Managing Forests for Snow, Water, and Sustainable Ecosystems

在北美西部，森林管理策略导致了年轻、茂密的森林更容易受到干旱、虫害和其他压力因素的影响。森林覆盖了地球陆地面积的30%，几乎覆盖了美国许多山区的100%。因此，森林结构的变化具有广泛的生态影响，超出了通常归因于气候变化的影响。指数显示，森林覆盖可能会使第一次融雪的日期增加或减少至多两周。由于山区积雪是天然的储水水库，第一次融雪的日期会影响夏季的河流流量。夏季河流流量对人类、生态系统和农业用水需求有着广泛的影响。森林覆盖的影响可以通过增加森林保有量和/或在林冠上开辟林隙来加以管理。这个研究项目正在开发和测试山区流域的水文模型，以确定森林变化与人类-生态-水文系统的关系。此外，该项目的研究人员正在与利益相关者合作，帮助学生了解与森林水管理有关的目标和问题，并向本科生和研究生提供最先进的空间数据分析和建模教育。森林管理人员想知道为可持续的水和森林管理应采取哪些具体的行动。光探测和测距（激光雷达）是一种遥感方法，它利用脉冲激光的形式来测量到地球的距离。激光雷达成像与空间显式建模相结合，代表了森林结构表征及其与森林-雪相互作用关系的突破。该研究项目利用并调整了现有的分布式水文模型，作为辅助森林管理决策的工具。具体而言，该项目涉及修改模型，以更好地表示复杂的森林-雪相互作用；利用高分辨率航空照片结合森林和积雪激光雷达数据集提取有意义的模型参数并检查模拟场；在不同的冬季天气条件下测试模型的可靠性；测试具有不同森林和气候制度的地点之间模型的可转移性，并与一个咨询小组合作分发模型和工具，以便它们可以应用于任何流域。

In western North America, forest management strategies have led to young, dense forests that are more susceptible to drought, insect infestation, and other stressors. Forests cover 30% of the earth's land area and almost 100% of many U.S. mountainous regions. Thus, a change in forest structure has broad ecological impacts, beyond those normally attributed to climate change. The PI has shown forest cover may either increase or decrease the date of the first snowmelt by up to two weeks. As mountainous snowpacks are a natural water storage reservoir, the date of the first snowmelt affects summer river flow. Summer river flow has wide ranging impacts on human, ecosystem, and agricultural water demands. The effect of forest cover may be managed by increasing forest retention and/or opening gaps in the canopy. This research project is developing and testing hydrologic models for mountainous watersheds to determine how forest change relates to the human-ecologic-hydrologic system. Additionally, the researchers on this project are working with stakeholders to help students understand the goals and concerns related to forest-water management and educating undergraduate and graduate students in state-of-the-art spatial data analysis and modeling.Forest managers would like to know what site-specific actions to take for both sustainable water and forest management. Light Detection and Ranging (Lidar) is a remote sensing method that uses light in the form of a pulsed laser to measure distances to the Earth. Lidar imaging, combined with spatially-explicit modeling, represent a break-through in forest structure characterization and its relation to forest-snow interactions. This research project is utilizing and adapting an existing distributed hydrologic model to be used as a tool to assist forest management decisions. Specifically, the project involves modifying the model to better represent complex forest-snow interactions; using high resolution aerial photos combined with forest and snow lidar datasets to extract meaningful model parameters and to check simulated fields; testing model reliability over varying winter weather regimes; testing model transferability between sites with different forests and climate regimes, and working with an advisory team to distribute the model and tools so that they can be applied to any watershed.