Lab-0012

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Revision as of 08:02, 31 May 2020 by Overeem (talk | contribs)

River Discharge Data Analysis

Model
[[Model:|]]
Duration
3.0 hrs
Updated
2020/05/31
Download
Run online using:
     Jupyter logo.png

Contributor(s)
    Irina Overeem at University of Colorado.

Introduction
RiverDischargeTimeseries.png
Topical learning objectives
  1. concept of river discharge and stage
  2. what are stage-discharge relationships
  3. what are some difficulties for relating stage to discharge

Python Skill Learning Objectives:

  1. Load csv data from a file using the Pandas library.
  2. Access data in DataFrames.
  3. Create plots of data in DataFrames.
  4. Save figures to file.

Classroom organization
We will be looking at data on river discharge - the volume of water transported through a given cross section per time- of the Colorado River.

This notebook lends itself well with a short introduction on the concept of river discharge, how it is measured and an introduction on gauging stations of the USGS. The data analysis part requires basic python handling skills, but is introductory level.

Students can run through notebook and are encouraged to do assignments by themselves (or as homework). A review and discussion of solutions by the instructor after completion by the participants is recommended.

Download associated file: RiverStageDischargeIntroduction.pdf
Concept Diagrams of Stage and Discharge Measurements

Learning objectives
Skills
  • Load csv data from a file using the Pandas library
  • Access data in DataFrames
  • Create plots of data in DataFrames
  • Save figures to file
Key concepts
  • River discharge and stage
  • Stage-discharge relationship

Lab notes
We will be looking at data on river discharge - the volume of water transported through a given cross section per time- of the Upper Colorado River.

River discharge data for many US rivers is available from the USGS water watch website: http://waterwatch.usgs.gov/?m=real&r=co

River stage data is typically measured by keeping track of the water surface height over time, i.e. stage, and this needs to be converted to discharge through a stage-discharge relationship.

Tabular data like this data with a combination of dates, name and data quality strings, and numbers are best handled by spreadsheets where entries such as dates and times are in some useful format. In Python the Python Data Analysis Library (a.k.a. Pandas) is really useful for this purpose.

We use one discharge data file downloaded for the USGS station at Kremmling, CO, for the Upper Colorado.

Acknowledgements
CSDMS, NSF Award

References