Lessons from Valencia: Why Early Warning Systems for Water Disasters Cannot Wait
On 29 October 2024, a meteorological phenomenon known as DANA (Depresion Aislada en Niveles Altos) dropped more than 445 millimetres of rain on the Valencia region of Spain in a single day, more than a year's worth of precipitation in a matter of hours. The resulting flash floods killed over 230 people, displaced thousands, and caused an estimated 29 billion euros in damage.
The scientific analysis that followed was alarming. A peer-reviewed study published in Nature Communications found that climate change had amplified the six-hour rainfall rate by 21% compared to preindustrial conditions, and that the area affected by extreme rainfall was 55% larger than it would have been without human-caused warming.
Valencia was not an isolated event. In 2025, water-related disasters worldwide caused nearly 5,000 deaths, displaced approximately 8 million people, and generated economic losses exceeding US$360 billion. Flash droughts, glacial lake outbursts, and floods appeared in regions where they were once rare.
The warning gap
The Valencia disaster exposed a real gap: the time between recognising a developing threat and communicating a useful warning to affected populations. It is not enough to have meteorological forecasts. Warning systems must translate raw data (rainfall rates, soil saturation, river levels, topography) into localised risk assessments that reach the right people in time for them to act.
This is the kind of problem that SWIM's Natural Disaster Module is designed to address. By integrating rainfall data, soil moisture measurements, topographic analysis, and water level readings, the module generates flash flood detection alerts, landslide susceptibility assessments, and infrastructure exposure scores for specific basins and communities.
From reactive to proactive
The traditional approach to flood and drought management is largely reactive, responding to events after they occur. Early warning systems shift this toward proactive risk management, but only if the underlying data is continuous, reliable, and usable.
SWIM combines Copernicus satellite data (including SAR-derived flood products from the Global Flood Monitoring service) with in-situ sensor networks to provide a multi-layered view of basin conditions. Machine learning models trained on historical data identify patterns that precede dangerous events, enabling alerts before conditions become critical.
In the pilot basin at Las Palmas Creek in Colombia, a region prone to flash floods and landslides, the SWIM team is developing and testing these capabilities with local authorities, including the Municipality of Envigado and EPM, Colombia's largest public utility.
Climate change demands digital resilience
The UN's 2025 Global Water Monitor reported that water-related hazards are now appearing in regions where they were historically rare. An equatorial cyclone affecting Indonesia, unprecedented glacial lake outburst floods in the Hindu Kush Himalaya, and the whiplash from drought to flood in Texas all point to a real acceleration of the water cycle.
Europe is not immune. The Mediterranean is warming faster than the global average, creating the conditions for more intense DANA events. The EU's climate adaptation strategy recognises the need for digital tools, but deployment remains patchy and uneven.
Projects like SWIM are part of the answer, not as a complete solution, but as a building block toward integrated, data-driven water risk management that can keep pace with a changing climate. The technology exists. The question is whether we deploy it widely enough, and quickly enough, to make a difference.
The SWIM project is funded by EUSPA under Horizon Europe grant agreement No. 101180055.
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.