GWBASE: An Algorithm for Screening Groundwater–Baseflow Coupling Using Paired USGS Well and Streamflow Records

Groundwater discharge contributes to stream baseflow, but coupling strength varies among catchments and transferable quantification methods are limited. We present GWBASE, an open-source Python algorithm that pairs U.S. Geological Survey (USGS) wells and gages within hydrographic catchments and ranks coupling at each gage by linear regression and mutual information (MI, capturing nonlinear and lagged dependence) on monthly ΔWTE–ΔQ records from baseflow-dominated months. We apply it to the Great Salt Lake Basin (Utah; ∼93,000km2 with 8752 USGS wells, 1906–2025). GWBASE ranked four terminal-gage catchments using a seasonally corrected, within-well regression as the primary estimate; three of the four show statistically significant groundwater–baseflow coupling. The ranking depends on the metric: absolute magnitude (cfs per foot) is dominated by the large Bear River catchment, whereas size-normalized sensitivity and coupling tightness identify the smaller Little Cottonwood Creek. The basin-scale aggregate (∼4 cfs per foot of basin-averaged decline) is dominated by Bear River and, once catchment-level uncertainty is propagated, is not distinguishable from zero. Because national groundwater records are predominantly intermittent, GWBASE resolves seasonal-to-interannual storage coupling rather than event-scale exchange. It is best used for screening and ranking catchment-scale coupling rather than yielding a single basin-scale coefficient.