This study provides the first estimates of the frequency distribution of observational and future maximum daily precipitation considering nonstationarity for the entire Brazilian territory. We assess observational data from 1980 to 2015 and projected data from 2020 to 2099 for two climate change scenarios and four downscaled climate models. We modelled extreme precipitation according to the extreme values theory and calculated the precipitation intensity associated with the return periods of 5, 10, 25, 50, and 100 years in nonstationary conditions. The results indicate that nonstationarity is identified in 17.5% of the study area during 1980–2015. The analysis of future climate projections indicated an increase in the return levels of extreme precipitation compared to the historical period in at least 90% of the national territory. It shows that engineering design must urgently consider the nonstationarity of extreme precipitation under the risk of increasingly unsafe infrastructure.

Created in 2014, the Serra da Gandarela National Park (SGNP), is repeatedly affected by wildfires. This Conservation Unit is located in the Iron Quadrangle (MG), in a transition zone between the Cerrado and the Atlantic Forest biomes, and is characterized by a complex mosaic of phytophysiognomies. The aim of this investigation was to compare the performance of two risk mapping models for forest fire in the SGNP and its surroundings, based on two different approaches, being one by multicriteria analysis, AHP method and the other a simple probability method, called Hot Spot History, which provided information on the areas of highest and lowest risk and their environmental and human characteristics. Spatial data from remote sensing and GIS were used to simulate, in sequence, the fire ignition, the fire spread and, finally, the risk of wildfire. The validation of the risk models was performed by the Kappa coefficient (K). The results showed that the model based on the History of Hot Points obtained greater accuracy (0.61) than the model generated by the AHP method (0.54). The Brazilian Savanna, Rupestrian Fields and Field coverings were the most susceptible to wildfire, as they are formed by herbaceous vegetations and are located very close to urban agglomerations and roads. The slopes oriented to the North and West were important for the prediction of wildfires slope and, on the other hand, the slope of the terrain was not important to discretize the areas of greater and lesser fragility to the referred ecological disturbance.

In order to assist in high-yield agricultural management in multiple cropping systems, it is essential to understand the link between the rainy season onset and crops sowing dates, since it considerably affects the management, yield and output. We built crop calendars derived from remote sensing products and investigated the link between sowing dates and the onset of the rainy season in irrigated and rainfed agriculture in Western Bahia, a new and important agricultural frontier in Brazilian Cerrado. Crop sowing dates were obtained from green-up dates from 2001 to 2019. Rainy season onset dates were determined using CHIRPS daily precipitation data. Results indicate that sowing occurs from 26 October to 15 November and the rainy season starts from 17 to 27 October. Rainfed sowing dates are strongly correlated to rainy season onset and are particularly affected in years where rains are delayed. Sowing dates in irrigated pixels occur up to 25 days earlier than rainfed and are not correlated to rainy season onset. Irrigated farms are sowing earlier and in a shorter window than rainfed, with a stronger resilience in years where rains are delayed, and have adapted their sowing operation towards a more intensive agriculture and efficient water use during the rainy season.

All the characteristics of the mountainous environment directly influence the coffee crops, and subsequently, on the final coffee note, that reflects the quality of the beverage produced in a region. Despite increasing coffee research, little is known about the influence of the water indices, factors, and the elements of climate on top-quality coffee production potential. Thus, the present study was carried out aiming to analyze the water indices, causes, and aspects of clime, to identify those that most contribute to the potential production of high-quality Arabica coffee beverages in a mountain environment. We considered harvesting the coffee fruits at the cherry stage in 26 municipalities in the Matas de Minas region in the Atlantic Forest Biome in the eastern state of Minas Gerais, and the International Cup of Excellence method was adopted for the sensory evaluation. The principal components analysis and the multiple linear regression (MLR) were used to relate the local environmental variables with the final grade of the coffee beverage. As a result, the Multiple Linear Regression model showed the value of 0.63 for R2. This result means that the joint variability of all the variables considered explained 63% of the changes in coffee beverage quality. And the altitude impact on the grade achieved for the coffee beverage produced in the Matas de Minas region, represented by β, was 0.008068, meaning that for every 100 meters of increase in the altitude, there is an approximate increment of 0.8 points in the final note achieved for the coffee beverage. Among all the environmental characteristics studied, the climatic factor altitude was the main contributor to the coffee top-quality production potential in the Matas de Minas region.

Coffee is among the most significant products in Brazil. Minas Gerais is the largest state producer of Arabica coffee. Coffee activity has excellent growth potential, which justifies the identification of new areas for expansion of the culture. This study aimed to determine factors that affect the spatial distribution of coffee plantations the most, as well as to identify areas with a greater aptitude for its expansion in the region of the Matas de Minas (63 municipalities). The MaxEnt software was used to elaborate a model capable of describing the area with the highest potential for estimating the probability of coffee adequacy. The elaboration of the model considered the records of occurrence, climatic and topographic variables of Matas de Minas, the second largest state producing region. The area under the curve (AUC), the omission rate and the Jackknife test were used for validation and analysis of the model. The model was accurate with an AUC of 0.816 and omission rate of 0.54% for the ‘test’. It was identified that the potential distribution of coffee in Matas de Minas is determined by changes in the annual maximum temperature, although it did not generate a significant gain when omitted, accounting for a considerable loss in the model. However, the most influential variables on the delineation of distribution were, the altitude and the annual average temperature. The most favorable areas for expansion of coffee culture in the Matas de Minas were found in the vicinity of the region of Alto Caparaó. Abbreviations used: A1 (altitude); A2 (maximum annual temperature); A3 (annual minimum temperature); BIO 1 (annual average temperature 1); BIO 4 (temperature seasonality), BIO 12 (annual precipitation); BIO 15 (precipitation seasonality); csv (comma-separated values); AUC (area under the curve).

O estudo da susceptibilidade a erosão laminar é pertinente na mesorregião da Zona da Mata de Minas Gerais, visto a predominância da cobertura de pastagem e pela expressiva degradação do solo. Neste estudo, objetivou-se compreender quais variáveis geodinâmicas são importantes na predição dos processos erosivos laminares e o melhor modelo preditivo entre oito, através de comparações multicritérios, possibilitando entender o fenômeno em uma bacia hidrográfica da mesorregião. Assim, utilizou-se o método de atribuição de notas pela Literatura (L) e Realidade de campo (RC), cuja ponderação de parcela dos processos erosivos (60%) laminares mapeados ponderou a nota das classes das variáveis pela área das mesmas. A integração das variáveis foi por testes de ponderação e integração total e parcial. A avaliação dos modelos gerados foi por estatística descritiva (Box-Plot), diferentes métodos de categorização (Manual, Natural Breaks e Geometrical Interval) e curva ROC com cálculo de eficiência AUC (40% das erosões mapeadas). Os resultados apontaram que a falta umidade é um fator importante para a ocorrência dos processos erosivos laminares, por outro lado, as variáveis morfométricas não foram importantes para a predição. Modelos baseados na RC (72,41% AUC médio) obteve eficiência consideravelmente maior do que a L (65,41% AUC médio), já quando comparado a integração de todas as variáveis geodinâmicas e somente as mais importantes e quando integrado com ponderação e sem ponderação, não houve considerável diferença estatística. O modelo mais eficiente obteve 76,3% AUC, considerado boa e estava adequado a realidade da área estudada. Study of Susceptibility to Sheet Erosion in a Watershed in Zona da Mata, Minas Gerais, BrazilABSTRACTThe study of susceptibility to surface erosion is relevant in the mesoregion of the Zona da Mata of Minas Gerais, given the predominance of pasture cover, the significant degradation of the soil and the stagnation of the agricultural sector. In this study, the objective was to understand which geodynamic variables are important in the prediction of surface erosive processes and the best predictive model among eight, through multicriteria comparisons, making it possible to understand the phenomenon in a watershed in the mesoregion. Thus, it was used the method of attributing grades by Literature (L) and Field Reality (RC), whose weighting of the mapped surface erosive (60%) processes weighted the grade of the variable classes by their area. The integration of the variables was through weighting tests and total and partial integration. The evaluation of the models generated was by descriptive statistics (Box-Plot), different methods of categorization (Manual, Natural Breaks and Geometrical Interval) and ROC curve with AUC efficiency calculation (40% of the mapped erosions). The results showed that the lack of moisture is an important factor for the occurrence of surface erosive processes, on the other hand, the morphometric variables were not important for the prediction. Models based on RC (72.41% average AUC) achieved considerably greater efficiency than L (65.41% average AUC), when compared to the integration of all geodynamic variables and only the most important ones and when integrated with weighting and without weighting, there was no considerable statistical difference. The most efficient model obtained 76.3% AUC, considered good and was adequate to the reality of the studied area.Key words: Geotechnologies; Comparison of Risk Models; Multicriteria Analysis