Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

Article


Paveh, Faraz Gorgin, Ramezani-Etedali, Hadi and Collins, Brian. 2022. "Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran." Journal of Arid Land. 14 (12), pp. 1361-1376. https://doi.org/10.1007/s40333-022-0108-7
Article Title

Evaluation of CRU TS, GPCC, AgMERRA, and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province, Iran

ERA Journal ID210794
Article CategoryArticle
AuthorsPaveh, Faraz Gorgin, Ramezani-Etedali, Hadi and Collins, Brian
Journal TitleJournal of Arid Land
Journal Citation14 (12), pp. 1361-1376
Number of Pages16
Year2022
PublisherSpringer
Place of PublicationChina
ISSN1674-6767
2194-7783
Digital Object Identifier (DOI)https://doi.org/10.1007/s40333-022-0108-7
Web Address (URL)https://link.springer.com/article/10.1007/s40333-022-0108-7
Abstract

In the past few decades, meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers. Based on the literature, meteorological datasets are not more accurate than synoptic stations, but their various advantages, such as spatial coverage, time coverage, accessibility, and free use, have made these techniques superior, and sometimes we can use them instead of synoptic stations. In this study, we used four meteorological datasets, including Climatic Research Unit gridded Time Series (CRU TS), Global Precipitation Climatology Centre (GPCC), Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), Agricultural Climate Forecast System Reanalysis (AgCFSR), to estimate climate variables, i.e., precipitation, maximum temperature, and minimum temperature, and crop variables, i.e., reference evapotranspiration, irrigation requirement, biomass, and yield of maize, in Qazvin Province of Iran during 1980–2009. At first, data were gathered from the four meteorological datasets and synoptic station in this province, and climate variables were calculated. Then, after using the AquaCrop model to calculate the crop variables, we compared the results of the synoptic station and meteorological datasets. All the four meteorological datasets showed strong performance for estimating climate variables. AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature. However, their normalized root mean square error was inferior to CRU for minimum temperature. Furthermore, they were all very efficient for estimating the biomass and yield of maize in this province. For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR. But for the estimation of biomass and yield, all the four meteorological datasets were reliable. To sum up, GPCC and AgCFSR were the two best datasets in this study. This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.

Article Publishing Charge (APC) FundingOther
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204101. Climate change impacts and adaptation
300499. Crop and pasture production not elsewhere classified
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Byline AffiliationsSyracuse University, United States
Imam Khomeini International University, Iran
University of Queensland
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