HR: 14:25h
AN: H13I-04    [Abstracts]
TI: Relationship Between Spatial Discretization and the Parameters and Model Performance of Precipitation-Runoff and Water Balance Models
AU: * Kling, H
EM: harald.kling@boku.ac.at
AF: Department of Hydrology & Water Resources, University of Arizona 1133 E. James E. Rogers Way, Tucson, AZ 85721, United States
AU: Gupta, H V
EM: hoshin.gupta@hwr.arizona.edu
AF: Department of Hydrology & Water Resources, University of Arizona 1133 E. James E. Rogers Way, Tucson, AZ 85721, United States
AB: The traditional approach in the application of precipitation-runoff or water balance models is to calibrate model parameters with observed input-output data. However, for the prediction in ungauged basins parameters have to be estimated a priori, for example from spatial data sets. Also for spatially distributed models, the a priori estimation of parameters using spatial data sets is desirable. In most studies to date, the spatial discretization of the model is not accounted for in the parameter estimation process. This study investigates the relationship between the spatial discretization, optimal parameter values, and the model performance. The results of precipitation-runoff and water balance modeling in several Austrian and US catchments will be presented. Models were applied repeatedly with varying spatial discretizations, from distributed to lumped, conducting a series of tests. The results show that the optimal values of some parameters are highly dependent on the spatial discretization, whereas other parameters are less sensitive. In general, the most sensitive parameters are ones that control highly threshold-like processes. Because of parameter interactions, the less sensitive parameters are also eventually affected by the spatial discretization. The model performance shows only little sensitivity to the spatial discretization, as long as model parameters are re-calibrated when moving from one spatial discretization to another. This has the consequence that the optimal parameters of lumped models have little correlation with their distributed counterparts, since the parameters have to overcome deficits in the spatial representation of threshold processes. Therefore, it is clear that these deficits need to be taken into account in any kind of a priori parameter estimation for lumped or semi-distributed models.
DE: 1804 Catchment
DE: 1816 Estimation and forecasting
DE: 1874 Ungaged basins
DE: 1876 Water budgets
SC: Hydrology [H]
MN: 2007 Fall Meeting