Evaluation of meteorological datasets in estimating the water footprint components of wheat and maize (case study: Qazvin, Iran)

Article


Ramezani-Etedali, Hadi, Gorginpaveh, Faraz, Kakvand, Parisa, Elbeltagi, Ahmed and Collins, Brian. 2024. "Evaluation of meteorological datasets in estimating the water footprint components of wheat and maize (case study: Qazvin, Iran)." AIMS Agriculture and Food. 9 (1), pp. 84-107. https://doi.org/10.3934/agrfood.2024006
Article Title

Evaluation of meteorological datasets in estimating the water footprint components of wheat and maize (case study: Qazvin, Iran)

ERA Journal ID211680
Article CategoryArticle
AuthorsRamezani-Etedali, Hadi, Gorginpaveh, Faraz, Kakvand, Parisa, Elbeltagi, Ahmed and Collins, Brian
Journal TitleAIMS Agriculture and Food
Journal Citation9 (1), pp. 84-107
Number of Pages24
Year2024
PublisherAIMS Press
Place of PublicationUnited States
ISSN2471-2086
Digital Object Identifier (DOI)https://doi.org/10.3934/agrfood.2024006
Web Address (URL)https://www.aimspress.com/aimsagri/article/2024/1/archive-articles
Abstract

Given the critical role of precise meteorological parameter estimation in water resources management, particularly concerning the water footprint (WF) concept and considering the scarcity of data, this study utilized thirty years of data from four meteorological datasets to estimate the WF of two main cereals, wheat and maize, in Qazvin province, Iran. AquaCrop was used to calculate the WF parameters based on a synoptic station and the closest datasets to the synoptic station. Coefficient of determination (R2), root-mean-square deviation (RMSE) and its normalization (NRMSE), and maximum error (ME) were used to compare the results. The results showed that these datasets efficiently estimate the WF components and can be used instead of synoptic stations. Also, all datasets were more efficient in estimating the green WF than the blue WF. The Global Precipitation Climatology Center (GPCC) dataset was the most efficient dataset in assessing the WF components for wheat, where the RMSE and NRMSE were 84.8 m3/ton and 17.18%. These amounts were 55.1 m3/ton and 12.96% for the green WF. For estimating the blue WF of maize, the Climatic Research Unit (CRU) datasets were the most efficient datasets in assessing the WF components of maize, which were 35.58 m3/ton and 15.91%. This study demonstrated the robustness of meteorological datasets in accurately estimating the components of the WF. Furthermore, the study advocates for the utilization of diverse datasets in estimating meteorological and crop parameters, recommending this approach for different crops across various regions.

Keywordscrop growth; cereal; el, crop pattern; multi-crop model; precipitation
Article Publishing Charge (APC) FundingOther
ANZSRC Field of Research 2020400513. Water resources engineering
Byline AffiliationsImam Khomeini International University, Iran
Syracuse University, United States
University of Tehran, Iran
Mansoura University, Egypt
Institute for Life Sciences and the Environment
Centre for Sustainable Agricultural Systems
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