Stationary and nonstationary behavior
Introduction
Stationarity and nonstationarity are terms you may have encountered previously if you've taken water or climate related courses and are important for understanding changes in the Earth System. But what do these terms actually mean? A stationary time series has statistical properties or moments (e.g., mean and variance) that do not vary in time. Stationarity, then, is the status of a stationary time series. Conversely, nonstationarity is the status of a time series whose statistical properties are changing through time. This step explores examples of stationarity and nonstationarity.
Conceptual Outcomes
1) Gain a conceptual understanding of stationarity and nonstationarity.
Practical Outcomes
1) Use open source tools to explore and visualize stationary and nonstationary data series.
Time Required
1-2 hours, depending on comfort level with modifying small amounts of code (e.g., file paths) and depth of responses to questions.
Computing/Data Inputs
1) Answers to various questions provided in the instructions.
2) Data visualizations created while working through the instructions.
Computing/Data Outputs
Computing/data outputs:
1) Answers to various questions provided in the instructions.
2) Data visualizations created while working through the instructions.
Hardware/Software Required
An interactive python environment (iPython or Jupyter) (all of which are included in the open source Anaconda distribution of python) with the Numpy, SciPy, matplotlib, and pandas libraries installed.
Instructions
Line by line instructions are embedded in the provided jupyter notebook: unit_1_step_1_notebook_new.ipynb ( 494kB Feb15 21)
See Additional files: colorado_river.csv (Comma Separated Values 554kB Aug17 20)
Additional Activities and Variants
The provided instructions and code can be adapted to any time series data that follow the same formatting as the example data from the Colorado River basin.
Related steps:
Other steps from this unit (Autocorrelation, Periodic Behavior)