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Publication Additional Information Download
Publication Type
Journal Article
Authorship
Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A.
Title
VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis
Year
2019
Publication Outlet
Environmental Modelling & Software, 112, 95-107
DOI
https://doi.org/10.1016/j.envsoft.2018.10.005
Citation
Razavi, S., Sheikholeslami, R., Gupta, H. V., & Haghnegahdar, A. (2019). VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis. Environmental Modelling & Software, 112, 95-107. https://doi.org/10.1016/j.envsoft.2018.10.005
Abstract
VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the “Variogram Analysis of Response Surfaces” framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model.
Program Affiliations
GWF: Global Water Futures
Project Affiliations
GWF-IMPC: Integrated Modelling Program for Canada
GWF-WNM: We Need More than Just Water: Assessing Sediment Limitation in a Large Freshwater Delta
Publication Stage
Published
Additional Information
Paradigm Shift
Download Links
https://doi.org/10.1016/j.envsoft.2018.10.005
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