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

doi:10.1007/s40333-022-0108-7

Journal of Arid Land

2022, Vol. 14

Issue (12): 1361-1376 DOI: 10.1007/s40333-022-0108-7

Research article

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

Faraz GORGIN PAVEH¹^,^*(

), Hadi RAMEZANI ETEDALI², Brian COLLINS³

¹Syracuse University, Syracuse 13244, USA
²Imam Khomeini International University, Qazvin 34149-16818, Iran
³Centre for Crop Science, The University of Queensland, Brisbane 4072, Australia

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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.

Key words： climate variables crop variables meteorological datasets precipitation reference evapotranspiration irrigation requirement Iran

Received: 04 July 2022 Published: 31 December 2022

Corresponding Authors: ^*Faraz GORGIN PAVEH (E-mail: fgorginp@syr.edu)

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Faraz GORGIN PAVEH, Hadi RAMEZANI ETEDALI, Brian COLLINS. 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, 2022, 14(12): 1361-1376.

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http://jal.xjegi.com/10.1007/s40333-022-0108-7 OR http://jal.xjegi.com/Y2022/V14/I12/1361

Table 1 Information about the climate variables in Qazvin Province during study period

Fig. 1 Study area and the location of synoptic station used in the study

Table 2 Information about the grid resolution and company's name of the four datasets used in this study

Fig. 2 Comparison of minimum temperature estimated by Climatic Research Unit gridded Time Series (CRU TS), Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), and Agricultural Climate Forecast System Reanalysis (AgCFSR) using K1 (a) and K4 (b) with the data from the synoptic station in Qazvin Province during 1980-2009. K1 represents only the closest point of the datasets to the synoptic station was measured, and K4 represents the average of the four closest points of the datasets to the synoptic station was calculated.

Table 3 Comparison of minimum temperature, maximum temperature, and precipitation estimated by CRU TS, GPCC, AgMERRA, and AgCFSR with the data from the synoptic station in Qazvin Province during 1980-2009

Fig. 3 Comparison of maximum temperature estimated by CRU TS, AgMERRA, and AgCFSR using K1 (a) and K4 (b) with the data from the synoptic station in Qazvin Province during 1980-2009

Fig. 4 Comparison of precipitation estimated by CRU TS, Global Precipitation Climatology Centre (GPCC), AgMERRA, and AgCFSR using K1 (a) and K4 (b) with the data from the synoptic station in Qazvin Province during 1980-2009

Fig. 5 Comparison of reference evapotranspiration estimated by CRU TS, AgMERRA, and AgCFSR using K1 (a), K4 (b), and K8 (c) with the data from the synoptic station in Qazvin Province during 1980-2009. K8 represents the average of the eight closest points of the datasets to the synoptic station was calculated.

Table 4 Statistical evaluation of reference evapotranspiration, irrigation requirement, biomass, and yield estimated by CRU TS, GPCC, AgMERRA, and AgCFSR in Qazvin Province from 1980 to 2009

Fig. 6 Comparison of irrigation requirement estimated by CRU TS, GPCC, AgMERRA, and AgCFSR using K1 (a), K4 (b), and K8 (c) with the data from the synoptic station in Qazvin Province during 1980-2009

Fig. 7 Comparison of biomass estimated by CRU TS, GPCC, AgMERRA, and AgCFSR using K1 (a), K4 (b), and K8 (c) with the data from the synoptic station in Qazvin Province during1980-2009

Fig. 8 Comparison of yield estimated by CRU TS, GPCC, AgMERRA, and AgCFSR using K1 (a), K4 (b), and K8 (c) with the data from the synoptic station in Qazvin Province during 1980-2009


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