HR: 11:20h
AN: H32C-05    [Abstracts]
TI: Modeling the uncertainty associated with the observation scale of space/time natural processes
AU: Lee, S
EM: seungjea@email.unc.edu
AF: University of North Carolina, Depart of Environmental Sciences and Engineering, Chapel Hill, NC 27599-7431 United States
AU: * Serre, M
EM: marc_serre@unc.edu
AF: University of North Carolina, Depart of Environmental Sciences and Engineering, Chapel Hill, NC 27599-7431 United States
AB: In many mapping applications of spatiotemporally distributed hydrological processes, the traditional space/time Geostatistics approaches have played a significant role to estimate a variable of interest at unsampled locations. Measured values are usually sparsely located over space and time due to the difficulty and cost of obtaining data. In some cases, the data for the hydrological variable of interest may have been collected at different temporal or spatial observation scales. Even though mixing data measured at different space/time scales may alleviate the problem of the sparsity of the data available, it essentially disregards the scale effect of estimation results. The importance of the scale effect must be recognized since a variable displays different physical properties depending on the spatial or temporal scale at which it is observed. In this study we develop a mathematical framework to derive the conditional Probability Density Function (PDF) of a variable at the local scale given an observation of that variable at a larger spatial or temporal scale, which properly models the uncertainty associated with the different observations scales of space/time natural processes. The developed framework allows to efficiently mix data observed at a variety of scales by accounting for data uncertainty associated with each observation scale present, and therefore generates soft data rigorously assimilated in the Bayesian Maximum Entropy (BME) method of modern Geostatistics to increase the mapping accuracy of the map at the scale of interest. We investigate the proposed approach with synthetic case studies involving observations of a space/time process at a variety of temporal and spatial scales. These case studies demonstrate the power of the proposed approach by leading to a set of maps with a noticeable increase of mapping accuracy over classical approaches not accounting for the scale effects. Hence the proposed approach will be useful for a wide variety of applications in hydrology and other earth sciences fields.
DE: 1819 Geographic Information Systems (GIS)
DE: 1839 Hydrologic scaling
DE: 1871 Surface water quality
DE: 1873 Uncertainty assessment (3275)
DE: 1894 Instruments and techniques: modeling
SC: Hydrology [H]
MN: Fall Meeting 2005