Preprints
https://doi.org/10.5194/hess-2021-325
https://doi.org/10.5194/hess-2021-325

  06 Aug 2021

06 Aug 2021

Review status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Improving the Pareto Frontier in multi-dataset calibration of hydrological models using metaheuristics

Silja Stefnisdóttir1,2, Anna E. Sikorska-Senoner3, Eyjólfur I. Ásgeirsson1,2, and David C. Finger1,2,4 Silja Stefnisdóttir et al.
  • 1School of Technology, Reykjavik University, Reykjavik, Iceland
  • 2Sustainability Institute and Forum (SIF), Reykjavik University, Reykjavik, Iceland
  • 3Department of Geography, University of Zurich, Zürich, Switzerland
  • 4Energy Institute, Johannes Kepler University, Linz, Austria

Abstract. Hydrological models are crucial tools in water and environmental resource management but they require careful calibration based on observed data. Model calibration remains a challenging task, especially if a multi-objective or multi-dataset calibration is necessary to generate realistic simulations of multiple flow components under consideration. In this study, we explore the value of three metaheuristics, i.e. (i) Monte Carlo (MC), (ii) Simulated Annealing (SA), and (iii) Genetic 5 Algorithm (GA), for a multi-data set calibration to simultaneously simulate streamflow, snow cover and glacier mass balances using the conceptual HBV model. Based on the results from a small glaciated catchment of the Rhone River in Switzerland, we show that all three metaheuristics can generate parameter sets that result in realistic simulations of all three variables. Detailed comparison of model simulations with these three metaheuristics reveals however that GA provides the most accurate simulations (with lowest confidence intervals) for all three variables when using both the 100 and the 10 best parameter sets for 10 each method. However, when considering the 100 best parameter sets per method, GA yields also some worst solutions from the pool of all methods’ solutions. The findings are supported by a reduction of the parameter equifinality and an improvement of the Pareto frontier for GA in comparison to both other metaheuristic methods. Based on our results, we conclude that GA-based multi-dataset calibration leads to the most reproducible and consistent hydrological simulations with multiple variables considered.

Silja Stefnisdóttir et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review Comment on hess-2021-325', Anonymous Referee #1, 06 Sep 2021
    • AC1: 'Reply on RC1', David C. Finger, 10 Sep 2021
  • RC2: 'Comment on hess-2021-325', Anonymous Referee #2, 06 Sep 2021
    • AC2: 'Reply on RC2', David C. Finger, 10 Sep 2021
  • AC3: 'Comment on hess-2021-325', David C. Finger, 07 Oct 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review Comment on hess-2021-325', Anonymous Referee #1, 06 Sep 2021
    • AC1: 'Reply on RC1', David C. Finger, 10 Sep 2021
  • RC2: 'Comment on hess-2021-325', Anonymous Referee #2, 06 Sep 2021
    • AC2: 'Reply on RC2', David C. Finger, 10 Sep 2021
  • AC3: 'Comment on hess-2021-325', David C. Finger, 07 Oct 2021

Silja Stefnisdóttir et al.

Silja Stefnisdóttir et al.

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Short summary
We combine multiple dataset calibration with metaheuristic calibration techniques, namely Mone Carlo (MC), Simulated Annealing (SA) and Genetic Algorithms (GA), to improve hydrological models. Our results demonstrate that GA improves the overall performance of hydrological models. This leads to precise scenario simulations and, accordingly, is a major achievement in hydrology.