HR: 1340h
AN: IN43B-1176    [Abstracts]
TI: Investigating Satellite Rainfall Based Flood Modeling in Anticipation of GPM: Understanding the Worth of Spatial Downscaling and Satellite Rainfall Uncertainty
AU: Rahman, S
EM: sayma.rahman@uconn.edu
AF: University of Connecticut, Department of Civil & Environmental Engineering 261 Glenbrook Road, Unit 2037, Storrs, CT 06269, United States
AU: Bagtzoglou, A C
EM: acb@engr.uconn.edu
AF: University of Connecticut, Department of Civil & Environmental Engineering 261 Glenbrook Road, Unit 2037, Storrs, CT 06269, United States
AU: * Yarbrough, L D
EM: ldyarbro@olemiss.edu
AF: University of Mississippi, Geology and Geological Engineering 118 Carrier Hall, University, MS 38677, United States
AU: Hossain, F
EM: fhossain@tntech.edu
AF: Tennessee Technological University, Civil and Environmental Engineering 1020 Stadium Drive Box 5015, Cookeville, TN 38505, United States
AB: Realistic flood modeling in medium-large river basins requires rainfall data at hydrologically relevant scales ranging from 1--5 km. However, satellite rainfall data has historically been available at spatial resolutions that can be considered somewhat coarse for predicting the dynamic flood phenomenon (~ 25--100km). As a natural response to this limitation that has persisted for over a decade, hydrologists have devised numerous statistical spatial downscaling schemes till now. With the proposed Global Precipitation Measurement (GPM) mission, satellite rainfall data will gradually become more available at smaller scales (~ 10 km) in the next decade, prompting us to re-evaluate the worth of spatial downscaling for flood modeling. In this study, we therefore seek an answer to the question--- Which is the better option for satellite rainfall based flood simulation when rainfall data is available at coarse scale---a) an error propagation based ensemble streamflow scheme at the coarse (native) resolution or a probabilistically downscaled based ensemble streamflow scheme? The study is performed on the 970 km² basin of the Upper Cumberland River in southeastern Kentucky bordering with Virginia and Tennessee. NASA satellite rainfall data products from the TRMM Multi-satellite Precipitation Analysis (TMPA) are used for the investigation. A statistical downscaling scheme of Perica and Foufoula-Georgiou (1996) and a satellite rainfall error modeling scheme of Hossain and Anagnostou (2006) are used for resolving the posed science question. Findings indicate that spatial downscaling does not unconditionally guarantee more accurate flood simulations as scale becomes smaller. The narrow range of uncertainty in flood simulation due to downscaling often misses the observed peak flow. On the other hand, error propagation at the native scale based on satellite rainfall uncertainty information tends to capture the natural variability during peak flows with much greater confidence.
DE: 0480 Remote sensing
DE: 1839 Hydrologic scaling
DE: 1854 Precipitation (3354)
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting