This repo is home to the API-backend of the Global River Runner project. The resources here are enough to deploy a pygeoapi server with the river-runner process. More information can be found here
The Global River Runner is a visualization simulating the path a raindrop would take, assuming it runs off into a stream and from then on to a terminating location, likely an inland water body or the ocean. A running list of interesting flow paths can be found here.
The basic operation is to issue a GET request of the form:
https://your-river-runner-domain.com/processes/river-runner/execution?lat=<y>&lng=<x>Performance is best if your request header includes Accept-Encoding: gzip and Accept: application/json.
Some domain to keep in mind:
| Domain | Version | Behind Cloudfront? |
|---|---|---|
| https://merit.internetofwater.app | stable | Yes |
| https://merit-nldi.internetofwater.app | stable | No |
| http://d1za2aav0xp6il.cloudfront.net | latest | Yes |
| https://merit-dev-z3iqgg3uaa-uc.a.run.app | latest | No |
Deployment requires an instance of pygeoapi and postgres with PostGIS installed.
The pygeoapi instance must include the river-runner process as well as several other modifications.
We have prepared docker images for both the latest versions of pygeoapi with the necessary modifications.
A copy of the hydrography database used is documented and available for download here.
See the deploy/cloud directory for Dockerfiles and pygeoapi configurations necessary to deploy an appropriately configured pygeoapi container to the cloud service of your choice. Note the need to use environmental variables URL (your desired service URL for the pygeoapi base path), and DBHOST, DBUSER,DBNAME, DBPASSWORD (to connect to your database). The pygeoapi river-runner is designed to be a stateless application, so your database must be deployed separately.
See the deploy/local directory for a docker-compose and pygeoapi configuration for a pygeoapi and postgis stack.