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Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation


Renard, B and Kavetski, D and Leblois, E and Thyer, M and Kuczera, G and Franks, SW, Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation, Water Resources Research, 47, (11) Article W11516. ISSN 1944-7973 (2011) [Refereed Article]

Copyright Statement

Copyright 2011 American Geophysical Union

DOI: doi:10.1029/2011WR010643


This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data and incorporating them into the Bayesian total error analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-runoff model GR4J is presented. It exploits a calibration period where dense rain gauge data are available to characterize the uncertainty in the catchment average rainfall using geostatistical conditional simulation. The inclusion of information about rainfall and runoff data uncertainties overcomes ill-posedness problems and enables simultaneous estimation of forcing and structural errors as part of the Bayesian inference. This yields more reliable predictions than approaches that ignore or lump different sources of uncertainty in a simplistic way (e.g., standard least squares). It is shown that independently derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff, and structural errors. In this case study, the total predictive uncertainty appears dominated by structural errors. Although further research is needed to interpret and verify this decomposition, it can provide strategic guidance for investments in environmental data collection and/or modeling improvement. More generally, this study demonstrates the power of the Bayesian paradigm to improve the reliability of environmental modeling using independent estimates of sampling and instrumental data uncertainties.

Item Details

Item Type:Refereed Article
Keywords:uncertainty hydrological modelling BATEA rainfall model
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:86417
Year Published:2011
Web of Science® Times Cited:155
Deposited By:Engineering
Deposited On:2013-09-14
Last Modified:2013-10-30

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