An analytic representation of the total soil carbon trajectory implied by the general mathematical framework of most soil carbon models

2021 ◽  
Author(s):  
Alexandre Stehlick ◽  
Ana Elisa Barioni ◽  
Paulino Ribeiro ◽  
Luís Gustavo Barioni
Keyword(s):  
Data Assimilation ◽  
Soil Carbon ◽  
Compartmental Models ◽  
Total Carbon ◽  
Model Parameters ◽  
Mathematical Framework ◽  
Decomposition Rates ◽  
Soil C ◽  
Forcing Function ◽  
Total Soil

<p>Most models of soil C dynamics can be expressed by the differential vector equation:</p> <p><strong>dC(t)/dt = f(t).K.C(t) + b(t)</strong></p> <p>where each element of the vector C(t) represents a carbon compartment with intrinsic decomposition rate (usually fast, slow and passive); K is the transition matrix between the compartments (decomposition rates and decomposition partitioning); the scalar function f(t) is a forcing function of the decomposition rates modifiers (e.g. soil moisture and temperature); and b(t) is the vector with rates of external C inputs for each compartment. Considering the case where only total soil carbon is measured, only the sum of C in all compartments can be used for model evaluation, calibration and data assimilation. Also, in most compartmental models there are too many parameters to be adjusted, leading to identifiability problems. Although some parameters can be constrained according to the model’s assumptions, identifiability is still problematic except for the simplest compartmental models. By working on the differential equation, it is possible to deduce an explicit representation of the total carbon trajectory, in a way that the number of necessary empirical parameters is reduced, without loss of generality or need of further assumptions. In this work we propose such a representation for the total carbon trajectory whose generality embraces implicitly the mechanism of models as Century, RothC and CQESTR. The solution requires less parameters than the original models do but still allows mapping the original model parameters and decomposition modifiers functions onto the solution. Additionally, we show how the main processes of decomposition of soil organic matter can be represented by the terms of the solution found. Finally, we present the solution behavior under extreme conditions of temperature, humidity and initial stocks. We expect our general framework to help improving model’s calibration and data assimilation procedures.</p>

Carbon storage in cotton soils of northern New South Wales

Soil Research ◽  
2003 ◽  
Vol 41 (5) ◽  
pp. 889 ◽  
Author(s):  
T. A. Knowles ◽  
B. Singh
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Soil Carbon ◽  
Inorganic Carbon ◽  
Carbon Pool ◽  
Total Carbon ◽  
Soil Types ◽  
Soil Carbon Pool ◽  
Total Soil

Soil carbon is an important component of the global carbon cycle with an estimated pool of soil organic carbon of about 1500 Gt. There are few estimates of the pool of inorganic carbon, but it is thought to be approximately 50% of the organic carbon pool. There is no detailed study on the estimation of the soil carbon pool for Australian soils.In order to quantify the carbon pools and to determine the extent of spatial variability in the organic and inorganic carbon pools, 120 soil cores were taken down to a depth of 0.90 m from a typical cotton field in northern NSW. Three cores were also taken from nearby virgin bushland and these samples were used as paired samples. Each soil core was separated into 4 samples, i.e. 0–0.15, 0.15–0.30, 0.30–0.60, and 0.60–0.90 m. Soil organic carbon was determined by wet oxidation and inorganic carbon content was determined using the difference between total carbon and organic carbon, and confirmed by the acid dissolution method. Total carbon was measured using a LECO CHN analyser. Soil organic carbon of the field constituted 62% (0–0.15 m), 58% (0.15–0.30 m), 60% (0.30–0.60 m), and 67% (0.60–0.90 m) of the total soil carbon. The proportion of inorganic carbon in total carbon is higher than the global average of 32%. Organic carbon content was relatively higher in the deeper layers (>0.30�m) of the studied soils (Vertosols) compared with other soil types of Australia. The carbon content varied across the field, however, there was little correlation between the soil types (grey, red, or intergrade colour) and carbon content. The total soil carbon pool of the studied field was estimated to be about 78 t/ha for 0–0.90 m layer, which was approximately 58% of the total soil carbon in the soil under nearby remnant bushland (136 t/ha). The total pool of carbon in the cotton soils of NSW was estimated to be 44.8 Mt C, where organic carbon and inorganic carbon constitute 34.9 Mt C and 9.9 Mt C, respectively. Based on the results of a limited number of paired sites under remnant vegetation, it was estimated that about 18.9 Mt of C has been lost from Vertosols by cotton cropping in NSW. With more sustainable management practices such as conservation tillage and green manuring, some of the lost carbon can be resequestered, which will help to mitigate the greenhouse effect, improve soil quality and may increase crop yield.

Carbon and nitrogen in the silt-size fraction and its HCl-hydrolysis residues from coarse-textured Canadian boreal forest soils

2014 ◽  
Vol 94 (2) ◽  
pp. 157-168 ◽  
Author(s):  
Caroline M. Preston ◽  
Charlotte E. Norris ◽  
Guy M. Bernard ◽  
David W. Beilman ◽  
Sylvie A. Quideau ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Soil Carbon ◽  
Forest Soils ◽  
Mineral Soil ◽  
Size Fraction ◽  
Soil C ◽  
Carbon And Nitrogen ◽  
Total Soil ◽  
C Stocks ◽  
Canadian Boreal Forest

Preston, C. M., Norris, C. E., Bernard, G. M., Beilman, D. W., Quideau, S. A. and Wasylishen, R. E. 2014. Carbon and nitrogen in the silt-size fraction and its HCl-hydrolysis residues from coarse-textured Canadian boreal forest soils. Can. J. Soil Sci. 94: 157–168. Improving the capacity to predict changes in soil carbon (C) stocks in the Canadian boreal forest requires better information on the characteristics and age of soil carbon, especially more slowly cycling C in mineral soil. We characterized C in the silt-size fraction, as representative of C stabilized by mineral association, previously isolated in a study of soil profiles of four sandy boreal jack pine sites. Silt-size fraction accounted for 13–31% of the total soil C and 12–51% of the total soil N content. Solid-state 13C nuclear magnetic resonance spectroscopy showed that silt C was mostly dominated by alkyl and O,N-alkyl C, with low proportions of aryl C in most samples. Thus, despite the importance of fire in this region, there was little evidence of storage of pyrogenic C. We used HCl hydrolysis to isolate the oldest C within the silt-size fraction. Consistent with previous studies, this procedure removed 21–74% of C and 74–93% of N, leaving residues composed mainly of alkyl and aryl C. However, it failed to isolate consistently old C; 11 out of 16 samples had recent 14C ages (fraction of modern 14C > 1), although C-horizon samples were older, with Δ14C from –17 to –476‰. Our results indicate relatively young ages for C associated with the silt-size fractions in these sites, for which mineral soil C storage may be primarily limited by good drainage and coarse soil texture, exacerbated by losses due to periodic wildfire.

Estimates of soil organic carbon stocks in central Canada using three different approaches

2002 ◽  
Vol 32 (5) ◽  
pp. 805-812 ◽  
Author(s):  
J S Bhatti ◽  
M J Apps ◽  
C Tarnocai
Keyword(s):  
Surface Layer ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Carbon ◽  
Soil Depth ◽  
Peat Soil ◽  
Regression Line ◽  
Organic C ◽  
Soil C ◽  
Total Soil

This study compared three estimates of carbon (C) contained both in the surface layer (0–30 cm) and the total soil pools at polygon and regional scales and the spatial distribution in the three prairie provinces of western Canada (Alberta, Saskatchewan, and Manitoba). The soil C estimates were based on data from (i) analysis of pedon data from both the Boreal Forest Transect Case Study (BFTCS) area and from a national-scale soil profile database; (ii) the Canadian Soil Organic Carbon Database (CSOCD), which uses expert estimation based on soil characteristics; and (iii) model simulations with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS2). At the polygon scale, good agreement was found between the CSOCD and pedon (the first method) total soil carbon values. Slightly higher total soil carbon values obtained from BFTCS averaged pedon data (the first method), as indicated by the slope of the regression line, may be related to micro- and meso-scale geomorphic and microclimate influences that are not accounted for in the CSOCD. Regional estimates of organic C from these three approaches for upland forest soils ranged from 1.4 to 7.7 kg C·m–2 for the surface layer and 6.2 to 27.4 kg C·m–2 for the total soil. In general, the CBM-CFS2 simulated higher soil C content compared with the field observed and CSOCD soil C estimates, but showed similar patterns in the total soil C content for the different regions. The higher soil C content simulated with CBM-CFS2 arises in part because the modelled results include forest floor detritus pool components (such as coarse woody debris, which account for 4–12% of the total soil pool in the region) that are not included in the other estimates. The comparison between the simulated values (the third method) and the values obtained from the two empirical approaches (the first two methods) provided an independent test of CBM-CFS2 soil simulations for upland forests soils. The CSOCD yielded significantly higher C content for peatland soils than for upland soils, ranging from 14.6 to 28 kg C·m–2 for the surface layer and 60 to 181 kg C·m–2 for the total peat soil depth. All three approaches indicated higher soil carbon content in the boreal zone than in other regions (subarctic, grassland).

A physics perspective on lidar data assimilation for mobile robots

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Yann Berquin ◽  
Andreas Zell
Keyword(s):  
Data Assimilation ◽  
Mean Free Path ◽  
Forward Model ◽  
Model Parameters ◽  
Lidar Data ◽  
Mathematical Framework ◽  
Model Parameter ◽  
Mapping Algorithm ◽  
Inference Algorithms ◽  
Coarse Model

Abstract This paper presents a new algorithm for lidar data assimilation relying on a new forward model. Current mapping algorithms suffer from multiple shortcomings, which can be related to the lack of clear forward model. In order to address these issues, we provide a mathematical framework where we show how the use of coarse model parameters results in a new data assimilation problem. Understanding this new problem proves essential to derive sound inference algorithms. We introduce a model parameter specifically tailored for lidar data assimilation, which closely relates to the local mean free path. Using this new model parameter, we derive its associated forward model and we provide the resulting mapping algorithm. We further discuss how our proposed algorithm relates to usual occupancy grid mapping. Finally, we present an example with real lidar measurements.

scholarly journals Source and Accumulation of Soil Carbon along Catena Toposequences over 12,000 Years in Three Semi-Natural Miscanthus sinensis Grasslands in Japan

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
David S. Howlett ◽  
J. Ryan Stewart ◽  
Jun Inoue ◽  
Masanori Saito ◽  
DoKyoung Lee ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Accumulation Rate ◽  
Miscanthus Sinensis ◽  
C4 Plant ◽  
Soil C ◽  
Radiocarbon Age ◽  
Total Soil ◽  
Natural Grasslands ◽  
The Difference

Miscanthus-dominated semi-natural grasslands in Japan appear to store considerable amounts of soil C. To estimate the long-term effect of Miscanthus vegetation on the accumulation of soil carbon by soil biota degradation in its native range, we measured total soil C from the surface to a 1.2 m depth along a catena toposequence in three annually burned grasslands in Japan: Kawatabi, Soni, and Aso. Soil C stock was estimated using a radiocarbon age and depth model, resulting in a net soil C accumulation rate in the soil. C4-plant contribution to soil C accumulation was further estimated by δ13C of soil C. The range of total soil C varied among the sites (i.e., Kawatabi: 379–638 Mg, Soni: 249–484, and Aso: 372–408 Mg C ha−1). Catena position was a significant factor at Kawatabi and Soni, where the toe slope soil C accumulation exceeded that of the summit. The soil C accumulation rate of the whole horizon in the grasslands, derived C mainly from C4 plant species, was 0.05 ± 0.02 (Average ± SE), 0.04 ± 0.00, and 0.24 ± 0.04 Mg C ha−1 yr−1 in Kawatabi, Soni, and Aso, respectively. Potential exists for long-term sequestration of C under M. sinensis, but the difference in the C accumulation rate can be influenced by the catena position and the amount of vegetation.

Soil carbon fractions based on their degree of oxidation, and the development of a carbon management index for agricultural systems

1995 ◽  
Vol 46 (7) ◽  
pp. 1459 ◽  
Author(s):  
GJ Blair ◽  
RDB Lefroy ◽  
L Lisle
Keyword(s):  
Soil Carbon ◽  
Population Pressure ◽  
Total Carbon ◽  
Agricultural Systems ◽  
Soil C ◽  
Carbon Management ◽  
Paired Samples ◽  
Labile C ◽  
Carbon Management Index ◽  
Fraction I

Increasing population pressure is increasing the demand on agricultural systems in many parts of the world and this has often led to the degradation of the soil resource. Soil carbon (C) is a major determinant of sustainability of agricultural systems and changes can occur in both total and active, or labile, C pools. A procedure is presented to determine the degree of lability of soil C. By treating a ground sample of soil with 333 mM potassium permanganate (KMnO4) to oxidize a proportion of the carbon and by determining the total carbon by combustion, two fractions of C can be measured. These fractions represent carbon of different lability, with fraction I representing the Labile C (CL), which is oxidized by 333 mM KMnO4, and fraction I1 representing the non-labile C (CNL), which is not oxidized by 333 mM KMnO4. On the basis of changes in total carbon (CT), a Carbon Pool Index (CPI) is calculated and, on the basis of changes in the proportion of labile C in the soil between a reference site and those subjected to agricultural practice or research treatments, a Lability Index (LI) is determined. These two indices are used to calculate a Carbon Management Index (CMI), with CMI = C Pool Index (CPI) xLability Index (LI) x 100. Analyses of paired samples (cropped and uncropped) from three sites in northern and central New South Wales, Australia, have shown a decline in CPI, a greater decline in LI and hence a decline in the CMI with cropping. Introduction of a legume into a wheat cropping system restored the CMI from 22 to 37 at the Warialda site. Analyses of paired samples from a sugarcane area in north Queensland have shown a decline in CMI in systems dominated by trash burning, but an increase in CMI in systems dominated by green cane trash management. Similar data from Brazil showed no increase in CT with mulching but a 48% increase in CMI due to an increase in the lability of C in the soil. The fractionation procedure and CMI outlined can be used to determine the state and rate of change in soil C of agricultural and natural systems.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 309
Author(s):  
Elena A. Mikhailova ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Mark A. Schlautman ◽  
Gregory C. Post ◽  
...  
Keyword(s):  
South Carolina ◽  
Ecosystem Services ◽  
Soil Carbon ◽  
Soil Depth ◽  
State Level ◽  
The United States ◽  
The State ◽  
Soil C ◽  
Low Country ◽  
Pee Dee

Sustainable management of soil carbon (C) at the state level requires valuation of soil C regulating ecosystem services (ES) and disservices (ED). The objective of this study was to assess the value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order, soil depth (0–200 cm), region and county using information from the State Soil Geographic (STATSGO) database. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.36B (i.e., $124.36 billion U.S. dollars, where B = billion = 109), $107.14B for SOC, and $17.22B for SIC. Soil orders with the highest midpoint value for SOC were: Ultisols ($64.35B), Histosols ($11.22B), and Inceptisols ($10.31B). Soil orders with the highest midpoint value for SIC were: Inceptisols ($5.91B), Entisols ($5.53B), and Alfisols ($5.0B). Soil orders with the highest midpoint value for TSC were: Ultisols ($64.35B), Inceptisols ($16.22B), and Entisols ($14.65B). The regions with the highest midpoint SOC values were: Pee Dee ($34.24B), Low Country ($32.17B), and Midlands ($29.24B). The regions with the highest midpoint SIC values were: Low Country ($5.69B), Midlands ($5.55B), and Pee Dee ($4.67B). The regions with the highest midpoint TSC values were: Low Country ($37.86B), Pee Dee ($36.91B), and Midlands ($34.79B). The counties with the highest midpoint SOC values were Colleton ($5.44B), Horry ($5.37B), and Berkeley ($4.12B). The counties with the highest midpoint SIC values were Charleston ($1.46B), Georgetown ($852.81M, where M = million = 106), and Horry ($843.18M). The counties with the highest midpoint TSC values were Horry ($6.22B), Colleton ($6.02B), and Georgetown ($4.87B). Administrative areas (e.g., counties, regions) combined with pedodiversity concepts can provide useful information to design cost-efficient policies to manage soil carbon regulating ES at the state level.

scholarly journals Spatially Related Sampling Uncertainty in the Assessment of Labile Soil Carbon and Nitrogen in an Irish Forest Plantation

2021 ◽  
Vol 11 (5) ◽  
pp. 2139
Author(s):  
Junliang Zou ◽  
Bruce Osborne
Keyword(s):  
Spatial Variability ◽  
Soil Carbon ◽  
Sampling Effort ◽  
Limited Information ◽  
Sampling Area ◽  
Soil C ◽  
Distribution Maps ◽  
Soil C And N ◽  
C And N ◽  
Highly Correlated

The importance of labile soil carbon (C) and nitrogen (N) in soil biogeochemical processes is now well recognized. However, the quantification of labile soil C and N in soils and the assessment of their contribution to ecosystem C and N budgets is often constrained by limited information on spatial variability. To address this, we examined spatial variability in dissolved organic carbon (DOC) and dissolved total nitrogen (DTN) in a Sitka spruce forest in central Ireland. The results showed moderate variations in the concentrations of DOC and DTN based on the mean, minimum, and maximum, as well as the coefficients of variation. Residual values of DOC and DTN were shown to have moderate spatial autocorrelations, and the nugget sill ratios were 0.09% and 0.10%, respectively. Distribution maps revealed that both DOC and DTN concentrations in the study area decreased from the southeast. The variability of both DOC and DTN increased as the sampling area expanded and could be well parameterized as a power function of the sampling area. The cokriging technique performed better than the ordinary kriging for predictions of DOC and DTN, which are highly correlated. This study provides a statistically based assessment of spatial variations in DOC and DTN and identifies the sampling effort required for their accurate quantification, leading to improved assessments of forest ecosystem C and N budgets.

scholarly journals Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes

2021 ◽  
Author(s):  
Felipe Bastida ◽  
David J. Eldridge ◽  
Carlos García ◽  
G. Kenny Png ◽  
Richard D. Bardgett ◽  
...  
Keyword(s):  
Microbial Diversity ◽  
Soil Carbon ◽  
Soil Microbial Communities ◽  
Ecosystem Functions ◽  
Arid Environments ◽  
Soil Microbial Diversity ◽  
Soil Biodiversity ◽  
Soil C ◽  
Soil Microbial ◽  
Biomass Ratio

AbstractThe relationship between biodiversity and biomass has been a long standing debate in ecology. Soil biodiversity and biomass are essential drivers of ecosystem functions. However, unlike plant communities, little is known about how the diversity and biomass of soil microbial communities are interlinked across globally distributed biomes, and how variations in this relationship influence ecosystem function. To fill this knowledge gap, we conducted a field survey across global biomes, with contrasting vegetation and climate types. We show that soil carbon (C) content is associated to the microbial diversity–biomass relationship and ratio in soils across global biomes. This ratio provides an integrative index to identify those locations on Earth wherein diversity is much higher compared with biomass and vice versa. The soil microbial diversity-to-biomass ratio peaks in arid environments with low C content, and is very low in C-rich cold environments. Our study further advances that the reductions in soil C content associated with land use intensification and climate change could cause dramatic shifts in the microbial diversity-biomass ratio, with potential consequences for broad soil processes.

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