David Niklaus Bresch

Early warning and anticipatory action can help reduce negative consequences from weather and climate extremes. To inform preemptive decisions and anticipatory actions, actors in disaster risk reduction such as humanitarian organisations are increasingly relying on impact forecasts, as these can provide more concrete and actionable information in comparison to hazard-based forecasts. However, impact forecasts commonly focus on direct impacts, leaving indirect impacts such as service accessibility, which are relevant for emergency response planning, too often unaccounted for. To address this gap, we develop an impact forecasting framework to predict disruption in service access resulting from weather extremes. We illustrate the framework by predicting healthcare access disruptions for two high-impact flood events that impacted Somalia and Sudan in 2023 and 2024. Our model is able to provide actionable and spatially-explicit information such as forecast maps of where people may experience loss of service accessibility, and our results show that the pipeline is able to capture observed impacts up to a week in advance. Additionally, including indirect impacts such as healthcare access disruption significantly increases the estimated scale of a disaster. Comparison of modelling results with ground reports, satellite observations, and survey data reveals that observed post-disaster service accessibility disruption rates can be reproduced. Limitations remain in the precision of flood forecast input data and incompleteness of exposure data. An uncertainty and sensitivity analysis reveals large regional discrepancies in the sensitivity to the model parameters, and that uncertainty in exposure and accessibility thresholds can exceed the hazard forecast uncertainty. Our results illustrate how a model built on open-source data can provide decision-relevant and people-centric impact forecasts, that can be used to inform anticipatory action. We expect our findings to help improving impact forecast models and anticipatory action frameworks aimed at reducing human suffering.

Early warning and anticipatory action can help reduce negative consequences from weather and climate extremes. To inform preemptive decisions and anticipatory actions, actors in disaster risk reduction such as humanitarian organisations are increasingly relying on impact forecasts, as these can provide more concrete and actionable information in comparison to hazard-based forecasts. However, impact forecasts commonly focus on direct impacts, leaving indirect impacts such as service accessibility, which are relevant for emergency response planning, too often unaccounted for. To address this gap, we develop an impact forecasting framework to predict disruption in service access resulting from weather extremes. We illustrate the framework by predicting healthcare access disruptions for two high-impact flood events that impacted Somalia and Sudan in 2023 and 2024. Our model is able to provide actionable and spatially-explicit information such as forecast maps of where people may experience loss of service accessibility, and our results show that the pipeline is able to capture observed impacts up to a week in advance. Additionally, including indirect impacts such as healthcare access disruption significantly increases the estimated scale of a disaster. Comparison of modelling results with ground reports, satellite observations, and survey data reveals that observed post-disaster service accessibility disruption rates can be reproduced. Limitations remain in the precision of flood forecast input data and incompleteness of exposure data. An uncertainty and sensitivity analysis reveals large regional discrepancies in the sensitivity to the model parameters, and that uncertainty in exposure and accessibility thresholds can exceed the hazard forecast uncertainty. Our results illustrate how a model built on open-source data can provide decision-relevant and people-centric impact forecasts, that can be used to inform anticipatory action. We expect our findings to help improving impact forecast models and anticipatory action frameworks aimed at reducing human suffering.

Early warning and anticipatory action can help reduce negative consequences from weather and climate extremes. To inform preemptive decisions and anticipatory actions, actors in disaster risk reduction such as humanitarian organisations are increasingly relying on impact forecasts, as these can provide more concrete and actionable information in comparison to hazard-based forecasts. However, impact forecasts commonly focus on direct impacts, leaving indirect impacts such as service accessibility, which are relevant for emergency response planning, too often unaccounted for. To address this gap, we develop an impact forecasting framework to predict disruption in service access resulting from weather extremes. We illustrate the framework by predicting healthcare access disruptions for two high-impact flood events that impacted Somalia and Sudan in 2023 and 2024. Our model is able to provide actionable and spatially-explicit information such as forecast maps of where people may experience loss of service accessibility, and our results show that the pipeline is able to capture observed impacts up to a week in advance. Additionally, including indirect impacts such as healthcare access disruption significantly increases the estimated scale of a disaster. Comparison of modelling results with ground reports, satellite observations, and survey data reveals that observed post-disaster service accessibility disruption rates can be reproduced. Limitations remain in the precision of flood forecast input data and incompleteness of exposure data. An uncertainty and sensitivity analysis reveals large regional discrepancies in the sensitivity to the model parameters, and that uncertainty in exposure and accessibility thresholds can exceed the hazard forecast uncertainty. Our results illustrate how a model built on open-source data can provide decision-relevant and people-centric impact forecasts, that can be used to inform anticipatory action. We expect our findings to help improving impact forecast models and anticipatory action frameworks aimed at reducing human suffering.

Early warning and anticipatory action can help reduce negative consequences from weather and climate extremes. To inform preemptive decisions and anticipatory actions, actors in disaster risk reduction such as humanitarian organisations are increasingly relying on impact forecasts, as these can provide more concrete and actionable information in comparison to hazard-based forecasts. However, impact forecasts commonly focus on direct impacts, leaving indirect impacts such as service accessibility, which are relevant for emergency response planning, too often unaccounted for. To address this gap, we develop an impact forecasting framework to predict disruption in service access resulting from weather extremes. We illustrate the framework by predicting healthcare access disruptions for two high-impact flood events that impacted Somalia and Sudan in 2023 and 2024. Our model is able to provide actionable and spatially-explicit information such as forecast maps of where people may experience loss of service accessibility, and our results show that the pipeline is able to capture observed impacts up to a week in advance. Additionally, including indirect impacts such as healthcare access disruption significantly increases the estimated scale of a disaster. Comparison of modelling results with ground reports, satellite observations, and survey data reveals that observed post-disaster service accessibility disruption rates can be reproduced. Limitations remain in the precision of flood forecast input data and incompleteness of exposure data. An uncertainty and sensitivity analysis reveals large regional discrepancies in the sensitivity to the model parameters, and that uncertainty in exposure and accessibility thresholds can exceed the hazard forecast uncertainty. Our results illustrate how a model built on open-source data can provide decision-relevant and people-centric impact forecasts, that can be used to inform anticipatory action. We expect our findings to help improving impact forecast models and anticipatory action frameworks aimed at reducing human suffering.

Early warning and anticipatory action can help reduce negative consequences from weather and climate extremes. To inform preemptive decisions and anticipatory actions, actors in disaster risk reduction such as humanitarian organisations are increasingly relying on impact forecasts, as these can provide more concrete and actionable information in comparison to hazard-based forecasts. However, impact forecasts commonly focus on direct impacts, leaving indirect impacts such as service accessibility, which are relevant for emergency response planning, too often unaccounted for. To address this gap, we develop an impact forecasting framework to predict disruption in service access resulting from weather extremes. We illustrate the framework by predicting healthcare access disruptions for two high-impact flood events that impacted Somalia and Sudan in 2023 and 2024. Our model is able to provide actionable and spatially-explicit information such as forecast maps of where people may experience loss of service accessibility, and our results show that the pipeline is able to capture observed impacts up to a week in advance. Additionally, including indirect impacts such as healthcare access disruption significantly increases the estimated scale of a disaster. Comparison of modelling results with ground reports, satellite observations, and survey data reveals that observed post-disaster service accessibility disruption rates can be reproduced. Limitations remain in the precision of flood forecast input data and incompleteness of exposure data. An uncertainty and sensitivity analysis reveals large regional discrepancies in the sensitivity to the model parameters, and that uncertainty in exposure and accessibility thresholds can exceed the hazard forecast uncertainty. Our results illustrate how a model built on open-source data can provide decision-relevant and people-centric impact forecasts, that can be used to inform anticipatory action. We expect our findings to help improving impact forecast models and anticipatory action frameworks aimed at reducing human suffering.

High quality data on natural hazard damages are crucial for effective disaster risk management. Yet, existing impact datasets remain limited and often biased toward Northern countries and monetary losses. To help address these gaps, we present ROUGE (Redcross Operations Unified Global Emergency database); a new socio-economic impact database obtained using textual operational reports from the International Federation of Red Cross and Red Crescent Societies. These reports are systematically collected and provide broad coverage of regions that are commonly underrepresented in existing impact datasets. Using large language models, we extract qualitative and quantitative information on a wide range of non-monetary impacts at national and sub-national scales. The resulting dataset documents socio-economic impacts of natural hazards on the population, infrastructure and economy with a spatial detail reaching the subregional level. This resource is designed to support research and applications that require geographically explicit information on socio-economic impacts of disasters, enabling more precise and inclusive analyses of socio-economic consequences of natural hazards worldwide.

Weather and climate extremes are transforming ecosystems in ways that threaten society through the disruption of nature’s contributions to people (NCP) from food and water security to disaster mitigation and mental well-being. While scientists and policy makers increasingly recognize the need to account for nature-related losses from climate-driven weather extremes, major research gaps remain, particularly in how climate-altered disturbance regimes will impact NCP and associated human well-being. Addressing this gap is critical for climate mitigation and adaptation planning, because the loss of NCP amplifies social-ecological vulnerability and exacerbates climate risks. However, this cross-systems dynamic remains underrepresented in most climate risk models. Climate risk modeling offers a probabilistic view of evolving hazard regimes under climate change, while ecological research provides mechanistic insight into ecosystem responses to disturbance. We argue that bridging these fields is essential to increasing societal resilience to climate change and propose an interdisciplinary research agenda to integrate social-ecological dynamics with a climate risk perspective. Advancing this agenda is critical to project, anticipate, and plan for ecosystem-related impacts of weather and climate extremes on society.

High quality data on natural hazard damages are crucial for effective disaster risk management. Yet, existing impact datasets remain limited and often biased toward Northern countries and monetary losses. To help address these gaps, we present ROUGE (Redcross Operations Unified Global Emergency database); a new socio-economic impact database obtained using textual operational reports from the International Federation of Red Cross and Red Crescent Societies (IFRC). These reports are systematically collected and provide broad coverage of regions that are commonly underrepresented in existing impact datasets. Using large language models (LLM), we extract qualitative and quantitative information on a wide range of non-monetary impacts at national and sub-national scales. The resulting dataset documents socio-economic impacts of natural hazards on the population, infrastructure and economy with a spatial detail reaching the subregional level. This resource is designed to support research and applications that require geographically explicit information on socio-economic impacts of disasters, enabling more precise and inclusive analyses of socio-economic consequences of natural hazards worldwide.

North Atlantic hurricanes rank among the costliest natural hazards. Their impacts have increased over recent decades and are projected to rise further with climate change. Yet catastrophe models used in (re)insurance remain largely static, reflecting historical climate with limited integration of near-term climate signals. We present a simplified tropical cyclone model to estimate climate-connected landfall rate changes for North Atlantic hurricanes. Using reanalysis and climate model data, we quantify hazard changes for near-present (+1.2°C) and future (+2°C) climates relative to the historical baseline (1980–2025). These changes are used to adjust a year event loss table with a static view of risk by resampling. We find the average annual loss increases by 15% in the near-present (+1.2°C) climate, and the 1-in-10-year loss increases by 11%. Results show substantially higher landfall and loss increases under future (+2°C) climate than previous studies, highlighting the critical but often overlooked role of frequency changes in hurricane risk assessment.