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Empirical estimation of daily global solar radiation with contrasting seasons of rain and drought characterize over tropical China

文献类型： 外文期刊

作者： Li, Mao-Fen 1 ; Guo, Peng-Tao 2 ; Dai, Shengpei 1 ; Luo, Hongxia 1 ; Liu, Enping 1 ; Li, Yuping 1 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Inst Sci & Tech Informat, Key Lab Pract Res Trop Crops Informat Technol Hai, Haikou 571101, Hainan, Peoples R China

2.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Hainan, Peoples R China

关键词： Wet season; dry season; ANOVA; Sunshine-based; Temperature-based

期刊名称：JOURNAL OF CLEANER PRODUCTION （ 影响因子：9.297； 五年影响因子：9.444 ）

ISSN： 0959-6526

年卷期： 2020 年 266 卷

页码：

收录情况： SCI

摘要： Despite a number of publications which have used readily available meteorological data to estimate daily Rs in a tropical environment with distinct dry and wet seasons, relatively few studies have focused on the dry and wet seasons independently. In this study, we compared 11 widely used empirical Rs estimation models using routinely measured meteorological data from the period 1957-2013 between the wet season and the dry season at 11 locations in tropical China. In terms of 4 statistical evaluation indicators, the top 3 performing sunshine-based models were, in rank order, M4, M5, and M2 over the entire year and M5, M2, and Ml (A-P model) during the dry and wet seasons. Meanwhile, the top 3 performing temperature-based models were, in rank order, M11, M10, and M7 over the entire year, M9, M10, and M11 in the dry season, and M11, M9, and M8 in the wet season. A comparison of the reported results revealed that the empirical models calibrated in the dry season performed better than in other seasons. For the temperature-based models, it was essential to develop the M6 (H-S), M7, and M9 models in the dry and wet seasons independently, eventhough their coefficients exhibited little significant difference between the dry and wet seasons according to ANOVA (P <= 0.5). This study revealed that daily Rs estimations could be developed with high accuracy during the dry and wet seasons independently in regions with contrasting seasons of rain and drought using only temperature data. (C) 2020 Elsevier Ltd. All rights reserved.

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