Interpoladores geoestatísticos na análise da distribuição espacial da precipitação anual e de sua relação com altitude

Carvalho, José Maria Franco de; Assad, Eduardo Delgado; Pinto, Hilton Silveira

doi:10.1590/s0100-204x2012000900008

Cited by 23 publications

(19 citation statements)

References 15 publications

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“…Kriging is a univariate geostatistical method widely used for its efficiency in data interpolation Assad, 2005;Viola et al, 2010;Pinto, 2012). The cartographic projection system was the conic of Lambert.…”

Section: Camargo Climate Classificationmentioning

confidence: 99%

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

et al. 2016

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Climate is the set of average atmospheric conditions that characterizes a region. It directly influences the majority of human activities, especially agriculture. Climate classification systems (CCSs) are important tools in the study of agriculture, enabling knowledge of the climatic characteristics of a region. Thus, we aimed to perform the climatic characterization of the State of Paraná using the methods proposed by Köppen and Geiger (1928), modified by Trewartha (1954) (KT), Thornthwaite (1948) (TH) and Camargo (1991) and modified by Maluf (2000) (CM), using data from the European Center for Medium-Range Weather Forecast (ECMWF) model. The results of spatial interpolation (virtual stations) were performed using the Kriging method in spherical shape with one neighbour and resolution of 0.25°. The CCSs displayed the ability to separate the warm and dry from cold and wet regions. The most predominant climates were Cfa (temperate humid with hot summers), C 1 rA'a' (sub-humid with little water deficiency, megathermal) and ST-UMi (humid subtropical with dry winter), according to KT, TH and CM, respectively. CM is an intermediate CCS between KT and TH.

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Section: Camargo Climate Classificationmentioning

confidence: 99%

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

et al. 2016

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“…Considerando a relevância do clima para o desenvolvimento vegetal, pode-se identificar a capacidade à produção de determinada cultura por meio do mapeamento de elementos climáticos (Meireles et al, 2007). Para confecção desses mapas podem ser utilizadas técnicas de interpolação, quando não se tem dados em quantidade suficiente à representação de variáveis, para estimar valores de um atributo em locais não amostrados, a partir de pontos amostrados na mesma região (Carvalho, 2012). Métodos de interpolação geoestatísticos possibilitam projetar a relação espacial de dados georreferenciados para obter superfícies contínuas, de fenômenos com distribuição espacial (Perin et al, 2015).…”

Section: Introductionunclassified

Mapeamento De Elementos Climáticos Da Região Das Matas De Minas – Mg Durante O Ciclo Fenológico Do Café Arábica

et al. 2018

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ResumoO Brasil é o maior exportador de café do mundo, sendo Minas Gerais, o Estado com maior participação na produção de café. Nesse contexto, o mapeamento dos elementos climáticos permite avaliar espacialmente as condições ambientais que influenciam na produção. O objetivo deste trabalho foi investigar a melhor forma de representar espacialmente o padrão de precipitação e temperatura do ar na região das Matas de Minas -MG, nas fases vegetativa e reprodutiva do ciclo fenológico do café arábica, e verificar a correlação entre dados das estações meteorológicas do Instituto Nacional de Meteorologia -INMET e do Banco de Dados Climáticos Globais -WorldClim, e expressar espacialmente, por meio de mapas, as diferenças entre as informações dos distintos bancos de dados. Notou-se que os modelos gerados a partir dos dados das estações meteorológicas foram generalistas, e não captaram as nuances das variações térmicas e pluviométricas, identificou-se diferenças entre as representações dos mapas gerados a partir das duas bases de dados, considerando o tamanho da área e a disponibilidade de dados. AbstractBrazil is the largest exporter of coffee in the world, as such Minas Gerais, is the State with the largest production of coffee. In this context, the mapping of these climate elements allows evaluate the spatial of the environmental conditions that pervade the productivity. The objective of this work was to investigate the best way to represent the pattern of rainfall and air temperature in the Matas de Minas region -MG, in the reproductive and vegetative phases of the Arabica coffee phenological cycle, and verify the correlation between the data of the National Institute of Meteorology -INMET, and the Global Climate Data -WoldClim, and spatially express by means of maps, the difference between the information of the different data bases. It was noted the models generated by the data of the meteorological stations were generalist, and not capture the nuances of thermal and pluviometry variability, it was identified differences between the representation of maps generated from the distinct data bases, considering the size of the area and the data availability.

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“…2012;CARVALHO et al, 2012;ASHRAF et al, 2013). Due to limited meteorological element information or gaps in weather data recorded at different Brazilian locations, essential variables have frequently been estimated and interpolated (ANDRADE et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

Ferreira

Sibaldelli

Morais

et al. 2017

SCA

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Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data. Ferreira, R. C. et al escassos estudos que avaliem a eficácia e precisão da utilização de dados climáticos estimados ou interpolados a partir de estações distantes da área agrícola de interesse. Assim, este estudo teve como objetivo demonstrar os impactos da variabilidade espacial dos principais elementos meteorológicos sobre a estimativa regional da produtividade de grãos de soja. Utilizaram-se dados observados em três estações meteorológicas em diferentes locais de Londrina, comparados por meio de análise de regressão. Calculou-se o balanço hídrico decendial para soja e estimaram-se as perdas de produtividade de grãos pelo método Zona Agroecológica. As temperaturas nos diferentes locais apresentaram semelhanças, enquanto a umidade relativa do ar e, principalmente, precipitação pluvial foram mais discrepantes. Recomenda-se muita cautela no uso e na escolha de uma única estação meteorológica para representar um município ou região, como acontece em várias regiões de importância agrícola e ambiental no Brasil. Modelos e resultados de estimativas de safras que não consideram tal...

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Interpoladores geoestatísticos na análise da distribuição espacial da precipitação anual e de sua relação com altitude

Cited by 23 publications

References 15 publications

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

Mapeamento De Elementos Climáticos Da Região Das Matas De Minas – Mg Durante O Ciclo Fenológico Do Café Arábica

Spatial variability of meteorological observations and impacts on regional estimates of soybean grain productivity

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