skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Thursday, May 23 until 2:00 AM ET on Friday, May 24 due to maintenance. We apologize for the inconvenience.

Search for: All records

Creators/Authors contains: "Kumar, Jitendra"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. 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.

    more » « less
  2. 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]]


    more » « less
  3. Abstract. Large changes in the Arctic carbon balance are expectedas warming linked to climate change threatens to destabilize ancientpermafrost carbon stocks. The eddy covariance (EC) method is an establishedtechnique to quantify net losses and gains of carbon between the biosphereand atmosphere at high spatiotemporal resolution. Over the past decades, agrowing network of terrestrial EC tower sites has been established acrossthe Arctic, but a comprehensive assessment of the network'srepresentativeness within the heterogeneous Arctic region is still lacking.This creates additional uncertainties when integrating flux data acrosssites, for example when upscaling fluxes to constrain pan-Arctic carbonbudgets and changes therein. This study provides an inventory of Arctic (here > = 60∘ N)EC sites, which has also been made available online(, last access: 25 January 2022). Our database currentlycomprises 120 EC sites, but only 83 are listed as active, and just 25 ofthese active sites remain operational throughout the winter. To map therepresentativeness of this EC network, we evaluated the similarity betweenenvironmental conditions observed at the tower locations and those withinthe larger Arctic study domain based on 18 bioclimatic and edaphicvariables. This allows us to assess a general level of similarity betweenecosystem conditions within the domain, while not necessarily reflectingchanges in greenhouse gas flux rates directly. We define two metrics basedon this representativeness score: one that measures whether a location isrepresented by an EC tower with similar characteristics (ER1) and a secondfor which we assess if a minimum level of representation for statisticallyrigorous extrapolation is met (ER4). We find that while half of the domainis represented by at least one tower, only a third has enough towers insimilar locations to allow reliable extrapolation. When we consider methanemeasurements or year-round (including wintertime) measurements, the valuesdrop to about 1/5 and 1/10 of the domain, respectively. With themajority of sites located in Fennoscandia and Alaska, these regions wereassigned the highest level of network representativeness, while large partsof Siberia and patches of Canada were classified as underrepresented.Across the Arctic, mountainous regions were particularly poorly representedby the current EC observation network. We tested three different strategies to identify new site locations orupgrades of existing sites that optimally enhance the representativeness ofthe current EC network. While 15 new sites can improve therepresentativeness of the pan-Arctic network by 20 %, upgrading as fewas 10 existing sites to capture methane fluxes or remain active duringwintertime can improve their respective ER1 network coverage by 28 % to 33 %. This targeted network improvement could be shown to be clearlysuperior to an unguided selection of new sites, therefore leading tosubstantial improvements in network coverage based on relatively smallinvestments. 
    more » « less
  4. Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, e.g., by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policy making or implementation. As a result, there is a critical mismatch between the point- and tree- scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policy making. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach. 
    more » « less
  5. null (Ed.)
  6. Abstract

    Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2and CH4fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO2and CH4fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low‐centered polygon (LCP) center, LCP transition, LCP rim, high‐centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM‐Microbe model against static‐chamber measured CO2and CH4fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low‐elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4emissions rates with greater seasonal variations than high‐elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2 + H2) is the most important factor determining CH4emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH4fluxes. The model underestimated the EC‐measured CH4flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4flux. The strong microtopographic impacts on CO2and CH4fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.

    more » « less