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Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation
Citation
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
Abstract
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 |
| Downloads: | 0 |
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