Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators

Area of Science:

• Hydrology
• Agronomy
• Environmental Science

Background:

• Accurate estimation of reference crop evapotranspiration (ET0) is vital for water management in agriculture.
• The Penman-Monteith (PM) model is accurate but requires extensive meteorological data.
• There is a need for simpler ET0 estimation methods with fewer input variables.

Purpose of the Study:

• To evaluate various ET0 estimation models under limited meteorological data conditions.
• To compare the performance of Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models against the PM model.
• To assess the effectiveness of Bayesian estimation in improving ET0 model accuracy.

Main Methods:

• Comparative analysis of PT, H-A, M-C, and F-R models against the standard PM model.
• Utilizing daily, monthly, and 10-day scale data for model evaluation.
• Applying Bayesian estimation techniques to refine ET0 predictions.

Main Results:

• The F-R model demonstrated the best performance among the tested models with limited data.
• F-R model achieved high correlation coefficients (R2) and acceptable errors (RMSE, MAE) across different time scales.
• Bayesian correction significantly reduced RMSE and MAE while improving the Willmott's Index (WI) for the F-R model.

Conclusions:

• The FAO-24 Radiation (F-R) model is a reliable alternative for ET0 estimation when meteorological data is scarce.
• Bayesian-enhanced F-R model offers improved accuracy for ET0 estimation under data-limited scenarios.
• This approach supports informed decisions in farmland hydrology and precision irrigation.