Using Process Based Snow Modeling and Lidar to Predict the Effects of Forest Thinning on the Northern Sierra Nevada Snowpack
Using Process Based Snow Modeling and Lidar to Predict the Effects of Forest Thinning on the Northern Sierra Nevada Snowpack
复制标题
使用基于过程的雪建模和激光雷达来预测森林疏伐对内华达山脉北部积雪的影响
DOI：
10.3389/ffgc.2020.00021
复制
发表时间：
2020
影响因子：
6.2
通讯作者：
A. Harpold
中科院分区：
文献类型：
--
作者：
S. Krogh;P. Broxton;P. Manley;A. Harpold
源头流域积雪和融雪的减少正在增加美国西部森林生态系统的水资源压力。森林间伐有可能通过减少林冠截留造成的升华损失来减少水分胁迫；然而，它也会增加积雪暴露在阳光和风中的时间。我们使用高分辨率（1米）能量和物质平衡雪物理和激光雷达测绘（SnowPALM）模型，研究了两种虚拟森林间伐情景对加利福尼亚州太浩湖盆地两个相邻流域（共54平方公里）积雪的影响，该流域正在规划森林间伐。SnowPALM真实地呈现了小规模的雪林相互作用，以模拟去除<10米和<20米树木的虚拟间伐实验的影响。一般来说，变薄导致峰值雪水当量和融雪量的总体增加。由于冠层升华的减少，遮荫林丛周围地区融雪量增加最多，而更开阔和暴露的地区由于积雪升华的增加，融雪量减少较少。在30 m林分尺度上，现有森林结构控制着间伐效果，其中平均叶面积指数（LAI）为3 ~ 3 m2/m2、林分高度为5 ~ 15 m的林分，积雪量（可达450 mm）和融雪量（可达650 mm）增加最多。尽管树木和林分尺度的间伐对融雪有影响，但宏观尺度的影响仅限于中低海拔斜坡（<2,300 masl）和每单位LAI去除的南向地区融雪体积的略微增加。开发了一种基于机器学习（随机森林）的决策支持工具来综合SnowPALM结果，并将其应用于相邻流域。这些结果将为加州正在进行的森林管理实践提供信息，并提高我们对与水管理相关的尺度上雪林相互作用影响的理解。
Reductions in snow accumulation and melt in headwater basins are increasing the water stress on forest ecosystems across the western US. Forest thinning has the potential to reduce water stress by decreasing sublimation losses from canopy interception; however, it can also increase snowpack exposure to sun and wind. We used the high-resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model to investigate the effect of two virtual forest thinning scenarios on the snowpack of two adjacent watersheds (54 km2 total) in the Lake Tahoe Basin, California, where forest thinning is being planned. SnowPALM realistically represents small-scale snow-forest interactions to simulate the impact of virtual thinning experiments in which trees <10 and <20 m are removed. In general, thinning results in an overall increase in peak snow water equivalent and snowmelt. Areas around sheltered tree clusters have the largest increases of snowmelt due to decreases of canopy sublimation, while more open and exposed areas show a small decrease due to increases in snowpack sublimation. At the 30-m forest stand scale, existing forest structure controls the efficacy of thinning, where forest stands with mean leaf area index (LAI) >3 m2/m2 and 5–15-m tall show the largest increases in snow accumulation (up to 450 mm) and melt volume (up to 650 mm). Despite the role of tree- and stand-scale thinning on snowmelt, macroscale effects were limited to slightly larger increases in melt volumes at mid to low elevation slopes (<2,300 masl) and south facing areas per unit of LAI removed. A decision support tool using machine learning (random forest) was developed to synthesize SnowPALM results, and was applied to neighboring watersheds. These results will inform ongoing forest management practices in California, and improve our understanding of the effects of snow-forest interactions at scales relevant to water management.