HR: 1400h
AN: H33D-03    [Abstracts]
TI: Toward Improved Calibration of Distributed Hydrologic Models via Uncertainty Analysis
AU: Forman, B A
EM: bforman@ucla.edu
AF: University of California at Los Angeles, Dept. of Civil and Env. Engin. 5732D Boelter Hall, Los Angeles, CA 90064, United States
AU: Vivoni, E R
EM: vivoni@nmt.edu
AF: New Mexico Institute of Mining and Technology, Dept. of Earth and Env. Science 801 Leroy Place, MSEC 244, Socorro, NM 87801, United States
AU: * Margulis, S A
EM: margulis@seas.ucla.edu
AF: University of California at Los Angeles, Dept. of Civil and Env. Engin. 5732D Boelter Hall, Los Angeles, CA 90064, United States
AB: Hydrologic models are generally dependent on physical and/or empirical parameters that may be difficult or impossible to measure directly. Sophisticated parameter calibration routines have been developed as a means of increasing model performance while both characterizing and reducing model uncertainty. Lumped models, which often contain a relatively low-dimensional parameter vector, in particular, have profited from such routines. As distributed models are employed more frequently to make use of spatially distributed measurements, there is a need to implement similar procedures to improve distributed model efficiency. However, application in distributed models often requires analysis of a larger parameter vector with a greater degree of dimensionality. Issues regarding parameter identifiability, cross-correlation, and search algorithms are further confounded by this increase in dimensionality. This research aims to demonstrate that fully distributed models (e.g. TIN-based Real-time Integrated Basin Simulator or tRIBS) can also benefit from implementation of similar calibration procedures (i.e., Generalized Likelihood Uncertainty Estimation or GLUE). Results from application of GLUE to12-month simulations (May 1996 to May 1997) of the Peacheater Creek basin near Eldon, Oklahoma, demonstrate the concept of "equifinality" between many different parameter sets. Furthermore, many of these parameter sets achieve Nash-Sutcliffe efficiencies greater than 0.7. GLUE not only increases model performance, but it provides estimates of model output uncertainty as well as discrete approximations of the posterior parameter probability distributions. These probability distributions can then be used as part of an ensemble-modeling scheme. A notable limitation in this procedure, however, is the computational burden of a Monte Carlo simulation using a complex hydrologic model.
DE: 1800 HYDROLOGY
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Joint Assembly