Agricultural and Forest Meteorology最新文献

{"title":"Observed declining strength of vegetation-atmosphere coupling","authors":"Shijie Li ,&nbsp;Guojie Wang ,&nbsp;Shanlei Sun ,&nbsp;Zefeng Chen ,&nbsp;Matteo Mura ,&nbsp;Jiao Lu ,&nbsp;Qi Liu ,&nbsp;Ji Li ,&nbsp;Daniel Fiifi Tawia Hagan ,&nbsp;Almudena García-García ,&nbsp;Jian Peng","doi":"10.1016/j.agrformet.2026.111051","DOIUrl":"10.1016/j.agrformet.2026.111051","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111051"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulatory mechanisms of spatiotemporal variations in aboveground and belowground net primary production in global terrestrial ecosystems","authors":"Tao Zhou ,&nbsp;Yanting Xiong ,&nbsp;Zhihan Yang ,&nbsp;Yuting Hou ,&nbsp;Yuqing Zhang ,&nbsp;Dan Liao ,&nbsp;Xiaodong Wang ,&nbsp;Dinghui Xu ,&nbsp;Pingfeng Li ,&nbsp;Peng Hou ,&nbsp;Wenji Zhao ,&nbsp;Guo Chen ,&nbsp;Benjamin Laffitte ,&nbsp;Xiaolu Tang","doi":"10.1016/j.agrformet.2026.111055","DOIUrl":"10.1016/j.agrformet.2026.111055","url":null,"abstract":"<div><div>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 (<em>R</em>² = 0.74) and BNPP (<em>R</em>² = 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 (<em>P</em> &lt; 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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111055"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon exchange in a tropical montane rainforest: Annual budgets, drivers, and anomalies","authors":"Peirong Liu ,&nbsp;Zhang Zhou ,&nbsp;Guilin Wu ,&nbsp;Xiaojuan Tong ,&nbsp;Tao Zhang ,&nbsp;Jingru Zhang ,&nbsp;Fangyuan Wang ,&nbsp;Dexiang Chen","doi":"10.1016/j.agrformet.2026.111049","DOIUrl":"10.1016/j.agrformet.2026.111049","url":null,"abstract":"<div><div>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 (<em>P</em> &lt; 0.1), 24.4 g C m^-2 year^-1 (<em>P</em> &gt; 0.1), and 12.78 g C m^-2 year^-1 (<em>P</em> &gt; 0.1), respectively. For seasonal fluxes, GPP was mainly controlled by solar-induced chlorophyll fluorescence (SIF), air temperature (<em>T</em>_a), and solar radiation (<em>R</em>_s); ER was predominantly influenced by the SIF and <em>T</em>_a; and NEP was primarily driven by <em>T</em>_a and <em>R</em>_s. On the interannual scale, <em>T</em>_a was the most important factor affecting GPP, ER, and NEP, followed by SIF, precipitation (PPT), and <em>R</em>_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 (<em>P</em> &lt; 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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111049"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-distance dispersion of birch pollen","authors":"Towa Yamane ,&nbsp;Masaru Inatsu ,&nbsp;Jun Kawano ,&nbsp;Takuto Sato ,&nbsp;Hiroyuki Kusaka","doi":"10.1016/j.agrformet.2026.111052","DOIUrl":"10.1016/j.agrformet.2026.111052","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111052"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Warming-induced changes in leaf phenology could amplify the effects of spring drought on tree seedlings","authors":"Miguel Muñoz-Mazón ,&nbsp;Rupert Seidl","doi":"10.1016/j.agrformet.2026.111022","DOIUrl":"10.1016/j.agrformet.2026.111022","url":null,"abstract":"<div><div>Droughts are not only becoming more frequent and intense but are also increasingly occurring at different times of the year. Seasonal shifts in water availability, including changes in early growing-season precipitation, could lead to more frequent spring droughts in temperate ecosystems. However, the ecological effects of warming combined with water shortages at the start of the vegetation period remain incompletely understood. We conducted a climate chamber experiment to examine how warming temperatures (current, + 2 °C and + 4 °C) and the timing of drought (spring vs. summer) interact to affect seedling survival and growth of four temperate tree species (<em>Picea abies, Pinus sylvestris, Fagus sylvatica, Sorbus aria</em>) with contrasting leaf habit (deciduous vs. evergreen) and drought tolerance (low vs. high).</div><div>Using high temporal resolution data from regional weather stations we simulated realistic seasonal changes in weather and drought conditions at the submontane-montane ecotone of the northern Alps. We found that spring drought impacted seedling survival more strongly than summer drought. Under warmer climate, these effects were potentially amplified via two pathways: (i) higher atmospheric water demand and (ii) shifts in phenology that, when not matched by shifts in drought timing, expose seedlings to drought during particularly vulnerable development stages. Our results highlight that warming-induced advances in leaf phenology may compound the effects of drought. The simultaneously reduced efficiency of species adaptations to drought under higher temperatures suggests that hotter droughts could increasingly challenge tree regeneration in temperate forest ecosystems in a warming world.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111022"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cropland evapotranspiration based on Sentinel-2 shortwave infrared data and ensemble Kalman filter","authors":"Lu Liu ,&nbsp;Yunjun Yao ,&nbsp;Qingxin Tang ,&nbsp;Xueyi Zhang ,&nbsp;Yufu Li ,&nbsp;Joshua B. Fisher ,&nbsp;Jiquan Chen ,&nbsp;Jia Xu ,&nbsp;Xiaotong Zhang ,&nbsp;Ruiyang Yu ,&nbsp;Zijing Xie ,&nbsp;Jing Ning ,&nbsp;Jiahui Fan ,&nbsp;Luna Zhang","doi":"10.1016/j.agrformet.2026.111035","DOIUrl":"10.1016/j.agrformet.2026.111035","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111035"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns and drivers of African carbon recovery after disturbance","authors":"Haotian Li ,&nbsp;Xianfeng Liu ,&nbsp;David Makowski ,&nbsp;Jean-Pierre Wigneron","doi":"10.1016/j.agrformet.2026.111061","DOIUrl":"10.1016/j.agrformet.2026.111061","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111061"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disentangling the climatic controls of maximum daily gross primary productivity across terrestrial ecosystems in the Northern Hemisphere","authors":"Zhaogang Liu ,&nbsp;Miao Dou ,&nbsp;Ming Zhao ,&nbsp;Yirui Xin ,&nbsp;Weikang Zhang","doi":"10.1016/j.agrformet.2026.111027","DOIUrl":"10.1016/j.agrformet.2026.111027","url":null,"abstract":"<div><div>Gross primary productivity (GPP), along with its spatial and temporal variations, is a critical component of the global carbon cycle. Maximum daily GPP (GPP_max) is an important indicator of vegetation's physiological capacity and a primary determinant of ecosystem-level GPP. However, the spatial and temporal dynamics of GPP_max and the underlying climatic controls remain poorly understood. This study synthesized GPP_max data from 859 site-years of eddy covariance observations across 103 flux tower sites in the Northern Hemisphere. We examined spatial patterns, interannual variability, and climatic drivers of GPP_max. GPP_max ranged from 0.87 g C m⁻² d⁻¹ to 16.87 g C m⁻² d⁻¹, with a mean of 9.07 ± 3.15 g C m⁻² d⁻¹. Temperate ecosystems exhibited the highest GPP_max, with a mean value of 11.06 ± 2.32 g C m⁻² d⁻¹, whereas arid zones showed the lowest, averaging 6.14 ± 3.24 g C m⁻² d⁻¹. Forest ecosystems showed significantly higher GPP_max than other vegetation types. GPP_max increased with latitude up to 50 °N, then declined. Climatic factors, including radiation, temperature, and water availability, explained 46 % of the spatial variability. Interannual variability in GPP_max ranged from 0.37 % to 56.14 %, with the highest variability in arid zones. Radiation, temperature, and water availability were the dominant climatic drivers at 41 %, 30 %, and 29 % of sites, respectively. These findings provide new insights into the climatic controls and variability of ecosystem photosynthetic capacity, which can improve GPP estimates and contribute to more accurate carbon modeling and enhance predictions of vegetation responses to climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111027"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the impact of different freezing-point depression equations on permafrost hydrothermal processes in the Arctic and Qinghai-Tibet Plateau with CLM5.0","authors":"Shenning Wang ,&nbsp;Ren Li ,&nbsp;Tonghua Wu ,&nbsp;Junjie Ma ,&nbsp;Wenhao Liu ,&nbsp;Shuhua Yang ,&nbsp;Yizhen Du ,&nbsp;Yao Xiao ,&nbsp;Xiaodong Wu ,&nbsp;Guojie Hu ,&nbsp;Jimin Yao ,&nbsp;Shengfeng Tang ,&nbsp;Xiaofan Zhu ,&nbsp;Jianzong Shi ,&nbsp;Yongping Qiao","doi":"10.1016/j.agrformet.2026.111034","DOIUrl":"10.1016/j.agrformet.2026.111034","url":null,"abstract":"<div><div>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 &amp; 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.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111034"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Annual dynamics of net ecosystem carbon dioxide exchange in differently managed tallgrass prairies under variable rainfall","authors":"Pradeep Wagle ,&nbsp;Brian K. Northup ,&nbsp;Xiangming Xiao ,&nbsp;Tanka Kandel ,&nbsp;Andres Cibils ,&nbsp;Stacey A. Gunter","doi":"10.1016/j.agrformet.2026.111047","DOIUrl":"10.1016/j.agrformet.2026.111047","url":null,"abstract":"<div><div>Tallgrass prairies are vital ecosystems that support regional biodiversity and play a crucial role in global carbon cycling. However, the management practices and disturbances they face can significantly alter their roles as carbon sinks or sources. Despite their importance, the carbon source-sink status of differently managed tallgrass prairies, especially under varying weather conditions, remains uncertain. This study utilized eddy covariance measurements of carbon dioxide (CO₂) fluxes from four co-located tallgrass prairie pastures with different management regimes, including prescribed spring burns, intensive and rotational grazing, and haying. The primary objectives were to thoroughly evaluate the dynamics of net ecosystem CO₂ exchange (NEE) and to investigate how diverse weather conditions affected carbon exchange across differently managed tallgrass prairies in central Oklahoma. The study period (2019-2024) experienced substantial variability in rainfall patterns. As expected, aboveground biomass and satellite-derived enhanced vegetation index (EVI) displayed distinct interannual variations. During the growing season (April-October, DOY ∼100-300), pastures generally behaved as net carbon sinks, with NEE ranging from -33 to -478 g C m⁻². However, the magnitude and duration of the carbon sink and the overall annual carbon balance showed considerable interannual variations. Annual NEE ranged from 104 g C m⁻² (carbon source) to -362 g C m⁻² (carbon sink). Interannual variations in forage production, vegetation dynamics, and NEE were mainly influenced by rainfall variability, with growing season rainfall strongly correlating with peak biomass and EVI (R² = 0.67-0.71). Although management practices modulated these rainfall effects on carbon exchange, random forest analysis showed that EVI was the primary predictor of NEE across pastures, reflecting its capacity to integrate the combined effects of meteorological factors on carbon uptake. Our findings emphasize the need for adaptive management strategies tailored to local rainfall patterns and forecasts to optimize forage production, increase carbon sequestration, and strengthen the resilience of grassland ecosystems.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"379 ","pages":"Article 111047"},"PeriodicalIF":5.7,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}