Investigating future projection of precipitation over Iraq using artificial neural network-based downscaling

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Keywords:

Climate change, CMIP5, Iraq, precipitation, ANN

Abstract

Global climate change will affect the precipitation and the temperature, and its effects need to be investigated. General circulation models (GCM) are one of the most used approaches to assessing the future effects of climate change. However, different GCMs have been proposed by researchers, and their success in the regions needs to be tested. Therefore, in this study, the performance of 29 GCMs in predicting precipitation in the Iraq region for 102 stations is evaluated using the artificial neural network-based statistical downscaling method. In order to evaluate the performance of these models, Nash Sutcliffe Model Efficiency Coefficient (NSE), normalized root mean square error (nRMSE), Kling-Gupta Efficiency (KGE), The Modified Index of Agreement (md), and Comprehensive Rating Index (CRI) are used. A comparison of the results shows that NorESM1-ME, FGOALS-g2, and NorESM1-M models performed well in estimating the historical precipitation of the region, and NorESM1-ME had the best representation. As a final step, future precipitation changes in Iraq were analyzed spatially and temporally under the RCP4.5 and RCP8.5 scenarios.

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Published

2024-01-20

How to Cite

Ibrahim, W. A., Gumus, V., & Seker, M. (2024). Investigating future projection of precipitation over Iraq using artificial neural network-based downscaling. Italian Journal of Agrometeorology, (2), 79–94. Retrieved from https://riviste.fupress.net/index.php/IJAm/article/view/1929

Issue

No. 2 (2023)

Section

RESEARCH ARTICLES

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Copyright (c) 2023 Wlat Abdulqader Ibrahim, Veysel Gumus, Mehmet Seker

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