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Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran
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  • Akbar Rahmati,
  • Aydin Bakhtar,
  • Afshin Shayeghi,
  • Zahra Kalantari,
  • Alireza Massah Bavani,
  • Navid Ghajarnia
Akbar Rahmati
Department of Irrigation and Drainage Engineering
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Aydin Bakhtar
Department of Water Engineering, Urmia University, Urmia, Iran
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Afshin Shayeghi
Water Engineering Department, Imam Khomeini International University (IKIU), Qazvin, Iran
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Zahra Kalantari
Department of Sustainable Development, Environmental Science and Engineering (SEED), KTH Royal Institute of Technology, Stockholm, Sweden

Corresponding Author:zahrak@kth.se

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Alireza Massah Bavani
Department of Irrigation and Drainage Engineering, College of Abureyhan, University of Tehran, Iran
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Navid Ghajarnia
Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, SE-10691, Stockholm, Sweden
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Access to spatio-temporally consistent precipitation data is a key prerequisite for hydrological studies, especially in data-scarce regions. Different global precipitation products offer an alternative way to estimate precipitation over areas with inadequate gauge distributions. However, before use of the datasets, the accuracy of these global estimations must be carefully studied at local and regional scale. This study evaluated 14 global precipitation products against gauge observations 2003-2012 in Karun and Karkheh basins in southwest Iran. Different categorical and statistical indices, including Kling-Gupta Efficiency (KGE), bias, correlation coefficient, and variability ratio, at varying spatial and temporal resolution were used to evaluate the products. KGE results at both daily and monthly time steps suggested that TMPA-3B42V7.0 and MERRA-2 outperformed all other products, while CMORPH-BLDV1.0 and PERSIANN-CDR was the best-performing product at daily and monthly time steps, respectively. ERA5-Land showed the highest positive bias compared with in-situ observations, particularly for mountainous southeastern parts of Karun basin. Overall, bias-adjusted products obtained by merging ground-based observations in the estimations outperformed the unadjusted versions. The spatial distribution of statistical error metrics indicated that almost all products showed their greatest uncertainties for mountainous regions, due to complex precipitation processes in these regions. These results can significantly contribute to various horological and water resources planning measures in the study region, including early flood warning systems, drought monitoring, and optimal dam operations.