Preprints
https://doi.org/10.5194/essd-2022-242
https://doi.org/10.5194/essd-2022-242
 
01 Aug 2022
01 Aug 2022
Status: this preprint is currently under review for the journal ESSD.

Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation

Jianxin Zhang1,2, Kai Liu1, and Ming Wang1 Jianxin Zhang et al.
  • 1School of National Safety and Emergency Management, Beijing Normal University, 100875 Beijing, China
  • 2School of Systems Science, Beijing Normal University, 100875 Beijing, China

Abstract. A complete global flood event record helps researchers analyse the distribution law of global floods and better formulate and manage disaster prevention and reduction policies. This study used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days quasi-globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that the floods extracted in this study can cover 81 % of the flood events in the DFO database and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection exceeding 0.5 reached 62 % in level-4 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60° S–60° N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.

Jianxin Zhang et al.

Status: open (until 26 Sep 2022)

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Jianxin Zhang et al.

Data sets

Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation Jianxin Zhang, Kai Liu, Ming Wang https://doi.org/10.5281/zenodo.6831384

Jianxin Zhang et al.

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Short summary
This study provides, for the first time, quasi-global flood day data obtained based on gravity satellite and precipitation data, compensating for the lack of a global flood database based on remote sensing observation data from Apr. 1st, 2002, to Aug. 31st, 2016. It is an important data foundation for analyzing the spatiotemporal distribution of large-scale floods and exploring the impact of Ocean-atmosphere oscillations on floods in different regions.