Document Type
Article
Publication Date
7-1-2010
Subjects
Streamflow -- Forecasting, Uncertainty -- Mathematical models, Hydrology -- Data processing
Abstract
Modeling of natural systems typically involves conceptualization and parameterization to simplify the representations of the underlying process. Objective methods for estimation of the model parameters then require optimization of a cost function, representing a measure of distance between the observations and the corresponding model predictions, typically by calibration in a static batch mode and/or via some dynamic recursive optimization approach. Recently, there has been a focus on the development of parameter estimation methods that appropriately account for different sources of uncertainty. In this context, we introduce an approach to sample the optimal parameter space that uses nonparametric block bootstrapping coupled with global optimization. We demonstrate the applicability of this procedure via a case study, in which we estimate the parameter uncertainty resulting from uncertainty in the forcing data and evaluate its impacts on the resulting streamflow simulations.
DOI
10.1029/2009WR007981
Persistent Identifier
http://archives.pdx.edu/ds/psu/8287
Citation Details
Ebtehaj, M., H. Moradkhani, and H. V. Gupta (2010), Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling, Water Resour. Res., 46, W07515.
Description
This is the publisher's final pdf. Originally published in Water Resources Research (http://www.agu.org/journals/Wr/) and is copyrighted by American Geophysical Union (http://www.agu.org/)