Improved process understanding of snow density and SWE across forested mountain landscapes from coordinated field observations and model analyses

通过协调的现场观测和模型分析，提高对森林山地景观的雪密度和 SWE 的过程理解

• 批准号: 1761441
• 负责人: Eric Small
• 金额: $ 53.1万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761441&HistoricalAwards=false
• 关键词: Improved; process; understanding; snow; density

Seasonal snow in mountains is a critical water resource for billions of people worldwide. The amount of water stored in snow, or snow water equivalent (SWE), depends on its depth and density, both of which vary in space and time. New technologies permit mapping of snow depth across large watersheds, but there is no similar advance for measuring snow density. Instead, snow density models are used, but different models can yield divergent results across mountain landscapes. Therefore, understanding why snow density models differ is essential for reducing uncertainty in SWE. This project will improve knowledge of the physical processes that affect snowpack density, focusing on how forests yield predictable variations in snowpack density across watersheds. The project will advance snow density modeling and thus predictions of SWE, snowmelt, and runoff. Snow density models will be used to transform snow depth datasets into SWE datasets, benefiting hydrologic research. The project will train one graduate student, develop two field classes, and support multiple undergraduate interns at the university and through programs that strives to increase diversity in geoscience students.Spatial variations in snowpack density are driven primarily by differential compaction due to the mass of overlying snow - density tends to increase with greater snow depth. In contrast, secondary processes lead to increased density as depth decreases (e.g., wind compaction) or decreased density as depth increases (e.g., new snowfall). The guiding hypothesis is that landscape properties and climate govern the relative importance of primary and secondary densification processes. Snow in open areas is typically deeper than in forests, so snowpack density is predicted to be greater in open areas. However, secondary processes can enhance or counteract this effect, depending on environmental factors. Hypotheses that assess the roles of primary and secondary densification effects will be tested using coordinated field investigations and modeling experiments. Field investigations in distinct snow climates with landscape controls (e.g., forest vs. open) will measure differences in snow density, depth, water content, and layer characteristics. Snowpack will be simulated with process-based modular snow models and reference models. Field data will be used to quantify model errors, evaluate model representation of primary and secondary effects, and test model sensitivity to meteorological uncertainty. Models will be further evaluated using data from the NASA SnowEx campaign and the second Snow Model Intercomparison Project. Robust models identified will be applied to produce datasets of density, SWE, and uncertainty for community usage.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

山区的季节性积雪是全球数十亿人的重要水资源。雪中储存的水量或雪水当量(SWE)取决于雪的深度和密度，这两者在空间和时间上都是不同的。新技术允许绘制大流域的积雪深度图，但在测量积雪密度方面没有类似的进步。取而代之的是，雪密度模型被使用，但不同的模型可以在不同的山脉景观中产生不同的结果。因此，了解雪密度模型不同的原因对于减少SWE中的不确定性至关重要。该项目将提高对影响积雪密度的物理过程的了解，重点关注森林如何在不同流域产生可预测的积雪密度变化。该项目将推进雪密度建模，从而预测SWE、积雪融化和径流。积雪密度模型将用于将积雪深度数据集转换为SWE数据集，从而有利于水文研究。该项目将培养一名研究生，开发两个实地课程，并支持大学的多名本科生实习生，并通过努力增加地学学生多样性的计划。积雪密度的空间差异主要是由差异压实驱动的，因为覆盖的积雪质量密度往往随着积雪深度的增加而增加。相反，次级过程导致密度随着深度的减少而增加(例如，风压实)或密度随着深度的增加而减少(例如，新的降雪)。指导性假设是景观特性和气候决定了初级致密化和次级致密化过程的相对重要性。开阔地区的积雪通常比森林更深，因此开阔地区的积雪密度预计会更大。然而，次级过程可以增强或抵消这种影响，具体取决于环境因素。评估一次和二次致密化效应作用的假设将通过协调的实地调查和模拟实验进行检验。在具有景观控制的不同雪地气候中的实地调查(例如，森林与开阔地带)将测量雪密度、深度、水分含量和层特征的差异。积雪将使用基于过程的模块化积雪模型和参考模型进行模拟。现场数据将用于量化模型误差，评估主要和次要影响的模型表示，并测试模型对气象不确定性的敏感性。模型将使用NASA SnowEx活动和第二个Snow Model国际比较项目的数据进行进一步评估。确定的稳健模型将被应用于产生密度、SWE和不确定性的数据集，供社区使用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Isolating forest process effects on modelled snowpack density and snow water equivalent

隔离森林过程对模拟积雪密度和雪水当量的影响

• DOI: 10.1002/hyp.14475
• 发表时间: 2022
• 期刊: Hydrological Processes
• 影响因子: 3.2
• 作者: Bonner, Hannah M.;Raleigh, Mark S.;Small, Eric E.
• 通讯作者: Small, Eric E.

A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA

来自美国科罗拉多州 Crested Butte 邻近森林和草地的气象和雪数据集

• DOI: 10.5281/zenodo.6618553
• 发表时间: 2022
• 期刊: Zenodo
• 作者: Bonner, Hannah M.;Smyth, Eric;Raleigh, Mark S.;Small, Eric E.
• 通讯作者: Small, Eric E.

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

通过树木摇摆监测估算针叶树冠层截获的雪量的挑战和能力

• DOI: 10.1029/2021wr030972
• 发表时间: 2022
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Raleigh, Mark S.;Gutmann, Ethan D.;Van Stan, II, John T.;Burns, Sean P.;Blanken, Peter D.;Small, Eric E.
• 通讯作者: Small, Eric E.

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

美国科罗拉多州 Crested Butte 邻近森林和草地站点的气象和降雪数据集

• DOI: 10.1029/2022wr033006
• 发表时间: 2022
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Bonner, Hannah M.;Smyth, Eric;Raleigh, Mark S.;Small, Eric E.
• 通讯作者: Small, Eric E.

Large‐diameter trees affect snow duration in post‐fire old‐growth forests

大直径树木影响火灾后老生长林的积雪持续时间

• DOI: 10.1002/eco.2414
• 发表时间: 2022
• 期刊: Ecohydrology
• 影响因子: 2.6
• 作者: Teich, Michaela;Becker, Kendall M. L.;Raleigh, Mark S.;Lutz, James A.
• 通讯作者: Lutz, James A.