Nonstationary flood risk assessment in coastal regions under climate change
Date
2021
Authors
Ghanbari, Mahshid, author
Arabi, Mazdak, advisor
Ettema, Robert, committee member
Schumacher, Russ, committee member
Bhaskar, Aditi, committee member
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Abstract
Coastal cities are exposed to multiple flood drivers including high tide, storm surge, extreme rainfall, and high river flows. The occurrence of these flood drivers, either in isolation or in combination, can cause significant risk to property and human life. Climate change is placing greater pressure on coastal communities by increasing frequency and intensity of flood events through sea level rise (SLR) and more extreme rainfall and storm events. Therefore, effective adaptation strategies are essential to reduce future flood risk in exposed communities. The planning and implementation of effective adaptation strategies require a comprehensive understanding of future flood hazards and risks under future climate conditions and adaptation options. The overarching goal of this dissertation is to improve the capacity to understand, estimate and mitigate future flood hazards and risks in coastal areas under uncertain climate change. To achieve this goal, first, a nonstationary mixture probability model was developed that enables simultaneous characterization of minor and major flood events under future sea level conditions. The probability model was used to estimate minor and major flooding frequency at 68 locations along the coasts of the Contiguous United States (CONUS). The results showed a significant increase in frequency of both minor and major flood events under future sea level conditions. However, the frequency amplification of minor and major flooding varied by coastal regions. While regions in the Pacific and southeast Atlantic coast are likely to be exposed to higher frequency amplification in major flooding, the Gulf and northeast Atlantic coastal regions should expect the highest minor flood frequency amplification. Second, the proposed mixture probability model was employed in a flood risk assessment framework to enable assessing future acute and chronic coastal flood risks under different SLR and adaptation levels. The HAZUS-MH flood loss estimation tool was used to estimate property damage. The application of the framework in Miami-Dade County revealed that as sea level rises, chronic risks from repetitive nonextreme flooding may exceed acute risks from extreme floods. Third, a nonstationary bivariate flood hazard assessment method was developed that enables estimation of future frequency of compound coastal-riverine flooding with consideration of impacts of climate change including SLR and variations in extreme river flows. The proposed method was employed at 26 paired tidal-riverine stations along the CONUS coast. Specifically, the joint return period of compound major coastal-riverine flooding, defined based on flood impact thresholds, was explored by mid-century. The results showed that under current climate conditions the northeast Atlantic and western part of the Gulf coasts are exposed to the highest compound major coastal-riverine flood probability. However, considering future SLR, emerging high compound major flooding probability was evident in the southeast Atlantic coast. The impact of changes in extreme river flows was found to be negligible in most of the locations. Finally, four stormwater intervention scenarios including gray (i.e., conventional centralized conveyance systems and water treatment plants) and green (i.e., decentralized infiltration measures) infrastructure systems, were assessed in New York City (NYC). The results revealed that in developed and urbanized cities like NYC, green systems should not be considered as a substitute for gray systems. Complementary benefits on flood and combined sewer outflow (CSO) reduction can be gained through integration of green and gray systems.
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Subject
extreme value analysis
mixture probability model
stormwater management
flood risk analysis
compound flood risk
sea level rise