Global Analysis of Hack’s Law: Decomposing the Geometric Origins of h > 0.5 Across Scales and Sampling Paradigms

Data and code repository for \"Global Analysis of Hack's Law: Decomposing the Geometric Origins of h > 0.5 Across Scales and Sampling Paradigms\" (Jiang et al., 2026, submitted to Water Resources Research).

This dataset contains 340,991 globally extracted drainage basins derived from MERIT Hydro (Yamazaki et al., 2019, ~90 m resolution), covering 180°W–180°E, 60°S–85°N. For each basin we provide per-basin Hack's law parameters (exponent h, prefactor
C, R²) computed under five sampling paradigms—along-stem and tributary-junction at both pooled and per-basin scopes, plus independent-outlet fitting—along with 23 environmental covariates spanning terrain, lithology and soil, landform, climate, ectonics, and basin-type categories, and four dimensionless shape descriptors (sinuosity, Schumm elongation ratio, convexity, Gravelius compactness coefficient).

The repository also includes the complete Python pipeline (17 scripts) that reproduces every figure in the manuscript (Fig 1–12 main + Fig A1–A5 appendix), the cached random forest feature-importance results (rf_importance_results_v1.pkl), and a
robustness-check JSON (rf_robustness_results.json) covering cross-fold Spearman ρ, threshold sensitivity, and gradient-boosting cross-validation.