A streamlined flood-specific evaluation framework: Assessing African riverine early warning systems

Intense hydrological events are rising across Africa, increasing flood risks. Flood Early Warning Systems (FEWS) are internationally acknowledged as fundamental tools for risk reduction and management. Their effectiveness depends on the integrated and balanced development of all essential components -risk knowledge, monitoring and forecasting, dissemination and communication, preparedness and response-yet their integration across operational systems is poorly documented. This study develops a flood-specific evaluation framework adapted from the UNDRR-WMO Multi-Hazard Early Warning System Custom Indicators (MHEWS-CI), reducing its 53 indicators to 25 through thematic consolidation and flood-specific adaptation. The framework is applied to assess 19 African riverine FEWS using publicly available documentation, as a proof-of-concept. Each indicator is assessed through a recognition-based approach: documented system functionalities are matched against three predefined development stages (absent, basic, advanced), lightning the reporting burden of MHEWS-CI. Scores are compared to illustrate relationships between system characteristics and development of components. Results reveal imbalances: 74% of systems demonstrate advanced monitoring and forecasting, but only 5% achieve advanced response capabilities. Web architectures and hydrological models favour monitoring and forecasting but show no corresponding advantage in response integration. Hybrid systems combining model forecasting with community engagement achieve the highest overall scores, suggesting that optimal development requires balancing technical sophistication with a participatory approach. These patterns may reflect institutional factors -governance fragmentation and insufficient operational integration-rather than causal effects of system characteristics. The framework delivers a streamlined, replicable instrument for insightful FEWS assessment, successfully corroborating prior research and supporting evidence-based identification of operational gaps and investment prioritisation.