Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
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Evaluation of Multi-criteria Performances of the TOPMODEL Simulations in a Small Forest Catchment based on the Concept of Equifinality of the Multiple Parameter Sets

  • Choi, Hyung Tae (Department of Forest Environment, Korea Forest Research Institute) ;
  • Kim, Kyongha (Department of Forest Environment, Korea Forest Research Institute) ;
  • Jun, Jae-Hong (Department of Forest Environment, Korea Forest Research Institute) ;
  • Yoo, Jae-Yun (Department of Forest Environment, Korea Forest Research Institute) ;
  • Jeong, Yong-Ho (Department of Forest Environment, Korea Forest Research Institute)
  • Received : 2006.06.20
  • Accepted : 2006.09.11
  • Published : 2006.10.30
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Abstract

This study focuses on the application of multi-criteria performance measures based on the concept of equifinality to the calibration of the rainfall-runoff model TOPMODEL in a small deciduous forest catchment. The performance of each parameter set was evaluated by six performance measures, individually, and each set was identified as a behavioral or non-behavioral parameter set by a given behavioral acceptance threshold. Many behavioral parameter sets were scattered throughout the parameter space, and the range of model behavior and the sensitivity for each parameter varied considerably between the different performance measures. Sensitivity was very high in some parameters, and varied depending on the kind of performance measure as well. Compatibilities of behavioral parameter sets between different performance measures also varied, and very few parameter sets were selected to be used in making god predictions for all performance measures. Since different behavioral parameter sets with different likelihood weights were obtained for each performance measure, the decision on which performance measure to be used may be very important to achieve the goal of study. Therefore, one or more suitable performance measures should be selected depending on the environment and the goal of a study, and this may lead to decrease model uncertainty.

Keywords

References

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