Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall

Shen, Yawen; Morsy, Mohamed M.; Huxley, Chris; Tahvildari, Navid; Goodall, Jonathan L.

doi:10.1016/j.jhydrol.2019.124159

Cited by 126 publications

(72 citation statements)

References 38 publications

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“…Nuisance flooding caused by rain and tide adversely affects social and economic activities by disrupting transportation systems (Jacobs et al, 2018; Suarez et al, 2005) and compromising the performance of storm sewers (Flood & Cahoon, 2015). These events can be exacerbated by the joint occurrence of high tides and even moderate rainfall events, given the interplay between tide and rainfall in coastal communities (Lian et al, 2015; Shen et al, 2019; Sreetharan et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

Zahura

Goodall

Sadler

et al. 2020

Water Resources Research

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Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics-based 1-D pipe/2-D overland flow models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real-time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Forest (RF), to serve as a surrogate model for urban flood predictions. The surrogate model was trained to relate topographic and environmental features to hourly water depths simulated by a high-resolution 1-D/2-D physics-based model at 16,914 road segments in the coastal city of Norfolk, Virginia, USA. Two training scenarios for the RF model were explored: (i) training on only the most flood-prone street segments in the study area and (ii) training on all 16,914 street segments in the study area. The RF model yielded high predictive skill, especially for the scenario when the model was trained on only the most flood-prone streets. The results also showed that the surrogate model reduced the computational run time of the physics-based model by a factor of 3,000, making real-time decision support more feasible compared to using the full physics-based model. We concluded that machine learning surrogate models strategically trained on high-resolution and high-fidelity physics-based models have the potential to significantly advance the ability to support decision making in real-time flood management within urban communities.

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Section: Introductionmentioning

confidence: 99%

Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

Zahura

Goodall

Sadler

et al. 2020

Water Resources Research

Self Cite

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“…Bilskie and Hagen (2018) define transition zones as areas where storm tide elevations are below rainfall‐runoff elevations but neither hazard is dominant compared to the combined rainfall + storm tide elevations. Shen et al (2019) define transition zones as areas where rainfall + storm tide elevations are at least 0.01 m higher than simulations of either hazard alone. Here, to focus on the compounding effect of the two drivers, we adopt a similar approach as Shen et al (2019) but define transition zones as areas where combined elevations are >0.2 m higher than elevations from either hazard alone (consistent with A C defined above).…”

Section: Resultsmentioning

confidence: 99%

“…Shen et al (2019) define transition zones as areas where rainfall + storm tide elevations are at least 0.01 m higher than simulations of either hazard alone. Here, to focus on the compounding effect of the two drivers, we adopt a similar approach as Shen et al (2019) but define transition zones as areas where combined elevations are >0.2 m higher than elevations from either hazard alone (consistent with A C defined above). Surge‐dominated zones are areas where storm tide elevations are within 0.2 m of the combined scenario, and rainfall‐dominated zones as areas where rainfall + tide elevations are within 0.2 m of the combined scenario.…”

Section: Resultsmentioning

confidence: 99%

“…Research efforts related to compound flooding have typically focused on either characterizing the statistical dependence between rainfall (or river flows) and storm surge (Cao et al, 2020; Couasnon et al, 2019; Hendry et al, 2019; Moftakhari et al, 2017; Ward et al, 2018; Wu et al, 2018; Zheng et al, 2014) or utilizing physics‐based models to investigate their compounding impacts on flood depths and extents (Bacopoulos et al, 2017; Herdman et al, 2018; Kumbier et al, 2018; Ray et al, 2011; Silva‐Araya et al, 2018; Torres et al, 2015). Recent work has also sought to delineate transition zones in coastal regions, where the influence of both rainfall and storm surge are important in driving total water levels (Bilskie & Hagen, 2018; Shen et al, 2019). Although numerous modeling studies have quantified interactions between rainfall‐runoff/river flows and storm surge through historical event reconstruction or synthetic scenarios, there has been considerably less work on quantifying compound flood hazard in terms of return periods of flood heights or inundation depths.…”

Section: Introductionmentioning

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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“…This section further verifies the performance of the proposed SC-FDO based MLP trainer by solving real-world application problems. Rainfall data are essential components of the hydrological cycle to assess flood risk [40] and predict rainfall forecasting [41]. However, missing rainfall values in the weather dataset reduces the accuracy and robustness of the hydrological data analysis.…”

Section: A Case Study: Missing Weather Data Imputationmentioning

confidence: 99%

Hybrid Sine Cosine and Fitness Dependent Optimizer for Global Optimization

et al. 2021

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Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall

Cited by 126 publications

References 38 publications

Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

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

Hybrid Sine Cosine and Fitness Dependent Optimizer for Global Optimization

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