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Ahmadpari, Hedieh, Khaustov, Vitaly, Amini, Ata. (1404). Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions. سامانه مدیریت نشریات علمی, (), -. doi: 10.22098/mmws.2025.18810.1726
Hedieh Ahmadpari; Vitaly Khaustov; Ata Amini. "Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22098/mmws.2025.18810.1726
Ahmadpari, Hedieh, Khaustov, Vitaly, Amini, Ata. (1404). 'Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22098/mmws.2025.18810.1726
Ahmadpari, Hedieh, Khaustov, Vitaly, Amini, Ata. Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22098/mmws.2025.18810.1726

Development of multiple linear regression models for annual reference evapotranspiration estimation under limited data conditions

مدل سازی و مدیریت آب و خاک
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 22 آذر 1404
نوع مقاله: Special Issue: New Approaches to Water and Soil Management and Modeling
شناسه دیجیتال (DOI): 10.22098/mmws.2025.18810.1726
نویسندگان
Hedieh Ahmadpari1؛ Vitaly Khaustov2؛ Ata Amini* 3
1Ph.D. Candidate, Hydrology of Land, Water Resources, Hydrochemistry, Russian State Hydrometeorological University, Saint Petersburg, Russia
2Candidate of Technical Sciences, Associate Professor at the Department of Engineering Hydrology of the RSHU, Saint Petersburg, Russia
3Professor, Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
چکیده
Accurate estimation of reference evapotranspiration (ET₀) is essential for agricultural water management, particularly in regions with limited data availability. The aim of this study was to evaluate multiple linear regression (MLR) models to estimate ET₀ at the annual scale. Meteorological data from the Kuhdasht synoptic station, Iran for a 25-year period (1998–2022) were used. ET₀ was calculated using the FAO-56 Penman-Monteith method implemented through the CROPWAT 8.0 software. A total of 31 MLR models were developed using the Regression option from the Analysis ToolPak of Microsoft Excel 2019 to quantify the relationship between ET₀ and climatic variables. Seven statistical indices were used to evaluate the performance of the MLR models in estimating ET₀. Results showed that 16 models achieved very high accuracy, with coefficients of determination (R²) greater than 0.92. Among single-variable models, wind speed ing up to 92% of ET₀ variability. Several two-variable models achieved R² = 0.92–0.96, and most three-variable models reached R² = 0.93–0.97. Four-variable models also performed strongly (R² ≈ 0.95–0.97), while the five-variable model yielded R² ≈ 0.97, similar to simpler models. Wind speed emerged as the most influential factor, highlighting that well-chosen two- or three-variable models can estimate ET₀ as effectively as more complex alternatives.
کلیدواژه‌ها
Reference evapotranspiration؛ FAO-56 Penman–Monteith؛ Multiple Linear Regression؛ Wind speed
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تعداد مشاهده مقاله: 61


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