Agricultural and Forest Meteorology最新文献_第2页

Do CMIP6 earth system models outperform their predecessors in simulating global vegetation changes? CMIP6地球系统模型在模拟全球植被变化方面是否优于它们的前辈？

IF 6.2 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-12 DOI: 10.1016/j.agrformet.2026.111067

Ruixuan Xu, Weiqing Zhao, Sen Cao, Zaichun Zhu

引用次数: 0

Robust estimation of daily photosynthesis from instantaneous observations through machine-learning integration of radiation and environmental drivers 通过集成辐射和环境驱动因素的机器学习，从瞬时观测中稳健估计每日光合作用

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-10 DOI: 10.1016/j.agrformet.2026.111064

Yanan Zhou, Xing Li, Jingyu Lin, Xi Liu

Remote sensing (RS) facilitates large-scale estimation of vegetation carbon and water fluxes, yet temporal mismatches persist between its instantaneous observations and the daily flux mean or sum required for ecological modeling. Traditional upscaling methods typically convert instantaneous flux observations to daily values through assuming that diurnal flux patterns are mainly driven by solar radiation, failing to capture real dynamics induced by other environmental factors (e.g., temperature and moisture). This introduces substantial errors, particularly in daily carbon flux estimation. To address this issue, we focus on gross primary production (GPP) and develop a new conversion factor model that integrates solar radiation and other key environmental drivers, enabling robust upscaling from instantaneous to daily scales. Using the FLUXNET2015 dataset, the conversion factor

γ_{e n v}

, defined as the ratio of instantaneous to daily GPP, was modeled using random forest, with vapor pressure deficit, soil water content, air temperature, and shortwave radiation as predictors. SHapley Additive exPlanations (SHAP) analysis was used to evaluate predictors’ contribution and response mechanisms. Results show that the proposed model outperformed traditional upscaling methods in daily GPP estimation, improving R² by up to 39% and reducing RMSE by up to 82%. Validation across diverse ecosystems, environmental stress levels, and drought conditions further confirmed its superior generalizability over conventional methods. Critically,

γ_{e n v}

retained high accuracy when driven by ERA5-Land reanalysis data instead of site-level tower measurements and reliably upscaled satellite-based instantaneous GPP snapshots to daily estimates, demonstrating scalability for large-scale applications. Moreover,

γ_{e n v}

effectively captured complex diurnal dynamics of vegetation photosynthesis under environmental stress, and through SHAP, revealed the growing role of water or temperature-related drivers in regulating GPP diurnal patterns as stress intensified. Overall, this study presents a structurally simple yet ecologically grounded solution to the temporal mismatch in RS-based GPP estimation, and offers valuable insights for upscaling other ecosystem fluxes.

遥感有助于大规模估算植被碳通量和水通量，但其瞬时观测值与生态建模所需的日通量平均值或总和之间存在时间不匹配。传统的升级方法通常通过假设日通量模式主要由太阳辐射驱动，将瞬时通量观测值转换为日值，而无法捕获由其他环境因素（如温度和湿度）引起的真实动态。这带来了很大的误差，特别是在每日碳通量估计中。为了解决这一问题，我们将重点放在初级生产总值（GPP）上，并开发了一种新的转换因子模型，该模型集成了太阳辐射和其他关键环境驱动因素，从而实现了从瞬时规模到日常规模的稳健升级。利用FLUXNET2015数据集，利用随机森林对转换因子γenv（定义为瞬时GPP与每日GPP的比值）进行建模，并以蒸汽压亏缺、土壤含水量、气温和短波辐射作为预测因子。采用SHapley加性解释（SHAP）分析评价预测因子的贡献和反应机制。结果表明，该模型在日常GPP估计中优于传统的上尺度方法，提高了39%的R²，降低了82%的RMSE。在不同生态系统、环境压力水平和干旱条件下的验证进一步证实了其优于传统方法的通用性。重要的是，当由ERA5-Land再分析数据驱动时，γenv保持了较高的精度，而不是现场级塔测量和可靠的升级卫星瞬时GPP快照，以达到每日估计，证明了大规模应用的可扩展性。此外，γ - env有效捕获了环境胁迫下植被光合作用的复杂日动态，并通过SHAP揭示了随着胁迫的加剧，水或温度相关驱动因素对GPP日模式的调节作用越来越大。总体而言，该研究为基于rs的GPP估算中的时间失配提供了一个结构简单但基于生态学的解决方案，并为其他生态系统通量的升级提供了有价值的见解。

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引用次数: 0

Coupling data assimilation and machine learning to improve land surface conditions and near-surface temperature and humidity forecasts 耦合数据同化和机器学习以改善地表条件和近地表温度和湿度预报

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-10 DOI: 10.1016/j.agrformet.2026.111063

Xinlei He , Shaomin Liu , Tongren Xu , Fei Chen , Zhitao Wu , Ziwei Xu , Xiang Li , Rui Liu

Enhancing the representation of land surface conditions and improving the accuracy of near-surface weather forecasts remain critical challenges for numerical weather prediction (NWP). This study coupled a hybrid data assimilation-machine learning framework (DL) with the Weather Research and Forecasting (WRF) model to quantify the impacts of incorporating soil moisture (SM) and vegetation data on land surface initialization and near-surface weather forecast accuracy. This was achieved by integrating satellite-based leaf area index (LAI) and multi-source SM data into the WRF model in the Southern Great Plains (SGP) of the United States. The results indicate that optimizing LAI and SM significantly improves the simulation of water table depth, evapotranspiration (ET), air temperature and humidity in the WRF model. In addition to SM, LAI optimization provides additional benefits to the WRF model in dry years. A series of comparison experiments were conducted across both dry and wet years to evaluate the accuracy of air temperature and humidity forecasts. The optimized vegetation and SM conditions from the DL method were used as initial conditions for the early days of the forecast period. The results confirm that the DL method effectively refines the land surface initial conditions at the beginning of the forecast period. This effect improves the estimation of near-surface atmospheric conditions (e.g., air temperature and humidity) and alters precipitation patterns during the forecast period. In addition, the integration of LAI and SM is more effective in improving forecasts in wet/normal years than dry years. Analysis of the forecast results illustrates that the DL method can optimize initial conditions and improve near-surface weather forecasts over the next month.

加强地表条件的表征和提高近地表天气预报的精度仍然是数值天气预报面临的关键挑战。本研究将混合数据同化-机器学习框架（DL）与天气研究与预报（WRF）模型相结合，量化纳入土壤湿度（SM）和植被数据对地表初始化和近地表天气预报精度的影响。这是通过将基于卫星的叶面积指数（LAI）和多源SM数据整合到美国南部大平原（SGP）的WRF模型中实现的。结果表明，优化LAI和SM显著改善了WRF模式对地表深度、蒸散发（ET）、气温和湿度的模拟。除了SM， LAI优化在干旱年份为WRF模型提供了额外的好处。在干湿两季进行了一系列对比试验，以评估气温和湿度预报的准确性。利用DL方法优化的植被和SM条件作为预报期前期的初始条件。结果表明，DL方法有效地细化了预测期开始时的地表初始条件。这种效应改善了对近地表大气条件（例如，空气温度和湿度）的估计，并改变了预报期间的降水模式。此外，LAI和SM的整合在湿/正常年比干旱年更有效地改善预报。对预报结果的分析表明，DL方法可以优化初始条件，提高未来一个月的近地面天气预报。

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引用次数: 0

Warm and wet spring compensated for the reduction in carbon sinks due to an extreme summer heatwave-drought event in 2022 in southern China 温暖湿润的春季弥补了2022年中国南方极端夏季热浪干旱事件造成的碳汇减少

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-07 DOI: 10.1016/j.agrformet.2026.111060

Yuanyuan Zhang , Fei Jiang , Yanlian Zhou , Guanyu Dong , Dongqiao Wu , Wei He , Jun Wang , Mousong Wu , Hengmao Wang , Lingyu Zhang , Mengwei Jia , Weimin Ju , Jing M. Chen

During the July-September (JAS) of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). It caused an unprecedented decline in vegetation photosynthesis, however, its impact on the regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using the Global Carbon Assimilation System (GCAS v2) by assimilating OCO-2 XCO₂ retrievals. Our results indicate that, relative to 2015-2021, the MLYR region experienced a 45.8 TgC reduction in land sink during JAS, consistent with the TRENDYv13 simulations. Combining our inverse results with satellite proxies for GPP, we find that an unusually wet spring in 2022 boosted vegetation growth in the MLYR, increasing gross primary productivity (GPP) by 46.1 TgC and strengthening the land sink by 24.0 TgC, thereby substantially offsetting the carbon sink reductions observed during JAS. Outside the MLYR region in southern China, annual land sink increased by 49.9 TgC in remaining areas (RAS), also greatly mitigating the impact of the DH2022 on the regional carbon balance. Overall, the annual land sink in MLYR decreased by only 7.1 TgC, whereas in southern China, it increased by 42.8 TgC. During JAS, the decreased land sink in MLYR was primarily driven by a decline in GPP in forests and grass/shrub, coupled with an increase in total ecosystem respiration in croplands. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing our understanding of the impacts of climate extremes on the regional carbon cycle.

2022年7 - 9月，中国南方地区，特别是长江中下游地区，遭受了一次破纪录的热浪干旱（DH2022）。它造成了植被光合作用的空前下降，但其对区域碳收支的影响尚不清楚。利用全球碳同化系统（GCAS v2），通过同化OCO-2 XCO2反演，评估了区域陆地碳通量对DH2022的响应。结果表明，与2015-2021年相比，在JAS期间，MLYR区域的陆地汇减少了45.8 TgC，与TRENDYv13模拟结果一致。将我们的反演结果与GPP的卫星代理相结合，我们发现2022年异常潮湿的春季促进了MLYR的植被生长，使总初级生产力（GPP）增加了46.1 TgC，使陆地汇增加了24.0 TgC，从而大大抵消了JAS期间观测到的碳汇减少。除长江三角洲地区外，其余地区（RAS）年土地汇增加49.9 TgC，也大大缓解了DH2022对区域碳平衡的影响。总体而言，年陆地汇仅减少了7.1 TgC，而南方增加了42.8 TgC。在JAS期间，MLYR土地汇的减少主要是由森林和草/灌木GPP的下降以及农田生态系统呼吸总量的增加所驱动的。通过对DH2022影响下中国南方土地碳动态的综合评估，加深了对极端气候对区域碳循环影响的认识。

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引用次数: 0

Net ecosystem carbon balance and greenhouse gas budget of a canola-wheat cropping system in the northern prairies 北方草原油菜-小麦种植系统净生态系统碳平衡与温室气体收支

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-07 DOI: 10.1016/j.agrformet.2026.111044

D. Ferland , C. Wagner-Riddle , P․K․C Pow , S․E. Brown , K․A. Congreves

Arable croplands are critical in the context of climate change, acting as both sources and potential sinks of greenhouse gases (GHG) in the form of carbon dioxide (CO₂) and nitrous oxide (N₂O). However, there is limited data on the carbon (C) balance and GHG budget (GHGB) from canola-wheat rotations in the northern Prairies of North America, an important agricultural region. We present micrometeorological GHG fluxes measured from January 2021 to April 2025 through two rotations of canola-wheat in Saskatchewan, Canada, to evaluate the interannual C dynamics and GHGB of the 4-year cropping sequence. Net ecosystem exchange (NEE) ranged from 87 to -239 g C m^-2, driven by variable meteorological conditions. Annual cumulative gross primary production (GPP) exceeded the cumulative ecosystem respiration (R_e) in all years except 2021—the second driest year on record for this region. The GHGB values were between 60 and 156 CO₂-eq m^-2 yr^-1 in the first 3 crop years, but were negative (i.e., GHG sink) in Year 4. Overall, this study presents the first multi-year, measurement-based assessment of canola-wheat rotations in the northern Prairies, showing that over a 4-year period, the cropping system was C neutral (net ecosystem carbon balance [NECB] = 2 ± 35 g C m^-2 yr^-1). The dataset provides the region-specific information needed to inform C policy and evaluate the agricultural sustainability of the northern Prairies. It also provides valuable information for canola and wheat crops, particularly the impact of harvest removals and N fertilizer application—practices that can be targeted for agricultural GHG emissions mitigation.

可耕地在气候变化的背景下至关重要，既是二氧化碳（CO2）和一氧化二氮（N2O）形式的温室气体（GHG）的来源，也是潜在的汇。然而，关于北美北部草原油菜-小麦轮作的碳(C)平衡和温室气体收支（GHGB）数据有限。本文利用加拿大萨斯喀彻温省油菜-小麦两个轮作在2021年1月至2025年4月测量的微气象温室气体通量，评估了4年种植序列的年际碳动态和温室气体排放量。净生态系统交换（NEE）在87 ~ -239 g C m-2之间，受不同气象条件的驱动。除2021年（该地区有记录以来第二干旱的年份）外，所有年份的年累积初级生产总值（GPP）均超过累积生态系统呼吸（Re）。前3个作物年的GHGB值在60 ~ 156 co2当量m-2年-1之间，但在第4年为负（即温室气体汇）。总体而言，本研究首次提出了对北部草原油菜-小麦轮作的多年期、基于测量的评估，表明在4年期间，种植系统是C中性的（净生态系统碳平衡[NECB] = 2±35 g C m-2年-1）。该数据集提供了为气候政策提供信息和评估北部草原农业可持续性所需的特定区域信息。它还为油菜籽和小麦作物提供了宝贵的信息，特别是可作为减缓农业温室气体排放目标的收获和氮肥施用方法的影响。

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引用次数: 0

Patterns and drivers of African carbon recovery after disturbance 扰动后非洲碳恢复的模式和驱动因素

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-07 DOI: 10.1016/j.agrformet.2026.111061

Haotian Li , Xianfeng Liu , David Makowski , Jean-Pierre Wigneron

Climate extremes and persistent deforestation pose significant threats to Africa's vegetation carbon stocks. However, the patterns of aboveground carbon (AGC) loss, recovery, and their driving factors in Africa remain poorly understood. Here, we utilize low-frequency microwave satellite data to analyze AGC dynamics across Africa during 2010-2020. Results indicate a small net AGC increase of +0.16 ± 0.03 PgC yr^-1 during the study period, composed of gross losses of −1.56 ± 0.26 PgC yr⁻¹ offset by gross gains of +1.72 ± 0.29 PgC yr⁻¹. The total loss in forested areas amount to -0.50 ± 0.07 PgC yr⁻¹, of which degradation accounting for twice as much loss as deforestation. In non-forested areas, the total AGC loss was −1. 06 ± 0.21 PgC yr⁻¹, primarily driven by wildfires (-0.78 PgC yr^-1). Following the 2015–2016 El Niño event, 66 % of affected regions exhibited AGC recovery ratios exceeding 100 % during 2015-2020, predominantly in non-forest vegetation, suggesting a higher recover ratio for non-forest vegetation. In contrast, the remaining 34 % of regions did not fully recover, with an average recovery rate of 58 %, predominantly concentrated in forested areas. A machine learning analysis based on random forest suggests that recovery ratios are primarily influenced by vapor pressure deficit (VPD), followed by precipitation and human footprint. Our study provides a comprehensive understanding of the dynamics of African AGC by distinguishing the loss into forest and non-forest vegetation, and also highlights the key drivers of AGC recovery after disturbances. These findings offer valuable insights for ecological conservation, climate adaptation, and global carbon budget assessments.

极端气候和持续的森林砍伐对非洲的植被碳储量构成了重大威胁。然而，非洲地上碳（AGC）损失、恢复的模式及其驱动因素仍然知之甚少。本文利用低频微波卫星数据分析了2010-2020年非洲地区的AGC动态。结果表明，在研究期间，AGC的净增长为+0.16±0.03 PgC -1，其中总损失为- 1.56±0.26 PgC -1，总收益为+1.72±0.29 PgC -1。森林面积的总损失为-0.50±0.07 PgC - 1年，其中退化造成的损失是毁林造成的损失的两倍。在非森林地区，总AGC损失为−1。6±0.21 PgC -1年（-0.78 PgC -1年）。2015-2016年El Niño事件发生后，2015-2020年期间，66%的受影响地区的AGC恢复率超过100%，主要是非森林植被，表明非森林植被的恢复率更高。相比之下，其余34%的地区没有完全恢复，平均恢复率为58%，主要集中在森林地区。基于随机森林的机器学习分析表明，回收率主要受蒸汽压差（VPD）的影响，其次是降水和人类足迹。我们的研究通过区分森林和非森林植被的损失，提供了对非洲AGC动态的全面理解，并强调了干扰后AGC恢复的关键驱动因素。这些发现为生态保护、气候适应和全球碳预算评估提供了有价值的见解。

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引用次数: 0

Regulatory mechanisms of spatiotemporal variations in aboveground and belowground net primary production in global terrestrial ecosystems 全球陆地生态系统地上、地下净初级生产量时空变化的调控机制

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-06 DOI: 10.1016/j.agrformet.2026.111055

Tao Zhou , Yanting Xiong , Zhihan Yang , Yuting Hou , Yuqing Zhang , Dan Liao , Xiaodong Wang , Dinghui Xu , Pingfeng Li , Peng Hou , Wenji Zhao , Guo Chen , Benjamin Laffitte , Xiaolu Tang

Net primary productivity is a critical component of terrestrial carbon cycling and an essential indicator of ecosystem carbon sequestration capacity. However, separating aboveground and belowground net primary productivity (ANPP and BNPP) and understanding the driving mechanisms of their spatial patterns remain challenging across global terrestrial ecosystems. Here, we used a modified multilayer perceptron network (MLP) built upon an updated database containing 5184 field observations to predict the spatiotemporal patterns of ANPP and BNPP and identify their driving mechanisms across global terrestrial ecosystems at 0.05° resolution. Results indicated that the MLP model satisfactorily predicted ANPP (R² = 0.74) and BNPP (R² = 0.73). Spatially, both ANPP and BNPP exhibited strong spatial heterogeneity, with a decreasing trend from the tropics toward the poles. Temporally, ANPP showed an increasing trend of 0.02 Pg C yr⁻², with a global mean of 33.4 ± 0.5 (mean ± standard error) Pg C yr⁻¹ from 1981 to 2018. Similarly, the mean total BNPP was 19.2 ± 0.73 Pg C yr⁻¹, with an increasing trend of 0.05 Pg C yr⁻². Quantitatively, significant trends were observed, with 65.4% and 60.8% of land areas showing increasing trend of ANPP and BNPP (P < 0.01), respectively. The spatial patterns of ANPP were mainly influenced by temperature and precipitation, while BNPP was controlled by soil properties. These findings highlight the importance of distinguishing ANPP and BNPP to better understand the driving mechanisms of carbon allocation strategies. These findings are crucial for advancing the understanding of vegetation dynamics in response to global climate change and improving terrestrial ecosystem carbon modeling.

净初级生产力是陆地碳循环的重要组成部分，也是生态系统固碳能力的重要指标。然而，在全球陆地生态系统中，分离地上和地下净初级生产力（ANPP和BNPP）并理解其空间格局的驱动机制仍然具有挑战性。本文基于5184个野外观测数据，采用改进的多层感知器网络（MLP）在0.05°分辨率下预测了全球陆地生态系统ANPP和BNPP的时空格局，并确定了它们的驱动机制。结果表明，MLP模型对ANPP （R2 = 0.74）和BNPP （R2 = 0.73）的预测较好。在空间上，ANPP和BNPP均表现出较强的空间异质性，从热带向两极呈下降趋势。时间上，ANPP呈增加趋势，为0.02 Pg C yr - 2, 1981 - 2018年全球平均值为33.4±0.5（平均±标准误差）Pg C yr - 1。同样，平均总BNPP为19.2±0.73 Pg C yr - 1，并有0.05 Pg C yr - 2的增加趋势。在数量上，有显著的趋势，65.4%和60.8%的土地面积呈现ANPP和BNPP增加的趋势（P < 0.01）。ANPP的空间格局主要受温度和降水的影响，而BNPP受土壤性质的控制。这些发现强调了区分ANPP和BNPP对于更好地理解碳分配策略的驱动机制的重要性。这些发现对于提高对全球气候变化下植被动态的认识和改进陆地生态系统碳模型至关重要。

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引用次数: 0

Improving the MCWLA agroecosystem model to better simulate methane emissions from paddy rice fields 改进MCWLA农业生态系统模型，更好地模拟稻田甲烷排放

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-04 DOI: 10.1016/j.agrformet.2026.111053

Hong Zhou , Fulu Tao , Yi Chen , Lichang Yin , Yibo Li

Rice cultivation stands out as a major greenhouse gas source, emitting 10–20% of global CH₄ emissions. How to accurately estimate CH₄ emissions from paddy rice and their mitigation potential has been key concerns. Agroecosystem models have unique advantages in understanding CH₄ processes, simulating CH₄ emissions dynamics, optimizing management practices, and quantifying mitigation potentials. However, current agroecosystem models need to be substantially improved for these purposes. In this study, we develop a comprehensive agroecosystem model, MCWLA-Rice 2.0, to better depict the production, oxidation, and emission processes of CH₄ and improve the simulation of root exudates, the effect of nitrate fertilizer on CH₄ emissions, and the decomposition of external organic carbon. We calibrate and validate the model and demonstrate its performance in simulating the rice cultivation system under different fertilizer and irrigation treatments at seven sites across Asia. Elaborating on both aboveground and belowground carbon-nitrogen coupling processes, MCWLA-Rice 2.0 is a valuable tool for simulating rice productivity and CH₄ emissions under various environments and managements, effectively supporting the development of climate-smart agriculture.

水稻种植是主要的温室气体来源，排放了全球10-20%的甲烷。如何准确估计水稻的甲烷排放及其减缓潜力一直是关键问题。农业生态系统模型在理解CH4过程、模拟CH4排放动态、优化管理实践和量化减排潜力方面具有独特的优势。然而，目前的农业生态系统模式需要为此进行大量改进。为了更好地描述CH4的产生、氧化和排放过程，本研究建立了MCWLA-Rice 2.0综合农业生态系统模型，并改进了对根系分泌物、硝态肥对CH4排放的影响以及外部有机碳分解的模拟。我们对该模型进行了校准和验证，并在亚洲7个地点模拟了不同施肥和灌溉处理下的水稻栽培系统。MCWLA-Rice 2.0详细阐述了地上和地下碳氮耦合过程，是模拟各种环境和管理下水稻生产力和CH4排放的宝贵工具，有效支持气候智慧型农业的发展。

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引用次数: 0

Temperature and photoperiod interactions influence the cessation of wood growth in three temperate and boreal conifers 温度和光周期相互作用影响三种温带和北方针叶树的木材生长停止

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-04 DOI: 10.1016/j.agrformet.2026.111056

Jianhong Lin , Cyrille B.K. Rathgeber , Patrick Fonti , Sergio Rossi , Henri Cuny , Edurne Martinez del Castillo , Katarina Čufar , J. Julio Camarero , Alessio Giovannelli , Harri Mäkinen , Peter Prislan , Walter Oberhuber , Hanuš Vavrčík , Jianguo Huang , Andreas Gruber , Vladimír Gryc , Václav Treml , Martin de Luis , Jožica Gričar , Nicolas Delpierre

Cambium phenology is a crucial process in wood production and carbon sequestration of forest ecosystems. Although cambium phenology has been widely studied, research specifically focusing on the cessation of wood formation remains limited. To better understand the influence of environmental and intrinsic factors on the cessation of wood formation, we built and compared three ecophysiological models (temperature sum model, photoperiod-influenced temperature sum model and soil moisture- and photoperiod-influenced temperature sum model) in their ability to predict the date of cessation of xylem cell enlargement (cE) in three major Northern Hemisphere conifer species (Black spruce, Norway spruce and Scots pine). We developed these models based on xylogenesis data collected for 130 site‐years across Europe and Canada. Our results demonstrate that the photoperiod-influenced temperature sum model is well-supported by data across all conifer species, with a RMSE of 9.2 days, suggesting that both temperature and photoperiod are critical drivers of wood growth cessation. However, incorporating soil moisture effects does not improve model performance. Our model effectively captures the inter-site variability in cE across a wide environmental gradient, with a fair model efficiency (ME = 0.51 ± 0.22), but performed less well for annual anomalies (ME = 0.10 ± 0.09). Additionally, we found that the total ring cell number also affected prediction accuracy. Using this model, we reconstructed historical trends in cE over the past six decades and found a trend to delayed cessation dates. This delay varied geographically, with slower shifts at higher latitudes and elevations, likely due to constrained cambial responses and conservative growth strategies in colder regions. Our model framework offers a simple yet accurate approach for predicting wood growth cessation at large spatial scales, providing a basis for integrating cambium phenology into land surface models and forest productivity assessments.

形成层物候是森林生态系统木材生产和固碳的重要过程。虽然形成层物候学已经被广泛研究，但专门关注木材形成停止的研究仍然有限。为了更好地了解环境和内在因素对木材停止形成的影响，我们建立了三种生态生理模型（温度和模型、光周期影响温度和模型和土壤水分和光周期影响温度和模型），并比较了它们预测北半球三种主要针叶树种（黑云杉、挪威云杉和苏格兰松）木质部细胞增大停止日期的能力。我们基于在欧洲和加拿大收集的130个站点年的木材发生数据开发了这些模型。结果表明，光周期影响的温度和模型得到了所有针叶树种数据的良好支持，RMSE为9.2 d，表明温度和光周期都是木材生长停止的关键驱动因素。然而，考虑土壤水分的影响并不能改善模型的性能。我们的模型有效地捕获了广泛环境梯度下的站点间cE变化，具有公平的模型效率（ME = 0.51±0.22），但对年异常表现不佳（ME = 0.10±0.09）。此外，我们发现总环胞数也影响预测精度。使用该模型，我们重建了过去六十年来cE的历史趋势，并发现了延迟戒烟日期的趋势。这种延迟在地理上有所不同，高纬度和高海拔地区的变化较慢，可能是由于在较冷地区受到限制的形成层响应和保守的生长策略。我们的模型框架提供了一个简单而准确的方法来预测大空间尺度上的木材生长停止，为将形成层物候学整合到陆地表面模型和森林生产力评估中提供了基础。

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引用次数: 0

Multi-layer model requires accurate information of vertical structure to realize its full potential in simulating gross primary production 多层模型需要准确的垂直结构信息，才能充分发挥其模拟初级生产总量的潜力

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-03 DOI: 10.1016/j.agrformet.2026.111036

Jiangliu Xie , Gaofei Yin , Shangrong Lin , Xiaozhou Xin , Hu Zhang , Xinjie Liu , Qinhuo Liu , Aleixandre Verger , Adrià Descals , Iolanda Filella , Josep Peñuelas

Photosynthesis, a vital process for carbon exchange between biosphere and atmosphere, has been integrated into terrestrial biosphere models (TBMs). TBMs employ upscaling methods such as big-leaf (BL), two-leaf (TL), or multi-layer (MTL) models to simulate canopy-scale photosynthesis, with MTL model theoretically offering the highest accuracy due to its detailed canopy representation. The comparative efficacy of these models in simulating gross primary production (GPP), however, remains uncertain. This study provides a systematic assessment of how the MTL differs from the BL and TL models in GPP estimation across global flux tower sites.

The results indicate that both the MTL and TL models performed better than BL model in simulating canopy GPP, reducing root mean square error (RMSE) by 32.23% and 26.51%, respectively, with the MTL (2.25 g C m⁻² d⁻¹) demonstrating a slightly improved accuracy compared to the TL model (2.44 g C m⁻² d⁻¹). Incorporating foliar clumping reduced the overestimation of GPP with mean error (ME) decreasing by 32%, 28.74%, and 6.94% for MTL, TL, and BL models, respectively; however, the specific impact varied among the models. The MTL model excelled in enabling layered simulations of photosynthesis, allowing for the identification of vertical heterogeneity in environmental responses. Nonetheless, its improvement in accuracy over simpler models like the TL model was limited without highly precise data on vertical structure. This study highlights that improved canopy structure data from LiDAR technologies, such as GEDI, is crucial for realizing the full potential of MTL models for accurately simulating carbon fluxes.

光合作用是生物圈与大气之间碳交换的重要过程，已被纳入陆地生物圈模型（tbm）。tbm采用大叶（BL）、两叶（TL）或多层（MTL）模型等升级方法来模拟冠层尺度的光合作用，其中MTL模型由于其详细的冠层表示，理论上提供了最高的精度。然而，这些模型在模拟初级生产总值（GPP）方面的比较功效仍然不确定。本研究对全球通量塔站点的GPP估算中MTL与BL和TL模式的差异进行了系统评估。结果表明，MTL模型和TL模型在模拟林冠GPP方面均优于BL模型，RMSE分别降低了32.23%和26.51%，其中MTL模型（2.25 g C m−2 d−1）的精度略高于TL模型（2.44 g C m−2 d−1）。加入叶面团块后，MTL、TL和BL模型的平均误差（ME）分别降低了32%、28.74%和6.94%；然而，具体影响因模型而异。MTL模型在实现光合作用的分层模拟方面表现出色，允许识别环境响应的垂直异质性。尽管如此，在没有高度精确的垂直结构数据的情况下，其精度比TL模型等简单模型的提高受到限制。这项研究强调了来自激光雷达技术（如GEDI）的改进的冠层结构数据对于实现MTL模型精确模拟碳通量的全部潜力至关重要。

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