Governance of Long-Duration Floods

October 10, 2019
A two-layer network model for Bayesian inference of the coupled regional flood duration scaling and atmospheric dynamic scaling of the flood duration.
A two-layer network model for Bayesian inference of the coupled regional flood duration scaling and atmospheric dynamic scaling of the flood duration. The complex interaction of geopotential height dipole index (GPH-di), vertical integral of water vapor (viWV), divergence of wind (divW), maximum of preceding cumulative exceeding flow (CEI), and flood duration is shown at the top. The boxplots indicate the scaling factors of flood duration (α, β) for each reservoir (dam) and their corresponding atmospheric dynamic scaling factors (γ1, γ2, γ3, γ4). The red and blue-colored plots demonstrate the probability of occurrence of long-duration flood (21 days and above) derived from the predictive flood duration model and the observed flood duration, respectively. Click image to enlarge.

Consequences of long-term inundation of floodplains, residential and commercial areas, and critical infrastructure systems cannot be fully comprehended without a clear understanding of the variability in the duration of the floods. Long-duration floods can cause substantial damages and prolonged interruptions to water resources facilities and critical infrastructure systems. Analyses of their causal structure are invaluable for evaluating reservoir and river system operation policies and for flood preparation. Despite its importance, most of the recent flood-related studies have not formally explained the physical mechanisms of long-duration flood events that can evoke substantial damages to properties and infrastructure systems.

In a recent article in Nature partner journal npj Climate and Atmospheric Science, Najibi, Devineni, and co-authors identified governing factors for long‐duration floods with a focus on explaining how flood duration scales with antecedent flow and atmospheric patterns, which are the primary contributing factors originating from the coupled land‐ocean‐atmosphere dynamic system. They found that long-duration floods can be related to persistent variables in the hierarchy of the climate and atmospheric system. Long-duration floods are triggered by high antecedent flow conditions, which are in turn caused by high moisture release from recurrent storm tracks. Atmospheric teleconnections are distinctively persistent and well developed for these events. For short-duration floods, these coupled patterns are insignificant. The authors summarized the leading factors of flood duration as maximum cumulative exceeding flow, blocking systems of pressure in the atmosphere, sufficient amount of moisture supply (water vapor), and the converging process for the available moisture (divergent wind). They developed a Bayesian network inference model to account for these relations and for predicting the likelihood of floods of varying duration using physics informed predictors. The model provides a deeper understanding of the nexus of antecedent flow regime, atmospheric blocking, and moisture transport/release mechanisms. This can ultimately aid in decision support systems for the protection of national infrastructure against long-duration flood events.

Written by 
Naresh Devineni, City University of New York (City College)

Nasser Najibi1, Naresh Devineni1, Mengqian Lu2, and Rui A. P. Perdigão3

1City University of New York (City College), USA

2Hong Kong University of Science and Technology, China

3University of Lisbon, Portugal