Property:LabDescription
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This exercise will provide some experience with methods used for predicting flood frequency and magnitude. We will be using the US Geological Survey (USGS) website to retrieve historical stream gauge data of the sort used to predict the likelihood of flood events of particular magnitudes during a given time interval. Such predictions are the basis for numerous engineering, restoration and development projects in and around rivers. 1. Practice using Python Pandas DataFrames to import, manipulate, and visualize data, 2. Learn some powerful tools for subsetting your dataframes, and 3. Practice visualizing data +
This lab consists of three notebooks that use the mesh generator ''dmsh'' and the mesh generator from Anuga to create unstructured grids that are passed into Landlab. The examples contain code for creating Landlab models using these other mesh generators as a starting point. Only the flow accumulation and drainage area components are demo'd, but there are many other questions one might want to ask about the influence of cell resolution (and local cell resolution) on different landscape evolution processes. +
This lab explores the physics behind unconfined groundwater aquifers. The lab is based on a 1D numerical model of an aquifer formed on top of a horizontal impermeable unit. The calculates the evolution of the aquifer's thickness profile, with an impermeable "wall" on one end, a seepage face on the other, and a constant recharge rate from above. Students can experiment with different values of hydraulic conductivity and different recharge rates. The model embedded in the notebook generates an animation showing the water table over time. One the model has been run, students can use matplotlib commands to plot the discharge and vertically averaged Darcian velocity. +
This lab is appropriate for advanced undergraduates and graduate students majoring in earth science/engineering.
We will use Landlab to generate a grid, use two different landscape evolution models (LEMs) to evolve a synthetic landscape, apply stochastic wildfires that increase erodibility, and plot various maps and graphs. Though no real-world data are used, the landscape relief is statistically realistic and roughly approximate to the Wasatch mountains in Utah. +
This lab is targeted at undergraduate students and illustrates how storm sequences interact with watershed properties to control infiltration and runoff.
Students will first use the PrecipitationDistribution landlab component to generate two month-long rainfall time series over the Hugo watershed: a higher intensity and a lower intensity scenario. Students will learn about rainfall intensity and storm parameters, and will use pandas to read a .csv and matplotlib to plot the rainfall time series.
Next, students will model what happens to the rain when it reaches the ground. Students will use the landlab component SoilInfiltrationGreenAmpt to model rainfall infiltration and will explore how hydraulic conductivity and hydraulic gradient affect the infiltration rate. The propagation of surface runoff will be simulated using the OverlandFlow component. Students will plot the resulting hydrographs and infiltration depths using matplotlib, and can explore how adjusting different parameters affects these outputs.
The lab includes assignment questions that prompt students to think about and explore how adjusting parameters for each aspect of the system - storm sequences and watershed properties - affects runoff and infiltration. +
This lab is useful for visualizing conductive heat flow in soil or rock, as the surface temperature varies. The notebook includes a simple 1D model of temperature evolution in a soil profile, and provides students with guidance on how to run the model and explore parameters. +
This lab uses a simple 1D numerical model to illustrate how a valley glacier grows and reaches equilibrium. Students can experiment by changing the Equilibrium Line Altitude, accumulation rate coefficient, valley slope, and other parameters. +
Topography is a Python library for fetching and caching land elevation data using the OpenTopography REST API. Topography provides access to the following global raster datasets:
* SRTMGL3 (SRTM GL3 90m)
* SRTMGL1 (SRTM GL1 30m)
* SRTMGL1_E (SRTM GL1 Ellipsoidal 30m)
* AW3D30 (ALOS World 3D 30m)
* AW3D30_E (ALOS World 3D Ellipsoidal, 30m)
* SRTM15Plus (Global Bathymetry SRTM15+ V2.1)
* NASADEM (NASADEM Global DEM)
* COP30 (Copernicus Global DSM 30m)
* COP90 (Copernicus Global DSM 90m)
The library includes an API, CLI, and BMI that accept the dataset type, a latitude-longitude bounding box, and the output file format. Data are downloaded from OpenTopography and cached locally. The cache is checked before downloading new data. Data from a cached file can optionally be loaded into an xarray DataArray using the experimental open_rasterio method.
More information on Topography can found in its documentation: https://bmi-topography.readthedocs.io. +
Using the Frost number code and grids of climate model input data (CMIP5), allows you to map predictions of permafrost occurrence. This is lesson 4 in a mini-course on permafrost. +
Waves enhance sediment resuspension from the seabed and shorelines. Wave power is often correlated to shoreline erosion and is used to assess feasibility of renewable wave energy generation. This lab demonstrates how to use the CSDMS Data Component to download surface wave properties from the WAVEWATCH III model output for a given time period, interpolate it to a specific location, and calculate the wave power over time at that point. +
We will be analyzing the data from a month long deployment of a Lowell Tilt Current Meter: https://lowellinstruments.com/products/tcm-1-tilt-current-meter/. These meters were deployed at the boat ramp of Bog Stream in North Pond, Maine (44.647773, -69.865923) from Sept. 25th 2020 - Oct. 22, 2020 as part of a final project for GE 254 at Colby College in conjunction with the 7-lakes alliance (https://www.7lakesalliance.org/). +
bmi_dbseabed package (https://github.com/gantian127/bmi_dbseabed) provides a set of functions that allows downloading of the dataset from dbSEABED (https://instaar.colorado.edu/~jenkinsc/dbseabed/), a system for marine substrates datasets across the globe.
bmi_dbseabed package also includes a Basic Model Interface (BMI), which converts the dbSEABED datasets into a reusable, plug-and-play data component for the PyMT modeling framework developed by Community Surface Dynamics Modeling System (CSDMS). +
bmi_era5 package (https://github.com/gantian127/bmi_era5) is an implementation of the Basic Model Interface (BMI) for the ERA5 dataset (https://confluence.ecmwf.int/display/CKB/ERA5). This package uses the CDS API (https://cds.climate.copernicus.eu/api-how-to) to download the ERA5 dataset and wraps the dataset with Basic Model Interface (BMI) for data control and query. +
bmi_nwis package (https://github.com/gantian127/bmi_nwis) is an implementation of the Basic Model Interface (BMI) for the USGS NWIS dataset (https://waterdata.usgs.gov/nwis). This package uses the dataretrieval package (https://github.com/USGS-python/dataretrieval) to download the NWIS dataset and wraps the dataset with Basic Model Interface (BMI) for data control and query. +
soilgrids package (https://github.com/gantian127/soilgrids) provides a set of functions that allow downloading of the global gridded soil information from SoilGrids https://www.isric.org/explore/soilgrids, a system for global digital soil mapping to map the spatial distribution of soil properties across the globe.
soilgrids package includes a Basic Model Interface (BMI), which converts the SoilGrids dataset into a reusable, plug-and-play data component for Community Surface Dynamics Modeling System (CSDMS) modeling framework. +
