不完全气象指标下参考作物蒸散量估算方法研究

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

作者信息

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

机构信息

College of Agronomy, Hebei Agricultural University, Baoding, Hebei, China.

Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

出版信息

Front Plant Sci. 2024 Jul 8;15:1354913. doi: 10.3389/fpls.2024.1354913. eCollection 2024.

DOI:10.3389/fpls.2024.1354913

PMID:39040513

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11260813/

Abstract

BACKGROUND

Accurate estimation of reference crop evapotranspiration (ET) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET, 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 ET in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET 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 (R) 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, R 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 ET in the absence of some meteorological indicators.

摘要

背景

准确估算参考作物蒸散量（ET）对于农田水文学、作物需水量以及精准灌溉决策至关重要。彭曼-蒙特斯（PM）模型在估算ET方面具有较高的准确性，但它需要许多不常见的气象数据输入。因此，需要一种理想的方法，在不影响估算准确性的前提下尽量减少输入数据变量的数量。本研究旨在分析在缺少某些气象指标的情况下各种ET估算方法的性能。以在ET估算方面具有高精度而闻名的彭曼-蒙特斯（PM）模型，作为气象指标充足条件下的标准值。对普里斯特利-泰勒（PT）、哈格里夫斯（H-A）、麦克劳德（M-C）和粮农组织-24辐射（F-R）模型进行了对比分析。采用贝叶斯估计方法改进ET估算模型。

结果

结果表明，与PM模型相比，在气象指标不足的情况下，F-R模型表现最佳。它在日、月和旬尺度上显示出更高的平均相关系数（R）：分别为0.841、0.937和0.914。相应的均方根误差（RMSE）分别为1.745、1.329和1.423，平均绝对误差（MAE）分别为1.340、1.159和1.196，威尔莫特指数（WI）值分别为0.843、0.862和0.859。经过贝叶斯校正后，R值保持不变，但RMSE显著降低，在日、月和旬尺度上平均分别降低了15.81%、29.51%和24.66%。同样，MAE显著下降，平均分别降低了19.04%、34.47%和28.52%，WI有所改善，平均分别提高了5.49%、8.48%和10.78%。