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

Abstract 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–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. Significance Statement Accurate snowpack predictions are essential for managing water resources in snow-dependent regions like the western United States, where snowmelt supports year-round supply. This study showed that applying elevation-based dynamic lapse rate corrections to downscaled meteorological data can significantly improve simulations of air temperature, snow water equivalent, snow depth, and snow cover. The dynamic lapse rate approach captured local, hourly variability through a computationally efficient estimation from gridded air temperature data. Notably, simulations using dynamic corrections outperformed those using a static lapse rate during an extreme snow drought year. As snow drought risks increase, these findings suggest that incorporating dynamic lapse rate corrections into land surface models can improve snowpack predictions and better inform water resource planning.