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-12-299-2020

Cited by 221 publications

(154 citation statements)

References 32 publications

Supporting

Mentioning

140

Contrasting

Unclassified

Order By: Relevance

“…dataset of SC depth distribution parameters could be generated from broader datasets with global distributions, such as the WoSIS database (Batjes et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Sulman

Harden

et al. 2020

Front. Environ. Sci.

View full text Add to dashboard Cite

Soils contain a large and dynamic fraction of global terrestrial carbon stocks. The distribution of soil carbon (SC) with depth varies among ecosystems and land uses and is an important factor in calculating SC stocks and their vulnerabilities. Systematic analysis of SC depth distributions across databases of SC profiles has been challenging due to the heterogeneity of soil profile measurements, which vary in depth sampling. Here, we fit over 40,000 SC depth profiles to an exponential decline relationship with depth to determine SC concentration at the top of the mineral soil, minimum SC concentration at depth, and the characteristic "length" of SC concentration decline with depth. Fitting these parameters allowed profile characteristics to be analyzed across a large and heterogeneous dataset. We then assessed the differences in these depth parameters across soil orders and land cover types and between soil profiles with or without a history of tillage, as represented by the presence of an Ap horizon. We found that historically tilled soils had more gradual decreases of SC with depth (greater e-folding depth or Z *), deeper SC profiles, lower SC concentrations at the top of the mineral soil, and lower total SC stocks integrated to 30 cm. The large database of profiles allowed these results to be confirmed across different land cover types and spatial areas within the Continental United States, providing robust evidence for systematic impacts of historical tillage on SC stocks and depth distributions.

show abstract

“…dataset of SC depth distribution parameters could be generated from broader datasets with global distributions, such as the WoSIS database (Batjes et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Sulman

Harden

et al. 2020

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Extrapolation 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, 2020) 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. We used a global dataset of soil depth (Webb, 2000) as the maximum soil depth to which we extrapolated.…”

Section: Extrapolation Of Soil Datasetsmentioning

confidence: 99%

“…Terrestrial ecosystem carbon turnover time (τ ) is the average time that carbon atoms spend in terrestrial ecosystems from the initial photosynthetic fixation until respiratory or non-respiratory loss (Bolin and Rodhe, 1973;Barrett, 2002;Carvalhais et al, 2014). Ecosystem turnover time is an emergent property that represents the macro-scale turnover rate of terrestrial carbon that results from different processes such as plant mortality and soil decomposition.…”

Section: Introductionmentioning

confidence: 99%

Apparent ecosystem carbon turnover time: uncertainties and robust features

Fan

Koirala

Reichstein

et al. 2020

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083∘) 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 of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of 37-6+3 yr, which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets (0.2<r<0.5) when compared to other regions (0.6<r<0.8). These uncertainties cast a shadow on current global estimates of τ in circumpolar regions, for which further geographical representativeness and clarification on variations in Csoil with soil depth are needed. Different GPP estimates contribute significantly to the uncertainties of τ mainly in semiarid and arid regions, whereas Cveg causes the uncertainties of τ in the subtropics and tropics. In spite of the large uncertainties, our findings reveal that the latitudinal gradients of τ are consistent across different datasets and soil depths. The current results show a strong ensemble agreement on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30–60∘ N) than in the subtropical and tropical zones (30∘ S–30∘ N). Additionally, while the strength of the τ–precipitation correlation was dependent on the Csoil data source, the latitudinal gradients also agree among different ensemble members. Overall, and despite the large variation in τ, we identified robust features in the spatial patterns of τ that emerge beyond the differences stemming from the data-driven estimates of Csoil, Cveg and GPP. These robust patterns, and associated uncertainties, can be used to infer τ–climate relationships and for constraining contemporaneous behavior of Earth system models (ESMs), which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback. The dataset of τ is openly available at https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).

show abstract

“…Simulating soil organic C under a framework also facilitates consistent pre-processing, quality checks and explicit definition of the simulation unit. This is important because often, datasets have a different formats and resolutions, which must be standardised and harmonised before running the simulation (Batjes et al, 2020). Datasets may need to be aggregated or disaggregated over space and time, depending on the data and the need.…”

Section: Simulating Soil C Dynamics Under a Frameworkmentioning

confidence: 99%

Simulation of soil carbon dynamics in Australia under a framework that better connects spatially explicit data with Rᴏᴛʜ C

Lee

Rossel

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model (Rᴏᴛʜ C) under a framework that connects new spatially-explicit soil measurements and data with the model. Doing so helped to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. Under this framework, we compiled continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Rᴏᴛʜ C and run simulations to predict the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4,043 sites in cropping, modified grazing, native grazing, and natural environments across Australia. The Rᴏᴛʜ C model uses measured C fractions, the particulate, humus, and resistant organic C (POC, HOC and ROC, respectively) to represent the three main C pools in its structure. The model explained 97–98 % of the variation in measured total organic C in soils under cropping and grazing, and 65 % in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to predict the potential for C accumulation in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model predicted a potential soil C increase of 13.58 (interquartile range 12.19–15.80), 14.21 (12.38–16.03), and 15.57 (12.07–17.82) Mg C ha−1 under cropping, modified grazing and native grazing, and 3.52 (3.15–4.09) Mg C ha−1 under natural environments. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C:N ratio, and cropping were the most important controls on POC change. Clay content and climate were dominant controls on HOC change. Consistent and explicit soil organic C simulations improve confidence in the model's predictions, contributing to the development of sustainable soil management under global change.

show abstract

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

Cited by 221 publications

References 32 publications

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Land Use and Land Cover Affect the Depth Distribution of Soil Carbon: Insights From a Large Database of Soil Profiles

Apparent ecosystem carbon turnover time: uncertainties and robust features

Simulation of soil carbon dynamics in Australia under a framework that better connects spatially explicit data with Rᴏᴛʜ C

Contact Info

Product

Resources

About