This special issue aims to advance the field of early warning systems (EWSs) and focuses on practical implementation, multi-hazard and impact-based integration, innovation, and evidence from operational EWSs. We are particularly interested in contributions that move beyond conceptual frameworks and that demonstrate how EWSs can effectively forecast (multiple) hazards and their interrelationships while taking the vulnerabilities and coping capacities of diverse populations in real-world settings into account and by enabling early action.

Scope and themes

We would like to invite original research, case studies, and review articles that address operational challenges and innovations in EWS implementation and cross-cutting EWS research. We would like to encourage submissions to have novel insights and contribute to one or more of the following areas:

1. Operational EWS implementation: Design, deployment, evaluation, and sustainability of operational warning systems, addressing single or multiple hazards in diverse institutional and resource contexts and across a range of forecasting lead times.
2. Technological innovation and data integration: Novel applications of AI and ML, low-cost sensors, satellite data, IoT, and citizen science for enhanced hazard monitoring, forecasting, and accessible warning dissemination.
3. Addressing multi-hazard interrelationships: Warning systems and forecasting approaches for triggering, cascading, compound, or consecutive hazards and their spatiotemporal interactions.
4. People-centred and vulnerability-focused approaches: Integration of diverse vulnerabilities, local knowledge systems, and inclusive dissemination strategies to ensure warnings reach and serve all populations.
5. Impact-based forecasting: Practical applications and operational use cases with novel physics-based, AI, and hybrid models for impact-based forecasting.

We welcome contributions that address, but are not limited to, the following hazard context, early warning early action value chain, and cross-cutting priorities:

Hazard coverage:

• Hydro-meteorological and climate hazards (e.g. floods, droughts, heatwaves, tropical cyclones)
• Geophysical hazards (e.g. earthquakes, tsunamis, volcanic eruptions, landslides)
• Multi-hazard contexts involving hazard interactions

Across the early warning early action value chain:

• Pillar I: Multi-hazard risk knowledge, vulnerability mapping, and impact-based forecasting
• Pillar II: Advances in multi-hazard observation, monitoring, and prediction
• Pillar III: Effective communication strategies, cultural adaptation, and accessible warning dissemination
• Pillar IV: Preparedness planning, early action protocols, and anticipatory actions for multiple hazards

Additional themes:

• Equity, justice, and inclusivity in EWS design and operation
• Sub-seasonal to seasonal (S2S) prediction for early warning
• Climate change adaptation through enhanced warning systems
• Performance evaluation and lessons learned from operational systems
• Bridging science–policy–practice gaps in EWS implementation

This special issue (SI) aims to enhance our understanding of the complex, cascading interactions between natural hazards, health systems, disease outbreaks, and societal health. By compiling a high-quality collection of papers, we seek to

1. provide an overview of the state of the art for multi-hazards and health research;
2. showcase new research on the health impacts of disasters, particularly when they coincide with disease outbreaks;
3. advance modelling and measurement capabilities for multi-hazard scenarios involving public health emergencies;
4. identify synergies and trade-offs in disaster risk reduction (DRR) and adaptation strategies.

Natural hazard emergencies are fundamentally a complex interaction of natural, anthropogenic, and biological processes. For example, the COVID-19 pandemic highlighted the operational challenges of responding to events like the 2020 Zagreb earthquake amidst lockdowns and travel restrictions. Similarly, devastating floods in Pakistan in 2022 led to outbreaks of cholera and diarrhoea. These events demonstrate that a limited understanding of the cascading effects of combined disasters and diseases creates major operational, ethical, and decision-making challenges for disaster management, humanitarian, and development organizations. However, until relatively recently, there has been little engagement between the multi-hazard and health research communities to understand how these processes interact and feed off each other.

International frameworks, such as the United Nation's Sendai Framework for Disaster Risk Reduction (SFDRR) and the latest Intergovernmental Panel on Climate Change reports (Assessment Report 6 cycle), recognize the need to move beyond single-hazard thinking and address the complexities of multiple and systemic risks. The scientific community has been called upon to improve our understanding of these spatiotemporal complexities. The pre-print paper, titled Invited perspective: Redefining Disaster Risk: The Convergence of Natural Hazards and Health Crises by Sairam and De Ruiter in NHESS, for example, explores the interconnections between natural hazards, health, and society, highlighting the need for a more integrated approach.

While separate communities have advanced research on multi-hazard and systemic risks, there is a clear need to bring together a dedicated body of work on the unique intersection of disasters, diseases, health, and health systems. This SI provides that opportunity, fostering cross-disciplinary learning and identifying new research avenues. The urgency of this topic is underscored by the compounding effects of climate change on health systems and health outcomes, as well as the spatial and temporal variability of exposures and vulnerabilities to these complex hazards. This SI is part of the RiskKAN (https://www.risk-kan.org/) working group on the same topic.

This special issue gathers already-published and future papers that describe and/or apply the global water resources and use model WaterGAP.

WaterGAP (www.watergap.de) is a global freshwater model that calculates human water use as well as water flows and storage on all continents (except Antarctica), taking into account the human influence on the natural freshwater system such as climate change, water abstractions, and dams. As one of the pioneers in the field of global hydrological modelling, it supports our understanding of the global freshwater system since 1996 for historical periods and the future. The model is continuously being improved to answer scientific questions driven by societal demands. WaterGAP is applied to assess water scarcity, droughts, and floods and to quantify the human impact on, for example, groundwater, wetlands, streamflow, and sea-level rise.

Landslide inventory maps (LIMs) are a basic tool for spatially representing landslides, forming a cornerstone for subsequent analyses in landslide research. Traditional methods of landslide mapping have historically relied on heuristic interpretation, resulting in varied accuracy, coverage, and timeliness. Their reliability is influenced by mapping errors arising from diverse techniques and base data. Recent research emphasizes geographic accuracy, thematic accuracy, and completeness/statistical representativeness as key factors defining the quality of LIMs.

The classification of susceptibility adds to the complexity of mapping efforts. Conventional methods often struggle with differences between the types of landslides due to variations in morphological and environmental factors. The integration of machine learning (ML) has revolutionized landslide mapping and modelling. ML's capacity to extract critical patterns from heterogeneous data sources enables precise classification of landslides, addressing challenges faced by conventional methods. Additionally, ML techniques offer a comprehensive view of the landscape and its dynamic changes and a comprehensive solution for assessing and mitigating landslide hazards by addressing challenges related to threshold determination, classification accuracy, and uncertainty evaluation.

We invite contributions addressing the following:

• metrics for evaluating mapping accuracy, errors, and uncertainty;
• statistical modelling of mapping errors and ML-based classification;
• quality assessment methods for landslide inventory maps;
• the impact of error propagation on susceptibility models, hazard assessment, and risk evaluation;
• model inter-comparisons;
• relating LIM quality to use limitations and decision-making at different land-management levels.

This special issue aims to advance the field of early warning systems (EWSs) and focuses on practical implementation, multi-hazard and impact-based integration, innovation, and evidence from operational EWSs. We are particularly interested in contributions that move beyond conceptual frameworks and that demonstrate how EWSs can effectively forecast (multiple) hazards and their interrelationships while taking the vulnerabilities and coping capacities of diverse populations in real-world settings into account and by enabling early action.

Scope and themes

We would like to invite original research, case studies, and review articles that address operational challenges and innovations in EWS implementation and cross-cutting EWS research. We would like to encourage submissions to have novel insights and contribute to one or more of the following areas:

1. Operational EWS implementation: Design, deployment, evaluation, and sustainability of operational warning systems, addressing single or multiple hazards in diverse institutional and resource contexts and across a range of forecasting lead times.
2. Technological innovation and data integration: Novel applications of AI and ML, low-cost sensors, satellite data, IoT, and citizen science for enhanced hazard monitoring, forecasting, and accessible warning dissemination.
3. Addressing multi-hazard interrelationships: Warning systems and forecasting approaches for triggering, cascading, compound, or consecutive hazards and their spatiotemporal interactions.
4. People-centred and vulnerability-focused approaches: Integration of diverse vulnerabilities, local knowledge systems, and inclusive dissemination strategies to ensure warnings reach and serve all populations.
5. Impact-based forecasting: Practical applications and operational use cases with novel physics-based, AI, and hybrid models for impact-based forecasting.

We welcome contributions that address, but are not limited to, the following hazard context, early warning early action value chain, and cross-cutting priorities:

Hazard coverage:

• Hydro-meteorological and climate hazards (e.g. floods, droughts, heatwaves, tropical cyclones)
• Geophysical hazards (e.g. earthquakes, tsunamis, volcanic eruptions, landslides)
• Multi-hazard contexts involving hazard interactions

Across the early warning early action value chain:

• Pillar I: Multi-hazard risk knowledge, vulnerability mapping, and impact-based forecasting
• Pillar II: Advances in multi-hazard observation, monitoring, and prediction
• Pillar III: Effective communication strategies, cultural adaptation, and accessible warning dissemination
• Pillar IV: Preparedness planning, early action protocols, and anticipatory actions for multiple hazards

Additional themes:

• Equity, justice, and inclusivity in EWS design and operation
• Sub-seasonal to seasonal (S2S) prediction for early warning
• Climate change adaptation through enhanced warning systems
• Performance evaluation and lessons learned from operational systems
• Bridging science–policy–practice gaps in EWS implementation

This special issue (SI) aims to enhance our understanding of the complex, cascading interactions between natural hazards, health systems, disease outbreaks, and societal health. By compiling a high-quality collection of papers, we seek to

1. provide an overview of the state of the art for multi-hazards and health research;
2. showcase new research on the health impacts of disasters, particularly when they coincide with disease outbreaks;
3. advance modelling and measurement capabilities for multi-hazard scenarios involving public health emergencies;
4. identify synergies and trade-offs in disaster risk reduction (DRR) and adaptation strategies.

Natural hazard emergencies are fundamentally a complex interaction of natural, anthropogenic, and biological processes. For example, the COVID-19 pandemic highlighted the operational challenges of responding to events like the 2020 Zagreb earthquake amidst lockdowns and travel restrictions. Similarly, devastating floods in Pakistan in 2022 led to outbreaks of cholera and diarrhoea. These events demonstrate that a limited understanding of the cascading effects of combined disasters and diseases creates major operational, ethical, and decision-making challenges for disaster management, humanitarian, and development organizations. However, until relatively recently, there has been little engagement between the multi-hazard and health research communities to understand how these processes interact and feed off each other.

International frameworks, such as the United Nation's Sendai Framework for Disaster Risk Reduction (SFDRR) and the latest Intergovernmental Panel on Climate Change reports (Assessment Report 6 cycle), recognize the need to move beyond single-hazard thinking and address the complexities of multiple and systemic risks. The scientific community has been called upon to improve our understanding of these spatiotemporal complexities. The pre-print paper, titled Invited perspective: Redefining Disaster Risk: The Convergence of Natural Hazards and Health Crises by Sairam and De Ruiter in NHESS, for example, explores the interconnections between natural hazards, health, and society, highlighting the need for a more integrated approach.

While separate communities have advanced research on multi-hazard and systemic risks, there is a clear need to bring together a dedicated body of work on the unique intersection of disasters, diseases, health, and health systems. This SI provides that opportunity, fostering cross-disciplinary learning and identifying new research avenues. The urgency of this topic is underscored by the compounding effects of climate change on health systems and health outcomes, as well as the spatial and temporal variability of exposures and vulnerabilities to these complex hazards. This SI is part of the RiskKAN (https://www.risk-kan.org/) working group on the same topic.

Landslide inventory maps (LIMs) are a basic tool for spatially representing landslides, forming a cornerstone for subsequent analyses in landslide research. Traditional methods of landslide mapping have historically relied on heuristic interpretation, resulting in varied accuracy, coverage, and timeliness. Their reliability is influenced by mapping errors arising from diverse techniques and base data. Recent research emphasizes geographic accuracy, thematic accuracy, and completeness/statistical representativeness as key factors defining the quality of LIMs.

The classification of susceptibility adds to the complexity of mapping efforts. Conventional methods often struggle with differences between the types of landslides due to variations in morphological and environmental factors. The integration of machine learning (ML) has revolutionized landslide mapping and modelling. ML's capacity to extract critical patterns from heterogeneous data sources enables precise classification of landslides, addressing challenges faced by conventional methods. Additionally, ML techniques offer a comprehensive view of the landscape and its dynamic changes and a comprehensive solution for assessing and mitigating landslide hazards by addressing challenges related to threshold determination, classification accuracy, and uncertainty evaluation.

We invite contributions addressing the following:

• metrics for evaluating mapping accuracy, errors, and uncertainty;
• statistical modelling of mapping errors and ML-based classification;
• quality assessment methods for landslide inventory maps;
• the impact of error propagation on susceptibility models, hazard assessment, and risk evaluation;
• model inter-comparisons;
• relating LIM quality to use limitations and decision-making at different land-management levels.

This special issue gathers already-published and future papers that describe and/or apply the global water resources and use model WaterGAP.

WaterGAP (www.watergap.de) is a global freshwater model that calculates human water use as well as water flows and storage on all continents (except Antarctica), taking into account the human influence on the natural freshwater system such as climate change, water abstractions, and dams. As one of the pioneers in the field of global hydrological modelling, it supports our understanding of the global freshwater system since 1996 for historical periods and the future. The model is continuously being improved to answer scientific questions driven by societal demands. WaterGAP is applied to assess water scarcity, droughts, and floods and to quantify the human impact on, for example, groundwater, wetlands, streamflow, and sea-level rise.