Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data
Study region: Iran, characterized by diverse climatic conditions, including arid, semi-arid, and humid subtropical regions, where ET₀ dynamics vary significantly due to climatic differences. Study focus: Reference evapotranspiration (ET₀) is a fundamental component of hydrological modelling and play...
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Elsevier
2025-06-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825001685 |
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| author | Ali Asghar Zolfaghari Maryam Raeesi Giuseppe Longo-Minnolo Simona Consoli Miles Dyck |
| author_facet | Ali Asghar Zolfaghari Maryam Raeesi Giuseppe Longo-Minnolo Simona Consoli Miles Dyck |
| author_sort | Ali Asghar Zolfaghari |
| collection | DOAJ |
| description | Study region: Iran, characterized by diverse climatic conditions, including arid, semi-arid, and humid subtropical regions, where ET₀ dynamics vary significantly due to climatic differences. Study focus: Reference evapotranspiration (ET₀) is a fundamental component of hydrological modelling and plays a critical role in agricultural water management. Reliable ET₀ predictions are essential for optimizing irrigation systems and estimating water demand. This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET₀ across these diverse climatic conditions. Daily ET₀ values were calculated using the FAO-56 Penman-Monteith model and validated against ground-based observations from 50 weather stations (2008–2017). The RF model was trained using ERA5-Land climatic variables (air temperature, relative humidity, and ET₀ from ERA5-Land) along with the day of the year (DOY). New hydrological insights for the region: Results demonstrated a high correlation between ERA5-Land temperature estimates and observed station data (Pearson correlation coefficient, r = 0.97; Root Mean Square Error, RMSE = 2.77°C), while relative humidity showed a weaker agreement (Normalized Root Mean Square Error, NRMSE = 21 %). The RF model outperformed traditional approaches in arid and semi-arid regions, achieving NRMSE values of 25 % and 28 %, respectively, with a 60 % improvement over humid regions. At the 8-day scale, predictive accuracy improved further (RMSE = 6.05 mm/8 days, r = 0.99). Beyond model performance, this study provides new insights into the spatiotemporal variability of ET₀ across different climatic zones. The findings indicate that temperature is the dominant climatic factor driving ET₀ variability, while relative humidity exhibits higher uncertainty, particularly in humid regions. Seasonal trends highlight notable summer ET₀ peaks exceeding 30 mm/day in arid zones, emphasizing the need for climate-adaptive irrigation strategies. The proposed methodology is computationally efficient, requiring minimal input variables, and demonstrates robust and scalable performance for large-scale ET₀ estimation. These findings provide a cost-effective solution for water resource management, drought monitoring, and climate change adaptation, particularly in data-scarce regions. |
| format | Article |
| id | doaj-art-4bb352e638a846579bb839ee08deffff |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-4bb352e638a846579bb839ee08deffff2025-08-20T03:47:31ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910234310.1016/j.ejrh.2025.102343Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land dataAli Asghar Zolfaghari0Maryam Raeesi1Giuseppe Longo-Minnolo2Simona Consoli3Miles Dyck4Faculty of desert studies, Semnan University, Semnan, Iran; Corresponding author.Faculty of desert studies, Semnan University, Semnan, IranDepartment of Agriculture, Food and Environment (Di3A), University of Catania, Catania, ItalyDepartment of Agriculture, Food and Environment (Di3A), University of Catania, Catania, ItalyDepartment of Renewable Resources, University of Alberta, Edmonton, AB T6G 2R3, CanadaStudy region: Iran, characterized by diverse climatic conditions, including arid, semi-arid, and humid subtropical regions, where ET₀ dynamics vary significantly due to climatic differences. Study focus: Reference evapotranspiration (ET₀) is a fundamental component of hydrological modelling and plays a critical role in agricultural water management. Reliable ET₀ predictions are essential for optimizing irrigation systems and estimating water demand. This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET₀ across these diverse climatic conditions. Daily ET₀ values were calculated using the FAO-56 Penman-Monteith model and validated against ground-based observations from 50 weather stations (2008–2017). The RF model was trained using ERA5-Land climatic variables (air temperature, relative humidity, and ET₀ from ERA5-Land) along with the day of the year (DOY). New hydrological insights for the region: Results demonstrated a high correlation between ERA5-Land temperature estimates and observed station data (Pearson correlation coefficient, r = 0.97; Root Mean Square Error, RMSE = 2.77°C), while relative humidity showed a weaker agreement (Normalized Root Mean Square Error, NRMSE = 21 %). The RF model outperformed traditional approaches in arid and semi-arid regions, achieving NRMSE values of 25 % and 28 %, respectively, with a 60 % improvement over humid regions. At the 8-day scale, predictive accuracy improved further (RMSE = 6.05 mm/8 days, r = 0.99). Beyond model performance, this study provides new insights into the spatiotemporal variability of ET₀ across different climatic zones. The findings indicate that temperature is the dominant climatic factor driving ET₀ variability, while relative humidity exhibits higher uncertainty, particularly in humid regions. Seasonal trends highlight notable summer ET₀ peaks exceeding 30 mm/day in arid zones, emphasizing the need for climate-adaptive irrigation strategies. The proposed methodology is computationally efficient, requiring minimal input variables, and demonstrates robust and scalable performance for large-scale ET₀ estimation. These findings provide a cost-effective solution for water resource management, drought monitoring, and climate change adaptation, particularly in data-scarce regions.http://www.sciencedirect.com/science/article/pii/S2214581825001685ET0Machine Learning Algorithm (MLA)Reanalysis DatasetWater Management |
| spellingShingle | Ali Asghar Zolfaghari Maryam Raeesi Giuseppe Longo-Minnolo Simona Consoli Miles Dyck Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data Journal of Hydrology: Regional Studies ET0 Machine Learning Algorithm (MLA) Reanalysis Dataset Water Management |
| title | Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data |
| title_full | Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data |
| title_fullStr | Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data |
| title_full_unstemmed | Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data |
| title_short | Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data |
| title_sort | daily reference evapotranspiration prediction in iran a machine learning approach with era5 land data |
| topic | ET0 Machine Learning Algorithm (MLA) Reanalysis Dataset Water Management |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825001685 |
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