Increasing Tropical Cyclone Precipitation Exposures over the Continental U.S.

doi:10.21203/rs.3.rs-1953782/v1

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Increasing Tropical Cyclone Precipitation Exposures over the Continental U.S.

https://doi.org/10.21203/rs.3.rs-1953782/v1

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Extreme tropical cyclone precipitation (TCP) can cause massive stress to society by generating inland flooding events. Historical observations have shown global and regional trends in TCP and climate models predict there will be substantial increases in TCP under anthropogenic global warming. Changes in both physical risk and in human society together determine the exposure to extreme weather events. However, they are rarely studied together and more knowledge is needed to be understood how they interact and to determine exposure at the local level. Here high-quality in-situ precipitation observations and gridded population and Gross Domestic Product (GDP) data were used to develop a social exposure index to quantify how TCP risk changes as a function of both physical risk and socioeconomic exposure. We demonstrate that while physical TCP risk is increasing in the United States, there are regional differences in exposure due to the heterogeneity of the population and economic development. Exposure hot spots are mostly concentrated in southern states with high population density. Rapid population growth and economic development have magnified societal exposure to those extreme TCP events. This multi-scale evaluation framework identifies locations where mitigation and adaptation to climate change are important.

Extreme precipitation from Tropical Cyclones (TC) can cause major inland flooding in both coastal and interior areas in the continental U.S. (CONUS). This can lead to significant economic damage. For example, Hurricane Harvey (2017) caused $136.7 billion in losses, mostly from rain-induced inland flooding¹. It ranks as the second most expensive disaster after Hurricane Katrina. Similar flooding disasters were caused by Hurricane Irene (2011), Hurricane Florence (2018), and Hurricane Ida (2021). Tropical Cyclone Precipitation (TCP) is likely to increase in the future because a warming climate will increase TC rain rate and reduce TC translation speed^2-4, causing more frequent stalling⁵. These changes in TCP patterns will elevate inland flooding risk⁶ and they can be further exacerbated by other factors such as the urban heat island effect⁷ and possible intensification effect from aerosols released from urban area⁸.

While the physical risk of TCP is changing, there have also been substantial shifts in regional demographics and economic development and these changes are likely to continue in the future⁹. Socio-economic trends in TC damages have been quantified for past TCs using inflation-adjusted GDP^10-13, and this has also been applied to project future changes¹⁴. Past studies have focused on trends in historical hurricane destruction over the CONUS. Some^10,15 have concluded that there is no trend in destruction because of the lack of trends in landfalling TC frequency and intensity¹³. In contrast, Klotzbach et al.¹¹ used a longer period of record to demonstrate that there are increasing trends in hurricane-related economic losses in CONUS. Increased socio-economic exposure is observed globally and regionally¹⁶ from TC wind damages^11,14,17, as well as other TC-related hazards¹⁰. While many discussions have focused on damages caused by TC winds, few¹⁸ have focused on impacts from TCP and inland flooding. In a changing climate, local mitigation, adaptation and risk management are of crucial importance to reduce the impact of TCs¹⁹. Here we use a long-term, high resolution TCP record (at 0.25°) derived from daily in-situ precipitation observations²⁰, high resolution population data (highest resolution of 1 km)²¹ and Gross Domestic Product (GDP, 1 km)²² to quantify spatial and temporal changes in physical TCP risk and socioeconomic exposure to TCP in CONUS over 70 years (1949 to 2018). We summarize changes at the local, state, and national levels to inform mitigation and adaptation strategies.

Temporal Trends in TCP

About 3.7 million km² (30%) of CONUS has consistent TCP records from 1949 to 2018 (Fig 1a) and 52,000 km² (0.4% of CONUS) have statistically significant increasing trends in annual TCP. More areas have increasing trends in annual maximum event TCP with 144,000 km² of areas (1.2% of CONUS) having statistically significant trends (Fig 1b). There are also some smaller areas with negative trends (~28,000 km² for annual TCP and only 1540 km² for annual maximum event TCP), although none are statistically significant. Most of the areas with increasing annual TCP trends are clustered in the mid-atlantic and south-eastern Atlantic coast (Fig 1a). In comparison, extreme TCP (Fig 1b) has a more expansive spatial pattern with significant increasing trends in the north-eastern Atlantic coast, Gulf coast, and some inland locations. There are different spatial patterns for increases in daily rain rate (SI 2), annual TCP days (SI 3) and durations of TCP events (SI 4) which, together, contribute to the increases in total and extreme TC in Fig 1. Our results generally agree with the spatial patterns shown in previous studies^23-25, although our analysis provides more detailed spatial patterns and is based on a longer time series.

Changes in TCP Probability

We have chosen 100 mm and 200 mm as the thresholds to quantify changes in the Return Periods (RP) of extreme TCP events across the CONUS because 100 mm is a general precipitation threshold for generating flooding²⁶ and 200 mm indicates an extreme precipitation event. The RPs are calculated for two overlapping 50-year time windows to evaluate changes between the early period (1949 to 1998) and the late period (1969 to 2018). Only 529,760 km² of areas in CONUS have < 25 years RP for >100 mm event TCP in the early period (Fig. 2a) and it increases to 904,750 km² in the late period (Fig 2b) with a relative increase of 70%. The > 200 mm TCP (Fig 2c & 2d) events are rarer but more destructive. We observe they have a ten-fold increase in the areas with < 25 years RP from 11,550 km² in the early period to 117,810 km² in the late period. Geographically, locations with a higher risk of extreme TCP are mostly found in coastal areas, but these areas of higher risk have shifted inland during the later period (Fig 2b & d).

Cities are where people and economic activities are concentrated and so they usually have greater exposure to extreme weather hazards²⁷. Rapid urbanization and elevated risk of extreme weather jointly exacerbate risk^28,29. We choose nine large cities along the coast and many have shown elevated TCP risk (Figure 3). Houston has the largest increase, with the 20-year event TCP increased from < 200 mm in the early period to > 400 mm in the late period. The tail of the distribution during the late period increases to > 600 mm as a result of extreme events like Tropical Storm Alison (2001), Hurricanes Ike (2008) and Harvey (2017) in the late period. Other large cities including New Orleans, Mobile, Tampa, Jacksonville, and Raleigh have also experienced substantial increases in TCP risk (Fig. 3b, c, d, e, f). Cities further to the north generally have lower TCP risk and demonstrate variable patterns of change in TCP risk. For example, TCP risk in New York City increased by 50%, while it did not change in Washington, D.C., and it decreased slightly in Boston. This is partly a function of the uncertainties in determining TCP risk in regions that experience fewer TCs.

TC rain rate generally follows the Clausius-Claypron equation and many previous studies already demonstrate that higher global mean atmospheric temperature will possibly lead to more precipitation^2,30,31. Our analysis shows substantial increases in TC rain rates over CONUS during the last 70 years (SI 2 & 5) and increased probabilities of higher rain rates in many major cities (SI 6). At the same time, the mean translation speed of TCs decreased from 10.55 kt in the early period to 10.37 kt (SI 7) in the late period (~1.7%). This reduction is most pronounced in the 0 to 20 kt range of the distribution. This agrees with Kossin et al.³², which showed a global shift towards lower TC translation speeds. Therefore, we demonstrate here that recent increases in TCP risk are due to increases in TC rain rate and decreases in TC translation speed.

Changes in socio-economic exposure to TCP

Substantial demographic and economic changes have occurred in the last 70 years in CONUS. The population in the southern U.S. “Sunbelt” has increased since 1950, which can be explained by economic growth, demand for amenities and housing supply³³. Meanwhile, these locations are prone to TC-related disasters because of their proximity to the ocean. We created a TCP socio-economic exposure index (TCPEI, see data and methods for details) by combining demographic and economic data with physical TCP risk. The TCPEI quantifies how socio-economic exposure to extreme TCP events has changed between the early period (1949 to 1998) and the late period (1969 to 2018). Overall, there is a general increase in TCPEI from the early period (Fig 4a & c) to the late period (Fig 4b & d). About 55 million people were exposed to > 100 mm TCP in the early period and this increased to 107 million people (92% relative increase) in the late period (Figure 4c & d). For example, the population with > 1TCPEI (the highest category for TCP > 200 mm, Fig 4g) has increased from zero to 4 million. In comparison, the population with > 0 TCPEI (lowest category for TCP > 200 mm) has increased from 4.9 million to 29 million, an ~6-fold increase. This indicates that populated areas are experiencing more substantial increases in exposure to extreme TCP events.

Large changes in TCPEI can be also observed regionally. By averaging the TCPEI within state boundaries, we demonstrate that most states have experienced increases in the median TCPEI, but the magnitude varies from state to state (SI 8). Louisiana, Florida, and North Carolina have the largest absolute increases (0.49 to 0.70 in median) and relative increases (97% to 100%) in population exposure for > 100 mm TCP (SI 8 a & b). Louisiana, Florida, and Texas have the largest increases in population exposure to > 200 mm TCP (0.19 to 0.29 increases in the median with 39% to 58% relative increases, SI 8 c&d). This pattern indicates that states with larger populations are experiencing significantly elevated exposures to extreme TCP events. Florida, Texas and North Carolina all rank high in the relative population growth from 1990 to 2020 (SI 9). Louisiana has a relatively slower population growth so its significant increase in population exposure is mostly due to increases in physical TC risk.

Economic activities are closely associated with population changes and can be substantially disturbed by extreme weather events like TCs. Similar to the population TCPEI, we have found substantial increases in the exposure of economic activities to extreme TCP events (SI 10). There are expansions of areas with high TCPEI in the late period (SI 10b & f) as compared with the early period (SI 10a & e). The > 200 mm TCPEI has a larger relative increase than the > 100 mm TCPEI. For example, the total GDP exposed to > 100 mm TCP increased by 2%, from $5.2 trillion to $5.3 trillion (SI 10c), while the GDP exposed to > 200 mm TCP increased by 124% from $581 billion to $1.3 trillion (SI 10g). Regionally, the majority of states had increases in median GDP TCPEIs (SI 11) and the relative increases are more pronounced for states located in the southern U.S. For example, Florida, Mississippi, Texas, Alabama, and Louisiana rank as the top 5 states highest relative increase (32% to 131%) in the 200 mm TCPEI (SI 11c & d).

There are large areas in the southern U.S. and coastal mid-Atlantic with significant increasing trends in annual total TCP and maximum event TCP during the last 70 years. Wide-spread increases in TCP probability are observed in coastal regions of the United States, and they are most pronounced in the southern U.S. Elevated TCP risk is also observed in most coastal cities. These changes in physical TCP risk are caused by an increase in TCP rain rates and a decrease in TC translation speed^6,34. Specifically, the largest decreases in TC translation speed are observed in the slowest moving TCs (< 20 knots) and for systems that are tracking towards the northwest (SI 7). These patterns generally agree with trends in historical TCP from previous studies^3,34,35 and the recently reported global reduction in TC translation speed^36,37.

The TCPEI demonstrates that changes in both population and GDP have significantly increased exposure to TCP. We found that the areas with high TCPEI have expanded substantially due to rapid urbanization and population growth in the southern and mid-Atlantic states of CONUS during the last 70 years (SI 12). Future regional population growth³⁸ and urbanization³⁹ may further intensify TCP flooding^6,28 and societal exposure to this hazard. In addition, the spatial heterogeneity in the population growth and economic development cause differences in local exposure to TCP hazards. Potential damage may increase exponentially due to changes in the magnitude and frequency of extreme TCP events⁴⁰. At the local level, government and citizen need more detailed mitigation strategies such as updated building codes, expanded insurance, infrastructure hardening¹¹ when faced with increases in exposure to extreme TCP. Our research quantifies how physical risk from TCP and societal exposure changed simultaneously across CONUS during the last 70 years. Geiger et al.¹⁴ predict an annual global exposure increase of 26% (33 million people) to TC winds for a 1°C increase in global mean surface temperature. We demonstrated that the number of people exposed to > 200 mm TCP has increased by 24 million (489% relative increase, Fig. 4g) and the GDP exposure to > 200 mm TCP has increased by $719 billion USD (124% relative increase, SI 10g). Future research needs to address how compound flood⁴¹ exposure (extreme floods generated by both rainfall and storm surge) changes by integrating and coupling earth system models, hydrological/hydraulic models, and socio-economic/land cover change models.

Precipitation data are from the Daily Global Historical Climatology Network (GHCN-D), which provides the most complete global daily precipitation record. These data can be accessed freely from National Centers for Environmental Information, NCEI (https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily). Daily precipitation observations are collected for TCP calculation from GHCN-D gauges that are within a moving daily boundary. This daily boundary is defined by four connected circles with 600 km radius to four TC centers recorded at 6-hour intervals from the International Best Track Archive for Climate Stewardship (IBTrACS). The IBTrACS data are also freely available from NCEI (https://www.ncdc.noaa.gov/ibtracs/). After a wind under-catch bias correction²⁰, we use Inverse Distance Weighting (IDW) interpolation⁴² to estimate the precipitation at a set of grid points with even spacing of 0.25° across the CONUS. IDW is defined as:

$${P}_{g}=\left[\left.\sum _{i=1}^{N}{\left({d}_{i}\right)}^{-u}{z}_{i}\right]/\left[\sum _{i=1}^{N}{\left({d}_{i}\right)}^{-u}\right.\right]$$

Where P_g is the precipitation estimation at each grid point, N is the number of gauges within a searching distance R = 30 km for each grid point, d_i is the distance from each gauge to a specific grid within R, and u is the weighting power, which is set up as the default value of 2. After P_g has been calculated for all included grids for each TCP day, only grids within 500 km of the storm track are finally recorded into our daily TCP database. The routine has been validated and optimized by comparing available TMPA observations²⁰ and is available upon request. To guarantee the consistency of the data, we only chose grids with at least one consistent gauge with 70 years of complete observations within R (30 km). 4816 grids (SI 1) were selected based on that criterion and most have more than 2 gauges within 30 km, which guarantees there are no missing observations in the time series. The 4816 gauges provide reasonable spatial coverage of the areas that are affected by TCP across the CONUS.

We obtained gridded population and Gross Domestic Product data for the whole world and rescaled these data to our 0.25° precipitation grid. The population for 1970 are from The Global Population Density Grid Time Series v1 and the 2020 population is from the Gridded Population of the World (GPW) v4. They have 1 km spatial resolution and are compiled by the Socioeconomic Data and Application Center (SEDAC) of Columbia University. GDP data at 1 km resolution are obtained for 1990 and 2015 from the Gridded global datasets for Gross Domestic Product and Human Development Index from 1990 to 2015²². We calculated the total population and GDP for each 0.25° grid by summing all 1 km grid values within each TCP grid box.

We aggregate daily TCP observations at each grid into event TCP based on the duration of each TC (from the start to the end of each TC event over CONUS) and summed total TCP in each year as the annual total TCP. A 70-year time series is constructed for annual total TCP, annual maximum event TCP, and annual maximum daily TCP at each grid. The Mann-Kendall non-parametric test^43,44 is used to detect trends in those time series and the Sen’s slope⁴⁵ is calculated for each time series. We use a 95% significance level for both non-parametric methods for all trend detections. We also calculate return periods for daily and event TCP samples at each grid by constructing empirical exceedance probability density⁴⁶. For a sample of all TCP observations (event or daily) at a specific grid, the non-parametric kernel density estimation (KDE) function is defined as

$${\widehat{f}}_{h }\left(p\right)= \frac{1}{nh}\sum _{i=1}^{n}K\left(\frac{p-{p}_{i}}{h}\right)$$

Where p₁, p₂, … p_n are observations of TCP at each grid with an unknown distribution; n is the sample size; h is defined as the bandwidth which we set up as 0.02 so that it gives enough detail about each KDE curve; K is the kernel smoothing function, which we used a Gaussian kernel smoother, expressed as

$$K\left({p}^{*},{p}_{i}\right)=exp\left(-\frac{{\left({p}^{*}-{p}_{i}\right)}^{2}}{{2\sigma }^{2}}\right)$$

Where σ is the length scale for the input space. After a KDE curve is fitted, we can estimate the Cumulative Distribution Function (CDF) and the Exceedance Probability Function (EPF) (1 – CDF). By using a linear interpolation approach⁴⁷, we find the Return Period (RP) estimate for each TCP threshold (e.g, > 100 mm). We estimated the RPs of daily and event TCP thresholds for all 4816 grids and compared the estimates from the early period (1949 to 1998) to the late period (1969 to 2018). We did not make extrapolations so the largest return period in our comparison is 50 years. We removed all locations where we are not able to determine a value from this empirical EPF calculation. We also assume both the event and daily TCP at each location follow a Poisson Distribution^6,48 and use it to calculate Confidence Intervals (CI) by the upper 95% and lower 5% boundary for each kernel. We compare the spatial distribution of RP for different magnitudes of TCP between the early and late periods and demonstrate how the whole TCP probability profile changes at night cities in the CONUS.

Tcp Exposure Index

To quantify society’s exposure to extreme TCP events, we created a TCP exposure index (TCPEI):

$$TCPEI=\frac{{\text{log}}_{10}SEC}{RP}$$

Where the SEC stands for Social Economic Criteria. In this study, we utilized two datasets: population and annual GDP in U.S. Dollars (USD). We use the logarithm of 10 for the SEC to re-scale the population and GDP for spatial analysis. The RP is the Return Period for a specific magnitude of TCP (e.g., 100 mm). The TCPEI increases as population or GDP increases or RP becomes shorter (higher physical TCP risk). Since there can be spatial and temporal changes in both the physical TCP risk and population/socio-economic activities, our TCPEI is used to quantify socioeconomic exposure.

Acknowledgments:

L.Z. is supported by the Faculty Start-up Funding and the Lucia Harrison Endowment Funding by the Department of Geography, Environment, and Tourism at Western Michigan University. S.Q. is supported by the Translational Data Analytics Institute and The Ohio State University.

Author Contributions:

L.Z. conceptualized and designed the research. L.Z. executed the data analysis and drafted the manuscript. S.Q. provided feedback on the research design and helped to edit the manuscript.

Competing Interests statement

The authors declare no competing interests.

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