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.

阿德里安·哈波尔德（Adrian Harpold）博士被授予NSF地球科学博士后奖学金，以与科罗拉多大学北极和高山研究所（Instaar）和科罗拉多州国家大气研究中心（NCAR）一起制定研究和教育计划。 他将调查森林结构如何控制海拔，地形和气候梯度跨气管和水文通量的分布。 Harpold博士将首先使用高分辨率的光检测和范围（LIDAR）衍生的植被，地形和积雪信息来量化雪植物相互作用，该植被，地形和积雪信息在加利福尼亚，科罗拉多州和新墨西哥州的季节性雪森林中的三个关键区域观测站（CZO）（CZO）。 此高分辨率数据还将用于在高度吸引人的立场和集水区中为陆地表面模型，NCAR开发的社区土地模型（CLM）提供信息。 这项研究的总体目标是改善在美国西部西部西部人类和自然系统的主要水源的地形复杂，森林景观中的水和能量通量的预测，这是美国半干旱西部的水源来源。径流。 然而，季节性的积雪很难在复杂的森林地形中进行衡量和模型，这损害了我们可靠地预测天气，气候和水资源的能力。 有证据表明，改善陆地表面模型中植被结构的表示方式是及时的，因为有证据表明，由于树木的状态而导致的植被结构发生了巨大变化，而温暖温度的早期融雪会改变北美森林中的雪绿植物相互作用。 Harpold博士将开发一系列的讲座，以将LiDar信息吸收到科罗拉多大学的一门课程中，并设计和运行针对年轻地球科学家的激光雷达分析的短途课程。