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. However, CF modeling is subject to four main sources of uncertainty: (i) the initial condition, (ii) the forcing (or boundary) conditions, (iii) the model parameters, and (iv) the 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; additionally, we 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 captures “hidden” nonlinear associations and interactions among the sources of uncertainty, thereby outperforming conventional MLR models. The 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 % 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.