The Brazilian Midwest has significant spatiotemporal variability in terms of precipitation and air temperature, making it more vulnerable to the occurrence of extreme weather events. The objective of this study is to characterize the trend of extreme climatic events regarding precipitation and air temperature in the Brazilian Midwest, and to analyze their relationship with Pacific and Atlantic Sea Surface Temperature anomalies (SSTAs). We used daily precipitation and air temperature data measured at 24 conventional weather stations. Pacific and Atlantic SSTA data were obtained from the Climate Prediction Center. The frequency of hot extremes had increased, while that of cold extremes had decreased significantly, thus highlighting the consistent warming across the Brazilian Midwest. The precipitation extremes had greater variability than the temperature extremes. Precipitation intensity increased in Amazonia, with no change in annual precipitation volume. The precipitation extremes in the Brazilian Savanna, Pantanal, and the Atlantic Forest did not have a well-defined pattern but indicated a trend towards a decrease in days with intense precipitation events. In general, the Equatorial Pacific and Atlantic Ocean (TNAI and TSAI) SSTAs were negatively correlated with precipitation extreme indices and positively correlated with air temperature extreme indices in the Amazon. However, the North Atlantic SSTAs were positively correlated with precipitation and air temperature extreme indices in the Brazilian Savanna and Pantanal. In addition, the Pacific SSTAs were positively correlated with precipitation intensity in the Atlantic Forest. Thus, the variability of the trends of precipitation and air temperature extreme indices in the Brazilian Midwest was observed, and it was surmised that this measure was significantly related to Pacific and Atlantic SSTAs.

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

The global and Amazon climate change, mainly in rainfall, due to the El Niño and La Ninã phenomena. The objective of this study was to analyze the relative frequency (RF) of rainfall, in different periods of the day, during occurrences of El Niño, La Niña events and neutrality condition. The research was carried out in the Southern mesoregion of the state of Amazonas (Apuí, Boca do Acre, Lábrea, Manicoré and Humaitá). Data were analyzed by the Spiegel´s method. The rainfall data were obtained from the Global Land Data Assimilation database from January 1st, 2000 to December 31st, 2018. Data were made available every three hours and integrated into six hours. RF of rainfall was higher in the afternoon in La Niña, Neutro and El Niño years in all municipalities. The RF in La Niña year was higher than during El Niño. The RF during the Neutral year was higher than during El Niño in Apuí and Boca do Acre, and had no difference in Lábrea, Manicoré and Humaitá. The rainfall in the southern Amazon mesoregion was more frequent during the afternoon.

A implantação de assentamentos rurais e a avaliação do efeito da dinâmica da mudança da cobertura nesses assentamentos é de extrema importância para determinação de políticas públicas. Assim, o objetivo desse estudo foi avaliar o impacto da implantação do assentamento rural Roseli Nunes e de sua dinâmica do uso do solo sobre parâmetros biofísicos da superfície. Para tanto, foram calculados o índice de vegetação da diferença normalizada (NDVI) e o albedo (αs), a temperatura (Ts) e o saldo de radiação (Rn) da superfície em áreas de preservação permanente, nativa, urbana, plantio de mandioca, plantio de banana, pastagem e solo exposto a partir de imagens Landsat 5 e Landsat 8. Os dados meteorológicos e as imagens de satélite foram obtidas durante o período seco nos anos 2000, 2010 e 2017. O NDVI e o Rn diminuíram e o αs e a Ts aumentaram entre 200 e 2010 em função da substituição da vegetação nativa em pequenas áreas agrícolas e urbanas. No entanto, apresentaram padrão inverso entre 2010 e 2017 no assentamento. As áreas de preservação permanente e nativas apresentaram os maiores valores de NDVI e Rn, e menores valores αs e Ts, enquanto as áreas com cultivos agrícolas, pastagem e solo exposto apresentaram menores valores de NDVI e Rn, e maiores valores αs e Ts. Por fim, os parâmetros biofísicos foram alterados significativamente com o desmatamento da área do assentamento entre 2000 e 2010, e a recuperação da vegetação entre 2010 e 2017, quando os valores dos parâmetros biofísicos foram próximos aos encontrados antes da implantação do assentamento.

Técnicas de sensoriamento remoto possibilita monitorar e identificar alterações que ocorrem na superfície terrestre, sejam elas ocasionadas por fenômenos naturais ou ação antrópica. O monitoramento dessa dinâmica tornou-se um importante instrumento de pesquisa, pois as transformações ocorridas na cobertura do solo alteram as trocas de radiação entre a superfície a atmosfera. Assim, o objetivo deste trabalho foi analisar os parâmetros biofísicos da superfície em diferentes uso e ocupação em Cuiabá. Foram utilizadas imagens do satélite Landsat 5 durante o ano de 2009. As estimativas dos parâmetros biofísicos foram produzidas a partir de uma camada vetorial com nove pixels sobre cada área. Essas áreas foram classificadas como área de solo exposto, área urbana, vegetação rasteira, área reflorestada, fragmento de floresta urbana e vegetação densa. O índice de vegetação da diferença normalizada (NDVI) apresentou variação de acordo com a cobertura do solo, sendo observado os maiores valores nas áreas com vegetação mais densa. Os maiores valores de albedo foram encontrados nas áreas com pouca ou ausência de vegetação. A Ts foi maior nas áreas com maior grau de antropização e baixa quantidade de vegetação. O Rn foi maior nas áreas com maior densidade vegetal e menor nos pontos com maior antropização. Enfim, os locais com maior quantidade de biomassa vegetal apresentaram maiores valores de NDVI e Rn, além de menores valores de αs e Ts. Effect of Coverage Change on Biophysical Parameters in Cuiabá, Mato Grosso A B S T R A C TRemote sensing techniques make it possible to monitor and identify changes that occur on the earth's surface, whether caused by natural phenomena or anthropic action. Monitoring these dynamics has become an important research tool, since the transformations in the ground cover alter the radiation exchanges between the surface and the atmosphere. Thus, the objective of this study was to analyze the surface biophysical parameters in different use and occupation in Cuiabá. Landsat 5 satellite images were used during 2009. Estimates of biophysical parameters were produced from a vector layer with nine pixels over each area. These areas were classified as exposed soil area, urban area, undergrowth, reforested area, urban forest fragment and dense vegetation. The normalized difference vegetation index (NDVI) varied according to soil cover, with the highest values being observed in areas with denser vegetation. The highest albedo values were found in areas with little or no vegetation. The Ts was higher in the areas with higher degree of anthropization and low amount of vegetation. Rn was higher in areas with higher plant density and lower in areas with greater anthropization. Finally, the sites with the highest amount of plant biomass presented higher NDVI and Rn values, as well as lower αs and Ts values.Keywords: Anthropization, Satellite image, Urbanization.

A substituição da vegetação nativa e a sazonalidade climática modificam os parâmetros biofísicos da superfície. Como 50% do Cerrado tem sido convertido em lavouras e pastagens, o objetivo desse estudo foi avaliar a variação temporal de parâmetros biofísicos em áreas nativas e convertidas em uma área de transição entre Cerrado e Pantanal no Mato Grosso. O estudo foi conduzido na Fazenda Experimental da UFMT (15° 51’ 15.23’’ S e 56° 04’ 13.50’’ W) com imagens do sensor TM-Landsat 5 entre abril e setembro de 2011. O Índice de Vegetação da Diferença Normalizada (NDVI), o albedo da superfície (α) e a temperatura da superfície (Ts) foram estimados em áreas de Cerrado strito sensu (CER), pastagem de Brachiaria brizantha (PAS) e mangueiral (MAN). Os maiores valores de NDVI e menores valores de α e Ts foram observados nas áreas mais densamente vegetadas. O Cerrado apresentou menores valores de α e a MAN apresentou maiores valores de NDVI ao longo de todo experimento. A substituição da cobertura nativa da região de transição de Cerrado e Pantanal por vegetação exótica modificou significativamente os parâmetros biofísicos avaliados, com potencial de modificar os balanços de radiação, energia, carbono da superfície. Palavras-chave: desmatamento; sensoriamento remoto; aquecimento regional. TEMPORAL VARIATION OF BIOPHYSICAL PARAMETERS OF THE SURFACE BY LANDSAT 5 IMAGES IN DIFFERENT SOIL COVERS IN A TRANSITIONAL AREA OF CERRADO AND PANTANAL IN MATO GROSSO The substitution of native vegetation and climatic seasonality modify the surface biophysical parameters. As 50% of the Cerrado has been converted to crops and pastures, the objective of this study was to evaluate the temporal variation of biophysical parameters in native areas and converted into a transition area between Cerrado and Pantanal in Mato Grosso. The study was conducted at the UFMT Experimental Farm (15° 51’ 15.23’’ S e 56° 04’ 13.50’’ W) with TM Landsat 5 images between April and September 2011. The Normalized Difference Vegetation Index (NDVI), and the surface albedo (α) and surface temperature (Ts) were obtained from a Cerrado stricto sensu (CER), a pasture of Brachiaria brizantha (PAS) and a mangrove (MAN). The highest NDVI values and lowest α and Ts values were observed in the most densely vegetated areas. The replacement of the native cover of the Cerrado and Pantanal transition region with exotic vegetation significantly modified the biophysical parameters evaluated, with the potential to modify the radiation, energy and carbon balances of the surface. Keywords: deforestation; remote sensing; regional warming.