Statistical emulation of hyper-resolution mechanistic snow modeling assesses forest management and the importance of tree arrangement

Abstract

Forests are changing rapidly due to drought, disease, wildfire, and forest management, with unknown impacts on snowmelt resources. While hyper-resolution forest hydrology models capture the effects of canopy cover amount and arrangement on snowpack, they are too complex for landscape-scale assessments. Here, we evaluated two statistical approaches to emulate high-resolution (1 m) mechanistic model maps of snow variables for future application in forest management and drew inferences about topographic vs. forest canopy controls. We tested a simple landscape classification using one or more of topographic northness, canopy cover, and surrounding forest arrangement and machine learning (ML) models of varying complexity to emulate maps of peak SWE, liquid water input, and snow cover duration (SCD) from the 3-D forest hydrology model SnowPALM, which was previously trained using lidar and daily Snowtography. We evaluated three winters at three mid-scale study areas (~50 ha) in the southwestern US spanning gradients of SCD and forest type. All approaches emulated areal mean values within <2 %. Landscape classification captured 50–80 % of spatial variability, while ML explained 88–98 %. Increasing ML complexity was needed to emulate snow maps having greater spatial variability, which tended to occur where SCD was greatest: at high/cold sites, in cold/wet winters, and in locations shaded by terrain or nearby trees. Warm/dry forests were adequately modeled using canopy cover. These results demonstrate a generalizable approach for future upscaling of forest snow measurements through sequential mechanistic and statistical modeling for hydrologically informed forest management.