HYDRoSWOT – HYDRoacoustic dataset in support of Surface Water Oceanographic Topography – is a data set that aggregates channel and flow data collected from the USGS streamgaging network and includes 200,000+ records of USGS acoustic Doppler current profiler (ADCP) discharge measurements. The data set includes a variety of fields including: mean depth, mean velocity, discharge, stage, water-surface width, maximum depth, maximum velocity, and streamgage metadata for more than 5,000 stations in the United States. The hydraulic and channel geometry data is stored in the USGS hydroacoustic Doppler current profiler tables reported in the SiteVisit field measurement database and polled from 45 individual National Water Information System (NWIS) hosts. The database tables were de-normalized into a single flat file intended for further study in various analytical software, such as a spreadsheet or data frame. Each station can possess multiple measurements, as indicated by the measurement n ...

This dataset focuses on reach-averaged estimation of river channel geometry, including top-width and depth, crucial for water flow prediction and flood mapping. Leveraging HYDRoacoustic data from the Surface Water Oceanographic Topography (HYDRoSWOT) program, we develop a machine learning model to predict channel geometry using data from the National Water Model, National Hydrologic Geospatial Fabric network, and other geospatial datasets. Our model demonstrates good fit within the Continental United States, with better performance observed in flatter regions. Covering nearly 2.7 million reaches in the US, this dataset is indexed to the National Hydrologic Geospatial Fabric network. However, in estuaries, particularly near river mouths where it widens into the coastal zone, there are no recorded Acoustic Doppler Current Profiler (ADCP) measurements in HYDRoSWOT, leading to unreliable model accuracy. Additionally, limitations in the training dataset, particularly the primary significant feature of ML models—100% annual exceedance probability discharge derived from the NWM—diminish skill in this exceedance probability, impacting the overall model goodness-of-fit. We provide estimates of channel geometry for two conditions: 100% and 50% annual exceedance probability, based on NWM historical retrospective data..