Authors: Mohammad Erfani, Mahdi Erfani, Austin Downey, and Erfan Goharian, University of South Carolina; Sagy Cohen, University of Alabama
Title: A Large Dataset of Fluvial Hydraulic and Geometry Attributes Derived from Cross Section Observations Across the U.S.A.
Abstract: The representation of river channel geometry is important for the hydrologic and hydraulic modeling and analysis of fluvial systems. Channel geometry is often estimated by simple empirical equations that can be applied across various spatial and temporal scales. However, such methods are limited as they do not employ many potentially relevant catchment and river attributes, hindering robust estimation of channel geometry. This study aims to introduce a new dataset, IFMHA (Inventory of Field Measurement for Hydraulic Attributes), to enable further research studies on channel geometry and hydraulic attributes. IFMHA is derived from the National Water Information System (NWIS) site inventory for surface water field measurements, and stream attributes of the National Hydrography Dataset (NHD). IFMHA includes 2,802,532 records from 10,050 sites (NWIS stream gaging stations). A series of physics, conceptual, and Machine Learning (ML)-based models are used in this study to augment the reported observations with key channel morphology parameters (i.e., channel mean depth, channel maximum depth, side slope, bottom width, and roughness) based on the available field attributes provided in IFMHA. The augmented attributes were compared with measured values in the HYDRoSWOT dataset, and the channel geometry parameters currently being used in NOAA National Water Model. The results show very strong predictability and a considerable improvement over existing estimates.