Model Of Carbon Dynamics Research Articles

The Stratification Analysis of Sediment Data for Lake Michigan

Accurately understanding the distribution of sediment measurements within large water bodies such as Lake Michigan is critical for modeling and understanding of carbon, nitrogen, silica, and phosphorus dynamics. Several water quality models have been formulated and applied to the Great Lakes to investigate the fate and transport of nutrients and other constituents, as well as plankton dynamics. This paper summarizes the development of spatial statistical tools to study and assess the spatial trends of the sediment data sets, which were collected from Lake Michigan, as part of Lake Michigan Mass Balance Study. Several new spatial measurements were developed to quantify the spatial variation and continuity of sediment data sets under concern. The applications of the newly designed spatial measurements on the sediment data, in conjunction with descriptive statistics, clearly reveal the existence of the intrinsic structure of strata, which is hypothesized based on linear wave theory. Furthermore, a new concept of strata consisting of two components defined based on depth is proposed and justified. The findings presented in this paper may impact the future studies of sediment within Lake Michigan and all of the Great Lakes as well.

Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration

Sequestering carbon into the soil can mitigate the atmospheric concentration of greenhouse gases, improving crop productivity and yield financial gains for farmers through the sale of carbon credits. In this work, we develop and evaluate advanced Bayesian methods for modelling soil carbon sequestration and quantifying uncertainty around predictions that are needed to fit more complex soil carbon models, such as multiple-pool soil carbon dynamic models. This paper demonstrates efficient computational methods using a one-pool model of the soil carbon dynamics previously used to predict soil carbon stock change under different agricultural practices applied at Tarlee, South Australia. We focus on methods that can improve the speed of computation when estimating parameters and model state variables in a statistically defensible way. This paper also serves as a tutorial on advanced Bayesian methods for fitting complex state-space models, which will be of interest to soil scientists and other environmental scientists more generally.

Microclimate and distribution of mangrove soil carbon in mud lobster (Thalassina anomala Herbst 1804) mounds

Although the role of mud lobsters (Thalassina anomala Herbst 1804) in influencing mangrove succession, structure and soil chemistry has been given a great deal of attention, there are still large knowledge gaps. We are unable to incorporate mud lobsters into model of carbon dynamics. This study examines the variations of microclimate (temperature and light intensity) in mud lobster mounds. The effects of burrowing activity of mud lobster on carbon distributions were investigated by comparing the content of organic matter (SOM) and organic carbon (SOC) extracted from both mud lobster mound and surrounding mangrove soils in Sibuti Wildlife Sanctuary (Sarawak, Malaysia). Our study demonstrates that mud lobsters, with microclimate (light and temperature) variation, are spatially distributed along the seaward and landward intertidal zone of the Sibuti mangrove. The transport of soil from deeper in the soil profile to the soil surface by the mud lobster resulted in significantly higher SOM and total C in freshly excavated mud than in older mud in the mound of the mud lobster. The vertical distribution of SOM was not influenced by the behavior of mud lobsters and had a slight negative impact on total soil C. There was no substantial evidence that the activity of the mud lobsters in mangrove soils increased SOM and total C. Because mangrove soils are known to trap or store belowground C, the burrowing activities may be doing the opposite by releasing the stored C to the surface. While mud lobsters have not had a significant impact on C, other processes (e.g., regeneration, productivity, and subsidence) are highly affected and may therefore have indirect effects on soil carbon stocks. Information on the contribution of mud lobster mound activities to C distribution in mangrove environment would be critical for national carbon estimation.

Divergent responses of soil organic carbon to afforestation

Large-scale afforestation is regarded as an effective natural climate solution. However, afforestation-induced changes in soil organic C (SOC) are poorly quantified due to the paucity of large-scale sampling data. Here, we provide the first comprehensive assessment of the afforestation impact on SOC stocks with a pairwise comparative study of samples from 619 control-and-afforested plot pairs in northern China. We found context-dependent effects of afforestation on SOC: afforestation increases SOC density (SOCD) in C-poor soils but decreases SOCD in C-rich soils, especially in deeper soil. Thus, the fixed biomass/SOC ratio assumed in previous studies could overestimate the SOC enhancement by afforestation. By extrapolating the sampling data to the entire region, we estimate that afforestation increased SOC stocks in northern China by only 234.9 ± 9.6 TgC over the last three decades. The study highlights the importance of including pre-afforestation soil properties in models of soil carbon dynamics and carbon sink projections. Forest planting is considered a natural climate solution, but effects on soil carbon are unclear. This study, in northern China, finds that planting forests increases carbon in soils poor in it and vice versa.

Calibration of a management-oriented greenhouse gas emission model for lakes and reservoirs under different distribution of environmental data

The management-oriented CICLAR lumped model for carbon dynamics and Greenhouse Gas (GHG) emission assessment, is presented. A metaheuristic calibration, through a Pareto based multi-objective particle swarm optimization (PSO), is used to automatically calibrate the model with data from the Capivari reservoir (southern Brazil). Two types of calibration are implemented: (1) with carbon dioxide (CO2), methane (CH4) flux, and carbon stock changes, and (2) with synthetic data based on a solution selected from (1). The calibration's performance was assessed by Nash-Sutcliffe and root means squared errors. Three synthetic scenarios are used to analyze the data distribution influence on calibration and GHG fluxes output. The results show that the spread of solutions is higher when the model is calibrated with less data (using only measured values) when compared to the ones obtained from the synthetic data series. Although there are differences between solutions calibrated with different scenarios, all of them characterized the reservoir, through the Global Warming Potential index (GWP), as a sinkhole of equivalent CO2. Moreover, the similarity among accumulated probability distribution obtained from those different scenarios, suggest that the model can be calibrated regardless of the temporal scopes of measurements.

МОДЕЛИРОВАНИЕ УГЛЕРОДНО-АЗОТНОГО ЦИКЛА ТУНДРЫ В ГОЛОЦЕНЕ

The observed increase in near-surface temperature can cause degradation of permafrost and an increase in greenhouse gas emissions. At the same time, the productivity of the tundra increases, and more intense photosynthesis means an increase in the absorption of carbon from the atmosphere. Therefore, the development of models of the carbonnitrogen cycle to study the contribution of tundra ecosystems is a special challenge. In this work, the modeling of carbon and nitrogen dynamics in tundra ecosystems of northern regions of Western Siberia were carried out using the reservoirflow model including vegetation, organic carbon compounds, dissolved organic and inorganic nitrogen. Input data of the atmospheric forcing were set according to the results of calculations with CLIMBER-2 model for the last 10 thousand years. Model estimates show a regional increase in mean annual air temperature and a decrease in the annual range over a period of 10–5.5 thousand years ago due to an increase in winter temperatures in this region. According to the results of calculations, carbon stocks in soil increase monotonically during the period under consideration. Calculated soil C values for modern climatic conditions are 14700 gC/m2 . The reserves of nitrate and ammonium forms of nitrogen are stabilized at the beginning of the calculation period. The results of calculations show that tundra ecosystems within the last 10 thousand years, including those under conditions of near-surface warming with increasing winter temperature and minor changes in summer temperature could accumulate organic matter, absorbing carbon-containing greenhouse gases from the atmosphere.

Correcting an Error in Some Interpretations of Atmospheric <sup>14</sup>C Data

The variable “∆14C”, commonly used in radiocarbon dating and tracing applications to quantify 14C levels, is a measure of the ratio of the radioisotope 14C to other carbon in a sample. After atmospheric nuclear testing in the 1950’s and 1960’s nearly doubled atmospheric 14C, the later evolution of ∆14C allowed insights into the dynamics of carbon exchange between the atmosphere and terrestrial and marine sinks. But a few authors without backgrounds in isotope measurements have confused ∆14C with excess 14C concentration. They erroneously interpret the present recovery of ∆14C to near its pre bomb test value as evidence that atmospheric 14C concentration has returned to its earlier value. From this they reach further incorrect conclusions about the fate of anthropogenic CO2 introduced into the atmosphere by fossil fuel burning. An estimate of the true time dependence of atmospheric 14C concentration over the past century, calculated from averaged atmospheric ∆14C and CO2 data is presented. The data show that 14C concentrations remain over 30% above 1950 values, and have begun to increase, even as ∆14C continues to fall. This confirms the prediction of a conventional model of the carbon cycle. The unconventional models of carbon dynamics motivated by the mistake, on the other hand, are excluded by the properly interpreted 14C data.

Soil organic carbon sequestration according to two Geoset long-term field experiments in the Moscow region

The feasibility of implementing the "4 ppm" initiative, which assumes an annual increase in organic carbon stocks of agricultural soils in the layer 0-40 cm, was estimated with the dynamic carbon model RothC in two long-term DAOS experiments in the Moscow region, conducted in neighbouring fields for 74 and 76 years. Treatments included absolute control, application of organic, mineral, organic and mineral fertilizers at increasing rates. One of the experiments showed the growth of C stocks 12‰ in the layer 0-20 cm in the first 20 years in treatments with mineral fertilization, and 17‰ with the additional application of manure in an average annual rate of 10 Mg·ha-1. The accumulation of C allowed increasing its stock by 18-25%. Still, with the subsequent decline in crop rotation productivity, there was a loss of part of the previously accumulated C. In another experiment, at close values of annual C input, there was a loss of initial C stock due to the history of land use. The crop rotation adjustment provided a 3-8 ‰ increase of soil C in the 0-20 cm layer in the first 20 years after introduction but was insufficient to match the "4 ppm" initiative. In the long term, the organic fertilizer system had an advantage over the mineral one in ensuring the stability of organic C stocks in the arable layer. However, the management of C sequestration was complicated in the non-equilibrium state of the carbon system "plant residues-organic fertilizer-soil".

Using Plant Functional Groups as a Strategy for Modeling Carbon Dynamics in Grassland Ecosystems

This paper argues for the use of plant functional groups as an important strategy for modeling carbon dynamics in grasslands. Carbon sequestration is paramount to help reduce climate change globally, and grasslands, representing 40% of all terrestrial area, can serve as primary locations of sequestration if optimal management strategies can be realized.

Twenty-Five Years of Aboveground Biomass and Carbon Accumulation Following Extreme Wind Damage in an Old-Growth Forest

Modeling of carbon dynamics at the landscape, regional, and continental scales is currently limited by few empirical studies of biomass and carbon accumulation after some types of disturbances. For temperate forests of North America, only three previous studies described biomass and carbon accumulation after wind disturbances, and those were limited by either coarse temporal resolution of the first several decades, or limited time span. Here, 25 years of aboveground biomass and carbon accumulation following severe wind disturbance of an old-growth hemlock-northern hardwoods forest of northwestern Pennsylvania are documented to characterize the temporal trends with fine temporal resolution and extend into the third decade post-disturbance. Mature undisturbed forest at the site supported roughly 296 Mg ha−1 live aboveground biomass and 148 Mg ha−1 of carbon. The disturbance reduced the aboveground woody biomass to ~7 Mg ha−1, and carbon to ~3.5 Mg ha−1. During regrowth, biomass and carbon accumulated slowly at first (e.g., 2–4 Mg ha−1 year−1 for biomass and 1–2 Mg ha−1 year−1 for carbon), but at increasing rates up through approximately 17 years post-disturbance, after which accumulation slowed somewhat to roughly 3.4 Mg ha−1 year−1 of biomass and 1.7 Mg ha−1 year−1 of carbon. It appears that the rates reported here are similar to rates observed after wind disturbance of other temperate forests, but slower than accumulation in some tropical systems. Notably, in tropical forests, post-windthrow accumulation is often very rapid in the first decade followed by decreases, while in the results reported here, there was slow accumulation in the first several years that increased in the second decade and then subsequently slowed.

Sensitivity of Boreal Carbon Stocks to Fire Return Interval, Fire Severity and Fire Season: A Simulation Study of Black Spruce Forests

Boreal forests store substantial amounts of carbon in vegetation and soil pools. The magnitude of these pools is related to fire regime attributes. Climate change is expected to alter boreal fire regimes, leading to changes in the amount of stored carbon. Quantifying these changes is of importance to understanding and managing global carbon budgets. We investigate how fire return interval (FRI) interacts with seasonal variation in fire intensity and severity to affect carbon stocks and fluxes in the boreal black spruce forests of Quebec, Canada. A size-class structured population model of stand dynamics was coupled with an established model of boreal carbon dynamics and linked to a simplified representation of fire regime that simulates the occurrence of fires and their direct effects on canopy tree mortality, surface fuels combustion and regeneration. We simulated carbon stocks and fluxes under seven levels of FRI and two fire seasons: spring and summer. We tested for an effect of these fire regime parameters on equilibrium mean C stocks. All dead organic matter and biomass carbon stocks were sensitive to FRI between 60 and 300 years. These C stocks were lower for summer fires that occurred under shorter FRIs. Net primary production was highest at FRIs between 150 and 300 years. Total C stocks were highest for FRIs from 150 to 700 years, varying little over this range. There was a small but significant difference of C pool sizes between stands with even and uneven tree-diameter distributions. This difference was greatest for FRIs of 150 years or less. Reductions in equilibrium C storage are forecasted for nearly 27% of the study region under expected end-of-century climates.

Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models

Soil organic matter (SOM) turnover increasingly is conceptualized as a tension between accessibility to microorganisms and protection from decomposition via physical and chemical association with minerals in emerging soil biogeochemical theory. Yet, these components are missing from the original mathematical models of belowground carbon dynamics and remain underrepresented in more recent compartmental models that separate SOM into discrete pools with differing turnover times. Thus, a gap currently exists between the emergent understanding of SOM dynamics and our ability to improve terrestrial biogeochemical projections that rely on the existing models. In this opinion paper, we portray the SOM paradigm as a triangle composed of three nodes: conceptual theory, analytical measurement, and numerical models. In successful approaches, we contend that the nodes are connected—models capture the essential features of dominant theories while measurement tools generate data adequate to parameterize and evaluate the models—and balanced—models can inspire new theories via emergent behaviors, pushing empiricists to devise new measurements. Many exciting advances recently pushed the boundaries on one or more nodes. However, newly integrated triangles have yet to coalesce. We conclude that our ability to incorporate mechanisms of microbial decomposition and physicochemical protection into predictions of SOM change is limited by current disconnections and imbalances among theory, measurement, and modeling. Opportunities to reintegrate the three components of the SOM paradigm exist by carefully considering their linkages and feedbacks at specific scales of observation.

The biophysical and socio-economic dimension of yield gaps in the southern Amazon – A bio-economic modelling approach

Farmers in the State of Mato Grosso are among Brazil's most productive soybean, maize and cotton producers, but are still far away from achieving potential yields as measured on experimental sites. The objective of this study was to decompose yield gaps in the Southern Amazon into their biophysical and socio-economic dimensions. In order to achieve this, the process-based MOdel of NItrogen and Carbon dynamics in Agro-ecosystems (MONICA) was coupled with the Mathematical Programming-based Multi-Agent Systems (MPMAS) software. Soybean, maize and cotton yield gaps were simulated for five macro-regions in Mato Grosso considering different climatic, edaphic and crop management conditions. The impact of socio-economic constraints on crop yields was assessed in form of full factorial design in which each factor was set to a baseline and unconstrained level. The simulation results show that biophysical yield gaps (due to water and nutrient deficit) account for 24% of potential yields (Yp), whereas an unrestricted access to machinery, labour, credit and technological innovation would lead to a reduction of yield gaps by 6.1% and an expansion of cropland by 22%. Yield gaps can be reduced through improved water- and nutrient management, appropriate cultivar-sowing date combinations and in part by a removal of socio-economic constraints. However, each solution comes with its own limitation either in form of increased pressure on limited environmental resources or incompatibility with individual farmer objectives. Future yield gap closure will depend on the access to arable land, environmental regulations preventing further deforestation as well as political and economic incentives for sustainable intensification.

To what extent do uncertainty and sensitivity analyses help unravel the influence of microscale physical and biological drivers in soil carbon dynamics models?

Soil respiration causes the second largest C flux between ecosystems and the atmosphere. Emerging soil carbon dynamics models consider the complex interplay of microscale interactions between the physical and biological drivers of soil organic matter decomposition occurring in the 3D soil architecture. They are expected to provide a way to upscale results to the macroscopic level and as such appear as an alternative modelling approach to the traditional “black-box” macroscopic models. However, these models still need to be tested under a broader range of their parameters values and structures than has been the case to date. We thus conducted uncertainty and global sensitivity analyses to test the robustness of previous predictions on dissolved organic carbon biodegradation obtained by one of these microscopic carbon dynamics models, LBioS. Six parameters of the carbon dynamics module of LBioS, associated with bacterial metabolism and three microscopic 3D descriptors of soil architecture were considered as uncertain inputs. We built two complete factorial designs in which the minimum and maximum of uncertainty intervals are considered. Each factorial design is assigned to a particular structure of the model, one including dormancy of bacteria and the other considering optimal bacterial activity. The scenarios took place in 3D computed tomography images of an undisturbed cultivated soil. The sensitivity indices at different simulations dates were computed with an ANOVA procedure taking into account main effects and interactions among factors. The uncertainty analysis shows that only in the limiting case of low accessibility of resources to bacteria the different microbial metabolisms tested can modify to a small extent the system responses, and uncertainty linked to parameters describing soil architecture becomes preponderant. In the case of optimal accessibility output variability is due predominantly to uncertainty of the microbial metabolism parameters. The sensitivity analysis suggests that whatever the structure of the model, the role of soil architecture in the microbial activity can be evidenced using either DOC or CO2 as proxy measures. Beyond these results, we stress that results of uncertainty and sensitivity analyses of soil carbon models need to be interpreted with caution, dependent as they are on the status of the model itself, as well as on the particular scenarios used in the uncertainty and sensitivity analyses.

Integrate carbon dynamics models for assessing the impact of land use intervention on carbon sequestration ecosystem service

The land use intervention caused a noticeable impact on carbon sequestration as an ecosystem service. To comprehensively project land use change impact on carbon sequestration, the carbon dynamics models in the ecosystem and the atmosphere were integrated, and characterization factors were calculated for different scenarios. In this study, to illustrate the proposed method, the CENTURY model was used to simulate the carbon dynamics for seven land use change scenarios under two climate change scenarios. Carbon dynamics in the atmosphere was simulated by the Bern2.5CC carbon cycle model. The impact on carbon sequestration was calculated based on the difference of carbon sequestration between the land use change scenario and the corresponding baseline and the decay of CO2 in the atmosphere. According to the simulations, we found that carbon storages and differences of the annual carbon sequestration rate were varied among land use change scenarios and between the two climate change scenarios. After land use conversion, an equilibrium status would be obtained after 100 to 200 years of growth. By integrating the carbon dynamics model with the atmosphere, the characterization factors (CF) were calculated for life cycle assessment. The values of CFs were significantly changed in different land use change scenarios, climate change scenarios and time horizons. The comparison to the previous assessment method indicated that the previous method was too conservative. The results suggested that the method in this study could provide a more reasonable assessment of the impact of land use intervention on carbon sequestration.

Applying a systems approach to assess carbon emission reductions from climate change mitigation in Mexico’s forest sector

The Paris Agreement of the United Nation Framework Convention on Climate Change calls for a balance of anthropogenic greenhouse emissions and removals in the latter part of this century. Mexico indicated in its Intended Nationally Determined Contribution and its Climate Change Mid-Century Strategy that the land sector will contribute to meeting GHG emission reduction goals. Since 2012, the Mexican government through its National Forestry Commission, with international financial and technical support, has been developing carbon dynamics models to explore climate change mitigation options in the forest sector. Following a systems approach, here we assess the biophysical mitigation potential of forest ecosystems, harvested wood products and their substitution benefits (i.e. the change in emissions resulting from substitution of wood for more emissions-intensive products and fossil fuels), for policy alternatives considered by the Mexican government, such as a net zero deforestation rate and sustainable forest management. We used available analytical frameworks (Carbon Budget Model of the Canadian Forest Sector and a harvested wood products model), parameterized with local input data in two contrasting Mexican states. Using information from the National Forest Monitoring System (e.g. forest inventories, remote sensing, disturbance data), we demonstrate that activities aimed at reaching a net-zero deforestation rate can yield significant CO2e mitigation benefits by 2030 and 2050 relative to a baseline scenario (‘business as usual’), but if combined with increasing forest harvest to produce long-lived products and substitute more energy-intensive materials, emissions reductions could also provide other co-benefits (e.g. jobs, illegal logging reduction). We concluded that the relative impact of mitigation activities is locally dependent, suggesting that mitigation strategies should be designed and implemented at sub-national scales. We were also encouraged about the ability of the modeling framework to effectively use Mexico’s data, and showed the need to include multiple sectors and types of collaborators (scientific and policy-maker communities) to design more comprehensive portfolios for climate change mitigation.

Mathematical Analysis of a Chemotaxis-Type Model of Soil Carbon Dynamic

The goal of this paper is to study the mathematical properties of a new model of soil carbon dynamics which is a reaction-diffusion system with a chemotactic term, with the aim to account for the formation of soil aggregations in the bacterial and microorganism spatial organization (hot spot in soil). This is a spatial and chemotactic version of MOMOS (Modelling Organic changes by Micro-Organisms of Soil), a model recently introduced by M. Pansu and his group. The authors present here two forms of chemotactic terms, first a “classical” one and second a function which prevents the overcrowding of microorganisms. They prove in each case the existence of a nonnegative global solution, and investigate its uniqueness and the existence of a global attractor for all the solutions.

The Temperature Sensitivity (Q10) of Soil Respiration: Controlling Factors and Spatial Prediction at Regional Scale Based on Environmental Soil Classes

AbstractThe temperature sensitivity of heterotrophic soil respiration is crucial for modeling carbon dynamics but it is variable. Presently, however, most models employ a fixed value of 1.5 or 2.0 for the increase of soil respiration per 10°C increase in temperature (Q10). Here we identified the variability of Q10 at a regional scale (Rur catchment, Germany/Belgium/Netherlands). We divided the study catchment into environmental soil classes (ESCs), which we define as unique combinations of land use, aggregated soil groups, and texture. We took nine soil samples from each ESC (108 samples) and incubated them at four soil moisture levels and five temperatures (5–25°C). We hypothesized that Q10 variability is controlled by soil organic carbon (SOC) degradability and soil moisture and that ESC can be used as a widely available proxy for Q10, owing to differences in SOC degradability. Measured Q10 values ranged from 1.2 to 2.8 and were correlated with indicators of SOC degradability (e.g., pH, r = −0.52). The effect of soil moisture on Q10 was variable: Q10 increased with moisture in croplands but decreased in forests. The ESC captured significant parts of Q10 variability under dry (R2 = 0.44) and intermediate (R2 = 0.36) moisture conditions, where Q10 increased in the order cropland<grassland<forest. Overall, the estimated heterotrophic CO2 release from the catchment was up to 45% lower when ESC‐ and moisture‐specific Q10 values were used instead of 1.5, suggesting that scaling Q10 on the basis of both ESC and moisture might be a promising concept for spatially continuous assessments of carbon turnover at regional scales.

Determining the within-field yield variability from seasonally changing soil conditions

Crop yield variations are strongly influenced by the spatial and temporal availabilities of water and nitrogen in the soil during the crop growth season. To estimate the quantities and distributions of water and nitrogen within a given soil, process-oriented soil models have often been used. These models require detailed information about the soil characteristics and profile architecture (e.g., soil depth, clay content, bulk density, field capacity and wilting point), but high resolution information about these soil properties, both vertically and laterally, is difficult to obtain through conventional approaches. However, on-the-go electrical resistivity tomography (ERT) measurements of the soil and data inversion tools have recently improved the lateral resolutions of the vertically distributed measurable information. Using these techniques, nearly 19,000 virtual soil profiles with defined layer depths were successfully created for a 30 ha silty cropped soil over loamy and sandy substrates in Central Germany, which were used to initialise the CArbon and Nitrogen DYnamics (CANDY) model. The soil clay content was derived from the electrical resistivity (ER) and the collected soil samples using a simple linear regression approach (the mean R2 of clay = 0.39). The additional required structural and hydrological properties were derived from pedotransfer functions. The modelling results, derived soil texture distributions and original ER data were compared with the spatial winter wheat yield distribution in a relatively dry year using regression and boundary line analysis. The yield variation was best explained by the simulated soil water content (R2 = 0.18) during the grain filling and was additionally validated by the measured soil water content with a root mean square error (RMSE) of 7.5 Vol%.

Introducing turgor-driven growth dynamics into functional-structural plant models.

In many scenarios the availability of assimilated carbon is not the constraining factor of plant growth. Rather, organ growth appears driven by sink activity in which water availability plays a determinant role. Current functional-structural plant models (FSPMs) mainly focus on plant-carbon relations and largely disregard the importance of plant water status in organogenesis. Consequently, incorporating a turgor-driven growth concept, coupling carbon and water dynamics in an FSPM, presents a significant improvement towards capturing plant development in a more mechanistic manner. An existing process-based water flow and storage model served as a basis for implementing water control in FSPMs. Its concepts were adjusted to the scale of individual plant organs and interwoven with the basic principles of modelling carbon dynamics to allow evaluation of turgor pressure across the entire plant. This was then linked to plant organ growth by applying the principles of the widely used Lockhart equation. This model successfully integrates a mechanistic understanding of plant water transport dynamics coupled with simple carbon dynamics within a dynamically developing plant architecture. It allows evaluation of turgor pressure on the scale of plant organs, resulting in clear diel and long-term patterns, directly linked to plant organ growth. A conceptual sap flow and turgor-driven growth model was introduced for functional-structural plant modelling. It is applicable to any plant architecture and allows visual exploration of the diel patterns of organ water content and growth. Integrated in existing FSPMs, this new concept fosters an array of possibilities for FSPMs, as it presents a different formulation of growth in terms of local processes, influenced by local and external conditions.