Climate assessment essentially involves a good understanding of rainfall and temperature patterns. As such, there are many factors to be considered while studying climate. Although, temperature and rainfall are playing an extremely important and manifold role in climatic research particularly in various environmental hazards. The aim of this study was to develop and validate a forecasting model that could predict temperature and rainfall and provide timely early warning in Hunza-Nagar. In this paper temperature and rainfall dataset (2007-2011) have used and developed a quantitative treatment using different statistical methods such as regression and time series/stochastic modeling. The regression analysis proposes that the rainfall increased with increasing temperature. It also found that trends in monthly mean maximum temperature indices increase from years 2007 to 2011 while the amount of rainfall has decreased. The available data presented that AR (1) model is most adequate for a forecast of temperature. These forecasts will be useful for public, private and government organization.

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This study is an attempt to map and measure the quality of life in the urban area of Karachi, Pakistan, by using Landsat-7 sensor, Enhanced Thematic Mapper (ETM+) combined with Census data through the state-of-the-art Geographic Information System (GIS). For this purpose, the physical environment of the city is determined by the variables of Normalized Difference Vegetation Index (NDVI), surface temperature and land cover/use. These are extracted from the satellite image data through various techniques of remote sensing. On the other hand, the socioeconomic variables were obtained from the 2000 Karachi District Census Reports, to represent the living environment of the city. Finally, the integration of the physical variables with the socioeconomic variables was conducted in a GIS framework using an aggregated Z Sum score approach, in order to derive the quality of life scores for the city of Karachi on the basis of Administrative-Spatial Units called Union Councils (UCs). The results effectively demonstrated the efficiency of the Index raster techniques to evaluate and map the quality of life over the study area. In addition, the GIS techniques also isolated the contributing variables that may be responsible for the spatial variability in the quality of life.

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