eCite Digital Repository
Bayesian analysis of input uncertainty in hydrological modelling: 1. Theory
Citation
Kavetski, D and Kuczera, G and Franks, SW, Bayesian analysis of input uncertainty in hydrological modelling: 1. Theory, Water Resources Research, 42 Article W03407. ISSN 0043-1397 (2006) [Refereed Article]
![]() | PDF Not available 150Kb |
Copyright Statement
Copyright 2006 by the American Geophysical Union.
Abstract
Parameter estimation in rainfall-runoff models is affected by uncertainties in the
measured input/output data (typically, rainfall and runoff, respectively), as well as model
error. Despite advances in data collection and model construction, we expect input
uncertainty to be particularly significant (because of the high spatial and temporal
variability of precipitation) and to remain considerable in the foreseeable future. Ignoring
this uncertainty compromises hydrological modeling, potentially yielding biased and
misleading results. This paper develops a Bayesian total error analysis methodology for
hydrological models that allows (indeed, requires) the modeler to directly and
transparently incorporate, test, and refine existing understanding of all sources of data
uncertainty in a specific application, including both rainfall and runoff uncertainties. The
methodology employs additional (latent) variables to filter out the input corruption
given the model hypothesis and the observed data. In this study, the input uncertainty is
assumed to be multiplicative Gaussian and independent for each storm, but the general
framework allows alternative uncertainty models. Several ways of incorporating vague
prior knowledge of input corruption are discussed, contrasting Gaussian and inverse
gamma assumptions; the latter method avoids degeneracies in the objective function.
Although the general methodology is computationally intensive because of the
additional latent variables, a range of modern numerical methods, particularly Monte
Carlo analysis combined with fast Newton-type optimization methods and Hessian-based
covariance analysis, can be employed to obtain practical solutions.
Item Details
Item Type: | Refereed Article |
---|---|
Keywords: | uncertainty hydrological modelling BATEA |
Research Division: | Engineering |
Research Group: | Environmental engineering |
Research Field: | Air pollution modelling and control |
Objective Division: | Environmental Management |
Objective Group: | Other environmental management |
Objective Field: | Other environmental management not elsewhere classified |
UTAS Author: | Franks, SW (Professor Stewart Franks) |
ID Code: | 86409 |
Year Published: | 2006 |
Web of Science® Times Cited: | 502 |
Deposited By: | Engineering |
Deposited On: | 2013-09-14 |
Last Modified: | 2013-10-14 |
Downloads: | 6 View Download Statistics |
Repository Staff Only: item control page