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Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq

This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corre...

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Main Authors: Zeri, Sarah Jabbar, Hamed, Mohammed Magdy, Wang, Xiaojun, Shahid, Shamsuddin
Format: Article
Language:English
Published: MDPI 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/107562/1/ShamsuddinShahid2023_UtilizingSatelliteDatatoEstablishRainfall.pdf
http://eprints.utm.my/107562/
http://dx.doi.org/10.3390/w15050852
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spelling my.utm.1075622024-09-23T06:13:47Z http://eprints.utm.my/107562/ Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq Zeri, Sarah Jabbar Hamed, Mohammed Magdy Wang, Xiaojun Shahid, Shamsuddin TA Engineering (General). Civil engineering (General) This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corrected (GSMaP GC) satellite precipitation datasets. Many probability distribution functions were used to fit the maximum yearly rainfall data. The Sherman equation was used to create intensity-duration-frequency (IDF) curves for rainfall intensities with 2-, 5-, 10-, 25-, 50-, and 100-year return periods, with the estimated coefficients of the best-fit distribution serving as the fitting parameters. The discrepancy between the IDF curves produced from the satellites and the observed data was used to bias correct the satellite IDF curves. The Generalized Extreme Value Distribution model best describes the hourly rainfall distribution of satellite data. GSMaP GC was the best option for creating IDF curves with higher correlations with observed data at Baghdad, Basra, and Mosul. The study indicates the necessity of gauge correction of satellite rainfall data to reduce under- and over-estimating observed rainfall. GSMaP GC can reasonably estimate rainfall in a predominantly arid climate region like Iraq. The generated IDF curves may be an important step toward achieving sustainable urban stormwater management in the country. MDPI 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/107562/1/ShamsuddinShahid2023_UtilizingSatelliteDatatoEstablishRainfall.pdf Zeri, Sarah Jabbar and Hamed, Mohammed Magdy and Wang, Xiaojun and Shahid, Shamsuddin (2023) Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq. Water, 15 (5). pp. 1-22. ISSN 2073-4441 http://dx.doi.org/10.3390/w15050852 DOI:10.3390/w15050852
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Zeri, Sarah Jabbar
Hamed, Mohammed Magdy
Wang, Xiaojun
Shahid, Shamsuddin
Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq
description This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corrected (GSMaP GC) satellite precipitation datasets. Many probability distribution functions were used to fit the maximum yearly rainfall data. The Sherman equation was used to create intensity-duration-frequency (IDF) curves for rainfall intensities with 2-, 5-, 10-, 25-, 50-, and 100-year return periods, with the estimated coefficients of the best-fit distribution serving as the fitting parameters. The discrepancy between the IDF curves produced from the satellites and the observed data was used to bias correct the satellite IDF curves. The Generalized Extreme Value Distribution model best describes the hourly rainfall distribution of satellite data. GSMaP GC was the best option for creating IDF curves with higher correlations with observed data at Baghdad, Basra, and Mosul. The study indicates the necessity of gauge correction of satellite rainfall data to reduce under- and over-estimating observed rainfall. GSMaP GC can reasonably estimate rainfall in a predominantly arid climate region like Iraq. The generated IDF curves may be an important step toward achieving sustainable urban stormwater management in the country.
format Article
author Zeri, Sarah Jabbar
Hamed, Mohammed Magdy
Wang, Xiaojun
Shahid, Shamsuddin
author_facet Zeri, Sarah Jabbar
Hamed, Mohammed Magdy
Wang, Xiaojun
Shahid, Shamsuddin
author_sort Zeri, Sarah Jabbar
title Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq
title_short Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq
title_full Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq
title_fullStr Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq
title_full_unstemmed Utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in Iraq
title_sort utilizing satellite data to establish rainfall intensity-duration-frequency curves for major cities in iraq
publisher MDPI
publishDate 2023
url http://eprints.utm.my/107562/1/ShamsuddinShahid2023_UtilizingSatelliteDatatoEstablishRainfall.pdf
http://eprints.utm.my/107562/
http://dx.doi.org/10.3390/w15050852
_version_ 1811681222564249600
score 13.211869

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