Missouri River Watershed: the Object for Hydrological Study and Uncertainty of Models

Abstract

The longest river (2540 mi) and the second drainage area (529 346 mi2) in the North America the Missouri River is one from the most interesting hydrological objects to study. The issue of uncertainty is the basis for any application of knowledge (“Uncertainty is an attribute of information.” From Zadeh, 2005) and has to be one of the main tasks in Earth’s systems study. Knowledge about natural systems (watershed in our case) may be only obtained by the analysis of the empirical (instrumental) data (observations). Principle of Uncertainty is the basic low in Physic. In Hydrology, the Uncertainty starts from the unveiling of the research task by the researcher. The main source of the uncertainty comes from the natural system “extraction” (unit’s boundaries) for modeling and from the limitations of data representing both time and space variability. The watershed has the formal determined boundary and this property places hydrology in the center of regional climate research. The uncertainty is considered in context of time and space with use of cybernetic model of the watershed and described on base of specification of the system in the coordinates on the Earth. The math model does not have criteria to verify itself (Godel's incompleteness theorems) – multitasks & multiscales studies have to be completed. The data analysis for Upper Missouri River provide a base for regionalization, a multi-scaled description of the structure of river watersheds and their interaction with climate characteristics, and uncertainty of the obtained knowledge. The formulation of the uncertainty for watershed helps to explain the scope of practical applications to be developed, and the tasks to study, communicate and educate the public/communities about water resources and environmental issues, including extreme events like drought/flooding. Science is one of many components of life but the scientist is the only holder of the “truth” and a creator of formal knowledge of the nature. “… data analysis assists in the formulation of a model … A model is merely your reflection of reality and, like probability, it describes neither you nor the world, but only a relationship between you and that world” (From Lindley, “Principles of Statistics”, 2006).