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 is an appropriate proxy for the nonlinear relationship between stage and discharge in natural systems. This study aims to develop a hierarchical data-driven approach to compute the power-law rating curve parameters (α, β) across the stream networks of CONtiguous United States (CONUS). The development of rating curve models is motivated by our interest in exploring a unifying solution linking rating curve parameters with hydro-climatological and geomorphological characteristics across CONUS. These can be applied to operational hydrological forecastins, such as the NOAA Office of Water Prediction NextGen framework, to enhance river routing and flood inundation mapping efforts. We used HYDRoacoustics in support of the Surface Water Oceanographic Topography (HYDRoSWOT), National Hydrography (NHDPlus v2.1), and STREAM-CATCHMENT (STREAMCAT) datasets for model development. Four empirical models—Multivariate regression, eXtreme Gradient boosting (XGBoost), Random Forest, Support Vector regression are compared. The first tier of models offers high accuracy but is limited to gauges, while the second-tier models offer a good compromise between accuracy and applicability across CONUS. We found XGBoost yielded R² of 0.67 and 0.55 for α, and 0.74 and 0.70 for β in the first and second-tier models. The spatial distribution of predicted α and β indicates sensitivity to elevation, aridity, and rainfall patterns.

Remote Sensing-derived Flood Inundation Maps (RS-FIM) are an attractive and commonly used source of evaluation benchmarks. Errors in model-predicted FIM (M-FIM) evaluation results due to biases in RS-FIM benchmarking are quantified by introducing a high-confidence benchmark FIM, which was manually delineated from ultra-resolution imagery, as Ground Truth. The evaluation results show considerable differences in M-FIM accuracy assessment when using lower-quality benchmarks. A RS-FIM enhancement (gap-filling) procedure is presented and its effect on FIM evaluation results is analyzed. The results show that the enhancement is insufficient for significantly improving the robustness of the evaluation. The impact of including/excluding Permanent Water Bodies (PWB) on FIM evaluation results is analyzed. The results show that including PWB in FIM evaluation can significantly inflate the model accuracy. A novel evaluation strategy is proposed and analyzed. The proposed evaluation strategy is based on excluding low-confidence grid cells and PWB from the M-FIM evaluation analysis. Low-confidence grid cells are those that were estimated to be flooded by the gap-filling procedure, but were not classified as such by the remote sensing analysis. The results show that the proposed evaluation strategy can dramatically improve the robustness of the evaluation, except when a considerable number of false positives exist in the RS-FIM. The analyses showcase the many challenges in FIM evaluation. We provide an in-depth discussion about the need for standards, user-centric evaluation, the use of secondary sources, and qualitative evaluation.