This is issue text, describe what should be done.

I think it's useful to make a copy of @EMayorga 's tutorial, but for well data, and see if we can get it to run. Then push it back to the tutorial materials.

• Get the notebook running with well points and a grid (grace or chirps)

• Explore some more rasterstats at the end of the notebook

• Clean up the notebook to be useful and clear to our data patron/ well data stakeholders

• Push back to the tutorial Github repository

This will be for the "human-caused" issues. Will need another one for hydrologic-caused issues.

Similar to #8, clip our precipitation raster to the same bounding region for potential further hydrologic analysis.

In addition to the well functional binary (YES/NO), we also have status messages, e.g.,

Status:Not functional|Quantity:Dry|Quality:Soft
Low yield|Normally operational
Dry pan|No operation in the dry season
No- broken down. Well polluted
No- broken down. WATER TABLE HAS DROPED

So we need to figure out some of these keywords in order to make better categories of well failure conditions.

Options

1. Create YAML file, to be sourced from the terminal before starting Jupyter notebooks, or
2. Create list of dependencies to be installed through conda.

Tasks:

• List dependencies.
• Create YAML file.
• Test environment.yaml
• Document guide for both methods of setting up environment.

There are ~1500 wells that have "Breakdown year:[YEAR]" in the STATUS message, e.g.,

This is useful information because it tells us the actual year where the well became nonfunctional, and we could use this date instead of the report date, which is often years later, for trying to pinpoint potential hydrologic causes.

Or, we could use the report date like we do with the rest of the data if we want to be consistent with the rest of the available data. Thoughts?

Take the global GRACE dataset, clip to Tanzania/Uganda bounding box, potentially do some zonal stats based on hydrological units

Right now, we have "wells" that are actually gravity fed, like a spring or a rainwater harvesting operation, e.g.,

These have no relation to GRACE data (since GRACE is groundwater), and therefore should be removed.

I am using GEE to calculate drought indexes (probably NDDI) from Landsat. I can plot changes in the drought indexes over time in the area of the well.

Should clip it so we only include in the eastern part of Africa.

Change address to

GRACE data

nc='https://opendap.jpl.nasa.gov:443/opendap/GeodeticsGravity/tellus/L3/mascon/RL05/JPL/CRI/netcdf/GRCTellus.JPL.200204_201701.GLO.RL05M_1.MSCNv02CRIv02.nc'

Tasks:

• Aggregate precipitation product to monthly precipitation [mm/month].
• Calculate mean monthly precipitation for study time. Can use this to get a general idea of the wet and dry season.