Estimating wheat production in west Iran using a simple water footprint approach.
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| Title: | Estimating wheat production in west Iran using a simple water footprint approach. |
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| Authors: | Ramezani Etedali, Hadi1 (AUTHOR) Ramezani@eng.ikiu.ac.ir, Kalanaki, Mahdi1,2 (AUTHOR), van Oel, Pieter2 (AUTHOR), Gorginpaveh, Faraz3 (AUTHOR) |
| Source: | Environment, Development & Sustainability. Jul2026, Vol. 28 Issue 7, p17335-17373. 39p. |
| Subject Terms: | *Water consumption, *Climate change models, *Supervised learning, *Atmospheric models, *Wheat farming, *Forecasting |
| Geographic Terms: | Iran |
| Abstract: | In this study, a simple approach for comparing future water footprints (WF) has been presented. Six General Circulation Models (GCMs) for three Representative Concentration Pathways (RCPs) were applied, during 1990–2019 (30 years). The LARS-WG model was used to calculate the different RCPs from the six GCM models for each of the ten selected locations in West Iran. Linear regression in the Python environment as a machine learning technique was applied to estimate future Wheat production for the preferred locations. Our model projections indicate that the blue and green WF could increase by an estimated 10–40 % by the year 2100. Concerning overall model performance, the BCC.CM1.1 and GISS-E2-R-CC models were not able to provide consistent results, while estimates of other models were quite accurate. Estimates for RCP 2.6 resulted in relatively higher values while estimates for RCP 8.5 resulted in relatively lower values. The lowest estimates for green and blue WF were found for Parsabad with RCP 2.6 with values of 99.7 and 2325.5 m3/ton respectively. The highest estimate for the green WF was found for Ilam with 718.8 m3/ton for RCP 8.5. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | In this study, a simple approach for comparing future water footprints (WF) has been presented. Six General Circulation Models (GCMs) for three Representative Concentration Pathways (RCPs) were applied, during 1990–2019 (30 years). The LARS-WG model was used to calculate the different RCPs from the six GCM models for each of the ten selected locations in West Iran. Linear regression in the Python environment as a machine learning technique was applied to estimate future Wheat production for the preferred locations. Our model projections indicate that the blue and green WF could increase by an estimated 10–40 % by the year 2100. Concerning overall model performance, the BCC.CM1.1 and GISS-E2-R-CC models were not able to provide consistent results, while estimates of other models were quite accurate. Estimates for RCP 2.6 resulted in relatively higher values while estimates for RCP 8.5 resulted in relatively lower values. The lowest estimates for green and blue WF were found for Parsabad with RCP 2.6 with values of 99.7 and 2325.5 m3/ton respectively. The highest estimate for the green WF was found for Ilam with 718.8 m3/ton for RCP 8.5. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 1387585X |
| DOI: | 10.1007/s10668-024-05605-2 |
