Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models
Abstract. Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in which concurrent or successive flood drivers synergize, producing larger impacts than those from individual drivers. CF modeling is yet subject to four main sources of uncertainty including (i) initial condition, (ii) forcing (or boundary) conditions, (iii) model parameters, and (iv) model structure. These sources of uncertainty, if not quantified and effectively reduced, cascade in series throughout the modeling chain and compromise the accuracy of CF hazard assessments. Here, we characterize cascading uncertainty using linked process-based and machine learning (PB-ML) models for a well-known CF event, namely Hurricane Harvey in Galveston Bay, TX. For this, we run a set of hydrodynamic model scenarios to quantify isolated and cascading uncertainty in terms of maximum water level residuals, and additionally, track the evolution of residuals during the onset, peak, and dissipation of Hurricane Harvey. We then develop multiple-linear regression (MLR) and PB-ML models to estimate the relative and cumulative contribution of the four sources of uncertainty to total uncertainty over time. Results from this study show that the proposed PB-ML model capture “hidden” nonlinear associations and interactions among the sources of uncertainty, thereby outperforming conventional MLR models. Model structure and forcing conditions are the main sources of uncertainty in CF modeling and their corresponding model scenarios, or input features, contribute to (56 %) 49 % of variance reduction in the estimation of (maximum) water level residuals. Following these results, we conclude that PB-ML models are a feasible alternative for quantifying cascading uncertainty in CF modeling.
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