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Characterizing Hydrologic and Econom...

Bowers, Corinne Casey,

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Characterizing Hydrologic and Economic risk due to Flooding Driven by Atmospheric Rivers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Characterizing Hydrologic and Economic risk due to Flooding Driven by Atmospheric Rivers // Corinne Casey Bowers.
作者:	Bowers, Corinne Casey,
面頁冊數:	1 electronic resource (195 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Sensitivity analysis. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30726800
ISBN:	9798381020311

Characterizing Hydrologic and Economic risk due to Flooding Driven by Atmospheric Rivers /
Bowers, Corinne Casey,

Characterizing Hydrologic and Economic risk due to Flooding Driven by Atmospheric Rivers /Corinne Casey Bowers. - 1 electronic resource (195 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Atmospheric rivers (ARs), sometimes called "rivers in the sky," are long, narrow bands in the atmosphere that carry high levels of moisture from the tropics to the midlatitudes. They are key components of the overall water balance in California, but they also can cause extreme precipitation and floods. The overall goal of this dissertation is to identify which ARs are most likely to cause damaging flooding. I address this goal through three main research objectives: the first connecting AR hazard to AR risk, the second quantifying the contribution of temporal compounding to AR-driven hydrologic and economic impacts, and the third accounting for probabilistic uncertainty.Chapters 2 and 3 address the first objective of connecting ARs to the flood damage and loss they cause to infrastructure and communities. In Chapter 2, I use tools from earthquake engineering and probabilistic risk analysis to build a model chain connecting ARs, precipitation, streamflow, inundation, damage, and loss in a single process-based modular framework. Each component model in the framework represents one physical process and reports an uncertainty range that can be propagated through the chain. I establish the value of this framework as a standalone theoretical contribution, then implement it in a case study along the lower Russian River in Sonoma County, CA to demonstrate the practical and actionable metrics that can be derived from the results. In Chapter 3, I reverse direction, starting with records of damage and loss and working backwards to identify what types of ARs could have caused that damage. I train random forest models in California to predict the probability of flood insurance claims based on hazard, exposure, and vulnerability information at the county- and census tract-level. The models achieve high levels of balanced accuracy and are able to consistently discriminate between damage and no-damage cases, even when trained on highly imbalanced datasets. The real value of the fitted models, though, comes from their ability to interrogate drivers of risk and extremeness. I use interpretable machine learning techniques to estimate variable importance and variable impact for each of the hazard, exposure, and vulnerability inputs. While hazard intensity variables contribute the majority of the models' explanatory power, exposure and vulnerability variables are jointly responsible for about a third of the predicted outcome, and a feature-by-feature analysis reveals interesting local trends.Chapters 4 and 5 focus on the contribution of temporal compounding to the hydrologic and economic impacts of ARs. Temporal compounding is the risk amplification that occurs when two or more AR storms occur in close succession. The series of ARs that affected California in the early months of 2023 are an example of the significant consequences that temporal compounding can cause when the hydrologic environment does not have time to recover between events. I define a new metric, AR sequences, to identify when temporal compounding is contributing to risk. In Chapter 4, I show that AR sequences are associated with higher levels of hydrologic hazard, namely extreme runoff and soil moisture, than ARs are on their own. Future climate projections under medium and very high greenhouse gas emissions scenarios reveal that sequences are projected to increase in frequency, intensity, and duration in the future, with the largest frequency increases occurring at the longest durations.

English

ISBN: 9798381020311Subjects--Topical Terms:

3560752
Sensitivity analysis.

Characterizing Hydrologic and Economic risk due to Flooding Driven by Atmospheric Rivers /
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520 $a Atmospheric rivers (ARs), sometimes called "rivers in the sky," are long, narrow bands in the atmosphere that carry high levels of moisture from the tropics to the midlatitudes. They are key components of the overall water balance in California, but they also can cause extreme precipitation and floods. The overall goal of this dissertation is to identify which ARs are most likely to cause damaging flooding. I address this goal through three main research objectives: the first connecting AR hazard to AR risk, the second quantifying the contribution of temporal compounding to AR-driven hydrologic and economic impacts, and the third accounting for probabilistic uncertainty.Chapters 2 and 3 address the first objective of connecting ARs to the flood damage and loss they cause to infrastructure and communities. In Chapter 2, I use tools from earthquake engineering and probabilistic risk analysis to build a model chain connecting ARs, precipitation, streamflow, inundation, damage, and loss in a single process-based modular framework. Each component model in the framework represents one physical process and reports an uncertainty range that can be propagated through the chain. I establish the value of this framework as a standalone theoretical contribution, then implement it in a case study along the lower Russian River in Sonoma County, CA to demonstrate the practical and actionable metrics that can be derived from the results. In Chapter 3, I reverse direction, starting with records of damage and loss and working backwards to identify what types of ARs could have caused that damage. I train random forest models in California to predict the probability of flood insurance claims based on hazard, exposure, and vulnerability information at the county- and census tract-level. The models achieve high levels of balanced accuracy and are able to consistently discriminate between damage and no-damage cases, even when trained on highly imbalanced datasets. The real value of the fitted models, though, comes from their ability to interrogate drivers of risk and extremeness. I use interpretable machine learning techniques to estimate variable importance and variable impact for each of the hazard, exposure, and vulnerability inputs. While hazard intensity variables contribute the majority of the models' explanatory power, exposure and vulnerability variables are jointly responsible for about a third of the predicted outcome, and a feature-by-feature analysis reveals interesting local trends.Chapters 4 and 5 focus on the contribution of temporal compounding to the hydrologic and economic impacts of ARs. Temporal compounding is the risk amplification that occurs when two or more AR storms occur in close succession. The series of ARs that affected California in the early months of 2023 are an example of the significant consequences that temporal compounding can cause when the hydrologic environment does not have time to recover between events. I define a new metric, AR sequences, to identify when temporal compounding is contributing to risk. In Chapter 4, I show that AR sequences are associated with higher levels of hydrologic hazard, namely extreme runoff and soil moisture, than ARs are on their own. Future climate projections under medium and very high greenhouse gas emissions scenarios reveal that sequences are projected to increase in frequency, intensity, and duration in the future, with the largest frequency increases occurring at the longest durations.
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