HR: 0800h
AN: H21A-0170    [Abstracts]
TI: Developing a Framework for Testing Distributed Hydrologic Models at the Hillslope Scale
AU: * Cristea, N C
EM: cristn@u.washington.edu
AF: University of Washington, 160 Wilcox Hall, Seattle, WA 98195-2700, United States
AU: Kampf, S K
EM: skampf@warnercnr.colostate.edu
AF: Colorado State University, 214 Natural Resources Building, Fort Collins, CO 80523-1472, United States
AU: Mirus, B B
EM: bmirus@pangea.Stanford.EDU
AF: Stanford University, Braun Hall, Building 320, Stanford, CA 94305-2115, United States
AU: Loague, K
EM: kloague@stanford.edu
AF: Stanford University, Braun Hall, Building 320, Stanford, CA 94305-2115, United States
AU: Burges, S J
EM: sburges@u.washington.edu
AF: University of Washington, 160 Wilcox Hall, Seattle, WA 98195-2700, United States
AB: Numerous hydrologic models solve Richards equation for the variably saturated subsurface domain. However, the scarcity of measured hydrologic states and variables and the scale discrepancies between observations and simulations pose a challenge in testing and evaluating such models. We develop a flexible framework for testing distributed hydrologic models at the hillslope scale. The proposed method consists of three major steps. First we generate "hypothetical realities" representing the hydrologic response of a synthetic watershed modeled after the 10.5 ha Tarrawarra catchment in Australia. The catchment was extensively monitored and has a relatively simple geometry with 0.5-1.5m deep soils overlaying bedrock and a fairly uniform grass cover. Eleven years of half-hourly time increment hydrological states and fluxes generically termed "hypothetical realities" have been generated using the complex Integrated Hydrology Model (InHM) representing fully coupled 3D variably saturated subsurface and 2D surface flow with high resolution. In the second step, simpler distributed hydrologic models can be evaluated against the hypothetical realities, which represent an error-free data set of hydrologic variables. The simpler distributed models are run first without calibration and then with calibration against different combinations of the observed data from the hypothetical realities. In the third step, further tests of distributed models incorporate event based and continuous simulations, variable spatial and temporal scales and increasing amounts and types of model input data and observed data.
DE: 1804 Catchment
DE: 1829 Groundwater hydrology
DE: 1830 Groundwater/surface water interaction
DE: 1847 Modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting