Abstract
Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis, and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the impact of using four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in Northern England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically and no product performed the best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data is available, but they should be used with caution. Until a more systematic international strategy for the collection of rainfall data ensures more complete global coverage of validation data, multiple reanalysis products should be used concurrently to capture the range of uncertainties.
Highlights
Climate Forecast System Reanalysis (CFSR) estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4%) and floodplain depth (28.6%)
Japanese Meteorological Agency reanalysis 55 (JRA-55) performed significantly worse than other reanalysis products across all of these aggregated measures
Reanalysis data has enabled flood risk assessments to be undertaken more widely. This analysis shows global or regional reanalysis data should not be considered as a replacement for local, high resolution, observations
Summary
The primary drivers of pluvial and fluvial flooding are precipitation events. The choice of precipitation data when simulating floods is critical. Inaccurate precipitation will undoubtedly lead to a spurious and potentially misleading understanding of the risk posed by a given event. This effect is further exacerbated when low-quality precipitation data is used to project risk into the future, with planning decisions being made based on the results. There is spatial variation in the availability and quality of precipitation data. High-quality data is often collected by national or regional authorities but can be inaccessible or difficult to obtain, continental or global precipitation datasets, such as reanalysis products, are a popular option despite their generally lower resolution and accuracy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
