Empirical estimation of daily global solar radiation with contrasting seasons of rain and drought characterize over tropical China. (1st September 2020)

• Record Type: Journal Article
• Authors: Li, Mao-Fen; Guo, Peng-Tao; Dai, Shengpei; Luo, Hongxia; Liu, Enping; Li, Yuping
• Is Part Of: Journal of cleaner production. Volume 266(2020)
• Publication Date: 2020-09-01
• Subjects: Wet season -- dry season -- ANOVA -- Sunshine-based -- Temperature-based; ρ Spearman's rank correlation coefficient -- RMSE Root mean square error (MJ/m−2day−1) -- MBE Mean bias error (MJ/m−2day−1) -- R2 Coefficient of determination -- Hum Daily relative humidity(%) -- CRM Coefficient of residual mass -- RRMSE Relative root mean square error (%) -- ANOVA One-way analysis of variance; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.121915
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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 M1 (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.