Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/65924
Type: Conference paper
Title: Characterizing model error in conceptual rainfall-runoff models using storm-dependent parameters
Author: Kuczera, G.
Kavetski, D.
Franks, S.
Thyer, M.
Citation: MODSIM 2005 16th International Congress on Modelling and Simulation: Modelling and Simulation Society of Australia and New Zealand, December 2005 / Andre Zerger and Robert M. Argent (eds.): pp. 2925-2931
Publisher: Modelling and Simulation Society of Australia and New Zealand
Publisher Place: Canberra, Australia
Issue Date: 2005
ISBN: 0975840029
9780975840009
Conference Name: International Congress on Modelling and Simulation (16th : 2005 : Melbourne, Victoria)
Editor: Zerger, A.
Argent, R.M.
Statement of
Responsibility: 
G. Kuczera, D. Kavetski, S. Franks and Thyer, M.
Abstract: Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by input, model and response error (Figure 1a). This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterization of model error in CRR modelling has been thwarted by poor conceptualizations of error propagation (Figure 1b) and the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. It is argued that CRR fluxes are fundamentally stochastic because they involve spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterization of model error. The hypothesis advanced in this paper is that CRR model error can be characterized by storm-dependent random variation of one or more CRR model parameters that affect fluxes. A simple sensitivity analysis is developed to assist in identifying the parameters most likely to behave stochastically. A Bayesian hierarchical model is formulated to explicitly differentiate between input, response and model error - this provides a very general framework for calibration and prediction, as well as the testing of hypotheses regarding model structure and data uncertainty. A case study using daily data from the Abercrombie catchment (Australia) and employing a 6-parameter CRR model demonstrates the considerable potential of this approach. Figure 2 illustrates the excellent fit to the observed data. Of particular significance is the use of posterior diagnostics to test key assumptions about errors. The assumption that the storm-dependent parameters are log-normally distributed is only partially supported by the data, which suggests that the parameter hyperdistributions have thicker tails. Further research is aiming to refine this approach to characterizing model error. (Figure Presented).
Keywords: conceptual rainfall-runoff modelling
parameter calibration, model error
input error
Bayesian parameter estimation
parameter variation
model determinism
Rights: Copyright status unknown
Published version: http://www.mssanz.org.au/modsim05/authorsH-K.htm#k
Appears in Collections:Aurora harvest
Civil and Environmental Engineering publications
Environment Institute publications

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