Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future

Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China

The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be leveraged to accurately monitor forest AGC, whereas machine learning is an important algorithm for the accurate prediction of AGC. Therefore, in this study, single Landsat 8 (L) remote sensing data, single Sentinel-2 (S) remote sensing data, and combined Landsat 8 and Sentinel-2 (L + S) data are used as data sources. Four machine learning methods, support vector regression (SVR), random forest (RF), XGBoost (extreme gradient boosting), and CatBoost (categorical boosting), are used to predict forest AGC based on two phases of forest sample plots in Shanghai. We chose the optimal model to predict the AGC and simulate the spatiotemporal distribution. The study shows that both machine learning models based on separate Landsat 8 OLI and Sentinel-2 satellite remote sensing data can accurately predict the AGC and spatiotemporal distribution of the Shanghai urban forest. Nevertheless, the accuracy of the combined data (L + S) and CatBoost-integrated AGC models is higher than the others, with fitting and validation accuracy R2 values of 0.99 and 0.70, respectively. The RMSE was also smaller at 0.67 and 6.29 Mg/ha, respectively. The uncertainty of the AGC spatial distribution in the Shanghai urban forest derived from the CatBoost model prediction from the 2016–2019 data was small and consistent with the actual situation. Furthermore, the statistics showed that the AGC of the Shanghai forest increased from 24.90 Mg/ha in 2016 to 25.61 Mg/ha in 2019

Assessing Carbon Sequestration Potential in State-Owned Plantation Forests in China and Exploring Feasibility for Carbon Offset Projects

In the pursuit of carbon neutrality, state-owned forests are prime candidates for carbon offset projects due to their unique tenure and management characteristics. Employing methodologies endorsed by the International Panel on Climate Change and logistic growth curves, this study assesses the carbon stocks and sequestration potential of established state-owned plantation forests across 31 Chinese provinces from 2023 to 2060, encompassing seven forestry industry groups. This study projects that by 2060, these forests will amass a carbon stock of 558.25 MtC, with the highest stock in Northeast China (122.09 MtC) and the lowest in Northwest China (32.27 MtC), notably showing the highest growth rate at 91.15%. Over the forecast period, they are expected to accumulate a carbon sink of 637.07 MtCO2e, translating to an average annual carbon sink of 17.22 MtCO2e and an average annual carbon sink per unit of 1.41 tons of CO2 per hectare per year. Additionally, state-owned forests have the potential to offset approximately 0.15%–0.17% of annual carbon emissions, aligning with international climate goals. However, it is essential to note that the conversion of these carbon sinks into tradable carbon credits is subject to specific methodology requirements. Therefore, the future development of carbon offset projects in China’s state-owned forests should consider the advancement of carbon market mechanisms, including the Chinese Certified Emission Reduction and the introduction of a carbon inclusion mechanism and natural forest methodology, to fully realize their potential contributions to carbon neutrality. In summary, these findings offer valuable insights for shaping the future of carbon offset initiatives within China’s state-owned forests.Forestry, Faculty ofNon UBCReviewedFacultyResearche

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R-s) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R-s) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R(s )was 44.07 t CO2 ha(-1) a(-1) R-s correlated significantly with soil temperature (P <0.01), which explained 69.7% of the variation in R-s at a diurnal scale. Soil moisture was correlated significantly with R-s on a daily scale except not during winter, indicating it affected R-s. A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R-s. The relationship between R-s and soil temperature during a day showed a clear hysteresis. R-s was significantly and positively (P <0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R-s.Peer reviewe

Estimating Crown Structure Parameters of Moso Bamboo: Leaf Area and Leaf Angle Distribution

Both leaf area (LA) and leaf angle distribution are the most important eco-physiological measures of tree crowns. However, there are limited published investigations on the two parameters of Moso bamboo (Phyllostachys edulis (Carri&egrave;re) J. Houz., abbreviated as MB). The aim of this study was to develop allometric equations for predicting crown LA of MB by taking the diameter at breast height (DBH) and tree height (H) as predictors and to investigate the leaf angle distribution of a MB crown based on direct leaf angle measurements. Data were destructively sampled from 29 MB crowns including DBH, H, biomass and the area of sampled leaves, biomass of total crown leaves, and leaf angles. The results indicate that (1) the specific leaf area (SLA) of a MB crown decreases from the bottom to the top; (2) the vertical LA distribution of MB crowns follow a &ldquo;Muffin top&rdquo; shape; (3) the LA of MB crowns show large variations, from 7.42 to 74.38 m2; (4) both DBH and H are good predictors in allometry-based LA estimations for a MB crown; (5) linear, exponential, and logarithmic regressions show similar capabilities for the LA estimations; (6) leaf angle distributions from the top to the bottom of a MB crown can be considered as invariant; and (7) the leaf angle distribution of a MB crown is close to the planophile case. The results provide an important tool to estimate the LA of MB on the standing scale based on DBH or H measurements, provide useful prior knowledge for extracting leaf area indexes of MB canopies from remote sensing-based observations, and, therefore, will potentially serve as a crucial reference for calculating carbon balances and other ecological studies of MB forests

High-temperature reaction kinetics of Inconel 617 in impure helium

Inconel 617 is the reference candidate material for the high-temperature gas-cooled reactor (HTGR) which is expected to contain traces of impurities in the primary circuit. The corrosion behaviors of Inconel 617 in a special impure helium atmosphere (PCO = 0μbar) were investigated at 750℃ and 980℃ in this work. The production of CO was observed above 830℃, which can be verified by the thermodynamic model of “microclimate reaction”. Compared with the corrosion phenomenon at 750℃, the oxide layer thinning, surface breakage, and mass loss were found at 980℃. Then, the reaction kinetics of this corrosion behavior at 980℃ was studied, and the influence factor controlling the reaction rate is the partial pressure of CO in the atmosphere. On this basis, a gas phase prediction model for the “microclimate reaction” is proposed, which can be utilized to predict the variation of CO content in the impure helium atmosphere during the corrosion process at 980℃. The model is in good agreement with the experimental gas data in this study and previous work. Further, the critical temperature of the microclimate reaction could be obtained by the kinetic model in this research, which is consistent with the previous results of thermodynamic calculation and corrosion experiments

High-Temperature Corrosion Behavior of Incoloy 800H Alloy in the Impure Helium Environment

The helium coolant in the primary circuit of the high-temperature gas-cooled reactor (HTGR) contains traces of impurities, which can induce the corrosion of superalloys when exposed to elevated temperatures. The superalloy damage caused by the corrosion could threaten the safe operation of the reactor. In this work, the corrosion behavior of a representative superalloy (chromium-rich iron base alloy Incoloy 800H) was investigated under the impure helium at different typical temperatures of HTGR. An experimental setup developed for studying the high-temperature corrosion of superalloys was used to investigate the chemical reactions and corrosion behaviors of Incoloy 800H. It was found that CO2 is an important oxygen source in the reaction with chromium, and CO is released as the product. In addition, the observation and computation of the critical temperature (TC) of the reaction between CO2 and carbon in the alloy show that TC is much lower than that (TA) of the microclimate reaction, which indicates that CO2 can protect the scale from destruction. Furthermore, the slight decarbonization of the alloy was found above TC. Also, a model developed by the thermodynamic analysis was proposed to explain the mechanism of slight decarbonization and predict the critical temperature when the CO2-C reaction occurs. This work presents a guideline for protecting the oxide scale of superalloys used in HTGR

Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County

Simulating spatiotemporal land use and land cover change (LUCC) data precisely under future climate scenarios is an important basis for revealing the carbon cycle response of forest ecosystems to LUCC. In this paper, a coupling model consisting of a back propagation neural network (BPNN), Markov chain, and cellular automata (CA) was designed to simulate the LUCC in Anji County, Zhejiang Province, under four climate scenarios (Representative Concentration Pathway (RCP) 2.6, 4.5, 6.0, 8.5) from 2024 to 2049 and to analyze the temporal and spatial distribution of bamboo forests in Anji County. Our results provide four outcomes. (1) The transition probability matrices indicate that the area of bamboo forests shows an expansion trend, and the largest contribution to the expansion of bamboo forests is the cultivated land. The Markov chain composed of the average transition probability matrix could perform excellently, with only small errors when simulating the areas of different land-use types. (2) Based on the optimized BPNN, which had a strong generalization ability, a high prediction accuracy, and area under the curve (AUC) values above 0.9, we could obtain highly reliable land suitability probabilities. After introducing more driving factors related to bamboo forests, the prediction of bamboo forest changes will be more accurate. (3) The BPNN_CA_Markov coupling model could achieve high-precision simulation of LUCC at different times, with an overall accuracy greater than 70%, and the consistency of the LUCC simulation from one time to another also had good performance, with a figure of merit (FOM) of approximately 40%. (4) Under the future four RCP scenarios, bamboo forest evolution had similar spatial characteristics; that is, bamboo forests were projected to expand in the northeast, south, and southwest mountainous areas of Anji County, while bamboo forests were projected to decline mainly around the junction of the central and mountainous areas of Anji County. Comparing the simulation results of different scenarios demonstrates that 74% of the spatiotemporal evolution of bamboo forests will be influenced by the interactions and competition among different land-use types and other driving factors, and 26% will come from different climate scenarios, among which the RCP8.5 scenario will have the greatest impact on the bamboo forest area and spatiotemporal evolution, while the RCP2.6 scenario will have the smallest impact. In short, this study proposes effective methods and ideas for LUCC simulation in the context of climate change and provides accurate data support for analyzing the impact of LUCC on the carbon cycle of bamboo forests