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Spatial estimation of monthly mean daily sunshine hours and solar radiation across mainland China

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  • Wu, Wei
  • Tang, Xiao-Ping
  • Yang, Chao
  • Guo, Nai-Jia
  • Liu, Hong-Bin

Abstract

Spatial databases of climate data in digital format are required for many agricultural and eco-environmental systems. This study compared 7 approaches for interpolating monthly mean daily sunshine hours and solar radiation over mainland China. The approaches included simple geostatistical approaches to incorporation of Universal Transverse Mercator (UTM) coordinates and elevation. Performance indicators (root mean square error, mean absolute percentage error, and modeling efficiency) showed thin plate smoothing spline with UTM coordinates and elevation (TPS) outperformed other models. Besides, multiple linear regression equations for estimating solar radiation using geographical parameters (UTM coordinates and elevation) and sunshine hours predicted by TPS performed well for the study site. Spatial datasets of annual and monthly mean daily sunshine hours and solar radiation with 1 km resolution were then obtained by the best performance models. Spatial and temporal variability was clearly observed in sunshine hours and solar radiation. For both annual and seasonal scenarios, higher values of sunshine hours and solar radiation existed in north and Tibetan Plateau and lower values were observed in the middle and southern China. Lower values of annual solar radiation were also found in northeastern China. Sunshine hours and solar radiation varied with time, especially from spring to summer and from summer to autumn. The accurate gridded datasets are expected to provide significant information on more efficient use of natural resources.

Suggested Citation

  • Wu, Wei & Tang, Xiao-Ping & Yang, Chao & Guo, Nai-Jia & Liu, Hong-Bin, 2013. "Spatial estimation of monthly mean daily sunshine hours and solar radiation across mainland China," Renewable Energy, Elsevier, vol. 57(C), pages 546-553.
    • Handle: RePEc:eee:renene:v:57:y:2013:i:c:p:546-553
    DOI: 10.1016/j.renene.2013.02.027
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    References listed on IDEAS

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    1. Li, Mao-Fen & Fan, Li & Liu, Hong-Bin & Wu, Wei & Chen, Ji-Long, 2012. "Impact of time interval on the Ångström-Prescott coefficients and their interchangeability in estimating radiation," Renewable Energy, Elsevier, vol. 44(C), pages 431-438.
    2. Dhungana, P. & Eskridge, K.M. & Weiss, A. & Baenziger, P.S., 2006. "Designing crop technology for a future climate: An example using response surface methodology and the CERES-Wheat model," Agricultural Systems, Elsevier, vol. 87(1), pages 63-79, January.
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    Cited by:

    1. Ming, Zeng & Shaojie, Ouyang & Hui, Shi & Yujian, Ge, 2015. "Is the “Sun” still hot in China? The study of the present situation, problems and trends of the photovoltaic industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1224-1237.
    2. Soulis, Konstantinos X. & Manolakos, Dimitris & Ntavou, Erika & Kosmadakis, George, 2022. "A geospatial analysis approach for the operational assessment of solar ORC systems. Case study: Performance evaluation of a two-stage solar ORC engine in Greece," Renewable Energy, Elsevier, vol. 181(C), pages 116-128.
    3. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    4. Sompop Moonchai & Nawinda Chutsagulprom, 2020. "Semiparametric Semivariogram Modeling with a Scaling Criterion for Node Spacing: A Case Study of Solar Radiation Distribution in Thailand," Mathematics, MDPI, vol. 8(12), pages 1-16, December.

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