Geology topics

Zheng Shi

Publications and source records attributed to Zheng Shi.

3 recordsLinked to original sources

USGS2021

Beyond bulk: Density fractions explain heterogeneity in global soil carbon abundance and persistence

Understanding the controls on the amount and persistence of soil organic carbon (C) is essential for predicting its sensitivity to global change. The response may depend on whether C is unprotected, isolated within aggregates, or protected from decomposition by mineral associations. Here, we present a global synthesis of the relative influence of environmental factors on soil organic C partitioning among pools, abundance in each pool (mg C g −1 soil), and persistence (as approximated by radiocarbon abundance) in relatively unprotected particulate and protected mineral-bound pools. We show that C within particulate and mineral-associated pools consistently differed from one another in degree of persistence and relationship to environmental factors. Soil depth was the best predictor of C abundance and persistence, though it accounted for more variance in persistence. Persistence of all C pools decreased with increasing mean annual temperature (MAT) throughout the soil profile, whereas persistence increased with increasing wetness index (MAP/PET) in subsurface soils (30–176 cm). The relationship of C abundance (mg C g −1 soil) to climate varied among pools and with depth. Mineral-associated C in surface soils (<30 cm) increased more strongly with increasing wetness index than the free particulate C, but both pools showed attenuated responses to the wetness index at depth. Overall, these relationships suggest a strong influence of climate on soil C properties, and a potential loss of soil C from protected pools in areas with decreasing wetness. Relative persistence and abundance of C pools varied significantly among land cover types and soil parent material lithologies. This variability in each pool's relationship to environmental factors suggests that not all soil organic C is equally vulnerable to global change. Therefore, projections of future soil organic C based on patterns and responses of bulk soil organic C may be misleading.

Global Change Biology↗

USGS2020

An open source database for the synthesis of soil radiocarbon data: ISRaD version 1.0

Radiocarbon is a critical constraint on our estimates of the timescales of soil carbon cycling that can aid in identifying mechanisms of carbon stabilization and destabilization and improve the forecast of soil carbon response to management or environmental change. Despite the wealth of soil radiocarbon data that have been reported over the past 75 years, the ability to apply these data to global-scale questions is limited by our capacity to synthesize and compare measurements generated using a variety of methods. Here, we present the International Soil Radiocarbon Database (ISRaD; http://soilradiocarbon.org , last access: 16 December 2019), an open-source archive of soil data that include reported measurements from bulk soils, distinct soil carbon pools isolated in the laboratory by a variety of soil fractionation methods, samples of soil gas or water collected interstitially from within an intact soil profile, CO 2 gas isolated from laboratory soil incubations, and fluxes collected in situ from a soil profile. The core of ISRaD is a relational database structured around individual datasets (entries) and organized hierarchically to report soil radiocarbon data, measured at different physical and temporal scales as well as other soil or environmental properties that may also be measured and may assist with interpretation and context. Anyone may contribute their own data to the database by entering it into the ISRaD template and subjecting it to quality assurance protocols. ISRaD can be accessed through (1) a web-based interface, (2) an R package (ISRaD), or (3) direct access to code and data through the GitHub repository, which hosts both code and data. The design of ISRaD allows for participants to become directly involved in the management, design, and application of ISRaD data. The synthesized dataset is available in two forms: the original data as reported by the authors of the datasets and an enhanced dataset that includes ancillary geospatial data calculated within the ISRaD framework. ISRaD also provides data management tools in the ISRaD-R package that provide a starting point for data analysis; as an open-source project, the broader soil community is invited and encouraged to add data, tools, and ideas for improvement. As a whole, ISRaD provides resources to aid our evaluation of soil dynamics across a range of spatial and temporal scales. The ISRaD v1.0 dataset is archived and freely available at https://doi.org/10.5281/zenodo.2613911 (Lawrence et al., 2019).

Earth System Science Data↗

USGS2017

Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m −2 yr −1 ), most models produced higher NPP (309 ± 12 g C m −2 yr −1 ) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m −2 yr −1 ), which mainly resulted from differences in simulated maximum monthly GPP (GPP max ). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C ( V c max_25 ), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO 2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPP max as well as their sensitivity to climate change.

Journal of Geophysical Research: Biogeosciences↗