Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019)

Batjes, N.H.; Ribeiro, Eloi; Oostrum, Ad van

doi:10.5194/essd-2019-164

Cited by 17 publications

(21 citation statements)

References 9 publications

(11 reference statements)

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“…For example, SOC stocks in northern ecosystems and wetlands are very large, but exhibit tremendous spatial heterogeneity and thus challenge our ability to estimate their contributions to global SOC stocks (Hugelius et al 2013, Hengl et al 2017, Jackson et al 2017, Malhotra et al 2019). Soil sampling efforts in non‐temperate regions (e.g., northern latitudes, the tropics, northern Africa) and central Asia have lagged behind those in other areas (Batjes et al 2020). Worldwide, limited deep soil sampling, which most investigators consider to be depths greater than 30 cm (Richter and Markewitz 1995), due to accessibility challenges (Richter and Markewitz 1995, Jobbagy and Jackson 2000) limits our understanding of deep, lateral, SOC heterogeneity.…”

Section: Expanding the Global Reach And Depth Of Standardized Soc Datmentioning

confidence: 99%

“…Briefly, the following are examples of best uses of the aforementioned networks. ISRIC has the largest global database (containing 150,000+ soil cores) and is best suited to questions of global variation in carbon stocks (Batjes et al 2020). ISCN, ISRaD, and SoDaH, on the other hand, also describe soil C stocks, but may be more useful for mechanistic questions as they contain information on other soil attributes such as pH, radiocarbon signatures and soil fractions, among other features; SoDaH also includes time‐series data.…”

Section: Research Network and Data Compilations Are Powerful Means Omentioning

confidence: 99%

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Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Billings

Lajtha

Malhotra

et al. 2021

Ecological Applications

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Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. We advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.

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Section: Expanding the Global Reach And Depth Of Standardized Soc Datmentioning

confidence: 99%

Section: Research Network and Data Compilations Are Powerful Means Omentioning

confidence: 99%

Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Billings

Lajtha

Malhotra

et al. 2021

Ecological Applications

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“…The dataset provides soil carbon stocks at soil depths of 0-30 cm, 30-100 cm and 100-200 cm and at a spatial resolution of 10 km. c. Harmonized World Soil Database (HWSD) was also used in this study which utilized over 16000 standardized soilmapping units worldwide which are harmonized into a global soil dataset (Batjes et al, 2016). HWSD is a 30 arc-second raster database that provides soil properties including organic carbon, PH, water storage capacity, soil depth at topsoil (0-30 cm) and subsoil (30-100 cm).…”

Section: Soil Organic Carbon Datasetsmentioning

confidence: 99%

“…Extrapolation, which involves using empirical numerical models, may cause arbitrary bias and higher uncertainty if the models are not appropriately chosen. Here we used the in-situ observational data from the World Soil Information Service (WOSIS) (Batjes et al, 2019) and the International Soil Carbon Network (ISCN) (Nave et al, 2017) to select the ensemble of the models that could best simulate soil carbon stocks at full depth. The approach (i) fit of each empirical model against cumulative Csoil with all data points up to 2m; then (ii) predicted the cumulative Csoil at full depth for each soil profile independently.…”

Section: Extrapolation Of Soil Datasetsmentioning

confidence: 99%

“…There are not only large differences between simulations and observation-based estimates of τ, studies also show differences in the current observation-based estimates themselves. Specifically, estimates of global total carbon stock are characterized by large uncertainties as different in-situ measurements and methods were used to derive total carbon stocks (Batjes, 2016;Hengl et al, 2017;Sanderman et al, 2017). Alongside recent soil carbon datasets (Tifafi et al, 2018), there are also several different global vegetation biomass (Thurner et al, 2014;Avitabile et al, 2016;Saatchi et al, 2017;Santoro et al, 2018) and GPP (Gross Primary Production) (Jung et al, 2017) products which may lead to substantial differences in the global τ distribution and its relationship with climate.…”

Section: Introductionmentioning

confidence: 99%

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Apparent ecosystem carbon turnover time: uncertainties and robust features

Fan¹,

Koirala²,

Reichstein³

et al. 2020

Preprint

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Abstract. The turnover time of terrestrial carbon (τ) controls the global carbon cycle – climate feedback and, yet, is poorly simulated by the current Earth System Models (ESMs). In this study, by assessing apparent carbon turnover time as the ratio between carbon stocks and fluxes, we provide a new, updated ensemble of diagnostic terrestrial carbon turnover times and associated uncertainties on a global scale using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble, we estimated the global average τ to be 42−5+9 years when the full soil depth is considered, longer than the previous estimates of 23−4+7 years. Only considering the top 1 m (assuming maximum active layer depth is up to 1 meter) of soil carbon in circumpolar regions yields a global τ of 35−4+9 years. Csoil in circumpolar regions account for two thirds of the total uncertainty in global τ estimates, whereas Csoil in non-circumpolar contributes merely 9.38 %. GPP (2.25 %) and Cveg (0.05 %) contribute even less to the total uncertainty. Therefore, the high uncertainty in Csoil is the main factor behind the uncertainty in global τ, as reflected in the larger range of full-depth Csoil (3152–4372 PgC). The uncertainty is especially high in circumpolar regions with a uncertainty of 50 % and the spatial correlations among different datasets are also low compared to other regions. Overall, we argue that current global datasets do not support robust estimates of τ globally, for which we need clarification on variations of Csoil with soil depth and stronger estimates of Csoil in circumpolar regions. Despite the large variation in both magnitude and spatial patterns of τ, we identified robust features in the spatial patterns of τ that emerge regardless of soil depth and differences in data sources of Csoil, Cveg and GPP. Our findings show that the latitudinal gradients of τ are consistent across different datasets and soil depth. Furthermore, there is a strong consensus on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30º N–60º N) than in subtropical and tropical zones (30º S–30º N). The identified robust patterns can be used to infer the response of τ to climate and for constraining contemporaneous behaviour of ESMs which could contribute to uncertainty reductions in future projections of the carbon cycle – climate feedback. The dataset of the terrestrial turnover time ensemble (DOI: 10.17871/bgitau.201911) is openly available from the data portal: https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).

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Computational Infrastructure of SoilGrids 2.0

Sousa¹,

Poggio²,

Dawes³

et al. 2020

IFIP Advances in Information and Communication Technology

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SoilGrids maps soil properties for the entire globe at medium spatial resolution (250 metres cell side) using state-of-the-art machine learning methods. The expanding pool of input data and the increasing computational demands of predictive models required a prediction framework that could deal with large data. This article describes the mechanisms set in place for a geo-spatially parallelised prediction system for soil properties. The features provided by GRASS GIS -mapset and region -are used to limit predictions to a specific geographic area, enabling parallelisation. The Slurm job scheduler is used to deploy predictions in a high-performance computing cluster. The framework presented can be seamlessly applied to most other geo-spatial process requiring parallelisation. This framework can also be employed with a different job scheduler, GRASS GIS being the main requirement and engine.

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Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019)

Cited by 17 publications

References 9 publications

Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Soil organic carbon is not just for soil scientists: measurement recommendations for diverse practitioners

Apparent ecosystem carbon turnover time: uncertainties and robust features

Computational Infrastructure of SoilGrids 2.0

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