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Improved parameter inference in catchment models: 2. Combining different kinds of hydrologic data and testing their compatibility
Author(s) -
Kuczera George
Publication year - 1983
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr019i005p01163
Subject(s) - compatibility (geochemistry) , pooling , surface runoff , inference , bayesian probability , computer science , bayesian inference , data mining , environmental science , artificial intelligence , engineering , ecology , chemical engineering , biology
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.