Real-time forecasting and mapping flood extent from integrated hydrologic models and satellite remote sensing

This report outlines a real‑time forecasting and flood‑mapping system that integrates hydrologic models, hydraulic simulations and satellite remote sensing to assess a major 2021 flood event in the Turkey River and Upper Iowa River basins in rural Iowa. It examines why accurate flood risk reduction requires combining diverse data sources—radar‑based rainfall estimates, numerical weather prediction, streamflow observations and satellite imagery—and shows how uncertainties propagate through each stage of the forecasting chain. The paper highlights that satellite observations alone are insufficient for medium‑sized basins, as cloud cover, revisit times and spatial resolution limit their usefulness during peak flooding. Chapters analysing inundation mapping and model–satellite comparison focus directly on disaster risk reduction by demonstrating the operational constraints of remote sensing and the essential role of hydrologic and hydraulic modelling in anticipating flood extent and impacts. 

The study recommends strengthening early‑warning capability by improving rainfall forecasting skill, integrating satellite data with model‑based inundation mapping, and enhancing channel geometry information to reduce hydraulic model uncertainty. It emphasises that effective disaster risk reduction depends on combining real‑time modelling with whatever satellite data are available, using remote sensing primarily for validation rather than primary detection. The authors conclude that operational systems should prioritise multi‑source data fusion, automated modelling workflows and continuous refinement of hydrologic and hydraulic models to support emergency managers with more reliable, timely flood forecasts.