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

Warner, D. L.; Bond‐Lamberty, B.; Jian, J.; Stell, E.; Vargas, R.

doi:10.1029/2019GB006264

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

Journal Article · 17 December 2019 · Global Biogeochemical Cycles

DOI: https://doi.org/10.1029/2019GB006264 · OSTI ID:1579755

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Delaware Geological Survey University of Delaware Newark DE USA
Pacific Northwest National Laboratory Joint Global Change Research Institute MD USA
Department of Geography University of Delaware Newark DE USA
Department of Plant and Soil Sciences University of Delaware Newark DE USA

Abstract Soil respiration (Rs), the soil‐to‐atmosphere CO ₂ 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.

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Sponsoring Organization:: USDOE

OSTI ID:: 1579755

Journal Information:: Global Biogeochemical Cycles, Journal Name: Global Biogeochemical Cycles Journal Issue: 12 Vol. 33; ISSN 0886-6236

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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