Abstract
Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012–2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools.
Highlights
Floods are among the most catastrophic natural disasters globally in terms of impact on human life and the economy
We evaluated the skill of the satellite-based Global Flood Detection System (GFDS), the Moderate Resolution Imaging Spectroradiometer (MODIS) Flood Map, and the streamflow generated by the Global Flood Awareness System (GloFAS) model to monitor floods
The results showed that the GFDS and the GloFAS have different flood extent time-variations for different flood cases
Summary
Floods are among the most catastrophic natural disasters globally in terms of impact on human life and the economy. In light of mitigating the impacts, global scale flood risk reduction measures such as early warning systems (e.g., flood forecasting systems), and satellite-based real-time detection and monitoring tools have been developed in the last decade. To be used at their full potential, it is essential that end users can assess the reliability of these data services and processing systems in terms of whether they correctly represent the flood occurrence and characteristics on the ground. Any global-scale validation of these systems is hampered by the lack or limited availability of in situ streamflow in many areas of the world [10,11]. This constraint is even more acute for validation of realtime flood forecasts. From the 2692 global stations with some daily streamflow data available between 2011 and 2015 currently provided by the Global Runoff Data Centre[11], only ~0.2%
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