Bridging the Research‐to‐Operations Gaps in NOAA Water Prediction Through Advances in Hydroinformatics

The U.S. National Water Model (NWM) produces streamflow forecasts for 2.7 million river reaches, generating roughly five terabytes of output daily. However, ongoing research‐to‐operations gaps prevent this forecasting capability from being fully usable—meaning researchers, water managers, and communities cannot easily access, understand, trust, and act on the model outputs. This paper examines advances in hydroinformatics coordinated through the Cooperative Institute for Research to Operations in Hydrology (CIROH) to systematically address these gaps. We organize the discussion around: (1) the Access Gap, where data formats and architectures optimized for model developers hinder end‐user access; (2) the Trust Gap, where users lack the evaluation and correction tools needed to confidently apply model outputs; (3) the Reproducibility Gap, where complex software environments make sharing and verification difficult; and (4) the Expertise Gap, where data analysis codes and decision support tools require substantial expertise to develop and use. We highlight, review, and discuss recent advancements in specific tools, platforms, and methods to close these gaps. We also explore how artificial intelligence (AI), from physics‐informed machine learning to large language model (LLM)‐based multi‐agent systems, is transforming hydroinformatics and reshaping research‐to‐operations in hydrology.