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

RAPID：植被调节的长波辐射对山区积雪和消融过程中积雪时空分布的作用

• 批准号: 1914598
• 负责人: James McNamara
• 金额: $ 3.89万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914598&HistoricalAwards=false
• 关键词: RAPID; role; vegetation; moderated; longwave

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.

山上的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化的雪，水资源管理者需要知道山上有多少雪以及雪的位置。但各地的积雪深度并不相同，因此需要进行全面的估计。不幸的是，由于交通问题，人工测量积雪太难了，从卫星上测量积雪的方法仍在开发中。卫星面临的一个挑战是树木。在森林里通常看不到雪。 2019年冬季，美国宇航局正在测试新的传感器，通过反复飞越美国西部的13个飞行区来测量飞机的积雪。这些传感器将提供大约每3米的雪深和含水量数据。其中一条飞行路线覆盖了爱达荷州博伊西附近的一个实验场，那里的科学家研究山区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区森林与雪之间的关系，同时NASA正在收集前所未有的积雪深度数据。 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供水来自山区积雪;了解控制积雪积累和融化的过程和特性对科学和社会至关重要。积雪和融化的模式受到许多气候和地形因素的影响，这些因素挑战了科学家建模和预测雪源供水的能力。最重要的挑战是森林对融化时间的影响。在一些地区，森林可以保存雪，而在另一些地区，森林可以促进雪的融化。获取足够的空间和时间分辨率的数据来回答关于森林对雪的影响的重要问题通常在财政和后勤上都是不允许的。在2019年冬季，NASA的SNOWEX项目将在积雪和融化季节每两周获得3米空间分辨率的雪深数据。 通过在这些活动中收集更多的地面数据，将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空间和时间分布的积雪分布数据相一致的分布式长波辐射数据。具体而言，该项目旨在回答空间可变的长波辐射在森林和开放环境中如何影响相对寒冷与相对温暖的雪地环境中的积累和融化。将部署一个辐射计阵列，以测量在积雪和融化季节不同密度的林冠下雪面长波辐射的空间分布。将高分辨率的NASA雪深和含水量数据与该项目资助的分布式辐射数据相结合，填补了雪-植被相互作用科学中的一个关键知识空白，为一名博士生和两名本科生提供培训，连接到一口井上在博伊西州立大学建立了教育和推广项目。该奖项反映了NSF的法定使命，并通过评估被认为值得支持使用基金会的知识价值和更广泛的影响审查标准。