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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #315302
Title: The Model Optimization, Uncertainty, and SEnsitivity analysis (MOUSE) toolbox: overview and application

Author
item Ascough Ii, James
item FISCHER, CHRISTIAN - Friedrick-Schiller University
item LIGHTHART, NATHAN - Colorad0 State University
item DAVID, OLAF - Colorad0 State University
item Green, Timothy
item KRALISCH, SVEN - Friedrick-Schiller University

Submitted to: Annual Hydrology Days Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 3/20/2015
Publication Date: 3/27/2015
Citation: Ascough II, J.C., C. Fischer, N.P. Lighthart, O. David, T.R. Green, and S. Kralisch. 2015. The model optimization, uncertainty, and sensitivity analysis (MOUSE) Toolbox: overview and application. In: Ramirez, J.A. (Ed.), Proc. Hydrology Days 2015, March 23-25, Fort Collins, Colorado. Colorado State University, Fort Collins, Colorado. pp. 17-28.

Interpretive Summary: Although much progress has been made towards optimization and sensitivity/uncertainty analysis of environmental models, the growing complexity of these types of models in representing real-world systems makes it increasingly difficult to fully comprehend model behavior, sensitivities and uncertainties. This presentation 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 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 (e.g., runoff peaks, low flow periods, or hydrograph recession periods); and 2) permits graphical visualization of the methods described above in addition to access and visualization of numerous tools contained in the Monte Carlo Analysis Toolbox (MCAT) including dotty plots, identifiability plots, and Dynamic Identifiability Analysis (DYNIA). Following a brief system overview, we present a basic application of MOUSE to the HyMod conceptual hydrologic model.

Technical Abstract: For several decades, optimization and sensitivity/uncertainty analysis of environmental models has been the subject of extensive research. Although much progress has been made and sophisticated methods developed, the growing complexity of environmental models to represent real-world systems makes it increasingly difficult to fully comprehend model behavior, sensitivities and uncertainties. This presentation 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 (e.g., runoff peaks, low flow periods, or hydrograph recession periods); and 2) permits graphical visualization of the methods described above in addition to access and visualization of numerous tools contained in the Monte Carlo Analysis Toolbox (MCAT) including dotty plots, identifiability plots, and Dynamic Identifiability Analysis (DYNIA). Following a brief system overview, we present a basic application of MOUSE to the HyMod conceptual hydrologic model.