Flood Risk Prediction under Global Vegetated Hydrodynamics: A Bayesian Network

Abstract

The increasing frequency and intensity of extreme events due to global warming and climate change is increasing flood risk. To act, rather than react, nature-based solutions (NBS) involving vegetation and wetlands are being explored on top of conventional solutions like dikes. WHY? There was a dire need for global study quantifying the potential of vegetation in reducing flood risk and eventually make a decision support tool which enables quick assessments about flood risk reduction in a vegetated hydrodynamic system. WHAT? The developed tool can predict flood risk anywhere in the world without rigorous modeling through user defined conditionalization of in-situ hydrodynamic or vegetation characteristics. HOW? Multivariate dependence among parameters of schematized system can exhibit characteristics of vegetated hydrodynamics. To ensure global representation of vegetated hydrodynamics a copula-based multivariate stochastic model has been developed which caters global ranges of each parameter, their probability distributions and the inter-parameter dependencies through ranked correlations. Numerical modeling has been carried out through XBeach non-hydrostatic model by resolving full spectrum of high and low frequency waves. A non-parametric Bayesian network-based flood risk prediction tool has been developed from the synthetic dataset developed from the simulations. SO? Bulk results conclude that saltmarshes attenuates waves by 87% and mangroves by 94% as a mean value. Wave attenuation, flood risk reduction and wave run-up manifests maximum dependence on offshore wave height, water depth, drag coefficient, vegetation height, frontal width, and forest length and least on offshore slope and vegetation density. NOW? The flood risk prediction tool would help decision makers in implementing NBS, in making better informed decisions about early warnings and policy making related to flood risk reduction and climate change adaptation by incorporating vegetation. NOVELTY? To the author's knowledge no such study exists which captures natural variability of hydrodynamics and vegetation together in a probabilistic model over global scales. Additionally, no such study exist which applies non-parametric Bayesian networks to predict flood risk. The dependence modeling of global vegetated hydrodynamic environments is also unique which skims out the most critical parameters.