article · Environmental Research Letters
Global Flood Models and Earth Observation are essential for understanding flood hazards in data-sparse regions, but validation is often constrained to limited historical events. A method termed Flood Expectation Per Pixel compares twenty years of MODIS satellite observations with simulated hazards from a Global Flood Model across four major African river basins: the Congo, Niger, Nile, and Volta. The analysis accounts for observation uncertainties such as cloud cover, vegetation, and burned areas. At return periods under twenty years, satellite data records less flooding than the model predicts, indicating that models tend to overpredict frequent events. Consistency between model outputs and satellite records improves for return periods between fifty and one hundred years. For events exceeding one hundred years, observations record more flooding than expected. Combining satellite observations and flood models offers a complementary approach to improve flood mitigation planning.
Reliable flood hazard data is vital for decision-makers seeking to mitigate disaster risk, particularly in regions where ground-level data is sparse. Comparing long-term satellite records with global flood models identifies where simulations are dependable and where they diverge. This helps planners understand model strengths across different event frequencies, supporting better-targeted flood risk mitigation policies.
This research presents an applied analytical method to validate flood hazard models using satellite data, relevant to disaster risk managers, civil protection agencies, and flood mitigation planners. The approach is tested across four major river basins, demonstrating applied research ready for operational testing by risk assessment bodies. While not an off-the-shelf software product, the methodology can guide organisations in combining satellite and modelled data to refine flood hazard mapping and infrastructure planning.
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Global Flood Models (GFMs) and Earth Observation (EO) play a crucial role in characterising flooding, especially in data-sparse, under-resourced regions of the world. However, validation studies are often limited to a handful of historic events and do not directly assess the ability of these products to simulate flood hazard - the probability that flooding will occur in a given location. As a result, it is difficult for stakeholders to decipher the ability of either models or observations to identify flood hazard and make decisions to mitigate for flooding. Here, we leverage flood observations from 20 years of MODIS data to compare the recorded flooding with what would be expected given the hazard simulated by a GFM. We devise an approach, Flood Expectation Per Pixel, and apply it across four large basins in Africa – Congo, Niger, Nile and Volta representing a variety of biomes. We estimate the uncertainty of EO to capture flood events due to burned areas, cloud cover and vegetation, incorporating uncertainty estimates when comparing to modelled hazard. We found that at lower return periods (<20 years), the EO data records less flooding than the GFM, suggesting GFMs overpredict frequent flooding. For return periods between 50-100 years), GFM and EO data show greater consistency given the uncertainties we consider. For large return periods (100 years) the EO observations show more flooding than expected given the GFM data, potentially due to data errors and non-fluvial flooding, however there are too few observations to draw significant conclusions at these return periods. The EO record indicates that the GFM can differentiate between flood return periods. We find EO and GFM complement each other and thus should be used in tandem to inform strategies to mitigate floods across the hazard spectrum from frequent to extreme flood events.
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DOI: 10.1088/1748-9326/abc216
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