Do Levees Redistribute Floodwater Risk? A Bayesian Assessment with the National Levee Database

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Abstract/Contents

Abstract

Since the 1800s, levees, embankments that prevent water from entering a floodplain, have played a critical role in U.S. flood management.
While physics-based models are used by engineers prior to construction projects to limit adverse impacts, flood victims, law suits, and academic papers have asserted that in redistributing floodwater, levees redistribute flood risk up or downstream—to communities without this infrastructure. However, there has been little empirical research examining the actual effect of levees on stage (surface water level). We examine this question at a national scale, extending the simple statistical method of a small-scale study (in IL, IA) to levees in the National Levee Database, then generalize this approach to a fully Bayesian treatment using a multi-level model.
While the original small-scale study found that levee construction significantly increased stage upstream and adjacent to levees, we find that this result does not generalize nationally. However, the mutli-level model offers many advantages over the previous methods in this application, and allows us to study effect heterogeneity, where preliminary results suggest that non-accredited levees and those in regions with high silt soil composition may be more likely to exhibit stage increases.

Description

Type of resource text
Date created June 3, 2022
Date modified December 5, 2022
Publication date July 22, 2022

Creators/Contributors

Author Verma, Miki ORCiD icon https://orcid.org/0000-0001-7793-4942 (unverified)
Thesis advisor Ho, Daniel

Subjects

Subject Levees
Subject Statistics
Subject Bayesian Models
Subject Flood control
Genre Text
Genre Thesis

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This work is licensed under a Creative Commons Zero v1.0 Universal license (CC0).

Preferred citation

Preferred citation
Verma, M. (2022). Do Levees Redistribute Floodwater Risk? A Bayesian Assessment with the National Levee Database. Stanford Digital Repository. Available at https://purl.stanford.edu/yk640qt4155

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Master's Theses, Symbolic Systems Program, Stanford University

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