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

山区的季节性积雪是全世界数十亿人的重要水资源。雪中储存的水量或雪水当量（SWE）取决于其深度和密度，这两者在空间和时间上都有变化。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展。相反，使用雪密度模型，但不同的模型可以产生不同的结果在山区景观。因此，了解为什么雪密度模型不同是至关重要的，以减少在SWE的不确定性。该项目将提高对影响积雪密度的物理过程的认识，重点是森林如何在各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，融雪和径流。雪密度模型将用于将雪深数据集转换为SWE数据集，有利于水文研究。该项目将培养一名研究生，开设两个实地课程，并通过努力增加地球科学专业学生多样性的计划，支持大学的多名本科实习生。积雪密度的空间变化主要是由上覆积雪质量引起的差异压实驱动的-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减小而增加（例如，风压实）或随着深度增加密度降低（例如，新降雪）。指导性假设是，景观特性和气候管理的相对重要性，初级和次级致密化过程。开阔地区的积雪通常比森林深，因此预计开阔地区的积雪密度更大。然而，二次加工可以增强或抵消这种影响，这取决于环境因素。将使用协调的现场调查和模拟实验来测试评估初级和次级致密化效应的作用的假设。在不同的雪地气候中进行实地调查，并进行景观控制（例如，森林与开阔地）将测量雪密度、深度、含水量和层特征的差异。积雪将使用基于过程的模块化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，评估模型对主要和次要影响的表示，并测试模型对气象不确定性的敏感性。将使用来自NASA SnowEx活动和第二个雪模型相互比较项目的数据对模型进行进一步评估。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.