HR: 14:10h
AN: H23K-03 INVITED    [Abstracts]
TI: Evaluation of Hydrologic Prediction Accuracy Using Gauge-Adjusted Radar Precipitation Input
AU: * Vieux, B E
EM: bvieux@ou.edu
AF: University of Oklahoma, School of Civil Engineering and Environmental Science, Natural Hazards and Disaster Research, National Weather Center 120 David L. Boren Blvd., Suite 3600, Norman, OK 73072, United States
AU: Looper, J P
EM: looperjp@ou.edu
AF: University of Oklahoma, School of Civil Engineering and Environmental Science, Natural Hazards and Disaster Research, National Weather Center 120 David L. Boren Blvd., Suite 3600, Norman, OK 73072, United States
AU: Moreno, M A
EM: maria@ou.edu
AF: University of Oklahoma, School of Civil Engineering and Environmental Science, Natural Hazards and Disaster Research, National Weather Center 120 David L. Boren Blvd., Suite 3600, Norman, OK 73072, United States
AB: Operational hydrologic prediction in real-time at the event scale depends on having accurate representation of rainfall over watershed areas. A major limitation to predictions in gauged and ungauged basins is the lack of precipitation observations that are accurate or representative. Radar rainfall that has been enhanced for accuracy with rain gauge observations represents a significant advance in hydrologic prediction. This study is motivated by research in operational hydrology where predictions are needed at stream locations in headwater basins and river basins in rural and urban settings. Setup of the physics-based distributed hydrologic model, Vflo, is accomplished using geospatial data to derive readily obtainable physical parameters and used with input derived from NEXRAD radar and a rain gauge network. Evaluation of the accuracy in streamflow predictions is accomplished with several types of rainfall products as input derived from radar and gauge data. While gauge- adjusted radar and gauge-only products agree well after quality enhancement procedures are applied, the hydrologic predictions derived from the precipitation products reveal that improved accuracy is obtained by enhancing radar accuracy through bias correction and quality control procedures. Hydrologic prediction accuracy is known to be affected by both the forcing products derived from radar/gauge observations and from model uncertainty. The achievable accuracy for various radar input datasets derived from adjustment of continuous radar and gauge data is examined in this presentation to gain an understanding of inherent uncertainties associated with data quality, sampling error and bias correction procedures applied to radar. Through the framework of the Distributed Modeling Intercomparison Project (DMIP2), hydrologic prediction accuracy obtained through comparison of the hydrograph volume and peak discharge produced from two radar products and a gauge-only product over an extended period reveals prediction uncertainty associated with gauge network density. Uncertainty in rainfall derived from a multi-radar mosaic is compared with hydrologic prediction uncertainty. Distributed hydrologic simulation accuracy for a ten year period of hourly rainfall is presented for the 1200 km² Blue River to test the uncertainty and accuracy of streamflow produced by a physics-based model in relation to the rainfall input product.
DE: 1847 Modeling
DE: 1848 Monitoring networks
DE: 1853 Precipitation-radar
DE: 1860 Streamflow
DE: 1874 Ungaged basins
SC: Hydrology [H]
MN: 2007 Fall Meeting