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An Open-Source Java-Based Toolbox for Environmental Model Evaluation: The MOUSE Software Application
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2015 ASABE Annual International Meeting 152189328.(doi:10.13031/aim.20152189328)
Authors: James C. Ascough II, Christian Fischer, Nathan P. Lighthart, Olaf David, Sven Kralisch
Keywords: Model calibration, Uncertainty, Sensitivity analysis, Parameter estimation.
Abstract. A consequence of environmental model complexity is that the task of understanding how environmental models work and identifying their sensitivities/uncertainties, etc. becomes progressively more difficult. Comprehensive numerical and visual evaluation tools have been developed such as the Monte Carlo Analysis Toolbox (MCAT) and OPTAS to help analyze environmental model input (state, parameter) and output spaces. While MCAT and OPTAS are useful for exploring model performance, sensitivity/uncertainty, and underlying assumptions regarding model structure, they both rely on additional software platforms to run. Therefore, the primary goal of this research study was to convert the tools found in MCAT and OPTAS to open-source, Java-based visual and numerical analysis components and integrate them within a fully standalone toolbox. This paper provides an overview of the Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components for the evaluation of environmental models. MOUSE is based on the OPTAS model calibration system developed for the Jena Adaptable Modeling System (JAMS) framework, is model-independent, and helps the modeler understand underlying hypotheses and assumptions regarding model structure, identify and select behavioral model parameterizations, and evaluate model performance and uncertainties. MOUSE offers well-established local and global sensitivity analysis methods, single- and multi-objective optimization algorithms, and uses GLUE methodology to quantify model uncertainty. MOUSE has a robust GUI that: 1) allows the modeler to constrain objective functions for specific time periods or events; and 2) permits graphical visualization of the methods described above in addition to visualization of numerous tools contained in MCAT including dotty plots, identifiability plots, and Dynamic Identifiability Analysis (DYNIA). In addition to an overview of MOUSE, a basic application of MOUSE to the HyMod conceptual hydrologic model is presented to further demonstrate the integrated model behavior, optimization, and sensitivity/uncertainty analysis tools.(Download PDF) (Export to EndNotes)