Modelling global solar radiation to optimise agricultural production
Abstract
Aim of study: To present a complete global radiation model that includes direct, diffuse sky and ground-reflected radiation, and compare the values with those obtained by the pyranometers.
Area of study: The data were analyzed at the meteorological station network in Extremadura, Spain, to validate the results calculated by the model.
Material and methods: The method uses the maps from meteorological station data are based on a single piece of daily solar radiation data for an area of 8,000 to 9,000 ha, whereas the maps created by the models are obtained using the spatial resolution of the digital elevation model, in this case 25 × 25m.
Main results: The analytical model used in the study obtained global radiation values with a difference of 1.44% relative to the values captured by the meteorological stations in Extremadura. Analysis of the data indicates that on days with a specific type of fog or very diffuse cloud, the global radiation captured by sensors is greater than it would be on clear-sky days in the same area. The method was suitable for calculating global solar radiation on any type of terrain with its corresponding diversity of crop types.
Research highlights: The research highlights the importance of understanding and modelling solar radiation for efficient use of water resources in agriculture. Adding these global radiation models to a GIS would provide a very valuable tool for developing regions.
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References
Bindi M, Gozzini B, 1998. Data spatial distribution in meteorology and climatology. Cost Action 79, Sept 28-Oct 3, 1997. Publ Office of the EU, Luxembourg. 226 pp.
Changnon SA, Changnon D, 2005. Importance of sky conditions on the record 2004 Midwestern crop yields. Phys Geogr 26: 99-111. https://doi.org/10.2747/0272-3646.26.2.99
Corripio JG, 2002. solarpath.zip. 18/09/2002. http://tsunami.geo.ed.ac.uk/~jgc/. 2002b.
Domínguez A, 2015. Modelado y análisis de parámetros agrometeorológicos para la optimización de la producción agrícola. Aplicación a las zonas regables de Extremadura. Doctoral thesis, Univ. de Extremadura, Badajoz, Spain.
Estévez J, Gavilán P, Giráldez J.V, 2011. Guidelines on validation procedures for meteorological data from automatic weather stations. J Hydrol 402 (1-2): 144-154. https://doi.org/10.1016/j.jhydrol.2011.02.031
Ferrante A, Mariani L, 2018. Agronomic management for enhancing plant tolerance to abiotic stresses: high and low values of temperature, light intensity and relative humidity. Horticulturae 4 (3): 21. https://doi.org/10.3390/horticulturae4030021
Gattioni N, Boca T, Rebella C, Di Bella C, 2011. Comparación entre observaciones meteorológicas obtenidas de estaciones convencionales y automáticas a partir de la estimación de parámetros estadísticos. Revista de Investigaciones Agropecuarias 37: 75-85.
Gozzini B, Hims S, 1998. Proceedings workshop dealing on spatialisation. Cost Action 79, Toulouse, 24-25 Sept 1996. Publ Office of the EU, Luxembourg. 149 pp.
Gozzini B, Paniagua S, 2000. Coordination and comparison of several interpolation methods of meteorological data (minimum temperature). Cost Action 79: Integration of data and methods in agroclimatology. Publ Office of the EU, Luxembourg. 102 pp.
Hantson S, Chuvieco E, 2011. Evaluation of different topographic correction methods for Landsat imagery. Int J Appl Earth Observ Geoinform 13 (5): 691-700. https://doi.org/10.1016/j.jag.2011.05.001
Kambezidis HD, 2016. Current trends in solar radiation modeling: the paradigm of MRM. J Fund Renew Energ Appl 06 (2). https://doi.org/10.4172/2090-4541.1000e106
Kaskaoutis D, Polo J, 2019. Editorial for the special issue "Solar Radiation, Modeling and Remote Sensing". Remote Sens 11 (10): 1198. https://doi.org/10.3390/rs11101198
Lynch P, 2008. The origins of computer weather prediction and climate modeling. J Comput Phys 227 (7): 3431-3444. https://doi.org/10.1016/j.jcp.2007.02.034
Matuszko D, 2012. Influence of the extent and genera of cloud cover on solar radiate n intensity. Int J Climatol 32: 2403-2414. https://doi.org/10.1002/joc.2432
Michalakes J, Dudhia J, Gill D, Henderson T, Klemp J, Skamarock W, Wang W, 2004. The weather research and forecast model: software architecture and performance. Workshop on the Use of High Performance Computing in Meteorology, 25-29 Oct 2004, Reading UK. https://doi.org/10.1142/9789812701831_0012
Mitjá A, Batalla E, 1982. Manual de radiación solar. Tablas para Catalunya. Universitat Politècnica de Catalunya i Instituto de Ciencias Energéticas -ETSIIB. Ed. Prensa XX1. Barcelona. 1: p. 137.
Muneer T, Younes S, Munawwar S, 2007. Discourses on solar radiation modeling. Renew Sust Energ Rev 11 (4): 551-602. https://doi.org/10.1016/j.rser.2005.05.006
Pandey CK, Kariyar AK, 2013. Solar radiation: models and measurement techniques. Journal of Energy 2013: Art ID 305207. https://doi.org/10.1155/2013/305207
Pérez-Burgos A, Bilbao J, de Miguel A, Román R, 2014. Analysis of solar direct irradiance in Spain. Energy Procedia 57: 1070-1076. https://doi.org/10.1016/j.egypro.2014.10.070
Polo J, Martín-Pomares L, Gueymard C.A, Balenzategui J.L, Fabero F, Silva JP, 2019. Fundamentals: quantities, definitions and units. In: Solar resource mapping-fundamentals and applications; green energy and technology; Polo J, Martín-Pomares L, Sanfilippo A, (Eds). Springer: Zurich, Switzerland. pp: 1-14. https://doi.org/10.1007/978-3-319-97484-2_1
Psiloglou BE, Kambezidis HD, Kaskaoutis DG, Karagiannis D, Polo JM, 2019. Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece. Renew Energ 146: 1372-1391. https://doi.org/10.1016/j.renene.2019.07.064
Sandaña P, Ramírez M, Pinochet D, 2012. Radiation interception and radiation use efficiency of wheat and pea under different P availabilities. Field Crops Res 127: 44-50. https://doi.org/10.1016/j.fcr.2011.11.005
Solanki SK, 2002. Solar variability and climate change: is there a link? Astron Geophys 43 (5): 9-13. https://doi.org/10.1046/j.1468-4004.2002.43509.x
WMO, 1982. Observing the weather from space. World Meteorological Organization, Geneva. https://public.wmo.int/en
Xiao S, Tian X, Liu Q, Wen J, Ma Y, Song ZA, 2018. A semi-empirical topographic correction model for multi-source satellite images. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci IV: 225-232. https://doi.org/10.5194/isprs-annals-IV-3-225-2018
Xue Y, Fennessy MJ, 1996. Impact of vegetation properties on U.S. summer weather prediction. J Geophys Res 101 (D3): 7419. https://doi.org/10.1029/95JD02169
Zelenka A, Pérez R, Seals R, Remé D, 1999. Effective accuracy of satellite-derived hourly irradiances. Theor Appl Climatol 1 (62): 199-207. https://doi.org/10.1007/s007040050084
Zhu X, Sun M, Wang Y, 2017. Correction of false topographic perception phenomenon based on topographic correction in satellite imagery. IEEE Trans Geosci Remote Sens 55 (1): 468-476. https://doi.org/10.1109/TGRS.2016.2608980
Zimmermann NE, 2000a. Shortwavc.aml, 9.08.2000. http://www.wsl.ch/staff/niklaus.zimmermann/programs/aml1_1.html
Zimmermann NE, 2000b. Diffuse.aml, 9.08.2000. http://www.wsl.ch/staff/niklaus.zimmermann/programs/aml1_4.html
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