Improving Representations of Snow-Vegetation Interactions in Land Surface Models

禤浩焯·哈波德博士被授予美国国家科学基金会地球科学博士后奖学金，与科罗拉多大学北极和高山研究所(INSTAAR)和科罗拉多州博尔德市国家大气研究中心(NCAR)共同开展研究和教育项目。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博士将首先在加利福尼亚州、科罗拉多州和新墨西哥州季节性积雪覆盖的森林中的三个临界区天文台(CZO)使用高分辨率光探测和测距(LiDAR)得出的植被、地形和积雪信息来量化雪与植被的相互作用。这些高分辨率数据还将用于在高度仪表化的林分和集水区提供信息和评估陆地表面模型，即NCAR开发的社区土地模型(CLM)。这项研究的总体目标是提高对美国西部地形复杂的森林景观中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、消融(蒸发、升华和融化)以及最终分配到大气与土壤和径流中发挥着核心作用。然而，在复杂的森林地带，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。改进植被结构在陆地表面模型中的表示方式是及时的，因为有证据表明，由于树木死亡而导致的植被结构的大规模变化，以及气温变暖导致的更早的积雪融化，将改变北美西部森林的雪与植被的相互作用。哈波德博士将为科罗拉多大学的一门课程开发一系列关于将激光雷达信息吸收到积雪模型中的讲座，并设计和开办一个面向年轻地球科学家的激光雷达分析短期课程。

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.