Linking statistical and hydrodynamic modeling for compound flood hazard assessment in tidal channels and estuaries

Abstract

A method to link bivariate statistical analysis and hydrodynamic modeling for flood hazard estimation in tidal channels and estuaries is presented and discussed for the general case where flood hazards are linked to upstream riverine discharge Q and downstream ocean level, H. Using a bivariate approach, there are many possible combinations of Q and H that jointly reflect a specific return period, T , raising questions about the best choice as boundary forcing in a hydrodynamic model. We show, first of all, how possible Q and H values depend on whether the definition of T corresponds to the probability of exceedance of "H OR Q " or "H AND Q ". We also show that flood hazards defined by "OR " return periods are more conservative than "AND " return periods. Finally, we introduce a new composite water surface profile to represent the spatially distributed hazard for return period T. The composite profile synthesizes hydrodynamic model results from the "AND " hazard scenario and two scenarios based on traditional univariate analysis, a "Marginal Q " scenario and a "Marginal H " scenario.

Increasing exposure of energy infrastructure to compound hazards: cascading wildfires and extreme rainfall

“…Even if the pipelines would be underground, and so less threatened directly by wildfire, measurements and control assets are highly vulnerable (meters in several locations had melted away in California wildfire incidents in October 2018) and disruptions/shutoffs are required before fire reaches houses (ICF 2018). Beyond these, the exposure of natural gas pipelines to compound hazards yet to be studied, otherwise the failure probability will be under/overestimated (Moftakhari et al 2017(Moftakhari et al , 2019. In January 2018, for example Montecito community in California experienced one of a kind compound hazards (wildfire followed by intense rainfall) that each of drivers in isolation may not produce such extensive mudslide-driven damage yielding federal Major Disaster Declaration (ICF 2018).…”

Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps

“…On the other hand, at the Tijuana River Valley site dominated by river flooding, end‐users requested maps related to the velocity of flooding to assess flood forces (which bear on safety) and shear stresses (which bear on erosion management). Use of technical terminology in collaborative flood modeling, and especially statistical concepts such as confidence levels, was disruptive of productive dialogue and discussion about flooding among end‐users. Flood hazard maps can and should be presented to end‐users with plain language such as “1% annual chance flood depth” even when the methods of statistical analysis and hydrodynamic modeling used to make the map are far from simple (e.g., Moftakhari et al, ). Additionally, flood maps depicting historical flooding events are advantageous for productive, nontechnical dialogue aimed at reducing flood vulnerabilities. Collaborative flood modeling aligns with the strategic plans of FEMA to support local leadership of FRM, creating a process for local stakeholders to contemplate flooding, map risks in ways that align with local decision‐making needs, and enhance deliberations over strategies and projects to manage risks.…”

“…Earth's Future to make the map are far from simple (e.g., Moftakhari et al, 2019). Additionally, flood maps depicting historical flooding events are advantageous for productive, nontechnical dialogue aimed at reducing flood vulnerabilities.…”

Why We Can No Longer Ignore Consecutive Disasters

“…For example, tropical cyclones and earthquakes are independent and distinctly different disasters, stemming from different hazard groups (atmospheric and geophysical respectively) and their impacts occur at very different temporal and spatial scales (Gill & Malamud, ). As single or univariate hazard risk assessments tend to either significantly overestimate or underestimate risk of compound events (Moftakhari et al, ). AghaKouchak et al () suggest to assess the risk of compound events caused by climatic extremes using a multivariate framework that can account for compound and concurrent events.…”