Agricultural and Forest Meteorology最新文献_第3页

Evaluating surface fluxes in WRF using eddy-covariance flux measurements in the Western and Eastern U.S. 利用美国西部和东部涡旋协方差通量测量评估WRF的地表通量

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.2026.111029

Fan Wu , Kenneth J. Davis , Li Zhang , Ray G. Anderson , Jason P. Horne , Sarah Goslee , William Munger , Chenxia Cai , Yu Yan Cui , Zhan Zhao , Min Zhong

Atmospheric boundary layer simulations in weather models, important elements of air quality simulations, are coupled with land surface parameterizations. The San Joaquin Valley (SJV) of California and the Multi-state Mid-Atlantic (MMA) feature diverse land uses, including agriculture, urban areas, and forests, which pose challenges for simulating surface fluxes. This study evaluates surface fluxes in the Weather Research and Forecasting (WRF) model using physical configurations adopted by state air quality agencies in California and Pennsylvania. We compared WRF simulations with year-long eddy-covariance flux measurements from 16 sites across the two regions. Results show that the Pleim-Xiu land surface model (PX LSM) exhibits substantial heat flux biases in the SJV but lacks systematic biases in the MMA. In the SJV, the model overestimates daytime (10:00-16:00 LST) sensible heat flux (H) by 260 W m^-2 (274%) and underestimates latent heat flux (LE) by 200 W m^-2 (68%) at irrigated croplands and orchards during spring and summer. In the MMA, PX LSM moderately overestimates both H and LE, with stronger partitioning into H over urban surfaces and into LE over vegetation. Daytime momentum fluxes are overestimated in both regions, while nighttime biases are inconsistent. Our findings suggest that in the SJV, heat flux biases are strongly associated with irrigation during the growing season, while in the MMA, model-data residuals are limited to modest errors in the Bowen ratio and depend on land cover. Improving WRF’s representation of irrigation and land use, potentially through satellite remote sensing, may enhance surface flux simulation accuracy.

天气模式中的大气边界层模拟是空气质量模拟的重要元素，它与陆地表面参数化相耦合。加利福尼亚州的圣华金河谷（SJV）和多州大西洋中部（MMA）具有不同的土地用途，包括农业、城市地区和森林，这对模拟地表通量构成了挑战。本研究使用加州和宾夕法尼亚州州空气质量机构采用的物理配置来评估天气研究与预报（WRF）模型中的地表通量。我们将WRF模拟与两个地区16个站点的一年涡旋协方差通量测量结果进行了比较。结果表明，Pleim-Xiu陆面模式（PX LSM）在SJV中存在较大的热通量偏差，而在MMA中缺乏系统偏差。在SJV中，春夏季灌溉农田和果园白天（10:00-16:00 LST）感热通量(H)高估260 W m-2(274%)，潜热通量（LE）低估200 W m-2（68%）。在MMA中，PX LSM对H和LE均有适度高估，对城市地表上的H和植被上的LE的划分更强。这两个地区白天的动量通量都被高估了，而夜间的偏差则不一致。我们的研究结果表明，在SJV中，热通量偏差与生长季节的灌溉密切相关，而在MMA中，模型数据残差仅限于Bowen比的适度误差，并取决于土地覆盖。可能通过卫星遥感改善水资源循环系统对灌溉和土地利用的反映，可以提高地表通量模拟的准确性。

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引用次数: 0

Carbon exchange in a tropical montane rainforest: Annual budgets, drivers, and anomalies 热带山地雨林的碳交换：年度预算、驱动因素和异常现象

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.2026.111049

Peirong Liu , Zhang Zhou , Guilin Wu , Xiaojuan Tong , Tao Zhang , Jingru Zhang , Fangyuan Wang , Dexiang Chen

Tropical forests store substantial carbon stocks and play important roles in biogeochemical carbon cycling. Understanding the drivers of carbon fluxes in tropical forests and how they respond to extreme events are crucial for predicting future global carbon dynamics. Utilizing a 12-year CO₂ flux dataset and meteorological variables from a tropical montane rainforest ecosystem in southern China. This study assessed the effects of climatic drivers on the seasonal and interannual variations in gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP), as well as the responses of carbon fluxes to extreme climate events. The ecosystem functioned as a strong carbon sink (NEP = 368 ± 121 g C m⁻²) across the study period. Both GPP and ER were generally higher in the wet season. Compared to the dry season, NEP values decreased by 18% during the wet season, primarily due to a temperature-induced increase in ER surpassing GPP. Annual GPP, ER, and NEP showed increasing trends of 32.32 g C m^-2 year^-1 (P < 0.1), 24.4 g C m^-2 year^-1 (P > 0.1), and 12.78 g C m^-2 year^-1 (P > 0.1), respectively. For seasonal fluxes, GPP was mainly controlled by solar-induced chlorophyll fluorescence (SIF), air temperature (T_a), and solar radiation (R_s); ER was predominantly influenced by the SIF and T_a; and NEP was primarily driven by T_a and R_s. On the interannual scale, T_a was the most important factor affecting GPP, ER, and NEP, followed by SIF, precipitation (PPT), and R_s. Extreme climate events, such as typhoons, significantly reduced GPP and NEP via physical pathways, while having a minimal effect on ER. Droughts notably enhanced GPP and ER (P < 0.05). In contrast, a severe drought in 2006 led to reductions in GPP, ER, and NEP of 11%, 12%, and 8%, respectively. Overall, this study addresses the lack of long-term research on CO₂ fluxes in the tropical rainforest of China and will improve the understanding and prediction of the forest carbon dynamics.

热带森林蕴藏着丰富的碳储量，在生物地球化学碳循环中发挥着重要作用。了解热带森林碳通量的驱动因素以及它们如何对极端事件作出反应，对于预测未来全球碳动态至关重要。利用中国南方热带山地雨林生态系统12年CO2通量数据和气象变量。本研究评估了气候驱动因素对总初级生产力（GPP）、生态系统呼吸（ER）和净生态系统生产力（NEP）的季节和年际变化的影响，以及碳通量对极端气候事件的响应。在整个研究期间，生态系统作为一个强大的碳汇（NEP = 368±121 g C m−2）。GPP和ER在雨季普遍较高。与旱季相比，雨季NEP值下降了18%，这主要是由于温度引起的ER增加超过了GPP。年GPP、ER和NEP分别增加32.32 g C m-2 （P > 0.1）、24.4 g C m-2 （P > 0.1）和12.78 g C m-2 （P > 0.1）。在季节通量上，GPP主要受太阳诱导的叶绿素荧光（SIF）、气温（Ta）和太阳辐射（Rs）的控制；ER主要受SIF和Ta的影响；在年际尺度上，Ta是影响GPP、ER和NEP的最重要因子，其次是SIF、降水（PPT）和Rs。台风等极端气候事件通过物理途径显著降低GPP和NEP，而对ER的影响最小。干旱显著提高了GPP和ER （P < 0.05）。相比之下，2006年的严重干旱导致GPP、ER和NEP分别下降了11%、12%和8%。总体而言，该研究解决了中国热带雨林二氧化碳通量长期研究的不足，将提高对森林碳动态的认识和预测。

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引用次数: 0

From past exceptional extremes to frequent future risks: How climate change shapes the fate of common wheat in France 从过去的异常极端天气到未来频繁的风险：气候变化如何影响法国普通小麦的命运

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.2026.111054

Mael Aubry , Benjamin Renard , Thomas Opitz , Renan Le Roux , Marie Launay , Iñaki García de Cortázar-Atauri , Carina Furusho-Percot

Climate change is reshaping the agroclimatic conditions for wheat, taking some past decennial events to frequent climatic features, substantially increasing the probability of crop stress and yield loss. We propose a method using ecoclimatic indicators combined with probability models, continuous-time regression and copulas, to evaluate — in non-stationary context —the future climate suitability of common wheat in France expressed as the probability of exceeding past decennial thresholds. Under a high-emission scenario, our analysis identifies four emerging risks from 2050 onward: early heat stress and warm nights during the flag-leaf-to-anthesis stage, late warm nights from anthesis to grain maturity, and devernalisation. Precipitation- and humidity-related extremes during the vegetative phase are projected to slightly increase or stagnate, with strong regional and model-specific variability. Mild winters are likely to become a dominant climatic feature by century’s end, while late cold stress events decline sharply. In contrast, reproductive-phase heat stress intensifies markedly, becoming a dominant agroclimatic constraint by century’s end, whereas risks linked to excess moisture or precipitation decrease. Drought-related stress remains mostly stable, due to shorter phenological cycles. By century’s end, under high emission scenario, compound hazards —such as simultaneous drought and heat stress during key reproductive stages—are projected to become 3 to 6 times more frequent than in the historical baseline. Compound mild-winter and wet- or damp-condition events increase 2.5- to 12-fold. Regional disparities emerge: the English Channel coast and the Paris Basin appear comparatively less exposed to these climatic risks, positioning them as potential, though not risk-free, future refuges for wheat cultivation. A low-emission mitigation pathway would reduce the frequency of these risks by a factor of 2 to 6, and reduce interannual risk variability by up to 45% relative to high emissions. These findings highlight priority targets for adaptation and underscore the urgent need for mitigation to safeguard wheat production.

气候变化正在重塑小麦的农业气候条件，使过去十年的一些事件成为频繁的气候特征，大大增加了作物胁迫和产量损失的可能性。我们提出了一种使用生态气候指标结合概率模型、连续时间回归和copulas的方法，来评估在非平稳背景下法国普通小麦的未来气候适宜性，该方法表示为超过过去十年阈值的概率。在高排放情景下，我们的分析确定了从2050年起出现的四个新风险：从旗叶到开花阶段的早期热应激和温暖的夜晚，从开花到谷物成熟的晚温暖的夜晚，以及去化。植被期与降水和湿度相关的极端事件预估将略有增加或停滞，具有很强的区域和模式特异性变率。到本世纪末，暖冬可能成为主要的气候特征，而晚冷应激事件将急剧减少。相反，生殖期热应激显著加剧，到本世纪末成为主要的农业气候制约因素，而与过量水分或降水有关的风险则减少。由于物候周期较短，与干旱相关的胁迫基本保持稳定。到本世纪末，在高排放情景下，复合危害——如关键生殖阶段同时发生干旱和高温胁迫——预计将比历史基线高出3至6倍。复合暖冬和潮湿或潮湿条件事件增加2.5至12倍。区域差异出现了：英吉利海峡沿岸和巴黎盆地受这些气候风险的影响相对较小，使它们成为未来小麦种植的潜在避难所，尽管并非没有风险。低排放缓解途径将使这些风险的发生频率降低2至6倍，并使年际风险变异率相对于高排放降低45%。这些发现突出了适应气候变化的优先目标，并强调了为保障小麦生产而采取减缓措施的迫切需要。

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引用次数: 0

Observed declining strength of vegetation-atmosphere coupling 观测到植被-大气耦合强度下降

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-02 DOI: 10.1016/j.agrformet.2026.111051

Shijie Li , Guojie Wang , Shanlei Sun , Zefeng Chen , Matteo Mura , Jiao Lu , Qi Liu , Ji Li , Daniel Fiifi Tawia Hagan , Almudena García-García , Jian Peng

Land-atmosphere coupling (LAC) directly influences the occurrence of extreme climate events. Traditionally, the studies of LAC strength have primarily used soil moisture as a proxy for land conditions. However, recent research has highlighted the significant role of vegetation–atmosphere coupling (VC) in the evolution of extreme climate events through its regulation of the water and energy cycles. Despite this progress, the global patterns and driving mechanisms of VC remain unclear. In this study, the index with a clear physical meaning, ω, defined as the relationship between the canopy conductance (g_c) and aerodynamic conductance (g_a), was introduced to represent VC values. Long-term (1981–2018) global annual VC values were derived using two high-quality reanalysis datasets (ERA5 and MERRA2) based on two different g_c models. Both g_c models exhibited similar spatial distributions that the highest VC values in Arid regions, the lowest in Humid regions, and intermediate values in Transition zones. Results showed 38.84–61.98 % of global land with decreasing VC trend. An attribution analysis using a nonlinear machine learning approach revealed that leaf area index (LAI) and wind speed dominated the VC changes across different climate zones. An increase in LAI reduced VC strength, whereas enhanced wind speed increased VC values. LAI was the dominant factor influencing VC through transpiration regulation (i.e., g_c) over Transition and Arid regions, while wind speed controlled VC variations via g_a over Humid regions. Our study analyzed the spatiotemporal changes in VC values and their driving mechanisms across global land areas. These findings contribute to a deeper understanding of vegetation-climate feedback and its role in amplifying extreme climate events.

陆地-大气耦合直接影响极端气候事件的发生。传统上，LAC强度的研究主要使用土壤湿度作为土地条件的代理。然而，近年来的研究强调了植被-大气耦合（VC）通过调节水和能量循环在极端气候事件演变中的重要作用。尽管取得了这些进展，但风险投资的全球模式和驱动机制仍不清楚。本文引入具有明确物理意义的指标ω，定义为冠层导度（gc）与空气动力导度（ga）之间的关系，来表示VC值。基于两种不同的gc模型，使用两个高质量的再分析数据集（ERA5和MERRA2）获得了长期（1981-2018）全球年度VC值。两种gc模型的空间分布相似，均表现为干旱区VC值最高，湿润区最低，过渡区处于中间值。结果表明，全球38.84 ~ 61.98%的土地呈下降趋势。利用非线性机器学习方法进行归因分析发现，叶面积指数（LAI）和风速在不同气候带的VC变化中起主导作用。LAI的增加降低了VC强度，而风速的增加增加了VC值。在过渡区和干旱区，LAI是通过蒸腾调节（即gc）影响VC的主导因子，而在湿润区，风速通过ga控制VC的变化。本研究分析了全球陆地区域VC值的时空变化及其驱动机制。这些发现有助于更深入地了解植被-气候反馈及其在放大极端气候事件中的作用。

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引用次数: 0

Short-distance dispersion of birch pollen 桦树花粉的短距离分散

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-02 DOI: 10.1016/j.agrformet.2026.111052

Towa Yamane , Masaru Inatsu , Jun Kawano , Takuto Sato , Hiroyuki Kusaka

This study aims to obtain fundamental information on birch pollen deposition data by field observation for the high-resolution, accurate pollen modeling. On the peak dispersal day in 2024, simple pollen collectors were installed just below and at three downwind points of an isolated birch tree line in Ebetsu, Hokkaido, Japan. Meteorological observations were also conducted at the site during the days. The birch pollen captured on slide glasses was imaged by a microscope. We automatically counted birch pollen grains by applying a machine learning algorithm You Only Look Once (YOLO) v5 to the images. The results suggested that the pollen count was highest in the point 200 m downstream from the tree line and diurnal variations were observed at all distances. The pollen counts in the downstream was correlated with air temperature with a statistical significance, but was correlated with wind speed with a marginal significance. The large-eddy simulation with the pollen advection supported the observation results, though the pollen deposition was more concentrated near the tree in the simulation.

本研究旨在通过野外观测获取白桦花粉沉积数据的基础信息，为高分辨率、准确的花粉建模提供依据。在2024年的传播高峰日，在日本北海道的Ebetsu，一个孤立的桦树线的下方和三个下风点安装了简单的花粉收集器。此外，天文台亦在该处进行气象观测。在载玻片上捕获的桦树花粉用显微镜成像。我们通过对图像应用机器学习算法You Only Look Once (YOLO) v5来自动计数桦树花粉颗粒。结果表明，花粉数量在距林木线下游200 m处最高，且各距离均有明显的日变化。下游花粉数与气温的相关性有统计学意义，与风速的相关性有边际显著性。具有花粉平流的大涡模拟支持观测结果，但模拟中花粉沉积更集中在树附近。

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引用次数: 0

Corrigendum to “Interannual climatic sensitivity of surface energy flux densities and evapotranspiration in a disturbed and rewetted ombrotrophic bog” [Agricultural and Forest Meteorology 367 (2025) 110501] “扰动和再湿润的共生沼泽中地表能量通量密度和蒸散的年际气候敏感性”[农林气象367(2025)110501]的勘误表

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-02-01 DOI: 10.1016/j.agrformet.2026.111043

J.L. Exler , J. Skeeter , S.H. Knox , A. Christen , R.D. Moore

引用次数: 0

The influence of spatial correlations in crop production on global crop failures in model simulations 模式模拟中作物生产空间相关性对全球作物歉收的影响

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-01-30 DOI: 10.1016/j.agrformet.2026.111021

Sifang Feng , Jakob Zscheischler , Zengchao Hao , Jonas Jägermeyr , Christoph Müller , Emanuele Bevacqua

Spatial correlation between climate variables may modulate concurrent regional crop failures and reduce global crop production. However, the influence of spatial correlation in crop production fields on globally aggregated production remains poorly understood. Systematically addressing this gap using observed crop production is challenging, as such observational datasets typically suffer from limited sample sizes and/or coarse spatial information. Here, using gridded global simulations from the Global Gridded Crop Model Intercomparison Phase 3 (GGCMI3), we quantify how spatial correlation between regional crop productions influences global production across different spatial scales for maize, wheat, soybean, and rice. By employing the mean of crop production from multiple crop models forced with reanalysis climate data, we find minimal influence of the correlations between the productions of major breadbasket regions on global breadbasket-aggregated production. This aligns with the fact that global major breadbasket regions are generally non-large and distant from each other, whereas spatial correlations in the crop production field influence global crop production through correlations between small and nearby areas. The correlation between crop production of areas characterized by small spatial scales (100–1000 km) enhances extremely low (5th percentile) global production by about 0.9-1.1 standard deviation of the global production on average. This correlation effect at small spatial scales is less important for weaker extremes of low global crop production. Finally, crop model simulations forced with bias-corrected climate simulations often are not able to reproduce the correlation effects seen in crop model simulations forced with reanalysis climate data, suggesting that bias-corrected climate model input may degrade correlation effects in GGCMI3 crop simulations. These model-based results highlight that spatial correlations are a critical driver of global production risk, stressing the need for improved cross-regional processes representation in crop models to enhance future food security risk assessments.

气候变量间的空间相关性可能调节同时发生的区域作物歉收并降低全球作物产量。然而，作物生产领域的空间相关性对全球总产量的影响尚不清楚。利用观测到的作物产量系统地解决这一差距具有挑战性，因为此类观测数据集通常样本量有限和/或空间信息粗糙。本文利用全球网格化作物模型比对阶段3 （GGCMI3）的网格化全球模拟，量化了区域作物生产之间的空间相关性如何影响玉米、小麦、大豆和水稻在不同空间尺度上的全球产量。通过使用再分析气候数据强迫的多种作物模型的作物产量平均值，我们发现主要产粮区产量之间的相关性对全球产粮区总产量的影响最小。这与全球主要产粮区通常面积不大且彼此相距遥远的事实相一致，而作物生产领域的空间相关性通过小区域和附近区域之间的相关性影响全球作物生产。小空间尺度（100-1000 km）区域作物产量的相关性使极低（第5百分位）全球产量平均提高约0.9-1.1个标准差。在小空间尺度上，这种相关效应对全球低作物产量的弱极端不太重要。最后，用偏校正气候模拟强迫的作物模式模拟往往不能再现用再分析气候数据强迫的作物模式模拟所看到的相关效应，这表明偏校正气候模式输入可能会降低GGCMI3作物模拟的相关效应。这些基于模型的结果强调，空间相关性是全球生产风险的关键驱动因素，强调需要改进作物模型中的跨区域过程代表性，以加强未来的粮食安全风险评估。

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引用次数: 0

The efficiency-level inconsistency of the SIF–GPP relationship in a subtropical evergreen forest in Okinawa, Japan 日本冲绳亚热带常绿森林SIF-GPP关系的效率水平不一致性

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-01-29 DOI: 10.1016/j.agrformet.2026.111050

Junjie Fu , Tomomichi Kato , Tomoki Morozumi , Kazuho Matsumoto , Shingo Taniguchi , Masahito Ueyama , Kanokrat Buareal , Tatsuya Miyauchi , Naohisa Nakashima , Tomoko Kawaguchi Akitsu

Monitoring of photosynthetic activity is essential for understanding forest carbon dynamics, particularly in structurally stable but physiologically dynamic ecosystems such as subtropical evergreen broadleaf forests. This study investigated the seasonal and diurnal dynamics of far-red and red solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), and related metrics in a subtropical evergreen broadleaf forest in Okinawa, Japan, on the basis of ground-based observations from 2020 to 2022. Far-red SIF consistently exhibited stronger and more stable correlations with GPP than red SIF, especially under overcast conditions and at daily temporal resolution. However, a significant environmental divergence was observed in the efficiency metrics. Environmental binning analyses revealed that light-use efficiency (LUE) responded strongly to changes in vapor pressure deficit (VPD), whereas SIF yield exhibited much weaker sensitivity. Random forest analysis further supported this divergence, showing a distinct, time-dependent sensitivity of SIF yield and LUE to VPD. Consistent with these environmental sensitivities, diurnal patterns showed that a pronounced inconsistency emerged at the efficiency level: the relationship between far-red SIF yield and LUE was generally weak or non-significant, indicating asynchronous regulation of fluorescence efficiency and carbon assimilation. Diurnal analyses showed that this decoupling could become more pronounced under certain conditions, such as during the afternoon. These findings refine the interpretation of SIF-based indicators in dense evergreen canopies and underscore the importance of the efficiency-level asynchronous recovery for improving SIF-based GPP models in subtropical ecosystems.

监测光合活动对于了解森林碳动态至关重要，特别是在结构稳定但生理动态的生态系统中，如亚热带常绿阔叶林。利用2020 - 2022年的地面观测资料，研究了日本冲绳亚热带常绿阔叶林远红和红光诱导的叶绿素荧光（SIF）、总初级生产力（GPP）及相关指标的季节和日动态。远红色SIF与GPP的相关性一直比红色SIF更强、更稳定，特别是在阴天条件下和在日时间分辨率下。然而，在效率指标中观察到显著的环境差异。环境分仓分析表明，光利用效率（LUE）对蒸汽压差（VPD）的变化响应强烈，而SIF产率的敏感性要弱得多。随机森林分析进一步支持了这种差异，显示出SIF产量和LUE对VPD具有明显的、随时间变化的敏感性。与这些环境敏感性相一致的是，日模式表明，在效率水平上出现了明显的不一致性：远红色SIF产量与LUE之间的关系普遍较弱或不显著，表明荧光效率和碳同化的异步调节。每日分析表明，在某些情况下，这种脱钩可能会变得更加明显，比如在下午。这些发现完善了常绿茂密林冠层中基于sif的指标的解释，并强调了效率级异步恢复对改进亚热带生态系统中基于sif的GPP模型的重要性。

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引用次数: 0

Evaluating the impact of different freezing-point depression equations on permafrost hydrothermal processes in the Arctic and Qinghai-Tibet Plateau with CLM5.0 基于CLM5.0评价不同冰点下降方程对北极和青藏高原冻土热液过程的影响

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-01-28 DOI: 10.1016/j.agrformet.2026.111034

Shenning Wang , Ren Li , Tonghua Wu , Junjie Ma , Wenhao Liu , Shuhua Yang , Yizhen Du , Yao Xiao , Xiaodong Wu , Guojie Hu , Jimin Yao , Shengfeng Tang , Xiaofan Zhu , Jianzong Shi , Yongping Qiao

Soil hydrothermal processes in permafrost regions are critical for land-atmosphere exchange but are challenging to simulate accurately in models, largely due to the parameterization of unfrozen water content. This study evaluated 11 freezing-point depression schemes, derived from combinations of three soil water characteristic curves (SWCCs) and four soil matric potential schemes, using CLM5.0 at six sites across the Arctic and Qinghai-Tibet Plateau (QTP). Results showed regionally dependent optimal schemes. For soil temperature, a combined effective porosity and cryosuction scheme (TEST3) reduced the RMSE by 0.51–0.52°C (7.1–8.3%) in the Arctic, while a cryosuction scheme (TEST10) was best on the QTP, reducing RMSE by 0.04–0.06°C. For soil moisture, a Van Genuchten SWCC scheme with effective porosity (TEST5) reduced RMSE by up to 0.018 m³/m³ (13.2%) in the Arctic, and TEST4/TEST5 performed best on the QTP. The explicit parameterization of residual water content in Brooks & Corey and Van Genuchten SWCCs was a key mechanism, correcting the default scheme's large soil moisture bias by up to 53% during freezing. Cryosuction increased unfrozen water, while effective porosity decreased it. However, model structural limitations caused unreliable matric potential output during freezing. Persistent biases at specific sites were attributed to inaccurate soil texture data, unaccounted lateral flow, and insufficient snow insulation representation. This study highlights the regional applicability of schemes and provides critical insights for improving permafrost simulations.

多年冻土区的土壤热液过程对陆地-大气交换至关重要，但由于未冻水含量的参数化，在模式中难以准确模拟。本研究利用CLM5.0软件对北极和青藏高原6个站点的11个冰点降低方案进行了评价，这些方案由3种土壤水分特征曲线（swcc）和4种土壤基质势方案组合而成。结果显示了区域相关的最优方案。对于土壤温度，有效孔隙度和低温吸力组合方案（TEST3）在北极降低RMSE 0.51 ~ 0.52°C(7.1 ~ 8.3%)，而低温吸力方案（TEST10）在QTP上效果最好，降低RMSE 0.04 ~ 0.06°C。对于土壤水分，具有有效孔隙度（TEST5）的Van Genuchten SWCC方案在北极减少RMSE高达0.018 m3/m3(13.2%)，而TEST4/TEST5在QTP上表现最好。在Brooks & Corey和Van Genuchten swcc中，剩余水分的显式参数化是一个关键机制，它在冻结期间纠正了默认方案中高达53%的土壤水分偏差。低温吸吸增加了未冻水，而有效孔隙率降低了。然而，模型结构的限制导致冻结过程中矩阵电位输出不可靠。特定地点的持续偏差归因于不准确的土壤质地数据、未考虑的横向流动和不充分的雪绝缘表征。这项研究强调了方案的区域适用性，并为改进永久冻土模拟提供了重要的见解。

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引用次数: 0

Cropland evapotranspiration based on Sentinel-2 shortwave infrared data and ensemble Kalman filter 基于Sentinel-2短波红外数据和集合卡尔曼滤波的农田蒸散发

IF 5.7 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology

Pub Date : 2026-01-27 DOI: 10.1016/j.agrformet.2026.111035

Lu Liu , Yunjun Yao , Qingxin Tang , Xueyi Zhang , Yufu Li , Joshua B. Fisher , Jiquan Chen , Jia Xu , Xiaotong Zhang , Ruiyang Yu , Zijing Xie , Jing Ning , Jiahui Fan , Luna Zhang

The latent heat of evapotranspiration (LE) is a vital element of agricultural water resources; accurately estimating cropland LE at a fine spatial resolution is crucial for monitoring agricultural drought and estimating crop water requirements. In this study, we propose a shortwave infrared-transformed reflectance (STR)-and ensemble Kalman filter (EnKF)-based Priestley–Taylor (STR–EnKF–PT) model to simulate daily cropland LE using Sentinel-2 data. To evaluate the STR–EnKF–PT model’ performance, we conducted an assessment using ground observations derived from 10 eddy covariance (EC) sites situated in various regions of the United States over the two-year period from 2019 through 2020.The results revealed that STR–EnKF–PT yielded better performance than the competing methods did at four validation sites; additionally, the coefficient of determination (R²) was 0.54∼0.84 at the 99 % confidence level, the root-mean-square error (RMSE) was 26.1∼38.0 W/m², the Kling–Gupta efficiency (KGE) value was 0.69∼0.91, and the bias was -16.3∼9.4 W/m². Crucially, the ensemble system demonstrated robust probabilistic forecasting capabilities across different validation sites, with an excellent mean reliability score of 0.0010, a mean continuous ranked probability score (CRPS) of 29.91 W/m², and appropriate spread-error relationships with a mean ratio of 1.239, providing reliable probability distributions of LE forecasts beyond deterministic estimates. STR–EnKF–PT was then used to depict the spatial patterns of cropland LE at a 20-m resolution in six different regions across the United States, and the results revealed that it is possible to accurately distinguish the LE status of cultivated land. One innovation is the use of soil moisture (SM) constraints derived from the STR to achieve high-resolution (20-m) cropland LE estimation. Furthermore, the incorporation of EnKF significantly enhances the estimated accuracy of the STR–EnKF–PT model and enables reliable probabilistic forecasting. This approach has significant practical implications for achieving the efficient utilization of cropland irrigation water and agricultural risk management processes.

蒸散发潜热是农业水资源的重要组成部分；在精细空间分辨率下准确估算农田LE对于监测农业干旱和估算作物需水量至关重要。在这项研究中，我们提出了一个基于短波红外变换反射率（STR）和集合卡尔曼滤波（EnKF）的Priestley-Taylor （STR - EnKF - pt）模型，利用Sentinel-2数据模拟农田日LE。为了评估STR-EnKF-PT模型的性能，我们利用2019年至2020年两年期间来自美国不同地区的10个涡动相关（EC）站点的地面观测数据进行了评估。结果表明，STR-EnKF-PT在4个验证位点的性能优于竞争对手的方法；此外，在99%置信水平下，决定系数（R2）为0.54 ~ 0.84，均方根误差（RMSE）为26.1 ~ 38.0 W/m2，克林-古普达效率（KGE）值为0.69 ~ 0.91，偏差为-16.3 ~ 9.4 W/m2。重要的是，该集成系统在不同验证点显示出稳健的概率预测能力，平均可靠度得分为0.0010，平均连续排序概率得分（CRPS）为29.91 W/m2，平均差差比为1.239，提供了可靠的LE预测概率分布，超出确定性估计。利用STR-EnKF-PT在20 m分辨率下对美国6个不同地区的耕地LE进行了空间特征描述，结果表明，STR-EnKF-PT能够准确区分耕地LE状况。其中一个创新是利用STR导出的土壤湿度（SM）约束来实现高分辨率（20米）农田LE估计。此外，EnKF的引入显著提高了STR-EnKF-PT模型的估计精度，实现了可靠的概率预测。这种方法对实现农田灌溉用水的有效利用和农业风险管理过程具有重要的实际意义。

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引用次数: 0