Abstract Changes in land use and land cover (LULC) can result in significant changes in a hydrographic basin flow regime. Future projections about LULC and its interference with water availability help to identify extreme events in advance and help propose appropriate management measures. Thus, this study aimed to make the LULC projection for the year 2030 for the Alto Rio Grande (ARG) sub-basin, located in Southeastern Brazil. This region was chosen because of its intense water resources use and for having recently faced water scarcity as result of prolonged droughts and inadequate water resources management. To identify the LULC trend for the year 2030, the Land Change Modeler (LCM) was used, the map obtained was inserted in the Soil and Water Assessment Tool (SWAT) model previously calibrated and validated for the region’ environmental and climatic conditions. The ARG sub-basin was affected by heavy rains in 2011, which resulted in changes in the landscape due to landslides. This particularity of the region contributed to the projection of LULC for the year 2030 to present an increase in forest and pastures to the agricultural areas detriment. When evaluating the impacts of these changes in water availability, it was observed that the SWAT model presented, for the same rainfall conditions, a reduction in peak streamflows of up to 59% and a reduction in the average monthly flow of up to 63% in 2030 in relation to the LULC observed in 2017. Thus, this study provides an important contribution by identifying a considerable reduction in water availability. These results will help to formulate strategies for water resources management and the adoption of measures to promote water security in the region.

Resumen Los cambios en el uso y la cobertura del suelo pueden resultar en cambios significativos en el régimen del flujo de una cuenca hidrográfica. Los estudios predictivos sobre el uso del suelo y su interferencia con la disponibilidad del agua ayudan a identificar eventos extremos con anticipación a fin de proponer medidas de gestión adecuadas. De esta forma, este estudio tuvo como objetivo realizar la predicción del uso del suelo para el año 2030 para la subcuenca del Alto Río Grande (ARG), ubicada en el sureste de Brasil. Esta región fue elegida por el uso intenso de los recursos hídricos y por haber enfrentado, recientemente, escasez de agua como resultado de sequías prolongadas y una gestión inadecuada de los recursos hídricos. Para la predicción del uso del suelo en 2030 se utilizó el Land Change Modeler (LCM), el mapa obtenido se insertó en el modelo Soil and Water Assessment Tool (SWAT) previamente calibrado y validado para las condiciones ambientales y climáticas de la región. La subcuenca ARG fue afectada por fuertes lluvias en 2011 que resultaron en cambios en el paisaje debido a deslizamientos de tierra. Esta particularidad de la región contribuyó para que la predicción del uso del suelo para el año 2030 presente un aumento de bosques y pastos en detrimento de las áreas agrícolas. Al evaluar los impactos de estos cambios en la disponibilidad del agua, se observó que el modelo SWAT presentó, para las mismas condiciones de precipitación, una reducción en los caudales pico de hasta 59% y una reducción en el caudal promedio mensual de hasta 63% en 2030 en relación con el uso del suelo observado en 2017. Es decir, este estudio hace un aporte importante al identificar una reducción considerable en la disponibilidad del agua. Estos resultados ayudarán a formular estrategias para la gestión de los recursos hídricos y la adopción de medidas para promover la seguridad hídrica en la región.

Abstract Forest burning susceptibility mapping is a tool to mitigate wildfires, with several methods to develop them. This study aimed to compare the Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), and Random Forest (RF) methods for mapping. Several variables were used to generate the maps. For MLR and RF methods, fire frequency between 1990 and 2010 was used as the response variable in the models. To validate the methods (AHP, MLR and RF), fire data between 2011 and 2018 were used in four stages. RF was the best method employed. Correct and incorrect values for this method were 74% and 26% and AUC 0.66. The sensitivity and specificity for the highest risk class were 31% and 96%. The low sensitivity values can be attributed to the randomness attributed to anthropic fire. The high specificity values point to a good separation of the higher risk class compared to the others.

Resumo:A integração de sistemas de informação geográfica com os métodos de decisão multicritério, vem proporcionando inúmeros benefícios para a resolução de problemas de planejamento urbano. Nesse sentido este trabalho apresenta a metodologia utilizada na avaliação e seleção de áreas para a implantação de uma universidade e de um loteamento universitário no município de Patos de Minas - MG. Foram encontrados um local para o loteamento com área de 25,83 ha, e um para a universidade com uma área de 31,47 ha, ambos com risco médio e alta compensação. Os resultados obtidos revelam que o uso da ferramenta SIG associada a análise multicritério são eficientes na escolha de locais para a implantação de empreendimentos que causarão impactos no ambiente a ser instalado

Abstract:The integration of geographic information with the methods of multicriteria decision systems, has provided numerous benefits to solving problems of urban planning. In this sense this work presents the methodology used in the evaluation and selection of areas for the implementation of a university and a college allotment in the city of Patos de Minas - MG. A location for the allotment with an area of 25.83 ha, and one for the university were found with an area of 31.47 ha, both with medium risk and high reward. The results show that the use of the GIS tool associated with multicriteria analysis are efficient in selecting sites for the implementation of projects that will have impacts on the environment to be installed