RAPID: The role of vegetation-moderated longwave radiation on the spatiotemporal distribution of snow during accumulation and ablation in mountain terrain

高山积雪为美国西部数百万人提供了水源。为了预测何时何地会有融雪，水资源管理者需要知道山上有多少雪以及雪的位置。但各地积雪的深度不尽相同，因此需要进行全面估算。不幸的是，由于访问问题，人工测量雪太难了，而且从卫星测量雪的方法仍在开发中。卫星面临的一个挑战是树木。卫星通常无法透过森林看到雪。2019年冬季，美国宇航局将在美国西部的13个飞行区域反复飞行，测试用于测量飞机积雪的新传感器。传感器将在非常大的区域大约每3米提供一次雪深和含水量数据。一条飞行路线覆盖了爱达荷州博伊西附近的一个试验场，科学家们在那里研究山地水文有着悠久的历史。该项目将通过在森林中进行新的气象测量来调查山林和雪之间的关系，同时NASA正在收集前所未有的雪深数据。所获得的知识可用于改进关键供水预测方法。美国西部大部分地区的主要水源是山区积雪；了解控制雪的积累和融化的过程和特性对科学和社会至关重要。积雪和融雪的模式受到许多气候和地形因素的影响，这对科学家模拟和预测雪源供水的能力提出了挑战。最重要的挑战是森林对融化时间的影响。在一些地区，森林保存了雪，而在另一些地区，森林加速了雪的融化。获取足够的空间和时间分辨率的数据来回答有关森林对雪的影响的重要问题，通常在财政和后勤上都是令人望而却步的。2019年冬季，NASA SNOWEX项目将通过积累和融化季节每两周获得3米空间分辨率的雪深数据。有了在这些活动中收集的额外地面数据，就有可能回答有关森林-雪相互作用的一些关键问题。本研究的目的是收集与积雪时空分布一致的分布长波辐射数据。具体来说，该项目旨在回答森林和开放环境中空间变化的长波辐射如何影响相对寒冷和相对温暖的雪环境中的积累和融化。利用辐射计阵列测量不同密度林冠下雪面长波辐射在积雪和融化季节的空间分布。将高分辨率NASA雪深和水含量数据与本项目资助的分布式辐射数据相结合，填补了雪-植被相互作用科学的关键知识空白，为一名博士生和两名本科生提供培训，并与博伊西州立大学建立了良好的教育和推广计划。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Mountain snow provides water to millions of people in the western United States. To predict when and where melting snow will be available, water managers need to know how much snow is in the mountains and where it is located. But snow is not the same depth everywhere, so comprehensive estimates are required. Unfortunately, manual measurements of snow are too hard because of access problems, and methods to measure snow from satellites are still being developed. One challenge faced by satellites is trees. Satellites can't usually see snow through a forest. In winter 2019, NASA is testing new sensors for measuring snow from aircrafts by repeatedly flying over 13 flight regions throughout the western US. The sensors will provide snow depth and water content data approximately every 3 meters of very large areas. One flight path covers an experimental field site near Boise, Idaho were scientists have a long history of studying mountain hydrology. This project will investigate the relationships between mountain forests and snow by making new meteorological measurements in forest at the same time that NASA is collecting unprecedented snow depth data. Knowledge gained can be used to improve critical water supply forecasting methods.Mountain snow provides the dominant water supply from most of the western US; understanding the processes and properties that control the accumulation and melt of snow is crucial for science and society. The patterns of snow accumulation and melt are impacted by many climate and terrain factors that challenge scientist's abilities to model and forecast snow-derived water supply. Foremost among the challenges is the impact that forests have on the timing of melt. In some regions forests preserve snow while in others they enhance melt. Obtaining data at sufficient spatial and temporal resolutions to answer important questions about the impact of forests on snow is typically financially and logistically prohibitive. In winter 2019, the NASA SNOWEX project will obtain snow depth data at 3 m spatial resolution every two weeks through the accumulation and melt season. With additional ground-based data collected during these campaigns, it will be possible to answer some critical questions about forest-snow interactions. The objective of this research is to collect distributed longwave radiation data coincident with spatially and temporally distributed snow distribution data. Specifically, the project seeks to answer how spatially variable longwave radiation in forested and open environments impact accumulation and melt in relatively cold vs relatively warm snowy environments. An array of radiometers will be deployed to measure the spatial distribution of longwave radiation at the snow surface under forest canopies of different density through the snow accumulation and melt seasons. Coupling the high resolution NASA snow depth and water content data with distributed radiation data funded by this project fill a critical knowledge gap in the science of snow-vegetation interactions, provide training for one PhD student and two undergraduate students, and connect to a well-established education and outreach programs at Boise State University.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.