Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models
Accurate estimates of reference evapotranspiration (ET) are of great significance to water resources planning and management, but the actual solar radiation (R), as the primary parameter for ET estimation, is difficult to obtain directly in most areas. Thus, studying the impacts of locally calibrated empirical solar radiation (R) models to improve the accuracy of Penman-Monteith (PM) is significant. Meanwhile, swarm intelligence algorithms have proved their potential in the domains of agriculture and hydrology, but few studies applied them in optimizing R models to improve ET estimation. This study used the particle swarm optimization (PSO), the gravitational search algorithm (GSA), and the mind evolutionary algorithm (MEA), respectively, to optimize the nine most common empirical R models, comprising three sunshine-based models (Angstrom, Ögelman, Bahel), three temperature-based models (Hargreaves, Bristow-Campbell, Hunt), and three combined-based models (Fan, Chen, El-Sebaii), and then integrated them into the PM for ET estimation at four climatic zones of China. The results showed that the Fan model obtained the most accurate R estimates in china, while the sunshine-based and temperature-based exerted significantly different applicability at different climatic zones. Regarding optimization algorithm, this study found that GSA performed better for the Ögelman model, Fan model, and Chen model when integrating into the PM equation for ET estimation, whereas MEA performed better for the Angstrom model, Hunt model, and El-Sebaii model. After optimization, PM obtained the most accurate estimates of ET at four climatic zones, with a regional spatial gradient in estimates accuracy of R from north to south of China. In terms of sunshine-based models, the PM performed better in TMZ, TCZ, and MPZ, whereas the PM performed better in SMZ, respectively; in terms of temperature-based models, the PM performed better in TMZ and SMZ, whereas the PM performed better in TCZ and MPZ, respectively. Overall, this study identifies the optimal R estimation model and optimization algorithm for ET estimation at four climatic zones, which provide regionally and nationally accurate water consumption information without actual measured R in China.
|WOS Research Area|
Engineering ; Geology ; Water Resources
Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:1
|Document Type||Journal Article|
|Department||School of Environmental Science and Engineering|
1.State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower,Sichuan University,Chengdu,610065,China
2.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518000,China
3.Center for Agricultural Water Research in China,China Agricultural University,Beijing,100091,China
4.State Engineering Laboratory of Efficient Water Use of Crops and Disaster Loss Mitigation,Institute of Environment and Sustainable Development in Agriculture,Chinese Academy of Agriculture Science,Beijing,100081,China
Xing，Liwen,Feng，Yu,Cui，Ningbo,et al. Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models[J]. Journal of Hydrology,2023,620.
Xing，Liwen.,Feng，Yu.,Cui，Ningbo.,Guo，Li.,Du，Taisheng.,...&Zhao，Lu.(2023).Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models.Journal of Hydrology,620.
Xing，Liwen,et al."Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models".Journal of Hydrology 620(2023).
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