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Key Points The NEON sites were estimated to have large soil organic carbon (SOC) loss in both topsoil and subsoil during 1984–2014 The carbon sequestration potential is limited in well‐developed and near carbon‐saturated soils in managed ecosystems Runoff/erosion and leaching, vertical translocation, and mineral sorption are dominant factors affecting SOC variation at National Ecological Observatory Network sites 

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.

 

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]]

 

 

Soil nitrous oxide (N 2 O) emissions are an important driver of climate change and are a major mechanism of labile nitrogen (N) loss from terrestrial ecosystems. Evidence increasingly suggests that locations on the landscape that experience biogeochemical fluxes disproportionate to the surrounding matrix (hot spots) and time periods that show disproportionately high fluxes relative to the background (hot moments) strongly influence landscape-scale soil N 2 O emissions. However, substantial uncertainties remain regarding how to measure and model where and when these extreme soil N 2 O fluxes occur. High-frequency datasets of soil N 2 O fluxes are newly possible due to advancements in field-ready instrumentation that uses cavity ring-down spectroscopy (CRDS). Here, we outline the opportunities and challenges that are provided by the deployment of this field-based instrumentation and the collection of high-frequency soil N 2 O flux datasets. While there are substantial challenges associated with automated CRDS systems, there are also opportunities to utilize these near-continuous data to constrain our understanding of dynamics of the terrestrial N cycle across space and time. Finally, we propose future research directions exploring the influence of hot moments of N 2 O emissions on the N cycle, particularly considering the gaps surrounding how global change forces are likely to alter N dynamics in the future. 

Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial–temporal scales. Here, we introduce a continentally distributed, publicly available data set collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. First, we detail the sampling design and methods used to collect and analyze soil inorganic N pool and net flux rate data from 47 terrestrial sites. We address methodological challenges in generating a standardized data set, even for a network using uniform protocols. Then, we evaluate sources of variation within the sampling design and compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2). We observed wide spatiotemporal variation in inorganic N pool sizes and net transformation rates. Site explained the most variation in NEON’s stratified sampling design, followed by plots within sites. Organic horizons had larger pools and net N transformation rates than mineral horizons on a sample weight basis. The majority of sites showed some degree of seasonality in N dynamics, but overall these temporal patterns were not matched by CESM2, leading to poor correspondence between observed and modeled data. Looking forward, these data can reveal new insights into controls on soil N cycling, especially in the context of other environmental data sets provided by NEON, and should be leveraged to improve predictive modeling of the soil N cycle.

 

Abstract. Tropical ecosystems contribute significantly to global emissionsof methane (CH4), and landscape topography influences the rate ofCH4 emissions from wet tropical forest soils. However, extreme eventssuch as drought can alter normal topographic patterns of emissions. Here weexplain the dynamics of CH4 emissions during normal and droughtconditions across a catena in the Luquillo Experimental Forest, Puerto Rico.Valley soils served as the major source of CH4 emissions in a normalprecipitation year (2016), but drought recovery in 2015 resulted in dramaticpulses in CH4 emissions from all topographic positions. Geochemicalparameters including (i) dissolved organic carbon (C), acetate, and soil pH and (ii) hydrological parameters like soil moisture and oxygen (O2)concentrations varied across the catena. During the drought, soil moisturedecreased in the slope and ridge, and O2 concentrations increased in thevalley. We simulated the dynamics of CH4 emissions with theMicrobial Model for Methane Dynamics-Dual Arrhenius and Michaelis–Menten (M3D-DAMM), which couples a microbialfunctional group CH4 model with a diffusivity module for solute and gastransport within soil microsites. Contrasting patterns of soil moisture,O2, acetate, and associated changes in soil pH with topographyregulated simulated CH4 emissions, but emissions were also altered byrate-limited diffusion in soil microsites. Changes in simulated availablesubstrate for CH4 production (acetate, CO2, and H2) andoxidation (O2 and CH4) increased the predicted biomass ofmethanotrophs during the drought event and methanogens during droughtrecovery, which in turn affected net emissions of CH4. A variance-basedsensitivity analysis suggested that parameters related to aceticlasticmethanogenesis and methanotrophy were most critical to simulate net CH4emissions. This study enhanced the predictive capability for CH4emissions associated with complex topography and drought in wet tropicalforest soils.