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)

JOURNAL OF HYDROLOGY

卷:600

Article

Sadeghi, Mojtaba¹  Shearer, Eric J.¹  Mosaffa, Hamidreza²  Gorooh, Vesta Afzali¹  Naeini, Matin Rahnamay¹  Hayatbini, Negin¹  Katiraie-Boroujerdy, Pari-Sima³  Analui, Bita¹  Nguyen, Phu¹  Sorooshian, Soroosh^1,4 

[1] Univ Calif Irvine, Dept Civil & Environm Engn, Henry Samueli Sch Engn, Ctr Hydrometeorol & Remote Sensing, Irvine, CA 92697 USA

[2] Shiraz Univ, Water Engn Dept, Shiraz 7196484334, Iran

[3] Islamic Azad Univ, Fac Marine Sci & Technol, Dept Meteorol, Tehran North Branch, Tehran 1651153311, Iran

[4] Univ Calif Irvine, Dept Earth Syst Sci, 3200 Croul Hall, Irvine, CA 92697 USA

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

DOI  :  10.1016/j.jhydrol.2021.126569

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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