TEMA will greatly improve Natural Disaster Management (NDM, e.g., for wildfires, floods) by automating precise semantic 3D mapping and disaster evolution prediction to achieve NDM goals in near-real-time. It will analyze and fuse many heterogeneous extreme data sources: smart drone and in-situ sensors, remote sensing data, topographical data, meteorological data/predictions and geosocial media data (text, image and videos). TEMA will focus on the extreme nature of the data, due to their varying resolution and quality, very large volume and update rate, different spatiotemporal resolutions and acquisition frequencies, real-time needs and multilingualism. It will develop an integrated, ground-breaking NDM platform, focusing on real-time semantic extraction from multiple heterogeneous data modalities and sources, on-the-fly construction of a meaningful semantically annotated 3D disaster area map, prediction of disaster evolution and improved communication between service providers and end-users, through automated process triggering and response recommendations. Semantic analysis computations will be distributed across the edge-to-cloud continuum, in a federated manner, to minimize latency. Extreme data analytics will be performed in a trustworthy and transparent way, by greatly advancing state-of-the-art AI and XAI approaches. The constantly updated 3D map and the disaster evolution predictions will form the basis for an advanced, interactive, Extended Reality (XR) interface, where the current situation will be visualized and different response strategies will be dynamically evaluated through simulation by NDM personnel. The innovative, scalable and efficient TEMA platform will provide precise NDM support, based on extreme data analytics. It will be validated on two critical disaster use-cases (wildfires and floods), in four EU countries, and will form the basis for the TEMA NDM-Analytics-as-a Service (NDM-AaaS) model.

Mountainous regions in Central Asia are vulnerable to consequences of climate change. Taking appropriate decisions for allocation of water over communities, the environment and key economic sectors such as agriculture and energy, is increasingly challenging due to economic and population growth as well as climate-induced changes in hydrological regimes in Central Asia’s main transboundary river basins. WE-ACT proposes to establish a climate sensitive Decision Support System for water allocation in two sub-catchments of a transboundary river basin in Central Asia, namely the Naryn and Kara Darya catchments of the Syr Darya river basin (covering parts of Kyrgyzstan and Uzbekistan). Based on an innovative water information system that captures a thorough understanding of water availability, demand, footprint and allocation in a glacier-fed river basin, WE-ACT will enable water managers to interact with an accessible and intuitive DSS to alleviate water stress for communities, the economy and the environment on the short- and long-term. WE-ACT will enable them to adapt the allocation of water resources to the known and expected effects of climate change, while encouraging the improvement of policies to correctly set water tariffs, reduce water footprints and increase water use efficiency in agriculture and energy sectors. The backbone of the project is a reliable data supply chain based on real-time monitoring, integrated water demand-, availability- and use modelling approach, machine-learning, and data storage in a transboundary context. This will be matched with an in-depth understanding of water policies and priorities that face increasing pressures of climate change, growing demand and water dependency. End-users of the project (hydrometeorological stations, integrated models, DSS for water allocation) will be carefully mapped, invited, involved and trained to establish and use meaningful results from the outset of the project.