Biomass Model Research Articles

Modeling pyrolysis kinetics of extracted biomass components with the bio-chemical percolation devolatilization model

Within the pyrolysis modeling of biomass, the solid is typically interpreted as a mixture of cellulose, hemicellulose, and lignin, where the components react independently before the products are linearly superimposed. This assumption applies to the bio-chemical percolation devolatilization model (Bio-CPD), which, as a network model, represents the physicochemical processes very realistically. A literature review regarding available structural and kinetic parameters for the Bio-CPD model revealed four hits. To investigate and evaluate the model predictivity, experiments from extracted lignocellulosic biomass components pyrolyzed separately in a fluidized bed reactor are used as reference data. Using single-component data simplifies the reaction chemistry by minimizing interactions between the components and best approximates the model assumptions. The comparison between experimentally obtained total volatile release rates and model predictions revealed the parameter set from Vizzini et al. (2008) for cellulose and the one from Sheng and Azevedo (2002) for hemicellulose as best-suitable candidates to predict the pyrolysis behavior for the boundary conditions of the fluidized bed reactor. For lignin, instead, the Genetti correlation (Genetti, Fletcher, and Pugmire, 1999) to estimate structural parameters based on the ultimate and proximate analysis led to better results than all other sets. Further, the analysis of light gas and tar fractions showed a high relevance to model secondary tar cracking reactions in the fluidized bed reactor system.

Assessing the potential of synthetic and ex situ airborne laser scanning and ground plot data to train forest biomass models

Abstract Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r$^{2}$), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52–122 %, r$^{2}$ decrease of 4–18 %). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21–118 %, r$^{2}$ decrease of 5–14 % compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r$^{2}$, as long as no more than 12–346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r$^{2}$ were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available.

The above-ground biomass model of mangrove forest in Panai Hilir, Indonesia

Mangrove forest is one of the forests with high biomass storage in the tropics besides mainland forests. The mangrove ecosystem has an ecological function as a carbon reducer through a sequestration process (C-sequestration). One technique that can be used to calculate biomass is the use of remote sensing technology using satellite imagery and the application of transformation of the vegetation index. This study aims to obtain the best biomass equation model from several vegetation indices, analyze the estimated biomass value from Landsat 8 imagery in 2022, and map the distribution of the estimated distribution of average above-ground biomass of mangrove forest in Panai Hilir using Landsat 7 imagery and Landsat 8 in 2002 and 2022. This study used a systematic sampling method with a random start with 70 plots. Biomass calculations are carried out using allometric equations. Modeling uses the dependent variable (biomass) and the independent variable (vegetation index). The vegetation indices used are NDVI, GNDVI, and TDVI. The results of this study showed that the chosen equation model was the GNDVI (Green Normalized Difference Vegetation Index) linear equation model y = 11, 479x – 2,5569 with r2 of 0,404. The average distribution of above-ground biomass of mangrove vegetation at research sites 2002 was 109,66 tons/ha, with the highest biomass distribution being 200,48 tons/ha. The average above-ground biomass content of mangrove vegetation at the research location in 2022 is 98,28 tons/ha, with the highest biomass distribution of 213,10 tons/ha. In calculations using the selected model and index, in 2022, a total estimated average biomass was obtained of 405,52 tons/ha.

The Use of Aquacrop Model for Soybean in Various Water Availability Within a Lysimeter System

The AquaCrop model is widely used under various agro-ecological conditions to reduce farm water consumption. The study aimed to simulate, validate, and measure the performance of AquaCrop models for canopy cover, biomass and soybean crop yields cultivated within a lysimeter. This research was conducted in the experimental field of the Faculty of Agriculture, the University of Jember, Indonesia (8°09'45.1" S, 113°42'58.2" E, 101 m a.s.l). There are four treatments in 4 lysimeters, namely P1 (irrigation based on recommendation), P2 (irrigation 95-105% FC), P3 (irrigation 75-85% FC) and P4 (irrigation 55-65% FC). The AquaCrop model is calibrated using canopy cover (CC) and then validated to predict the biomass and soybean yield. The experiment revealed that the model simulates better CC, biomass, and soybean yields with full irrigation than deficit irrigation. The performance of the AquaCrop model for soybeans of the Deja 2 variety in predicting CC, biomass, and soybean yield is impressive and reasonable. For the CC we found R2 ranges from 0.956 to 0.995, RMSE 10.389% to 3,293%, NRMSE 0.154% to 0.051%, NSE 0.918 to 0.992, and d 0.980 to 0.998. For biomass the R2 is 0.842, RMSE 0.111 t ha-1, NRMSE 0.017%, NSE 0.712, and d 0.937. For soybeans production the R2 is 0.999, RMSE 0.045 t.ha-1, NRMSE 0.018%,, NSE 0.908 and d 0.970. This study demonstrated that based on WUE, 55-65% FC irrigation is the most efficient application.

Allometric Aboveground Biomass Models for Northern Delhi Ridge Forest

Bio energy obtained through forest biomass is a promising renewable energy option that provides a more environmentally sustainable alternative to fossil resources by reducing the net flux of greenhouse gases to the atmosphere. India, a developing nation is also in the process of building forest biomass models to study the potential of forests in short and long - term carbon mitigation. Earlier, traditional and destructive sampling were the popular methods for the aforesaid purpose. Recently, non - destructive methods have been adopted applying different allometric equations mostly developed outside India. Thus, there was a need to develop non- destructive allometric aboveground (AGB) models complementing the Indian climate. Also, there was a scarcity of domestic/local AGB models for arid, semi- arid, northern tropical thorn forest of India. The objectives of this study were to develop site-specific and mixed- species allometric models to predict AGB, at the Northern Delhi Ridge Forest (NDRF) using nonlinear mathematical functions. The model has a wide scope and can be applied to adjoining areas as well having similar climatic conditions. Three allometric combinations were tried to fit the aboveground biomass data obtained from the ridge forest. A three- parametric Richard’s model (with predictor variable x=D2H) was best fitted to AGB (R2adj= 0.9463) values for larger trees. Meanwhile, for juvenile plants, a three- parametric Richard function was best fitted (R2adj= 0.9733) when (x = DH) was used as a predictor variable. Cross- validation of model parameters exhibited statistical stability.

Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest

Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R2) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R2 = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery.

Growth characteristics and biomass model of Cupressus gigantea sapling

Growth characteristics and biomass model of Cupressus gigantea sapling

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

Forests, commonly viewed as the Earth’s lungs, play a crucial role in mitigating greenhouse gas emissions, regulating the globe, and maintaining ecological equilibrium. The assessment of aboveground biomass (AGB) serves as a pivotal indicator for evaluating forest quality. By integrating remote sensing images with a small number of ground-measured samples to map, forest AGBs can significantly reduce time and labor costs. Current research mainly focuses on improving the accuracy of mapping forest AGBs, such as integrating multiple-sensors remote sensing data and models. However, due to uncertainties associated with remote sensing images and complexities inherent in forest structures, the accuracy of mapping forest AGBs is constrained by both the quantity and distribution of ground samples available. The development of transfer learning methods can fully utilize ground-based measurement data and enable the application of samples across regions and time. To evaluate the potential of transfer learning methods in mapping forest AGBs, this study conducted a spatial–temporal transfer of spectral variables (SVs) and prediction models (PMs) using a direct-push transfer method, and a new evaluation metric, relative change of R-squared (RCRS), was proposed to assess the transferability of SVs and PMs. The results showed that the transferability of SVs and PMs in the spatial target domain is obviously greater than that in the temporal target domain. Compared to the temporal target domain, the RCRS for transfer SVs in the spatial target domain was lower by 20.89 (oak) and 20.88 (Chinese fir) and for transfer PMs by 24.16 (oak) and 24.79 (Chinese fir). Tree species is also one of the main factors affecting the spatial and temporal transfer of SVs, and it is challenging to transfer SVs between different tree species. The results also show that nonparametric models have better generalization performance, and their transferability is much greater than that of parametric models.

Estimation of Agronomic Characters of Wheat Based on Variable Selection and Machine Learning Algorithms

Wheat is one of the most important food crops in the world, and its high and stable yield is of great significance for ensuring food security. Timely, non-destructive, and accurate monitoring of wheat growth information is of great significance for optimizing cultivation management, improving fertilizer utilization efficiency, and improving wheat yield and quality. Different color indices and vegetation indices were calculated based on the reflectance of the wheat canopy obtained by a UAV remote sensing platform equipped with a digital camera and a hyperspectral camera. Three variable-screening algorithms, namely competitive adaptive re-weighted sampling (CARS), iteratively retains informative variables (IRIVs), and the random forest (RF) algorithm, were used to screen the acquired indices, and then three regression algorithms, namely gradient boosting decision tree (GBDT), multiple linear regression (MLR), and random forest regression (RFR), were used to construct the monitoring models of wheat aboveground biomass (AGB) and leaf nitrogen content (LNC), respectively. The results showed that the three variable-screening algorithms demonstrated different performances for different growth indicators, with the optimal variable-screening algorithm for AGB being RF and the optimal variable-screening algorithm for LNC being CARS. In addition, using different variable-screening algorithms results in more vegetation indices being selected than color indices, and it can effectively avoid autocorrelation between variables input into the model. This study indicates that constructing a model through variable-screening algorithms can reduce redundant information input into the model and achieve a better estimation of growth parameters. A suitable combination of variable-screening algorithms and regression algorithms needs to be considered when constructing models for estimating crop growth parameters in the future.

Unraveling the effects of sodium carbonate on hydrothermal liquefaction through individual biomass model component and machine learning-enabled prediction

Despite sodium carbonate (Na2CO3) being commonly utilized as a catalyst in actual biomass hydrothermal liquefaction (HTL), its impact on individual biomass components hasn't been well-examined. This study thus delves into the role of Na2CO3 in HTL of biomass model components (carbohydrate, lignin, protein, and lipid) at varying conditions. Na2CO3 at 5 wt% amplified carbohydrate degradation into biocrude and aqueous-gaseous products (AG), resonating with previous work on carbohydrate-rich feedstocks. While Na2CO3 has a marginal effect on lignin HTL, it negatively influences lipid HTL. Here, 5 wt% and 13.5 wt% Na2CO3 decrease the biocrude yield from 95.6% to less than 10%, simultaneously increasing the AG yield to approximately 90%. This is presumably due to the interaction of lipid decomposition intermediates (fatty acids) with sodium cations, resulting in water-soluble soap. For protein HTL, a low Na2CO3 concentration (5 wt%) has no significant impact on product formation, but excessive Na2CO3 (27 wt%) converts a considerable portion of biocrude into AG. In addition to these explorations that provided insights for actual biomass HTL, we also developed a machine learning model to adequately predict HTL biocrude yield by taking Na2CO3 effect into consideration. The Adaboost machine learning model displayed the most satisfactory prediction performance (training and testing R2: 0.96, 0.8) among the three investigated machine learning models. The feature importance analysis reveals lipid content and Na2CO3 concentration as pivotal factors over HTL process conditions and other biochemical components. This research provides fundamental insights into the Na2CO3 role in HTL product formation pathway and offers an intelligent and precise prediction model for HTL biocrude yield, thereby advancing the development of more efficient and sustainable bioenergy production processes.

Biomass allocation and equations for silver birch (Betula pendula) and downy birch (Betula pubescens) in Estonia

ABSTRACT The values of the fractional distribution of aboveground (AGB) and belowground biomass (BGB), root/shoot ratios (R/S) and stemwood density were estimated for silver (Betula pendula) and downy birch (Betula pubescens) along site index and age gradients for Estonia. Biomass models were used, in which the diameter at breast height (D1.3, measured above root collar), tree height, age and site index served as independent variables. The models for AGB and BGB were elaborated based on the data of 117 and 30 model trees, respectively. Strong allometric relationships were revealed between D1.3 and total AGB (with foliage), stem mass and BGB. For trees with D1.3 < 15 cm, individual models predicted similar biomass patterns for both species, for larger trees, species-specific models are suggested. The relative share of stem mass in AGB was 80–90% for all site index and age classes, being significantly higher for silver birch. The average relative share of stembark, branches, leaves and the R/S, were significantly higher for downy birch. Mean oven-dry mass stemwood density for silver and downy birch was 0.573 g cm−3, there was no statistically significant difference between the species. The mean R/S ratio was 0.29 and 0.43 for silver and downy birch, respectively.

Study of the Slow Pyrolysis of Lignin, Hemicellulose, and Cellulose and the Effect of Their Interaction in Plant Biomas

The pyrolysis of two types of raw materials of plant origin (sawdust and sunflower husks), components of the organic matter of biomass (hemicellulose, cellulose, and lignin), and model mixtures preparedfrom components in accordance with their fractions in the raw materials was studied. Pyrolysis of the materials was carried out using TGA and a laboratory setup with a fixed bed reactor. The distribution and composition of the products were determined at pyrolysis temperatures of 350, 425, 500, and 575C. Experimental data obtained with the biomass samples and model mixtures were compared with calculated values obtained based on the pyrolysis of individual components and their fractions in the biomass. The possibility of predicting the distribution of pyrolysis products depending on the component composition was investigated. Theinfluence of intercomponent interaction in biomass on the yield and composition of pyrolysis products was revealed.

Above ground tree biomass modeling using machine learning algorithms in western Terai Sal Forest of Nepal

The monitoring of forest biomass is a crucial biophysical parameter in forest ecosystems, as it provides valuable information for managing forests sustainably and tracking carbon circulation statistics. To achieve sustainable forest management, it is essential to monitor and study forest resources, particularly biomass. This study aimed to model above ground tree biomass (AGTB) using Machine Learning Algorithms (MLAs) in the western terai Sal forest of Nepal. AGTB was calculated using a systematic inventory sample plot, while spectral and textural variables were processed and masked for the study area using Sentinel-2A satellite imagery. Three MLAs namely support vector machine (SVM), random forest (RF), and stochastic gradient boosting (SGB), were employed for modeling with eight categorized variable datasets. Among the MLAs, the RF algorithm with a combination of gray-level co-occurrence matrix (GLCM) and raw bands (RB) dataset variable demonstrated the best performance, with a low RMSE value of 78.81 t ha−1 in the test data. However, the AGTB range from this model ranged from 118.34 to 425.97 t ha−1. The study found that traditional indices, raw bands, and GLCM texture from near-infrared were important variables for AGTB. Nevertheless, the RF algorithm and the dataset combination of GLCM plus raw bands (RB) exhibited excellent performance in all model runs. Thus, this pioneering study on comparative MLAs-based AGTB assessment with multiple datasets variables can provide valuable insights for new researchers and the development of novel approaches for biomass/carbon estimation techniques in Nepal.

Simulation of Furfural and Levulinic Acid Production from Lignocellulosic Biomass

Furfural and levulinic acid are chemicals that can be produced from lignocellulosic biomass. In this research, mass balance simulation of furfural and levulinic acid production at scale of 100 tons of biomass/year was examined. This research using various models of kinetic reaction and biomass to solvent ratio from lignocellulosic biomass raw materials consisting of cellulose, hemicellulose, lignin, ash, and water composition of 35%, 30%, 25%, 3%, and 7%. The kinetic models used in this research were separate kinetic models and simultaneous model (model-3). The separate kinetic models were divided into two models which separate kinetic model 1 (model-1) and separate kinetic model 2 (model-2). SuperPro Designer 9.0 software was used to calculate mass balance simulation. From the research, it was found that variations in kinetic reaction model affected furfural and levulinic acid production. Higher biomass to solvent ratio produced higher furfural and levulinic acid production. The highest furfural produced from simulation process was kinetic reaction model-2 with a biomass to solvent ratio of 1:30 and 0.67 liter furfural/hour. While the highest levulinic acid produced from the simulation process was kinetic reaction model-1 with a biomass to solvent ratio of 1:30 and 2.37 liter levulinic acid/hour.

Biomass and carbon stocks in mangrove-afforested areas, central coastal areas of Bangladesh

Mangroves are the remarkably productive ecosystems of the world which losses its natural existence to a great portion and presently emphasis is given on its plantation for future conservation. Thus, present study focused on the development of biomass models for the Sonneratia caseolaris (L.) and Sonneratia apetala (Buch-Ham), and assessment of carbon stock for the plantation of central coastal areas of Bangladesh. Semi-destructive approach was applied for estimating the biomass of trees. The best-fit TAGB models for S. apetala and S. caseolaris plantations were Ln (TAGB) = -1.89 + 2.10 * Ln(D) + 0.23*Ln(H), and Ln (TAGB) = -3.09 + 2.31*Ln(D) + 0.35*Ln(H), respectively. Carbon stocks in TAGB and below-ground were estimated for a young mangrove plantation (<20 years) with Sonneratia apetala and Sonneratia caseolaris; and for an older plantation (>40 years) with Sonneratia apetala separately. The mean ecosystem carbon stock (above- and below-ground) of young and mature were estimated to be 350.53 ± 14.23 t C/ha and 483.64 ± 16.35 t C/ha, respectively which varied significant (p < 0.05) between the sites. The current study recommended assessing the biomass of studied species using the site and species-specific allometric models for the correct carbon accounting.

Environmental Effects on the Spatiotemporal Variability of Sardine Distribution Along the Portuguese Continental Coast

Scientific tools capable of identifying distribution patterns of species are important as they contribute to improve knowledge about biodiversity and species dynamics. The present study aims to estimate the spatiotemporal distribution of sardine (Sardina pilchardus, Walbaum 1792) in the Portuguese continental waters, relating the spatiotemporal variability of biomass index with the environmental conditions. Acoustic data was collected during Portuguese spring acoustic surveys (PELAGO) over a total of 16,370 hauls from 2000 to 2020 (gap in 2012). We propose a spatiotemporal species distribution model that relies on a two-part model for species presence and biomass under presence, such that the biomass process is defined as the product of these two processes. Environmental information is incorporated with time lags, allowing a set of lags with associated weights to be suggested for each explanatory variable. This approach makes the model more complete and realistic, capable of reducing prediction bias and mitigating outliers in covariates caused by extreme events. In addition, based on the posterior predictive distributions obtained, we propose a method of classifying the occupancy areas by the target species within the study region. This classification provides a quite helpful tool for decision makers aiming at marine sustainability and conservation. Supplementary materials accompanying this paper appear on-line.

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties.

Correlations between dendrometric and allometric variables of Eucalyptus benthamii under agroforestry systems in the subtropic of Brazil

The growing interest in integrated agricultural production systems has increased the search for information about the arboreal component, thus, information about the correlations and associations between dendrometric and allometric variables are important. This study investigated the results obtained through simple correlation analysis and its breakdown via path analysis of dendrometric and allometric variables of Eucalyptus benthamii under different integrated systems and monoculture. The following variables were evaluated: diameter at 1.3 m above ground, total height, volume with and without bark, crown radius and biomass components stem wood and bark, branches, leaves and total aboveground biomass. Descriptive statistics showed the great variance of the data with coefficients of variation from 18 to 85.08%. All correlations (Spearman) between allometric and dendrometric variables were positive and 89% were statistically significant (p &lt; 0.05). For the total aboveground biomass as dependent variable, the variables with the greatest direct effects were wood biomass (0.26), volume with bark (0.17) and branch biomass (0.15). With the results of the simple correlation any variable can be used in the modeling of the total aboveground biomass. However, the path analysis makes it clear that some variables have weak associations even with significant correlations of great magnitude.

New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada

Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (&lt;5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha−1), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.

Estimating changes of forest carbon storage in China for 70 years (1949–2018)

In the realm of forest resource inventory and monitoring, stand-level biomass carbon models are especially crucial. In China, their importance is underscored as they form the bedrock for estimating national and international forest carbon storage. This study, based on the data from 52,700 permanent plots in the 9th National Forest Inventory (NFI) of China, was directed towards developing these models. After computing biomass and carbon storage per hectare using specific tree models for 34 species groups, we devised robust volume-derived biomass and carbon storage models for 20 forest types. The application of these models and historical data reveals notably a decline in China's forest carbon storage to 4.90Pg by the late 1970s due to aggressive forest exploitation. However, subsequent conservation and afforestation campaigns have affected a recovery, culminating in a storage of 8.69Pg by the 9th NFI. Over the past 40 years, China's forest carbon storage has surged by 3.79Pg, split between natural forests (2.25Pg) and planted forests (1.54Pg). In benchmarking against three pre-existing models, we discerned discernible biases, underscoring the need for larger modeling sample sizes. Overall, our models stand as a monumental stride in accurately gauging forest carbon storage fluctuations in China, both regionally and nationally.