Main Objectives Before Monday 2/1/2021 & Thursday 2/4/2021

[maxtitcomb]

• [ ] Data exploration: Continue running the template data listed in our code section and check to see accuracy of results.
• [ ] Look for scientific literature & other sources online of people who have done similar projects.
• [ ] GIS work to take a look at spatial relationships. (Fires, vegetation, land cover)
• [ ] Take a look at the raw data and plot trends you think might matter -Plot changes in mean data per year -Show varied highs/lows between each site -etc.

Old Goals to Continue:

• [ ] Share paper links on drivers of DO and other factors to take into account for our forecast
• [ ] Plot data sources from Table 2 and look for trends
• [ ] Brainstorm some additions that we could make to the template code to make it more accurate
  • If you make changes to template code, make your own branch

[maxtitcomb]

Useful Links:

Detailed Information on site: Barco Lake https://www.neonscience.org/field-sites/barc

Detailed Information on site: Posey Creek https://www.neonscience.org/field-sites/pose

Cumulitive NEON data search: https://data.neonscience.org/data-products/explore

Spatial data: https://www.neonscience.org/data-samples/data/spatial-data-maps https://neon.maps.arcgis.com/home/index.html

[maxtitcomb]

Possible GIS data visualizations:

• Modeling nutrients from agricultural runoff that cause imbalances in dissolved oxygen levels. https://learn.arcgis.com/en/projects/model-water-quality-using-interpolation/
  • Example map: https://downloads.esri.com/learnarcgis/model-water-quality-using-interpolation/dissolved-oxygen-levels-in-chesapeake-bay.pdf
• 3D geostatistical interpolation to predict the oxygen levels over vertical area: https://learn.arcgis.com/en/projects/interpolate-3d-oxygen-measurements-in-monterey-bay/

[maxtitcomb]

A photo showing some of the highlights I got out of the in-class forecasting activity focusing on Barco lake.