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Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors


Renard, B and Kavetski, D and Kuczera, G and Thyer, M and Franks, SW, Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resources Research, 46, (5) pp. 1-22. ISSN 1944-7973 (2010) [Refereed Article]

Copyright Statement

Copyright 2010 American Geophysical Union

DOI: doi:10.1029/2009WR008328


Meaningful quantification of data and structural uncertainties in conceptual rainfallrunoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors describing rainfall uncertainty. Compared with traditional lumped additive error approaches, the quantification of the total predictive uncertainty in the runoff is improved when rainfall and/or structural errors are characterized explicitly. However, the decomposition of the total uncertainty into individual sources is more challenging. In particular, poor identifiability may arise when the inference scheme represents rainfall and structural errors using separate probabilistic models. The inference becomes ill‐posed unless sufficiently precise prior knowledge of data uncertainty is supplied; this ill‐posedness can often be detected from the behavior of the Monte Carlo sampling algorithm. Moreover, the priors on the data quality must also be sufficiently accurate if the inference is to be reliable and support meaningful uncertainty decomposition. Our findings highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall‐runoff data with large unknown errors. Bayesian total error analysis can overcome these problems using independent prior information. The need for deriving independent descriptions of the uncertainties in the input and output data is clearly demonstrated.

Item Details

Item Type:Refereed Article
Keywords:uncertainty hydrological modelling model structure rainfall 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:86415
Year Published:2010
Web of Science® Times Cited:487
Deposited By:Engineering
Deposited On:2013-09-14
Last Modified:2013-10-30

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