Geosciences and Information Technology Section

The Geosciences and Information Technology Section (GITS) performs applied studies dealing with environmental issues that have surface water, ground water, or soils components. GITS focuses on solving problems in a larger context and has state-of-the-art capabilities for both data analysis and predictive modeling. GITS uses advanced communication, data storage, and visualization techniques to analyze data and to communicate with others and uses advanced methods and tools for problem solving.

Recent ground water projects have addressed areas such as flow and contaminant transport modeling in porous and fractured media, optimal design of subsurface containment systems, adaptive sampling plan design, stochastic simulation of the extent of contamination, and distributed data management. Surface water projects have included studying the effects of hydropower generation and scheduling on downstream ecosystems and developing flow and quality models for a major river basin in Asia to evaluate country-wide development plans, assess various remediation options, and formulate consistent management strategy to meet quality and supply objectives.

The GITS staff has expertise in a wide range of areas including:

• Mathematical modeling of ground water
• Vadose and phreatic zone modeling
• Stochastic modeling
• Multi-phase modeling
• Analytical and mathematical model development
• Field and laboratory test modeling and evaluation
• Fracture flow and transport



• Adaptive Sampling Design
• Bayesian statistics for incorporating "soft" data
• Value of information analysis
• Stochastic simulation
• Directed sampling plans
• Risk based sampling design



• Mathematical modeling of surface water
• Statistical parameter generation
• River flow modeling
• Water quality modeling
• Optimal design of release schedules
• Stochastic simulation
• Overland flow modeling
• Erosion and sediment transport modeling


• Computer Science
• Data base design
• Distance communication
• High performance computing
• Parallel computing
• Mathematical optimization
• Visualization