Continuous Risk Monitoring and Assessment (CRMA) for Operational Impact-Based Forecasting: A Bayesian Network method for Flood and Drought Hazards in East Africa 

Continuous Risk Monitoring and Assessment (CRMA) is widely used in financial auditing and cyber-risk management to update risks in real-time and escalate them as conditions evolve. Hydrometeorological early warning systems typically operate in a cycle of repeated hazard and threshold monitoring, usually daily for floods and monthly or seasonally for droughts. The current study introduces a method tailored for operational Impact-Based Forecasting (IBF) for flood and drought hazards in East Africa, developed under the Complex Risk Analytics Fund(CRAF'd) project. The method formalizes existing monitoring practices into a continuous, conditional, evidence-driven hydrometeorological risk assessment process, in which evolving observations, forecasts, and expert knowledge are systematically integrated, documented, and auditable across time. The method combines forecast and observation indicators using probabilistic Bayesian networks to aggregate risks and provide decision support. For drought, it uses multi month Combined Drought Indicators (CDI) as observed antecedent conditions, along with ECMWF SEAS5 standard precipaiton index (SPI) ensemble forecasts across agricultural seasons. For floods, antecedent rainfall and soil saturation indicators from satellite observations are fused with short-range ensemble precipitation forecasts from NOAA GEFS. In both hazard contexts, Bayesian Networks encode expert knowledge through Conditional Probability Tables(CPT) to represent compound risk mechanisms, temporal persistence, spatial coverage, and data confidence, enabling transparent, uncertainty quantification and reproducible inference of evolving risk states. The output produces admin-2–level traffic-light risk communcation categories linked to anticipatory action decision pathways. Validation results from pilot study demonstrate that Bayesian Networks implemented using the Python pgmpy library enable cost-effective and repeatable continuous risk monitoring when combined with analysis-ready, cloud-optimized datasets. The results show that parsimonious hazard modelling, using Prefect automation tool for operational impact-based forecasting, a calendar-based web app, and structured CPT management support transparent risk assessment, traceable record-keeping, and auditable decision histories. Integration with storymaps complements this method by enabling event-based climate storylines that link risk knowledge with operational decision communication.