Quantifying the Impacts of Dynamic Lapse Regimes on Snow Simulations over Complex Terrains

Accurate characterization of gridded meteorological distributions in complex terrain—especially relationships between meteorological fields and altitude—is essential for simulating snowpack dynamics. This is challenging due to sparse long-term observations and strong spatial variability in near-surface meteorology. This study evaluated elevation-based dynamic lapse rate corrections—which account for local, hourly variations in temperature, pressure, humidity, and longwave radiation—against the commonly assumed static lapse rate of ?6.5°C km?1 in a 1-km resolution land surface model. We examined snow water equivalent (SWE), snow depth (SD), snow cover (SC), and 2-m air temperature (T2) across western U.S. coastal mountains. Both correction methods improved T2 and snow simulations relative to those without correction, with similarly broad improvements over the simulation period (June 2006 through December 2020). Dynamic correction led to marginally broader snow improvements in some regions relative to no correction—for example, in high-elevation Upper Cascade leeward regions, SWE root mean square error improved across 91% of the area with dynamic correction, compared to 89% with static correction. While overall improvement differences between methods were small for the full simulation period, dynamic correction demonstrated clearer advantages during an extreme snow drought year. SC improvements were less pronounced, but dynamic correction better detected snow during the drought year, while static correction performed slightly better during a surplus year. Although future climate scenarios were not modeled, the increasing prevalence of snow droughts highlights the value of this approach for improving snowpack simulations under a warming climate.