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Empirical models of monthly and annual albedo in managed boreal forests of interior Norway

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Abstract

An 11-year remotely sensed surface albedo dataset coupled with historical meteorological and stand-level forest management data for a variety of stands in Norway’s most productive logging region is used to develop regression models describing temporal changes in forest albedo following clear-cut harvest disturbance events. Datasets are grouped by dominant tree species, and two alternate multiple regression models are developed and tested following a potential-modifier approach. This result in models with statistically significant parameters (p < 0.05) that explain a large proportion of the observed variation, requiring a single canopy modifier predictor coupled with either monthly or annual mean air temperature as a predictor of a stand’s potential albedo. Models based on annual mean temperature predict annual albedo with errors (RMSE) in the range of 0.025–0.027, while models based on monthly mean temperature predict monthly albedo with errors ranging between of 0.057–0.065 depending on the dominant tree species. While both models have the potential to be transferable to other boreal regions with similar forest management regimes, further validation efforts are required. As active management of boreal forests is increasingly seen as a means to mitigate climate change, the presented models can be used with routine forest inventory and meteorological data to predict albedo evolution in managed forests throughout the region, which, together with carbon cycle modeling, can lead to more holistic climate impact assessments of alternative forest harvest scenarios and forest product systems.

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Notes

  1. H40 is a productivity index used in forestry and is the height in meters for the dominant trees at 40-yrs. breast height age (age after tree has reached 1.3 m). Thus, a productivity of “14” indicates the top height in meters of the stand by 40 years of breast height age.

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Acknowledgments

The MODIS L3 data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA (https://lpdaac.usgs.gov/get_data). This work was performed under the project “Decision Support Models for Increased Harvest and Climate-motivated Forest Policies” funded by the Norwegian Research Council, grant number: 210464.

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Authors and Affiliations

  1. Industrial Ecology Programme, Energy and Process Engineering, Norwegian University of Science and Technology, Høgskoleringen 5, E-1, 7491, Trondheim, Norway

    Ryan M. Bright & Anders H. Strømman

  2. Norwegian Forest and Landscape Institute, P.O. Box 115, 1431, Ås, Norway

    Rasmus Astrup

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  1. Ryan M. Bright
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  2. Rasmus Astrup
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  3. Anders H. Strømman
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Correspondence to Ryan M. Bright.

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Bright, R.M., Astrup, R. & Strømman, A.H. Empirical models of monthly and annual albedo in managed boreal forests of interior Norway. Climatic Change 120, 183–196 (2013). https://doi.org/10.1007/s10584-013-0789-1

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