Property:LabCOIntro
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Sediment pulses are synthetically introduced to simulate erosive conditions, which may be caused by fire or landslide occurrences in the landscape, to quantify sediment yield across river network using the Landlab NetworkSedimentTransporter (NST) component. +
This Jupyter Notebook is best for people who already have a conceptual understanding of earth surface processes and want to learn how these apply to numerical modeling, landscape evolution, and sediment flux. Although no Python skills are required to run the notebook, those with an intermediate understanding of Python will be able to learn more by reading the code. +
This is a self-contained jupyter notebook using meander py. It is recommended the instructor introduces concepts of meandering rivers, and students read papers on meadner migration rate - i.e. the papers directly associated with the model. Students can run through the examples, and then assignments can be worked on plenary in a lab style class or as homework.
It is recommended that results are being discussed plenary after the completion of the exercises. +
This lab couples two coastal models: CoastSat and Coastal Evolution Model (CEM) in a jupyter notebook to allow users to explore shoreline change using real, observed shorelines and wave data.
We use CoastSat to download and extract shorelines from satellite imagery. These shorelines feed into the CEM where they are evolved by historical wave characteristics from a nearby buoy. +
This lab includes examples of using the Topography data component in a Jupyter Notebook, in a shell script, and in a Python program. Try editing these examples to get data from different locations around the globe. +
This lab includes examples of using the GeoTiff data component in a Jupyter Notebook, in a shell script, and in a Python program. Try editing these examples to download, open, and display data from other GeoTIFF files. +
This lab is appropriate for advanced undergraduates and graduate students majoring in earth science/engineering.
We will be looking at data on river discharge--the volume of water transported through a given cross section per time--in the Colorado River.
This Jupyter Notebook lends itself to a short introduction on the concept of river discharge and how it is measured, as well as an introduction to the gauging stations of the USGS.
The data analysis requires basic Python data handling skills, but the coding is introductory level.
Students can run the Notebook, and they're encouraged to do assignments on their own or as homework. A review and discussion of solutions by the instructor after completion by the participants is recommended. +
This lab is designed for undergraduate and graduate students studying earth sciences. The notebooks give participants the opportunity to interactively explore the topics covered. +
This lab is the first in a series of introduction to permafrost process modeling, designed for inexperienced users. In this first lesson, we explore the Air Frost Number model and learn to use such models in the CSDMS Python Model tool (Pymt). We implemented a basic configuration of the Air Frost Number (as formulated by Nelson and Outcalt in 1987). This lab is designed to gain some experience with running a numerical model, changing model inputs, and analyzing model output. Specifically, this first lab looks at what controls permafrost occurrence and compares the occurrence of permafrost in Russia.
Basic theory on the Air Frost Number is presented in File:FrostNumberModel Lecture1.pptx.
This lab will likely take ~ 1.5 hours to complete in the classroom. This time assumes you are unfamiliar with pymt and need to learn setting parameters, and looking at output (otherwise it will be much faster).
If you have never used the Python Modeling Tool (Pymt), learn how to use it at: https://pymt.readthedocs.io/en/latest. The Pymt allows you to set up simulations, and run them and then analyze data using Python. +
This lab is the fourth in a series of introduction to permafrost process modeling, designed for inexperienced users. In this lesson, we explore the future of permafrost under a globally changing climate in the CSDMS Python Modeling Tool (Pymt). We implemented a basic configuration of the Air Frost Number (as formulated by Nelson and Outcalt in 1987). This series of labs is designed for inexperienced modelers to gain some experience with running a numerical model, changing model inputs, and analyzing model output. This fourth lab looks at permafrost occurrence trends over the 21st century for different regions in the Arctic
Basic theory on the Air Frost Number is presented in File:FrostNumberModel Lecture1.pptx.
This lab will likely take ~ 1,5 hours to complete in the classroom. Since this is the fourth lab in a mini-series, this estimated time assumes you are familiar with the Pymt and have now learned setting parameters, saving runs, downloading data and looking at output. If this is not the case, either start with Lab 1 in this series, or learn how to use Pymt (https://pymt.readthedocs.io/en/latest/install.html). The Pymt allows you to set up simulations and run notebooks.
We will use netcdf files for output, this is a standard output from all CSDMS models. If you have no experience with visualizing these files, Panoply software will be helpful (https://www.giss.nasa.gov/tools/panoply/). Find instructions on how to use this software: https://csdms.colorado.edu/wiki/Labs_WMT_VisualizeOutput.
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 +
This lab is the second in a series of introduction to permafrost process modeling, designed for inexperienced users. In this second lesson, we explore the Kudryavstev model and learn to use this model in the CSDMS Python Modeling Tool (Pymt). We implemented the Kudryavstev model (as formulated in Anisimov et al.1997). It is dubbed the Ku-model.
This series of labs is designed for inexperienced modelers to gain some experience with running a numerical model, changing model inputs, and analyzing model output. Specifically, this lab looks at what controls soil temperature and active layer thickness and compares model output with observed longterm data collected at permafrost active layer thickness monitoring sites in Fairbanks and Barrow, Alaska.
Basic theory on the Kudryavstev model is presented in these slides File:KudryavtsevModel Lecture2.pptx
This lab will likely take ~ 1.5 hours to complete in the classroom. This time assumes you now have gained some familiarity with the Pymt and have learned how to set parameters, save runs, download data and look at output. If this is not the case, either start with Lab 1 in this series, or learn how to use Pymt (https://pymt.readthedocs.io/en/latest/install.html). The Pymt allows you to set up simulations and run notebooks. +
This lab is the third in a series of introduction to permafrost process modeling. In this third lesson, we further use the Air Frost number model and learn to use this model to create spatially varying predictions of permafrost in the CSDMS Python Modeling Tool (Pymt). We implemented the Air Frost number model (as formulated in Nelson and Outcalt, 1987). This pseudo-2D implementation is named the Frost-number GEO model.
This series of labs is designed for inexperienced modelers to gain some experience with running a numerical model, changing model inputs, and analyzing model output. Specifically, this lab combines the simple model with a climate reanalysis dataset, modified from the CRU-NCEP climate data over the 20th century.
Basic information on the CRU_AK data component is presented in these slides. File:FrostNumberGEOModel Lecture3.pptx
This lab will likely take 1.5 -2 hrs to complete in the classroom. This time assumes you now have gained some familiarity with the Pymt and have learned how to set parameters, save runs, download data and look at output. If this is not the case, either start with Lab 1 in this series, or learn how to use Pymt (https://pymt.readthedocs.io/en/latest/install.html).
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 +
This lab replicates and improves upon simulations originally run by Frances Dunn and Stephen Darby, reported in Darby et al. 2015. This simulation is driven by climate predictions (daily temperature and precipitation) obtained from the Hadley Centre (HadRM3P) Regional Climate Model. The Q0 realization is utilized in this lab.<br><br>
<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> +
