Coevolution of Snowpack and Tree Growth in a Mediterranean Montane Forest

Abstract:

Montane snowpack in the Sierra Nevada provides critical water resources for ecological functions and downstream communities. Climate change is reducing snow accumulation challenging water management and intensifying environmental water stress. Forest removal is one of the few means by which we can manage the snowpack in montane forests and mitigate the effect of climate. Decades of research have focused on understanding and quantifying the effect of forest removal on snow accumulation and melt, from empirical paired watershed experiments to high resolution snow simulations. However, little is known about the long-term effects of increasing snowpack and water yield on tree growth, and how tree growth impacts snowpack through canopy interception losses, shading, and emission of longwave radiation. This research uses a 1-m resolution process-based snow model (SnowPALM) capable of representing small scale snowpack and trees interactions, coupled with an ecohydrological model (RHESSys) that resolves water, energy and carbon cycling to simulates tree growth, to understand and quantify the coevolution of the snowpack and tree growth on a montane forest in the Sierra Nevada, California. We take advantage of multi-temporal lidar data to validate historical tree growth rates and to represent the 1-m trees structure in the snow model over time. Simulations under several scenarios including historical forest structure, moderate and intense thinning, and the different modelling decisions about subsurface processes are investigated. Results from this study are expected to shed more light into the coevolution of montane forests and snowpack response to forest treatments, which will help in long-term planning of forest restoration in the Sierra Nevada.