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

文献类型： 外文期刊

第一作者： Sun, Xuguang

作者： Sun, Xuguang;Zhang, Baoyuan;Gao, Ruocheng;Gu, Limin;Zhen, Wenchao;Sun, Xuguang;Zhang, Baoyuan;Dai, Menglei;Ma, Kai;Gu, Xiaohe;Dai, Menglei;Jing, Cuijiao;Gu, Limin;Zhen, Wenchao;Gu, Shubo;Gu, Shubo;Zhen, Wenchao

作者机构：

关键词： reference crop evapotranspiration; Penman-Monteith; FAO-24 radiation; meteorological indicators; Bayesian estimation

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.1； 五年影响因子：5.3 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Background Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model.Results Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively.Conclusion Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

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