Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters

George Kuczera*, Dmitri Kavetski, Stewart Franks, Mark Thyer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

244 Citations (Scopus)

Abstract

Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the uncertainty in the observed forcing/response data and the structural error in the model. This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterisation of model error in CRR modelling has been thwarted by the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. This paper argues that the fluxes in CRR models should be treated as stochastic quantities because their estimation involves spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterisation of model error. The hypothesis advanced in this paper is that CRR model error can be characterised by storm-dependent random variation of one or more CRR model parameters. A simple sensitivity analysis is used to identify the parameters most likely to behave stochastically, with variation in these parameters yielding the largest changes in model predictions as measured by the Nash-Sutcliffe criterion. A Bayesian hierarchical model is then formulated to explicitly differentiate between forcing, response and model error. It provides a very general framework for calibration and prediction, as well as for testing hypotheses regarding model structure and data uncertainty. A case study calibrating a six-parameter CRR model to daily data from the Abercrombie catchment (Australia) demonstrates the considerable potential of this approach. Allowing storm-dependent variation in just two model parameters (with one of the parameters characterising model error and the other reflecting input uncertainty) yields a substantially improved model fit raising the Nash-Sutcliffe statistic from 0.74 to 0.94. Of particular significance is the use of posterior diagnostics to test the key assumptions about the data and model errors. The assumption that the storm-dependent parameters are log-normally distributed is only partially supported by the data, which suggests that the parameter hyper-distributions have thicker tails. The results also indicate that in this case study the uncertainty in the rainfall data dominates model uncertainty.

Original languageEnglish
Pages (from-to)161-177
Number of pages17
JournalJournal of Hydrology
Volume331
Issue number1-2
DOIs
Publication statusPublished - 30 Nov 2006

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