Modeling and analysis of the spatiotemporal variations of photosynthetically active radiation in China during 1961–2012

Wang, Lunche; Gong, Wei; Hu, Bo; Lin, Aiming; Li, Hui

doi:10.1016/j.rser.2015.04.174

Cited by 29 publications

(15 citation statements)

References 51 publications

(79 reference statements)

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“…Some radiation modelling studies have been carried out around the world, for example, Karakoti et al () tried to evaluate seven empirical models at 23 sites in India and the results showed that the input combinations of sunshine, temperature and relative humidity gave more accurate estimations; Hassan et al () presented some temperature‐based models for solar radiation study in different locations in Egypt. Unfortunately, direct observations of PAR and its components have not been conducted at meteorological and radiometric stations and no data are available for many regions around the world up to now, especially for PAR d (Wang et al , ; ; Cruse et al , ). For this reason, the direct and diffuse PAR components have to be calculated from global solar radiation or diffuse radiation; the model accuracies may not be so good and rely greatly on the input data (Alados et al , ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of diffuse photosynthetically active radiation using different soft computing techniques

Wang

Kisi

et al. 2017

Quart J Royal Meteoro Soc

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Knowledge of diffuse photosynthetically active radiation (PARd) is important for many applications dealing with climate change, environmental engineering and terrestrial productivity. It is necessary to estimate the PARd using different techniques due to the absence of direct observations of this radiometric flux in most parts of the world. In this study, Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) with grid partition (ANFIS‐GP), ANFIS with subtractive clustering (ANFIS‐SC) and M5 model tree (M5Tree) are optimized and applied for predicting hourly PARd in different ecosystems. Hourly climatic variables at six stations from the AmeriFlux network are used for training, validating and testing the above models. It is observed that the mean bias error (MBE) values range −11 to +18%, −16 to +9% and −10 to +9% for ANFIS‐GP, ANFIS‐SC and M5Tree models, respectively; root mean square errors (RMSE) range 25–44%, 22–41% and 29–51%, respectively for different stations in the testing period. The results show that the ANFIS‐SC model can generally bring more accurate estimations than other models, and the statistical indices (MBE and RMSE) at CA_Gro stations (mixed forests) are lower than other stations for each model. The models underestimate some high PARd values (>900 µmol m−2 s−1) for some stations, which may due to the differences of data ranges and distributions. This study will lay the foundation for accurately mapping regional and global distributions of PARd and its associated ecological applications.

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Section: Introductionmentioning

confidence: 99%

Prediction of diffuse photosynthetically active radiation using different soft computing techniques

Wang

Kisi

et al. 2017

Quart J Royal Meteoro Soc

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“…Wang et al . () revealed the spatial distribution and variation trends in the PAR from an empirical estimation method based on measured global solar radiation ( R s ) from 115 radiation observation sites of the China Meteorological Administration (CMA), and then, geographic information technology (spatial statistics and analysis methods) was used to improve the spatial resolution of this parameter. Zhu et al .…”

Section: Introductionmentioning

confidence: 99%

“…Spatial distribution and long-term trends have been reported in many early studies; for example, the PAR in Europe has been discussed by Leuchner et al (2011). Wang et al (2015b) revealed the spatial distribution and variation trends in the PAR from an empirical estimation method based on measured global solar radiation (R s ) from 115 radiation observation sites of the China Meteorological Administration (CMA), and then, geographic information technology (spatial statistics and analysis methods) was used to improve the spatial resolution of this parameter. Zhu et al (2010) reported the variability in PAR for eight different regions of China based on an empirical model that used sunshine duration and 4 years of observational data; however, the performance of the estimation model and the reasons for the spatial distribution and long-term trends were not evaluated.…”

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Trends of photosynthetically active radiation over China from 1961 to 2014

Tang

Liu

et al. 2018

Intl Journal of Climatology

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The aim of this study was to reveal the long‐term variation properties of photosynthetically active radiation (PAR) over China. A series of reliable empirical PAR estimation equations suitable for all‐sky conditions were developed using data measured in situ from 2005 to 2014, which were obtained from the Chinese Ecosystem Research Network (CERN). These equations were then combined with a hybrid model to reconstruct the PAR from 1961 to 2014 for 724 weather stations belonging to the China Meteorological Administration (CMA). There was a decreasing linear trend in the PAR (with an amplitude of −0.009 mol m−2 day−1 year−1) from 1961 to 1981 and a steady low level of −0.003 mol m−2 day−1 year−1 during 1981–2014 for all of China. The PAR over the Tibetan Plateau (TP) region increased from 1961 to 1984; then, this linear trend slightly changed. The breakpoint years for the northwest China (NWC), southwest China (SWC), north China (NC), northeast China (NEC) and east China (EC) regions were 1985, 1981, 1996, 1998 and 1979, respectively. In addition, the aerosol optical depth, ozone column concentration, cloud cover and water vapour content attenuated the PAR that would otherwise have reached the earth’s surface by approximately 7.59, 1.12, 18.13 and 6.20%, respectively.

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“…The large-scale utilization of solar energy in buildings is one of the most promising technologies to solve the global energy shortage problem and reduce the carbon dioxide (CO 2 ) emissions. Sun being the ultimate source of energy, plays an important role in driving the atmospheric heat engine to operate and sustain the atmospheric processes and to control the entire activity of all living organisms on earth and is also necessary in building energy reproduction and assessment [1][2][3][4][5][6][7][8][9][10][11][12][13]. The radiation scattered by atmospheric constituents is called diffuse radiation where~6% of this radiation goes back to space, and about 20% of the incident radiation reach the earth's surface [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study of Ground Measurement and Satellite‐Derived Data of Global and Diffuse Radiation

Mahima

Karakoti

Pathak

et al. 2018

Env Prog and Sustain Energy

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The comparative study of measured and calculated data of India Meteorological Department (IMD) and National Aeronautics and Space Administration (NASA) for 23 locations of India for 15 years (1986–2000) is presented. Global and diffuse radiation data are used in the study and to make the study comprehensive, three statistical predictors, viz., mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE) are used for the comparison. The results show that for global radiation, MPE is maximum of −36.65% for Shillong and minimum of 0.93% for New Delhi. MBE is found to be maximum of 1.63 kWh/m2 for Shillong while it is minimum (0.04 kWh/m2) for Nagpur. RMSE is maximum (2.14 kWh/m2) for Shillong and minimum for Ranchi (0.19 kWh/m2). For diffuse radiation, MPE is highest for Bhavnagar (100%) and lowest for Hyderabad (81.33%). MBE is maximum of −7.73 kWh/m2 and minimum of −4.62 kWh/m2 for Srinagar. RMSE is maximum for Bhavnagar (8.26 kWh/m2) and minimum for Srinagar (4.73 kWh/m2). © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13060, 2019

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Modeling and analysis of the spatiotemporal variations of photosynthetically active radiation in China during 1961–2012

Cited by 29 publications

References 51 publications

Prediction of diffuse photosynthetically active radiation using different soft computing techniques

Prediction of diffuse photosynthetically active radiation using different soft computing techniques

Trends of photosynthetically active radiation over China from 1961 to 2014

A Comprehensive Study of Ground Measurement and Satellite‐Derived Data of Global and Diffuse Radiation

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