Model Of Carbon Dynamics Research Articles

Stochastic modeling of fine particulate organic carbon dynamics in rivers

The majority of particulate organic matter standing stock in streams is < 1 mm in diameter, and the mobile phase is primarily very fine particles. Such fine particles transport downstream in a series of deposition and resuspension events mediated by interactions with coarser bed sediment, yielding fine particle retention over a wide range of time scales. This retention controls the opportunity for biogeochemical processing of particulate organic carbon in streams. We present a conceptual model of particulate organic carbon transport in rivers categorized in three cyclic processes: (i) migration of fine particles from the water column to the underlying and surrounding sediments, (ii) fine particle transport and retention within the bed sediments, and (iii) resuspension of fine particles back to the water column. We developed a stochastic model to describe the transport and retention of fine suspended particles in rivers, including advective delivery of particles to the streambed, transport through pore waters, and reversible filtration within the streambed. We then apply this model to observations of fine particle transport in two small streams, and show that the stochastic mobile-immobile model supports improved interpretation of particulate organic carbon dynamics under base flow conditions. Analysis of in-stream solute and particle data shows that particles engage in multiple deposition and resuspension events during downstream transport, and that long-term retention in the streambed produces extended slow releases to the stream even during base flow conditions. We also show how multiscale stochastic modeling can be used to incorporate local observations of particle retention in predictions of whole-stream particle dynamics.

Accountable Accounting: Carbon-Based Management on Marginal Lands

Substantial discussion exists concerning the best land use options for mitigating greenhouse gas (GHG) emissions on marginal land. Emissions-mitigating land use options include displacement of fossil fuels via biofuel production and afforestation. Comparing C recovery dynamics under these different options is crucial to assessing the efficacy of offset programs. In this paper, we focus on forest recovery on marginal land, and show that there is substantial inaccuracy and discrepancy in the literature concerning carbon accumulation. We find that uncertainty in carbon accumulation occurs in estimations of carbon stocks and models of carbon dynamics over time. We suggest that analyses to date have been largely unsuccessful at determining reliable trends in site recovery due to broad land use categories, a failure to consider the effect of current and post-restoration management, and problems with meta-analysis. Understanding of C recovery could be greatly improved with increased data collection on pre-restoration site quality, prior land use history, and management practices as well as increased methodological standardization. Finally, given the current and likely future uncertainty in C dynamics, we recommend carbon mitigation potential should not be the only environmental service driving land use decisions on marginal lands.

DRAINMOD–DSSAT model for simulating hydrology, soil carbon and nitrogen dynamics, and crop growth for drained crop land

Integrated agricultural systems modeling represents an effective research tool to meet the evolving challenges facing agricultural production and environmental quality. An integrated, process-based model was developed to simulate the impacts of the changing environment and different water and farming management practices on the hydrology, water quality, and crop growth and yield for artificially drained cropping systems. The new model; named DRAINMOD–DSSAT, was developed by integrating three different process based models: the hydrological model, DRAINMOD; the soil carbon and nitrogen (N) dynamics model; DRAINMOD-NII, and selected crop modules of the DSSAT–CSM model; CROPGRO and CERES-Maize. The integration of the three component models is implemented at the source code level and allows for daily interactions and feedback among simulated climatic conditions, soil water and nitrogen, and crop growth. DRAINMOD–DSSAT performance was evaluated using a 10-yr dataset collected from a corn–soybean production system on a subsurface drained field in Iowa, with corn receiving low, medium, and high N fertilization rates. The model was calibrated using the data collected from the high-N treatment, and validated for the other two treatments. Annual and monthly subsurface drainage outflows were predicted with modeling efficiencies (NSE) of 0.95 and 0.83, respectively. The NSE's for annual and monthly NO3–N mass losses were 0.87 and 0.70 for the high N-treatment, 0.93 and 0.86 for the medium N-treatment, 0.94 and 0.67 for the low N-treatment, respectively. Predicted and measured crop yields were accurately predicted with an absolute percent error less than 8% in 27 of the 30 simulated plot-years (3 plots×10yrs). Nitrogen removal in crop grain was reasonably predicted. This first model application suggested the potential capability of DRAINMOD–DSSAT of simulating the hydrology, water quality, and crop growth and yield for corn and soybean production on artificially drained fields in response to varying climatic conditions and nutrient management practices. Further research, using more intense field measurements, is needed to validate the model for its intended use.

Mineralization and fragmentation rates of bark attached to logs in a northern boreal forest

Predicting the characteristics of coarse woody debris (CWD) and its importance for biodiversity, carbon and nutrient cycling requires narrowing the uncertainties in bark decomposition rate assessments. We estimated bark decomposition rates and compared them to wood decomposition rates of birches (Betula pubescens and Betula pendula), Siberian fir (Abies sibirica), Siberian pine (Pinus sibirica) and Siberian spruce (Picea obovata) in the northern boreal forest of the Komi Republic, Russia. Estimates were made based on the mass loss of bark attached to above-ground fallen and leaning logs dated to have fallen from 1 to 168years previous to sampling. A single-exponential model was used to estimate the mass loss of bark attached to the logs. Decomposition rate estimates of all log bark included mass loss due to fragmentation. Mineralization rate as mass loss per surface area was estimated for non-fragmented bark pieces. The initial bark mass of the tree base was higher compared to that of the rest of the log; it did not depend neither on the tree species nor on the log size. The proportional rate of bark mineralization was the same for Siberian fir, Siberian pine and Siberian spruce logs – 0.040yr−1. The mineralization rate of birch bark was 0.009yr−1. Bark fragmentation accelerated mass loss. Variation in bark decomposition rates was explained by tree species and log diameter and did not depend on tree mortality mode. The bark turn-over time (t95) was 302, 224, 149, 140 and 117years for birch, fir, spruce, Siberian pine with diameter more than 41cm, and Siberian pine with diameter less than 40cm, respectively. Bark decomposed faster than wood for fir (0.034 vs. 0.026yr−1), spruce (0.051 vs. 0.030yr−1) and Siberian pine with diameters less than 40cm (0.054 vs. 0.008yr−1) and (0.065 vs. 0.008yr−1) for logs with diameters more than 41cm. Birch bark decomposed slower than birch wood (0.025 vs. 0.032yr−1). The different decomposition rates of wood and bark suggest that considering wood and bark together as one substrate can result in a less accurate portrayal of decomposition patterns. These bark decomposition rates can be used for modelling carbon dynamics in similar ecosystems. Knowing the turnover time of log bark for these tree species also facilitates the prediction of the quality of CWD in biodiversity studies in boreal forests.

Organic Carbon Dynamics in Different Soil Types After Conversion of Forest to Agriculture

AbstractState‐of‐the‐art predictive models of soil organic carbon (SOC) dynamics associated with land use changes are unable to reflect the diversity of tropical soil types as the knowledge of contrasting site‐specific factors in mediating the response of the SOC pool is sparse. This paper examines the influence of soil type and management on SOC dynamics following the conversion of forests to annual cropping in Ghana. Soil from primary forests and from areas with short (2–7 years) and long (20 years) histories of maize cultivation was sampled from a Vertisol dominated by smectite and Ultisol dominated by kaolinite. Wet sieving was used to separate soil fractions below and above 250 µm. SOC concentrations and δ13C signatures of SOC in soil fractions and bulk soil were determined. SOC stocks were calculated by the commonly used fixed depth approach and by the equivalent soil mass approach. After 20 years of cultivation of the Vertisol, the total SOC content was 40 per cent lower than under forest, and about 95 per cent of the forest‐derived SOC had been lost. After 20 years of cultivation of the Ultisol, total SOC content was only about 20 per cent lower than under forest and merely 30 per cent of the forest‐derived SOC had been lost. Both soil types were managed as they would typically be in small scale farming systems, thus the higher SOC losses and the substantial loss of forest‐derived SOC from the Vertisol question the conventional concept of smectite having a higher SOC‐stabilizing potential than kaolinite under field conditions. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Spatiotemporal variability analysis of diffuse radiation in China during 1981–2010

Abstract. Solar radiation is the primary driver of terrestrial plant photosynthesis and the diffuse component can enhance canopy light use efficiency (LUE), which in turn influences the carbon balance of terrestrial ecosystems. In this study we calculated the spatial data of diffuse radiation in China from 1981 to 2010, using a radiation decomposition model and spatial interpolation method based on observational data. Furthermore, we explored the spatiotemporal characteristics of diffuse radiation using GIS and trend analysis techniques. The results show the following: (1) The spatial patterns of perennial average of annual diffuse radiation during 1981–2010 are complex and inhomogeneous in China, generally lower in the north and higher in the south and west. The perennial average ranges from 1730.20 to 3064.41 MJ m−2 yr−1 across the whole country. (2) There is an increasing trend of annual diffuse radiation in China from 1981 to 2010 on the whole, with mean increasing amplitude of 7.03 MJ m−2 yr−1 per decade. Whereas a significant downtrend was observed in the first 10 years, distinct anomalies in 1982, 1983, 1991 and 1992 occurred due to the eruptions of El Chinchon and Pinatubo. (3) The spatial distribution of the temporal variability of diffuse radiation showed significant regional heterogeneity in addition to the seasonal differences. Northwestern China has the most evident downtrend, with highest decreasing rate of 6% per decade, while the Tibetan Plateau has the most evident uptrend, with highest increasing rate of up to 9% per decade. Such quantitative spatiotemporal characteristics of diffuse radiation are essential in regional scale modeling of terrestrial carbon dynamics.

Modelling carbon dynamics and response to environmental change along a boreal fen nutrient gradient

As the global climate changes so will the balance of carbon dioxide (CO2) sequestration and release from peatlands. The changes occurring within peatlands will be complex, making their future carbon (C) responses difficult to predict. We studied three boreal fen peatlands along a nutrient gradient in northern Ontario, Canada. Specifically, we investigated how abiotic and biotic responses of the fens interacted with seasonal and annual hydrologic variability. We further assessed the role that a local disturbance, such as inundation due to beaver dams, has in confounding fen C dynamics. We conducted these assessments by applying the process-based model Wetland-DNDC, which we parameterized, calibrated and corroborated with field data, to examine how C dynamics were affected by fen type (rich, intermediate and poor), historical and projected climate and local flooding disturbance. We found inter-annual variability in greenhouse gas (CO2 and methane, CH4) dynamics to be large among fens, with larger coefficients of variation for heterotrophic respiration and CH4 effluxes (a range of 40–85% and 31–58%, respectively) than gross primary production (a range of 9–26%). This annual variability in C effluxes correlated to meteorological conditions and water table, although the specific drivers differed among fens. Scenarios of prolonged flooding and drought had opposite effects on C effluxes. Drought increased CO2 efflux by 43%, 175% and 65% for rich, intermediate and poor fens, respectively, and decreased CH4 efflux by 11% and 14% for the intermediate and poor, but increased CH4 efflux by 19% for the rich fen. In contrast, shallow flooding decreased CO2 by 40%, 72% and 57% in the rich, intermediate and poor fen, and increased CH4 effluxes by 87%, 202% and 269%, respectively. Flooding by beaver dams during a prolonged drought moderately decreased CO2 (29%, 66% and 53%) but strongly increased CH4 efflux by 121%, 261% and 333%, respectively, than the flooding only. We conclude that the interacting controls on greenhouse gas dynamics differ among fens types, contributing to the magnitude of effluxes and their response to changing conditions. We propose that effects of water table level, peat nutrient status, and vegetation composition, and their interactions, on C processes require enhanced understanding to define natural ranges of spatial and temporal variability. Only then will it be possible to adequately predict future peatland C responses to climate and other environmental changes. As such, short-term experimental results or field studies should not be used to predict future peatland C dynamics without an understanding of the natural range of variation in the environmental drivers and the response of C processes to those drivers.

Plankton community respiration, net ecosystem metabolism, and oxygen dynamics on the Louisiana continental shelf: Implications for hypoxia

We conducted a multi-year study of the Louisiana continental shelf (LCS) to better understand the linkages between water column metabolism and the formation of hypoxia (dissolved oxygen <2mlO2l−1) in the region. Water column community respiration rates (WR) were measured on 10 cruises during spring, summer and fall seasons from 2003 to 2007 at multiple sites distributed across the Louisiana continental shelf, overlapping the region where bottom-water hypoxia occurs. We found consistent broad scale patterns in WR rates that followed depth and salinity gradients across the shelf. Observed WR rates were highest at low salinity inner shelf stations (<30m depth) and decreased with increasing water depth. Surface waters had higher WR rates than bottom waters, a pattern most pronounced near the Mississippi river during spring and early summer. Surface water WR rates were highest in eastern transects and decreased westward; a trend that was not evident in bottom waters. WR tended to be higher in spring and summer compared to fall months, but overall the seasonal variability was small. We combined the WR rate measurements with contemporaneous measurements of phytoplankton productivity rates (reported in Lehrter et al., 2009, Continental Shelf Research, 29: 1861–1872) to estimate net water column metabolism. There was consistent evidence of net heterotrophy, particularly in western transects, and in deeper waters (>40m depth), indicating a net organic carbon deficit on the LCS. We offer a simple scale argument to suggest that riverine and inshore coastal waters may be significant sources of organic carbon to account for this deficit. This study provided unprecedented, continental shelf scale coverage of heterotrophic metabolism, which is useful for constraining models of oxygen, carbon, and nutrient dynamics along the LCS.

Spatial and temporal variability of soil biological activity in the Province of Québec, Canada (45–58 °N, 1960–2009)—calculations based on climate records

Climate records of air temperature (AT) and total precipitation (TP) are standard inputs for soil carbon dynamic models, i.e., for calculating temperature and moisture effects on soil biological activity. In this study our objective was to determine both spatial and temporal differences in soil biological activity in the Province of Quebec, Canada. Soil biological activity was here calculated on a daily basis with the ICBM re_clim parameter using data from weather stations. When keeping soil and crop properties constant, re_clim (unitless) allows us to assess relative differences in soil biological activity. The magnitude of the temporal changes in re_clim, AT and TP were analyzed using Sen’s slope, which is a nonparametric method used to determine the presence of a trend component. The re_clim varied across Quebec from 0.50 (58 °N) to a high of 1.66 (45 °N). Considering only the area with significant agricultural production, re_clim varied from 0.99 at Gaspe (48 °N) to 1.66 at Philipsburg (45 °N), i.e., soil organic carbon (SOC) decomposition rate is 68 % higher at the latter site (1.66/0.99) and correspondingly more C input is needed to maintain SOC. Soil biological activity increased from 1960 to 2009, with a mean slope variation in re_clim of about +10 %. The temporal variation in AT had more influence than that of TP. For 1980–2009 the mean annual slope of re_clim was significantly different from zero for 29 out of 49 climate records (mean = +14 %; N = 29). We also emphasize that analysis of seasonal changes in AT is an issue that needs further attention, as well as modeling climate-induced changes in SOC dynamics based on future climate scenarios.

Uncertainty analysis of modeled carbon fluxes for a broad-leaved Korean pine mixed forest using a process-based ecosystem model

The uncertainty in the predicted values of a process-based terrestrial ecosystem model is as important as the predicted values themselves. However, few studies integrate uncertainty analysis into their modeling of carbon dynamics. In this paper, we conducted a local sensitivity analysis of the model parameters of a process-based ecosystem model at the Chaibaishan broad-leaved Korean pine mixed forest site in 2003–2005. Sixteen parameters were found to affect the annual net ecosystem exchange of CO2 (NEE) in each of the three years. We combined a Monte Carlo uncertainty analysis with a standardized multiple regression method to distinguish the contributions of the parameters and the initial variables to the output variance. Our results showed that the uncertainties in the modeled annual gross primary production and ecosystem respiration were 5–8% of their mean values, while the uncertainty in the annual NEE was up to 23–37% of the mean value in 2003–2005. Five parameters yielded about 92% of the uncertainty in the modeled annual net ecosystem exchange. Finally, we analyzed the sensitivity of the meteorological data and compared two types of meteorological data and their effects on the estimation of carbon fluxes. Overestimating the relative humidity at a spatial resolution of 10 km × 10 km had a larger effect on the annual gross primary production, ecosystem respiration, and net ecosystem exchange than underestimating precipitation. More attention should be paid to the accurate estimation of sensitive model parameters, driving meteorological data, and the responses of ecosystem processes to environmental variables in the context of global change.

DRAINMOD-FOREST: Integrated Modeling of Hydrology, Soil Carbon and Nitrogen Dynamics, and Plant Growth for Drained Forests

We present a hybrid and stand-level forest ecosystem model, DRAINMOD-FOREST, for simulating the hydrology, carbon (C) and nitrogen (N) dynamics, and tree growth for drained forest lands under common silvicultural practices. The model was developed by linking DRAINMOD, the hydrological model, and DRAINMOD-N II, the soil C and N dynamics model, to a forest growth model, which was adapted mainly from the 3-PG model. The forest growth model estimates net primary production, C allocation, and litterfall using physiology-based methods regulated by air temperature, water deficit, stand age, and soil N conditions. The performance of the newly developed DRAINMOD-FOREST model was evaluated using a long-term (21-yr) data set collected from an artificially drained loblolly pine ( L.) plantation in eastern North Carolina, USA. Results indicated that the DRAINMOD-FOREST accurately predicted annual, monthly, and daily drainage, as indicated by Nash-Sutcliffe coefficients of 0.93, 0.87, and 0.75, respectively. The model also predicted annual net primary productivity and dynamics of leaf area index reasonably well. Predicted temporal changes in the organic matter pool on the forest floor and in forest soil were reasonable compared to published literature. Both predicted annual and monthly nitrate export were in good agreement with field measurements, as indicated by Nash-Sutcliffe coefficients above 0.89 and 0.79 for annual and monthly predictions, respectively. This application of DRAINMOD-FOREST demonstrated its capability for predicting hydrology and C and N dynamics in drained forests under limited silvicultural practices.

Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia

Accurate estimation of non-photosynthetic biomass is critical for modeling carbon dynamics within grassland ecosystems. We evaluated the cellulose absorption index (CAI), widely used for monitoring non-photosynthetic vegetation coverage, for non-photosynthetic biomass estimation. Our analysis was based on in situ hyperspectral measurements, during the growing seasons of 2009 and 2010, in the desert steppe of Inner Mongolia. ASD (Analytical Spectral Device)-derived and Hyperion-derived CAI were found to be effective for non-photosynthetic biomass estimation, yielding relative error (RE) values of 26.4% and 26.6%, respectively. The combination of MODIS (Moderate Resolution Imaging Spectroradiometer)-derived (MODIS2-MODIS5)/(MODIS2+MODIS5) and (MODIS6-MODIS7)/(MODIS6+MODIS7) showed a high multiple correlation (multiple correlation coefficient, r= 0.884) with ASD-derived CAI. A predictive model involving the two MODIS indices gave greater accuracy (RE=28.9%) than the TM (Landsat Thematic Mapper)-derived indices. The latter were the normalized difference index (NDI), the soil adjusted corn residue index (SACRI), and the modified soil adjusted crop residue index (MSACRI). These indices yielded RE values of more than 42%. Our conclusions have great significance for the estimation of regional non-photosynthetic biomass in grasslands, based on remotely sensed data.

Modeling Carbon Dynamics in Paddy Soils in Jiangsu Province of China with Soil Databases Differing in Spatial Resolution

A number of process-based models have been developed for quantifying carbon (C) sequestration in agro-ecosystems. The DeNitrification-DeComposition (DNDC) model was used to simulate and quantify long-term (1980–2008) soil organic carbon (SOC) dynamics in the important rice-producing province, Jiangsu, China. Changes in SOC storages were estimated from two soil databases differing in spatial resolution: a county database consisting of 68 polygons and a soil patch-based database of 701 polygons for all 3.7 Mha of rice fields in Jiangsu. The simulated SOC storage with the coarse resolution county database ranged between 131.0–320.6 Tg C in 1980 and 170.3–305.1 Tg C in 2008, respectively, while that estimated with the fine resolution database was 201.6 and 216.2 Tg C in 1980 and 2008, respectively. The results modeled with the soil databases differing in spatial resolution indicated that using the soil input data with higher resolution substantially increased the accuracy of the modeled results; and when lacking detailed soil datasets, the DNDC model, parameterized with the most sensitive factor (MSF) method to cope with attribute uncertainty, could still produce acceptable results although with deviations of up to 60% for the case study reported in this paper.

Simulated nitrogen deposition affects wood decomposition by cord-forming fungi

Anthropogenic nitrogen (N) deposition affects many natural processes, including forest litter decomposition. Saprotrophic fungi are the only organisms capable of completely decomposing lignocellulosic (woody) litter in temperate ecosystems, and therefore the responses of fungi to N deposition are critical in understanding the effects of global change on the forest carbon cycle. Plant litter decomposition under elevated N has been intensively studied, with varying results. The complexity of forest floor biota and variability in litter quality have obscured N-elevation effects on decomposers. Field experiments often utilize standardized substrates and N-levels, but few studies have controlled the decay organisms. Decomposition of beech (Fagus sylvatica) blocks inoculated with two cord-forming basidiomycete fungi, Hypholoma fasciculare and Phanerochaete velutina, was compared experimentally under realistic levels of simulated N deposition at Wytham Wood, Oxfordshire, UK. Mass loss was greater with P. velutina than with H. fasciculare, and with N treatment than in the control. Decomposition was accompanied by growth of the fungal mycelium and increasing N concentration in the remaining wood. We attribute the N effect on wood decay to the response of cord-forming wood decay fungi to N availability. Previous studies demonstrated the capacity of these fungi to scavenge and import N to decaying wood via a translocating network of mycelium. This study shows that small increases in N availability can increase wood decomposition by these organisms. Dead wood is an important carbon store and habitat. The responses of wood decomposers to anthropogenic N deposition should be considered in models of forest carbon dynamics.

Effects of climatic data low-pass filtering on the ICBM temperature- and moisture-based soil biological activity factors in a cool and humid climate

The air temperature ( T air ), total precipitation (TP) and potential evapotranspiration (PET) are standard input data for soil carbon dynamic models, i.e., for calculating temperature and moisture effects on soil biological activity. The resolution needed depends on objectives, the complexity of models and inbuilt pedotransfer functions. The Introductory Carbon Balance Model (ICBM) soil climate front end model calculates a multiplicative soil-temperature ( r e_temp ) and -moisture ( r e_wat ) factor with a daily time-step to estimate soil biological activity, i.e., r e_crop = r e_temp × r e_wat . Our objective was to determine how much r e_temp , r e_wat and r e_crop are affected by low-pass filtering of the climatic input data for a cool, humid temperate region. To achieve this we conducted spectral analysis on T air , TP, PET and r e_crop in the frequency domain. Thereafter we applied Fourier low-pass filters of 5, 15, 30, 60 and 180 days on T air , TP, PET and tracked their effects through the soil climate model's state variables and outputs. This was done using a sandy and a heavy clay soil and an 89-year daily time-series from a meteorological station in Quebec (Canada). The Fourier spectra showed that the variance for T air , PET and r e_crop was dominated by an annual cycle, as could be expected. There was no yearly cycle for TP. The variation in r e_temp explained most of the variance in r e_crop . The soil climate module outputs were not sensitive to low-pass filtering of PET. A daily time-step was needed to avoid overestimating r e_crop for the sandy soil. Using a weekly time-step for TP and T air allowed us to explain about 80% of the variance in r e_crop for the heavy clay soil. This study also indicates that the standard leaf (and green) area index functions for calculating transpiration should receive more attention, since they have significant effects on the state and output variables.

Validation of soil organic carbon dynamics model in the semi-arid tropics in Niger, West Africa

The fertility of sandy soils in the Sahelian zone (SZ) is extremely low. This poor soil fertility is one of the limiting factors of crop production in the SZ. Therefore, it is imperative to improve or to maintain soil fertility through various agricultural management methods. Further, it is well known that soil organic matter plays an important role in improving the physico-chemical properties of these sandy infertile soils. Therefore, it is essential to develop a suitable tool for the appropriate evaluation of soil organic carbon (SOC) dynamics in the SZ. Therefore, the Rothamsted carbon model (Roth-C) was verified in 32 treatments of two long-term field experiments with and without crop residue application. These experiments were performed by ICRISAT. The performance of the model was evaluated by statistical methods using four indices (RMSE: root mean square error, LOFIT: lack of fit, r: correlation coefficient, and M: mean difference). As a result, the predicted SOC values in the case without crop residue management decreased with time in approximately 10 cultivated years. In contrast, in the case with crop residue application, the predicted SOC remained roughly equal to the initial SOC value during the term observed. Mostly, the Roth-C-modelled values agreed well with the actual value. RMSE and LOFIT, the statistical indicators of agreement between predicted and observed values, showed a significant conformity between the predicted and observed SOC values in all the 32 treatments. This fact means that Roth-C can estimate long-term SOC dynamics of several technical options that developed with short-term trials. Moreover the annual carbon requirement for SOC maintaining can be calculate if enough number of cases was estimated. And also analysis of regional carbon dynamics was made possible with using Roth-C model. It will contribute to show the sustainable development in SZ against global warming and other climatic changes.

Quantifying management-driven changes in organic matter turnover in an agricultural soil: An inverse modeling approach using historical data and a surrogate CENTURY-type model

Abstract In order to facilitate inverse modeling of time-series data and/or parameter estimation for process-based models of soil organic carbon (SOC) dynamics, we developed and applied a new surrogate CENTURY SOC model (SCSOC). While SCSOC has mass balance and decomposition kinetics equations for residue and soil organic matter (SOM) pools identical to those of CENTURY, it differs by being: (1) decoupled from models of plant growth, nutrient cycling, and hydrologic processes; (2) capable of employing daily, monthly, or annual timesteps; (3) solvable using widely available non-linear regression software. Here, we describe SCSOC and demonstrate that its numerical solution to the CENTURY SOC equations is highly accurate for both daily and monthly timesteps, provided the monthly physical forcing function inputs are appropriately derived from daily-resolution hydrologic model results. As an example of the proper application of the tool to obtain site-specific parameters and to investigate hypotheses using historical data, we analyzed SOC data from a single subplot of the Morrow Plots (Urbana, Illinois, U.S.A.) that has been cropped continuously in corn since 1876 using management practices that were common for east-central Illinois. The impact of different approaches typically used during calibration were examined by objectively estimating a variety of parameter sets that both govern SOM decomposition kinetics and define the initial mass and fractionation of the SOM. The results suggest that mean SOM turnover times were circa 5-fold longer during the period 1876–1954 relative to 1955–1995, when modern management practices were employed, and significantly different from default CENTURY predictions during both. Modified versions of SCSOC were also constructed in order to (1) show that uncertainty about historical soil erosion does not confound this interpretation of the data and (2) explore alternative assumptions about the structure of the model. One such alternate model showed that unexpectedly rapid decomposition of the corn-derived SOM combined with depletion of slowly-decomposing prairie-derived SOM could explain the post-1955 acceleration in decomposition.

Individual-Based Modeling of Carbon and Nitrogen Dynamics in Soils

The fate of soil carbon and nitrogen compounds in soils in response to climate change is currently the object of significant research. In particular, there is much interest in the development of a new generation of micro-scale models of soil ecosystems processes. Crucial to the elaboration of such models is the ability to describe the growth and metabolism of small numbers of individual microorganisms, distributed in a highly heterogeneous environment. In this context, the key objective of the research described in this article was to further develop an individual-based soil organic matter model, INDISIM-SOM, first proposed a few years ago, and to assess its performance with a broader experimental data set than previously considered. INDISIM-SOM models the dynamics and evolution of carbon and nitrogen associated with organic matter in soils. The model involves a number of state variables and parameters related to soil organic matter and microbial activity, including growth and decay of microbial biomass, temporal evolutions of easily hydrolysable N, mineral N in ammonium and nitrate, CO2 and O2. The present article concentrates on the biotic components of the model. Simulation results demonstrate that the model can be calibrated to provide good fit to experimental data from laboratory incubation experiments performed on three different types of Mediterranean soils. In addition, analysis of the sensitivity toward its biotic parameters shows that the model is far more sensitive to some parameters, i.e., the microbial maintenance energy and the probability of random microbial death, than to others. These results suggest that, in the future, research should focus on securing better measurements of these parameters, on environmental determinants of the switch from active to dormant states, and on the causes of random cell death in soil ecosystems.

Modeling carbon dynamics in subsoils using simple models

AbstractSoil C dynamics below the plow layer have been little studied, despite a suspected large C‐stabilization potential of subsurface horizons. The objective of this study was to test two simple models (model A: single compartment for C3‐ and for C4‐derived C; model B: division of C3‐ and C4‐derived C into active and passive compartments) in their ability to simulate the C dynamics in subsoil horizons of a Haplic Phaeozem after conversion from C3 (rye) to C4 cropping (maize). The models were calibrated on an unfertilized maize soil and then validated on a maize soil with NPK fertilization. Both models simulated well C3‐C and C4‐C dynamics in the investigated soil depths (20–40 cm and 40–60 cm). In all cases, the model efficiency EF was &gt; 0, which indicated that the simulated values described the trend in the measured data better than the mean of the observations. However, we observed some inconsistency in the obtained parameter set (e.g., a higher proportion of passive C for C4‐derived than for C3‐derived C or a very low decomposition rate constant for passive C4‐C in 40–60 cm), which we assume to result from data restrictions on the investigated soils. More detailed data on SOC pools and turnover rates in subsoils which are generally not yet available for most experimental plots is vitally needed—especially for applying more sophisticated C‐dynamics models.

Carbon dynamics in topsoil and in subsoil may be controlled by different regulatory mechanisms

AbstractIt is estimated that in excess of 50% of the soil carbon stock is found in the subsoil (below 20–30 cm). Despite this very few studies have paid attention to the subsoil. Although surface and subsurface horizons differ in pedological, environmental and physicochemical features, which are all likely to affect the mechanisms and biological actors involved, models of carbon dynamics tend to assume that the underlying processes are identical in all horizons, but with lower gross fluxes in the subsurface. The aim of this study was to test this assumption by analysing factors governing organic matter decomposition in topsoil (from depths of 5–10 cm) and subsoil (from depths of 80–100 cm). To this end, we established incubations that lasted 51 days, in which factors that were thought to control organic matter mineralization were altered: oxygen concentration, soil structure and the energetic and nutritional status. At the end of the incubation period, the microbial biomass was measured and the community level physiological profiles established. The mineralization per unit organic carbon proved to be as important in the subsoil as it was in surface samples, in spite of lower carbon contents and different catabolic profiles. Differences in the treatment effects indicated that the controls on C dynamics were different in topsoil and subsoil: disrupting the structure of the subsoil caused a 75% increase in mineralization while the surface samples remained unaffected. On the other hand, a significant priming affect was found in the topsoil but not in the subsoil samples. Spatial heterogeneity in carbon content, respiration and microbial communities was greater in subsoil than in topsoil at the field scale. These data suggest greater attention should be paid to the subsoil if global C dynamics is to be fully understood.