HR: 09:00h
AN: H51J-05    [Abstracts]
TI: Exploiting Natural Stimuli for "Seeing" into Watershed/Groundwater Basins
AU: * Yeh, T J
EM: yeh@hwr.arizona.edu
AF: University of Arizona, John Harshbarger Building 1133 E. North Campus Drive, Tucson, AZ 85721 United States
AB: Inverse modeling is an ultimate quantitative approach for characterizing parameters of watershed or basin-scale hydrologic models. Tomographic surveys make an inverse problem better posed because each survey cross-validates the others so that the modeling result approaches reality. Tomographic surveys using different types of energy provide coverage and perspectives of a watershed or basin at difference scales. Fusion of different tomographic surveys expands and enhances the capability of a particular type of survey, provides cross-validation, and drives new sampling strategies. Watershed or basin scale tomographic surveys require energy sources of great strengths. Spatially and temporally varying natural stimuli (i.e., precipitation, barometric pressure, river stage, lightning, earthquakes, etc.) are ideal energy sources for this purpose. The overall objective of this study is to develop a Dynamic Data Driven Application System (DDDAS) for characterizing hydrologic basins using spatially and temporally varying natural stimuli as energy sources for naturally recurrent, basin-scale tomographic surveys (i.e., CAT scans). The DDDAS will be context-aware, self-configuring, self-compositing, and self-optimizing. Specifically, we will define autonomic components, develop autonomic subsurface simulations as dynamic compositions of autonomic models, and design key autonomic middleware services to support the hydrologic applications. In addition, a hydrologic stochastic information fusion technology that is based on the Bayesian formalism is included in the DDDAS. Thus, the DDDAS will enable hydrologists to dynamically collect and process hydrologic data, and provide unbiased forecasts and associated uncertainties. These uncertainties then are fed back to the system to dynamically change data sampling and simulation strategies. As a result, near-real time and high-resolution monitoring, characterizing, and forecasting of hydrologic processes in watersheds and basins become possible.
UR: http://www.hwr.arizona.edu/yeh
DE: 1830 Groundwater/surface water interaction
DE: 1835 Hydrogeophysics
DE: 1848 Monitoring networks
DE: 1849 Numerical approximations and analysis
DE: 1879 Watershed
SC: Hydrology [H]
MN: Fall Meeting 2005