Date of Award
Floods, Hydrology, Atmosphere, Infrastructure Systems, Statistical Modeling
Our preliminary survey showed that most of the recent flood-related studies did not formally explain the physical mechanisms of long-duration and large-peak flood events that can evoke substantial damages to properties and infrastructure systems. These studies also fell short of fully assessing the interactions of coupled ocean-atmosphere and land dynamics which are capable of forcing substantial changes to the flood attributes by governing the exceeding surface flow regimes and moisture source-sink relationships at the spatiotemporal scales important for risk management. This dissertation advances the understanding of the variability in flood duration, peak, volume, and timing at the regional to the global scale, and quantifies their causal mechanisms that include land surface conditions, large-scale climatic patterns, and mesoscale atmospheric teleconnections. Analyzing recent trends in the frequency and duration of global floods indicates that there is a significant upward trend in the statistics of their annual probability distribution, and large-scale climate teleconnections play a major role in modulating this trend. A comprehensive hydroclimate-informed framework is then presented to identify the physical causative relationship between floods of varying duration and peak and their regional land surface conditions, rainfall statistics, and moisture transports and convergences. Then, flood duration is modeled using the antecedent exceeding flow, large-scale atmospheric circulation patterns, interrelated ocean-atmospheric conditions, and dynamics of moisture transport systems in a physically informed Bayesian network framework. Statistical scaling relationships of floods with basin-wide geomorphologic characteristics and precipitation variability are then estimated. This is followed by an understanding of the spatial manifestation of widespread simultaneous heavy precipitation events (SHPEs), including quantifying their risk footprints. Subsequently, an experimental study has been designed to study the flood risk propagation in the river network along with deriving the relationship between infrastructure failure probability and the probability of SHPEs. Ultimately, this full conditional probability based framework provides a multi-angle precautional insight on better management and maintenance of critical infrastructure systems such as flood control dams, water supply reservoirs, bridges, and power plants.
This dissertation contributes the following aspects to the growing literature on the hydroclimatology of floods and its impacts: 1) Global trends in the duration and frequency of observed floods, and their driving atmospheric teleconnection together with their impacts are revealed, 2) A comprehensive hydroclimate-informed framework is developed to assess the variability of flood duration, peak, volume, and timing at different spatiotemporal scales conditioned on large-scale climate and atmospheric teleconnections, 3) The cause-effect relationship between floods of varying duration (and peak), regional preceding dry/wet conditions and large-scale atmospheric circulations are statistically modeled, 4) Statistical scaling of flood duration, peak, and volume with the regional geomorphologic and precipitation patterns are established, 5) Spatial manifestation of widespread heavy precipitation events are derived and their projected geometric attributes on the ground are modeled and predicted, and 6) Flood risk propagation across the river network and vulnerabilities of critical infrastructure systems such as the flood control dams to the specific driver of flood (e.g., simultaneous extreme rainfall) are quantified.
Najibi, Nasser, "Hydroclimate Drivers and Atmospheric Dynamics of Floods" (2019). CUNY Academic Works.
Civil Engineering Commons, Climate Commons, Environmental Engineering Commons, Fresh Water Studies Commons, Hydraulic Engineering Commons, Hydrology Commons, Meteorology Commons, Oceanography Commons, Statistical Methodology Commons, Statistical Models Commons, Water Resource Management Commons