DOI: 10.5281/zenodo.15110783
title: Harmonized global datasets of soil carbon and heterotrophic respiration from data-driven estimates, with derived turnover time and Q10

description: We collected all available global soil carbon (C) and heterotrophic respiration (RH) maps derived from data-driven estimates, sourcing them from public repositories and supplementary materials of previous studies (Table 1). All spatial datasets were converted to NetCDF format for consistency and ease of use.

Because the maps had varying spatial resolutions (ranging from 0.0083° to 0.5°), we harmonized all datasets to a common resolution of 0.5° (approximately 50 km at the equator). We then merged the processed maps by computing the mean, maximum, and minimum values at each grid cell, resulting in harmonized global maps of soil C (for the top 0–30 cm and 0–100 cm depths) and RH at 0.5° resolution.

Grid cells with fewer than three soil C estimates or fewer than four RH estimates were assigned NA values. Land and water grid cells were automatically distinguished by combining multiple datasets containing soil C and RH information over land.

Soil carbon turnover time (years), denoted as τ, was calculated under the assumption of a quasi-equilibrium state using the formula:

τ = CS / RH

where CS is soil carbon stock and RH is the heterotrophic respiration rate. The uncertainty range of τ was estimated for each grid cell using:

τmax = CS+ / RH−  τmin = CS− / RH+

where CS+ and CS− are the maximum and minimum soil C values, and RH+ and RH− are the maximum and minimum RH values, respectively.

To calculate the temperature sensitivity of decomposition (Q10)—the factor by which decomposition rates increase with a 10 °C rise in temperature—we followed the method described in Koven et al. (2017). The uncertainty of Q10 (maximum and minimum values) was derived using τmax and τmin, respectively.  

 

The harmonized dataset files available in the repository are as follows:

·       harmonized-RH-hdg.nc: global soil heterotrophic respiration map

·       harmonized-SOC100-hdg.nc: global soil C map for 0–100 cm

·       harmonized-SOC30-hdg.nc: global soil C map for 0–30 cm

·       Q10.nc: global Q10 map 

·       Turnover-time_max.nc: global soil C turnover time estimated using maximum soil C and minimum RH

·       Turnover-time_min.nc: global soil C turnover time estimated using minimum soil C and maximum RH 

·       Turnover-time_mean.nc: global soil C turnover time estimated using mean soil C and RH

·       Turnover-time30_mean.nc: global soil C turnover time estimated using the soil C map for 0-30 cm

 

More details are provided in:

Shoji Hashimoto, Akihiko Ito, Kazuya Nishina (submitted) 

 

Reference

Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).

 

Table1 : List of soil carbon and heterotrophic respiration datasets used in this study.






Dataset



Repository/References (Dataset name)



Depth



ID in NetCDF file***





Global soil C



Global soil data task 2000 (IGBP-DIS)1



0–100



3,-





 



Shangguan et al. 2014 (GSDE)2,3



0–100, 0–30*



1,1





 



Batjes 2016 (WISE30sec)4,5



0–100, 0–30



6,7





 



Sanderman et al. 2017 (Soil-Carbon-Debt) 6,7



0–100, 0–30



5,5





 



Soilgrids team and Hengl et al. 2017 (SoilGrids)8,9



0–30**



-,6





 



Hengl and Wheeler 2018 (LandGIS)10



0–100, 0–30



4,4





 



FAO 2022 (GSOC)11



0–30



-,2





 



FAO 2023 (HWSD2)12



0–100, 0–30



2,3





Circumpolar soil C



Hugelius et al. 2013 (NCSCD)13–15



0–100, 0–30



7,8





Global RH



Hashimoto et al. 201516,17



-



1





 



Warner et al. 2019 (Bond-Lamberty equation based)18,19



-



2





 



Warner et al. 2019 (Subke equation based)18,19



-



3





 



Tang et al. 202020,21



-



4





 



Lu et al. 202122,23



-



5





 



Stell et al. 202124,25



-



6





 



Yao et al. 202126,27



-



7





 



He et al. 202228,29



-



8





*The vertical depth intervals did not exactly match 100 cm and 30 cm. Therefore, weighted means were calculated for the 0–100 cm and 0–30 cm depths. **Only the soil C stock data for the 0–30 cm depth is officially provided in the repository. ***IDs for 0–100cm/0–30cm

 

References

1.         Global soil data task. Global Gridded Surfaces of Selected Soil Characteristics (IGBP-DIS). Preprint at https://doi.org/10.3334/ORNLDAAC/569 (2000).

2.         Shangguan, W., Dai, Y., Duan, Q., Liu, B. & Yuan, H. A global soil data set for earth system modeling. J. Adv. Model. Earth Syst. 6, 249–263 (2014).

3.         Land-atmosphere interaction research group at Sun Yat-sen University. The global soil dataset for Earth system modeling. http://globalchange.bnu.edu.cn/research/soilw (2014).

4.         Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).

5.         ISRIC World Soil Information. WISE derived soil properties on a 30 by 30 arc-seconds global grid. https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/dc7b283a-8f19-45e1-aaed-e9bd515119bc (2016).

6.         Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. Proc. Natl. Acad. Sci. 114, 9575–9580 (2017).

7.         Sanderman, J. Soil-Carbon-Debt. https://github.com/whrc/Soil-Carbon-Debt (2017).

8.         SoilGrids team. SoilGrids-global gridded soil information. https://files.isric.org/soilgrids/latest/data_aggregated/ (2020).

9.         Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12, e0169748 (2017).

10.       Hengl, T. & Wheeler, I. Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. Zenodo https://doi.org/10.5281/ZENODO.2536040 (2018).

11.       FAO. Global soil organic carbon map. https://data.apps.fao.org/catalog/dataset/global-soil-organic-carbon-map (2022).

12.       FAO. Harmonized world soil database v2.0. https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/ (2023).

13.       Hugelius, G. et al. A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region. Earth Syst. Sci. Data 5, 393–402 (2013).

14.       Hugelius, G. et al. The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13 (2013).

15.       Bolin Centre for Climate Research. The northern circumpolar soil carbon database gridded data in network common data form (NetCDF-files). https://bolin.su.se/data/ncscd/netcdf.php (2013).

16.       Hashimoto, S. et al. Global spatiotemporal distribution of soil respiration modeled using a global database. Biogeosciences 12, 4121–4132 (2015).

17.       Hashimoto, S. Global gridded soil respiration, heterotrophic respiration, autotrophic respiration, and Q10 value (Hashimoto et al. 2015, Biogeosciences). Zenodo https://doi.org/10.5281/ZENODO.4708444 (2021).

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20.       Tang, X. et al. Spatial and temporal patterns of global soil heterotrophic respiration in terrestrial ecosystems. Earth Syst. Sci. Data 12, 1037–1051 (2020).

21.       Tang, X. et al. A globally gridded heterotrophic respiration dataset based on field observations. figshare https://doi.org/10.6084/M9.FIGSHARE.8882567 (2019).

22.       Lu, H. et al. Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models. Environ. Res. Lett. 16, 054048 (2021).

23.       Lu, H. Global soil respiration and its components dataset derived from the Random Forest method. Zenodo https://doi.org/10.5281/ZENODO.4686669 (2021).

24.       Stell, E., Warner, D., Jian, J., Bond‐Lamberty, B. & Vargas, R. Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions? Glob. Change Biol. 27, 3923–3938 (2021).

25.       Stell, E., Warner, D. L., Jian, J., Bond-Lamberty, B. P. & Vargas, R. Global gridded 1-km soil and soil heterotrophic respiration derived from SRDB v5. https://doi.org/10.3334/ORNLDAAC/1928 (2021).

26.       Yao, Y. et al. A data‐driven global soil heterotrophic respiration dataset and the drivers of its inter‐annual variability. Glob. Biogeochem. Cy. 35, e2020GB006918 (2021).

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