Unraveling the Controls on Snow Disappearance in Montane Conifer Forests Using Multi‐Site Lidar
Unraveling the Controls on Snow Disappearance in Montane Conifer Forests Using Multi‐Site Lidar
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DOI：
10.1029/2020wr027522
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发表时间：
2021-12
影响因子：
5.4
通讯作者：
H. Safa;S. Krogh;J. Greenberg;T. Kostadinov;A. Harpold
中科院分区：
文献类型：
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作者：
H. Safa;S. Krogh;J. Greenberg;T. Kostadinov;A. Harpold
积雪消失日期（SDD）通过改变水分有效性、森林火情和地表能量收支来影响山地森林的生态水文动态。林冠层通过竞争过程调节SDD；浓密的冠层拦截降雪并增强长波辐射，同时遮蔽积雪免受短波辐射和风的影响。有限的地面积雪存在和缺失观测限制了我们揭示影响山地森林SDD的主要过程的能力。我们应用激光雷达衍生的方法来估算内华达山脉两个相对温暖的地点和落基山脉两个较冷的地点的分数积雪面积（fSCA），并将其与SDD联系起来。除后期积雪和低fSCA外，低植被密度下的雪滞留时间都比高植被密度下的雪滞留时间长。与茂密的林冠下地区相比，温暖森林在开阔地区的雪滞留时间始终更长，尤其是在南向斜坡上。与开放地区相比，寒冷森林在低密度冠层下的雪滞留时间更长，特别是在朝北的斜坡上。我们利用这一实证分析来进行过程推断，并开发了一个初始框架来预测包含地形和植被结构作用的SDD。在我们的框架的基础上，有必要提供更好的森林管理建议，以便在复杂的地形和异质冠层结构中保留积雪。
Snow disappearance date (SDD) affects the ecohydrological dynamics of montane forests, by altering water availability, forest fire regime, and the land surface energy budget. The forest canopy modulates SDD through competing processes; dense canopy intercepts snowfall and enhances longwave radiation while shading snowpack from shortwave radiation and sheltering it from the wind. Limited ground‐based observations of snow presence and absence have restricted our ability to unravel the dominant processes affecting SDD in montane forests. We apply a lidar‐derived method to estimate fractional snow cover area (fSCA) at two relatively warm sites in the Sierra Nevada and two colder sites in the Rocky Mountains, which we link to SDD. With the exception of late season snowpack and low fSCA, snow retention is longer under low vegetation density than under high vegetation density in both warm and cold sites. Warm forests consistently have longer snow retention in open areas compared to dense under canopy areas, particularly on south‐facing slopes. Cold forests tend to have longer snow retention under lower density canopy compared to open areas, particularly on north‐facing slopes. We use this empirical analysis to make process inferences and develop an initial framework to predict SDD that incorporates the role of topography and vegetation structure. Building on our framework will be necessary to provide better forest management recommendations for snowpack retention across complex terrain and heterogenous canopy structure.