Evaluation of a Physics-Based Tropical Cyclone Rainfall Model for Risk Assessment

Xi, Dazhi; Lin, Ning; Smith, James A.

doi:10.1175/jhm-d-20-0035.1

Cited by 36 publications

(42 citation statements)

References 46 publications

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“…We simulate storm tides (storm surge plus astronomical tide) for each event with the advanced circulation (ADCIRC) hydrodynamic model 16,17 , using a high-resolution mesh that spans the entire North Atlantic basin and has been previously validated 18 (Methods). We estimate rainfall fields using the physics-based Tropical Cyclone Rainfall (TCR) model, which has previously been used to assess historical rainfall climatology 19,20 , project changes in rainfall hazard 21 , and simulate flood impacts 22,23 (Methods). To evaluate the impact of SLR, we incorporate spatially-varied, probabilistic SLR projections for 2100 from ref.…”

Section: Introductionmentioning

confidence: 99%

Tropical cyclone climatology change greatly exacerbates US joint rainfall-surge hazard

Gori

Lin

et al. 2021

Preprint

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Tropical cyclones (TCs) are among the largest drivers of extreme rainfall and surge, but current and future TC joint hazard has not been well quantified. We utilize a physics-based approach to simulate TC rainfall and storm tides and quantify their joint hazard under historical conditions and a future (SSP5 8.5) climate projection. We find drastic increases in the frequency of exceeding joint historical 100-yr hazard levels by 2100, with a 10–36 fold increase along the southern US coast and 30–195 fold increase in the northeast. The joint hazard increase is induced by sea-level rise and TC climatology change; the relative contribution of TC climatology change is higher than that of sea-level rise for 96% of the coast due to large increases in rainfall. Increasing storm intensity and decreasing translation speed are the main TC change factors that cause higher rainfall and storm tides and up to 25% increase in their dependence.

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Section: Introductionmentioning

confidence: 99%

Tropical cyclone climatology change greatly exacerbates US joint rainfall-surge hazard

Gori

Lin

et al. 2021

Preprint

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“…Outer rain bands falling several hours before landfall could cause high flows within the lower Cape Fear River and interact with storm tides (Gori et al, 2020), and neglecting this possibility could result in an underestimation of the compound hazard. However, existing parametric rainfall models are unable to simulate outer rain band structure (Xi et al, 2020), and the relative importance of outer rain bands compared to inner core rainfall varies greatly across different TC events and locations. Although the TCR model is not able to simulate outer rain bands for single TC events, the model produces accurate outer rainfall estimates compared to historical averages.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, TCR has been shown to satisfactorily reproduce TC rainfall climatology (Zhu et al, 2013), as well as spatial patterns of rainfall totals and modeled flood peaks for TC events (Lu et al, 2018). Recent work has validated TCR using observed TC events across the U.S. Gulf and Atlantic coasts (Xi et al, 2020). Although TCR has been shown to perform well, the model does not explicitly simulate outer rain band precipitation, which results from stratified clouds that develop raindrops via complicated microphysical processes and are beyond the ability of simplified physics to capture.…”

Section: Methodsmentioning

confidence: 99%

“…Although TCR has been shown to perform well, the model does not explicitly simulate outer rain band precipitation, which results from stratified clouds that develop raindrops via complicated microphysical processes and are beyond the ability of simplified physics to capture. However, Xi et al (2020) showed that TCR is still able to predict rainfall for locations far from the TC center, and the magnitudes are approximately correct compared to the statistics of historical observations. With awareness of the uncertainty of rainfall estimates far from the TC center, the model is a useful tool for use in risk assessment due to its ability to accurately represent rainfall return periods and rainfall accumulations.…”

Section: Methodsmentioning

confidence: 99%

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Tropical Cyclone Compound Flood Hazard Assessment: From Investigating Drivers to Quantifying Extreme Water Levels

Gori

Lin

2020

Earth's Future

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Compound flooding, characterized by the co-occurrence of multiple flood mechanisms, is a major threat to coastlines across the globe. Tropical cyclones (TCs) are responsible for many compound floods due to their storm surge and intense rainfall. Previous efforts to quantify compound flood hazard have typically adopted statistical approaches that may be unable to fully capture spatio-temporal dynamics between rainfall-runoff and storm surge, which ultimately impact total water levels. In contrast, we pose a physics-driven approach that utilizes a large set of realistic TC events and a simplified physics-based rainfall model and simulates each event within a hydrodynamic model framework. We apply our approach to investigate TC flooding in the Cape Fear River, NC. We find TC approach angle, forward speed, and intensity are relevant for compound flood potential, but rainfall rate and time lag between the centroid of rainfall and peak storm tide are the strongest predictors of compounding magnitude. Neglecting rainfall underestimates 100-year flood depths across 28% of the floodplain, and taking the maximum of each hazard modeled separately still underestimates 16% of the floodplain. We find the main stem of the river is surge-dominated, upstream portions of small streams and pluvial areas are rainfall dominated, but midstream portions of streams are compounding zones, and areas close to the coastline are surge dominated for lower return periods but compounding zones for high return periods (100 years). Our method links joint rainfall-surge occurrence to actual flood impacts and demonstrates how compound flooding is distributed across coastal catchments. Plain Language Summary Compound flooding can result when multiple sources of flooding (such as storm surges at the coast and inland rainfall) overlap in space or time and interact in such a way that overall flooding is exacerbated. Tropical cyclones (TCs) often produce both storm surges and intense rainfall, and have caused many observed compound floods. We analyze compound flooding from TCs by using a large set of realistic TC events and a suite of physics-based models that estimate TC rainfall, storm tides, and simulate their interaction in a coastal study area (Cape Fear River, NC). We find that compound flooding is more severe during TCs that produce high-intensity rainfall and when significant rainfall occurs near the time of peak ocean water level. It is crucial to incorporate rainfall in coastal flood hazard assessment, and we find that neglecting rainfall results in an underestimation of extreme water levels in the study area. It is also necessary to model rainfall and storm surges together within physics-based models to accurately represent their interaction and compounding during TC events.

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“…However, there are still large uncertainties in quantifying changes in the risk of compound flooding due to the insufficient skill of climate models in simulating extreme precipitation caused by storms (Zhang et al, 2019a;Roberts et al, 2020;Vannière et al, 2020). Alternatively, previous efforts have been made to develop parametric tropical cyclone rainfall models (Marks and DeMaria, 2003;Lonfat et al, 2007;Langousis and Veneziano, 2009;Zhu et al, 2013;Emanuel, 2017;Brackins and Kalyanapu, 2020;Xi et al, 2020). The parametric tropical cyclone rainfall models are listed in Table 2, including R-CLIPER (Marks and DeMaria, 2003;Tuleya et al, 2007), IPET (IPET 2006), PHRaM (Lonfat et al, 2007), MSR (Langousis and Veneziano, 2009), RMS (Grieser and Jewson, 2012) and TCRM (Zhu et al, 2013;Emanuel, 2017;Xi et al, 2020).…”

Section: Storm Surge and Heavy Rainfallmentioning

confidence: 99%

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

et al. 2021

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Compound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.

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Evaluation of a Physics-Based Tropical Cyclone Rainfall Model for Risk Assessment

Cited by 36 publications

References 46 publications

Tropical cyclone climatology change greatly exacerbates US joint rainfall-surge hazard

Tropical cyclone climatology change greatly exacerbates US joint rainfall-surge hazard

Tropical Cyclone Compound Flood Hazard Assessment: From Investigating Drivers to Quantifying Extreme Water Levels

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

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