Models have an inherent uncertainty. The difficulty in fully characterizing the subsurface environment makes uncertainty an integral component of groundwater flow and transport models, which dictates the need for continuous monitoring and improvement. Building and sustaining confidence in closure decisions and monitoring networks based on models of subsurface conditions require developing confidence in the models through an iterative process. The definition of model validation is postulated as a confidence building and long-term iterative process (Hassan, 2004a). Model validation should be viewed as a process not an end result. Following Hassan (2004b), an approach is proposed for the validation process of stochastic groundwater models. The approach is briefly summarized herein and detailed analyses of acceptance criteria for stochastic realizations and of using validation data to reduce input parameter uncertainty are presented and applied to two case studies. During the validation process for stochastic models, a question arises as to the sufficiency of the number of acceptable model realizations (in terms of conformity with validation data). Using a hierarchical approach to make this determination is proposed. This approach is based on computing five measures or metrics and following a decision tree to determine if a sufficient number of realizations attain satisfactory scores …

This 8-page brochure describes the Office of Industrial Technologies' financial assistance programs--National Industrial Competitiveness through Energy, Environment, and Economics (NICE3) and Inventions and Innovation (I and I). Cost-shared grants awarded by NICE3 provide up to $500,000 to industry-state partnerships for demonstrations of clean and energy-efficient technologies. I and I awards grants of up to $200,000 to inventors who are establishing technical performance or in the early stages of development or commercialization of energy-efficient technologies.