HR: 14:30h
AN: H43E-05    [Abstracts]
TI: Impact of Spatial Interpolation Methods for Precipitation on Ensemble Streamflow Simulation From Watershed Models
AU: * Hwang, Y
EM: yhwang@colorado.edu
AF: University of Colorado, 1333 Grandview Ave., Boulder, CO 80309 United States
AU: Clark, M P
EM: clark@vorticity.colorado.edu
AF: CIRES, University of Colorado, 1333 Grandview Ave., Boulder, CO 80309 United States
AU: Rajagopalan, B
EM: Rajagopalan.Balaji@colorado.edu
AF: University of Colorado, CEAE, Campus Box 428, Boulder, CO 80309 United States
AB: Watershed models are used for simulating basin streamflows based on spatially sparse precipitation and temperature observations. The sparse observations are typically interpolated on a regular grid or a subbasin as inputs to the hydrologic models. Given the paucity in observations and nonhomogenous nature of the precipitation process, differences in interpolation methods can potentially impact the simulated streamflow. Of course, hydrologic model parameter uncertainty also contribute to the errors, but in this paper we focus on the uncertainty due to interpolation methods. To this end, first we developed a two-step process in which the precipitation occurrence is first generated via a logistic regression model, and the amounts are then estimated using a Multiple Linear Regression (MLR) and Locally Weighted Polynomial Regression (LWP). The two-step approach is shown to capture the spatial variability of precipitation effectively than other competing traditional methods. Secondly, interpolated precipitation estimates are input into the watershed model, Precipitation Runoff Modeling System (PRMS) to estimate daily and consequently, monthly and seasonal streamflows. Streamflow estimates from PRMS are obtained for three methods of precipitation interpolation, MLR, LWP and the currently used method in PRMS, Climatological MLR (CMLR). Streamflows are compared on a variety of attributes. We find that the MLR and LWP methods perform much better in simulating the streamflows compared to CMLR. Ensembles of precipitation from the two methods (MLR and LWP) coupled with the logistic regression for precipitation occurrence, are generated to subsequently generate ensembles of streamflows from the watershed model. This approach captures the input uncertainty.
UR: http://ceae.colorado.edu/~yhwang/AGUmay
DE: 1800 HYDROLOGY
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
DE: 1860 Runoff and streamflow
SC: Hydrology [H]
MN: 2005 Joint Assembly