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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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Developing a Modeling Framework to Simulate Compound Flooding: When Storm Surge Interacts With Riverine Flow
Compound flooding is a physical phenomenon that has become more destructive in recent years. Moreover, compound flooding is a broad term that envelops many different physical processes that can range from preconditioned, to multivariate, to temporally compounding, or spatially compounding. This research aims to analyze a specific case of compound flooding related to tropical cyclones where the compounding effect is on coastal flooding due to a combination of storm surge and river discharge. In recent years, such compound flood events have increased in frequency and magnitude, due to a number of factors such as sea-level rise from warming oceans. Therefore, the ability to model such events is of increasing urgency. At present, there is no holistic, integrated modeling system capable of simulating or forecasting compound flooding on a large regional or global scale, leading to the need to couple various existing models. More specifically, two more challenges in such a modeling effort are determining the primary model and accounting for the effect of adjacent watersheds that discharge to the same receiving water body in amplifying the impact of compound flooding from riverine discharge with storm surge when the scale of the model includes an entire coastal line. In this study, we investigated the possibility of using the Advanced Circulation (ADCIRC) model as the primary model to simulate the compounding effects of fluvial flooding and storm surge via loose one-way coupling with gage data through internal time-dependent flux boundary conditions. The performance of the ADCIRC model was compared with the Hydrologic Engineering Center- River Analysis System (HEC-RAS) model both at the watershed and global scales. Furthermore, the importance of including riverine discharges and the interactions among adjacent watersheds were quantified. Results showed that the ADCIRC model could reliably be used to model compound flooding on both a watershed scale and a regional scale. Moreover, accounting for the interaction of river discharge from multiple watersheds is critical in accurately predicting flood patterns when high amounts of riverine flow occur in conjunction with storm surge. Particularly, with storms such as Hurricane Harvey (2017), where river flows were near record levels, inundation patterns and water surface elevations were highly dependent on the incorporation of the discharge input from multiple watersheds. Such an effect caused extra and longer inundations in some areas during Hurricane Harvey. Comparisons with real gauge data show that adding internal flow boundary conditions into ADCIRC to account for river discharge from multiple watersheds significantly improves accuracy in predictions of water surface elevations during coastal flooding events.
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- PAR ID:
- 10281344
- Date Published:
- Journal Name:
- Frontiers in Climate
- Volume:
- 2
- ISSN:
- 2624-9553
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract. In coastal regions, compound flooding can arise from a combination of different drivers such as storm surges, high tides, excess river discharge, and rainfall. Compound flood potential is often assessed by quantifying the dependence and joint probabilities of the flood drivers using multivariate models. However, most of these studies assume that all extreme events originate from a single population. This assumption may not be valid for regions where flooding can arise from different generation processes, e.g., tropical cyclones (TCs) and extratropical cyclones (ETCs). Here we present a flexible copula-based statistical framework to assess compound flood potential from multiple flood drivers while explicitly accounting for different storm types. The proposed framework is applied to Gloucester City, New Jersey, and St. Petersburg, Florida as case studies. Our results highlight the importance of characterizing the contributions from TCs and non-TCs separately to avoid potential underestimation of the compound flood potential. In both study regions, TCs modulate the tails of the joint distributions (events with higher return periods) while non-TC events have a strong effect on events with low to moderate joint return periods. We show that relying solely on TCs may be inadequate when estimating compound flood risk in coastal catchments that are also exposed to other storm types. We also assess the impact of non-classified storms that are neither linked to TCs or ETCs in the region (such as locally generated convective rainfall events and remotely forced storm surges). The presented study utilizes historical data and analyzes two populations, but the framework is flexible and can be extended to account for additional storm types (e.g., storms with certain tracks or other characteristics) or can be used with model output data including hindcasts or future projections.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fclim.2020.609610
