Improving Representations of Snow-Vegetation Interactions in Land Surface Models

改进地表模型中雪与植被相互作用的表示

• 批准号: 1144894
• 负责人: Adrian Harpold
• 金额: $ 17万
• 依托单位: Harpold Adrian A
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144894&HistoricalAwards=false
• 关键词: Improving; Representations; Snow; Vegetation; Interactions

Dr. Adrian Harpold has been awarded an NSF Earth Sciences Postdoctoral Fellowship to develop a research and education program with the Institute of Arctic and Alpine Research (INSTAAR) at the University of Colorado and the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. He will investigate how forest structure controls the distribution of snowpacks and hydrologic fluxes across gradients of elevation, topography, and climate. Dr. Harpold will first quantify snow-vegetation interactions using high resolution Light Detection and Ranging (LiDAR) derived vegetation, terrain, and snowpack information at three Critical Zone Observatories (CZO) in seasonally snow-covered forests in California, Colorado, and New Mexico. This high resolution data will also be used to inform and evaluate a land surface model, the NCAR-developed Community Land Model (CLM), in highly-instrumented stands and catchments. The overall goal of this study is to improve the predictions of water and energy fluxes in topographically complex, forested landscapes across the Western U.S.Seasonal mountain snowpacks are the major source of water for human and natural systems in the semi-arid Western U.S. The interactions between vegetation and climate play a central role in the accumulation, ablation (evaporation, sublimation, and melt), and ultimate partitioning of snowpacks to the atmosphere versus soils and runoff. Seasonal snowpacks are difficult to measure and model in complex forested terrain, however, which compromises our ability to reliably predict weather, climate, and water resources. Improving how vegetation structure is represented in land surface models is timely, as evidence suggests massive changes in vegetation structure due to tree-dieoff and earlier snowmelts from warming temperatures will alter snow-vegetation interactions in western North American forests. Dr. Harpold will develop a series of lectures on assimilating LiDAR information into snowpack models for a course at the University of Colorado, as well as design and run a short-course on LiDAR analysis targeted to young earth scientists.

Adrian Harpold博士已被授予NSF地球科学博士后奖学金，与科罗拉多大学的北极和阿尔卑斯山研究所（INSTAAR）和位于科罗拉多博尔德的国家大气研究中心（NCAR）一起开发研究和教育计划。 他将研究森林结构如何控制积雪和水文通量在海拔、地形和气候梯度上的分布。 Harpold博士将首先在加州、科罗拉多和新墨西哥州的季节性积雪覆盖的森林中的三个关键区观测站（CZO）使用高分辨率光探测和测距（LiDAR）获得的植被、地形和积雪信息来量化雪-植被相互作用。 这些高分辨率数据还将用于为高度仪器化的林分和集水区的陆面模型（NCAR开发的社区土地模型（CLM））提供信息和进行评估。 本研究的总体目标是改善美国西部地形复杂的森林景观中的水和能量通量的预测。季节性山地积雪是美国西部半干旱地区人类和自然系统的主要水源。植被和气候之间的相互作用在积雪的积累、消融和消融中起着核心作用。（蒸发，升华和融化），以及积雪最终分配到大气中与土壤和径流。 然而，在复杂的森林地形中，季节性积雪很难测量和建模，这损害了我们可靠地预测天气、气候和水资源的能力。 改进植被结构在陆面模型中的表现方式是及时的，因为有证据表明，由于树木死亡和气温变暖导致的早期融雪导致的植被结构的巨大变化将改变北美西部森林的雪-植被相互作用。 Harpold博士将为科罗拉多大学的一门课程开设一系列关于将LiDAR信息融入积雪模型的讲座，并设计和开设一门针对年轻地球科学家的LiDAR分析短期课程。