Ajay Srivastava, Assistant Professor, Department of Remote Sensing, Birla Institute of Technology - Ranchi, INDIA 835 215

Abstract: In this study a systematic approach has been made for the analysis of Ken graben area by integrating the remote sensing data with the hydrologic data to study the subsurface geological and geomorphological details and to demonstrate the aquifer geometry, ground water quality in the region. The approach involves regional interpretation of geomorphological and structural features exposed at the surface and relating the same to the subsurface. The region has varying thickness of alluvium composed of alternating sand/kankar and clay strata deposited on an uneven basement. In the present study the geological, geomorphological and structural aspects of the terrain have been carried out using IRS LISS I/ II data. The subsurface features of importance in the ground water exploration such as buried channels have been identified. Efforts have been made to generate a digital elevation model of the subsurface topography with the help of depth to bedrock contours. This has facilitated identification of the areas with favourable aquifer disposition and subsurface geomorphic features that are potential sites for ground water development. Two different types of basement depressions are present in the study area, which affect aquifer geometry, ground water potential and quality. Digital elevation model (DEM) of the basement topography has been prepared by converting set of depth to bedrock contours to another set of contours. Variations in tone and texture associated with vegetation and geological features coupled with inferred ground water migration pattern in the study area have enabled the delineation of the brackish ground water pockets that are in close agreement with the field investigation. An overlay of the enhanced image on the digital terrain model of the basement has enabled an understanding of the exact subsurface geometry of the aquifers and their relationship to the surficial geomorphic features. Ground water hydrogeological status has been inferred from an integration of the information from structural, lithological and vegetational information, DEM along with available geologic, as well as topographic and hydrologic data.

Keywords: DEM, Basement topography, Remote Sensing, GIS, Aquifer geometry, Water quality, Fence diagram and cross-sections.

Isam Salih M M¹, Håkan B.L Pettersson¹, Åke Sivertun² and Eva Lund¹
1. Department of Radiation Physics, IMV, Linköping University, S-582 85 Linköping, Sweden
2. Department of Computer and Information Science (IDA), Linköping University, S-581 83 Linköping, Sweden

Abstract: This study describes approaches to create surface maps of radon in groundwater based on measurements of radon (222Rn) in drilled bedrock wells at unevenly distributed sites and uranium bedrock maps from the South East of Sweden, the Östergotland county (N 58°14’ – N 58°56’ and E 14°53’ – E 16°06’), see figure 1. Geostatistical techniques of inverse distance weighted (IDW), kriging and cokriging were compared in terms of their interpolation power and correlation between the produced radon in the water layer and the bedrock uranium layer. The goal of these analyses and calculations is to improve our understanding concerning the factors influencing the transport of radon. Therefore, these interpolation techniques were investigated by optimizing parameters that are used in the specific interpolation. Using the IDW interpolator method at fixed radius enabled us to determine the linkage or search distances for auto correlation, and linkage between radon in water and bedrock. This method showed good agreement with the cokriging method when using uranium concentration as a secondary variable. Good interpolation layers (with least root mean square errors RMSE=232) were obtained by kriging. However, the kriged radon surface showed poor correlation with bedrock uranium layers. The best radon in water layer that match with uranium in bedrock layer was produced using IDW interpolator (RMSE=377, using all points). The correlation coefficient (R2) is 0.5 while for the kriging method the best correlation is R2 = 0.1. A compromise between the two approaches is demonstrated.

Keywords:  radon, uranium, groundwater, bedrock, GIS, Kriging, IDW 

B. D. Kulkarni, Indian Institute of Tropical Meteorology, Pashan Pune-8

Abstract: In this paper a generalized physical approach of estimating areal probable maximum precipitation (PMP) for the non-orographic region of the Godavari river basin has been developed. In this method, highest average areal rain depths of different size areas and duration from the major rainstorms were considered, using 105 years rainfall data (1891-1995). The transposition limits of major rainstorms have been identified. The Depth-Area-Duration (DAD) rain depths were then moisture maximized at its original location of occurrence and then transposed at different grid points. After applying various corrections, Probable Maximum Precipitation (PMP) values at different grid points were estimated. By using this method, generalized PMP estimates at different locations were obtained and with the help of these estimated PMP values generalized charts for 1000, 5000 and 10,000 km2 areas have been prepared. These PMP maps for different size areas and duration will be very useful for estimating design storm rain depths of PMP magnitudes for any sub-catchment in the Godavari basin whose areas area falling in range of 1000 to 10,000 km2.

Keywords: Probable Maximum Precipitation (PMP), Depth-Area-Duration (DAD) analysis, Transposition, Moisture Maximization, Dew point Temperature, Rainstorm and Perceptible Water

Enayetur Raheem, Lecturer and Sekander Hayat Khan, Professor, Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka- 1000, Bangladesh.

Abstract: Probabilistic considerations have been practiced in determining the capacity of a dam after the introduction of probability theory of dams by P. A. P. Moran (1954). Various researchers determined the capacity by using stationary distribution of the dam content, mean of the first emptiness time, and by specifying the probability of overflow of a dam. In this study, after highlighting the methods used by the design engineers using probabilistic consideration at various stages of the process, capacity has been determined by using the probability of emptiness and overflow simultaneously. As an example, riverflow data of Mitta Mitta River of Australia has been considered. The data was available only for a short period of time. So long inflow sequences have been generated by keeping intact the statistical properties of the historical data, and then determined the capacity.

Keywords:  Stochastic simulation, dam process, behavior analysis 

Barry M. Evans¹, David W. Lehning¹, Kenneth J. Corradini¹, Gary W. Petersen², Egide Nizeyimana¹, James M. Hamlett³, Paul D. Robillard³, and Rick L. Day^2
1Environmental Resources Research Institute, ²Department of Crop and Soil Sciences, ³Department of Agricultural and Biological Engineering, The Pennsylvania State University, University Park, PA 16802.

Keywords: GIS, watershed modeling, TMDL, nutrients, nonpoint source pollution