Accounting for Fine‐Scale Forest Structure is Necessary to Model Snowpack Mass and Energy Budgets in Montane Forests. Issue 12 (3rd December 2021)

• Record Type: Journal Article
• Authors: Broxton, Patrick D.; Moeser, C. David; Harpold, Adrian
• Is Part Of: Water resources research. Volume 57:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-03
• Subjects: snow -- modeling -- forest -- lidar -- LSMs; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR029716
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy‐mediated processes, we applied a high‐resolution energy balance snowpack model at two forested sites at a fine (1 m 2 ) and coarse (100 m 2 ) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine‐scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under‐canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100‐m and 1‐m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine‐scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance. Plain Language Summary: In semiarid regions, such as the western United States, water supply depends critically on snowpack in mountain forests. These forests are undergoing rapid changes due to wildfire, insect infestation, forest thinning, and climate change. Therefore, it is critically important to understand how these changes will affect snowpack, ecosystem health, and downstream water resources. However, our current generation of land surface models (LSMs) does not do a good job at simulating snowpack responses to forest disturbances because of the simplistic representation of forest in these models. Here, we show, through comparison between ultra‐fine resolution (1 m) and coarser resolution (100 m) snow simulations, that at a typical LSM resolution, a snow model configuration that accounts for canopy edge environments performs better than a model configuration that simply divides the landscape based on fractional vegetation cover, as is done in many LSMs. Furthermore, most remaining biases can be explained using high‐resolution forest structure information, indicating that lidar‐derived forest‐structure metrics could be used to improve snow model simulation in LSMs. These improvements could help LSMs to better simulate how forest disturbance influences snow accumulation and melt. Key Points: Subdividing forests into open and canopy covered regions, as in most LSMs, overestimates below canopy radiation and snow ablation rates Snowpack simulations on larger model grids are improved by accounting for the effect of near‐canopy environments Land surface models could benefit from lidar‐based representations of canopy edges and finer‐scale canopy structure information