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

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.

Keywords

• crop growth
• precipitation
• multi-crop model
• crop pattern
• cereal