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A history matching approach for calibrating hydrological models

Authors
  • Bhattacharjee, Natalia V.1, 2
  • Ranjan, Pritam3
  • Mandal, Abhyuday2
  • Tollner, Ernest W.4
  • 1 University of Washington, Institute for Health Metrics and Evaluation, Seattle, USA , Seattle (United States)
  • 2 University of Georgia, Department of Statistics, Athens, USA , Athens (United States)
  • 3 Indian Institute of Management Indore, OM&QT, Indore, MP, India , Indore (India)
  • 4 University of Georgia, College of Engineering, Athens, USA , Athens (United States)
Type
Published Article
Journal
Environmental and Ecological Statistics
Publisher
Springer US
Publication Date
Feb 27, 2019
Volume
26
Issue
1
Pages
87–105
Identifiers
DOI: 10.1007/s10651-019-00420-9
Source
Springer Nature
Keywords
License
Yellow

Abstract

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.

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