This paper presents a comprehensive real-time forecasting and mapping cycle of a regional flood event, encompassing quantitative precipitation forecasting, runoff production and routing, and inundation mapping. The objective of this study is to highlight the significant uncertainties inherent in each step of the fully automated cycle, despite the utilization of state-of-the-art models and remote sensing technologies. The case study focuses on a significant flood event that occurred in the Turkey River and Upper Iowa River, in rural Iowa, United States, resulting in localized damage and disruption to several small communities. The novelty of this study is that it demonstrates the limited utility of satellite-based remote sensing in the absence of other forecasting and mapping system elements, emphasizing the need for the timely integration of information from diverse sources to accurately forecast and map floods. To achieve this, we assembled and analyzed precipitation data from weather radars, streamflow estimates derived from river stages and rating curves, and cross-sectional data from river channels to characterize the movement of the flood wave. These data were integrated into hydrologic and hydraulic models to generate flood inundation estimates for the more severely affected areas. Remote sensing imagery was obtained and used as reference to assess the accuracy of the modeled inundated areas. Our findings illustrate that, despite the increasing availability of satellite data sources, there are still significant limitations to tracking inundation using satellite remote sensing, particularly for medium-sized basins. Flood modeling processes are not merely complementary to satellite-based flood estimation, but essential for comprehensive flood risk assessment.
Krajewski et al. (Thu,) studied this question.