Net Ecosystem Exchange Research Articles

Upside down and the game of C allocation.

Non-structural carbohydrates (NSCs) represent the primary carbon (C) reserves and play a crucial role for plant functioning and resilience. Indeed, these compounds are involved in the regulation between C supply and demand, and in the maintenance of hydraulic efficiency. NSCs are stored in parenchyma of woody organs, which is recognized as a proxy for reserve storage capacity of tree. Notwithstanding the importance of NSCs for tree physiology, their long-term regulation and trade-offs against growth were not deeply investigated. This work evaluated the long-term dynamics of mature tree reserves in stem and root, proxied by parenchyma features, and focusing on the trade off and interplay between the resources allocation in radial growth and reserves in stem and coarse root. In a Mediterranean beech forest, NSCs content, stem and root wood anatomy analysis, and eddy covariance data, were combined. The parenchyma fraction (RAP) of beech root and stem was different, due to differences in axial parenchyma (AP) and narrow ray parenchyma (nRP) fractions. However, these parenchyma components and radial growth showed synchronous inter-annual dynamics between the two organs. In beech stem, positive correlations were found among soluble sugars content and nRP, and among starch content and the AP. Positive correlations were found among Net Ecosystem Exchange (NEE) and AP of both organs. In contrast, NEE was negatively correlated to radial growth of root and stem. Our results suggest a different contribution of stem and roots to reserves storage, and a putative partitioning in the functional roles of parenchyma components. Moreover, a long-term trade-off of C allocation between growth and reserve pool was evidenced. Indeed, in case of C source reduction, trees preferentially allocate C towards reserves pool. Conversely, in high productive years, growth represents the major C sink.

Increased salinity decreases annual gross primary productivity at a Northern California brackish tidal marsh

Tidal marshes sequester 11.4–87.0 Tg C yr−1 globally, but climate change impacts can threaten the carbon capture potential of these ecosystems. Tidal marshes occur across a wide range of salinity, with brackish marshes (0.5–18 ppt (parts per thousand)) dominating global tidal marsh extents. A diverse mix of freshwater- and saltwater-tolerant plant and microbial communities has led researchers to predict that carbon cycling in brackish wetlands may be less sensitive to changes in salinity than fresh- or saltwater wetlands. Rush Ranch, a well-monitored brackish tidal wetland of the San Francisco Bay National Estuarine Research Reserve, experiences highly variable annual salinity regimes. Within a five-year period (2014–2018), Rush Ranch experienced particularly extreme drought-induced salinization during the 2014 and 2015 growing seasons. During drought years, tidal channel salinity rose from a 15 year baseline of 4.7 ppt to growing season peaks of 10.3 ppt and 12.5 ppt. Continuous eddy covariance data from 2014 to 2018 demonstrate that during drought summers, gross primary productivity (GPP) decreased by 24%, whereas ecosystem respiration remained similar among all five years. Stepwise linear regression revealed that salinity, not air temperature or tidal height, was the dominant driver of annual GPP. A random forest model trained to predict GPP based on environmental data from low salinity years (i.e. naive to salinization) significantly over predicted GPP in drought years. When growing season salinities were doubled, annual estimates of net ecosystem exchange of CO2 decreased by up to 30%. These results provide ecosystem-scale evidence that increased salinity influences CO2 fluxes dominantly through reductions in GPP. This relationship provides a starting point for incorporating the effect of changes in salinity in wetland carbon models, which could improve wetland carbon forecasting and management for climate resilience.

Estimating Net Carbon and Greenhouse Gas Balances of Potato and Pea Crops on a Conventional Farm in Western Canada

AbstractQuantifying the emissions of the three main biogenic greenhouse gases (GHGs), carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4), from agroecosystems is crucial. In this study continuous measurements of N2O, and CH4 emissions from potato and pea crops in southwest British Columbia, Canada were made using the eddy‐covariance (EC) technique. Flux footprint analysis, coupled with EC and manual nonsteady state chamber measurements, was used to address the spatial heterogeneity resulting from the field edge at the study site. Flux footprint corrections had a larger effect on N2O fluxes than CO2 fluxes because of a more pronounced difference in N2O fluxes between the crop and edge areas. After flux footprint corrections, the potato and pea crops were both weak CO2 sinks with annual net ecosystem exchange values of −57 ± 9 and −97 ± 16 g C m−2 yr−1, respectively. However, after taking carbon (C) export via crop harvest and C import via seeding into account, the potato crop shifted to being a moderate C source of 284 ± 55 g C m−2 yr−1, while the pea crop became near C neutral, sequestering only 30 ± 26 g C m−2 yr−1. Annual GHG balances, quantified by converting N2O and CH4 to CO2 equivalents as pulse emissions using respective global warming potentials on a 100‐year timescale, were 417 ± 88 and 152 ± 106 g CO2e m−2 yr−1 for the potato and pea crops, respectively, with N2O contributing the largest proportion to annual total GHG balances and outweighing the CO2 uptake from the atmosphere.

The effect of charcoal production on carbon cycling in African biomes

AbstractUsing biomass for charcoal production in sub‐Saharan Africa (SSA) may change carbon stock dynamics and lead to irreversible changes in the carbon balance, yet we have little understanding of whether these dynamics vary by biome in this region. Currently, charcoal production contributes up to 7% of yearly deforestation in tropical regions, with carbon emissions corresponding to 71.2 million tonnes of CO2 and 1.3 million tonnes of CH4. With a projected increased demand for charcoal in the coming decades, even low harvest rates may throw the carbon budget off‐balance due to legacy effects. Here, we parameterized the dynamic global vegetation model LPJ‐GUESS for six SSA biomes and examined the effect of charcoal production on net ecosystem exchange (NEE), carbon stock sizes and recovery time for tropical rain forest, montane forest, moist savanna, dry savanna, temperate grassland and semi‐desert. Under historical charcoal regimes, tropical rain forests and montane forests transitioned from net carbon sinks to net sources, that is, mean cumulative NEE from −3.56 ± 2.59 kg C/m2 to 2.46 ± 3.43 kg C/m2 and −2.73 ± 2.80 kg C/m2 to 1.87 ± 4.94 kg C/m2 respectively. Varying charcoal production intensities resulted in tropical rain forests showing at least two times higher carbon losses than the other biomes. Biome recovery time varied by carbon stock, with tropical and montane forests taking about 10 times longer than the fast recovery observed for semi‐desert and temperate grasslands. Our findings show that high biomass biomes are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. Therefore, the prolonged biome recoveries imply that current charcoal production practices in SSA are not sustainable, especially in tropical rain forests and montane forests, where we observe longer recovery for vegetation and litter carbon stocks.

Photosynthesis rate, radiation and water use efficiencies of irrigated potato in a semi-arid climate using Eddy covariance techniques

AbstractUnderstanding the photosynthetic behaviour, radiation use efficiency (RUE) and water use efficiency (WUE) of potatoes in response to diurnal and seasonal fluctuations in weather, is important for optimizing growth and production. The net ecosystem exchange (NEE) of carbon dioxide (CO2), is a parameter used to measure the balance between gross CO2 assimilation (also referred to as gross primary production (GPP)) and ecosystem respiration (Reco). The objectives of this study were (i) to quantify the NEE of potato grown in two contrasting agro-ecologies and partition NEE into Reco and GPP; (ii) to describe seasonal patterns in daily photosynthesis, RUE and WUE in potato; and (iii) to analyse the variability in photosynthesis rate, RUE and WUE as affected by weather and crop growth stage. We measured CO2 and water vapour fluxes using eddy covariance techniques in potato fields in two production regions of South Africa. The winter crop had higher mean RUE and WUE values, whereas the summer crop absorbed more CO2. RUE had a negative relationship with incident radiation. To optimize dry matter production, WUE and RUE, potato crops must be well established and develop a full canopy early during the season. Maintaining the crop for a longer period in the field has yield benefits, although they appear to decline over time due to reduced efficiencies as the crop matures. The RUE observed over the growing period in the present study was high, relative to findings by other field studies, despite relatively warm growing conditions that were likely sub-optimal for potato.

Interannual variability in the ecosystem CO2fluxes at a paludified spruce forest and ombrotrophic bog in the southern taiga

Abstract. Climate warming in high latitudes impacts CO2 sequestration of the northern peatlands through the changes in production and decomposition processes. The response of the net CO2 fluxes between ecosystems and the atmosphere to climate change and weather anomalies can vary across forest and non-forest peatlands. To better understand the differences in CO2 dynamics at forest and non-forest boreal peatlands induced by changes in environmental conditions, the estimates of interannual variability in the net ecosystem exchange (NEE), total ecosystem respiration (TER), and gross primary production (GPP) was obtained at two widespread peatland ecosystems – paludified spruce forest and the adjacent ombrotrophic bog in the southern taiga of west Russia using 6 years of paired eddy covariance flux measurements. Both positive and negative annual and growing season air temperature and precipitation anomalies were observed in the period of measurement (2015–2020). Flux measurements showed that, in spite of the lower growing season TER (332±17 … 339±15 gC m−2) and GPP (442±13 … 464± 11 gC m−2) rates, the bog had a higher CO2 uptake rates (NEE was -132±11 … -108±6) than the forest, except for the warmest and the wettest year of the period (2020), and was an atmospheric CO2 sink in the selected years, while the forest was a CO2 sink or source, depending on the environmental conditions. Growing season NEE at the forest site was between -142±48 and 28±40 gC m−2, TER between 1135±64 and 1366±58 gC m−2, and GPP between 1207±66 and 1462±107 gC m−2. Annual NEE at the forest was between -62±49 and 145±41 gC m−2, TER between 1429±87 and 1652±44 C m−2, and GPP between 1345±89 and 1566±41 gC m−2, respectively. Under the anomalously warm winter conditions with sparse and thin snow cover (2019/2020), the increased daily GPP, TER, and net CO2 uptake at the forest was observed, while at the bog, the changes in CO2 fluxes between the warm and cold winters were not significant. This study suggests that the warming in winter can increase the CO2 uptake of the paludified spruce forests of the southern taiga in non-growing seasons.

Machine Learning and Pattern Analysis Identify Distinctive Influences from Long-term Weekly Net Ecosystem Exchange at Four Deciduous Woodland Locations

A methodology integrating correlation, regression (MLR), machine learning (ML), and pattern analysis of long-term weekly net ecosystem exchange (NEE) datasets are applied to four deciduous broadleaf forest (DBF) sites forming part of the AmeriFlux (FLUXNET2015) database. Such analysis effectively characterizes and distinguishes those DBF sites for which long-term NEE patterns can be accurately predicted using the recorded environmental variables, from those sites cannot be so delineated. Comparisons of twelve NEE prediction models (5 MLR; 7 ML), using multi-fold cross-validation analysis, reveal that support vector regression generates the most accurate and reliable predictions for each site considered, based on fits involving between 16 and 24 available environmental variables. SVR can accurately predict NEE for datasets for DBF sites US-MMS and US-MOz, but fail to reliably do so for sites CACbo and MX-Tes. For the latter two sites the predicted versus recorded NEE weekly data follow a Y ≠ X pattern and are characterized by rapid fluctuations between low and high NEE values across leaf-on seasonal periods. Variable influences on NEE, determined by their importance to MLR and ML model solutions, identify distinctive sets of the most and least influential variables for each site studied. Such information is valuable for monitoring and modelling the likely impacts of changing climate on the ability of these sites to serve as long-term carbon sinks. The periodically oscillating NEE weekly patterns distinguished for sites CA-Cbo and MX-Tes are not readily explained in terms of the currently recorded environmental variables. More detailed analysis of the biological processes at work in the forest understory and soil at these sites are recommended to determine additional suitable variables to measure that might better explain such fluctuations.

Mapping the net ecosystem exchange of CO2 of global terrestrial systems

Mapping the net ecosystem exchange of CO2 of global terrestrial systems

Opportunities for Adaptation to Climate Change of Extensively Grazed Pastures in the Central Apennines (Italy)

Future climate change is expected to significantly alter the growth of vegetation in grassland systems, in terms of length of the growing season, forage production, and climate-altering gas emissions. The main objective of this work was, therefore, to simulate the future impacts of foreseen climate change in the context of two pastoral systems in the central Italian Apennines and test different adaptation strategies to cope with these changes. The PaSim simulation model was, therefore, used for this purpose. After calibration by comparison with observed data of aboveground biomass (AGB) and leaf area index (LAI), simulations were able to produce various future outputs, such as length of growing season, AGB, and greenhouse gas (GHG) emissions, for two time windows (i.e., 2011–2040 and 2041–2070) using 14 global climate models (GCMs) for the generation of future climate data, according to RCP (Representative Concentration Pathways) 4.5 and 8.5 scenarios under business-as-usual management (BaU). As a result of increasing temperatures, the fertilizing effect of CO2, and a similar trend in water content between present and future, simulations showed a lengthening of the season (i.e., mean increase: +8.5 and 14 days under RCP4.5 and RCP8.5, respectively, for the period 2011–2040, +19 and 31.5 days under RCP4.5 and RCP8.5, respectively, for the period 2041–2070) and a rise in forage production (i.e., mean biomass peak increase of the two test sites under BaU: +53.7% and 62.75% for RCP4.5. and RCP8.5, respectively, in the 2011–2040 period, +115.3% and 176.9% in RCP4.5 and RCP8.5 in 2041–2070, respectively,). Subsequently, three different alternative management strategies were tested: a 20% rise in animal stocking rate (+20 GI), a 15% increase in grazing length (+15 GL), and a combination of these two management factors (+20 GI × 15 GL). Simulation results on alternative management strategies suggest that the favorable conditions for forage production could support the increase in animal stocking rate and grazing length of alternative management strategies (i.e., +20 GI, +15 GL, +20 GI × 15 GL). Under future projections, net ecosystem exchange (NEE) and nitrogen oxide (N2O) emissions decreased, whereas methane (CH4) rose. The simulated GHG future changes varied in magnitude according to the different adaptation strategies tested. The development and assessment of adaptation strategies for extensive pastures of the Central Apennines provide a basis for appropriate agricultural policy and optimal land management in response to the ongoing climate change.

Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing

Abstract. Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.

Weekly carbon dioxide exchange trend predictions in deciduous broadleaf forests from site-specific influencing variables

Weekly net-ecosystem-exchange (NEE) data recorded and verified over multiple years, together with site-specific influential ecological variables, for distinct deciduous-broadleaf-forest (DBF) sites in North America can reveal useful relationships regarding their functions as long-term carbon dioxide (CO2) sources and sinks. Machine learning (ML) and regression models have greater success at predicting weekly NEE from some DBF sites than others, from the available site variables. In particular, support vector regression (SVR) and extreme gradient boosting (XGG) ML methods perform better than multi-linear regression in the weekly NEE predictions they generate using up to 24 influential variables. The DBF sites studied are distinguished into those that have followed distinctive, albeit fluctuating seasonal NEE trends, and those that are characterized by abrupt fluctuations in NEE across the leaf-on season. ML models predict weekly NEE for the former sites more reliably than for the latter sites. Consideration of the relative influence of the variables on the XGB and regression model NEE predictions identifies which variables are most influential at specific sites. Short wave radiation (in and out) and air temperature are found to be variables exerting substantial influence on the prediction models for the sites studied. From the prediction results and the relative influences of the available environmental variables, it is concluded that complex processes are involved at those sites showing rapid NEE fluctuations in the leaf-on seasons that are not readily detectable from the environmental variables currently being continuously recorded at those sites.

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.

Addressing Effects of Environment on Eddy-Covariance Flux Estimates at a Temperate Sedge-Grass Marsh

We examined the net-ecosystem-exchange (NEE)-based annual carbon-balance estimates obtained from eddy-covariance (EC) measurements at an unmanaged sedge-grass marsh ecosystem (Třeboň, Czech Republic, 49°1′ N, 14°46′ E), seeking methods to improve the EC measurements in inhomogeneous environment. The data filtering procedure was developed using three thresholds: (a) a stationarity test; (b) a stability {u}_{*}-threshold; and (c) a high relative humidity RH-threshold. This procedure was tested in 2014, a year without significant floods and drought events led to a stable water table, reducing the effect of soil respiration on the EC measurements. Estimates of annual carbon-balance were reduced from 182 to 234 ± 12 gC m−2 year−1 for the initial data to 39–44 ± 8 gC m−2 year−1 after the RH ≤ 95% filtering and to 24–26 ± 7 gC m−2 year−1 after the further {u}_{*} ≥ 0.1 m s−1 filtering. Applying the precipitation/fog threshold reduced this balance to 10–12 ± 7 gC m−2 year−1, closer to carbon neutrality. Up to 9.5% of this identified shift occurred during apparent nocturnal downslope katabatic drainage flows or plume descent coming from the nearby town of Třeboň. High-RH conditions account for up to 27% of this shift. Moreover, both conditions together account for an additional 67% of the identified carbon-balance change. Removing these non-ecosystem-related processes brings EC measurements closer to values of an unmanaged-ecosystem productivity, providing a better NEE-based estimate for the net ecosystem production. The presented procedure is applicable to EC measurements conducted at different wetlands or terrestrial ecosystems with similar conditions.

Water level variation at a beaver pond significantly impacts net CO2 uptake of a continental bog

Abstract. The carbon (C) dynamics of northern peatlands are sensitive to hydrological changes owing to ecohydrological feedbacks. We quantified and evaluated the impact of water level variations in a beaver pond (BP) on the CO2 flux dynamics of an adjacent, raised Sphagnum–shrub-dominated bog in southern Canada. We applied the CoupModel to the Mer Bleue bog, where the hydrological, energy and CO2 fluxes have been measured continuously for over 20 years. The lateral flow of water from the bog to the BP was estimated by the hydraulic gradient between the peatland and the BP's water level and the vertical profile of peat hydraulic conductivity. The model outputs were compared with the measured hydrological components, CO2 flux and energy flux data (1998–2019). CoupModel was able to reproduce the measured data well. The simulation shows that variation in the BP water level (naturally occurring or due to management) influenced the bog net ecosystem exchange (NEE) of CO2. Over 1998–2004, the BP water level was 0.75 to 1.0 m lower than during 2017–2019. Simulated net CO2 uptake was 55 gCm-2yr-1 lower during 1998–2004 compared to 2017–2019 when there was no BP disturbance, which was similar to the differences in measured NEE between those periods. Peatland annual NEE was well correlated with water table depth (WTD) within the bog, and NEE also shows a linear relation with the water level at the BP, with a slope of −120 gCO2-Cm-2yr-1m-1. The current modelling predicts that the bog may switch from CO2 sink to source when the BP water levels drop lower than ∼ 1.7 m below the peat surface at the eddy covariance (EC) tower, located on the bog surface 250 m from the BP. This study highlights the importance of natural and human disturbances to adjacent water bodies in regulating the net CO2 uptake function of northern peatlands.

青藏高原高寒草甸和荒漠碳交换特征及其气象影响机制

PDF HTML阅读 XML下载 导出引用 引用提醒 青藏高原高寒草甸和荒漠碳交换特征及其气象影响机制 DOI: 10.5846/stxb202112303709 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金-联合基金项目（U21A2021）；青海省科技厅创新平台建设专项（2022-ZJ-Y11）；中国气象局创新发展专项（CXFZ2022P022） Carbon exchange characteristics and meteorological influence mechanism of Alpine Meadow and Desert in Qinghai-Tibet Plateau 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:以青藏高原玛沁地区高寒草甸和沱沱河地区高寒荒漠草原为观测研究站，利用涡动协方差技术获取高寒生态系统水平上的CO2通量以及水和能量通量，通过REddyProc、随机森林（Random Forest，RF）进行了数据后处理，探究了不同下垫面典型环境因子对净生态系统CO2交换量（Net Ecosystem Exchange，NEE）的影响机制。结果表明：1）玛沁高寒草甸在6-7月以吸收为主，表现为碳汇，吸收峰值出现在11：00-12：00（北京时，下同）之间，而在3、4、5、8月以排放为主，表现为碳源，排放峰值出现在21：00-23：00之间；沱沱河高寒荒漠在3-8月以吸收为主，表现为净碳汇，吸收峰值出现在13：00-14：00之间；整个生长季前后（3-8月），玛沁和沱沱河的累计NEE分别为79.50 g C/m2和79.24 g C/m2，都表现为碳汇。2）不同尺度不同下垫面，气象因子对NEE的重要程度不同，小时尺度上，高寒草甸辐射对NEE的重要性最大，高寒荒漠草原蒸散发对NEE的重要性最大；日尺度上，高寒草甸土壤含水率对NEE的重要性最大，高寒荒漠草原风速对NEE的重要性最大；3）生态系统呼吸（Ecosystem Respiration，Reco）和NEE都受到总初级生产力（Gross Primary Productivity，GPP）的显著限制，高寒荒漠草原GPP对NEE的影响远大于高寒草甸GPP对NEE的影响；4） NEE与蒸散发呈显著相关性，表明水分条件是控制高寒草甸和高寒荒漠草原碳和水收支变化的最重要因素。 Abstract:In this study, the alpine meadow in Maqin area of Qinghai-Tibet Plateau and the alpine desert steppe in Tuotuo Rivers area were taken as the observation stations. The eddy covariance technique was used to obtain the CO2 flux, water and energy flux at the level of alpine ecosystem. The data were post-processed by REddyProc and Random Forest (RF), and the variation characteristics of Net Ecosystem Exchange (NEE) at different time scales and different underlying surfaces were analyzed, so as to accurately evaluate and compare the carbon budget under different underlying surfaces. The influence mechanism of meteorological factors on CO2 flux of alpine meadow and alpine desert grassland was explored, which provided reference and data support for the research on carbon and water cycle of alpine meadow and alpine desert grassland in Qinghai-Tibet Plateau. The results showed that:1) Maqin alpine meadow mainly absorbed carbon sink from June to July, and the absorption peak appeared between 11:00 and 12:00 (Beijing, the same below). However, in March, April, May and August, the main emission was carbon source, and the emission peak appeared between 21:00 and 23:00. The alpine desert of Tuotuo Rivers was mainly absorbed from March to August, showing net carbon sink, and the absorption peak appeared between 13:00 and 14:00; around the whole growing season (March-August), the cumulative NEE of Maqin and Tuotuo Rivers were 79.50 g C/m2 and 79.24 g C/m2, respectively, showing carbon sink. 2). The correlation between carbon flux and meteorological factors was different in different scales and underlying surfaces. On the hourly scale, the importance of alpine meadow radiation (Rg) to carbon flux was the largest. Evapotranspiration (ET) in alpine desert steppe was the most important factor for carbon flux. On the daily scale, soil water content (Soil _VWC) was the most important factor for carbon flux in alpine meadow, while wind speed (WS) was the most important factor for carbon flux in alpine desert steppe. 3) Ecosystem Respiration (Reco) and Net Ecosystem Exchange (NEE) were significantly restricted by total primary productivity (GPP), and 91% of the GPP contributed to Reco and 9% to NEE; in alpine desert steppe, 40% of the GPP contributed to Reco and 60% to NEE. 4) NEE was significantly correlated with ET, indicating that water condition was the most important factor controlling the changes of carbon and water budget in alpine meadow and alpine desert steppe. 5) Human activities intensified the impact of climate change and the susceptibility of carbon balance, and became the inducing factors of weak carbon sources in March, April, May and August in Maqin. 参考文献 相似文献 引证文献

Modelling managed forest ecosystems in Sweden: An evaluation from the stand to the regional scale

Incorporation of a forest management module in the dynamic vegetation model LPJ-GUESS has allowed the study and predictions of management treatment effects on the carbon cycle and on forest ecosystem structure. In this study, LPJ-GUESS is evaluated at the regional scale against observational data from the Swedish National Forest Inventory. Simulated standing volume is compared against observations for the four most common forest types in the country. Furthermore, eddy-covariance flux measurements from the Integrated Carbon Observation System (ICOS) are used to evaluate model predictions of net ecosystem exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (Reco) at the site scale. The model results suggest an adequate representation of standing volume in monocultures of Norway spruce and Scots pine for regional simulations in southern and central Sweden, after an updated parameterization of the species. For northern Sweden, the standing volume in Norway spruce monocultures was overestimated with the updated parameter values. At the stand scale, the model produced mixed results for carbon fluxes when evaluated against eddy-covariance data for two sites, one in central and one in southern Sweden. The interannual variation of GPP was well captured for the central Swedish site, but the modelled average GPP for the period 2015–2019 was overestimated by 9%. For the southern Swedish site, GPP was underestimated by 15% for the corresponding period and the simulated interannual variation was half of the observed. The seasonal estimates of modelled net ecosystem exchange (NEE) deviated from observations and the simulated standing volume was underestimated by 25% for both sites. The results highlight further potential to perform species-specific calibration to capture latitudinal gradients in key ecosystem properties, and to incorporate additional characteristics of site quality which could benefit model accuracy at the scale of individual forest stands, both regarding simulated carbon fluxes and forest stand variables.

Above- and belowground interplay: Canopy CO2 uptake, carbon and nitrogen allocation and isotope fractionation along the plant-ectomycorrhiza continuum.

In forests, mycorrhizal fungi regulate carbon (C) and nitrogen (N) dynamics. We evaluated the interplay among ectomycorrhizas (ECM), ecosystem C fluxes, tree productivity, C and N exchange and isotopic fractionation along the soil-ECM-plant continuum in a Mediterranean beech forest. From bud break to leaf shedding, we monitored: net ecosystem exchange (NEE, a measure of the net exchange of C between an ecosystem and the atmosphere), leaf area index, stem growth, N concentration, δ13 C and δ15 N in rhizosphere soil, ectomycorrhizal fine root tips (ERT), ECM-free fine root portions (NCR) and leaves. Seasonal changes in ERT relative biomass were strictly related to NEE and mimicked those detected in the radial growth. The analysis of δ13 C in ERT, leaves and NCR highlighted the impact of canopy photosynthesis on ERT development and an asynchronous seasonal C allocation strategy between ERT and NCR at the root tips level. Concerning N, δ15 N of leaves was negatively related to that of ERT and dependent on seasonal 15 N differences between ERT and NCR. Our results unravel a synchronous C allocation towards ERT and tree stem driven by the increasing NEE in spring-early summer. Moreover, they highlighted a phenology-dependent 15 N fractionation during N transfer from ECM to their hosts. This evidence, obtained in mature beech trees under natural conditions, may improve the knowledge of Mediterranean forests functionality.

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

Long-Term (2007 to 2018) Energy and CO2 Fluxes over an Agriculture Ecosystem in the Southeastern Margin of the Tibetan Plateau

Long-term eddy covariance flux observations over complex topography are crucial for improving the understanding of the turbulent exchanges between the land and atmosphere. Based on a 12-year (2007–2018) record dataset measured with the eddy covariance technique over the Dali agriculture ecosystem in the southeastern margin of the Tibetan Plateau, we investigated the diurnal, seasonal, and interannual variations of the sensible heat flux (Hs), latent heat flux (LE), and carbon dioxide flux (Fc), and their controlling variables. The results showed that Hs and LE exhibited similar diurnal and seasonal variations, while the amplitude of LE was clearly larger than that of Hs throughout the year. The turbulent fluxes showed remarkable fluctuation on the annual scale. The annual average Hs (LE) increases (decreases) from approximately 8 (110) W·m−2 during 2007–2013 to 20 (79) W·m−2 during 2014–2018. The annual cumulative net CO2 ecosystem exchange (NEE) increases significantly from approximately −739 g·C·m−2·yr−1 during 2007–2013 to −218 g·C·m−2·yr−1 during 2014–2018. The relationship between turbulent fluxes and meteorological variables was also examined. Wind speed (WS) is found to be the dominant controlling factor for the Hs on different temporal scales and their correlation coefficients increase when the timescales vary from daily to annual scale; whereas the product of WS and vapor pressure deficit (VPD) is the major meteorological variable controlling the LE over all temporal scales. The net radiation (Rn) is the dominating factor for Fc on daily and monthly timescales, while WS becomes the most controlling factor for Fc on an annual scale. Notably, surface energy and CO2 fluxes are also greatly influenced by the vegetation cover surrounding the measurement site.

Optimizing 4 years of CO2 biospheric fluxes from OCO-2 and in situ data in TM5: fire emissions from GFED and inferred from MOPITT CO data

Abstract. Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often co-emitted with CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using Measurements of Pollution in The Troposphere (MOPITT) v8 data is performed to estimate fire emissions which are then converted into CO2 fire emissions (called FIREMo) through the use of the emission ratio. These optimized CO2 fire emissions are used to rebalance the CO2 net ecosystem exchange (NEEMo) and respiration (RhMo) with the global CO2 growth rate. Subsequently, in a second step, these rebalanced fluxes are used as priors for a CO2 inversion to derive the NEE and ocean fluxes constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ (IS) CO2 data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Emissions Database (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal, and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 (FIRE3) and GFED4.1s (FIRE4) fires, as well as with an inversion using both Carnegie–Ames–Stanford Approach (CASA)-GFED3 NEE and GFED3 fire priors (priorCMS). After comparison at the different scales, the inversions are evaluated against Total Carbon Column Observing Network (TCCON) data. Comparison of the flux estimates shows that at the global scale posterior net flux estimates are more robust than the different prior flux estimates. However, at the regional scale, we can observe differences in fire emissions among the priors, resulting in differences among the NEE prior emissions. The derived NEE prior emissions are rebalanced in concert with the fires. Consequently, the differences observed in the NEE posterior emissions are a result of the balancing with fires and the constraints provided by CO2 observations. Tropical net flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger net CO2 sources are derived with posterior fire emissions using either FIRE4 or FIREMo in the OCO-2 inversion, in particular for most tropical regions during the 2015 El Niño year. Similarly, larger net CO2 sources are also derived with posterior fire emissions in the in situ data inversion for Tropical Asia. Evaluation with CO2 TCCON data shows lower biases with the three rebalanced priors than with the prior using CASA-GFED3. However, posteriors have average bias and scatter very close each other, making it difficult to conclude which simulation performs better than the other. We observe that the assimilated CO2 data have a strong influence on the global net fluxes among the different inversions. Inversions using OCO-2 (or IS) data have similar emissions, mostly as a result of the observational constraints and to a lesser extent because of the fire prior used. But results in the tropical regions suggest net flux sensitivity to the fire prior for both the IS and OCO-2 inversions. Further work is needed to improve prior fluxes in tropical regions where fires are a significant component. Finally, even if the inversions using the FIREMo prior did enhance the biases over some TCCON sites, it is not the case for the majority of TCCON sites. This study consequently pushes forward the development of a CO–CO2 joint inversion with multi-observations for a possible stronger constraint on posterior CO2 fire and biospheric emissions.