Desenvolvimento, avaliação e aplicação de um algoritmo para espacialização global dos climas árido, tropical e temperado

• Autor(a) principal: Sampaio, Marcelly da Silva
• Data de Publicação: 2012
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFMT
• Texto Completo: http://ri.ufmt.br/handle/1/1224
• topic: Classificação climática; Regressão linear múltipla; Mudanças climáticas; CNPQ::ENGENHARIAS::ENGENHARIA SANITARIA::RECURSOS HIDRICOS; Climate classification; Multiple linear regression model; Climate change

Climate classification systems are used to characterize variations in the components of the climate on a local, regional or global scale, in order to delimit homogeneous areas, to characterize the biological and physical environment, but also to distinguish the key features of climatic behavior. Among the several existing classification systems, stand out those developed by Köppen-Geiger and Thornthwaite. Both used different climatic elements for classification of climate. Köppen-Geiger was based on vegetation, temperature and precipitation data. Moreover Thornthwaite has introduced new concepts and was based on the evapotranspiration and water balance, classifying the climate on scales of moisture. The aim of this study was to develop, evaluate and implement an algorithm to classify arid, tropical and temperate climates of Earth's surface, based on the moisture index of the climatic classification of Thornthwaite and in the climatic limits defined by Köppen-Geiger climatic classification, using geographic database of average annual mean air temperature and total rainfall. The algorithm was developed in stages. In a first analysis, meteorological elements data observed from 39 INMET' stations located in the state of Minas Gerais and surrounding areas were used for calculating the climatic water balance, following the method of Thornthwaite and Mather and for estimation of evapotranspiration by the method of Penman- Monteith-FAO. From these values we calculated the moisture index for each of the 39 stations. Then we developed the multiple linear regression model based on the annual moisture index as dependent variable and independent variables as the mean annual average air temperature and annual total rainfall. After generating the model, we evaluated its performance using global data of air temperature and rainfall of high resolution interpolated climate surfaces of land surface, excluding the region of Antarctica to the spatial distribution of the moisture index estimated by the algorithm in a GIS environment , for the period 1950- 2000 (Worldclim data) and for the periods of 1990-2020, 2020-2050 and 2050-2080 (CCCMA data) of future climate change scenarios A2 and B2 of the Intergovernmental Panel on Climate Change (IPCC AR3). Based on multiple linear regression model developed, it was explained approximately 92% (R2 value) of the behavior of the moisture index by using data of rainfall and air temperature. The algorithm showed good performance for the characterization of terrestrial areas with arid, tropical and temperate climates, as the results were corresponding to those found in the literature. Regarding the studies on climate change it was observed that the conditions of arid climate increased sharply in the periods analyzed, particularly in the A2 - 2080 emission scenario, considered the worst case scenario. Whereas there are still gaps relevant to currently available knowledge on some aspects of climate mitigation, as well as on the availability of climate data, the regression model developed and the methodology used to assess climate variability in this study may be useful to reduce uncertainties about the current climate and future, facilitating the decision-making related to mitigation of climate change.