Canopy and Terrain Interactions Affecting Snowpack Spatial Patterns in the Sierra Nevada of California. Issue 11 (8th November 2019) - Zheng, Zeshi; Ma, Qin; Jin, Shichao; Su, Yanjun; Guo, Qinghua; Bales, Roger C.
Abstract: Airborne light detection and ranging is an emerging measurement tool for snowpack estimation, and data are now emerging to better assess multiscale snow depth patterns. We used airborne light detection and ranging measurements from four sites in the southern Sierra Nevada to determine how snow depth varies with canopy structure and the interactions between canopies and terrain. We processed the point clouds into snow depth rasters at 0.5×0.5‐m 2 resolution and performed statistical analysis on the processed snow depth data, terrain attributes, and vegetation attributes, including the individual tree bole locations, canopy crown area, and canopy height. We studied the snow depth at such a fine scale due in part to the spatial heterogeneity introduced by canopy interception and enhanced melting caused by tree trunks in forested areas. We found that the dominant direction of a tree well, the area around the tree bole that has shallower snowpack, is correlated with the local aspect of the terrain, and the gradient of the snow surface in a tree well is correlated with the tree's crown area. The regression‐tree based XGBoost model was fitted with the topographic variables and canopy variables, and about 71% of snow depth variability can be explained by the model. Key Points: Lidar point density affects snow depth estimates in dense forest with a warmer snowpack, over runoff‐producing elevations in the Sierra In a tree well, more snow accumulates in the downslope direction from the tree bole Compared to the dense forest spanning the rain‐snow transition elevations, the same forest with half trees will have about 10% deeper snow