THE VERTICAL DISTRIBUTION OF SOIL ORGANIC CARBON AND ITS RELATION TO CLIMATE AND VEGETATION

2000 ◽  
Vol 10 (2) ◽  
pp. 423-436 ◽  
Author(s):  
Esteban G. Jobbágy ◽  
Robert B. Jackson
2014 ◽  
Vol 962-965 ◽  
pp. 1386-1391
Author(s):  
Li Li Huo ◽  
Xian Guo Lv ◽  
Da Song Lin

To investigate how reclamation of wetlands in three different soil types impacts the vertical distribution of soil organic carbon (SOC) content in soil profiles, contents and density of soil organic carbon (SOC) in soil profiles of three types of wetland soils in wetland, soybean and paddy field in Sanjiang Plain were determined. Both soybean and paddy field were reclaimed from wetland. We observed that reclamation significantly reduced SOC content in 0-10,10-20 and 20-30 cm soil layers in meadow albic soil and meadow marsh soil, and 0-10,10-20,20-30 and 30-40 cm soil layers in peat bog soil, there were no significant difference in SOC contents in soil layers under 0-30 or 0-40 cm before and after reclamation. After reclamation, SOC density in three types of wetland soils decreased, and SOC density in soybean field were higher than that in paddy field. Either in wetlands or farm lands in the three types of wetland soils, most of the SOC storage in 0-100 cm soil layer was stored in 0-50 cm soil layer. Though wetland reclamation reduced the SOC content, it hasn’t changed the regularity of SOC vertical distribution. The relationships between SOC content and soil depth in wetlands and farm lands all could be described by exponential functions in three types of soils. The specific functions are useful to estimate and predict the regional SOC pool by models.


2016 ◽  
Vol 66 (1) ◽  
Author(s):  
Carmen Rosa Montes ◽  
José Joaquin Ramos Miras ◽  
Ana María San José Wery

The vertical distribution of soil organic carbon (SOC), considered to be a key component of the carbon cycle, is still poorly understood in tropical highest mountain ecosystems such as the Andean paramo. The estimation of the SOC in the presence and absence of anthropic intervention, will help to define policies to mitigate CO2 emissions into the atmosphere from this ecosystem. The aim of this research was to determine soil organic carbon sequestration at three soil depths under two types of soil use in the paramo of Sumapaz, Colombia. The soil variations of pH, phosphorus, aluminum, bulk density, carbon sequestration, cation exchange capacity, texture and to estimate the vertical distribution of soil organic carbon SOC, were evaluated, respectively. Two sites were selected to establish the soil estimations according to soil use: natural vegetation cover and potato (Solanum tuberosum L.) crop. Samples were taken from 0-25, 25-50 and > 50 cm soil depths. Consequently, eight physical-chemical variables were analyzed in terms of the SOC sequestration estimated for each soil depth and soil. The averages for SOC under natural vegetation cover were: 188 tC.ha-1 to 25 cm, 183 tC.ha-1 to 50 cm, and 178 tC.ha-1 at soil depths below 50cm. For potato (Solanum tuberosum L.) crops, SOC sequestration were: 119 tC.ha-1 to 25 cm, 83 tC.ha-1 to 50 cm, and 71.8 tC.ha-1 at soil depths below 50cm. These results allow to support the soil management strategies that addressed to preserve SOC and regulate water level within the ecosystem of the Andean paramo.


Author(s):  
R. Sauzède ◽  
J. E. Johnson ◽  
H. Claustre ◽  
G. Camps-Valls ◽  
A. B. Ruescas

Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.


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