Coefficient correction of Ångström–Prescott equation for China and its influence on solar radiation and reference crop evapotranspiration at different temporal and spatial scales

Peng, Zhigong; Chen, He; Wang, Zheng; Zhang, Baozhong; Zhang, Suyang; Gong, Liqin; Cai, Jiabing; Li, Wangcheng; Zhang, Qian

doi:10.1016/j.jclepro.2022.134013

Cited by 1 publication

(2 citation statements)

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“…The optimal machine‐learning algorithm was then used to estimate the relevant time–scale A‐P coefficients at the 839 weather stations without R s measurements. The Kriging method was selected to interpolate the 839–site A‐P coefficients (Peng et al, 2022). Additionally, the four machine learning algorithms were also directly used to establish the ET 0 estimation (ML‐ET 0 ) models based on the above variables and been compared with the PM‐ET 0 .…”

Section: Methodsmentioning

confidence: 99%

“…Unfortunately, Sabziparvar et al (2013) and Liu et al (2014) have reported remarkable spatial variations in the A‐P coefficients that can cause significant errors in regional and national R s and ET 0 estimates. Thus, calibrating the A‐P coefficients with actual local R s measurements is essential for minimizing R s errors (Peng et al, 2022). In addition to the spatial variation, temporal fluctuation of the A‐P coefficients has also attracted the attention of researchers (Liu, Mei, Li, Zhang, Wang, et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Chen,

Feng,

et al. 2024

Hydrological Processes

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Accurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements.

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Section: Methodsmentioning

confidence: 99%