20 Dec 2021
20 Dec 2021
Status: a revised version of this preprint is currently under review for the journal HESS.

A Novel Objective Function DYNO for Automatic Multi-variable Calibration and Application to Assess Effects of Velocity versus Temperature Data for 3D Lake Models Calibration

Wei Xia1,2,3, Taimoor Akhtar4, and Christine A. Shoemaker1,2,3 Wei Xia et al.
  • 1Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore
  • 2Department of Industrial Systems Engineering and Management, National University of Singapore, 117576, Singapore
  • 3Energy and Environmental Sustainability for Megacities (E2S2) Phase II, Campus for Research Excellence and Technological Enterprise (CREATE), 138602, Singapore
  • 4RWDI Consulting Engineers and Scientists, N1G 4P6, ON, Canada

Abstract. This study introduced a novel Dynamically Normalized objective function (DYNO) for multi-variable (i.e., temperature and velocity) model calibration problems. DYNO combines the error metrics of multiple variables into a single objective function by dynamically normalizing each variable's error terms using information available during the search. DYNO is proposed to dynamically adjust the weight of the error of each variable hence balancing the calibration to each variable during optimization search. The DYNO is applied to calibrate a tropical hydrodynamic model where temperature and velocity observation data are used for model calibration simultaneously. We also investigated the efficiency of DYNO by comparing the result of using DYNO to results of calibrating to either temperature or velocity observation only. The result indicates that DYNO can balance the calibration in terms of water temperature and velocity and that calibrating to only one variable (e.g., temperature or velocity) cannot guarantee the goodness-of-fit of another variable (e.g., velocity or temperature). Our study suggested that both temperature and velocity measures should be used for hydrodynamic model calibration in real practice. Our example problems were computed with a parallel optimization method PODS but DYNO can also be easily used in serial applications.

Wei Xia et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-601', Anonymous Referee #1, 05 Jan 2022
    • AC1: 'Reply on RC1', Wei Xia, 25 Mar 2022
  • RC2: 'Comment on hess-2021-601', Anonymous Referee #2, 19 Jan 2022
    • AC2: 'Reply on RC2', Wei Xia, 25 Mar 2022
  • RC3: 'Comment on hess-2021-601', Christoph Schürz, 07 Feb 2022
    • AC3: 'Reply on RC3', Wei Xia, 25 Mar 2022

Wei Xia et al.

Model code and software

DYNO-pods Wei Xia

Wei Xia et al.


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Short summary
The common practice of calibrating lake hydrodynamics models only to temperature data is shown to be unable to reproduce the flow dynamics well. We proposed a new dynamically normalized objective function (DyNO) for multi-variable calibration to be used with parallel or serial optimization methods. DyNO is successfully applied to simultaneously calibrate the temperature and velocity of a 3-dimensional tropical lake model.