SWIM Quarterly Update, Q3 2025: Field Deployment and First Data

SWIM Quarterly Update, July 2025

The SWIM project reached a major milestone this quarter with the deployment of the WAMO sensor platform at our primary pilot site. Here is a summary of progress.

WAMO deployment at La Fe Reservoir

On 12 June 2025, the WAMO (Water Monitoring) IoT sensor platform developed by consortium partner e.Ray was installed at La Fe Reservoir in Colombia. The platform is now operational, transmitting 19 water quality and environmental parameters hourly via LTE. This marks the beginning of continuous, autonomous in-situ monitoring at the pilot site, an important step for validating SWIM’s satellite-sensor data fusion approach.

Module development progress (WP2)

All three core modules have advanced significantly:

• Water Quality Module: Computing chlorophyll-a (NDCI), turbidity, total suspended matter, algal bloom indices (FAI, SABI, ABDI), and Secchi depth from Sentinel-2 data, with ACOLITE atmospheric correction applied.

• • Water Balance Module: ERA5-Land climate reanalysis data and GloFAS hydrological outputs are being processed, with automated watershed delineation via HydroSHEDS.

  • • Natural Disaster Module: Integration of rainfall, soil moisture, topography, and water level data for the Las Palmas Basin is underway.

  • WOLF calibration engine (WP2)

• Rayner Software has made strong progress on the WOLF calibration engine, which cross-validates satellite-derived water quality parameters against laboratory data from EPM and WAMO in-situ measurements.

Stakeholder partnerships formalised (WP4)

Formal Memoranda of Understanding have been signed with EPM, the Municipality of Envigado, and the University of Envigado. More than 30 functional and non-functional requirements have been gathered through fieldwork, interviews, and workshops with these stakeholders.

Looking ahead

With WAMO now operational and the modules progressing toward integration, Q4 will focus on the DSS design, continued data collection, and the development of the machine learning pipeline.

The SWIM project (grant agreement No. 101180055) is funded by EUSPA under Horizon Europe.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EUSPA. Neither the European Union nor the granting authority can be held responsible for them.