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1. 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)

   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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2. 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)

   Data Description:

   To 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.

   Files in collection (32):

   Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts

   gNATSGO TIF files:

   ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]
   ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]
   ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]
   ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]
   ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]
   ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]
   ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]
   ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]
   ├── depthWT_30arc_us.tif                                                        [depth to water table]
   ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]
   ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]
   ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]
   ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]
   ├── pondingFre_30arc_us.tif                                                     [ponding frequency]
   ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]
   ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]
   ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]
   ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]
   ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]
   └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]

   SOC TIF files:

   ├──30cm SOC mean.tif                             [30 cm depth soil SOC]
   ├──100cm SOC mean.tif                           [100 cm depth soil SOC]
   ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]
   └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]

   site observations csv files:

   ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

   ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

   
   Data format:

   Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.

   Geospatial projection: 

   GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]]

    

    

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3. Changes in Soil Chemistry After 17 Years in Urban and Rural Forest Patches

   https://doi.org/10.3389/fevo.2022.786809  

   Yesilonis, Ian ; Giorgio, Vincent ; Hu, Yinhong ; Pouyat, Richard ; Szlavecz, Katalin ( February 2022 , Frontiers in Ecology and Evolution)

   Cycling of carbon (C), nitrogen (N), calcium (Ca), phosphorus (P), and sulfur (S) is an important ecosystem service that forest soils provide. Humans influence these biogeochemical processes through the deposition of atmospheric pollutants and site disturbances. One way to study these potential anthropogenic trajectories is through long-term monitoring in association with human-caused environmental gradients such as urban-rural gradients. The objective of this study was to characterize changes in surface soil chemistry of urban, suburban and rural forest patches in the Baltimore Metropolitan area. Soil composite samples (0–10 cm) were analyzed for macro- and micronutrients, pH, and C. A total of 12 sites in forest patches dominated by white oak ( Quercus alba ) and tulip poplar ( Liriodendron tulipifera ) were established in 2001, and resampled in 2018. We hypothesized that after almost two decades (1) concentrations of N, Ca, and P, as well as soil pH would be higher, especially in urban forest patches due to local deposition; (2) S levels would be lower due to decreased regional atmospheric deposition and; (3) total soil C would increase overall, but the rate of increase would be higher in the urban end of the gradient due to increased NPP. Overall, means of Ca concentration, pH, and C:N ratios significantly changed from 2001 to 2018. Calcium increased by 35% from 622 to 844 mg kg –1 , pH increased from 4.1 to 4.5, and C:N ratios decreased from 17.8 to 16.7. Along the gradient, Ca, N, P, and S were statistically significant with Ca concentration higher in the urban sites; S and N higher in the suburban sites; and P lower in the urban sites. Confounding factors, such as different geologic parent material may have affected these results. However, despite the unique site conditions, patterns of surface soil chemistry in space and time implies that local and regional factors jointly affect soil development in these forest patches. The increase in pH and Ca is especially notable because other long-term studies demonstrated changes in the opposite direction. 

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4. 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 ( February 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⁻¹d⁻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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5. The effect of repeated hurricanes on the age of organic carbon in humid tropical forest soil

   https://doi.org/10.1111/gcb.17265  

   Mayer, Allegra C ; McFarlane, Karis J ; Silver, Whendee L ( April 2024 , Global Change Biology)

   Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30‐year period that spanned repeated hurricanes. We used a nonlinear matrix model of soil C pools and fluxes (“soilR”) and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times of soil C in 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the 30‐year period. Between 1988 and 2018, the occluded C stock increased and δ¹³C in all pools decreased, while changes in particulate and mineral‐associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris‐deposited C, and possible microbial scavenging of old and young C in the particulate and mineral‐associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that the increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, making soil C more sensitive to future tropical forest stressors.

    

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