Spatial heterogeneities of current and future hurricane flood risk along the U.S. Atlantic and Gulf coasts

Journal Pre-proof Spatial heterogeneities of current and future hurricane flood risk along the U.S. Atlantic and Gulf coasts

Muhammad Sajjad, Ning Lin, Johnny C.L. Chan PII:

S0048-9697(20)30214-X

DOI:

https://doi.org/10.1016/j.scitotenv.2020.136704

Reference:

STOTEN 136704

To appear in:

Science of the Total Environment

Received date:

4 November 2019

Revised date:

13 January 2020

Accepted date:

13 January 2020

Please cite this article as: M. Sajjad, N. Lin and J.C.L. Chan, Spatial heterogeneities of current and future hurricane flood risk along the U.S. Atlantic and Gulf coasts, Science of the Total Environment (2020), https://doi.org/10.1016/j.scitotenv.2020.136704

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© 2020 Published by Elsevier.

Journal Pre-proof Spatial Heterogeneities of Current and Future Hurricane Flood Risk along the U.S. Atlantic and Gulf Coasts

Muhammad Sajjad

1, 2 *

, Ning Lin 2 , Johnny C. L. Chan

1

1

Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong SAR. 2 Department of Civil and Environmental Engineering, Princeton University, USA. *Corresponding Author: [email protected] (Muhammad Sajjad)

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ORCID (Corresponding Author): 0000-0002-1576-1342

Journal Pre-proof Spatial Heterogeneities of Current and Future Hurricane Flood Risk along the U.S. Atlantic and Gulf Coasts Abstract: We evaluate the spatial heterogeneities of hurricane flood risk along the United States (U.S.) Atlantic and Gulf coasts under two different climate scenarios (current and future). The flood hazard is presented as the hurricane surge flood level with 1% annual exceedance probability (100-year flood) under the two scenarios, where the future scenario considers the effect of hurricane climatology change and sea level rise towards late-21st-century under a high

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emission scenario (RCP8.5). This hazard information is combined with estimated vulnerability and disaster resilience of coastal communities to map the relative current and

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future risk employing different risk definitions. Several geographical techniques and spatial

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distributional models (e.g., spatial autocorrelation, spatial hotspot analysis, and spatial

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multivariate clustering analysis) are applied to systematically analyze the risk and identify statistically significant hotspots of the highest risk. Most of the high-risk hotspots are found

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in the Gulf coast region, particularly along the west coast of Florida. However, two out of

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three risk evaluation approaches also indicate New York City as a risk hotspot under the

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future climate—showing that the resultant risk is sensitive to the co nsideration of evaluation factors (i.e., hazard, vulnerability, and resilience). Additionally, we apply a machine- learning algorithm-based spatial multivariate approach to map the spatially distinct groups based on the values of risk, hazard, vulnerability, and resilience. The results show that the counties in the highest risk group (value > 3rd quartile, 15% of total counties, including New York City) in the future lack specifically in the community capital and the social components of community resilience. This assessment of coastal risk to hurricane flood has important policy-relevant implications to provide a focus-for-action for risk reduction and resilience enhancement for the U.S., where 6.5 million households live in the hurricane flood-prone areas.

Journal Pre-proof Keywords: Climate Change; Natural Hazards; Hurricane Storm Surge; Geographic Information System (GIS); Spatial Risk Distribution; Coastal Resilience 1. Introduction Hurricanes are believed to be major natural hazards due to their strong winds, storm surges, and rainfall in coastal areas globally. Since 1980, landfalling hurricanes in the continental U.S. have caused two-thirds of the global total damages from natural hazards (Mohleji and Pielke, 2014; Weinkle et al., 2018). For example, in 2005, Hurricane Katrina—known to be

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the most devastating disaster in the U.S.—produced the highest flooding in the history of the U.S., resulting in more than USD100 billion in terms of damages and causing approximately

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2,000 mortalities. Similarly, damages and fatalities associated with Hurricanes Sandy in 2012;

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Harvey, Irma, and Maria in 2017, and Florence and Michael in 2018 have highlighted the

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power of hurricanes to cause destruction on even one of the most advanced societies (Freeman and Ashley, 2017; Garner et al., 2017; Lin et al., 2016, 2012; Reed et al., 2015;

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Shuckburgh et al., 2017). Among all three hazards from hurricanes (e.g., wind, rainfall, and

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storm surges), storm surges are responsible for most of the damages and life- loss (Lin et al.,

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2012). As climate change progresses and sea level rises, the hurricane-associated storm surges are expected to become more risky as compared to today due to larger inundation and increased inland flooding (Grinsted et al., 2013; Lin et al., 2016; Trenberth et al., 2015; Weinkle et al., 2018). Additionally, this increase in surge hazard coupled with growing influx of population and capital investments, urbanization, and economic activities along the coasts in the 21st century could result in even worse consequences (Fang et al., 2014; Weinkle et al., 2018). It is imperative to improve our understanding regarding the current and future storm surge risk for corrective decision making and smart resources allocation in order to effectively mitigate the impacts.

Journal Pre-proof Risk assessment is an important tool used as a rational foundation for decision making (Sajjad and Chan, 2019; Vojinovic et al., 2016). There are several viewpoints when it comes to risk assessment (Gao et al., 2014). In the context of natural hazards, the United Nations Development Program (2005) states risk of communities as the probability of penalties (i.e. economic damage or loss of life) due to the interaction between vulnerable societal conditions and hazard potential; given as Risk = Hazard x Vulnerability. This perspective represents the traditional risk assessment approach, which considers two essential conditions

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(hazard potential and vulnerability level) for the realization of risk and has widely been

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adopted by researchers in this field (Fang et al., 2014; Gao et al., 2014; Liu et al., 2018;

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Vojinovic et al., 2016). However, this definition of risk neglects the systematic functionality of societies to cope with and recover fast after the hazard (also referred to as resilience), as

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vulnerability is more like a characteristic of resisting the pressure as a consequence of natural

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hazards (Cimellaro, 2016). This consideration has led to another, comparatively new, narrative of risk assessment that considers natural hazard potential and resilience of

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communities (Angeler et al., 2018; Sajjad and Chan, 2019; Tran et al., 2017). Although some

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researchers provide the same definition of vulnerability and resilience (Field et al., 2012;

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Kammouh et al., 2018) and many other argue that they are different concepts with overlaps in some areas (Cutter, 2016), several researchers state the comparison between vulnerability and resilience, suggesting that vulnerability has more to do with resisting the stress, while resilience is the inherent property in communities to recover from disasters in a reasonably short time and perform better in the future (Cutter et al., 2008; Kammouh et al., 2018). Inpractice traditional risk models may be improved by considering hazard, vulnerability, and resilience, grasping more comprehensiveness in the estimated risk (Chan and Kepert, 2010). This broader approach will provide opportunities to reduce vulnerabilities as well as strengthen community resilience for risk mitigation and adaptation to future ha zards, which is

Journal Pre-proof also stressed in the Sendai Framework for Disaster Risk Reduction (2015) adopted by the United Nations member states (Aitsi-Selmi et al., 2015). This study evaluates the current and future spatially relative risk of coastal counties along the U.S. Atlantic and Gulf coasts to hurricane- induced flooding from different risk perspectives (i.e., consideration of hazard vulnerability, and resilience as provided in Table 1). To map the relative risk, we take the hurricane surge flood level with 1% annual exceedance probability (100-year flood) under current and future climate scenarios as the hazard. The future scenario

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accounts for the hurricane climatology change and sea level rise towards late-21st century under a high emission scenario (RCP8.5). This hazard information, obtained from a recent

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study, is combined with the estimated community disaster resilience and the social

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vulnerability, also based on established studies, using different risk assessment perspectives

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to estimate as well as map the spatially relative risk in the study area. To study the spatial heterogeneities in the estimated relative risk, we apply different spatial

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distributional models to identify the statistically-significant spatial clustering of high-risk

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locations (hotspots) under current and future climate scenarios to provide information for the

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prioritization of immediate or gradual actions. We also apply a machine- learning based approach to map the spatially distinct groups of counties with similar levels of risk, hazard, vulnerability, and community resilience. This analysis helps us to provide a focus-for-action to inform the professionals in the field of risk planning/management for corrective decisions to enhance community resilience through proper policy implications. The paper is divided into five sections. Section 2 describes several datasets used in our analysis along with the techniques and models employed for risk evaluation, mapping, and the identification of statistically significant risk hotspots. The results from these procedures, including spatial hazard assessment, spatial risk distribution, hotspots identification under both current and future climate scenarios, and spatial multivariate grouping, are presented in

Journal Pre-proof Section 3. Section 4 discusses the focus- for-action along with possible implications of the results for risk management and resilience enhancement. Lastly, Section 5 summarizes the conclusions of this study, details current limitations, and proposes future research directions. 2. Materials and methods Considering the above- mentioned different conditions for the realization of risk and its assessment, we present three different perspectives of risk assessment to hurricane- induced coastal flooding. We compute three different risk indicators considering hazard (H),

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vulnerability (V), and resilience (R), see Table 1. These indices enable us to compare regions from three different risk perspectives, providing broader opportunities for decision making

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and resource treatment for risk reduction and resilience enhancement. Details on these risk

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2.1. Hazard assessment and mapping

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components (hazard, vulnerability, and resilience) are provided in the following sub-sections.

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Evaluating spatial extents of hurricane-induced storm surge can progressively help provide

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important insights for hazard mapping to aid risk reduction. However, few if any of the existing studies have analyzed the future hurricanes-associated storm surge risk (Hallegatte,

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2007). Using synthetic hurricane track data generated from physical mechanisms-based models is helpful to overcome this shortcoming (Emanuel, 2017; Garner et al., 2017; Hallegatte, 2007; Lin et al., 2012). This modelling approach differs from other statistical models that only take the historical observations of hurricanes into account. The basic idea of using synthetic hurricanes is to compensate for the data series shortness via comprehending the underlaying physical mechanisms (Hallegatte, 2007). These tracks can further be generated in several different environments such as under future climate projections to evaluate the climate change influence on hurricane climatology. From there, this information can be used in hydrodynamic models to compute the storm tide (combination of surge and astronomical tide) heights and return levels under current and future climatic conditions,

Journal Pre-proof providing current and future hurricane flood hazard data. This hazard combined with existing socio-economic

vulnerabilities and

community resilience

information can provide

opportunities to evaluate different risk perspectives. This assessment might enable us to estimate the possible influence of future climate change on the risk level of different areas to hurricane- induced flooding along with its spatial variations. Such risk information can be channeled into risk planning to make the communities more resilient through policy implications.

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In this paper, we use the 100- year hurricane surge flood height for each coastal county (n=171) along the U.S. Atlantic and Gulf coasts estimated by Marsooli et al. (2019) using the above

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discussed climatology- hydrodynamic approach as the hazard. This hazard information

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considers the future climatology of hurricanes along with projected sea level rise owing to climate change towards the end of 21st century. To estimate the hazard, a large number of hurricane

tracks

were

generated

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synthetic

for

the

Atlantic

basin

using

the

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deterministic/statistical hurricane model from Emanuel et al. (2008). The current (1980-2005)

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climatic conditions are based on the National Centers for Environmental Prediction (NCEP) reanalysis (Kalnay et al., 1996) and the future (2070-2095) climate conditions are based on

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projections by six climate models under the RCP85 scenario. The RCP85 represents the high range of non-climate policy setups and considers the development of the world following business as usual (fossil fuel aided energy consumption and emissions). The ADvanced CIRCulation model (ADCIRC), a hydrodynamic model, was used (with a basin-scale mesh) to simulate the storm tide induced by each track (Marsooli and Lin, 2018). Further details on this model can be seen at www.adcirc.org. The probabilistic sea- level rise projection obtained from Kopp et al. (2014) (Kopp et al., 2014) is combined with the distribution of storm tide to estimate the return level for coastal flood relative to the local Mean Higher High Water. Further information on this can be seen in Marsooli et al. (2019).

Journal Pre-proof The mean (over all coastal counties) estimated flood heights are 1.58 ±1.12 m and 3.32 ±1.32 m under the current and future climates, respectively (Supplementary Table S1). The maximum flood height under the current climate is expected to increase from 4.3 m to 6.8 m under the future climate scenario. Marsooli et al. (2019) concluded that climate change is likely to aggravate the hurricane-related flood hazards along the U.S. Atlantic coast with a significant spatial variation. However, the actual risk level of coastal communities does not just depend upon hazards but also the interacting socio-economic vulnerability and disaster

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resilience (Sajjad et al., 2019b). For example, the communities which are comparatively less

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exposed to natural hazards but highly vulnerable (or less resilient) might experience larger

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impacts as compared to those subjected to higher hazard level but les s vulnerable (or highly resilient). This narrative implies that the spatial distribution can vary differently after

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combining the hazard information with vulnerability and resilience information. Therefore,

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the end- user information for decision- making to take appropriate measures, policy development, and delineating the action plans for future risk reduction must consider the

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vulnerability and resilience in combination with hazards, and not only the hazard (see Sub-

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sections 2.2, and 2.3 for detail on vulnerability and resilience). To be comparable across

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physically different risk components, we normalize the hurricane flood height values for each of the two scenarios using a min- max normalization approach (Sajjad and Chan, 2019). This approach distributes all the values between 0 and 1 to make the data non-dimensional. The formula for normalization is given as; ( ( where

is the normalized value,

) ) represents original value,

and

is the

maximum and minimum variable (in this case hazard) values, respectively, among all the coastal counties (n = 171). 2.2. Vulnerability of communities

Journal Pre-proof Vulnerability estimation is an important aspect of risk assessment process as it can pro vide useful information for risk management and enhancing the sustainability through identifying the vulnerable communities (Gu et al., 2018). As the conceptualization of vulnerability very much depends on the scientific area in which it is used, the definition of vulnerability differs according to discipline/field of study (Ramieri et al., 2011). The Inter- governmental Panel on Climate Change (IPCC) states vulnerability as the susceptibility of a community/system to disruptions such as natural hazards. This narrative is also reflected in the vulnerability

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definition put forth by the United Nations Office for Disaster Risk Reduction, which define

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vulnerability as the circumstances and characteristics of communities making them

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susceptible to the effects of hazards (Frigerio et al., 2018). Though there exists a large disparity in the concept of vulnerability in different research context, it is based on two

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dominant perspectives: (a) considering the status of communities at risk and (b) taking

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exposure as given and seeking the patterns of damages among the affected communities(Gao

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et al., 2014).

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The vulnerability in this study is defined as the underlying socio-economic characteristics and conditions that make certain communities suffer more during disasters, inferring an unequal

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stress of disasters among the exposed communities. This narrative takes vulnerability as a multidimensional and dynamic phenomenon as the status of communities change constantly. There are several vulnerability indices to environmental hazards developed by different researchers (Cutter et al., 2003; Cutter and Finch, 2008; Flanagan et al., 2011; Frigerio et al., 2018, 2016; Gao et al., 2014; Zhou et al., 2014). We use a social vulnerability index (SVI) proposed by Flanagan et al. (2011) due to its wide use and focus on assisting disaster preparedness. The SVI is based on fifteen potential variables under four domains (i.e. socioeconomic status, household composition and disability, language and minority status, and transportation and housing) at census-tract level presenting the overall vulnerability status. We retrieve the data on all these variables for the year 2016 (available at: https://svi.cdc.gov).

Journal Pre-proof Later, we use the min- max standardization method to make the SVI indicators nondimensional (distribute the index values between 0 and 1) using Eq. 1 and computed the SVI following Flanagan et al. (2011) for each coastal county (n = 171) along the U.S. Atlantic and Gulf coasts. The values range between 0.07 – 0.94 with an average value of 0.38 over all the coastal counties in the study area (n = 171). Further details on these variables can be seen in Flanagan et al. (2011). For future risk assessment, we use the future population projections to compute the SVI

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(SVIF). For this purpose, we use the population scenarios consistent with Shared Socio-

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economic Pathways (SSPs), available from the National Center for Atmospheric Research’s

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(NCAR) Integrated Assessment Modelling (IAM) group and the City University of New York

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Institute for Demographic Research (Gao, 2017; Jones and O’Neill, 2016). These spatially explicit population scenarios (1 km resolution) are developed under different SSP alternative

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future pathways of societal change. These population projections are available for the period

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2010-2100 with a ten-year interval. For SVIF, we compute the population (for each 1 Km cell)

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averaged over the years 2070, 2080, and 2090 under SSP-5 using Raster Calculator in ArcGIS. This population is summed over each coastal county and used for SVIF computation and later

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for future risk assessment. It is noted that we use the minimum and maximum values from current population in Eq. 1, which results in the maximum value slightly higher than 1. This is done to reflect the influence of population change in the future. The values for SVI F range between 0 - 1.6 with an average of 0.48 over all the coastal counties in the study area (n = 171). Though this is not a full comprehension of the future vulnerability, the SVIF at least accounts for the future population, which drives most of the socio-economic sectors. However, for smaller geographical areas and localized studies, it would be better to have higher resolution demographic projections to reduce the uncertainty in the projected population, which might result in more reliable future vulnerability. The projections of other demographic indicators could also improve this future vulnerability estimation resulting in better risk

Journal Pre-proof assessments It is noted that we use the population under the SSP-5 scenario as it results in the radiative forcing pathway almost similar to RCP8.5 (used in future hazard assessment). 2.3. Community Resilience to Natural Hazards Being a multi-dimensional concept, resilience involves different subjects under various disciplines including but not limited to ecology, disaster science, psychology, and socioeconomies (Adger et al., 2005; Ayyub, 2015, 2014; Li et al., 2019; Linkov et al., 2014). Like vulnerability, the definition of resilience differs according to research goals. However,

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contrary to vulnerability, which is more of a characteristic of tackling the stress as a result of natural hazards, the resilience is more concerned with the capacities and capabilities of

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communities—in an system-of-systems fashion—to recover from disasters in an efficient

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amount of time and perform better in future (Kammouh et al., 2018). This consideration of

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community resilience to disasters in risk assessment process grasps the natural and man-made capabilities to withstand, cope, and recover fast from any internal or external shocks (flooding

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in our case), which are not appreciated in a typical risk assessment approach (Gao et al., 2014;

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Sajjad et al., 2019b; Sajjad and Chan, 2019).

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Following several other researchers, resilience in this study is defined as the community’s inherent characteristic to cope with, do well during disasters, recuperate fast, and perform better in future (Cutter et al., 2014; Sajjad et al., 2018; Sajjad and Chan, 2019). We employ a capital approach for the comprehension of community resilience to natural hazards, which is a well- used approach (Burton, 2015; Cutter et al., 2010; Cutter and Derakhshan, 2018; Sajjad et al., 2019b). This approach considers communities as integrated systems in which several subsystems (capitals) contribute to the overall functionality of the communities (Cutter and Derakhshan, 2018). To present the community resilience to natural hazards, we use a community resilience index from Cutter and Derakhshan (2018), which is based on 49 determinants categorized within six capitals (i.e., social, economic, environmental,

Journal Pre-proof community capital, institutional, and infrastructural) (Cutter et al., 2014, 2010). The data on these determinants are obtained from the U.S. Census Bureau and the American Community Survey five- year estimates for 2010-2014. The original values for resilience index from Cutter and Cutter and Derakhshan (2018) range from 1 – 6 and the average value is 2.73 over all six capitals. Readers are encouraged to see Cutter and Derakhshan (2018) for further details. To make it consistent with the calculation of hazard and vulnerability, we use the min- max standardization (as presented in Eq. 1) to make the index non-dimensional (distribute values

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between 0 and 1) before incorporating into risk assessment equations (Table 1). 2.4. Spatial pattern identification of current and future hurricane flood risk

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To analyze the geographical heterogeneities of the risk under both climates (i.e. NCEP-based

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current and RCP8.5-based future climates), we use several spatial models (Sajjad et al.,

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2019a). We begin with evaluating the global spatial autocorrelation in the estimated risk using a global Moran’s I index (Anselin, 1988). This approach results in a single Moran’s I

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index value between -1 and 1, which indicates the spatial autocorrelation. A value closer to

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+1, -1, and 0 indicates the strong positive, strong negative, and absence of spatial

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autocorrelation, respectively (Gu et al., 2018; Sajjad et al., 2019a). Additionally, for more comprehensive insights into the locations of geographical dependence and to map the local spatial heterogeneities in the risk, we use the Local Indicators of Spatial Association (LISA) (Ord and Getis, 1995). LISA helps us identify the statistically significant (95% confidence) clustering of high risk (hot-spots presented by High- High clusters), low risk (cold-spot presented by Low-Low clusters), and spatial outliers (presented by High- low and Low-High clusters) in space. Furthermore, we use a Getis-Ord Gi* based sensitivity analysis to explore how changing (lowering or raising) the confidence level influences the indication of spatial clustering in the risk (Frigerio and De Amicis, 2016). We analyze this sensitivity at three confidence levels (i.e. 90%, 95%, and 99%).

Journal Pre-proof Lastly, we use the Multivariate Clustering Analysis technique—a relatively new exploratory method—to catalog the counties into statistically distinct spatial groups. This technique is based on the unsupervised machine- learning algorithms and identifies statistically distinct natural clusters in the data (Duda et al., 2000; Jain, 2010). The classification technique is termed unsupervised as it does not require any set of pre-classified features for training to identify the statistically distinct geographical clusters. Given the number of clusters, the tool looks for the solution where within-cluster features are as similar as possible, and all the

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groups (clusters) are as dissimilar as possible, while the similarity or dissimilarity among the

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features is based on the attribute set specified for the analysis. We use the well-recognized

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Calinski-Harabasz pseudo F-statistic for the determination of the maximum number of spatially distinct clusters (Calinski and Harabasz, 1974). This approach eliminates the odds

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of prejudging the number of clusters and provides more reliable information about the

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number of clusters in data. We apply this tool under different risk, hazards, vulnerability, and resilience settings to provide a wide range of information to professionals in the field of risk

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and resilience management. The corresponding maps are generated to present the

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geographical distribution of the identified clusters for spatial referencing. To the best of our

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knowledge this clustering method has not been applied to risk mapping earlier, particularly in the context of current and future hurricane flood risk.

3. Results

3.1. Hazard Assessment along the U.S. Atlantic and Gulf coasts There exists a large geographical variation in the distribution of hurricane flooding with a 1% annual exceedance probability from the climatology-hydrodynamic modeling under the two climate scenarios, with southern regions being at relatively higher hazard level (Figure 1). Conversely, the estimated percent-change in the hazard under the future scenario is larger for northern counties. The top-ten counties with the highest 100-year return level of

Journal Pre-proof hurricane flood under current climate show that most of the counties with higher hazard level belong to Louisiana (LA) and Florida (FL), see Table 2. Whereas, under the future climate, most of the counties (7 out of 10) belong to LA. The largest percent change in the hurricane flood hazard is estimated to be observed by Rockland county in New York state followed by Bergen and Ocean counties in New Jersey (see Table 2 for top ten counties). It is observed that all top-ten counties according to percent change are located along the northeastern coast of the U.S. Furthermore, the statistically significant (p = 0.05) spatial

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clustering results show that the counties of New Jersey and New York are more likely to be the hotspots of the maximum change in the hazard under the future climate (Figure 1). This

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implies that while the relative hurricane flood hazard of east coast of the U.S. under both

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scenarios is lower as compared to the Gulf of Mexico region, the largest future change in the

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hazard (under the RCP8.5 projection) will be experienced by the east coast of the U.S., particularly along the coastline of New York, New Jersey, Massachusetts, and Maryland

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(Table 2, and Figure 1), mainly due to sea level rise (Marsooli et al., 2019).

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3.2. Coastal geographies of hurricane flood risk under current and future climate scenarios Overall, the east coast of the U.S. is at lower risk (indexes < 0.5 std. dev.) to hurricane flooding as compared to the Gulf of Mexico region under both climate scenarios (i.e., current-NCEP and future-RCP85, Figure 2). While the estimated risk under both climate scenarios from all three methods shows a higher degree of risk for counties of the Gulf of Mexico region, the geographical disparity in the relative risk level is evident from different risk assessment methods. For example, the HV method shows larger number of counties at highest risk (std. dev. > 2.5) as compared to HR and HVR under the current condition (this includes current population). This is because the HV approach does not account for the higher disaster resilience in these counties and as a result, indicate them at relatively higher

Journal Pre-proof risk. It is noticeable that under the current scenario, the counties from Florida and Texas are at relatively higher risk from all three risk assessment methods, indicating higher threat of hurricane flooding for these counties. Additionally, the results show that maximum number of counties from Florida, as compared to other states, are at higher risk as estimated by all the methods under both current and future scenarios. Along the entire east coast, only New York city is identified at comparatively high risk under the future scenario (indexes > 1.5 std. dev.)—from HVR and HV risk assessment approaches. When all three risk conditions

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(hazard, vulnerability, and resilience) are considered, New York City is identified at the

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highest risk (std. dev. > 2.5) among all the coastal counties under the future scenario (this

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includes future population based on SSP5 scenario). The indication of New York City at the highest risk under the future scenario from HVR is due to the contribution of comparatively

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higher expected hazard potential in the future along with an increase in social vulnerability

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due to change in population. It is important to note that while the change in hazard potential is higher for the northeastern counties of the study area (Figure 1), the change in the relative

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risk—from all risk assessment approaches—is comparatively higher for most of the southern

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counties, particularly the counties in Florida (bottom panel in Figure 2). Along the east coast,

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only New York City is indicated to experience higher change in the risk. The hotspots of relatively highest risk are situated in the Gulf coast under the two scenarios, particularly the west coast of Florida (Figure 3). Both HVR and HV approaches indicate the New York region as the hotspot of highest risk (95% confidence), which is critical given the higher population density in the region (~10,194 / km²). It is important to note that this region is not indicated as the hotspot from HR approach—which is exclusive of vulnerability. The sensitivity analysis results show that lowering or levitating the significance barrier (i.e. 90%, 95%, and 99% confidence levels) influence the indication of spatial clustering of the risk estimated by a specific or different risk assessment approaches under the two climate scenarios (Supplementary Figure S1). For example, New York City is indicated as a risk

Journal Pre-proof cluster with 95% confidence under the NCEP-HVR scenario whereas, the confidence for this indication under NCEP-HR (99%) and NCEP-HV (a mix of 95 and 99%) is different (Supplementary Figure S1). The similar is observed for the future scenario. This situation suggests that considering hazard, vulnerability, and resilience together, the New York City is indicated as a high-risk cluster (95% confidence), which calls for risk reduction and resilience enhancement measures. Similar to change in risk under the current to the future scenario, the clustering of change- in-risk is mostly evident in the Gulf of Mexico region,

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particularly in Florida (bottom panel of Figure 3), though this indication varies under HVR,

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HR, and HV assessment approaches. New York City is the only region indicated as the

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cluster of high change- in-risk along the east coast—with the strongest indication from the HVR approach. This empirically-based spatial heterogeneities in the change in hazard

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potential and the risk under the two scenarios suggests that rather relying only on hazard

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economic vulnerability and resilience.

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information, the risk planning and management practices should also account for the socio-

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3.3. Multivariate spatial grouping analysis

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Two statistically distinct spatial groups are identified based on current hurricane flood risk, hazard, vulnerability, and resilience with the Calinski- Harabasz pseudo F-statistics = 111.3 (Figure 4). The current risk for the counties in the red group is higher (above 3rd quartile) and consist of approximately 30% of overall counties. Most of these counties belong to the Gulf of Mexico regions with some counties from eastern coast (New York and North Carolina). The resilience status of these counties is identified below 1 st quartile whereas the social vulnerability is above 3rd quartile. The multivariate clustering analysis based on the risk under the future projection and its three considerations (i.e. hazard, vulnerability, and resilience) results into five distinct geographical groups with a pseudo F-statistic of 111.6 (Figure 5). The results show that 5 out of 171

Journal Pre-proof counties (shown in red) are in the highest future risk group and belong to Florida and New York states. It is critical to note that the resilience of these counties is the lowest and the social vulnerability is the highest as compared to all the other counties in the study area (Figure 6 box-plot). The second highest group based on the future risk is shown in purple and included 11% counties with most of them from Florida and Texas states. It is evident that the hazard level plays an important role towards its higher risk as the resilience of this group is better than the red group and the vulnerability is lower. This suggests that, even if the red

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group is expected to experience a lower level of hazard, its low resilience and high social

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vulnerability increase its risk. This calls for appropriate measures to reduce the social

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vulnerability and enhance the resilience to mitigate the hurricane flood-associated impacts in

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the future.

The results for multivariate clustering analysis to find the optimal distinct spatial groups

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based on future risk and six resilience sub-systems are shown in Figure 6. For this analysis,

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we focus on the future risk as this can provide important information to be utilized into future

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coastal risk reduction actions and resilience enhancement measures. Three geographicallydiverse groups are found based on the Calinski-Harabasz pseudo F-statistics (pseudo F =

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46.58). The first statistically distinct group is shown in red, which has comparatively highest future risk level under the future scenario. The results show that 15% counties are in the highest risk group. The counties from Florida, New York, Texas, and Mississippi are included in this group with Florida containing highest number of counties in the high-risk group among all these states. Importantly, these counties lack particularly in social and community capital resilience, as shown in box-plot in Figure 4. Other two groups include 38% and 47% remaining counties shown in blue (representing intermediate level of risk) and green (relatively low risk), respectively. It is notable that the counties in low-risk category (green) have higher economic and social resilience as compared to the counties in other two risk categories.

Journal Pre-proof 4. Discussion The risk from flooding due to hurricanes is evolving along the U.S. coast owing to global warming and sea level rise, which is critical for coa stal population and infrastructure. This situation calls for comprehensive risk assessments for risk- informed planning to reduce the hurricane flood-associated costs. This study presents three different natural hazard risk assessment perspectives and evaluates the current and future spatially relative coastal flood risk for the U.S. Atlantic and Gulf coasts considering both change in storm flood and

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population growth in the future. The different perspectives of risk assessment presented in this study supply a broader range of information to professionals in disaster risk reduction

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and resilience management fields. While the risk maps help understand the overall

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geographic distribution of the risk under the both scenarios, the hotspot maps highlights the

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regions where the risk is clusters. Similarly, the results from multivariate clustering analysis indicate the areas of highest risk along with providing the possible reasons behind this higher

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risk (i.e., increase in hazard, vulnerability, or resilience). The HV approach indicates higher

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risk level for whole Gulf of Mexico region as compared to HVR and HR under the current climate (Figure 2), showing how different considerations of risk can result in different risk

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levels and spatial distribution. The indication of New York City as the hotspot of the future highest risk as well as change in the risk (95% confidence) is critical given the socioeconomic importance of the region. However, higher resolution demographic projections— similar to population projections—could help further verify this indication. Macro-scale initiatives have been taken by New York City to construct and restore wetlands in the coastal areas (Adnan and Kreibich, 2016), which might help reduce the impacts from future hazard and climate change. While the results from this study suggest that the New York is a high risk cluster (Figure 3), the community disaster resilience in New York City is comparatively less (Figures 4, 5, and 6). Therefore, to improve the disaster risk reduction measures in New York City, the community disaster resilience (particularly community capital and social resilience)

Journal Pre-proof should also be considered in addition to wetland construction. The spatial variations in change- in-hazard, change- in-risk, and spatial clustering of the change under the two scenarios (Figures 1-3) make it clear that why it is important to consider vulnerability and resilience information in combination with hazard information for corrective decision making and prioritization of risk reduction actions. The results from this study could act as a road- map for further high-resolution evaluation of the regions indicated as statistically significant hotspots of the risk. These higher resolution studies may evaluate the resilience indicators

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performance to form the overall resilience of communities as presented by Sajjad et al.

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(2019b). This evaluation could help taking targeted actions at local levels for resilience

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enhancements as stressed in target 13.1 (resilience strengthening and improving adaptive

(Climate Actions) (Sajjad et al., 2019b).

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capacity of communities to climate-related hazards) of the Sustainable Development Goal-13

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Also, this study provides support to reduce the hurricane-associated risks via prioritizing the

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higher risk areas and enhancing the community resilience to natural hazards in the U.S. The

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results from the multivariate spatial grouping analysis are particularly interesting as well as important for the professionals in the field of risk management and resilience enhancement.

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For example, about 30% counties in comparatively higher risk group under current climate scenario (NCEP climate) are mostly situated in the Gulf region and have comparatively low resilience and high social vulnerability (Figure 4). Similarly, the 15% counties which are found to be at the highest risk in the future (including New York City) lack specifically in the community capital and the social resilience along with having lower economic, environmental, and institutional resilience (Figure 6). This implies that these counties should work on these sub-systems (particularly the community capital and the social resilience) of the community resilience to disasters in order to minimize the hurricane flooding-associated impacts in the future. Hence, the applicability of the current results is helpful for the

Journal Pre-proof communities in the study area to enhance their disaster resilience resulting in reduced impacts and hurricane flooding-associated costs. 4.1. Current Limitations and the way forward The authors do acknowledge the current limitations of the results present in this study. The hazard information used here do not account for the increase in the water level due to breaking waves as the hydrodynamic model used neglects the wave-setup. Integrating spectral wave model and hydrodynamic model can remove this limitation. However, it would

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increase the computational costs significantly—particularly when the tracks are larger in numbers. Another limitation is the non-availability of higher resolution projections of

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population and other demographic indicators as the population used in this study is sparse

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which might not be useful for localized regions (smaller areas). Similarly, the resilience index

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used here do not consider the specific flood interventions (e.g., polders and coastal embankments construction) such as adopted by many coastal regions. Depending upon the

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data availability, the consideration of such interventions in risk assessment can change the

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spatial risk variation along with influencing the magnitude of hazard. Lastly, though the study

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provides substantial information for risk and resilience planning, the spatial patterns could further be validated using flood damage or economic losses data on county level if freely available—which we were unable to find. However, the general spatial risk pattern is similar to the coastal flood risk maps produced by the Federal Emergency Management Agency (FEMA) (available at https://bit.ly/37Gafk9). The hurricane- induced flooding also exacerbates risk to critical coastal infrastructure, ecosystems, and real estate in the U.S. and beyond. Therefore, these aspects of hurricane flood risk should also be studied. However, these assessments on such larger scales could be expensive along with requiring higher computation capabilities. While the risk assessment results from this study are important for risk planning and management, the study can assist

Journal Pre-proof in further analysis to aid risk reduction and resilience enhancement via using the hazard maps. For example, the information on the hazard in combination with several other datasets (i.e., coastal infrastructure) can reveal regions where immediate or gradual measure s are needed to reduce the impacts—left for follow-up studies. 5. Conclusions This paper details the hurricane- induced flood risk to coastal communities under current and future climates from three different perspectives of risk assessment. Different geographical

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models are applied to empirically evaluate the spatial heterogeneities in the risk along with providing suggestions on focus- for-action to reduce the hurricane flood-associated future

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risks in the study area. The risk assessment results show that New York City is expected to be

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at comparatively higher risk to hurricane flooding in the future. The counties which are

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estimated to be at higher risk in the future lack specifically in the community capital and social components of community resilience. The findings from this study are important to

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inform the professionals in the fields of risk planning and management for corrective decision

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making improving coastal resilience to natural hazards. This study can also act as a road- map

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to scale-down the regions (e.g., high-risk hotspots) in the U.S. for further detailed evaluations. In the wake of climate change and its expected influence on global tropical cyclone basins, this study acts as a tool to conduct similar analysis for tropical cyclo nes-prone regions such as East Asia and Indian sub-continent. However, the data availability on cyclone record and socio-economic characteristics on the desired assessment scale is important. Acknowledgements This work is partly supported by a Research Studentship from the City University of Hong Kong (CityU, 000618) and the U.S. National Science Foundation (1520683). S. M. is partly funded by a Research Activities Fund (000669) from the Chow Yei Ching School of Graduate Studies at CityU to

Journal Pre-proof visit Princeton University. All the data used in this study are freely available and the resources are provided within the paper. The authors declare no conflict of interest.

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Figures

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Percent Change

Percent Change LISA

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Figure 1 Spatial assessment of hazard (represented by hurricane flood with a 1% annual probability) under current and future climates along with the change in the flood under both climates. The spatial distribution of hurricane flood under NCEP and RCP85 are presented as 1 standard deviation—showing how much the values vary from the mean. Whereas, percent change is calculated as ((y2 - y1) / y1) * 100, where y2, and y1 are the flood values under future and current climates respectively. The percent change LISA map offers the statistically significant spatial clustering (p = 0.05) of counties with comparatively higher (in red) and lower (in green) change in the hazard in the future as compared to current climate. The inset map shows the New York and New Jersey coastal regions .

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Figure 2 Spatial distribution of the risk under both climate scenarios (i.e. current—NCEP, and future—RCP85) as calculated by three different risk models. The results are presented as one standard deviation interval — showing how much the values vary from the mean. The bottom panel shows the relative change in the risk under current and future climate scenarios estimated using different risk assessment approaches. The inset map shows the New York and New Jersey coastal regions .

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Figure 3 Statistically significant (p = 0.05) hotspot identification of the risk under both climate scenarios (i.e. current—NCEP, and future—RCP85) as calculated by three different risk models. The bottom panel shows the statistically significant (95% confidence) clustering of relative change in the risk under current and future climate scenarios estimated using different risk assessment approaches. The inset map shows the New York and New Jersey coastal regions.

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Figure 4 Multivariate spatial clustering results showing statistically distinct spatial groups based on current hurricane-induced flood risk, hazard (NCEP), vulnerability, and resilience. The box-plot shows the average scores (standardized) of each variable for all the counties indicated in the specific group.

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Figure 5 Multivariate spatial clustering results showing statistically distinct spatial groups based on future flood risk, hazard (RCP85), vulnerability, and resilience. The box-plot shows the average scores (standardized) of each variable for all the counties indicated in the specific group.

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Figure 6 Multivariate spatial clustering results showing statistically distinct spatial groups based on future flood risk and six resilience sub-systems (i.e., community capital, economy, environment, infrastructure, institutional, and social). The parallel box-plot (right) illustrates the grouping results and the map (left) is produced for spatial reference to counties in different categories within each group. The number of clusters is based on the highest pseudo F-statistics. The inset bar-chart shows the frequency of counties in each group. The colors scheme is defined by future risk values (e.g. r ed showing the counties expec ted to experience relatively high risk, blue showing the counties with inter mediate level of risk, and the green showing the counties estimated to be at relatively low risk in future). The spatial grouping results are only calculated for the risk model considering all three aspects of the risk—e.g., hazard, vulnerability, and resilience. The box-plot shows the average scores (standardized) of each variable for all the counties indicated in the specific group.

Journal Pre-proof Table 1 Different perspectives of risk assessment. The NCEP and RCP85 represent the current and future hazard scenarios, respectively (discussed in Section 2.1).

Risk assessment considerations

Relationship between risk and its different elements

Code (used hereafter)

Hazard, Vulnerability

NCEP/RCP85-HV

Hazard, Resilience

NCEP/RCP85-HR

NCEP/RCP85-HVR

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Resilience

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Hazard, Vulnerability, and

Journal Pre-proof Table 2 Top-ten counties (state) listed according to hurricane flood values under NCEP and RCP85 climate along with percent change in ST under both scenarios. The percent change is calculated as ((y2 - y1) / y1) * 100, where y2, and y1 are the hurricane flood values under future and current climates , respectively.

Serial (Rank)

NCEP

RCP85

Percent Change

St. Bernard (LA)

Plaquemines (LA)

Rockland (NY)

2

Taylor (FL)

Vermilion (LA)

Bergen (NJ)

3

Franklin (FL)

St. Bernard (LA)

Ocean (NJ)

4

Plaquemines (LA)

Iberia (LA)

Norfolk (MA)

5

Jefferson (TX)

Galveston (TX)

Suffolk (MA)

6

Cameron (LA)

Terrebonne (LA)

Atlantic (NJ)

7

Iberia (LA)

Cameron (LA)

Essex (NJ)

8

Vermilion (LA)

Taylor (FL)

Worcester (MD)

9

Lee (FL)

Jefferson (TX)

Monmouth (NJ)

Collier (FL)

St. Mary (LA)

Hudson (NJ)

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Journal Pre-proof Statement on conflict of interest

The authors declare no conflict of interests in conducting this research.

Muhammad Sajjad On behalf of all authors

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Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong SAR. Email: [email protected]

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Graphical abstract

Journal Pre-proof

Highlights Spatial heterogeneities of hurricane flood risk are analyzed for the U.S.

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High-risk hotspots are mostly found in the Gulf region, particularly along the west coast of Florida.

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Two out of three approaches indicate New York City as a risk hotspot under the future climate.

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The highest risk group counties (in future) lack in community capital and social resilience.

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The results are important to provide a focus-for-action for risk reduction and resilience enhancement.

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