Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for simulating snowpack dynamics. This is challenging at spatial resolutions smaller than common gridded meteorological datasets (e.g., resolutions smaller than 5 to 250 km) due to sparse long-term temperature measurements at those resolutions and local factors like cool air pooling and inversions. Near-surface air temperatures (Ta) are often assumed to decrease with elevation at a constant rate of 6.5°C km-1, leading to significant model errors in snow evolution and other key processes. This study evaluated the impact of local dynamical adjustments to downscaled Ta on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization land surface model. Forcings were derived from remote sensing and reanalysis precipitation products and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric products at the downscaled 1-km resolution. Hourly lapse rates at each grid cell were calculated by applying linear regression to Ta and elevation from neighbor grids (one grid length in the x or y direction) at the native MERRA-2 resolution and applied to the downscaled 1-km Ta product. We assessed the impact on simulated snow cover and depth across simulations forced with the downscaled Ta (1) without lapse rate correction, (2) corrected with a static lapse rate (6.5°C km-1), and (3) corrected with the dynamic hourly lapse rate. We evaluated model skill improvement with dynamic or static lapse rate correction, and no correction against satellite-derived products. Both lapse rate correction methods led to similar improvements on average, relative to no correction. However, dynamic lapse rate correction showed more pronounced improvements in simulating perennial snowpacks at mid-elevations and in deficit years, indicating the method can better resolve heterogeneous snowpack conditions that are key for water resource management.

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.