HR: 1340h
AN: H33E-1419    [Abstracts]
TI: Characterization of Spatio-Temporal Dynamics Utilizing Climatic Information
AU: * Khalil, A F
EM: fka2102@columbia.edu
AF: Department of Earth and Environmental Engineering Columbia University, 918 Mudd 500 W. 120th St. , New York, NY 10027 United States
AU: Kwon, H
EM: hk2273@columbia.edu
AF: Department of Earth and Environmental Engineering Columbia University, 918 Mudd 500 W. 120th St. , New York, NY 10027 United States
AU: Lall, U
EM: ula2@columbia.edu
AF: Department of Earth and Environmental Engineering Columbia University, 918 Mudd 500 W. 120th St. , New York, NY 10027 United States
AU: Ahn, H
EM: hosung_ahn@nps.gov
AF: NPS - South Florida Ecosystem Office, FL 33030, USA., 950 N. Krome Ave., Homestead, FL 33030 United States
AB: Capturing the time varying spatial dynamics is essential for hydrologic systems. This research intends to develop an operational methodology to systematically address the spatio-temporal issues exploiting dynamic Bayesian methodology. A framework is constructed and employed to obtain near-optimal probabilistic forecasts in time and space. Providing multi-scale knowledge of some global climatic factors, state, and exogenous conditions, the framework is applied for the rainfall data of the Everglades National Park which exhibits 'long-memory' or regime like quasi-oscillatory behavior that may derive from low frequency climatic modes. Nonstationary covariance functions will be fused to capture the spatial dependency of the rainfall at the target locations. For many hydrologic systems this methodology can provide valuable management information.
DE: 1816 Estimation and forecasting
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
SC: Hydrology [H]
MN: Fall Meeting 2005