A Rapid Forecasting and Mapping System of Storm Surge and Coastal Flooding

Abstract

6A prototype of an efficient and accurate rapid forecasting and mapping system (RFMS) of storm 7 surge is presented. Given a storm advisory from the National Hurricane Center, the RFMS can 8 generate a coastal inundation map on a high-resolution grid in one minute (reference system Intel 9 Core i7-3770K). The foundation of the RFMS is a storm surge database consisting of high-10 resolution simulations of 490 optimal storms generated by a robust storm surge modeling system, 11 CH3D (Curvilinear-grid Hydrodynamics in 3D) -SSMS. The RFMS uses an efficient Quick 12 Kriging interpolation scheme to interpolate the surge response from the storm surge database, 13 which considers tens of thousands of combinations of five landfall parameters of storms: central 14 pressure deficit, radius to maximum wind, forward speed, heading direction, and landfall location. 15 The RFMS is applied to Southwest Florida using data from Hurricane Charley in 2004, Hurricane 16 Irma in 2017, and to Florida Panhandle using data from Hurricane Michael in 2018 and validated 17 with observed high water mark data. The RFMS results agree well with observation and direct 18 simulation of the high-resolution CH3D-SSMS. The RFMS can be used for real-time forecasting 19 during a hurricane or "what-if" scenarios for mitigation planning and preparedness training, or to 20 produce a probabilistic flood map. The RFMS can provide more accurate surge prediction with 21 uncertainties if NHC can provide more accurate storm forecasts in the future. By incorporating 22 storms for future climate and sea level rise, the RFMS could be used to generate future flood maps 23 for coastal resilience and adaptation planning.24 25

Towards an efficient storm surge and inundation forecasting system over the Bengal delta: chasing the Supercyclone Amphan

“…This model solves the shallow-water equations using finite-element and finitevolume schemes in an unstructured grid that can combine tri-and quad-elements. The model is applicable in baroclinic as well as barotropic ocean circulation problems for a broad range of spatial scales, from the creek scale to the ocean basins (Ye et al, 2020;Zhang et al, 2020Zhang et al, , 2016a. It has already been shown to have an excellent performance in reproducing shallow-water processes over the Bengal shoreline and elsewhere, including the coastal tide (Krien et al, 2016), wave set-up (Guérin et al, 2018), and storm surge flooding (Bertin et al, 2014;Krien et al, 2017;Fernández-Montblanc et al, 2019).…”

“…Storm surge modelling is, however, computationally expensive and has proven to be challenging in real-time-forecasting mode (Glahn et al, 2009;Murty et al, 2017). Several practical solutions exist to curb the real-time constraints of cyclone forecasts, such as soft real-time computation based on looking up an extensive database of precomputed cyclones (Condon et al, 2013;Yang et al, 2020), coarse-resolution modelling (Suh et al, 2015), and modelling without wave-coupling (Murty et al, 2017). Over the past decades, unstructured-grid modelling systems are getting more and more popular due to their efficiency in resolving the topographic features and their reduced computational cost compared to structured-grid equivalents (Ji et al, 2009;Lane et al, 2009;Melton et al, 2009;Fortunato et al, 2017;Khalid and Ferreira, 2020).…”

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

“…The SLOSH model is currently being used by the NHC for real-time forecasting of potential hurricane storm surges across the entire seaboard of the United States [10,11,46]. A major advantage of the SLOSH model is its ability to reproduce historical hurricane storm surges based on the HURDAT2 dataset [23,46,48].…”

Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network

“…Though these differences are minor, the total energy is proportional to the square of the wave height. Thus, these minor differences are essential to capture as they can lead to dramatically different forecasts, affecting not only wave energy estimates, but also forecasts of hurricane-induced waves [35][36][37] and storm surges [38,39]. Consequently, the joint EMD-LSTM model displays a dramatically lower RMSE for wave forecasts compared to LSTM alone and thus, wherever possible, should be used.…”