Beyond the flood map: an open-source framework for forecasting cascading impacts of natural hazards on people and essential services

Abstract. 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.