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Item HDFR: A Hydrologic Data and Modeling System with On-Demand Access to Environmental Sensing Data for Decision Making(IEEE, 2023-01) Luna, Daniel; Hernández, Felipe; Liang, Yao; Liang, Xu; Computer Science, Luddy School of Informatics, Computing, and EngineeringThis 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.