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J. Douglas, R. Clarke, S. Newton (1976)
The use of likelihood functions to fit conceptual models with more than one dependent variableJournal of Hydrology, 29
W. Madow, T. Anderson (1959)
An Introduction to Multivariate Statistical Analysis
G. Box, G. Tiao (1973)
Bayesian inference in statistical analysisInternational Statistical Review, 43
G. Kuczera (1982)
Improved inference of catchment model parameters
K. Langford, P. O'shaughnessy (1977)
A study of canopy interception in native forests and conifer plantations.
G. Kuczera (1983)
Improved parameter inference in catchment models: 1. Evaluating parameter uncertaintyWater Resources Research, 19
H. Theil (1971)
Principles of econometrics
G. Kuczera (1982)
On the relationship between the reliability of parameter estimates and hydrologic time series data used in calibrationWater Resources Research, 18
Often some of the parameters of catchment models fitted to runoff data are poorly determined thereby making the task of developing useful regionalization relationships more difficult. The Bayesian methodology developed in part 1 is extended to utilize several kinds of hydrologic data in parameter inference, the goal being to improve the precision of poorly determined parameters. The concept of compatibility is developed using statistical hypothesis tests. Different kinds of data are said to be compatible if differences between their fitted parameters are not statistically significant. The pooling of incompatible data may undermine the model's ability to predict runoff and also induce bias in the parameters. A hierarchy of three levels of information is introduced to enable systematic checking for compatibility. Finally, a case study is presented. Using data on runoff, soil moisture, and interception, it is shown that substantial reductions in parameter uncertainty can be realized; also the importance of compatibility testing is demonstrated.
Water Resources Research – Wiley
Published: Oct 1, 1983
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