Luna, DanielHernández, FelipeLiang, YaoLiang, Xu2025-02-192025-02-192023-01Luna, D., Hernández, F., Liang, Y., & Liang, X. (2023). HDFR: A Hydrologic Data and Modeling System with On-Demand Access to Environmental Sensing Data for Decision Making. 2023 17th International Conference on Ubiquitous Information Management and Communication (IMCOM), 1–8. https://doi.org/10.1109/IMCOM56909.2023.10035593https://hdl.handle.net/1805/45859This paper introduces the Hydrologic Disaster Forecasting and Response (HDFR), an online data and modeling integration software system that facilitates the machine-to-machine access to and the management of environmental sensing data from space and ground products. Available data sources include in-situ measurements from weather and hydrographic stations; remote sensing products from Doppler precipitation radars in the United States, Earth-monitoring satellites that measure precipitation, soil moisture, and snow cover; and numerical weather prediction model outputs from the U.S. National Weather Service. Additionally, the HDFR system provides a suite of hydrologic modeling tools; including data fusion, storm severity assessment, and hydrologic model preprocessing for the Distributed Hydrology Soil Vegetation Model (DHSVM); that are seamlessly incorporated with the diverse suite of data products. Two example workflows demonstrate how this unified framework could help bridge the gap between the online and on-demand accessing of growing wealth of Earth-observing data and hydrologic prediction for scientific and engineering applications.enPublisher Policyremote sensingearth-observing data retrievaldata integrationArticle