Lab-0005: Difference between revisions

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|LabDateContributedOrUpdated=2020/03/26
|LabDateContributedOrUpdated=2020/03/26
|LabDescriptionShort=Investigate river sediment supply to the ocean by exploring the effects of climate changes on river fluxes. We also look at the effect of humans on rivers: the building of a reservoir.
|LabDescriptionShort=Investigate river sediment supply to the ocean by exploring the effects of climate changes on river fluxes. We also look at the effect of humans on rivers: the building of a reservoir.
|LabCODuration=1.0 hrs
|LabCODuration=2.0 hrs
|LabModelDocumentation=HydroTrend
|LabModelDocumentation=HydroTrend
|LabAssociatedLesson=Jupyter Notebook
|LabAssociatedLesson=Jupyter Notebook

Revision as of 08:03, 31 May 2020

Sediment Supply to the Global Ocean

Duration
2.0 hrs
Updated
2020/03/26
Download
download
Run online using:
  1. Jupyter
     Jupyter logo.png

Contributor(s)
    Irina Overeem at INSTAAR - University of Colorado.

Introduction
Lab3sedsupply.png
Investigate river sediment supply to the ocean by exploring the effects of climate changes on river fluxes. We also look at the effect of humans on rivers: the building of a reservoir. See also the spreadsheet lab: https://csdms.colorado.edu/mediawiki/images/RiverFluxtoOceanSpreadsheetLab.zip

Classroom organization
In this lab, we are using a theoretical river basin of ~1990 km2, with 1200m of relief and a river length of ~100 km. All parameters that are shown by default once the HydroTrend Model is loaded are based on a present-day, temperate climate.

Whereas these runs are not meant to be specific, we are using parameters that are realistic for the Waiapaoa River in New Zealand. The Waiapaoa River is located on North Island and receives high rain and has erodible soils, so the river sediment loads are exceptionally high. It has been called the 'dirtiest small river in the world'. A more detailed description of applying HydroTrend to the Waipaoa basin, New Zealand has been published in WRR: http://dx.doi.org/10.1029/2006WR005570. To learn more about HydroTrend and its approach to sediment supply modeling, you can download the presentation: SedimentSupplyModeling02_2013.ppt.

This lab will run HydroTrend simulation with Python Modeling Tool (Pymt). If you have never used the Pymt, learn how to use it: https://pymt.readthedocs.io/en/latest/install.html. The Pymt allows you to set up simulations and run notebooks.

You can create an account at CSDMS JupyterHub and test the Jupyter Notebook for this lab. Please follow the instruction at: https://github.com/csdms/pymt/blob/master/notebooks/README.md. If you are a faculty at an academic institution, it is possible to work with us to get temporary teaching accounts. Work directly with us by emailing: csdms@colorado.edu.

Download associated file: SedimentSupplyModeling02_2013.ppt
To learn more about HydroTrend and its approach to sediment supply modeling

Learning objectives
Skills
  • use Pymt to run HydroTrend Model
  • familiarize with a basic configuration of the HydroTrend Model
  • make small changes to key input parameters
  • hands-on experience with visualizing output in Python
Key concepts
  • explore the HydroTrend base-case river simulation
  • how does a river system respond to climate change
  • how do human affect river sediment loads

Lab notes
Launch binder hydrotrend2.png
Launch binder
You can launch binder to directly run the Jupyter Notebook for this lab through a web browser.

>> Open a new browser window and open the Pymt read the docs page at: https://pymt.readthedocs.io/en/latest/examples.html

>> You will see that there are several example models. In this lab we will select the HydroTrend model.

>> Click on the 'Launch Binder' box and it will allow you to see this lab as a Jupyter Notebook.

>> You can execute the Jupyter notebook code cells using shift -enter.

Requirements
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References
  • Kettner, A.J., and Syvitski, J.P.M., 2008. HydroTrend version 3.0: a Climate-Driven Hydrological Transport Model that Simulates Discharge and Sediment Load leaving a River System. Computers & Geosciences, 34(10), 1170-1183. doi: 10.1016/j.cageo.2008.02.008