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<p>The Coastline Evolution Model (CEM) addresses predominately sandy, wave-dominated coastlines on time-scales ranging from years to millenia and on spatial scales ranging from kilometers to hundreds of kilometers. Shoreline evolution results from gradients in wave-driven alongshore sediment transport. The model has been used to represent varying geology underlying a sandy coastline and shoreface in a simplified manner and enables the simulation of coastline evolution when sediment supply from an eroding shoreface may be constrained. CEM also supports the simulation of human manipulations to coastline evolution through beach nourishment or hard structures. To learn more about the models in this lab, specifically the Coastal Evolution Model, CEM, you can download the presentation.</p>
<p>This lab includes experiments to couple the terrestrial and coastal domains. We will be looking at a river supplying sediment to a coastal zone, along which wave-driven longshore transport occurs. We will learn about the effect of incoming wave fields, the effect of sediment supply to the coast, and whether this supply happens through a single delta channel or multiple delta channels. Many deltas are classified as wave-dominated deltas, the Arno Delta in Italy is one example.</p>
<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> +
Although we understand the process of cratering reasonably well we have a limited understanding about how cratering and the cratered surface influences the role of other longer-term surface processes. For example, on heavily cratered surfaces of the Moon, Mars, and Mercury, which have not been altered by plate tectonics like on Earth, how do the craters affect the flow and collection of water? Of wind? And of the sediments transported by both water and wind? These are fundamental questions in our understanding of planetary bodies and their potential habitability.
This lab introduces what we know about impact cratering, some of the principles we use to simulate a cratered surface (after Howard, 2007), and allows students to explore the effects of stream-power erosion and diffusive erosion on a cratered landscape. Throughout this lab, quantities relevant to Mars are used, but impact cratering is a universal process across our solar system and the models could just as easily be applied to any cratered surface, while the surface processes could be modified to reflect the relevant processes on that planetary body. +
Answer these questions before running the notebook.<br><br>
1. What do you think will happen to total relief (defined as the maximum minus the minimum elevation, here area is fixed) and channel slope at steady state if $K_{sp}$ is uniformly increased?
2. What do you think will happen to total relief and channel slope at steady state if $U$ is uniformly increased?
3. How do you think a steady-state landscape with a uniform low rock uplift rate will respond if rock uplift is uniformly increased (relative to a steady base level)? How will channel slopes change through time? +
In this lab we explore what BMI is and how to use it through a pair of Jupyter Notebooks. The first notebook presents a simple "Heat" model which simulates the diffusion of temperature on a uniform rectangular plate with Dirichlet boundary conditions. The second notebook demonstrates how to run the "Heat" model through its BMI. +
In this lab, it includes two Jupyter Notebooks. In the first notebook, we will explore the Ku model to simulate the active layer thickness and soil temperature. In the second notebook, we will use the active layer depth results from Ku model to drive a depth-dependent hillslope diffusion model over the Eight Mile Lake study site. The second notebook gives one very simplistic example for how Ku can be used alongside landscape geomorphology models. +
In this lab, we are using a theoretical river basin of ~1990 km<sup>2</sup>, 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, located on North Island, 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 has been published in ''Water Resources Research'': http://dx.doi.org/10.1029/2006WR005570. To learn more about HydroTrend and its approach to sediment supply modeling, download the presentation listed below.
<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> +
In this lab, we will explore a series of Jupyter Notebooks that show how to use ''pymt'' to run and couple models. This lab will first introduce ''pymt'' and demonstrate how to setup, initialize, run and finalize a model. Then, it will show how to use ''pymt'' to run a standalone model (Hydrotrend) and couple two models (CEM + Waves). The lab also includes additional Jupyter Notebooks for other model components in ''pymt'' (e.g., Frost Number Model, Kudryavtsev Model). +
In this lab, we will use different Landlab components for modeling earth surface processes. This lab includes two Jupyter Notebooks. One illustrates running the deAlmeida overland flow component in an extremely simple-minded way on a real topography, then shows it creating a flood sequence along an inclined surface with an oscillating water surface at one end. The other one illustrates how you can use Landlab to construct a simple two-dimensional numerical model on a regular (raster) grid, using a simple forward-time, centered-space numerical scheme. +
In this lab, we will use the WAVEWATCH III Data Component to download the model output from the WAVEWATCH III model, including significant wave height, peak wave period, peak wave direction, windspeed in east-west direction, and windspeed in the north-south direction. These datasets are interpolated to a location on the Northern Gulf of Mexico continental shelf (28.8°N, 267.4°E). Wave power is then calculated using the significant wave height and peak wave period for this location. +
In this lab, you will learn how to use Topography and ERA5 Data Components to download terrain and precipitation datasets. You will also learn how to use the Landlab components (FlowAccumulator, ChannelProfiler, OverlandFlow) and the Landlab utility (get_watershed_mask) for watershed delineation and overland flow simulation. +
In this lab, you will learn how to use the soilgrids package to download the soil property datasets for data visualization. You will explore the SoilGrids map services and write your own code to download multiple soil datasets. +
In this lab, you will learn how to use the bmi_nwis package to download the instantaneous datasets at the USGS Gage and then create time series plots for stream flow and gage height. +
In this lab, you will learn how to use the bmi_dbseabed package to download the marine substrates dataset (e.g., carbonate) for data visualization. +
In this lab, you will learn how to use the bmi_era5 package to download the ERA5 hourly reanalysis data on single level for 2 meter temperature and total precipitation in Colorado. +
Lab is designed for undergraduate students majoring in earth sciences (hydrology, environmental sciences, hydrogeology). The notebook is demonstration style and gives participants the possibility to explore interactively. +
Lab is designed for undergraduate students majoring in earth sciences (hydrology, environmental sciences, glaciology, atmosphere and ocean sciences). The notebook is demonstration style and gives participants the possibility to explore interactively. +
Lab is designed for undergraduate students majoring in earth sciences (hydrology, environmental sciences, glaciology, atmosphere and ocean sciences). The notebook is demonstration style and gives participants the possibility to explore interactively. +
Landslides are frequent hazards in Puerto Rico which are mainly caused by the steep terrain and heavy rainfall from hurricanes and other tropical weather systems. For example, Hurricane Maria hit the island of Puerto Rico on September 20th, 2017 and triggered more than 40,000 landslides in Puerto Rico (see details at https://www.usgs.gov/supplemental-appropriations-for-disaster-recovery-activities/landslides-triggered-hurricane-maria).
In this lab, we will calculate the hourly landslide susceptibility for the area that has high concentration of landslide of Puerto Rico during Hurricane Maria. We will use the Topography and ERA5 Data Components as well as Landlab to prepare several datasets to calculate the landslide susceptibility. +
Permafrost covers nearly 85% of Alaska. The map shows the permafrost distribution in this area (Picture source https://permafrost.gi.alaska.edu/sites/default/files/AlaskaPermafrostMap_Front_Dec2008_Jorgenson_etal_2008.pdf). A warming climate brought higher temperatures which may cause some permafrost to thaw. This can lead to geologic hazards such as landslides, ground subsidence, erosion and other severe surface distortions.
In this lab, we will use the Kudryavtsev (Ku) model (a pymt component) to calculate the active layer thickness for Eight Mile Lake in Alaska. Active layer is the top layer of the permafrost where the annual maximum temperature reaches 0°C and the temperature shift over diurnal and seasonal cycles. Then we will use the active layer thickness as the input for a Landlab component (DepthDependentDiffuser) to simulate the hillslope diffusion process. The ERA5 and Topography Data Components are used to prepare the inputs for the Ku model. +
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> +
