A review on the global soil datasets for earth system modeling

Dai, Yongjiu; Shangguan, Wei; Wang, Dagang; Wei, Nan; Xin, Qinchuan; Yuan, Hua; Zhang, Shupeng; Liu, Shaofeng; Yan, Fapeng

doi:10.5194/soil-2018-32

Cited by 4 publications

(7 citation statements)

References 48 publications

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“…In addition, the SoilGrids provides higher SOM contents than the GSDE over northern middle and high latitudes (Figures S1e and S3e), which lead to higher values of saturated soil water content and lower values of thermal conductivities of dry soils as well as volumetric heat capacities of soil solids (Figures S5b, S14a, and S14d). Dai, Shangguan, et al () evaluated several global soil composition data sets including the GSDE and SoilGrids with the soil profile database WoSIS (Batjes et al, ), and found that SoilGrids and GSDE ranked first and second in terms of the accuracy. However, this evaluation had some limitations because quite a number of WoSIS soil profiles were considered in the compilation of the GSDE and SoilGrids, indicating that the two data sets were not independently validated.…”

Section: Resultsmentioning

confidence: 99%

A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Dai

Xin

Wei

et al. 2019

J Adv Model Earth Syst

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131

107

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Modeling land surface processes requires complete and reliable soil property information to understand soil hydraulic and heat dynamics and related processes, but currently, there is no data set of soil hydraulic and thermal parameters that can meet this demand for global use. In this study, we propose a fitting approach to obtain the optimal soil water retention parameters from ensemble pedotransfer functions (PTFs), which are evaluated using the global coverage National Cooperative Soil Survey Characterization Database and show better performance for global applications than our original ensemble estimations (median values of PTFs) as done in Dai et al. (2013, https://doi.org/10.1175/JHM-D-12-0149.

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Section: Resultsmentioning

confidence: 99%

A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Dai

Xin

Wei

et al. 2019

J Adv Model Earth Syst

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131

107

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“…Several studies have calibrated and validated microbial decomposition models (Robertson et al, ; Wieder et al, , ) using globally gridded soil databases such as the Harmonized Word Soils Database (HWSD, FAO/IIASA/ISRIC/ISSCAS/JRC, ) and the Northern Circumpolar Soil Carbon Database (NCSDC, Tarnocai et al, ). However, these global databases do not contain uncertainty estimates (Dai et al, ), and previous studies have identified significant differences between SOC estimates from these databases or between grid‐scale estimates from these databases and point‐scale in situ observations (Tifafi, Guenet, & Hatté, ; Figure S1). In addition, there is still no reliable globally gridded database of plant litter input.…”

Section: Introductionmentioning

confidence: 99%

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

Zhang

Goll

Wang

et al. 2020

Global Change Biology

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First‐order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial‐MIneral Carbon Stabilization model), which we compared to the widely used first‐order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.

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“…The access to spatial explicit, consistent and reliable soil data is essential to model and map the status of soil resources globally at increasing detailed resolution in order to respond and assess world global issues (Arrouays et al, 2014;FAO, 2015;Hengl et al, 2014;Omuto et al, 2013). Furthermore, soil datasets are also one of the most important inputs for Earth System Models (ESM), to address, for example, the importance of terrestrials sinks and sources for greenhouse gases (Dai et al, 2018;Luo et al, 2016). At the same time, soils in ESM are one of the largest sources of uncertainty (Dai et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, soil datasets are also one of the most important inputs for Earth System Models (ESM), to address, for example, the importance of terrestrials sinks and sources for greenhouse gases (Dai et al, 2018;Luo et al, 2016). At the same time, soils in ESM are one of the largest sources of uncertainty (Dai et al, 2018). This is why in recent years there has being a growing effort to improve access and quality of soil datasets, being one of the key goals of pillar 4 of the global soil partnership sponsored by the Food and Agriculture Organization of the United Nations (Batjes et al, 2017;Omuto et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

CHLSOC: The Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

Pfeiffer¹,

Padarian²,

Osorio³

et al. 2019

Preprint

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Abstract. One of the critical aspects in modelling soil organic carbon (SOC) predictions is the lack of access to soil information which is usually concentrated in regions of high agricultural interest. In Chile, most soil and SOC data to date is highly concentrated in 25 % of the territory that has intensive agricultural or forestry use. Vast areas beyond those forms of land use have few or no soil data available. Here, we present a new database of SOC for the country, which is the result of an unprecedented national effort under the frame of the Global Soil Partnership that help to build the largest database on SOC to date in Chile named “CHLSOC" comprising 13,612 data points. This dataset is the product of the compilation from numerous sources including unpublished and difficult to access data, allowing to fill numerous spatial gaps where no SOC estimates were publicly available before. The values of SOC compiled in CHLSOC range from 6×10−5 to 83.3 percent, reflecting the variety of ecosystems that exists in Chile. Profiting from the richness of geochemical, topographic and climatic variability in Chile, the dataset has the potential to inform and test models trying to predict SOC stocks and dynamics at larger spatial scales. Dataset available at https://www.doi.org/10.17605/OSF.IO/NMYS3 (Pfeiffer et al., 2019b).

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A review on the global soil datasets for earth system modeling

Cited by 4 publications

References 48 publications

A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

CHLSOC: The Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

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