This research aims to enhance scholarly understanding of snow dynamics, the remotely sensed snowpacks, and the calculation of basin-wide snow water equivalent in mountainous terrain. Mountain snow is a critical source of meltwater. However, forecasting snow distributions and total water equivalence in mountain basins is limited due to complex terrain, challenging environmental conditions, and lack of observations. Laser altimetry can provide detailed observations of snow depth, but an estimate of snow density is required to evaluate the total basin water equivalence. This study uses laser altimetry surveys and empirically modelled snow densities to estimate mountainous basin-wide snow-water equivalent (SWE). Between 2017 and 2020, seven laser altimeter surveys during late winter and spring were conducted in the LaJoie Basin, Coast Mountains, British Columbia (B.C.), a strategic hydroelectric power reservoir. The laser-derived snow depths averaged between 1.4 and 2.1 m for non-glacierized terrain, while glacierized terrain weighted averages ranged between 2.2 and 5.4 m. The laser-derived depths were combined with empirical snow density models to derive distributed SWE for the Lajoie Basin. Ten linear and three non-linear snow density empirical models were tested and developed, from which (a) a snow course multi-parameter, non-linear relation and (b) snow pillow robust (Huber loss) linear regressions yielded this study’s lowest root mean squared errors (51.65 and 74.12 kg m-3, respectively). For non-glacierized terrain, the multi-parameter, non-linear model produced basin-wide SWE averages between 0.56 and 1.06 m.w.e. and propagated uncertainties from ± 0.1 to ± 0.14 m.w.e. Conversely, glacierized terrain exhibited weighted SWE averages between 0.82 and 2.93 m.w.e., with estimates of uncertainty ranging from ± 0.31 to ± 0.52 m.w.e. The robust linear regressions yielded non-glacierized SWE averages from 0.49 to 0.92 m.w.e., with uncertainties between ± 0.14 and ± 0.2 m.w.e. The weighted SWE averages in glacierized terrain ranged between 0.77 and 1.92 m.w.e., with estimates of uncertainty between ± 0.29 and ± 0.45 m.w.e. The SWE estimates from lidar and modelled density are comparable to snow pillow observations at the watershed, which demonstrates the effectiveness of these coupled techniques and improves our forecasting capabilities.
