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Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization

Karolien Scheerlinck, Valentijn Pauwels (UGent) , Hilde Vernieuwe (UGent) and Bernard De Baets (UGent)
(2009) WATER RESOURCES RESEARCH. 45.
Author
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Abstract
It is well known that one of the major problems in the application of land surface models is the determination of the various model parameters. In most cases, only one or a limited number of variables are used to estimate these parameters. This study evaluates the use of two fundamentally different global optimization methods, multistart weight-adaptive recursive parameter estimation (MWARPE) and particle swarm optimization (PSO), for the estimation of hydrologic model parameters on the basis of data for multiple variables. MWARPE iteratively uses the linear recursive filter equations in a Monte Carlo setting and therefore does not rely on the explicit minimization of an objective function. However, a major drawback of the MWARPE method is the high dimensionality, determined by the number of observations, of the matrix to be inverted. On the other hand, PSO is a stochastic optimization method based on the collective strength of a population of individuals with flocking or herding behavior, as observed in a wide number of biological systems. In situ observations of net radiation; latent, sensible, and ground heat fluxes; and the soil moisture profile are used to determine the parameters of a simplified water and energy balance model. Both optimization methods are analyzed in terms of model performance and computational efficiency. Comparable results, expressed in terms of the root mean square error values, were obtained for both methods. However, it was found that MWARPE tends to slightly overfit the data.
Keywords
PREDICTION, GENETIC ALGORITHM, RADIATIVE-TRANSFER MODEL, INVERSION, MULTIOBJECTIVE CALIBRATION, DESIGN

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MLA
Scheerlinck, Karolien, et al. “Calibration of a Water and Energy Balance Model: Recursive Parameter Estimation versus Particle Swarm Optimization.” WATER RESOURCES RESEARCH, vol. 45, 2009, doi:10.1029/2009WR008051.
APA
Scheerlinck, K., Pauwels, V., Vernieuwe, H., & De Baets, B. (2009). Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization. WATER RESOURCES RESEARCH, 45. https://doi.org/10.1029/2009WR008051
Chicago author-date
Scheerlinck, Karolien, Valentijn Pauwels, Hilde Vernieuwe, and Bernard De Baets. 2009. “Calibration of a Water and Energy Balance Model: Recursive Parameter Estimation versus Particle Swarm Optimization.” WATER RESOURCES RESEARCH 45. https://doi.org/10.1029/2009WR008051.
Chicago author-date (all authors)
Scheerlinck, Karolien, Valentijn Pauwels, Hilde Vernieuwe, and Bernard De Baets. 2009. “Calibration of a Water and Energy Balance Model: Recursive Parameter Estimation versus Particle Swarm Optimization.” WATER RESOURCES RESEARCH 45. doi:10.1029/2009WR008051.
Vancouver
1.
Scheerlinck K, Pauwels V, Vernieuwe H, De Baets B. Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization. WATER RESOURCES RESEARCH. 2009;45.
IEEE
[1]
K. Scheerlinck, V. Pauwels, H. Vernieuwe, and B. De Baets, “Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization,” WATER RESOURCES RESEARCH, vol. 45, 2009.
@article{783862,
  abstract     = {{It is well known that one of the major problems in the application of land surface models is the determination of the various model parameters. In most cases, only one or a limited number of variables are used to estimate these parameters. This study evaluates the use of two fundamentally different global optimization methods, multistart weight-adaptive recursive parameter estimation (MWARPE) and particle swarm optimization (PSO), for the estimation of hydrologic model parameters on the basis of data for multiple variables. MWARPE iteratively uses the linear recursive filter equations in a Monte Carlo setting and therefore does not rely on the explicit minimization of an objective function. However, a major drawback of the MWARPE method is the high dimensionality, determined by the number of observations, of the matrix to be inverted. On the other hand, PSO is a stochastic optimization method based on the collective strength of a population of individuals with flocking or herding behavior, as observed in a wide number of biological systems. In situ observations of net radiation; latent, sensible, and ground heat fluxes; and the soil moisture profile are used to determine the parameters of a simplified water and energy balance model. Both optimization methods are analyzed in terms of model performance and computational efficiency. Comparable results, expressed in terms of the root mean square error values, were obtained for both methods. However, it was found that MWARPE tends to slightly overfit the data.}},
  author       = {{Scheerlinck, Karolien and Pauwels, Valentijn and Vernieuwe, Hilde and De Baets, Bernard}},
  issn         = {{0043-1397}},
  journal      = {{WATER RESOURCES RESEARCH}},
  keywords     = {{PREDICTION,GENETIC ALGORITHM,RADIATIVE-TRANSFER MODEL,INVERSION,MULTIOBJECTIVE CALIBRATION,DESIGN}},
  language     = {{eng}},
  pages        = {{22}},
  title        = {{Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization}},
  url          = {{http://doi.org/10.1029/2009WR008051}},
  volume       = {{45}},
  year         = {{2009}},
}
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