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More information on the examples used in this lab can be found in the documentation: https://elbeejay.github.io/meshing-with-landlab/.
The source code for the examples can be found at https://github.com/elbeejay/meshing-with-landlab. +
River discharge data for many US rivers is available from the USGS WaterWatch 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 these, 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.
<p>This lab can be run on either the <em>lab</em> (for educators) or <em>jupyter</em> (for general use) instance of the OpenEarthscape JupyterHub: just click one of the links under the <strong>Run online using</strong> heading at the top of this page, then run the notebook in the "CSDMS" kernel.</p>
<p>If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk.</p> +
Start at the top by reading each block of text and sequentially running each code block (shift - enter OR got to the Cell pulldown menu at the top and choose Run Cells).
If you just change one code block and rerun only that code block, only the parts of the code in that code block will be updated. (E.g. if you change parameters but don't reset the code blocks that initialize run time or topography, then these values will not be reset.)<br>
<p>This lab can be run on the <em>lab</em> (for educators) and <em>jupyter</em> (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the <strong>Run online using</strong> heading at the top of this page, then run the notebook in the "CSDMS" kernel.</p>
<p>If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk.</p> +
The lab consists of two main parts:
Sections 1-2 demonstrates the use of a function "create_network_from_raster" to link DEM created from other Landlab component.
Section 3-5 demonstrates NST application with synthetically generated sediment pulses.
The user may begin the lab from Section 3 to focus on NST application.
<p>This lab can be run on the <em>lab</em> (for educators) and <em>jupyter</em> (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the <strong>Run online using</strong> heading at the top of this page, then run the notebook in the "CSDMS" kernel.</p>
<p>If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk.</p> +
The lab has two Notebooks:
The first (Prepare_bathymetry_wave_inputs.ipynb), could be run on your local environment or any outside CSDMS jupyterhub environment in order to use CoastSat.
The second (cem_waves_notebook.ipynb), could be run on CSDMS jupyterhub environment , or local machine (only linux or osx). make sure you Install Pymt cem https://anaconda.org/conda-forge/pymt_cem +
This lab can be run on the <i>lab</i> (for educators) and <i>jupyter</i> (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel. Note that the notebook uses a little over 3 GB of memory.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
This lab can be run on the lab (for educators) and jupyter (for general use) instances of the OpenEarthscape JupyterHub: just click one of the links under the Run online using heading at the top of this page, then run the notebook in the "CSDMS" kernel.
If you don't already have a JupyterHub account, follow the instructions to sign up at https://csdms.colorado.edu/wiki/JupyterHub. If you're an educator, you can get JupyterHub accounts for students--please contact us through the CSDMS Help Desk: https://csdms.github.io/help-desk. +
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 (https://pymt.readthedocs.io/en/latest/examples.html)
>> You will see that there are several example models.
>> 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.
In this lab we will select the Frost number GEO component as the driving model, we will combine it with temperature data from the CMIP5 climate simulations.
If you have done the previous labs, you now are familiar with the approach of the Air Frost number, it uses the mean annual air temperature of a location (MAAT), as well as the yearly temperature amplitude. In the Air Frost parametrization the mean monthly temperature of the warmest month and coldest month set that amplitude. In the model coupling here we have assumed the warmest month is July and the coldest month is January. The 'degree thawing days' are above 0 C, the 'degree freezing days' are below 0 C. To arrive at the cumulative freezing degree days and thawing degree days, the annual temperature curve is approximated by a cosine as defined by the warmest and coldest months, and one can integrate under the cosine curve (see also in Frost number presentation). The CMIP component is a 'data component' processed from the CRU-NCEP Climate Model Intercomparison Project - 5, also called CMIP 5. Data presented include the mean annual temperature for each gridcell, mean July temperature and mean January temperature over the period 1902 -2100. This dataset presents the mean of all the CMIP5 models, and the original atmosphere ocean models were run for the representative concentration pathway RCP 8.5. In RCP 8.5, greenhouse gas emissions continue to rise throughout the 21st century.
>> Link the Frost number GEO component with the CMIP component >> In the Frost number component parameters adapt the time to start in 1950, for 150 years. Set the model dimensions to xdim=40 and ydim=30. Make sure you request the frost-number-air output file.
>> Then open up the settings for the CMIP5 data component. Make sure you start reading the data from 1950 onwards. Make sure you match the xdim and ydim with the Frost number grid dimensions. In addition, you will need to specify what region in large PanArctic dataset you do want to read. The CMIP5 data are on a half-degree latitude-longitude grid, so it consist of 720 by 360 grid cells. The upper left corner of the grid is at the international date line. For Alaska, set the column offset as 26, and the row offset as 32. Of course, the climate models keep track of 100's of physical variables; for our simple approximation we only need temperature. So this data component is subsampled to only provide monthly air temperature to other components. In the component here, the data is masked to only comprise the present-day permafrost region. The rest of the grid has 'no data' values.
Download the zip file with your simulation output from the run status window. If you unzip the file you will find your output files, we will use the one called 'frostnumber__air.nc'. You can open this file with Panoply and plot the steady Frost number over 150 years by clicking on the 'create plot' icon
What are typical mean annual temperatures over the North Slope of Alaska in the 1950's?
Do you see much change in the mean annual temperatures at the end of the 21st century?
What does the Frost Number map look like for 1955, and for 2095?
What do these specific Frost numbers imply for the likelihood of permafrost occurrence?
We assume that the disappearance of permafrost (as predicted by the Frost number) indeed means all 3m of soil is thawed.
What would be a rough estimation of the amount of new carbon potentially exposed to the global atmosphere and carbon cycle?
How does that amount compare to the total amount of carbon in the atmosphere? (Hint, see Schuur et al., 2015).
Discuss the factors that would make this first-order estimation problematic?
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 (https://pymt.readthedocs.io/en/latest/examples.html)
>> You will see that there are several example models.
>> 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. +
