Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations. It is widely used in engineering and geophysics fields to assess and predict the likelihood of various outcomes. This paper describes a UQ platform called UQ-PyL (Uncertainty Quantification Python Laboratory), a flexible software platform designed to quantify uncertainty of complex dynamical models. UQ-PyL integrates different kinds of UQ methods, including experimental design, statistical analysis, sensitivity analysis, surrogate modeling and parameter optimization. It is written in Python language and runs on all common operating systems. UQ-PyL has a graphical user interface that allows users to enter commands via pull-down menus. It is equipped with a model driver generator that allows any computer model to be linked with the software. We illustrate the different functions of UQ-PyL by applying it to the uncertainty analysis of the Sacramento Soil Moisture Accounting Model. We will also demonstrate that UQ-PyL can be applied to a wide range of applications. Name of software: Uncertainty Quantification Python Laboratory (UQ-PyL) Programming language: Python Operating system: Windows, Linux and MacOS Availability: http://www.uq-pyl.com Documentation: http://www.uq-pyl.com User interface: Graphical user interface or command-line License: Free under a GNU General Public License (www.gnu.org) agreement. 
A GUI platform for uncertainty quantification of complex dynamical models