A history matching approach for calibrating hydrological models
Calibration of hydrological time-series models is a challenging task since these models give a wide spectrum of output series and calibration procedures require significant amount of time. From a statistical standpoint, this model parameter estimation problem simplifies to finding an inverse solution of a computer model that generates pre-specified time-series output (i.e., realistic output series). In this paper, we propose a modified history matching approach for calibrating the time-series rainfall-runoff models with respect to the real data collected from the state of Georgia, USA. We present the methodology and illustrate the application of the algorithm by carrying a simulation study and the two case studies. Several goodness-of-fit statistics were calculated to assess the model performance. The results showed that the proposed history matching algorithm led to a significant improvement, of 30% and 14% (in terms of root mean squared error) and 26% and 118% (in terms of peak percent threshold statistics), for the two case-studies with Matlab-Simulink and SWAT models, respectively.
KeywordsContour estimation Gaussian process model History matching Hydrology Inverse problem Prediction
The authors would like to thank the Editor, the Associate Editor and two reviewers for their thorough and helpful reviews. Ranjan’s research was partially supported by the Extra Mural Research Fund (EMR/2016/003332/MS) from the Science and Engineering Research Board, Department of Science and Technology, Government of India. Mandal and Tollner’s research was partially supported by 104B State Water Resources Research Institute Program, USA Grant G16AP00047. We would like to thank NASA DEVELOP National Program’s node at the Center for Geospatial Research, UGA for providing resources on SWAT modeling.
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