Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

TitleSpatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale
Publication TypeJournal Article
Year of Publication2019
AuthorsWarner, D. L., Bond-Lamberty B., Jian J., Stell E., and Vargas R.
JournalGlobal Biogeochemical Cycles
Volume33
Number12
Pages1733-1745
Abstract / Summary

Soil respiration (Rs), the soil‐to‐atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.

URLhttp://dx.doi.org/10.1029/2019gb006264
DOI10.1029/2019gb006264
Journal: Global Biogeochemical Cycles
Year of Publication: 2019
Volume: 33
Number: 12
Pages: 1733-1745
Publication Date: 12/2019

Soil respiration (Rs), the soil‐to‐atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.

DOI: 10.1029/2019gb006264
Citation:
Warner, D, B Bond-Lamberty, J Jian, E Stell, and R Vargas.  2019.  "Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale."  Global Biogeochemical Cycles 33(12): 1733-1745.  https://doi.org/10.1029/2019gb006264.