Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84096
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Type: Journal article
Title: A strategy for diagnosing and interpreting hydrological model nonstationarity
Author: Westra, S.
Thyer, M.
Leonard, M.
Kavetski, D.
Lambert, M.
Citation: Water Resources Research, 2014; 50(6):5090-5113
Publisher: Wiley
Issue Date: 2014
ISSN: 1944-7973
1944-7973
Statement of
Responsibility: 
Seth Westra, Mark Thyer, Michael Leonard, Dmitri Kavetski, and Martin Lambert
Abstract: This paper presents a strategy for diagnosing and interpreting hydrological nonstationarity, aiming to improve hydrological models and their predictive ability under changing hydroclimatic conditions. The strategy consists of four elements: (i) detecting potential systematic errors in the calibration data; (ii) hypothesizing a set of “nonstationary” parameterizations of existing hydrological model structures, where one or more parameters vary in time as functions of selected covariates; (iii) trialing alternative stationary model structures to assess whether parameter nonstationarity can be reduced by modifying the model structure; and (iv) selecting one or more models for prediction. The Scott Creek catchment in South Australia and the lumped hydrological model GR4J are used to illustrate the strategy. Streamflow predictions improve significantly when the GR4J parameter describing the maximum capacity of the production store is allowed to vary in time as a combined function of: (i) an annual sinusoid; (ii) the previous 365 day rainfall and potential evapotranspiration; and (iii) a linear trend. This improvement provides strong evidence of model nonstationarity. Based on a range of hydrologically oriented diagnostics such as flow-duration curves, the GR4J model structure was modified by introducing an additional calibration parameter that controls recession behavior and by making actual evapotranspiration dependent only on catchment storage. Model comparison using an information-theoretic measure (the Akaike Information Criterion) and several hydrologically oriented diagnostics shows that the GR4J modifications clearly improve predictive performance in Scott Creek catchment. Based on a comparison of 22 versions of GR4J with different representations of nonstationarity and other modifications, the model selection approach applied in the exploratory period (used for parameter estimation) correctly identifies models that perform well in a much drier independent confirmatory period.
Keywords: hydrological modeling; nonstationarity; model selection; model diagnostics; AIC; GR4J
Description: Article first published online: 24 JUN 2014
Rights: © 2014. American Geophysical Union. All Rights Reserved.
RMID: 0030000006
DOI: 10.1002/2013WR014719
Appears in Collections:Civil and Environmental Engineering publications

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