Agricultural and Forest Meteorology最新文献_第9页

Precipitation variability stabilizes soil respiration through opposing effects on autotrophic and heterotrophic respiration in alpine meadows of the northeastern Qinghai-Tibetan Plateau

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-10 DOI: 10.1016/j.agrformet.2025.110984

Boliang Cui , Chuanyan Zhao , Fei Zang , Shuangjin Ma , Linsong Wang , Kelong Chen , Zhongren Nan

Variations in precipitation related to climate change affect soil carbon cycling processes in terrestrial ecosystems, particularly soil respiration (Rs). However, how Rs and its components-heterotrophic respiration (Rh) and autotrophic respiration (Ra)-respond to precipitation changes remains largely unclear in alpine ecosystems, given their distinct substrate sources and biological processes. In this study, we investigated the effects of altered precipitation levels on Rs and its components through a 3-year field experiment, where precipitation was adjusted by ±50 % in the alpine meadows of the northeastern Qinghai-Tibetan Plateau (QTP). Our results showed that precipitation variability did not significantly affect total Rs, but it increased Ra and decreased Rh, leading to a stable overall Rs. Specifically, increased precipitation (IP) and decreased precipitation (DP) reduced Rh by 22.75 % and 20.60 %, respectively, while Ra was elevated by 56.39 % and 40.24 % compared to the control (CK). Regression analysis revealed a significant exponential relationship between Rs and temperature. Both IP and DP treatments reduced the temperature sensitivity (Q₁₀) of Rs and its components compared to CK, suggesting that deviations from typical moisture levels suppress the response of Rs to temperature changes. The direct negative effect of IP on Rs was mitigated by a positive indirect effect through fungal richness, while DP produced opposite indirect effects via Rh and Ra, resulting in a weak overall impact on Rs. These site-specific results reveal the different responses of Ra and Rh to changing precipitation and suggest that extreme changes in precipitation impact soil microbial richness, suppress Rh, and weaken the decomposition and release of soil organic carbon in alpine meadows on the QTP.

与气候变化相关的降水变化影响陆地生态系统中土壤碳循环过程，特别是土壤呼吸。然而，考虑到不同的基质来源和生物过程，Rs及其组分异养呼吸（Rh）和自养呼吸（Ra）如何响应降水变化在高山生态系统中仍不清楚。结果表明，降水变率对总Rs影响不显著，但会增加Ra，降低Rh，使总Rs保持稳定。其中，与对照（CK）相比，降水量增加（IP）和降水量减少（DP）分别使Rh降低22.75%和20.60%，Ra升高56.39%和40.24%。回归分析显示Rs与温度呈显著的指数关系。与对照相比，IP和DP处理均降低了Rs及其组分的温度敏感性（Q10），表明偏离典型水分水平抑制了Rs对温度变化的响应。IP对Rs的直接负面影响被真菌丰富度的正面间接影响所抵消，而DP通过Rh和Ra产生相反的间接影响，导致Rs的整体影响较弱。这些站点特异性结果揭示了Ra和Rh对降水变化的不同响应，表明降水的极端变化影响了QTP上高寒草甸土壤微生物丰富度，抑制了Rh，减弱了土壤有机碳的分解和释放。

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引用次数: 0

Wind dynamics drives the changes of the 2001–2023 grass pollen seasons in Córdoba (southern Spain) 风动力驱动2001-2023年Córdoba（西班牙南部）草花粉季节变化

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-05 DOI: 10.1016/j.agrformet.2025.110955

M.A. Hernández-Ceballos , R. López-Orozco , M.J. Tenor-Ortiz , C. Galán , H. García-Mozo

Aerobiological studies have reported an increasing severity of pollen seasons, both in terms of duration and total pollen amount. Climate change has also influenced global terrestrial near-surface wind speeds, yet its connection to pollen dynamics remains underexplored in southern Europe. This study analyzes long-term trends in grass (Poaceae) pollen season parameters in Córdoba, Spain (2001–2023), with a particular focus on wind dynamics—including air mass trajectories and wind speed—as well as other meteorological variables such as temperature and rainfall. By examining surface wind patterns in detail, the analysis also allows identification of potential pollen sources during different phases of the pollen season (pre- and post-peak). The results reveal a significant positive trend in pollen season duration (+3.7 days year⁻¹), associated with an earlier onset (–0.8 days year⁻¹) and a delayed end (+1.3 days year⁻¹). Temporal variations in air mass arrivals significantly influence grass pollen concentrations, with distinct patterns observed between pre- and post-peak periods. The pre-peak period is characterized by lower wind speeds (average 1.53 ± 0.05 m/s, with a negative trend of –0.007 m/s year⁻¹) and slow-moving air masses, favoring local sources and atmospheric accumulation processes that lead to progressively increasing concentrations. In contrast, the post-peak period is marked by higher wind speeds (average 1.82 ± 0.05 m/s, with a positive trend of +0.004 m/s year⁻¹) and the dominance of faster northerly and north-westerly flows, indicating a greater influence of distant sources. Additionally, the study identifies a positive contribution of precipitation during the season to the extension of the pollen season with an earlier onset and delayed end. These findings provide a comprehensive understanding of how meteorological factors, especially wind dynamics, shape temporal and spatial patterns in grass pollen seasons in southern Spain.

有氧生物学研究报告了花粉季节的严重性，无论是在持续时间还是花粉总量方面。气候变化也影响了全球陆地近地表风速，但其与南欧花粉动态的关系仍未得到充分研究。本研究分析了西班牙Córdoba草（禾科）花粉季节参数的长期趋势（2001-2023），特别关注风动力学（包括气团轨迹和风速）以及其他气象变量（如温度和降雨量）。通过详细检查地面风的模式，分析还可以识别花粉季节不同阶段（峰前和峰后）的潜在花粉来源。结果显示花粉季持续时间（每年+3.7天）有显著的积极趋势，与早期开始（每年-0.8天）和延迟结束（每年+1.3天）有关。气团到达的时间变化显著影响草花粉浓度，在高峰前后观察到不同的模式。高峰前的特点是风速较低（平均1.53±0.05米/秒，负趋势为-0.007米/秒一年），气团移动缓慢，有利于当地来源和大气积累过程，导致浓度逐渐增加。相比之下,高峰期过后是伴随着更高的风速(平均1.82±0.05 m / s,一个积极的趋势,+ 0.004 m / s年⁻¹)和更快的北风和north-westerly流的主导地位,表明更远处的影响。此外，该研究还确定了季节降水对花粉季节延长的积极贡献，其开始时间早，结束时间晚。这些发现提供了对气象因素，特别是风动力，如何影响西班牙南部草花粉季节时空格局的全面理解。

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引用次数: 0

The influence of estimation window configuration on machine learning-based soybean yield estimation across black soil regions 估算窗口配置对黑土地区基于机器学习的大豆产量估算的影响

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-04 DOI: 10.1016/j.agrformet.2025.110957

Shuyuan Huang , Yujie Liu , Jiahao Chen , Ermei Zhang , Tao Pan

The configuration of phenology-based time windows, which determines how environmental variables are temporally aggregated, plays a pivotal role in crop yield estimation. However, the quantitative effects of different window configurations on model performance and uncertainty require further investigation. This study systematically assesses the effects of four window-configuration strategies (fixed, observation, rule-based, and sliding) on soybean yield estimation across the black soil regions of China and the USA. Multi-source remote sensing and meteorological datasets were integrated with three machine learning algorithms: RF, XGBoost, and LSTM. Results show that dynamic windows (observation, rule-based, and sliding) can better align environmental fluctuations with crop phenological stages, resulting in modest yet consistent improvements in accuracy compared to fixed windows. The LSTM–sliding window combination achieves the largest RMSE decrease (48.4-56.6%), followed by LSTM–rule-based windows (32.9-38.2%) and LSTM–observation windows (11.8-22.0%). A trade-off is identified: while sliding windows (SWs) provide the highest accuracy, they also show greater interannual variability, higher computational cost, and lower interpretability. In comparison, rule-based windows (RBWs) exhibit a moderate decline in accuracy but demonstrate lower inter-group variability, with ΔR² approximately one-third that of SW, offering more stable predictions. RBWs also exhibit better generalizability than observation windows, which rely on limited ground phenology data. Uncertainty decomposition reveals that, although the primary source of variation originates from input features and model structures, the configuration of the estimation window contributes approximately 11.9-13.7% to the total variation, indicating a secondary yet consistent factor influencing estimation stability. This study offers an analytical framework for quantifying the interactions among window design, algorithm type, and feature selection, thereby providing practical insights for future data-driven crop yield modeling.

基于物候的时间窗的配置决定了环境变量如何在时间上聚集，在作物产量估计中起着关键作用。然而，不同窗口配置对模型性能和不确定性的定量影响需要进一步研究。本研究系统地评估了四种窗口配置策略（固定、观察、基于规则和滑动）对中国和美国黑土地区大豆产量估算的影响。多源遥感和气象数据集集成了三种机器学习算法：RF、XGBoost和LSTM。结果表明，动态窗口（观测窗口、基于规则的窗口和滑动窗口）可以更好地将环境波动与作物物候阶段结合起来，与固定窗口相比，准确性得到了适度但持续的提高。lstm -滑动窗口组合的RMSE降幅最大（48.4-56.6%），其次是lstm -基于规则的窗口（32.9-38.2%）和lstm -观测窗口（11.8-22.0%）。确定了一种权衡：虽然滑动窗口（SWs）提供了最高的准确性，但它们也显示出更大的年际变化，更高的计算成本和更低的可解释性。相比之下，基于规则的窗口（RBWs）显示出适度的准确性下降，但显示出较低的组间变异性，ΔR²约为SW的三分之一，提供更稳定的预测。与依赖于有限地面物候数据的观测窗口相比，RBWs也表现出更好的泛化性。不确定性分解表明，虽然变异的主要来源是输入特征和模型结构，但估计窗口的配置对总变异的贡献约为11.9-13.7%，表明影响估计稳定性的次要但一致的因素。本研究为量化窗口设计、算法类型和特征选择之间的相互作用提供了一个分析框架，从而为未来数据驱动的作物产量建模提供了实用的见解。

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引用次数: 0

Remote sensing proxies underestimate fire-induced gross primary productivity loss and overestimate recovery in forests 遥感代用物低估了火灾造成的总初级生产力损失，高估了森林的恢复

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-03 DOI: 10.1016/j.agrformet.2025.110963

Xinyi Fan , Qinggaozi Zhu , Yingnan Wei , Ning Yao , Gang Zhao , Qiang Yu , Genghong Wu

Wildfires significantly alter terrestrial carbon cycling by reducing vegetation productivity and reshaping ecosystem functioning, yet satellite-based estimates of gross primary productivity (GPP) remain highly uncertain under fire disturbance. Here, we evaluated five global GPP products—BESS GPP (process-based), FLUXCOM and FluxSat GPP (machine learning-based), GOSIF GPP (derived from reconstructed solar-induced chlorophyll fluorescence, SIF), MODIS GPP (light-use efficiency–based)—together with three complementary proxies: GOSIF (reconstructed SIF), the near-infrared reflectance of vegetation (NIRv), and leaf area index (LAI). These products were benchmarked against eddy covariance (EC) tower GPP measurements from ten fire-affected sites (five forest sites, five grass/shrub sites) with multi-year pre- and post-fire records. Results show that satellite proxies generally underestimated fire-induced GPP loss, with forest sites showing the largest discrepancy: EC GPP declined by ∼94%, compared to 47–88% from satellites. During recovery, most satellite products overestimated post-fire carbon gain and underestimated recovery time, often signaling premature recovery in forests. In contrast, grass and shrub ecosystems showed faster rebound and closer agreement with satellite estimates. Among these products, BESS GPP and GOSIF better reproduced immediate loss and recovery time, though still underestimated persistent suppression and overestimated cumulative uptake. Moreover, EC data further revealed reduced post-fire GPP sensitivity to light, temperature, and vapor pressure deficit in forests, which satellite products failed to capture. These findings highlight systematic biases in current satellite proxies, emphasize the challenges in monitoring forest recovery, and underscore the need for disturbance-responsive models and expanded flux benchmarks to improve post-fire carbon cycle assessments.

野火通过降低植被生产力和重塑生态系统功能显著地改变了陆地碳循环，但在火灾干扰下，基于卫星的总初级生产力（GPP）估计仍然高度不确定。在这里，我们评估了五种全球GPP产品- bess GPP（基于过程的），FLUXCOM和FluxSat GPP（基于机器学习的），GOSIF GPP（源自重建太阳诱导叶绿素荧光，SIF）， MODIS GPP（基于光利用效率的）-以及三个互补代理：GOSIF（重建SIF），植被近红外反射率（NIRv）和叶面积指数（LAI）。这些产品以10个受火灾影响的地点（5个森林地点，5个草/灌木地点）的涡动相关（EC）塔GPP测量值为基准，具有多年的火灾前后记录。结果表明，卫星代用物普遍低估了火灾引起的GPP损失，其中森林样地的差异最大：EC GPP下降了~ 94%，而卫星代用物的GPP下降了47-88%。在恢复过程中，大多数卫星产品高估了火灾后的碳增益，低估了恢复时间，往往预示着森林的过早恢复。相比之下，草和灌木生态系统表现出更快的反弹，与卫星估计更接近。在这些产品中，BESS GPP和GOSIF更好地再现了即时损失和恢复时间，尽管仍然低估了持续抑制和高估了累积吸收。此外，EC数据进一步揭示了火灾后森林GPP对光、温度和蒸汽压赤字的敏感性降低，而卫星产品未能捕捉到这些数据。这些发现突出了当前卫星代理的系统性偏差，强调了监测森林恢复方面的挑战，并强调了需要扰动响应模型和扩大通量基准，以改进火灾后碳循环评估。

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引用次数: 0

Design and performance evaluation of a UAV-relayed LoRaWAN network for microclimate monitoring of standing live trees in forests 无人机中继LoRaWAN网络的设计与性能评估，用于森林中直立活树的小气候监测

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-03 DOI: 10.1016/j.agrformet.2025.110968

Yuewei Ma , Yuan He , Wenbin Li , Qingsong Li , Heng Chen

Effective monitoring of forest microclimates—including tree, soil, and atmospheric conditions—is essential for understanding ecosystem dynamics and advancing forest management. Yet, in dense or primary forests, traditional wireless sensor networks (WSNs) and existing IoT systems are limited by signal attenuation, restricted range, energy demands, and insufficient sensing depth. Integrated systems capable of stable wireless communication and environmental monitoring, together with evaluation of RF signals, remain lacking. Potential coupling between signal behavior and microclimate is also underexplored. This study proposes and evaluates an AI–IoT framework tailored to a closed-canopy forest stand, composed of: (1) multi-source nodes that monitor trunk, soil, and air microclimate variables while recording RSSI for link-quality tracking; (2) a UAV-relayed LoRaWAN system in which a UAV-mounted gateway ascends from the forest floor to above the canopy, enabling height-resolved propagation analysis and modeling of RSSI as a function of distance and elevation; and (3) a cloud-based platform that receives data streams and supports environmental monitoring and short-term forecasting via LSTM models. Field experiments at the study stand showed that elevating the UAV gateway to 50 m improved RSSI by >30 dB and reduced packet loss from over 60 % to below 5 % over ∼1 km. The LSTM model achieved high predictive fidelity for temperature- and humidity-related variables, with mean absolute percentage errors typically below 5 %, while soil and intermittent meteorological variables exhibited moderate to lower accuracy. By jointly analyzing RSSI and co-located microclimate observations within the above-canopy clearance zone characteristic of the Baicaowa stand, the framework provides preliminary, site-specific evidence of short-term coupling between signal strength and the thermo-hydric state of trees and soils. These relationships remain correlative and specific to the monitored stand and period, and their generality and causal mechanisms will require cross-site, multi-season, and experimental validation.

有效监测森林小气候——包括树木、土壤和大气条件——对于了解生态系统动态和推进森林管理至关重要。然而，在茂密或原始森林中，传统的无线传感器网络（wsn）和现有的物联网系统受到信号衰减、受限范围、能量需求和传感深度不足的限制。能够稳定的无线通信和环境监测以及射频信号评价的综合系统仍然缺乏。信号行为与小气候之间的潜在耦合也未得到充分探讨。本研究提出并评估了一个针对封闭冠层林分的AI-IoT框架，该框架由：(1)监测树干、土壤和空气微气候变量的多源节点组成，同时记录RSSI以进行链路质量跟踪；(2)无人机中继的LoRaWAN系统，其中无人机安装的网关从森林地面上升到冠层以上，实现RSSI作为距离和海拔函数的高度分辨传播分析和建模；(3)基于云的平台，接收数据流，并通过LSTM模型支持环境监测和短期预报。研究站的现场实验表明，将无人机网关提升到50米可将RSSI提高30 dB，并在1公里内将数据包丢包率从60%以上降低到5%以下。LSTM模型对温度和湿度相关变量的预测保真度很高，平均绝对百分比误差通常低于5%，而土壤和间歇性气象变量的预测精度为中等至较低。通过联合分析RSSI和同地小气候观测数据，该框架为信号强度与树木和土壤热水状态之间的短期耦合提供了初步的立地特异性证据。这些关系仍然与监测的林分和时期相关且特定，其普遍性和因果机制将需要跨站点、多季节和实验验证。

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引用次数: 0

Carbon dioxide dynamics across three stages of tropical peatland conversion to oil palm plantations 热带泥炭地转化为油棕种植园三个阶段的二氧化碳动态

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-02 DOI: 10.1016/j.agrformet.2025.110956

Frankie Kiew , Ryuichi Hirata , Takashi Hirano , Guan Xhuan Wong , Joseph Wenceslaus Waili , Kim San Lo , Kaido Soosaar , Kuno Kasak , Ülo Mander , Lulie Melling

This study represents the first long-term investigation spanning from a tropical peat swamp forest (PSF) to its conversion into an oil palm plantation (OPP), offering valuable data for assessing carbon dioxide (CO₂) dynamics across different conversion stages. The conversion of tropical peat swamp forests to oil palm plantations has significant implications for CO₂ dynamics. However, ecosystem-scale studies investigating CO₂ dynamics across different stages of land conversion are lacking. This study used the eddy covariance (EC) technique to measure the net ecosystem exchange (NEE) of CO₂ above a tropical peat swamp forest in Sarawak, Malaysia, from 2011 until it was cleared in 2017 and ultimately converted into an OPP in 2018. Our study found that the removal of forest biomass during land preparation led to a substantial increase in annual NEE from 25 ± 179 (2011 to 2016) to 2732 ± 655 g C m⁻² year⁻¹ (2017 to 2019). This increase was attributed to an 83 % reduction in gross primary productivity (GPP) and a 14 % reduction in ecosystem respiration (R_eco). The near-ground environmental conditions also significantly changed across the conversion stages, inducing drier conditions compared to the forest. These changes were found to affect the controlling factors of nighttime NEE during conversion, resulting in a negative relationship with both air temperature and vapor pressure deficit above canopy, in contrast to the typical relationship with groundwater level observed before conversion. The conversion is also found to cause significant reduction in overall ecosystem photosynthetic activity as evidenced by the reduction in maximum gross photosynthetic rate (P_max), photosynthetic photon flux density (PPFD), quantum yeild (α), and dark respiration (RE_d). Although ecosystem-scale assessments of CO₂ dynamics provide insights into how ecosystems respond to changes in relation to land conversion, it is crucial to assess other respiration components, such as soil respiration and aboveground woody debris, for a more comprehensive analysis.

这项研究代表了首次从热带泥炭沼泽森林（PSF）到其转化为油棕种植园（OPP）的长期调查，为评估不同转化阶段的二氧化碳（CO2）动态提供了有价值的数据。热带泥炭沼泽森林向油棕种植园的转变对二氧化碳动态具有重要意义。然而，在生态系统尺度上调查不同土地转化阶段二氧化碳动态的研究是缺乏的。本研究使用涡动相关（EC）技术测量了2011年至2017年马来西亚沙捞越热带泥炭沼泽森林上空二氧化碳的净生态系统交换（NEE），并最终在2018年将其转化为OPP。我们的研究发现，在整地过程中森林生物量的去除导致年NEE从25±179（2011 - 2016）大幅增加到2732±655 g C m−2（2017 - 2019）。这一增长归因于总初级生产力（GPP）下降83%和生态系统呼吸（Reco）下降14%。在整个转换阶段，近地环境条件也发生了显著变化，导致与森林相比更为干燥。这些变化影响了转换过程中夜间NEE的控制因子，导致其与冠层以上的气温和水汽压亏缺呈负相关，而与转换前观测到的地下水水位呈典型的负相关。这种转化还导致生态系统整体光合活性的显著降低，如最大总光合速率（Pmax）、光合光子通量密度（PPFD）、量子产率（α）和暗呼吸（REd）的降低。虽然生态系统尺度的二氧化碳动态评估提供了关于生态系统如何响应与土地转化有关的变化的见解，但评估其他呼吸成分（如土壤呼吸和地上木屑）对于更全面的分析至关重要。

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引用次数: 0

Beyond surface fluxes: Observational and computational needs of multilayer canopy models – A walnut orchard test case 超越地表通量：多层冠层模型的观测和计算需求——一个核桃园试验案例

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-02 DOI: 10.1016/j.agrformet.2025.110960

Gordon B. Bonan , Sean P. Burns , Edward G. Patton

Land surface models simulate fluxes exchanged between the land and atmosphere in weather and climate models. The prevailing modeling paradigm uses a big-leaf canopy parameterization that is not vertically-resolved. Multilayer canopy models have received interest over the past several years as a means to improve surface fluxes and enable new science. We present results from a comparison of the Community Land Model (CLM) multilayer canopy model (CLM-ml v2) and observations of air temperature, specific humidity, wind speed, and fluxes (net radiation, sensible heat, latent heat, momentum) at multiple heights in and above a walnut orchard during the Canopy Horizontal Array Turbulence Study (CHATS). The dataset provides a benchmark with which to test multilayer models. Above-canopy sensible heat, latent heat, and momentum fluxes are well simulated under a range of atmospheric regimes spanning strongly unstable, weakly unstable, near-neutral, weakly stable, and strongly stable, as are vertical profiles of fluxes within the canopy. Vertical profiles of wind speed closely match the observations under all stability regimes. Vertical profiles of air temperature and specific humidity are well simulated except for strongly stable conditions, when the first-order turbulence closure cannot represent within-canopy non-local vertical mixing that would otherwise transport the cool air produced by radiative cooling of the upper canopy downward to the lower canopy. Our model–data comparison highlights the potential of multilayer models to simulate the surface air space. The multilayer canopy model is simpler and more consistent with theory than is the CLM big-leaf canopy model, and it modernizes the canopy physics for theoretical and computational advances compared with CLM’s outdated ad-hoc parameterizations. Nonetheless, our analysis points to further modeling needs and identifies observations central to model testing. Measurements of within-canopy micrometeorology and leaf gas exchange are needed in addition to above-canopy fluxes.

在天气和气候模式中，陆地表面模式模拟陆地和大气之间交换的通量。流行的建模范例使用不是垂直解析的大叶冠参数化。多层冠层模型作为一种改善地表通量和促进新科学发展的手段，在过去几年中引起了人们的兴趣。我们将社区陆地模型（CLM）多层冠层模型（CLM-ml v2）与冠层水平阵列湍流研究（CHATS）期间核桃园内外多个高度的气温、比湿、风速和通量（净辐射、感热、潜热、动量）的观测结果进行了比较。该数据集为测试多层模型提供了一个基准。在强不稳定、弱不稳定、接近中性、弱稳定和强稳定的一系列大气状态下，很好地模拟了冠层上感热、潜热和动量通量，以及冠层内通量的垂直剖面。风速的垂直剖面与所有稳定状态下的观测结果非常吻合。空气温度和比湿度的垂直剖面得到了很好的模拟，但在强稳定条件下，一阶湍流闭合不能代表冠层内部的非局部垂直混合，否则会将上层冠层辐射冷却产生的冷空气向下输送到下层冠层。我们的模型与数据对比突出了多层模型模拟地表空气空间的潜力。多层冠层模型比CLM大叶冠层模型更简单，更符合理论，与CLM过时的自组织参数化相比，它在理论和计算方面实现了冠层物理的现代化。尽管如此，我们的分析指出了进一步的建模需求，并确定了模型测试的核心观察结果。除了冠层上通量外，还需要测量冠层内微气象学和叶片气体交换。

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引用次数: 0

Hydrological processes govern methane flux fluctuations in a subtropical floodplain 水文过程控制着亚热带洪泛区甲烷通量的波动

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-02 DOI: 10.1016/j.agrformet.2025.110965

Xueran Wang , Rongrong Wan , Guishan Yang , Xiaosong Zhao , Bing Li , Xingwang Fan , Yixue Hong , Haoran Wang , Jipeng Song , Zhiyu Song , Yu Jiang

Floodplain methane (CH₄) emissions represent a significant component of the global CH₄ budget. However, their response to escalating extreme drought events remains poorly understood, mainly due to high temporal variability under alternating wet-dry conditions. To address this gap, we conducted two years of in-situ CH₄ flux measurements using the chamber technique across alternating hydrological cycles (2022–2023) in the Poyang Lake floodplain, during which the region experienced a prolonged drought. Our results showed that CH₄ emissions during non-flooding periods (1.82 ± 1.36 mg CH₄ m^–2 h^–1) (mean ± standard deviation) were significantly higher than those during flooding periods (1.26 ± 0.96 mg CH₄ m^–2 h^–1). Notably, CH₄ fluxes in the autumn growing period (2.04 ± 1.43 mg CH₄ m^–2 h^–1) were 35 % higher than in the spring (1.51 ± 1.21 mg CH₄ m^–2 h^–1) under drought conditions. Further analysis revealed that, apart from air temperature, CH₄ fluxes were primarily regulated by vegetation during non-flooding periods and by fluctuating water levels and flooding duration that influence biogeochemical processes during flooding periods. The enhanced temperature sensitivity of CH₄ emissions emerged as a key factor for the higher autumn emissions compared to spring, which is directly linked to the shortened flooding period in the Poyang Lake floodplain. These findings underscore the critical role of extreme drought in reshaping hydrological conditions and CH₄ emissions in floodplain wetlands, with important implications for predicting wetland responses under future climate change scenarios.

漫滩甲烷（CH4）排放是全球CH4收支的一个重要组成部分。然而，它们对不断升级的极端干旱事件的反应仍然知之甚少，这主要是由于在干湿交替条件下的高时间变异性。为了解决这一差距，我们在鄱阳湖漫滩上进行了为期两年的CH4通量原位测量，使用了室内技术，跨越交替水文循环（2022-2023），在此期间该地区经历了长期干旱。结果表明：非淹水期CH4排放量（1.82±1.36 mg CH4 m-2 h-1）显著高于淹水期（1.26±0.96 mg CH4 m-2 h-1）；干旱条件下，秋季生育期CH4通量（2.04±1.43 mg CH4 m-2 h-1）比春季生育期（1.51±1.21 mg CH4 m-2 h-1）高35%。进一步分析表明，除气温外，CH4通量主要受非洪涝期植被和洪涝期影响生物地球化学过程的水位波动和洪水持续时间的调节。CH4排放的温度敏感性增强是导致秋季排放高于春季的关键因素，这与鄱阳湖漫滩汛期缩短有直接关系。这些发现强调了极端干旱在重塑洪泛区湿地水文条件和CH4排放中的关键作用，对预测未来气候变化情景下湿地的响应具有重要意义。

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引用次数: 0

Drivers and thresholds of carbon and water flux dynamics in a semi-humid urban forest ecosystem 半湿润城市森林生态系统碳水通量动态的驱动因素和阈值

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-01 DOI: 10.1016/j.agrformet.2025.110933

Tuqiang Chen , Han Li , Jinhui Jeanne Huang

Urban forests play a critical role in regulating microclimates, sequestering carbon, and providing various ecosystem services. Due to urban land use changes and human activities, urban water and thermal environments have been significantly altered. This has made urban ecosystems more complex than natural ecosystems. However, our understanding of the magnitude, driving factors, and environmental response thresholds of carbon and water fluxes in urban forests remains limited. This study measured carbon and water fluxes in a semi-humid urban forest in China using eddy covariance technology over four consecutive growing seasons (May–October) from 2020 to 2023. Multi-year averages of gross primary production (GPP), evapotranspiration (ET), and water use efficiency (WUE) during the growing seasons were 1177.1 g C m⁻² yr⁻¹, 520.5 mm yr⁻¹, and 2.2 g C kg⁻¹ H₂O, respectively. Under non-drought conditions, canopy conductance (Gc), GPP, and ET were significantly (p < 0.05) higher than under drought conditions. Higher soil water content (SWC) partially alleviated the negative effects of high net radiation (Rn), air temperature (Ta), and vapor pressure deficit (VPD) on GPP and ET during droughts, although it was not the primary driver of their variability. Structural equation modeling revealed that under drought conditions, GPP was primarily regulated by atmospheric demand (e.g., VPD), whereas ET was primarily controlled by energy availability (e.g., Rn and Ta), with SWC exerting a positive influence on both GPP and ET. In contrast, under non-drought conditions, energy availability dominated the regulation of GPP and ET. Threshold analyses further revealed that GPP and ET responded nonlinearly to environmental drivers, initially increasing with Rn, Ta, and VPD but declining after reaching specific thresholds. These findings enhance our understanding of the mechanisms underlying carbon and water flux dynamics in urban forest ecosystems, particularly in the context of drying and warming conditions.

城市森林在调节小气候、固碳和提供各种生态系统服务方面发挥着关键作用。由于城市土地利用变化和人类活动，城市水热环境发生了显著变化。这使得城市生态系统比自然生态系统更加复杂。然而，我们对城市森林碳和水通量的大小、驱动因素和环境响应阈值的理解仍然有限。本研究利用涡动相关技术测量了2020 - 2023年中国半湿润城市森林连续4个生长季节（5 - 10月）的碳和水通量。在生长季节的多年平均初级生产总值（GPP），蒸散（ET）和水利用效率（WUE）分别为1177.1克- m - 1年（⁻），520.5毫米- 1年（⁻）和2.2克- kg（⁻）。非干旱条件下，冠层导度（Gc）、GPP和ET显著高于干旱条件（p < 0.05）。较高的土壤含水量（SWC）在一定程度上缓解了干旱期间高净辐射（Rn）、气温（Ta）和水汽压差（VPD）对GPP和ET的负面影响，尽管它不是GPP和ET变率的主要驱动因素。结构方程模型表明，干旱条件下，GPP主要受大气需求（如VPD）的调控，而ET主要受能量可用性（如Rn和Ta）的调控，其中SWC对GPP和ET均有正向影响。非干旱条件下，能量可用性主导GPP和ET的调控。阈值分析进一步揭示了GPP和ET对环境驱动因素的非线性响应。最初随着Rn、Ta和VPD的增加而增加，但在达到特定阈值后下降。这些发现增强了我们对城市森林生态系统中碳和水通量动力学机制的理解，特别是在干燥和变暖的条件下。

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引用次数: 0

Climatic control on soil microbial methane uptake across forest biomes 气候对森林生物群落土壤微生物甲烷吸收的控制

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2025-12-01 DOI: 10.1016/j.agrformet.2025.110942

Yongping Kou , Manuel Delgado-Baquerizo , Wenqiang Zhao , Xiangzhen Li , Yanhong Wu , Xiaohu Wang , Jiangtao Xiao , Haijian Bing , Qing Liu

Soil microbial methane (CH₄) uptake is critical for mitigating global warming and is sensitive to climate change. However, how climatic changes regulate the capacity of forest soils to uptake CH₄ across environmental gradients remains largely unclear. Here, we investigated the distribution and key drivers of the CH₄ oxidation potential (MOP) across 26 forests along a latitudinal gradient of 4000 km with structural equation modeling and multiple regression. We found that climate was a fundamental driver of MOP, with soil MOP peaking in the subtropical zone and being the lowest in the temperate zone. Structural equation modeling provided evidence that soil MOP was directly driven by changes in the aridity index and indirectly by regulating plant biomass, followed by soil properties. We also found that the environmental context influenced MOP within particular biomes and vegetation types. For example, the cold temperate zone exhibited a significant positive correlation between soil copper content and MOP, suggesting copper as a key factor explaining the variation in soil MOP in this region, as the particulate methane monooxygenase that catalyzes the oxidation of CH₄ is a copper-bound membrane metalloenzyme. Within coniferous broad-leaved forests, soil manganese emerged as a significant predictor of soil MOP, because CH₄ oxidation could be coupled to the reduction of manganese oxides, highlighting its biome-specific role in ecosystem functioning. In addition, methanotrophic richness was most important for explaining soil MOP in coniferous forests due to the lower alpha diversity of methanotrophs observed here. Our study provides solid evidence that climate and local environmental conditions regulate CH₄ sinks in forest ecosystems, with implications for predicting terrestrial carbon cycling under global climate change.

土壤微生物对甲烷（CH4）的吸收对减缓全球变暖至关重要，对气候变化非常敏感。然而，气候变化如何调节森林土壤跨环境梯度吸收CH4的能力仍不清楚。利用结构方程模型和多元回归分析方法，在4000 km的纬度梯度上研究了中国26个森林CH4氧化电位（MOP）的分布及其驱动因素。气候是土壤MOP的根本驱动因素，土壤MOP在亚热带最高，在温带最低。结构方程模型表明，土壤MOP直接受干旱指数变化的影响，间接受植物生物量调节的影响，其次是土壤性质的影响。我们还发现，在特定的生物群系和植被类型中，环境背景会影响MOP。例如，冷温带土壤铜含量与MOP呈显著正相关，表明铜是解释该地区土壤MOP变化的关键因素，因为催化CH4氧化的颗粒甲烷单加氧酶是一种铜结合的膜金属酶。在针叶林中，土壤锰是土壤MOP的重要预测因子，因为CH4氧化可能与锰氧化物的还原耦合，突出了其在生态系统功能中的生物群系特异性作用。此外，甲烷营养丰富度是解释针叶林土壤MOP最重要的原因，因为这里观察到的甲烷营养菌α多样性较低。我们的研究为气候和局部环境条件对森林生态系统中CH4汇的调节提供了有力证据，对预测全球气候变化下陆地碳循环具有重要意义。

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引用次数: 0