Abstract

In Pakistan, the solar analogue has been addressed but its surface geographical parameterization has given least attention. Inappropriate density of stations and their spatial coverage particularly in difficult peripheral national territories, little or no solar radiation data, nonsatisfactory sunshine hours data, and low quality of ground observed cloud cover data create a situation in which the spatial modeling of Extraterrestrial Solar Radiation(ESR) and its ground parameterization got sufficient scope. The Digital Elevation Model (DEM) input into Geographic Information System (GIS) is a compatible tool to demonstrate the spatial distribution of ESR over the rugged terrains of the study domain. For the first time, distributed modeling of ESR is done over the rugged terrains of Pakistan, based on DEM and ArcGIS.. Results clearly depict that the complex landforms profoundly disrupt the zonal distribution of ESR in Pakistan. The screening impact of topography is higher on spatial distribution of ESR in winter and considerably low in summer. The combined effect of topography and latitude is obvious. Hence, the model was further testified by plotting Rb (ratio of ESR quantity over rugged terrain against plane surface) against azimuth at different latitudes with different angled slopes. The results clearly support the strong screening effect of rugged terrain through out the country especially in Himalayas, Karakoram and Hindukush (HKH), western border mountains and Balochistan Plateau. This model can be instrumental as baseline geospatial information for scientific investigations in Pakistan, where substantial fraction of national population is living in mountainous regions.

Detecting change on the face of the globe using GIS (Geographic Information System) aided by remotely sensed imagery is now becoming an indispensable tool in managing the resources of our planet. The present study with the help of GIS and remote sensing (RS) is also a similar attempt in recording and quantifying change in land use and land cover in district Pishin both in spatial and temporal extents. Satellite imagery was acquired from the USGS official website from three LANDSAT satellites. Theses satellites are LANDSAT 5, LANDSAT7 and LANDSAT 8. The data were acquired for the years 1992, 2003 and 2013. Satellite imagery was processed in ArcMap 10.1 and maximum likelihood supervised image classification was applied in reaching the goal of detecting change. The result of the analysis revealed that built-up area was increased by 5.84%; vegetation was increased by 3.89%; water bodies were increased by 0.05% and bare surfaces were decreased by 9.78%. The decrease in the barren surfaces was attributed to the increase in vegetation and built-up area which replaced the barren land in the study area. This paper also shows the significance and potential of digital change detection methods in managing the resources of our environment and keeping an eye on the land use and land cover of our Earth.

Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatialtemporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.