Interpretable machine learning for predicting rating curve parameters using channel geometry and hydrological attributes across the United States

The increasing occurrences of global flood events, amidst climate change, highlight the need for hydrological data availability over large geographical domains for robust decision-making. Hydrological rating curves translate fluvial stage to streamflow and play a pivotal role in various applications, including flood inundation modeling and river geomorphology. Power law regression is found to be an appropriate proxy between stage and discharge in non-linear systems. We develop a two-tier data-driven approach to predict the rating curve parameters (α, β) across the stream networks of the CONtiguous United States (CONUS). The development of rating curve models is motivated by our interest in exploring a unifying solution for representing hydrological rating curves within large stream networks. We used HYDRoacoustics in support of the Surface Water Oceanographic Topography (HYDRoSWOT), National Hydrography (NHDPlus v2.1), and STREAM-CATCHMENT (STREAMCAT) datasets. Four empirical models, Multivariate regression, eXtreme Gradient boosting (XGBoost), Random Forest, and Support Vector regression, are compared. Tier-1 XGB models offer high accuracy in prediction (R² = 0.70) but are only applicable at gauge sites, while Tier-2 XGB models offer a good compromise between accuracy (R² = 0.55) and applicability across the NHDPlus stream network in CONUS. We explored the influence of channel geometry and hydrometeorological attributes on rating curve parameters across NHDPlus.