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An improved calibration and uncertainty analysis approach using a multicriteria sequential algorithm for hydrological

Hongjing Wu1, Bing Chen2, Xudong Ye1

  • 1Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL, A1B 3X5, Canada.

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A new multicriteria sequential calibration and uncertainty analysis (MS-CUA) method enhances hydrological model performance. This approach improves computational efficiency and provides more reliable water resource management results compared to traditional methods.

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Area of Science:

  • Environmental science
  • Water resource management
  • Hydrology

Background:

  • Hydrological models are crucial for water resource management but face challenges in calibration and uncertainty analysis.
  • Efficient and reliable methods are needed to improve the performance of these complex models.

Purpose of the Study:

  • To introduce and evaluate a novel multicriteria sequential calibration and uncertainty analysis (MS-CUA) method.
  • To enhance the efficiency and reliability of hydrological modeling processes.

Main Methods:

  • The study proposed and implemented a multicriteria sequential calibration and uncertainty analysis (MS-CUA) method.
  • Performance was evaluated through two case studies, comparing MS-CUA against the sequential uncertainty fitting algorithm (SUFI-2) and generalized likelihood uncertainty estimation (GLUE).

Main Results:

  • The MS-CUA method demonstrated improved computational efficiency by rapidly identifying highest posterior density regions.
  • It yielded superior model calibration, evidenced by higher Nash-Sutcliffe Efficiency (NSE) values (e.g., 0.91, 0.97, 0.74).
  • MS-CUA also provided more balanced uncertainty analysis, indicated by higher Prediction-to-Observation ratio (P/R) values (e.g., 1.23, 2.15, 1.00).

Conclusions:

  • The MS-CUA method offers a significant advancement in hydrological modeling.
  • It provides a more efficient and reliable approach for model calibration and uncertainty analysis in water resource management.