The Method of Anchored Distributions (MAD): Principles and Implementation as a Community Resource
This project will develop a community-based, open-source computational platform for Bayesian inverse modeling and conditional simulations in hydrology. It is driven by the need to provide easily-accessible tools for applications and a platform for broad-based, long-term development, built to meet the challenges brought upon by the ever increasing complexity of modern computational tools, the diversity of data types and the breadth and depth of the subject matters needed for applications. The project is inspired by the success experienced by other communities with a long tradition of community-based development efforts. We will construct a kernel that will include a modular computational platform and a user-interface that will allow users and developers to link the kernel with their models. The kernel will be built using open-source architecture. The primary focus will be on developing a standalone version (for large-scale applications at the user?s computers). The kernel will be interoperable with a wide array of community-based resources (such as CUAHSI?s hydrologic information systems) and libraries (such as R). With these concepts and principles, various types of data could be assimilated, simplifying assumptions could be adopted at the user?s discretion, and powerful numerical models and analytical tools, current or under development, could be used as a focal point for long-term community-based development. The project developers will coordinate the project with CUAHSI. CUHASI will act as the custodian of the MAD computational platform. This implies securing the integrity of the MAD kernel, updating it and making it available to users and developers. This effort is in line with CUAHSI's overall strategy.
MAD will facilitate the science discovery process by making advanced inverse modeling tools available to users and an inviting development platform for developers. It will enhance the quality of hydrologic science by encouraging collaboration between hydrologists and earth scientists, and provide a platform for statisticians and computer scientists. MAD will provide educators and students with access to modern, well-documented computational tools, and with the motivation to experiment with them, because it will be understood as a general and extensible tool that is useful far beyond the classroom. MAD will make a contribution to the growing culture of community-based, open-source analytical tools that make science more accessible. Long-standing, well-tested, transparent and hence better-trusted analytical tools form the base for a constructive public discourse on matters relating to environmental regulations.
