  Hydraulic geometry parameters describing river hydrogeomorphic is important
for flood forecasting. Although well-established, power-law hydraulic geometry
curves have been widely used to understand riverine systems and mapping
flooding inundation worldwide for the past 70 years, we have become
increasingly aware of the limitations of these approaches. In the present
study, we have moved beyond these traditional power-law relationships for river
geometry, testing the ability of machine-learning models to provide improved
predictions of river width and depth. For this work, we have used an
unprecedentedly large river measurement dataset (HYDRoSWOT) as well as a suite
of watershed predictor data to develop novel data-driven approaches to better
estimate river geometries over the contiguous United States (CONUS). Our Random
Forest, XGBoost, and neural network models out-performed the traditional,
regionalized power law-based hydraulic geometry equations for both width and
depth, providing R-squared values of as high as 0.75 for width and as high as
0.67 for depth, compared with R-squared values of 0.57 for width and 0.18 for
depth from the regional hydraulic geometry equations. Our results also show
diverse performance outcomes across stream orders and geographical regions for
the different machine-learning models, demonstrating the value of using
multi-model approaches to maximize the predictability of river geometry. The
developed models have been used to create the newly publicly available
STREAM-geo dataset, which provides river width, depth, width/depth ratio, and
river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream
reaches across the rivers and streams across the contiguous US.
