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1. Dominant Edaphic Controls on Particulate Organic Carbon in Global Soils

   https://doi.org/10.1111/gcb.17619  

   Guo, Ziyu; Liu, Jianzhao; He, Liyuan; Rodrigues, Jorge_L Mazza; Chen, Ning; Zuo, Yunjiang; Wang, Nannan; Zhu, Xinhao; Sun, Ying; Zhang, Lihua; et al (December 2024, Global Change Biology)

   ABSTRACT The current soil carbon paradigm puts particulate organic carbon (POC) as one of the major components of soil organic carbon worldwide, highlighting its pivotal role in carbon mitigation. In this study, we compiled a global dataset of 3418 data points of POC concentration in soils and applied empirical modeling and machine learning algorithms to investigate the spatial variation in POC concentration and its controls. The global POC concentration in topsoil (0–30 cm) is estimated as 3.02 g C/kg dry soil, exhibiting a declining trend from polar regions to the equator. Boreal forests contain the highest POC concentration, averaging at 4.58 g C/kg dry soil, whereas savannas exhibit the lowest at 1.41 g C/kg dry soil. We developed a global map of soil POC density in soil profiles of 0‐30 cm and 0–100 cm with an empirical model. The global stock of POC is 158.15 Pg C for 0–30 cm and 222.75 Pg C for 0–100 cm soil profiles with a substantial spatial variation. Analysis with a machine learning algorithm concluded the predominate controls of edaphic factors (i.e., bulk density and soil C content) on POC concentration across biomes. However, the secondary controls vary among biomes, with solid climate controls in grassland, pasture, and shrubland, while strong vegetation controls in forests. The biome‐level estimates and maps of POC density provide a benchmark for modeling C fractions in soils; the various controls on POC suggest incorporating biological and physiochemical mechanisms in soil C models to assess and forecast the soil POC dynamics in response to global change. 

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2. 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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3. Dissolved Phosphorus Leaching Reflects the Dynamic Interaction Between Hydrology and Soil Phosphorus Kinetics

   https://doi.org/10.1002/hyp.70285  

   Mumbi, Rose_C K; Williams, Mark R; Ford, William I; Camberato, James J; Penn, Chad J (October 2025, Hydrological Processes)

   ABSTRACT Hydrologic processes and soil phosphorus adsorption/desorption kinetics affect subsurface nutrient transport; however, their interaction is not well understood. In this study, we investigated the effect of hydrologic variables including flow rate, preferential flow, soil–water contact time, rainfall intensity, and soil moisture on dissolved reactive phosphorus (DRP) leaching. Ten undisturbed soil columns (30 × 30 × 30 cm) were collected from an agricultural field in Indiana, USA. Seven rainfall simulations were conducted under varying rainfall intensity and soil moisture conditions to create an array of subsurface flow rates. Results showed that leachate flow rates, preferential flow, soil–water contact time, and DRP concentration varied substantially among soil columns and rainfall simulations, with both connectivity and soil adsorption/desorption kinetics controlling DRP transport. Leachate comprised of either > 90% or < 10% event water had the lowest DRP flow‐weighted mean concentration (FWMC; 0.12–0.85 mg L⁻¹). This suggests that minimal and maximum soil–water interaction yielded small DRP desorption from the surface soil and large DRP adsorption in subsoils, respectively. Leachate that was comprised of a mixture of water sources tended to have the greatest DRP FWMC (0.97–3.11 mg L⁻¹) resulting in a parabolic relationship between water source/soil contact time and DRP. Rainfall infiltration and interaction with surface soil promoted DRP desorption, with subsequent matrix‐derived preferential flow facilitating the transport of DRP‐rich water through the subsoil. Quantifying the connection between hydrology and phosphorus kinetics provides new insights into the impact of preferential flow on DRP leaching and is essential for predicting DRP transport and developing management practices for decreasing DRP loss. 

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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. Hurricanes fertilize mangrove forests in the Gulf of Mexico (Florida Everglades, USA)

   https://doi.org/10.1073/pnas.1908597117  

   Castañeda-Moya, Edward; Rivera-Monroy, Victor H.; Chambers, Randolph M.; Zhao, Xiaochen; Lamb-Wotton, Lukas; Gorsky, Adrianna; Gaiser, Evelyn E.; Troxler, Tiffany G.; Kominoski, John S.; Hiatt, Matthew (March 2020, Proceedings of the National Academy of Sciences)

   Hurricanes are recurring high-energy disturbances in coastal regions that change community structure and function of mangrove wetlands. However, most of the studies assessing hurricane impacts on mangroves have focused on negative effects without considering the positive influence of hurricane-induced sediment deposition and associated nutrient fertilization on mangrove productivity and resilience. Here, we quantified how Hurricane Irma influenced soil nutrient pools, vertical accretion, and plant phosphorus (P) uptake after its passage across the Florida Coastal Everglades in September 2017. Vertical accretion from Irma’s deposits was 6.7 to 14.4 times greater than the long-term (100 y) annual accretion rate (0.27 ± 0.04 cm y −1 ). Storm deposits extended up to 10-km inland from the Gulf of Mexico. Total P (TP) inputs were highest at the mouth of estuaries, with P concentration double that of underlying surface (top 10 cm) soils (0.19 ± 0.02 mg cm −3 ). This P deposition contributed 49 to 98% to the soil nutrient pool. As a result, all mangrove species showed a significant increase in litter foliar TP and soil porewater inorganic P concentrations in early 2018, 3 mo after Irma’s impact, thus underscoring the interspecies differences in nutrient uptake. Mean TP loading rates were five times greater in southwestern (94 ± 13 kg ha −1 d −1 ) mangrove-dominated estuaries compared to the southeastern region, highlighting the positive role of hurricanes as a natural fertilization mechanism influencing forest productivity. P-rich, mineral sediments deposited by hurricanes create legacies that facilitate rapid forest recovery, stimulation of peat soil development, and resilience to sea-level rise. 

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