Reduced snowpack in the Rockies presents a severe drought risk for Southern Alberta. Mountain snowmelt in the Bow and Oldman basins maintains reservoir levels, crop irrigation, municipal and industrial supply, and ecosystem services. As climate and consumption pressures increase, accurate snow water equivalent (SWE) data is crucial for water supply forecasts, which impact allocations and emergency drought measures. Field snowpack monitoring does not provide accurate spatial basin SWE estimates, and regional-scale uncertainties tend to be proportionally greatest when snowpack is low and drought-risk high. To reduce uncertainty, a monitoring framework that provides absolute spatially explicit SWE data is needed. Airborne lidar can map snow depth accurately at high resolution (Hopkinson et al., 2004), and depth imputation combined with modeled density can produce SWE for small meso-scale (~100 km²) watersheds (Barnes, 2023). The goal of this study was to test lidar sampling and imputation in an operational basin-scale SWE and runoff forecasting framework. Two lidar sensors were flown in March (Galaxy) and April (Titan) 2024, to collect 76 snow depth transects (~1 km wide, >2,000 km²) over the Bow and Oldman headwaters (>400 km north-south, >50 km east-west) near coincident with field samples at 28 sites. For 85 transect intersections, snow depth covariance was high (r² 0.70, RMSE 0.12m), with a small but acceptable bias of -0.04m or -5% (r² 0.94, n 198). Depths were aggregated to 30m and used to train a random forest imputation with summertime Landsat band composites and ASTER GDEM terrain derivatives as driver layers (out of bag accuracy > 0.9). Field validation showed a bias of -5% (March) and -10% (April), which is explainable by differences in data resolution and sampling time. Average field densities were applied to imputed depths to produce a 30m gridded SWE product and a point-in-time data input to the Raven Hydrological Modelling Framework v3.8 (Craig et al. 2020). Applying the lidar-imputed SWE to a calibrated model of the Oldman Basin (1838 km²) enabled a mid-simulation correction for negative drift in areas where modelled SWE was under-estimated. The remote sensing sampling to runoff modeling framework tested enables regional supply forecasting with greatest benefits during times of water stress.