Cartography of climate variables using gradients, expert systems and GIS
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Abstract
A method is presented for the generation of continuous maps of climatic variables in complex relief zones by using gradients, reference weather stations and expert criteria. This proposal uses a geographic information system to describe the geographic factors forming that form microclimates in the study region and fuzzy Mamdani inference models to integrate knowledge of local climate into an expert system. In this way it is possible to assign an arbitrary number of gradients and reference values to patterns describing locations within the study region. A case study for the variable minimum temperature is included in the study. In it maps at scales 1:250000 and 1:100000 of the variable for the eastern region of Cuba were generated. The process is fully automated, requiring human intervention only for cartographic improvement purposes of the final maps.
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Delgado-TéllezR., & Peña-de la CruzA. (2019). Cartography of climate variables using gradients, expert systems and GIS. Revista Cubana De Meteorología, 25(2). Retrieved from http://rcm.insmet.cu/index.php/rcm/article/view/464
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Bukovsky, M. S. & Karoly, D. J. 2009. “Precipitation Simulations Using WRF as a Nested Regional Climate Model”. : 2152-2159.
Fernando, H. J. S.; Hacker, J. P.; Katopodes Chow, F.; Pardyjak, E.; Dunn, P.; Pratt, T.; Hoch, S.; Steenburgh, J.; Whiteman, D.; Pu, Z. & de Wekker, S. F. J. 2017. Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program. Reporte Final del Programa, no. N00014-11-1-0709, call no. OMB no. 0704-0188, Notre Dame, Estados Unidos.: University of Notre Dame, p. 43.
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Kanjilal, V.; Liu, H. & Schneider, M. 2010. “Plateau Regions: An Implementation Concept for Fuzzy Regions in Spatial Databases and GIS”. In: Hüllermeier, E., Kruse, R. & Hoffmann, F. , (eds.)Hutchison, D., Kanade, T., Kittler, J., Kleinberg, J. M., Mattern, F., Mitchell, J. C., Naor, M., Nierstrasz, O., Pandu Rangan, C., Steffen, B., Sudan, M., Terzopoulos, D., Tygar, D., Vardi, M. Y. & Weikum, G. (ed. ser.), Computational Intelligence for Knowledge-Based Systems Design, vol. 6178, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 624-633, ISBN: 978-3-642-14048-8, DOI: 10.1007/978-3-642-14049-5_64, Available: Available: http://link.springer.com/10.1007/978-3-642-14049-5_64 , [Consulted: October 19, 2017].
Kryza, M.; Wałaszek, K.; Ojrzyńska, H.; Szymanowski, M.; Werner, M. & Dore, A. J. 2017. “High-Resolution Dynamical Downscaling of ERA-Interim Using the WRF Regional Climate Model for the Area of Poland. Part 1: Model Configuration and Statistical Evaluation for the 1981-2010 Period”. Pure and Applied Geophysics, 174(2): 511-526, ISSN: 0033-4553, 1420-9136, DOI: 10.1007/s00024-016-1272-5.
Lecha, L. B.; Paz, L. R. & Lapinel, B. 1994. El Clima de Cuba. La Habana, Cuba.: Editorial Academia, 186 p.
Li, D.; Storch, H. von & Geyer, B. 2016. “Testing Reanalyses in Constraining Dynamical Downscaling”. Journal of the Meteorological Society of Japan. Ser. II, 94A: 47-68, DOI: 10.2151/jmsj.2015-044.
Maceira, D.; Fong G., A.; William, F.; Alverson, S. & Wachter, T. (ed. ser.). 2005. Cuba: Parque Nacional La Bayamesa. (ser. Rapid Biological Inventories), Chicago: The Field Museum , p. 81.
Mamdani, E. H. 1974. “Application of fuzzy algorithms for control of simple dynamic plant”. Proceedings of the Institution of Electrical Engineers, 121(12): 1585, ISSN: 0020-3270, DOI: 10.1049/piee.1974.0328.
Montenegro, U. 1989. Procedimientos metodológicos para la investigación y caracterización del clima en las montañas de cuba. .
Montenegro, U. 1993. Caracterización Climática de las montañas de la región oriental de Cuba. .
Morales, G. M. 2002. Perfeccionamiento del manejo de la información en las regiones especiales de desarrollo sostenible de la república de cuba, mediante la aplicación de los sistemas de información geográfica. Tesis presentada en opción al grado científico de Doctor en Ciencias Geográficas, Habana, Cuba.: Instituto Técnico Militar (ITM).
Nuñez, A. & Viña, N. 1989. Regiones Naturales y Antrópicas. (ser. Nuevo Atlas nacional de Cuba.), Habana, Cuba.: Academia de ciencias de Cuba.
Planos, E.; Boudet, D.; Gonzalez, I.; Carrillo, E.; Hernández, M. & al;, et. 2012. Atlas Climático de Cuba. digital, Ministerio de las Fuerzas Armadas Revolucionarias.
Planos, E.; Guevara, V. & Rivero, R. 2013. Impacto del Cambio Climático en Cuba y medida de Adaptación. La Habana, Cuba.: Editorial AMA., ISBN: 978-959-300-039-0.
Stepinski, T. F. & Jasiewicz, J. 2011. “Geomorphons-A New Approach to Classification of Landforms”. Proceedings of Geomorphometry 2011, : 109-112.
Theobald, D. M.; Stevens, D. L.; White, D.; Urquhart, N. S.; Olsen, A. R. & Norman, J. B. 2007. “Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications”. Environmental Management, 40(1): 134-146, ISSN: 0364-152X, 1432-1009, DOI: 10.1007/s00267-005-0199-x.
TNC. 2018. The Nature Conservancy. The Nature Conservancy, Available: Available: https://www.nature.org/en-us/ , [Consulted: October 30, 2018].
Vázquez, R. & Solano, O. 2013. “Modelación espacial de la lluvia y la evapotranspiración teniendo en cuenta parámetros geográficos.”. Revista Ciencias de la Tierra y el Espacio., 14(1), ISSN: 1729-3790.
von Asmuth, J. R.; Maas, K.; Knotters, M.; Bierkens, M. F. P.; Bakker, M.; Olsthoorn, T. N.; Cirkel, D. G.; Leunk, I.; Schaars, F. & von Asmuth, D. C. 2012. “Software for hydrogeologic time series analysis, interfacing data with physical insight”. Environmental Modelling & Software, 38(0): 178-190, ISSN: 1364-8152, DOI: 10.1016/j.envsoft.2012.06.003.
Wang, T.; Hamann, A.; Spittlehouse, D. L. & Murdock, T. Q. 2012. “ClimateWNA-high-resolution spatial climate data for western North America”. : 16-29.