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1. Modeling microbial carbon fluxes and stocks in global soils from 1901 to 2016

   https://doi.org/10.5194/bg-21-2313-2024  

   He, Liyuan; Rodrigues, Jorge_L Mazza; Mayes, Melanie A; Lai, Chun-Ta; Lipson, David A; Xu, Xiaofeng (May 2024, Biogeosciences)

   Abstract. Soil microbes play a crucial role in the carbon (C) cycle; however, they have been overlooked in predicting the terrestrial C cycle. We applied a microbial-explicit Earth system model – the Community Land Model-Microbe (CLM-Microbe) – to investigate the dynamics of soil microbes during 1901 to 2016. The CLM-Microbe model was able to reproduce the variations of gross (GPP) and net (NPP) primary productivity, heterotrophic (HR) and soil (SR) respiration, microbial (MBC) biomass C in fungi (FBC) and bacteria (BBC) in the top 30 cm and 1 m, and dissolved (DOC) and soil organic C (SOC) in the top 30 cm and 1 m during 1901–2016. During the study period, simulated C variables increased by approximately 12 PgC yr−1 for HR, 25 PgC yr−1 for SR, 1.0 PgC for FBC and 0.4 PgC for BBC in 0–30 cm, and 1.2 PgC for FBC and 0.7 PgC for BBC in 0–1 m. Increases in microbial C fluxes and pools were widely found, particularly at high latitudes and in equatorial regions, but we also observed their decreases in some grids. Overall, the area-weighted averages of HR, SR, FBC, and BBC in the top 1 m were significantly correlated with those of soil moisture and soil temperature in the top 1 m. These results suggested that microbial C fluxes and pools were jointly governed by vegetation C input and soil temperature and moisture. Our simulations revealed the spatial and temporal patterns of microbial C fluxes and pools in response to environmental change, laying the foundation for an improved understanding of soil microbial roles in the global terrestrial C cycle. 

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2. Macroecology Differentiation Between Bacteria and Fungi in Topsoil Across the United States

   https://doi.org/10.1029/2023GB007706  

   He, Liyuan; Viovy, Nicolas; Xu, Xiaofeng (October 2023, Global Biogeochemical Cycles)

   Abstract Bacteria and fungi possess distinct physiological traits. Their macroecology is vital for ecosystem functioning such as carbon cycling. However, bacterial and fungal biogeography and underlying mechanisms remain elusive. In this study, we investigated bacterial versus fungal macroecology by integrating a microbial‐explicit model—CLM‐Microbe—with measured fungal (FBC) and bacterial biomass carbon (BBC) from 34 NEON sites. The distribution of FBC, BBC, and FBC: BBC (F:B) ratio was well simulated across sites, with variations in 99% (P < 0.001), 97% (P < 0.001), and 99% (P < 0.001) being explained by the CLM‐Microbe model, respectively. We found stronger biogeographic patterns of FBC relative to BBC across the United States. Fungal and bacterial turnover rates showed similar trends along latitude. However, latitudinal trends of their component fluxes (carbon assimilation, respiration, and necromass production) were distinct between bacteria and fungi, with those latitudinal trends following inverse unimodal patterns for fungi and showing exponential declining responses for bacteria. Carbon assimilation was dominated by vegetation productivity, and respiration was dominated by mean annual temperature for bacteria and fungi. The dominant factor for their necromass production differs, with edaphic factors controlling fungal and mean annual temperature controlling bacterial processes. The understanding of fungal and bacterial macroecology is an important step toward linking microbial metabolism and soil biogeochemical processes. Distinct fungal and bacterial macroecology contributes to the microbial ecology, particularly on microbial community structure and its association with ecosystem carbon cycling across space. 

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3. Hydrologic Export of Soil Organic Carbon: Continental Variation and Implications

   https://doi.org/10.1029/2021GB007161  

   Hararuk, O.; Jones, S. E.; Solomon, C. T. (June 2022, Global Biogeochemical Cycles)

   Abstract Soil is the largest terrestrial carbon (C) reservoir and a large potential source or sink of atmospheric CO_₂. Soil C models have usually focused on refining representations of microbe‐mediated C turnover, whereas lateral hydrologic C fluxes have largely been ignored at regional and global scales. Here, we provide large‐scale estimates of hydrologic export of soil organic carbon (SOC) and its effects on bulk soil C turnover rates. Hydrologic export of SOC ranged from nearly 0 to 12 g C m⁻²yr⁻¹amongst catchments across the conterminous United States, and total export across this region was 14 (95% CI 4‐41) Tg C/yr. The proportion of soil C turnover attributed to hydrologic export ranged from <1% to 20%, and averaged 0.97% (weighted by catchment area; 95% CI 0.3%–2.6%), with the lowest values in arid catchments. Ignoring hydrologic export in C cycle models might lead to overestimation of SOC stocks by 0.3–2.6 Pg C for the conterminous United States. High uncertainty in hydrologic C export fluxes and potentially substantial effects on soil C turnover illustrate the need for research aimed at improving our mechanistic understanding of the processes regulating hydrologic C export. 

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4. Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

   https://doi.org/10.5281/zenodo.8057232  

   wang, zhuonan; Kumar, Jitendra; R., Samantha Weintraub-Leff; Todd-Brown, Katherine; Mishra, Umakant; Sihi, Debjani (January 2023, Zenodo)

   {"Abstract":["Data Description<\/strong>:<\/p>\n\nTo improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.<\/p>\n\nFiles in collection (32):<\/p>\n\nCollection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts<\/p>\n\ngNATSGO TIF files:<\/p>\n\n├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]\n├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]\n├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]\n├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]\n├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]\n├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]\n├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]\n├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]\n├── depthWT_30arc_us.tif                                                        [depth to water table]\n├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]\n├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]\n├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]\n├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]\n├── pondingFre_30arc_us.tif                                                     [ponding frequency]\n├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]\n├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]\n├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]\n├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]\n├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]\n└── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]<\/p>\n\nSOC TIF files:<\/p>\n\n├──30cm SOC mean.tif                             [30 cm depth soil SOC]\n├──100cm SOC mean.tif                           [100 cm depth soil SOC]\n├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]\n└──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]<\/p>\n\nsite observations csv files:<\/p>\n\nISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data<\/p>\n\nISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data<\/p>\n\n\nData format<\/strong>:<\/p>\n\nGeospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.<\/p>\n\nGeospatial projection<\/strong>: <\/p>\n\nGEOGCS["GCS_WGS_1984",\n DATUM["D_WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["Degree",0.017453292519943295]]\n(base) [jbk@theseus ltar_regionalization]$ g.proj -w\nGEOGCS["wgs84",\n DATUM["WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["degree",0.0174532925199433]]\n<\/code>\n\n <\/p>"]} 

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5. Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

   https://doi.org/10.1029/2023JG007702  

   Wang, Zhuonan; Kumar, Jitendra; Weintraub‐Leff, Samantha R.; Todd‐Brown, Katherine; Mishra, Umakant; Sihi, Debjani (February 2024, Journal of Geophysical Research: Biogeosciences)

   Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R² = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R² = 0.23) and SoilGrids 2.0 (R² = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models. 

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