Groundwater model service driven by open data and social networks

This service will facilitate the management of groundwater bodies by means of numerical models using open data and public feedback (social networks).

The following presentation on this service was given at the MELODIES: Exploiting Open Data conference in October 2016. Slides are available here.

Service Q&A

Who are the target users of this service?

The main targets of this service are environmental regulating agencies (national, regional and local), consulting companies for whom environmental modelling is an accessory activity, R&D institutes working in environmental research, and citizens.

How will these users access this service?

Via a dedicated web service. These services will be similar to the CUAHSI web schema and will use OGC WaterML standard. The web service will allow third party mobile applications to communicate new data to the service and take part in recalibrating the products.

What products does this service provide?

Output data from the various groundwater models hosted by Hydromodel Host - groundwater level and chloride concentration in particular.

How will these products benefit users?

Users will be able to use this product to assist in their management of groundwater.

Which Open Data sources drive this service?

  1. Water level and withdrawals from the Catalan Water Agency
  2. Water level and withdrawals from CUADL
  3. Precipitation and temperature from the Spanish Meteorological Service

as Linked Open Data, via this link and this link

What processing is performed on this data?

Data is periodically and automatically obtained from open databases (primarily climatic conditions data) and public agencies (water level observations and detractions) and input into our bespoke hydrological model.  Data contributed by citizens through the web portal will also contribute to the model input data to improve the results.

How does this service use Linked Open Data?

We have designed an ontology to describe groundwater data either from measuring sites or from model results. This ontology is applied to the numerical model results and transformed into Linked Open Data RDF files. This set of files is stored in Strabon, located on the cloud platform. We have designed a set of Web Services to query the output data as a public interface.

How Open Data has improved this service

The major advantage is using a standard protocol because it simplifies all the interfaces between modules.

How the Shared Platform has improved this service

The service has been designed for the cloud platform from the very beginning. The true value of the platform will become clearer to us towards the end of the project.

How LOD and/or visualisation tools have improved this service

We are not using the visualisation tools yet and the advantages of publishing LOD will be more evident once we can begin to link to output from other work packages.

Our biggest challenges so far...

An innovative feature of the development in MELODIES will be the incorporation of data contributed by citizens, which will feed back to improving the models. The biggest challenge is how to mix information with very different levels of precision and we have solved this y using the trend of the value and weighting it accordingly.