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

Authors: Biederman, Joel; DWIVEDI, R. - University Of Arizona; BROXTON, P.D. - University Of Arizona; WOOLLEY, T. - The Nature Conservancy; LEONARD, J.M. - The Nature Conservancy; SVOMA, B. - Salt River Project; ROBLES, M.D. - The Nature Conservancy

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/8/2025
Publication Date: 7/9/2025
Citation: Biederman, J.A., Dwivedi, R., Broxton, P., Woolley, T., Leonard, J., Svoma, B., Robles, M. 2025. Statistical emulation of hyper-resolution mechanistic snow modeling assesses forest management and the importance of tree arrangement. Journal of Hydrology. 662. Article 133886. https://doi.org/10.1016/j.jhydrol.2025.133886.
DOI: https://doi.org/10.1016/j.jhydrol.2025.133886

Interpretive Summary: As water becomes increasingly scarce in the western US, it is critical to understand how forests regulate snowpack water supplies. In our prior work, we developed means to measure snowpack and snowmelt in relation to forest management and wildfires, and we trained an ultra-high precision forest hydrology model to assess the impacts of forest change on water provisioning for forests and people. However, our prior methods required too much computing power to be practical for informing large-scale management. In this paper, we develop several upscaling approaches of low complexity to allow forest managers to assess the hydrologic impacts of forest management or disturbances such as wildfire. We found that very simple models using only the degree to which the land slopes north or south and the amount and arrangement of trees could predict peak snowpack, snowmelt timing, and snowmelt volume quite well for warm/dry sites with short-duration snowpacks. More complex machine learning models with up to 9 variables were needed to reproduce snow maps accurately in cold/wet areas where snow lasts longer. The results of this study provide practical solutions and guidance for forest managers wishing to include hydrologic impacts in their decision making.

Technical 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) echanistic 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.