Natural hazards, such as floods, wildfires, droughts, and storms, are becoming increasingly frequent and severe due to the synergistic effects of climate change, environmental degradation, and the intensification of human activities. The growing complexity of these events demands long-term, high-resolution, and cross-domain environmental data to support monitoring, early warning, and comprehensive risk assessment. Environmental Research Infrastructures (RIs) provide an essential foundation for such an integrated approach by enabling standardized data collection, harmonized observation protocols, and shared analytical tools. Recent advances in open science and FAIR data practices, alongside emerging technologies such as artificial intelligence, cloud computing, and digital twins, have expanded the potential of RIs to enable real-time monitoring and forecasting. However, persistent challenges remain in linking these infrastructures across domains, ensuring interoperability, and translating scientific outputs into decision-ready knowledge for diverse stakeholders, including civil protection agencies, policy-makers, and the public.
This Research Topic aims to strengthen the contribution of Environmental Research Infrastructures to understanding, forecasting, and managing natural hazards within multi-hazard frameworks. It seeks to explore how improved coordination, data integration, and innovative technologies can enhance the capacity of RIs to deliver timely and actionable intelligence. The goal is to highlight practical examples and theoretical developments that demonstrate the integration of heterogeneous environmental observations, the application of machine learning and digital twin technologies, and the coupling of scientific data with operational and policy mechanisms. In doing so, the Research Topic will support progress toward a resilient and risk-informed society through the more effective use of RI services in disaster risk reduction and climate adaptation.
The scope of this Research Topic encompasses cross-domain environmental monitoring and the operational use of RIs for hazard-related applications. It encourages contributions from research, technical, and policy communities to foster integration and knowledge transfer across sectors.
To gather further insights into how Environmental Research Infrastructures can enhance natural hazard monitoring and risk mitigation, we welcome articles addressing, but not limited to, the following themes: • Integrated and multi-hazard monitoring across atmospheric, terrestrial, freshwater, and marine systems • Interoperability and data harmonization following FAIR data principles • Near real-time data integration and early warning system development • Application of artificial intelligence, machine learning, and digital twins in hazard detection and forecasting • Case studies showcasing RI-enabled hazard monitoring, forecasting, or emergency response • Linking RI outputs to risk assessment frameworks and policy implementation • Transnational coordination, user engagement, and access in hazard-related RI services • Governance, sustainability, and long-term operation of RIs for natural hazard monitoring • Capacity building, training, and knowledge transfer through RI networks
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Data Report
Editorial
FAIR² Data
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.