2017 ESA Annual Meeting (August 6 -- 11)

COS 21-9 - Seasonally dynamic influence of forest canopy and topography on surface temperatures in California mountain landscapes

Monday, August 7, 2017: 4:20 PM
B114, Oregon Convention Center
Frank Davis, Bren School of Environmental Science & Management, University of California, Santa Barbara, CA, Nicholas W. Synes, School of Geographical Sciences and Urban Planning, Arizona State University, Geoffrey A. Fricker, Geography, University of California, Los Angeles, Los Angeles, CA, Janet Franklin, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, Josep M. Serra-Diaz, Harvard Forest, Harvard University, Petersham, MA and Ian M. McCullough, Bren School of Environmental Science & Management, University of California, Santa Barbara, Santa Barbara, CA
Background/Question/Methods

Microclimate is a crucial determinant of seedling establishment and could regulate plant species range adjustments under climate change. Seedling survival and growth depends on conditions close to the soil surface where temperature regimes are influenced by a host of dynamic factors controlling surface energy balance, notably solar insolation, cold air drainage, overstory and surrounding vegetation, snow cover and soil moisture. At two study sites in the Sierra Nevada mountains -- one at 300-400m in foothill woodlands and grasslands, the other at 2000-2100m in mixed conifer forest -- we deployed networks of 288 near-surface (5 cm above the ground) temperature sensors spanning the local range of topography and vegetation structure. We analysed LIDAR and hyperspectral imagery acquired by the NEON Airborne Observation Platform (AOP) to characterize fine-grained topography, vegetation and moisture variables. Generalized boosted regression models were developed to relate local environmental conditions to daily minimum, mean and maximum near-surface temperatures.

Results/Conclusions

The statistical power of the regression models varied with atmospheric transmittance: models of minimum temperature performed better when atmospheric transmittance was high; models of maximum temperature performed better when atmospheric transmittance was low. Radiation and local canopy density were important predictors of maximum temperature, although their relative influence was scale-dependent and varied both seasonally and by study site. In summer months at the high elevation site, canopy density within a 2.5 m radius around the temperature sensor became more important than canopy at a 10 m radius – indicative of a higher sun angle in summer and the resultant change in the source of shading. Results suggest that it is feasible to model very fine-grained surface temperature variation as a function of the time-varying interaction of insolation, topography and overstory vegetation, thus allowing more sophisticated modelling of microenvironments for seedling establishment under climate change.