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The geosciences are a highly suitable field of application for optimizing modelparameters and experimental designs especially because many data arecollected.  In this paper, the weighted least squares estimator for optimizing modelparameters is presented together with its asymptotic properties. A popularapproach to optimize experimental designs called local optimal experimentaldesigns is described together with a lesser known approach which takes intoaccount the potential nonlinearity of the model parameters. These twoapproaches have been combined with two methods to solve their underlyingdiscrete optimization problem.  All presented methods were implemented in an open-source MATLAB toolboxcalled the <i>Optimal Experimental Design Toolbox</i> whose structure andapplication is described.  In numerical experiments, the model parameters and experimental design wereoptimized using this toolbox. Two existing models for sediment concentrationin seawater and sediment accretion on salt marshes of different complexityserved as an application example. The advantages and disadvantages of theseapproaches were compared based on these models.  Thanks to optimized experimental designs, the parameters of these modelscould be determined very accurately with significantly fewer measurementscompared to unoptimized experimental designs. The chosen optimizationapproach played a minor role for the accuracy; therefore, the approach with theleast computational effort is recommended

geoscientific model development

Optimization of model parameters and experimental designs with the Optimal Experimental Design Toolbox (v1.0) exemplified by sedimentation in salt marshes