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Valencia DANA Event: 227 Dead, 552km Flooded, Digital Tools Save Lives

Extreme rainfall overwhelmed Valencia, Spain. Digital tools helped manage the crisis, highlighting the need for proactive digitalisation in water risk management.

In this image there is a boat, people, digital board, water, buildings, cloudy sky and objects.
In this image there is a boat, people, digital board, water, buildings, cloudy sky and objects.

Valencia DANA Event: 227 Dead, 552km Flooded, Digital Tools Save Lives

An unprecedented disaster struck Valencia, Spain on October 29, 2024, when a DANA event brought extreme rainfall, with Turís municipality recording a staggering 772 litres/m2 in just 24 hours. This catastrophic event resulted in 227 fatalities, hundreds missing, and over 337 municipalities affected, with 552 square kilometres flooded. The main channel, designed to drain 400 m3/s, was completely overwhelmed.

The DANA event highlighted the urgent need for proactive management models based on digitalisation and advanced prediction for water risk management. Two existing tools within the Xylem Vue platform, Real-Time What-if Scenario (RTWIS) and Leak Detection, proved indispensable during the emergency response. Miren Aldecoa, Operations EU at Xylem Vue, described the disaster as 'unprecedented', with mud and sludge destroying roads, pipelines, and services.

Xylem Vue focused on four main actions during the recovery process: analysis, planning and execution, communication and coordination, and digitalisation. However, the lack of adequate digital sensors in the sewer network hindered operational decision-making during the emergency. Despite this, the platform's tools helped manage the crisis more effectively than documented flood management plans allowed for.

The DANA event in Valencia underscored the vital role of digitalisation and collaboration in emergency management. While the event overwhelmed existing plans, tools like those offered by Xylem Vue proved invaluable. As future events are inevitable, investing in digital infrastructure and advanced prediction models is crucial for saving lives and optimising resources under pressure.

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