4.7 Article

Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)

期刊

JOURNAL OF HYDROLOGY
卷 600, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.jhydrol.2021.126569

关键词

Remote sensing; Heavy precipitation; CONNECT algorithm; PERSIANN-CCS; MERRA-2

资金

  1. U.S. Department of Energy (DOE) [DE-IA0000018]
  2. California Energy Commission (CEC) [300-15-005]
  3. University of California [4600010378 TO15 Am 22]
  4. Maseeh Fellowship
  5. NOAA/NESDIS/NCDC [NA09NES4400006, 20091380-01]
  6. NOAA/NESDIS/NCDC (NCSU CICS)
  7. National Oceanic and Atmospheric Administration [ST133017CQ0058]
  8. Riverside Technology, Inc.
  9. NVIDIA Corporation
  10. office of the Vice-Chancellor for Research for Graduate Students, University of California, Irvine

向作者/读者索取更多资源

The study suggests that the increase in flood numbers in Iran during early spring is related to the increase in intensity and volume of heavy precipitation events. Atmospheric river conditions affect heavy precipitation events in Iran, with most of the atmospheric river pathways involving Africa and the Red Sea.
In recent years, the number of floods following unprecedented rainfall events have increased in Iran during early spring (March 21st to April 20th, referred to in Iran as the month of Farvadin). While numerous studies have addressed changes in climate extremes and precipitation trends at different temporal scales from daily to annual across the country, analyses of short-duration and heavy precipitation, especially during recent years, are rarely considered. Furthermore, most studies investigate the variations in extremes and total precipitation using a limited number of synoptic weather stations across Iran. This study assesses the variations in heavy precipitation (precipitation with intensities greater than or equal to 3 mm/3 h) at 0.04 degrees spatial and 3-hourly temporal resolution during the month of Farvardin. In addition, the effect of atmospheric river conditions over Iran and their possible link to intensifying heavy precipitation is explored. For this purpose, the CONNected-objECT (CONNECT) algorithm is applied on a precipitation dataset, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and an Integrated Water Vapor Transport (IVT) dataset from the NASA Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2). The results suggest that the increase in the number of floods in recent years is related to the increase in the intensity and volume of heavy precipitation events, although the frequency and duration of heavy precipitation events have not changed significantly. Furthermore, the results show that atmospheric river conditions over the country are present during the same window as each year's most extreme events. It is found that 8 out of 13 of the largest ARs over Iran come from moisture plumes with pathways over the African and Red Sea.

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