Abstract The parametric hurricane rainfall model (PHRaM), firstly introduced in 2007, has been widely used to forecast and quantify tropical-cyclone-induced rainfall (TC rainfall). The PHRaM is much more computationally efficient than global climate models, but PHRaM cannot be effectively utilized in the context of climate change because it does not have any parameters to capture the increase of tropospheric water vapor under the warming world. This study develops a new model that incorporates tropospheric water vapor to the PHRaM framework, named as the PHRaM with moisture (PHRaMM). The PHRaMM is trained to best fit the TC rainfall over the western North Pacific (WNP) unlike the PHRaM trained with the TCs over the continental US. The PHRaMM reliably simulates radial profile of TC rainfall and spatial distribution of accumulated rainfall during landfall in the present climate with the better prediction skills than existing statistical and operational numerical models. Using the PHRaMM, we evaluated the impacts of TC intensity and environmental moisture increase on TC rainfall change in a future climate. An increased TC intensity causes TC rainfall to increase in the inner-core region but to decrease in the outer region, whereas an increased environmental moisture causes the TC rainfall to increase over the entire TC area. According to the both effects of increased TC intensity and environmental moisture, the PHRaMM projected that the WNP TC rainfall could increase by 4.61–8.51% in the inner-core region and by 17.96–20.91% over the entire TC area under the 2-K warming scenario.
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Elevated risk of tropical cyclone precipitation and pluvial flood in Houston under global warming
Abstract Pluvial floods generated by tropical cyclones (TCs) are one of the major concerns for coastal communities. Choosing Houston as an example, we demonstrate that there will be significantly elevated risk of TC rainfall and flood in the future warming world by coupling downscaled TCs from Model Intercomparison Project Phase 6 models with physical hydrological models. We find that slower TC translation speed, more frequent stalling, greater TC frequency, and increased rain rate are major contributors to increased TC rainfall risk and flood risk. The TC flood risk increases more than the rainfall. Smaller watersheds with a high degree of urbanization are particularly vulnerable to future changes in TC floods in a warming world.
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- Award ID(s):
- 1854929
- NSF-PAR ID:
- 10332183
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 16
- Issue:
- 9
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- 094030
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract 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.
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Abstract Riverine flooding associated with landfalling tropical cyclones (TCs) in the western North Pacific basin is responsible for some of the most severe socioeconomic losses in East Asian countries. However, little is known about the spatial and temporal patterns of TC flooding and its climate controls, which constrain the predictive understandings of flood risk in this highly populated region. We provide a climatological characterization of TC flooding over China based on an exceptional network of stream gauging stations across the entire country. The most extreme floods in central and northeastern China are associated with TCs despite infrequent TC visits in these regions. Temporal variations in TC flooding demonstrate a mixture of controls tied to surface temperature anomalies in the northern hemisphere. The established links between TC flooding and climate controls present a potentially predictive tool of TC flood risk over China and other East Asian countries under future climate conditions.
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Abstract Tropical cyclone (TC) events are major drivers of compound flooding due to the interaction of wind‐driven storm surge and TC rainfall. Traditionally, coastal flood risk models have only taken into account surge flooding, even though it is known that the role of rainfall‐runoff is critical. There is limited understanding about the types of TC events that are capable of producing significant compounding and how site conditions at the coast affect the extent to which storm surge and rainfall‐runoff interact. This study investigates a suite of historical TCs making landfall near the Cape Fear River Estuary, NC, through a loosely coupled physical modeling methodology in order to draw conclusions about the spatial and temporal patterns of storm surge and rainfall that are able to induce significant compound impacts. Results indicate that intense outer rain bands falling over inland portions of the study area can be a driver of river‐surge compounding (increasing river levels by up to 0.36 m), while intense eyewall rainfall along the coast can result in localized compound impacts to coastal streams and tributaries if peak rainfall occurs near the time of peak storm tide. These localized compound impacts can result in defined interaction zones, where neither storm tide alone nor rainfall‐runoff alone can fully explain the observed maximum water levels. These results provide insight about the relative timing and spatial patterns of rainfall and storm surge that are capable of inducing compound flooding during TC events.
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Abstract One of the most costly effects of climate change will be its impact on extreme weather events, including tropical cyclones (TCs). Understanding these changes is of growing importance, and high resolution global climate models are providing potential for such studies, specifically for TCs. Beyond the difficulties associated with TC behavior in a warming climate, the extratropical transition (ET) of TCs into post-tropical cyclones (PTCs) creates another challenge when understanding these events and any potential future changes. PTCs can produce excessive rainfall despite losing their original tropical characteristics. The present study examines the representation of PTCs and their precipitation in three high resolution (25–50 km) climate models: CNRM, MRI, and HadGEM. All three of these models agree on a simulated decrease in TC and PTC events in the future warming scenario, yet they lack consistency in simulated regional patterns of these changes, which is further evident in regional changes in PTC-related precipitation. The models also struggle with their represented intensity evolution of storms during and after the ET process. Despite these limitations in simulating intensity and regional characteristics, the models all simulate a shift toward more frequent rain rates above 10 mm h−1in PTCs. These high rain rates become 4%–12% more likely in the warmer climate scenario, resulting in a 5%–12% increase in accumulated rainfall from these rates.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-9326/ac1e3d
