Where the Models and the Satellites Disagree: Global Patterns of River Discharge Anomalies from SWOT

Data and code for: "Where the Models and the Satellites Disagree:Global Patterns of River Discharge Anomalies from SWOT" INPUT DATASETS (not re-hosted; all public, see manuscript Data Availability Statement)SWOT L4 SoS v3 | GRDR v1.0.0 | SWORD v16 | MERIT-Basins | GRanD v1.1 | BoM Water Data Online FILESreach_level_anomaly_table.csv One row per SWOT-GRDR matched reach (75,191 reaches). reach_id (SWORD ID), region (GRDR/MERIT region 1-8), n_obs, width (m), flags, reliability_score, reliability (low/medium/high_confidence), median_z, abs_median_z, anomaly_score, dam_name, dam_dor, dam_dist_km (nearest GRanD dam within 15 km), facc (SWORD drainage area, km2), max_specific_discharge (m3/s/km2), physically_implausible (True/False), n_rch_up, is_confluence.sword_to_merit_crosswalk.csv SWORD reach -> MERIT-Basins/GRDR ID match. sword_reach_id, comid, distance_deg, uparea (km2), facc (km2), log10_ratio.scored_observations.csv.gz Every SWOT observation scored against GRDR. swot_key (continent prefix + SWORD ID), grdr_region, date, swot_q_m3s, percentile, grdr_q01..grdr_q99_m3s (GRDR seasonal quantiles, +/-15 days).regenerate_remaining_figures_standalone.py Code to reproduce the figures.