Hurricane Scenario Generation for Uncertainty Modeling of Coastal and Inland Flooding

Hurricanes often induce catastrophic flooding due to both storm surge near the coast, and pluvial and fluvial flooding further inland. In an effort to contribute to uncertainty quantification of impending flood events, we propose a probabilistic scenario generation scheme for hurricane flooding using state-of-art hydrological models to forecast both inland and coastal flooding. The hurricane scenario generation scheme incorporates locational uncertainty in hurricane landfall locations. For an impending hurricane, we develop a method to generate multiple scenarios by the predicated landfall location and adjusting corresponding meteorological characteristics such as precipitation. By combining inland and coastal flooding models, we seek to provide a comprehensive understanding of potential flood scenarios for an impending hurricane. To demonstrate the modeling approach, we use real-world data from the Southeast Texas region in our case study.

Investigating the forecast predictability for fluvial flooding from tropical cyclones

<p>Global ensemble forecast models have been shown to have good skill in forecasting the track probabilities of tropical cyclones worldwide, but less well-studied is their ability to predict the hazards resulting from tropical cyclones, which in the case of fluvial flooding can extend far from the landfall location traditionally focussed on in operational tropical cyclone warnings. This work aims to investigate the key factors that influence the predictability of fluvial flood severity from tropical cyclones, using forecasts from the Global Flood Awareness System (GloFAS). GloFAS is jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF) and is designed to provide a global overview of upcoming flood events to decision makers as part of the Copernicus Emergency Management Service, producing probabilistic river discharge forecasts driven by global ECMWF ensemble forecasts coupled to a hydrological model. This presentation will explore the chain of uncertainty through the forecasting process for several recent tropical cyclone flood events including Hurricane Iota and Cyclone Nivar. It investigates the influence on the overall predictability and uncertainty of the fluvial flood forecasts of various components of the forecasting chain, including the track, intensity, and precipitation forecasts for the tropical cyclone, and the hydrological catchment conditions and modelling.</p>

Tropical cyclones over the western north Pacific since the mid-nineteenth century

Tropical cyclone (TC) activities over the western North Pacific (WNP) and TC landfall in Japan are investigated by collecting historical TC track data and meteorological observation data starting from the mid-nineteenth century. Historical TC track data and TC best track data are merged over the WNP from 1884 to 2018. The quality of historical TC data is not sufficient to count the TC numbers over the WNP due to the lack of spatial coverage and different TC criteria before the 1950s. We focus on TC landfall in Japan using a combination of TC track data and meteorological data observed at weather stations and lighthouses from 1877 to 2019. A unified TC definition is applied to obtain equivalent quality during the whole analysis period. We identify lower annual TC landfall numbers during the 1970s to the 2000s and find other periods have more TC landfall numbers including the nineteenth century. No trend in TC landfall number is detected. TC intensity is estimated by an annual power dissipation index (APDI). High APDI periods are found to be around 1900, in the 1910s, from the 1930s to 1960s, and after the 1990s. When we focus on the period from 1977 to 2019, a significant increasing trend of ADPI is seen, and significant northeastward shift of TC landfall location is detected. On the other hand, TC landfall location shifts northeastward and then southwestward in about 100-year interval. European and US ships sailed through East and Southeast Asian waters before the weather station network was established in the late nineteenth century. Then, we focus on TC events in July 1853 observed by the US Naval Japan Expedition of Perry’s fleet and August 1863 by a UK Navy ship that participated in two wars in Japan. A TC moved slowly westward over the East China Sea south of the Okinawa Islands from 21 to 25 July 1853. Another TC was detected in the East China Sea on 15–16 August 1863 during the bombardment of Kagoshima in southern Japan. Pressure data are evaluated by comparing the observations made by 10 naval ships in Yokohama, central Japan during 1863–1864. The deviation of each ship pressure data from the 10 ships mean is about 2.7–2.8 hPa.

Hurricane Bond Price Dependency on Underlying Hurricane Parameters

Hurricane bonds are parametric in nature as they have a dual-exercise structure: the first exercise is conditional on the hurricane’s physical landfall location and the second is conditional upon the embedded option ending in-the-money. We propose a coupled and physically-based hurricane bond pricing model via Monte Carlo simulation that resolves the dual exercise, which was not addressed in extant loss-based catastrophe bond pricing models. This coupled model is developed at the nexus of atmospheric science and finance by integrating hurricane risk modeling and option pricing. By applying this model to price a parametric hurricane bond, we demonstrate how a hurricane bond’s price is sensitive to its underlying hurricane’s physical parameters – genesis, heading, translation speed, velocity, and radius.

Communication and Hazard Perception Lessons from Category Five Hurricane Michael

Hurricane Michael made landfall on 10 October 2018 as only the third Saffir Simpson Hurricane Wind Scale (SSHWS) category 5 storm in the USA in the named era. The storm’s intensity, rapid intensification, October landfall, high inland winds, and uncommon landfall location all combined to complicate the communication and preparation efforts of emergency managers (EMs) and broadcast meteorologists (BMs), while clouding the comprehension of the public. Interviews were conducted with EMs, BMs, and a small public sample to hear their stories and identify and understand common themes and experiences. This information and previous research was used to inform the creation of questions for a large sample public survey. Results showed that 61% of our sample did not evacuate, and approximately 80% either underestimated the intensity, misinterpreted or did not believe the forecast, or realized the danger too late to evacuate. Hazard perception from a survey of the public revealed that wind followed by tornadoes, and falling trees were the major concerns across the region. According to their counties of residence, participants were divided into Coastal or Inland, and Heavily Impacted or Less Impacted categories. Inland participants expressed a significantly higher concern for wind, tornadoes, falling trees, and rainfall/inland flooding than Coastal participants. Participants from Heavily Impacted counties showed greater concern for storm surge, tornadoes, and falling trees than participants from Less Impacted counties. These results reinforce the continued need for all parties of the weather enterprise to strengthen communication capabilities with EMs and the public for extreme events.

Forecast Accuracy Matters for Hurricane Damage

I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane’s predicted landfall location result in higher damage. This relationship holds across a wide range of model specifications and when controlling for ex-ante uncertainty and potential endogeneity. Using a counterfactual exercise I find that the cumulative reduction in damage from forecast improvements since 1970 is about $82 billion, which exceeds the U.S. government’s spending on the forecasts and private willingness to pay for them.

The Utilization of Regression and Analysis of Variance to Model Hurricane Storm Surge Sedimentation on Coastal Marshes along the Northern Gulf of Mexico Coastline

<p>Coastal marshes along the northern Gulf of Mexico coastline provide very important ecosystem services such as serving as habitat for a variety of flora and fauna and providing flood protection for inland areas. A growing body of research has documented how hurricane storm surge sedimentation has increased the elevation of coastal marshes along the northern Gulf of Mexico coastline. This study investigates spatial variations in sediment distribution on McFaddin National Wildlife Refuge, Texas, USA, which is in the geographic region that was impacted by the right-front quadrant of Hurricane Ike. This research builds upon a prior study on hurricane storm surge sedimentation in which the sediment deposits from hurricanes’ Audrey, Carla, Rita, and Ike were identified on a marsh transect on McFaddin National Wildlife Refuge. The purpose of this study was to discover how hurricane storm surge sedimentation spatially varies in relation to the landfall location of Hurricane Ike. Fieldwork conducted in 2017-2018 involved digging shallow pits on four coastal marsh transects between Sabine Pass, Texas and High Island, Texas. Elevations were measured at each pit site along all four transects using a telescopic lens and stadia rod. The transects extend 880-1630 meters, with pit sites beginning near the coastline and extending landward. Results obtained in the field indicate that the Hurricane Ike sediment deposit has been found on all four transects, and that the deposit decreases in thickness moving landward along each transect. Furthermore, the observational results of this study were used in Regression Analyses to model hurricane storm surge sediment deposit thickness based on pit site distance inland, pit site elevation, and distance from the landfall of Hurricane Ike. Moreover, Analysis of Variance revealed whether distance inland, distance from landfall location, and the interaction between distance inland and distance from landfall location had any significant effect on storm surge deposit thickness. Actual sediment deposit thicknesses measured in the field were compared to the Regression and Analysis of Variance results. Results show that the Power Law Curve from the Regression Analyses was the most robust predictor of pit site sediment thickness based on distance inland, with an R<sup>2</sup> value of 0.538. Additionally, the Regression and Analysis of Variance results revealed that transect distance from the landfall location of Hurricane Ike was the only independent variable that could not predict or explain storm surge deposit thickness; which is very likely due to all four transects being in the right-front quadrant of landfalling Hurricane Ike. The findings of this study provide improved understanding of the spatial relationship between storm surge sedimentation and storm surge heights, valuable knowledge about the sedimentary response of coastal marshes subject to storm surge deposition, and useful guidance to public policy aimed at combating the effects of sea-level rise on coastal marshes along the northern Gulf of Mexico coastline.</p><p> </p>

Bayesian Networks for storm surge estimation in Mississippi (US)

<p><strong>Bayesian Networks for storm surge estimation in Mississippi (US)</strong></p><p><strong>A. Prida<sup>1,</sup> A. Diaz Loaiza<sup>1</sup>, J. Bricker<sup>1</sup>, R. Meynadier<sup>2</sup>, O. Morales-Napoles<sup>1</sup>, T. Duong<sup>3</sup>, R. Ranasinghe<sup>3</sup>, A. Luijendijk<sup>1</sup></strong></p><p>The unprecedented damage due to flood caused by hurricanes like Katrina (2005) has reinforced the interest of the hydraulic community to improve the storm surge estimation for the North Gulf of Mexico. Very high-resolution hydrodynamic models have been traditionally used for this end. However, these models are computationally very expensive. In this paper, a Bayesian Network (BN) is built to estimate storm surge at the coastal areas of Mississippi. A catalogue of HURDAT2 historical hurricanes is simulated in Delft3D FM to generate a surge data base that is used for the training of the Bayesian Network. The storm surge obtained from Delft3D FM is validated against observations recorded during a past historical event. The landfall location, the maximum wind speed, the forward speed and the forward direction of the hurricane at landfall are the other variables considered in the Bayesian Network. The Bayesian Network is validated by inferring values from past historical events in the model and comparing the modeled surge to observations.</p>