4.7 Article

Application of the GPM-IMERG Products in Flash Flood Warning: A Case Study in Yunnan, China

期刊

REMOTE SENSING
卷 12, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/rs12121954

关键词

flash flood; Integrated Multi-Satellite Retrievals for Global Precipitation Measurement; Rainfall Triggering Index; Yunnan

资金

  1. National Key R&D Program of China [2018YFC1508105]
  2. National Natural Science Foundation of China (NSFC) [41471430]
  3. Young Scientists Fund of the National Natural Science Foundation of China [51909052]
  4. Natural Science Foundation of Tianjin [18JCQNJC08800]
  5. Research on Key Technologies of Flash Flood Prevention Based on Multi-machine Learning Model [2019KJ086]
  6. National Natural Science Foundation of China [41901343]
  7. Key R&D Program of Ministry of Science and Technology [2018YFC1506500]
  8. State Key Laboratory of Resources and Environmental Information System
  9. China Postdoctoral Science Foundation [2018M630037, 2019T120021]
  10. Open Fund of the State Key Laboratory of Remote Sensing Science [OFSLRSS201909]
  11. Research on Flash Flood Warning Based on Machine Learning: A Case Study of Yunnan Province [52XB1903]

向作者/读者索取更多资源

NASA's Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is a major source of precipitation data, having a larger coverage, higher precision, and a higher spatiotemporal resolution than previous products, such as the Tropical Rainfall Measuring Mission (TRMM). However, there rarely has been an application of IMERG products in flash flood warnings. Taking Yunnan Province as the typical study area, this study first evaluated the accuracy of the near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-hourly temporal resolution. Then the performance of the two products was analyzed with the improved Rainfall Triggering Index (RTI) in the flash flood warning. Results show that (1) IMERG-F presents acceptable accuracy over the study area, with a relatively high hourly correlation coefficient of 0.46 and relative bias of 23.33% on the grid, which performs better than IMERG-E; and (2) when the RTI model is calibrated with the gauge data, the IMERG-F results matched well with the gauge data, indicating that it is viable to use MERG-F in flash flood warnings. However, as the flash flood occurrence increases, both gauge and IMERG-F data capture fewer flash flood events, and IMERG-F overestimates actual precipitation. Nevertheless, IMERG-F can capture more flood events than IMERG-E and can contribute to improving the accuracy of the flash flood warnings in Yunnan Province and other flood-prone areas.

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