Método de elaboração de mapas dos cenários climáticos futuros para o Brasil.

Human activities are altering greenhouse gas concentrations in the atmosphere and causing global climate change. The issue of impacts of human-induced climate change has become increasingly important in recent years. The objective of this work was to develop a database of climate information of the future scenarios using a Geographic Information System (GIS) tools. Future scenarios focused on the decades of the 2020?s, 2050?s, and 2080?s (scenarios A2 and B2) were obtained from the General Circulation Models (GCM) available on Data Distribution Centre from the Third Assessment Report (TAR) of Intergovernmental Panel on Climate Change (IPCC). The TAR is compounded by six GCM with different spatial resolutions (ECHAM4:2.8125×2.8125º, HadCM3: 3.75×2.5º, CGCM2: 3.75×3.75º, CSIROMk2b: 5.625×3.214º, and CCSR/NIES: 5.625×5.625º). The mean monthly of the climate variables was obtained by the average from the available models using the GIS spatial analysis tools (arithmetic operation). Maps of mean monthly variables of mean temperature, minimum temperature, maximum temperature, rainfall, relative humidity, and solar radiation were elaborated adopting the spatial resolution of 0.5° X 0.5° latitude and longitude. The method of elaborating maps using GIS tools allowed to evaluate the spatial and distribution of future climate assessments. Nowadays, this database is being used in studies of impacts of climate change on plant disease of Embrapa projects.

A method for the estimation of potential evapotranspiration and/or open pan evaporation over Brazil.

This paper presents a simple regression model to estimate potential evapotranspiration and/or open pan evaporation data for a wide network of stations in Brazil. The model uses the readily available data sets like geocoordinates (latitude) and precipitation as inputs. Potential evapotranspiration presents a high correlation with the precipitation during summer months and with latitude during winter months. It also shows association with longitude and elevation; the magnitude of variation appears to be very small. This model gave a R2 varying from 0.460 to 0.902 for different months. The model is also extended to weekly periods of individual years ant tested with the open pan evaporation data of Bebedouro and Mandacaru. The agreement between observed and predicted values appears to be good.