neglect the
effect of wave setup on the total water level, which has been estimated
to contribute up to 10-20% to total extreme water levels in the
Shanghai region (Melet et al., 2018).
Using the mesh generation module of Surface-water Modeling System (SMS),
we develop a computational mesh that extends between latitudes 21°N and
41°N and longitudes 116.5°W and 129.5°W. The computational domain
consists of an unstructured triangular mesh with 108,440 nodes and
209,844 elements. The mesh resolution is <100 m nearshore and
gradually increases to > 50 km in deeper waters. The
bathymetry of the model is based on the bathymetry survey of Yangtze
River Estuary in 2012 (Zhu and Bao, 2016) and GEBCO (GEneral Bathymetric
Chart of the Oceans)-2014 global 30 arc sec gridded data
(https://www.gebco.net/data_and_products/historical_data_sets/). The
vertical datum is in mean sea level (referenced to year 2000). The
manning’s n bottom roughness coefficient varies spatially over the grid
based on a global database for sediments on the seabed
(http://portal.gplates.org/static/html/seafloor.html). The water
level at the open boundaries is specified by eight major tidal
constituents (K1, K2, M2, N2, O1, P1, Q1, and S2). The tidal
information, including the amplitudes and phases, are obtained from the
global model of ocean tides TPXO8-ATLAS with a 1/30⁰resolution (Egbert and Erofeeva, 2002). At closed boundaries, i.e.,
island and mainland shorelines, the normal flow is set to zero whereas
the tangential velocity is determined based on the free-slip boundary
condition. While it is difficult to validate the ADCIRC modeling for
storm surges in Shanghai, as a critical TC parameter –radius of
maximum wind–is not available in the historical record for the
Northwest Pacific Basin, the ADCIRC model has been found to produce
storm surge estimations with relatively high accuracy in a number of
studies (e.g., Westerink et al., 2008; Marsooli and Lin, 2018). Our
evaluation on the model performance in simulating the astronomical
tides, which can contribute to over 75% of the storm tides in Shanghai,
shows that the ADCIRC model satisfactorily captures the dominant
dynamics of major tide constituents along the Shanghai coast (Fig. S2).
We use analytical/parametric models to calculate the surface wind and
pressure fields associated with the synthetic TCs to drive the
hydrodynamic modeling. We use the parametric model of Holland (1980) to
calculate the radial profile of pressure, which is related to the
maximum wind speed, radius of maximum wind, and pressure deficit. We use
the analytical model of Emanuel and Rotunno (2011) to calculate the wind
field. The model calculates the 1-min axisymmetric wind field
(associated with the storm) at the gradient level based on the storm
characteristics including maximum wind speed and radius of maximum wind.
We convert the gradient wind to the surface level (10 m above the sea
surface) with a velocity reduction factor of 0.85 (Georgiou et al.,
1983) and an empirical expression of inflow angles (Bretschneider,
1972). We add to the storm wind the surface environmental wind estimated
as a fraction (0.55, rotated counter-clockwise by 20o)
of the storm translation velocity, to account for the asymmetry of the
wind field (Lin and Chavas, 2012). Finally, for use in the ADCIRC
modeling we adjust the 1-minute wind to a 10-minute average with a
reduction factor of 0.893 (Powell et al., 1996). This framework of TC
modeling for storm surge analysis has been implemented by various
previous studies (e.g., Lin et al., 2012; Marsooli et al. 2019). Also,
Xu et al. (2020) used a similar method to estimate the wind field, and
their estimated wind hazard based on the NCEP synthetic dataset compared
well with historical wind observations in the Shanghai region.