Managing Forests for Snow, Water, and Sustainable Ecosystems

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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.