From forecasts to action: testing a new impact-based flood early warning system

Flood Forecasting and Early Warning Systems (FFEWS) are key to reduce flood impacts by providing timely information to individuals, communities, and authorities. However, during the July 2021 floods in Europe, major gaps were observed between forecasts, warnings, and protective actions. In the impacted region of Limburg in the Netherlands, only 55% of people in flood-prone areas received an evacuation warning, and just 41% took emergency measures. This highlights a critical weakness in the FFEWS chain: the translation of forecasts into actionable warnings that effectively trigger response. Impact-based forecasting (IbF) has been promoted as an important step in bridging this gap, shifting the focus from hazard forecasting to forecasting societal consequences of potential flooding. Despite increasing interest in IbF, most FFEWS still focus mainly on hazards and are not tailored to forecast users and the specific actions they can trigger. Moreover, FFEWS effectiveness is often only assessed by the skill of flood hazard warnings, while there is little research on whether warnings lead to effective responses. To address this issue, we developed an impact-based flood forecasting, early warning, and response system (IbF-FEWS) using the Geographical, Environmental, and Behavioral (GEB) platform. This system consists of three novel interconnected components: (i) a flood forecast module, in which probablistic ensemble rainfall forecasts force a combined hydrological-hydrodynamic model to generate ensemble forecasted flood maps; (ii) a warning module, in which these flood maps are transformed into lead-time–dependent flood probability maps and evaluated against two action-based hazard thresholds: damaging water-level ranges and exposure of critical infrastructure. Each threshold is associated with recommended emergency measures (e.g. placing sandbags). Then, for each postal code, flood probabilities are filtered using a predefined probability threshold to identify flooded areas, after which the fraction of affected buildings or flooded area within the postal code area is evaluated to determine whether a warning is issued; and (iii) a decision-making module, in which households decide whether to implement the recommended measures based on their responsiveness to warnings, modeled as a binary state classifying households as either responsive or non-responsive. We demonstrate the system for the July 2021 flood event in the Geul catchment in the South of the Netherlands, showing how probabilistic, impact-based, and action-oriented warnings can lead to earlier and more effective early action. The results demonstrate the potential reduction in flood damage had such a system been operational during the 2021 event.