Evaluating the Feasibility of Scaling the FIER Framework for Large-Scale Flood Inundation Prediction

Abstract. Floods are a recurring global threat, causing lives lost, property damage, and agricultural impacts. Accurate and timely flood inundation forecasts are crucial for effective disaster preparedness and mitigation. However, traditional flood forecasting methods often face challenges in terms of computational demands and data requirements, particularly when applied to large geographic areas. This study presents a novel approach to scaling a data-driven flood forecasting framework, Forecasting Inundation Extents using REOF (Rotated Empirical Orthogonal Function) (FIER), to large geographic regions. FIER leverages historical satellite imagery and streamflow data to predict flood inundation extents without relying on complex hydrodynamic models. We demonstrate the effectiveness of applying FIER over a large geographic extent using watershed boundaries to create individual FIER models and then mosaicking the results geographically to provide large flood inundation predictions. The Upper Mississippi Alluvial Plain in the United States was used as a test region. We evaluated multiple buffer sizes for watersheds for generating the data-driven FIER models to reduce edge effects along watershed boundaries when mosaicking the individual FIER implementations. The FIER method using watersheds, coupled with different forecast lead times from the National Water Model operational streamflow forecasts, was used to accurately predict the extent of surface water for select flood and low flow use cases. Our results show that the scaled FIER approach using watersheds yields higher accuracies for different error metrics, including the Structural Similarity Index Measure (SSIM), RMSE, and MAE. The metrics for the watershed-scaling approach resulted in SSIM ranging from 0.699–0.804, RMSE range of 7.15–8.60, and an MAE range of 1.09–1.88 compared to a baseline area with SSIM ranging from 0.643–0.693, RMSE range of 8.112–11.681, and an MAE range of 1.969–1.989. We found that scaling FIER using a watershed approach yielded statistically significant better performance compared to the baseline area: this is particularly true when using buffer sizes for the watersheds of 0–10 km and when applying a post-processing correction to the FIER outputs. This approach offers a promising solution for large-scale flood forecasting, particularly in data-scarce regions or ungauged basins. Future research will focus on refining the framework to incorporate additional hydrological variables and improve the accuracy of long-range flood inundation predictions.