A group of researchers, with the Valencian Institute for Research with AI (VRAIN) and the University Research Institute of Water and Environmental Engineering (Somemi) from the UPV (Universitat Politècnica de València) at the helm, is working on the development of a system responsible for monitoring, preventing, managing and coordinating climate emergencies early. The project, PGTEC, started in September 2025 and will end in June 2026.
This system is supported by cutting-edge technologies, such as AI and digital twins, as well as cyber-physical systems. When ready, it will use integration of meteorological data, such as river flows and volume of impounded water, as well as official alerts; through various organizations using REST API, which facilitates communication between systems.
In addition, it will use intelligent data models, in order to facilitate interoperability and data exchange between systems and information sources. The platform will also integrate AI into hydrological models thanks to the use of predictive models with AI to anticipate extreme weather events and be able to evaluate their impact on hydrographic basins.
To achieve this, the platform will also combine digital twins with multi-agent AI, with the mission of generating virtual replicas that simulate the environment in real time to improve anticipation. Above all, AI agents with interoperable data spaces will be employed to securely share and reuse critical information in real time.
The platform collects, analyzes and models environmental and climate data from different sources. All are integrated into data spaces that can be exchanged to allow public administrations, emergency management organizations and companies in the climate sector to access information in real time.
The development of this system is being carried out within the framework of the Platform Project for the Management of Early Prevention of Climate Emergencies (PGTEC), financed by the Reference Center for Data Spaces (CRED) of the Ministry for Digital Transformation and Public Service.
The two UPV entities that are in charge of the project intervene. So far they have developed two use cases: the WATER4CAST web platform, for the management of the Júcar basin; and fire risk management and implementation of the TETIA hydrological model in the basins that flow into L’Horta Sud de Valencia.
Using real-time observation data from different entities, the system will obtain key weather forecasts with three types of classifications: very short-term forecast (2-3 days), short-term forecast (10-15 days), subseasonal and 8 weeks. The entities from which the data will be obtained are the Júcar Hydrographic Confederation (CHJ, which provides hydrological data in real time), the State Meteorological Agency (AEMET, with real-time meteorological data), AVRSE of the Generalitat Valenciana, the Meteoalerta Plan of AEMET and AVAMET.
The development of this tool is framed not only in the recent events in the province of Valencia with the DANA of October 2024, but also in the devastating forest fires in California, or those that devastated the northwest of the peninsula this summer. The increase in the incidence of these types of events related to climate change highlights the challenges faced by climate emergency response systems.
As explained Vicent Botti, Director of VRAIN at the UPV, and principal investigator of this project “Anticipatory action saves human lives, saves time, money and dignity by allowing informed decisions to be made before the crisis occurs. This tool ensures a more efficient allocation of resources, real-time decision making, for example prioritizing emergencies and reallocating resources to areas of greatest need, and optimizing coordination between those responsible for infrastructure, leading to a more urgent, effective and sustainable response to current and future challenges in urban, metropolitan and rural environments.
Furthermore, he emphasizes that the idea when developing this platform “is that some of the public administrations can continue it due to the amount of public data that will be managed and the usefulness for monitoring, predicting and warning of risks, as well as the optimization of transport routes (public, ambulances or traffic) in the event of extreme weather events.s».
