waterdata
Tuesday tutorials: data access, time series and spatial data analyses
These spatial data analyis notebooks cover:
- indexing/selecting in multi-dimensional arrays (numpy)
- labeled n-dimensional arrays (xarray)
- accessing gridded DayMet data, and visualizing gridded data (xarray, matplotlib, cartopy? rioxarray?)
- computing statistics/aggregation (xarray)
Materials to draw from
WHW19 tutorials
- Data access and time-series statistics cyberseminar
- Gridded climate data(sets) cyberseminar
- Observatory for Gridded Hydrometeorology (OGH)? See also this OGH presentation.
Other resources
- GHW19 vector tools. https://geohackweek.github.io/vector/
- GHW19 raster tools. https://github.com/geohackweek/raster-2019
- OHW19 tutorials: https://oceanhackweek.github.io/ohw19/curriculum_2019.html
Yakima watershed focus: Upper Yakima (HUC8 code: 17030001)
For a quick and easy exploration of this watershed (or any other HUC in the lower 48), you can go to https://modelmywatershed.org, an application I've been involved in. Type "upper yakima" in the text box in the upper right, select the HUC 8 Upper Yakima entry, then click on the Select button that comes up. You can add geospatial context via the Layers box (streams/grids/boundaries), and get quick view of the distribution of general watershed properties (precip/air temp/elev/slope/stream order) on the column to the left.
Setting up the conda environment
conda env create -f environment.yml
conda activate whwwaterdata; ipython kernel install --user --name whwwaterdata