Integrating Modeled Flood Maps From the National Water Model and Diverse Sources for Enhanced Forecasting and Preparedness

ABSTRACT Flood mapping is critical for flood forecasting, preparedness, and risk management. In the United States, multiple federal agencies and organizations generate or archive flood inundation maps (FIM) using diverse models, data sources, and standards. The National Water Center (NWC) currently provides the only national‐scale operational FIM through its Height above nearest drainage (HAND) ‐based workflow; however, most other authoritative flood datasets remain isolated and underutilized due to inherent discrepancies in models, formats, standards, and limited accessibility. In this study, we present an integrated relational database accompanied by Python‐based tools and workflows that together enable the acquisition of flood maps from multiple agency inventories or models, the generation of baseline HAND maps for corresponding areas, and optimization of FIM datasets for efficient storage, visualization, and cross‐model comparison. This framework complements the NWC's operational system by allowing multiple‐source FIM integration and evaluation within a unified environment. The functionality is equally applicable at the local level for training, comparison, and teaching practical differences in action between flood models. Leveraging different sources of flood datasets under a common framework enhances confidence in the results and promotes informed action, including improved early warning and response.