Shuffled complex evolution approach for effective and efficient global minimization
Journal of Optimization Theory and Applications
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Combining Field Data and Computer Simulations for Calibration and Prediction
SIAM Journal on Scientific Computing
Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology
Computational Statistics & Data Analysis
A DSS generator for multiobjective optimisation of spreadsheet-based models
Environmental Modelling & Software
Short communication: New unstructured mesh water quality model for coastal discharges
Environmental Modelling & Software
Generating time-series of dry weather loads to sewers
Environmental Modelling & Software
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach
Environmental Modelling & Software
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Models of environmental systems are simplified representations of the reality. For this reason, their results are affected by systematic errors. This bias makes it difficult to get reliable uncertainty estimates of model parameters and predictions. A relatively simple way of considering this bias when using deterministic models is to add a statistical representation of the bias to the model output in addition to observation error and to jointly estimate model parameters, bias and observation error. When assuming Normal distributions for bias and observation error, this leads to a relatively simple likelihood function that can easily be evaluated. Nevertheless, the sampling from the posterior distribution still requires long Markov chains to be calculated which can be prohibitive for computationally demanding models. In order to extend the range of applicability of this technique to computationally demanding models, we suggest to replace Markov chain sampling by a Normal approximation to the posterior of the parameters and to estimate prediction uncertainty by linearized error propagation. We tested this procedure for a didactical example and for an application of the biogeochemical-ecological lake model BELAMO to long-term data from Lake Zurich. This is a good test application because the strong coupling of output variables makes it difficult to avoid bias in the results of this model. These tests demonstrate the applicability of the suggested procedure, the approximate reproduction of the results of the full procedure for the didactical example, and meaningful results for the lake model. For the latter, the results demonstrate that the assumption of a realistic likelihood function leads to the conclusion that prediction uncertainty may be high.