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Köppen-Geiger and Camargo climate classifications for the Midwest of Brasil

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Abstract

There is a wide variety of climates on the planet’s surface, and climate classifications are tools for delimiting and describing prevailing climate types. Köppen classification relates types of climate to types of vegetation, while Camargo classification seeks to map climatic types based on thermal and water factors. In Brazil, the Midwest region is a major agricultural producer but still lacks detailed climate information. In this sense, we aimed to compare Köppen and Geiger (1928) with Camargo (1991) methods for climate classification in the Midwest of Brazil. For this purpose, we used data on daily global solar radiation; mean, maximum, and minimum air temperature; relative humidity; wind speed; and precipitation from 2160 weather stations, which were obtained from the NASA/POWER platform. Components of normal climatological water balance were calculated using the Thornthwaite and Mather (1955) method, with an available water capacity of 100 mm. Köppen and Geiger (1928) system uses data on mean annual temperature, annual precipitation, coldest month mean temperature, warmest month mean temperature, and driest month precipitation. The method of Camargo (1991), modified by Maluf (2000), uses the following meteorological elements: mean annual temperature, coldest month mean temperature, annual water surplus and deficit, and water deficit months. The similarity between classification methods was verified by agglomerative hierarchical clustering and Tukey’s test at 95% reliability. The most predominant climate class according to Camargo (1991) was TR-UMi (humid tropical climate), representing 33.63% of the entire territory of the Midwest of Brazil. According to Köppen and Geiger (1928), six climate types were observed in the Midwest region, with a predominance of class Aw (tropical climate with dry winter), representing 58.50% of the entire region. While Köppen and Geiger (1928) showed a macroscale scope, Camargo (1991) classification had a mesoscale approach. The latter was more suitable for agricultural purposes, mainly because it provided information on prevailing water conditions in the region.

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References

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. Rome: FAO, 300 p. (FAO – Irrigation and Drainage Paper, 56)

  • Aparecido LEO, de Rolim GS, Richetti J, de Souza PS, Johann JA (2016) Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil. Ciência e Agrotecnologia 40(4):405–417

    Article  Google Scholar 

  • Aparecido LEO, Torsoni GB, da Silva Cabral de Moraes JR, de Meneses KC, Lorençone JA, Lorençone PA (2020) Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019. Environ Dev Sustain (2020). https://doi.org/10.1007/s10668-020-00807-w

  • Alvares CA, Stape JL, Sentelhas PC, de Moraes G, Leonardo J, Sparovek G (2014) Köppen's climate classification map for Brazil. Meteorologische Zeitschrift 22(6):711–728

  • Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5:180214

    Article  Google Scholar 

  • de Camargo AP (1991) Climatic classification for zoning of agroclimatic aptitude. Brazilian Journal of Agrometeorology 8:126–131

    Google Scholar 

  • Cardoso MRD, Marcuzzo FFN, Barros JR (2014) Climatic classification of Köppen-Geiger for the state of Goiás and the Federal District. ACTA Geográfica 8:16

  • Chen T, Zhang H, Chen X, Hagan DF, Wang G, Gao Z, Shi T (2017) Robust drying and wetting trends found in regions over China based on Köppen climate classifications. Journal of Geophysical Research: Atmospheres 122(8):4228–4237

    Google Scholar 

  • De Matos RM, Da Silva PF, Borges VE, Galvão T, Sobrinho LMFS, Neto JD (2018) Use of climate classification systems for the municipality of Barbalha – CE. Revista Brasileira de Geografia Física 11(3):877–885

    Article  Google Scholar 

  • Engelbrecht CJ, Engelbrecht FA (2016) Shifts in Köppen-Geiger climate zones over southern Africa in relation to key global temperature goals. Theor Appl Climatol 123(1–2):247–261

    Article  Google Scholar 

  • Every JP, Li L, Dorrell DG (2020) Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations. Renew Energy 147:2453–2469

    Article  Google Scholar 

  • Gallardo C, Gil V, Hagel E, Tejeda C, Castro M (2013) Assessment of climate change in Europe from an ensemble of regional climate models by the use of Koppen–Trewartha classification. Int J Climatol 33:2157–2166

    Article  Google Scholar 

  • He WP, Zhao SS, Wu Q, Wan S (2019) Simulating evaluation and projection of the climate zones over China by CMIP5 models. Clim Dyn 52(5–6):2597–2612

    Article  Google Scholar 

  • Holdridge LR (1967) Life zone ecology. No. (rev. ed.). Tropical Science Center, San Jose, pp 206

  • Izzo M, Rosskopf CM, Aucelli PP, Maratea A, Méndez R, Pérez C, Segura H (2010) A new climatic map of the Dominican Republic based on the Thornthwaite classification. Phys Geogr 31(5):455–472

    Article  Google Scholar 

  • Jo S, Ahn JB, Cha DH, Min SK, Suh MS, Byun YH, Kim JU (2019) The Köppen-Trewartha climate-type changes over the CORDEX-East Asia phase 2 domain under 2 and 3 °C global warming. Geophys Res Lett 46:14030–14041

    Article  Google Scholar 

  • Köppen W, Geiger R (1928) Klimate der Erde. Justus Perthes, Gotha

    Google Scholar 

  • Krige D (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of Chemical, Metal and Mining Society of South Africa 52:119–139

    Google Scholar 

  • Kuinchtner A; Buriol GA (2016) Climate of the State of Rio Grande do Sul according to the climatic classification of Köppen and Thornthwaite. Disciplinarum Scientia| Naturais e Tecnológicas, v. 2, n. 1, p. 171–182

  • Maluf JRT (2000) New climatic classification of the Rio Grande do Sul. Revista Brasileira de Agrometeorologia 8(1):141–150

    Google Scholar 

  • Martins E, Aparecido LE, De O, Santos LPS, De Mendonça JMA, De SPS (2015) Weather influence in yield and quality coffee produced in South Minas Gerais region. Coffee Science 10:499–506

    Google Scholar 

  • Miller A (1931) Climatology. Methuen & Co. LTDA. Londres, p 304

  • Mohamed HA, Mohamed AA (2017) Classification of climates of Sudan using aridity indices. Sudan Journal of Desertification Research, v. 2, n. 1

  • Moreto VB, Rolim GS, Zacarin BG, Vanin AP, de Souza LM, Latado RR (2017) Agrometeorological models for forecasting the qualitative attributes of Valência oranges. Theor Appl Climatol 130:847–864. https://doi.org/10.1007/s00704-016-1920-9

    Article  Google Scholar 

  • Moreto VB, Rolim G, De S (2015) Estimation of annual yield and quality of ‘Valencia orange’ related to monthly water deficiencies, African Journal of Agricultural Research. 10:543–553. https://doi.org/10.5897/AJAR2014.9090

  • Müller MDS, Dourado-Neto D, Timm LIC, Reichardt K, Sartori FF, Felisberto G (2018) Climate analysis for agricultural improvement of the economic Community of West African States according to Kppen and Thornthwaite. Afr J Agric Res 13(23):1198–1212

    Article  Google Scholar 

  • Nóbrega RS (2010) Critical thinking on climate classifications: from Köppen to Strahler. Revista Brasileira de Geografia Física 3(1):18–22

    Article  Google Scholar 

  • Remedio AR, Teichmann C, Buntemeyer L, Sieck K, Weber T, Rechid D, Hoffmann P, Nam C, Kotova L, Jacob D (2019) Evaluation of new CORDEX simulations using an updated Köppen–Trewartha climate classification. Atmosphere 10(11):726

    Article  Google Scholar 

  • Rolim GS, Aparecido LEO (2015) Camargo, Köppen and Thornthwaite climate classification systems in defining climatical regions of the state of São Paulo. Brazil International Journal of Climatology 36:636–643

    Article  Google Scholar 

  • Rolim GS; Camargo MBP; Lania DG; Moraes JFL (2007) Köppen and Thornthwaite climate classification and their applicability in the determination of Agroclimatic Zones for the State of São Paulo. Bragantia, v. 66, n. 4, p. 711–720, maio

  • Rolim GS, de Oliveira Aparecido LE, de Souza PS, Augusto R, Lamparelli C, dos Santos ÉR (2020) Climate and natural quality of Coffea arabica L. drink. Theor Appl Climatol 141:87–98. https://doi.org/10.1007/s00704-020-03117-3

  • Rubel F, Brugger K, Haslinger K, Auer I (2017) The climate of the European Alps: shift of very high resolution Köppen-Geiger climate zones 1800–2100. Meteorol Z 26(2):115–125

    Article  Google Scholar 

  • Sá Junior A, Carvalho LG, Silva FF, Alves MC (2012) Application of the Köppen classification for climatic zoning in the state of Minas Gerais. Brazil Theoretical and Applied Climatology 108:1–7

    Article  Google Scholar 

  • Silva MP; Marujo LG (2012) Analysis of an intermodal model for the flow of soybean production in the Brazilian west center. J. Transp. Lit., Manaus , v. 6, n. 3, p. 90–106

  • Sparks (2018) sasapower: A NASA POWER global meteorology, surface solar energy and climatology data client for R. Journal of OpenSource Software, 3(30), 1035. 10.21105/joss.010351

  • Strahler AH, Strahler AN (2005) Physical geography: Science and systems of the Human Environment. Wiley, New York, p 794

  • Terassi PMDB, Silveira H (2013) Application of climatic classification systems to the Pirapó-PR river basin. Formação (Online), v. 1, n. 20

  • Thornthwaite CW (1948) An approach towards a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  • Thornthwaite CW, Mather JR (1955) The water balance. Publications in Climatology 8(1):104–114

    Google Scholar 

  • Trewartha GT (1954) An introduction to climate. New York: McGraw-Hill. 402p

  • Wang C, Li Y, Myint SW, Zhao Q, Wentz EA (2019) Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States. Landsc Urban Plan 192:103668

    Article  Google Scholar 

  • Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244

Download references

Funding

This research was supported by the Science and Technology of Mato Grosso do Sul—Campus of Naviraí, IFMS—Federal Institute of Education, Naviraí, Brasil.

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Authors and Affiliations

  1. Agricultural Engineering Laboratory, IFMS Campus of Naviraí, Navirai, MS, 79950-000, Brazil

    Lucas Eduardo de Oliveira Aparecido, José Reinaldo da Silva Cabral de Moraes, Kamila Cunha de Meneses, Guilherme Botega Torsoni, Rafael Fausto de Lima & Cícero Teixeira Silva Costa

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  1. Lucas Eduardo de Oliveira Aparecido
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  2. José Reinaldo da Silva Cabral de Moraes
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  3. Kamila Cunha de Meneses
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  5. Rafael Fausto de Lima
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  6. Cícero Teixeira Silva Costa
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Correspondence to Lucas Eduardo de Oliveira Aparecido.

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de Oliveira Aparecido, L.E., da Silva Cabral de Moraes, J.R., de Meneses, K.C. et al. Köppen-Geiger and Camargo climate classifications for the Midwest of Brasil. Theor Appl Climatol 142, 1133–1145 (2020). https://doi.org/10.1007/s00704-020-03358-2

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