The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt

The Science of the Total Environment
Ahmed ElbeltagiJinsong Deng

Abstract

Spatial-temporal information of different water resources is essential to rationally manage, sustainably develop, and optimally utilize water. This study focused on simulating future water footprint (WF) of two agronomically important crops (i.e., wheat and maize) using deep neural networks (DNN) method in Nile delta. DNN model was calibrated and validated by using 2006-2014 and 2015-2017 datasets. Moreover, future data (2022-2040) were obtained from three Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, and incorporated into DNN prediction set. The findings showed that determination-coefficient between historical-predicted crop evapotranspiration (ETc) varied from 0.92 to 0.97 for two crops. The yield prediction values of wheat-maize deviated within the ranges of -3.21% to 3.47% and -4.93% to 5.88%, respectively. Based on the ensemble of RCP, precipitation was forecasted to decease by 667.40% and 261.73% in winter and summer in western as compared to eastern, respectively, which will ultimately be dropped to 105.02% and 60.87%, respectively parallel to historical. Therefore, the substantial fluctuations in precipitation caused an obvious decrease in green WF of wheat (i.e., 24.96% and 37.44%) in western and easte...Continue Reading

References

Mar 24, 2016·Journal of the Science of Food and Agriculture·Lijie QinHongshi He
Jul 25, 2017·Scientific Reports·Naga Manohar Velpuri, Gabriel B Senay
Nov 8, 2017·The Science of the Total Environment·Mohammad Badrul MasudMonireh Faramarzi
Jan 27, 2019·The Science of the Total Environment·Pasquale GarofaloMirko Castellini

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Citations

Oct 22, 2021·Environmental Science and Pollution Research International·Ahmed ElbeltagiAbu Reza Md Towfiqul Islam

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