Flooding is one of the most frequent and disastrous natural hazards triggered by extreme precipitation, high river runoff, hurricane storm surges, and compounding effects of various flood drivers. This study introduces a new multi-source remote sensing approach that leverages both multi-spectral optical imagery and the weather- and illumination-independent characteristics of Synthetic Aperture Radar (SAR) data to streamline, automate, and map geographically reliable flood inundation extents. Utilizing the near real-time and cloud computing capabilities of Google Earth Engine (GEE), this process facilitates data acquisition and enables large-scale flood monitoring in an expeditious manner. Two major hurricanes along the U.S. Gulf Coast were evaluated: (1) the 2021 Hurricane Ida to the south of New Orleans, Louisiana and (2) the 2017 Hurricane Harvey to the east of Houston, Texas. We devised a change detection and thresholding framework using multi-temporal SAR imagery and validated the results with flood extent maps derived from Landsat 8 and Sentinel-2 optical imagery. We demonstrate that constant threshold values for flood extraction from SAR change detection indices are not ubiquitously suitable for all geographies, thus we outline a heuristic that can be used to select thresholds suitable for specific sites through a fully automated sensitivity analysis. The results indicated high agreement between the SAR and optical imagery (77–80%), with SAR providing the benefit of under-cloud detection. Furthermore, our results contribute to scaling the SAR approach to produce rapid and accurate information for decision-makers and emergency responders during time-sensitive flood events.

Compound flooding frequently threatens life and assets of people who live in low‐lying coastal regions. Co‐occurrence or sequence of extremes (e.g., high river discharge and extreme coastal water level) is of paramount importance as it may result in flood hazards with potential impacts larger than each extreme in isolation. Here, we use a coupled approach, that is, bivariate statistical analysis linked to hydrodynamic modeling, to quantify compounding effects of flood drivers and generate flood hazard maps near Savannah, Georgia. Also, we integrate wetland elevation correction in digital elevation models to improve hydrodynamic simulations of compound events and hence the accuracy of flood hazard (inundation and velocity) maps. Using statistical measures, we analyze compounding effects of terrestrial/coastal flood drivers and wetland elevation correction on maximum floodwater height (MFH) and velocity (MFV) for 50‐year return period scenarios. In addition, we compare our results to MFH and MFV patterns of Hurricane Matthew that hit the West Atlantic Coasts on October 2016. The statistical measures indicate significant differences among the scenarios, partly explained by wetland elevation correction. Inundation and velocity maps suggest that a proposed composite, that is, synthesis of marginal Q, marginal H, and “AND” scenarios, can lead to the lowest average underestimation of MFH (−0.35 m) and overestimation of MFV (0.20 m/s) within wetland areas. We conclude that a thorough compound flooding assessment should leverage statistical analysis and hydrodynamic modeling of extremes including corrections of coastal digital elevation models.

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.