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• W1525050347 abstract "[1] The determination of the parameters for hydrologic models has been the subject of a large number of studies during the last 3 decades. A multitude of methods have been developed for this purpose. Generally, the mismatch between the model simulations and the observations is lumped into an objective function, which is then is optimized by these methods. This can lead to parameter values that result in a good model performance under certain (e.g., low flow) conditions but not under other (e.g., high flow) conditions. The objective of this paper is to demonstrate a calibration algorithm which leads to a good model performance under all boundary conditions. This algorithm is referred to as Multistart Weight-Adaptive Recursive Parameter Estimation (MWARPE). For this purpose the equations of the Extended Kalman Filter (EKF) have been used recursively in a Monte-Carlo approach, strongly increasing the chance that a globally optimal parameter set is obtained instead of a local optimum. The method has been applied to a rainfall-runoff model for the Zwalm catchment in Belgium, using a 1-year, a 2-year, and a 3-year calibration period. The results have been compared to the Shuffled Complex Evolution (SCE)-UA method. A synthetic study revealed that for narrow parameter limits the SCE-UA algorithm outperformed MWARPE, while for broad parameter limits the opposite occurred. For the test case using in situ observed data, the SCE-UA method resulted in slightly lower RMSE values than MWARPE, but MWARPE performed better outside the calibration period. It has been found that MWARPE can bypass local optima in the determination of the final parameter set. Also, the best initial parameter sets (with the lowest RMSE) do not lead to the best final parameter values. To apply the method only four parameters need to be specified, more specifically the number of starting points, the number of iterations per starting point, one parameter used to initialize the model error covariance matrix, and the observation error. For this reason, the method could be a simple alternative to more complex methods if model parameters have to be determined when time and/or computational power are limited." @default.
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• W1525050347 date "2008-04-01" @default.
• W1525050347 modified "2023-10-16" @default.
• W1525050347 title "A multistart weight-adaptive recursive parameter estimation method" @default.
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• W1525050347 doi "https://doi.org/10.1029/2007wr005866" @default.
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