HR: 11:15h
AN: H52A-04    [Abstracts]
TI: Forecast uncertainty in semi-arid flash flood modeling using radar rain input
AU: Unkrich, C
EM: Carl.Unkrich@ARS.USDA.GOV
AF: USDA-ARS-SWRC, 2000 E. Allen Road, Tucson, AZ 85719, United States
AU: * Yatheendradas, S
EM: soni@nmt.edu
AF: Department of Earth and Environmental Science, New Mexico Tech, 801 Leroy Place, MSEC, Socorro, NM 87801, United States
AU: Gupta, H
EM: hoshin.gupta@hwr.arizona.edu
AF: Department of Hydrology and Water Resources, The University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, United States
AU: Wagener, T
EM: thorsten@engr.psu.edu
AF: Department of Civil and Environmental Engineering, Pennsylvania State University, 212 Sackett Building, University Park, PA 16802, United States
AU: Goodrich, D
EM: Dave.Goodrich@ARS.USDA.GOV
AF: USDA-ARS-SWRC, 2000 E. Allen Road, Tucson, AZ 85719, United States
AU: Schaffner, M
EM: Mike.Schaffner@noaa.gov
AF: National Weather Service, Binghamton Weather Forecast Office, 32 Dawes Drive, Johnson City, NY 13790, United States
AU: Stewart, A
EM: astewart@hwr.arizona.edu
AF: Department of Hydrology and Water Resources, The University of Arizona, 1133 E James E. Rogers Way, Tucson, AZ 85721, United States
AB: Flash floods are extremely dangerous hazards in the semi-arid southwest US at short temporal scales, posing a significant danger to life and property. Attempts to mitigate this flood risk using model-based forecasting are subject to uncertainties in the model and the data. This study reports on such an attempt using the distributed, semi-arid mechanistic rainfall-runoff model KINEROS2 driven by the NEXRAD WSR-88D DHR-based high resolution radar rainfall input. Sources of operational uncertainty considered in an integrated manner include rainfall estimates, model parameters, and initial conditions. Using a variance-based comprehensive global sensitivity analysis on both real and synthetic data from several events, the high predictive uncertainty in the modeled response was seen to be heavily dominated by operational event-specific biases in the radar rainfall depth estimates. Uncertainties in specific influential model parameters and initial conditions to be preferentially reduced were recognized, which show hillslopes to be more influential than channels on the outlet response in small basins. An inconsistency in behavioral/optimal model parameter set values was seen across events. This indicates the requirement of a computationally intensive Monte-Carlo setup that can incorporate such currently wide source uncertainty ranges with continuous incoming event information updating at local Weather Forecast Offices
DE: 1804 Catchment
DE: 1817 Extreme events
DE: 1821 Floods
DE: 1853 Precipitation-radar
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting