Soil Respiration Model Research Articles

Identification of Priority Supply Areas for Carbon Sinks Based on Ecosystem Service Flow: A Case Study for the Hexi Region in Northwestern China

The development and implementation of regional protection plans for ecosystem carbon storage services have been recognized as crucial actions for mitigating global climate change. However, the supply areas of carbon sequestration in terms of ecosystem service flows in inland regions are still less evaluated. The goal of this study is to identify the priority-ranked supply areas for carbon sinks. Here, we conducted a case study in the Hexi Region of northwestern China and proposed a framework to quantify the priority supply areas for carbon sinks from the perspective of ecosystem service flows. Firstly, we quantified the carbon service supply and demand areas by combining carbon models (i.e., the Carnegie–Ames–Stanford Approach model and soil respiration models) with socioeconomic and natural factors. Then, we introduced a breaking point formula to estimate ecosystem service flow, specifically focusing on distance or range. Finally, we determined priority supply areas for carbon sinks based on the Zonation model. The results showed that significantly higher carbon sequestration values were detected in the Qilian Mountains, ranging from 2.0 to 3.0 t hm−2, in comparison with desert oasis areas, where the supply values ranged from 0 to 0.01 t hm−2. The urban areas and rural settlements within the study area are characterized by higher values of carbon emissions compared to those in the Qilian Mountains and deserts. The carbon flow analysis demonstrated that the middle and northern parts of the study area, being characterized by lower precipitation and sandy landscapes, were identified as locations with low carbon sequestration fluxes (<1.0 t hm−2). In addition, the mountainous regions were identified as the main highest priority area for ecosystem carbon sequestration, covering 8.33% of total area of the Hexi Region. Our findings highlighted the importance of the Qilian Mountains in terms of sustaining carbon sequestration service supply in the Hexi Region and targeted ecological protection practices to be implemented going forward.

Spatial–Temporal Changes and Driving Factor Analysis of Net Ecosystem Productivity in Heilongjiang Province from 2010 to 2020

Net ecosystem productivity (NEP) is an important indicator for the quantitative evaluation of carbon sources/sinks in terrestrial ecosystems. An improved CASA model and soil respiration model, combined with MODIS and meteorological data, are utilized to estimate vegetation NEP from 2010 to 2020. A Theil–Sen trend analysis, a Mann–Kendall test, the Hurst index, and geographical detector methods were employed to analyze the spatiotemporal variations in NEP in Heilongjiang Province and its driving factors. The results show the following: (1) The overall NEP in Heilongjiang Province exhibited a fluctuating upward trend from 2010 to 2020, with a growth rate of 4.74 g C·m−2·yr−1, and an average annual NEP of 404 g C·m−2·yr−1. Spatially, NEP exhibits a distribution pattern of “low from east to west to high from north to south in the central region”, with 99.27% of the area being a carbon sink. (2) Significant regional differences were observed in the spatial trend of NEP changes, with 78.39% of regions showing increasing trends and 17.53% showing decreasing trends. Future NEP changes are expected to continue, with regions showing a persistent increase (58.44%), potential decrease (19.95%), potential increase (5.65%), and persistent decrease (11.88%). (3) The geographical detector results indicate that altitude is the dominant factor affecting NEP, followed by slope, temperature, population density, etc. The interaction-detector results show that the interaction between each factor shows an increasing trend, and the interaction between any two factors is higher than that of a single factor. The research results can provide scientific references for reducing emissions, increasing sinks, and protecting ecosystems in Heilongjiang Province.

Global soil respiration estimation based on ecological big data and machine learning model

Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R2 = 0.78216, root mean squared error (RMSE) = 285.8964 gCm−2y−1, and mean absolute error (MAE) = 180.4186 gCm−2y−1, which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Application of the ensemble of T&P models for estimating the respiration of forest soils in the zone of temperate continental climate

The aim of the study was by using an ensemble of T&P models, i.e. empirical models of soil respiration dependent on temperature, precipitation and other meteorological and soil parameters, to show the dependence of the choice of optimal empirical models of soil respiration on the soil types in forest ecosystems, in this case Entic Podzol and Haplic Luvisol, as well as on options for parameterizing models using soil or air temperatures, precipitation and soil organic carbon stock. Location and time of the study. Temperate continental climate zone of the Central Russia: mixed forest in the north shore of Oka River and deciduous forest on the south shore of Oka River. The data were collected in 1998–2022. Methods. An ensemble of empirical models of soil respiration was used, linking soil respiration to monthly mean soil or air temperatures and precipitation and soil organic carbon stock. At the same time, a method for determining soil respiration at zero degrees was proposed based on the results of a comparative analysis of simulated data with field measurements. Results. For the forest Entic Podzol, i.e. the well-drained soil poor in organic carbon, the model that relates soil respiration to soil temperature, precipitation and organic carbon storage was the best, whereas for Haplic Luvisol, i.e. the soil with good water retention capacity, the Reich-Hashtmoto model with a quadratic dependence on temperature at an exponent point was the best model. Conclusions. When choosing optimal empirical models of soil respiration, it is important to take into account such soil parameters as organic carbon stock and the ability to retain water.

Characteristics of carbon sources and sinks and their relationships with climate factors during the desertification reversal process in Yulin, China

Research on carbon sources/sinks in desert ecosystems is of great importance to understand the carbon cycle and its response to climate change. Net primary productivity (NPP) and net ecosystem productivity (NEP) are the two most important indictors for quantitatively evaluating carbon storage and can be used to indicate the response of terrestrial ecosystems to climate change. In this study, we used remote sensing data, meteorological data and vegetation type data to estimate the NPP and NEP using CASA model and soil respiration model from 2000 to 2020 in the region of Yulin, which is a typical desertification reversal region in the Mu Us Sandy Land. The spatial and temporal features of the NPP and NEP and their relationships with temperature and precipitation were determined. The results showed that both the annual NPP and NEP showed an increasing trend from 2000 to 2020 in the region of Yulin, where the terrestrial ecosystem acted as a carbon source until 2001 but turned into a sink thereafter. The carbon storage showed an increasing trend with a rate of 0.50 Tg C·a−1 from 2000 to 2020. Both the mean annual NPP and the total NEP increased from the west to the east of the region in spatial distribution. The total NEP indicated that the area with a carbon sink accounted for 89.22% of the total area, showing a carbon accumulation of 103.0 Tg C, and the carbon source area accounted for 10.78% of the total area with a carbon emission of 4.40 Tg C. The net carbon sequestration was 99.44 Tg C in the region of Yulin during the period from 2000 to 2020. Temperature had no significant effects on NPP and NEP for most areas of the region, while precipitation had a positive effect on the increasing NPP in 75.3% of areas and NEP in 30.07% of areas of the region. These results indicated that it is of utmost significance to protect terrestrial ecosystems from degradation, and ecological restoration projects are essential in combating desertification, which would be helpful for soil water conservation and could effectively increase carbon storage in desert ecosystems.

Ecosystem carbon storage considering combined environmental and land-use changes in the future and pathways to carbon neutrality in developed regions

Assessing the carbon storage capacity of terrestrial ecosystems is crucial for land management and carbon reduction policymaking. There is still a knowledge gap regarding how ecosystem carbon storage will be impacted by combined environmental and land-use factors and their spatial-temporal changes, especially in developed regions where urbanization has slowed down. This study investigated how developed regions in subtropical and tropical areas might increase carbon storage and achieve carbon neutrality, using Guangdong Province in South China as an example. Based on the sustainable development assumption, three land-management scenarios were developed and simulated for 2020–2060 using the Patch-generating Land Use Simulation model. Without considering disturbance and natural losses, carbon storage was estimated by net ecosystem productivity (NEP)—the difference between net primary productivity (NPP) and heterotrophic respiration (HR). NPP was predicted using an artificial neural network model trained by historical NPP data and 16 environmental and land-use variables. HR was predicted using soil respiration models from previous research. Based on the balance between carbon storage and emissions, we predicted the allowable fossil fuel consumption to achieve net-zero CO2 emissions in 2060. The results show that Guangdong's total carbon storage changes from 73.7 MtC in 2020 to 70.6–74.8 MtC in 2060 under different scenarios. Nonlinear relationships exist between the carbon stored and the areas of different land-use types. Topography, temperatures, and land-use configurations jointly lead to significantly varied carbon storage between croplands and between forests in space and time. Protecting and regenerating forests in subtropical areas and forest edges is more effective than afforestation in lowland tropical areas for storing carbon. Net-zero CO2 emissions rely more on reducing emissions than land management. To achieve this, the proportion of fossil energy in total energy consumption should be lowered from 75.5 % in 2020 to ~25 % in 2060.

Soil Temperature, Organic-Carbon Storage, and Water-Holding Ability Should Be Accounted for the Empirical Soil Respiration Model Selection in Two Forest Ecosystems

Soil respiration (SR) is a main component of the carbon cycle in terrestrial ecosystems, and being strongly affected by changes in the environment, it is a good indicator of the ecosystem’s ability to cope with climate change. This research aims to find better empirical SR models using 25-year-long SR monitoring in two forest ecosystems formed on sandy Entic Podzol and loamy Haplic Luvisol. The following parameters were considered in the examined models: the mean monthly soil or air temperatures (Tsoil or Tair), the amount of precipitation during the current (P) and the previous (PP) months, and the storage of soil organic carbon (SOC). The weighted non-linear regression was used for model parameter estimations for the normal, wet, and dry years. To improve the model resolutions by magnitude, we controlled the slope and intercept of the linear model comparison between the measured and modeled data through the change in R0—SR at zero soil temperature. The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R2) were used for the estimation of the goodness of model performances. For the sandy Entic Podzol, it is more appropriate to use the models dependent on SOC (TPPC). While for the loamy Haplic Luvisol, the Raich–Hashimoto model (TPPrh) with the quadratic Tsoil or Tair dependency shows the better results. An application of Tsoil for the model parameterization gives better results than Tair: the TPPC model was able to adequately describe the cold-period SR (Tsoil ≤ 2 °C); the TPPrh model was able to avoid overestimations of the warm-period SR (Tsoil > 2 °C). The TPPC model parameterized with Tsoil can be used for the quality control of the cold-period SR measurements. Therefore, we showed the importance of accounting for SOC and the water-holding ability when the optimal SR model is chosen for the analysis.

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

A biophysical model to simulate seasonal variations of soil respiration in agroecosystems in China

Characterizing the seasonal patterns of soil respiration using a biophysical model is crucial for understanding the mechanisms of the terrestrial carbon (C) cycle. We developed a biophysical soil respiration model including four submodels that were associated with the functions of soil temperature, moisture, leaf area index (LAI), and soil organic C, and the model parameters were determined and calibrated. The modeling reproducibility was examined in various crop fields at our study site, and the modeling performance was also validated in twenty-five other agroecosystems in China. The monthly and annual soil respiration was estimated using the model, and the modeling uncertainty was evaluated. The results showed that the soil respiration model had good modeling reproducibility in various crop fields at our site. The measured soil respiration in the different agroecosystems in China validated our model well. The R2, root mean squared error (RMSE), model efficiency (ME), and mean absolute error (MAE) were within the ranges of 0.449‒0.932, 0.526‒1.465 μmol m−2 s−1, 0.176‒0.796, and 0.312‒1.377 μmol m−2 s−1, respectively, across the twelve agroecosystem sites for validation. The measured soil respiration was significantly (P < 0.001) correlated with the modeled soil respiration across all twenty-six validation sites. Adding LAI significantly improved the modeling of soil respiration when the performance of the model (with LAI) vs. that without LAI was compared. The modeled monthly soil respiration was within the range of 0.016 ± 0.001‒0.117 ± 0.006 Pg C mo−1 in 2010 in agroecosystems in China. The black soils in Northeast China are fertile, which resulted in high temperature sensitivity and a clear increasing tendency of soil respiration from winter to summer in 2010 in agroecosystems in Northeast China. The established biophysical soil respiration model was proven to be effective for characterizing the temporal variations of soil respiration in various agroecosystems in China.

Strengthening grassland carbon source and sink management to enhance its contribution to regional carbon neutrality

Grassland ecosystems are an important part of terrestrial ecosystems. It is of great significance to fully tap the potential of grassland carbon sinks to achieve the goal of carbon neutrality in the region and even worldwide. Previous studies have focused on analyzing the temporal and spatial distributions of grassland carbon sink capacity and lacked a systematic discussion of grassland carbon source/sink management. Taking three grasslands in the Inner Mongolia Autonomous Region of China as an example, this study characterized grassland carbon source/sink capacity through net ecosystem productivity (NEP), combined with a soil respiration model, Sen + MK (Mann-Kendall) trend test method and other methods, revealed the spatiotemporal distribution characteristics of NEP and carbon emissions (CEs) in the study area from 2000 to 2019, and used spatiotemporal and geographical weighted regression (GTWR) model to clarify the potential mechanism of grassland sink increase. Suggestions for grassland carbon source/sink management are also discussed. The study found that the CEs in the study area from 2000 to 2019 could be roughly divided into three stages: slow growth period (2000–2009), rapid growth period (2009–2011) and stable development period (2011–2019). Although grassland carbon sinks could not completely offset regional CEs, grassland ecosystems had a very large carbon sink potential, which could make important contributions to the realization of regional carbon neutrality goals. Grassland carbon sources/sinks were jointly affected by climate change and human activities, and the degree of impact varied from year to year, with the impact of human activities changing more significantly. On this basis, the study puts forward policy recommendations from four aspects: regional priority, dynamic monitoring and management, control of grazing intensity and implementation of ecological compensation, and it proposed a sustainable development pattern of “carbon trading-ecological compensation-grassland sink increase”. The research results have important practical guiding significance for the management of grassland sink increases and regional carbon neutral development strategies in Inner Mongolia under the background of climate change.

Response of Bare Soil Respiration to Air and Soil Temperature Variations According to Different Models: A Case Study of an Urban Grassland

Soil respiration is an important component of the global carbon cycle and is highly responsive to disturbances in the environment. Human impacts on the terrestrial ecosystem lead to changes in the environmental conditions, and following this, changes in soil respiration. Predicting soil respiration and its changes under future climatic and land-use conditions requires a clear understanding of the processes involved. The observation of CO2 fluxes was conducted at an urban grassland, where plants were removed and respiration from bare soil was measured. Nine soil respiration models were applied to describe the temperature dependence of heterotrophic soil respiration. Modified models were suggested, including a linear relationship of the temperature sensitivity and base respiration coefficients with soil temperature at various depths. We demonstrate that modification improves the simulated soil respiration. The exponential and logistic models with linear dependences on the model parameters from the soil temperatures were the best models describing soil respiration fluxes. Variability of the apparent temperature sensitivity coefficient (Q10) was demonstrated, depending on the model used. The Q10 value can be extremely high and does not reflect the actual relationships between soil respiration and temperature. Our findings have important implications for better understanding and accurately assessing the carbon cycling characteristics of terrestrial ecosystems in response to climate change in a temporal perspective.

NEP Estimation of Terrestrial Ecosystems in China Using an Improved CASA Model and Soil Respiration Model

NEP Estimation of Terrestrial Ecosystems in China Using an Improved CASA Model and Soil Respiration Model

黄河流域生态功能区植被碳汇估算及其气候影响要素

PDF HTML阅读 XML下载 导出引用 引用提醒 黄河流域生态功能区植被碳汇估算及其气候影响要素 DOI: 10.5846/stxb202203110591 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 水沙科学与水利水电工程国家重点实验室(清华大学)2021年对外开放基金(sklhse-2021-A-06);辽宁省兴辽英才项目资助;国家自然科学基金项目(52079060) Estimation of vegetation carbon sink in the Yellow River Basin ecological function area and analysis of its main meteorological elements Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于改进的CASA模型和土壤呼吸模型,综合遥感数据、气象数据、植被数据等多源数据类型,以黄河流域为研究对象,估算生态功能区植被碳源/汇,探讨其时空变化及主要气候影响要素。结果显示:(1)黄河流域植被净初级生产力(NPP)在2000-2020年整体呈波动上升趋势,多年平均NPP值为317.18 gC m-2 a-1;林地＞耕地＞沼泽＞草地,水体、戈壁、风蚀劣地和裸沙地的NPP总体处于较低值。空间尺度上,黄河流域NPP呈现出南高北低的分布特点,高值区集中在黄土高原农业与草原生态区与汾渭盆地农业生态区交界处,空间差异显著。(2)土壤微生物呼吸量极大值出现2018年,为15.82 gC m-2 a-1,最小值出现在2004年为14.34 gC m-2 a-1;空间上呈现从东南往西北、从东往西依次递减的空间分布格局。(3)黄河流域总体呈碳汇属性(碳汇区域超过60%),年均固碳量约为111.02 MgC/a,年均排碳量为-9.96 MgC/a,年均净碳汇总量约为111.41 MgC/a。(4)黄河流域植被碳汇的形成与降水和太阳辐射的相关性最大,气温的影响面积仅占12.44%;当气温为30℃,降水150 mm,太阳辐射550 MJ/m2时,植被固碳能力效果最强。研究结果为区域全面准确地评估陆地生态系统碳源/汇,实现黄河流域高质量发展,达到碳中和目标提供参考依据。 Abstract:Based on the improved CASA (Carnegie-Ames-Stanford Approach) model and soil respiration model, this paper integrates multi-source data, such as remote sensing data, meteorological data and vegetation data, to estimate vegetation carbon sources/sinks in ecological function areas, and explore their spatio-temporal variations and main climate influencing elements in the Yellow River Basin. The results show that (1) the overall Net Primary Productivity (NPP) of vegetation in the Yellow River Basin presented a fluctuating upward trend from 2000 to 2020, with a multi-year average NPP value of 317.18 gC m-2 a-1. The NPP of various vegetation types showed that: forest land>cropland>swamp>grassland, and water bodies, Gobi, wind eroded poor land and bare sand land were at lower values overall. On the spatial scale, the NPP in the Yellow River Basin shows the distribution characteristics of high in the south and low in the north, and the high value areas are concentrated at the junction of the Loess Plateau agricultural and grassland ecological zone and the Fenwei Basin agroecological zone, with significant spatial differences. (2) The maximum value of soil microbial respiration appeared in 2018 at 15.82 gC m-2 a-1, and the minimum value appeared in 2004 at 14.34 gC m-2 a-1; spatially it showed a spatial distribution pattern decreasing from southeast to northwest and from east to west. (3) The Yellow River Basin has a carbon sink attribute in general (the carbon sink area exceeds 60%) The average annual carbon sequestration is about 111.02 MgC/a, the average annual carbon emission is -9.96 MgC/a, and the total annual net carbon sink is about 111.41 MgC/a. (4) The formation of vegetation carbon sink in the Yellow River Basin is mostly correlated with precipitation and solar radiation, and the influence area of air temperature only accounts for 12.44%; the effect of vegetation carbon sequestration capacity is the strongest when the temperature is 30℃, precipitation is 150 mm, and solar radiation is 550 MJ/m2. The results of the study provide a reference basis for a comprehensive and accurate regional assessment of carbon sources/sinks in terrestrial ecosystems to achieve high-quality development in the Yellow River Basin and meet the carbon neutrality target. 参考文献 相似文献 引证文献

2000-2020年华北地区植被固碳能力时空变化特征及其气象影响分析

PDF HTML阅读 XML下载 导出引用 引用提醒 2000-2020年华北地区植被固碳能力时空变化特征及其气象影响分析 DOI: 10.5846/stxb202110072771 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 以清洁能源淡化海水促进生态修复和应对气候变化的可行路径及综合效益研究(SGGEIG00JYJS2000053);国家重点研发计划项目(2019YFC1510204);国家气象中心预报员专项(Y202116) Spatial and temporal patterns of carbon sequestration and their responses to climatic factors in North China from 2000 to 2020 Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:净生态系统生产力(NEP)是估算植被生态系统碳源／汇的重要指标,开展区域NEP时空变化特征分析,对科学评估植被生态系统固碳能力及其气候变化应对措施具有重要意义。研究基于土壤呼吸模型和TEC模型,利用2000-2020年气象数据和遥感数据,采用线性趋势分析、Hurst指数和相关分析等统计方法,对华北地区固碳能力时空变化特征、影响因素及其未来变化趋势进行分析。结果表明:2000-2020年,华北地区年均NEP为228.8 gC/m2,在空间分布上呈现从西北向东南逐步递增的趋势,总体上表现为净固碳。从变化趋势来看,华北地区生态系统的固碳能力总体呈增加趋势,分布面积占比达到88.8%,达到显著增加趋势的面积比例为54.5%(P<0.05),其中河北北部、北京北部、山西西北部、山东西部等地NEP每年每平方米增加9.0 gC以上。华北大部地区NEP年际波动较小,有79.6%的区域多处于较高稳定、高稳定的等级。从变化趋势来看,华北地区有84.9%区域NEP具有强持续性,且NEP未来仍将持续增加的区域面积占比达到51.4%。华北地区NEP的空间分布和年际变化主要受降水的影响,达到显著相关的区域面积占总面积的44.7%,但与气温、日照呈弱正相关,而且不同区域相关性差异明显。其中降水影响空间格局差异极为明显,华北中北部NEP与降水呈显著正相关,南部呈负相关。 Abstract:Net ecosystem productivity (NEP) is an important index for estimating carbon source/sink of regional vegetation system. Analyzing the distribution characteristics and changing trend of net ecosystem productivity spatially is of great significance to scientifically assess carbon sequestration capacity of ecosystems and formulate policies and measures to cope with climate change. Using the soil respiration model and terrestrial ecosystem carbon flux model (TEC model), combined with MODIS and meteorological data, this paper estimated the NEP in North China from 2000 to 2020. And its spatial distribution characteristics and changing trend were analyzed, using methods of linear regression, the Hurst index, coefficient of variation and other methods. Results showed that: from 2000 to 2020, the annual average NEP in North China was about 228.8 gC/m2. In spatial distribution, the NEP showed an increasing trend from northwest to southeast, representing carbon sink as a whole. From spatial distribution of change trend, the carbon sequestration capacity of North China ecosystems was increasing, especially in the north of Hebei, the north of Beijing, the northwestern Shanxi, the western Shandong with increase of NEP over 9.0 gC/m2. The area of increasing trend with the NEP accounted for 88.8% of the research area, of which 54.5% showed a significantly increasing trend (P<0.05). As for the coefficient of variation (CV), the NEP status in North China presented slight fluctuations with 79.6%% of the area in higher stability and the highest stability. From the NEP change trend analysis, 84.9% of the area in North China was persistent in the future. The NEP in 51.4% of the regions will continue to increase in the future. The spatial distribution and interannual variation of the NEP values were mainly influenced by the values of precipitation, of which 44.7% showed a positive correlation with precipitation. The changes of NEP were slightly positively associated with temperature and sunshine. The responses of NEP in North China to the meteorological factors were diverse in spatial difference of the North China, especially differences by the influence of precipitation. There is a significantly positive correlation between NEP and precipitation in the northern part of North China, and a negative correlation in the south. 参考文献 相似文献 引证文献

Encoding diel hysteresis and the Birch effect in dryland soil respiration models through knowledge-guided deep learning

Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of the Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

Spatial–temporal pattern of vegetation carbon sequestration and its response to rocky desertification control measures in a karst area, in Guangxi Province, China

AbstractThe terrestrial ecosystem in southwestern China has been a long‐term carbon sink, but its spatial–temporal pattern during the previous two decades and its response to karst rocky desertification control measures (KRDC) still merit further quantification, particularly where higher levels of carbon capture by vegetation are proposed in the context of a ‘carbon neutrality’ goal. Satellite datasets, field data from previous studies, and the geostatistical soil respiration model enabled a focus on the karst landscape in Guangxi Province in Southwest China, to probe the spatial–temporal pattern of the ability to capture carbon during 2001–2020 and assess its response to KRDC. The results showed that the interannual variation in the average net ecosystem productivity (NEP) exhibited a remarkable tendency to increase at a rate of 4.15 g C m−2 yr−1 (p &lt; 0.01). A major period of increase was identified from 2006 to 2011, which represented the second stage of the KRDC. The terrestrial ecosystems of karst area have always acted as carbon sinks. Human activity was the dominant factor in the development of NEP and covered an area of 79.30%. The karst area offers climate change characteristics of ‘wetness’ and ‘light stress,’ which can promote the ability of vegetation carbon to serve as a carbon sink (0.18 g C m−2 yr−1). Furthermore, the closing mountains for afforestation is the most effective way to artificially increase the carbon sink. Quantifying vegetation ecosystem carbon sinks and sources in this study is highly significant for understanding regional carbon dynamics and guiding carbon capture policies to design and mitigate land degradation.

Accounting for Carbon Sink and Its Dominant Influencing Factors in Chinese Ecological Space

Ecological space (ES), including forest ecological space (FES) and grassland ecological space (GES) in this study, is the land with natural attributes and the main functions of providing ecological services, which has a huge potential capacity for carbon sink (CS). The interannual fluctuation of the CS in ES is severe, which is affected by factors such as precipitation and temperature, but it is still controversial which is the dominant factor in affecting the fluctuation process of the CS in ES. To this end, the multi-source remote sensing monitoring data on the fine-grid scale were collected in this study, including the land use and land cover remote sensing monitoring data, the data products of moderate-resolution imaging spectroradiometer (including land surface water index, photosynthetically active radiation, enhanced vegetation index, gross primary productivity), and meteorological data (including precipitation and temperature). By coupling the vegetation photosynthesis model and soil respiration model, the CS in CES from 2010 to 2020 was calculated, and the interannual fluctuation trends and stability of CS in CES were analyzed. Furthermore, the correlation coefficient and partial correlation coefficient equation between the CS of CES with precipitation and temperature were constructed to explore the correlation between interannual fluctuation of CS in CES with meteorological factor, and to determine the dominant position of precipitation and temperature in affecting the fluctuation process of the CS in CES. The research results show that the annual average CS of per unit area in CES was 233.78 gC·m−2·a−1, and the cumulative CS was 11.83 PgC. The GES and FES contributed 6.33 PgC and 5.49 PgC of CS, respectively. From 2010 to 2020, the CS of CES showed an upward trend and was generally in a relatively stable state (the mean value of the coefficient of variation was 0.6248). However, the year with severe fluctuation of was found in this study (from 2013 to 2015), the reason is that the precipitation was too low in 2014, which indicated that climate change, especially the change of precipitation, played a important role in the fluctuation of CS in CES. The results of correlation analysis confirmed the above analysis. The change of CS in CES is highly positively correlated with the change of precipitation (the correlation coefficient is 0.085), and weakly positively correlation with temperature (the correlation coefficient was 0.026). The precipitation is the dominant influencing factor, which has a positive effect on CS in CES. Within a climate environment dominated by precipitation, precipitation and temperature jointly affect the CS in CES. It should be noted that in some regions with variable climate, precipitation and temperature had relatively little impact on CS in CES. Their fluctuations may depend more on the ecosystem’s own ecological services’ regulation ability and their response degree to changes in atmospheric CO2 concentration.

Spatial-Temporal Changes of Carbon Source/Sink in Terrestrial Vegetation Ecosystem and Response to Meteorological Factors in Yangtze River Delta Region (China)

As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO2 concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m−2·a−1, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands &gt; grasslands &gt; wetlands ≈ croplands. (3) The area with the NEP change rate (θslope) &gt; 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θslope &lt; 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai &gt; Zhejiang Province &gt; Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions.

Spatiotemporal Evolution Characteristics and the Climatic Response of Carbon Sources and Sinks in the Chinese Grassland Ecosystem from 2010 to 2020

With the increase in global carbon dioxide emissions, China has put forward the goals of a carbon peak and carbon neutrality (double carbon) and formulated an action plan to consolidate and enhance the carbon sink capacity of the ecosystem. The Chinese grassland ecosystem (CGE) is widely distributed and is the key link for China to achieve the double carbon objectives. However, there is a relative lack of research on carbon sources and sinks in the CGE, so it is urgent to integrate and analyze the carbon sources and sinks in the grassland ecosystem on the national scale. Based on the refined grid data, the net ecosystem productivity (NEP) of the CGE was estimated by coupling the vegetation production model and soil respiration model. The results showed that the cumulative carbon sequestration of the CGE was 14.46 PgC from 2010 to 2020. In terms of spatial distribution, this shows that the differentiation characteristics are high in the northwest of China and low in the southeast of China, which strongly corresponds with the 400 mm isohyet and 0 °C isotherm of China. The results of the correlation analysis showed that the NEP of the CGE was positively correlated with precipitation and negatively correlated with temperature; that is, precipitation mainly promotes the accumulation of NEP, and temperature mainly inhibits it. The coupling effect of temperature and precipitation jointly affects the spatial change of carbon sources and sinks of the CGE. This study can provide a scientific basis for government departments to formulate targeted policies to deal with climate change, which is of great significance for China to improve ecosystem management, ensure ecological security and promote the realization of China’s double carbon goal.

Effect of Organic Material Amendments on Soil Respiration in Tobacco Fields of Central Henan

A field experiment was conducted to study the effects of different organic material amendments on soil respiration in a flue-cured tobacco field. Five treatments were set up:no fertilizer (NF), chemical fertilizer (NPK), chemical fertilizer+ryegrass (NPKG), chemical fertilizer+wheat straw (NPKS), and chemical fertilizer+tobacco straw biochar (NPKB). The results showed that:① Compared with that under NPK, NPKG and NPKS decreased the temperature sensitivity (Q10) of total soil respiration and heterotrophic respiration, whereas NPKB increased the Q10 of heterotrophic respiration. The two-factor fitting model of soil respiration and soil hydrothermal factors accounted for 50%-80% of the variation in soil respiration. ② The addition of organic materials significantly increased the content of soil soluble organic carbon (DOC) and root dry matter. Soil heterotrophic respiration(Rh) was significantly positively correlated with DOC content, and soil autotrophic respiration(Ra) was significantly parabolically correlated with root biomass, with an R2 of 0.327-0.634. ③ Soil respiration increased first and then decreased during the tobacco growth period. Compared with that under the NF treatment, the NPK treatment significantly promoted soil respiration and its components. Compared with those of the NPK treatment, Rsrates were significantly increased by 20.08%, 10.32%, and 9.88% under the NPKG, NPKS, and NPKB treatments, respectively; Rh rate increased by 24.21%, 16.51%, and 11.68% respectively, and Ra rate was increased by 15.12% in the NPKG treatment. In summary, straw returning and biochar addition significantly increased Rh by increasing soil DOC, thereby promoting Rs. Incorporation of ryegrass not only increased the Rh but also increased Ra by promoting the growth and development of roots and therefore the Rs.