HR: 11:35h
AN: H12E-05    [Abstracts]
TI: Model-Data Integration to Provide a Probabilistic Assessment of the Role of Snowmelt in Runoff Production in the Pacific Northwest
AU: * Wigmosta, M S
EM: mark.wigmosta@pnl.gov
AF: Pacific Northwest National Laboratory, 902 Battelle Boulevard P.O. Box 999, Richland, WA 99352, United States
AU: Coleman, A M
EM: andre.coleman@pnl.gov
AF: Pacific Northwest National Laboratory, 902 Battelle Boulevard P.O. Box 999, Richland, WA 99352, United States
AU: Gill, K
EM: Kashif.Gill@pnl.gov
AF: Pacific Northwest National Laboratory, 902 Battelle Boulevard P.O. Box 999, Richland, WA 99352, United States
AU: Leung, R
EM: Ruby.Leung@pnl.gov
AF: Pacific Northwest National Laboratory, 902 Battelle Boulevard P.O. Box 999, Richland, WA 99352, United States
AU: Vail, L W
EM: lance.vail@pnl.gov
AF: Pacific Northwest National Laboratory, 902 Battelle Boulevard P.O. Box 999, Richland, WA 99352, United States
AU: Prasad, R
EM: Rajiv.Prasad@pnl.gov
AF: Pacific Northwest National Laboratory, 902 Battelle Boulevard P.O. Box 999, Richland, WA 99352, United States
AB: We utilize an integrated combination of spatially distributed hydrologic modeling with remotely-sensed and ground based measurements to evaluate the contribution of snowmelt to runoff over a range of flow conditions and geographic locations in the Pacific Northwest. The contribution of snowmelt to runoff varies widely across the region depending on geographic location, elevation, land cover, and local meteorological conditions. Rain-on- snow events are relatively common within the snow transition zone and have contributed to a number of large floods. Snotel sites provide information on precipitation and changes in snow water equivalent at higher elevations, however, ground-based observation are generally lacking in the transition zone. We utilize an approach that allows the hydrologic model to be updated with remotely sensed spatial snow properties and measured streamflow using an Ensemble Kalman-based data assimilation strategy that accounts for uncertainty in meteorology, model parameters, and the observations used for updating. Although designed for ensemble streamflow forecasting, in this application the model provides a process based method to integrate multiple data sets across various spatial and temporal scales, allowing a probabilistic assessment of the role of snowmelt in runoff production.
DE: 1817 Extreme events
DE: 1833 Hydroclimatology
DE: 1847 Modeling
DE: 1855 Remote sensing (1640)
DE: 1860 Streamflow
SC: Hydrology [H]
MN: 2007 Fall Meeting