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Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran
Czech Univ Life Sci Prague, Fac Environm Sci, Praha Suchdol 16500, Czech Republic..
Urmia Univ, Dept Water Engn, Orumiyeh, Iran..
Imam Khomeini Int Univ IKIU, Water Engn Dept, Qazvin, Iran..
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Water and Environmental Engineering.ORCID iD: 0000-0002-7978-0040
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2022 (English)In: Journal of Hydrology: Regional Studies, E-ISSN 2214-5818, Vol. 44, p. 101269-, article id 101269Article in journal (Refereed) Published
Abstract [en]

Study region: Karkheh and Karun basins (29 degrees-35 degrees N, 46 degrees-52 degrees E) are two large river basins (area 51,000 and 67,000 km2, respectively) with complex topography in southwest Iran.Study focus: Access to spatio-temporally consistent precipitation data is a key requirement for hydrological studies, especially in data-scarce regions. This study evaluated 14 global precipi-tation products against gauge observations from 2003 to 2012 in Karun and Karkheh basins, southwest Iran. Different categorical and statistical indices at varying spatial and temporal res-olution, including Kling-Gupta Efficiency (KGE), bias, correlation coefficient, and variability ratio, were used to evaluate the products.New hydrological insights for the region: For daily time steps, TMPA-3B42V7.0, MERRA-2, and CMORPH-BLDV1.0 outperformed all other products, with KGE > 0.3. TMPA-3B42V7.0, MERRA-2, and PERSIANN-CDR were the best-performing products for monthly time steps, with KGE> 0.5. ERA5-Land showed the highest positive bias (bias>1.5) 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 pro-cesses in these regions. These results can contribute significantly to hydrological and water re-sources planning measures in the study region, including early flood warning systems, drought monitoring, and optimization of dam operation.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 44, p. 101269-, article id 101269
Keywords [en]
Global precipitation estimation products, Spatio-temporal performance evaluation, Statistical error analysis, Categorical index, Iran
National Category
Water Engineering
Identifiers
URN: urn:nbn:se:kth:diva-322805DOI: 10.1016/j.ejrh.2022.101269ISI: 000894507800002Scopus ID: 2-s2.0-85144022776OAI: oai:DiVA.org:kth-322805DiVA, id: diva2:1725701
Note

QC 20230214

Available from: 2023-01-11 Created: 2023-01-11 Last updated: 2023-02-14Bibliographically approved

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Kalantari, Zahra

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