SWOT, empiricism, and river modelling

Introduction: This dataset provides SWOT Ensemble Spearman correlation coefficients (SES) between machine learning (ML) model discharge estimates and SWOT Water Surface Elevation (WSE) observations for rivers in SWORD with at least 20 corresponding quality-filtered SWOT observations. For further methodological details, please refer to the associated paper. File Names: combined_SES_dataframe_versionD.rds Dataset Features: Temporal Coverage: January 2015 - December 2025 (depends on the reach and the model) Spatial Resolution: global SWORD reaches "reach_id" Description: id of each reach the high-resolution centerline point is associated with. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach id (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach) continent number: 1 = Africa, 2 = Europe, 3 = Siberia, 4 = Asia, 5 = Australia, 6 = South America, 7 = North America, 8 = Arctic of North America, 9 = Greenland SWOT version used: Version D Column names for rds files: reach_id: id of each reach SES: SWOT Ensemble Spearman correlation between machine learning (ML) model discharge estimates and SWOT Water Surface Elevation (WSE) observations model: name of the model used Trad_ensemble : mean discharge from HyMAP, CaMa-Flood, HRR-GLOFAS and ERA5 RAPID model estimates ML_ensemble : mean discharge from δHBV2 and GRADES_hydroDL model estimates geometry