Maritime transportation is crucial to national economic development as it offers a low-cost, safe, and efficient alternative for movement of freight compared to its land or air counterparts. River and channel dredging protocols are often adopted in many ports and harbors of the world to meet the increasing demand for freight and ensure safe passage of larger vessels. However, such protocols may have unintended adverse consequences on flood risks and functioning of coastal ecosystems and thereby compromising the valuable services they provide to society and the environment. This study analyzes the compound effects of dredging protocols under a range of terrestrial and coastal flood drivers, including the effects of sea level rise (SLR) on compound flood risk, vessel navigability, and coastal wetland inundation dynamics in Mobile Bay (MB), Alabama. We develop a set of hydrodynamic simulation scenarios for a range of river flow and coastal water level regimes, SLR projections, and dredging protocols designed by the U.S. Army Corps of Engineers. We show that channel dredging helps increase bottom (‘underkeel’) clearances by a factor of 3.33 under current mean sea level and from 4.20 to 4.60 under SLR projections. We find that both low and high water surface elevations (WSEs) could be detrimental, with low WSE (< -1.22 m) hindering safe navigation whereas high WSE (> 0.87 m) triggering minor to major flooding in the surrounding urban and wetland areas. Likewise, we identify complex inundation patterns emerging from nonlinear interactions of SLR, flood drivers, and dredging protocols, and additionally estimate probability density functions (PDFs) of wetland inundation. We show that changes in mean sea level due to SLR diminish any effects of channel dredging on wetland inundation dynamics and shift the PDFs beyond pre-established thresholds for moderate and major flooding. In light of our results, we recommend the need for integrated analyses that account for compound effects on vessel navigation and wetland inundation, and provide insights into environmental-friendly solutions for increasing cargo transportation.
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When floods hit the road: Resilience to flood-related traffic disruption in the San Francisco Bay Area and beyond
As sea level rises, urban traffic networks in low-lying coastal areas face increasing risks of flood disruptions. Closure of flooded roads causes employee absences and delays, creating cascading impacts to communities. We integrate a traffic model with flood maps that represent potential combinations of storm surges, tides, seasonal cycles, interannual anomalies driven by large-scale climate variability such as the El Niño Southern Oscillation, and sea level rise. When identifying inundated roads, we propose corrections for potential biases arising from model integration. Our results for the San Francisco Bay Area show that employee absences are limited to the homes and workplaces within the areas of inundation, while delays propagate far inland. Communities with limited availability of alternate roads experience long delays irrespective of their proximity to the areas of inundation. We show that metric reach, a measure of road network density, is a better proxy for delays than flood exposure.
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- Award ID(s):
- 1739027
- PAR ID:
- 10220013
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 6
- Issue:
- 32
- ISSN:
- 2375-2548
- Page Range / eLocation ID:
- eaba2423
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract This paper describes an integrated climatological‐hydrodynamic method that couples probabilistic hurricane model, storm surge model, inundation model, coastal protection data, and sea level rise projections to estimate tropical cyclone‐induced coastal flood inundation hazard in a coastal megacity‐Shanghai, China. We identify three “worst‐case” scenarios (extracted from over 5,000 synthetic storms) that generate unprecedentedly high flood levels in Shanghai. Nevertheless, we find that the mainland Shanghai is relatively safe from coastal flooding under the current climate, thanks to its high‐standard seawall protection. However, the city is expected to be increasingly at risk due to future sea level rise, with inundation two times and 20 times more likely to occur by mid‐ and late‐21st century, respectively, and inundation depth and area to greatly increase (e.g., 60%–1,360% increase in the inundation area for the “worst cases” by 2,100). The low‐lying and poorly protected area (e.g., Chongming Island) is likely to be moderately affected by flood events with long return periods at the current state but could be largely inundated in future sea‐level‐rise situations.
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Spatiotemporal implications of flooding on relocation risk in rural and urban coastal municipalitiesThe research on coastal hazards predicts substantial adverse impacts of chronic and episodic flooding on populated coastal areas. Despite the growing evidence about anticipated flood risks, many coastal communities are still not adapting. The observed disconnect between science on physical impacts and adaptation decisionmaking in part reflects stakeholders’ inability to envision the implications of these impacts on socioeconomic systems and the built environment in their jurisdictions. This inertia is particularly apparent in the discourse on flood-driven displacement and relocation. There is a lack of knowledge about direct and indirect flood impacts on community attributes and services that contribute to relocation decision-making. This study holistically evaluates the flood exposure on municipal features vital for socioeconomic stability, livelihoods, and quality of life across spatiotemporal scales. As such, it uses a more nuanced approach to relocation risk assessment than those solely focused on direct inundation impacts. It measures flood exposure of land use, land cover, and sociocultural and economic dimensions that are important drivers of relocation in selected rural and urban areas. The approach uses a 50-year floodplain to delineate populated coastal locations exposed to 2% Annual Exceedance Probability (AEP) storm surge projections adjusted for 2030, 2060, and 2090 sea level rise (SLR) scenarios. It then evaluates the potential impacts of this flood exposure on different types of land uses and critical socioeconomic assets in rural (Dorchester and Talbot Counties, Maryland, USA) and urban (Cities of Hampton, Norfolk, Portsmouth, and Virginia Beach, Virginia, USA) settings. The results show that some urban land uses, such as open space, military and mixed-use, and rural residential and commercial areas, might experience significantly more flooding. There are also notable differences in the baseline flood exposure and the anticipated rate and acceleration in the future among selected communities with significant implications for relocation planning.more » « less
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The emergence of mobile platforms equipped with Global Positioning System technology enables real-time data collection affording opportunities for mining data applicable to rapid flood inundation assessment. The collected data can be employed to complement existing methods for rapid flood inundation assessment, such as remote sensing, to enhance situational awareness. In particular, telemetry-based digital trace data related to human activity have intrinsic advantages to be used for inundation assessment. In this study, we investigate the use of Mapbox telemetry data, which provides human activity indices with high spatial and temporal resolutions, for application in rapid flood inundation assessment. Using data from Hurricane Harvey in 2017 in Harris County, Texas, we (1) study anomalous fluctuations in human activities and analyze the differences in activity level between inundated and non-inundated areas and (2) investigate changes in the concentration of human activity, to explore the disruption of human activity as an indicator of flood inundation. Results show that both analyses can provide valuable rapid insights regarding flood inundation status. Anomalous activities can be significantly higher/lower in flooded areas compared with non-flooded areas. Also, the concentration of human activity during the flood propagation period across affected watersheds can be observed. This study contributes to the state of knowledge in smart flood resilience by investigating the application of ubiquitous telemetry-based digital trace data to enhance rapid flood inundation assessment. Accordingly, the use of such digital trace data could provide emergency managers and public officials with valuable insights to inform impact evaluation and response actions.more » « less
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Abstract Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father’s Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.aba2423
