Agricultural and Forest Meteorology最新文献

{"title":"Contrasting below- and above-canopy climate regulation services of a temperate forest during heatwaves","authors":"J. Zhou ,&nbsp;M. van der Molen ,&nbsp;A.J. Teuling","doi":"10.1016/j.agrformet.2025.110485","DOIUrl":"10.1016/j.agrformet.2025.110485","url":null,"abstract":"<div><div>Heatwaves have significant effects on ecosystems and human populations. Human habitability is impacted severely as human exposure to heatwaves is projected to increase. Future risk of heatwaves requires effective measures for adaptation to persistent hot temperature extremes and ambitious mitigation to limit further increases in heatwave severity.</div><div>At local scales, afforestation and reforestation could be a potential approach of modifying the (near-)surface energy budget and temperature, in this way alleviating heatwave impacts. In this study, thermal characteristics and energy fluxes across open-site, below-canopy, and above-canopy environments are analysed and compared, to investigate canopy's dual functions in affecting above-canopy macroclimate and acting as a thermal insulator that regulates understory microclimate and litter layer environment. Using high-resolution sub-daily datasets from the Loobos flux tower site in the Netherlands, complemented by routine weather data from 3 nearby meteorological stations, we analysed temperatures at three levels of Loobos (23.5 m, 7.5 m, and litter layer) along the same vertical profile and compared them with those measured at nearby open sites.</div><div>During heatwave periods, the cooling effects of the canopy on litter layer temperature are up to 12.5 K while the canopy may also amplify the temperature above it by up to 5 K between 15 and 23 pm accompanied with increasing sensible heat. In the conditions of daytime, the site-average canopy effects increase quasi-linearly (R² &gt; 0.78) with the rising open-site temperature. This research reveals the ability of the forest in providing contrasting climate regulation ecosystem services on both below-canopy and above-canopy environments, in which the canopy's potential in accommodating the temperature of near-surface environments during both day and nocturnal times to alleviate impacts from compound heatwaves is highlighted.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"366 ","pages":"Article 110485"},"PeriodicalIF":5.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal simulation of net ecosystem productivity in the Tibetan Plateau region using multi-scale data assimilation for terrestrial ecosystem process model","authors":"Changhui Ma ,&nbsp;Si-Bo Duan ,&nbsp;Cong Xu ,&nbsp;Wenhua Qin ,&nbsp;Feng Wang ,&nbsp;Lei He","doi":"10.1016/j.agrformet.2025.110471","DOIUrl":"10.1016/j.agrformet.2025.110471","url":null,"abstract":"<div><div>Accurately elucidating the spatio-temporal pattern of net ecosystem productivity (NEP) in grasslands on the Tibetan Plateau (TP) is essential for understanding the feedback mechanisms of the carbon cycle to climate and grazing. Parameter localization through data assimilation for terrestrial ecosystem process model is the dominant approach to accurately simulate NEP. However, current studies calibrate ecosystem process model by grassland types, neglecting the spatial variation of model parameters within the same type. Consequently, the calibrated model struggles to characterize the spatially diverse ecological mechanisms. Therefore, we propose a multi-scale parameterization scheme for the CENTURY model, described as follows. (1) Calibrate model parameters in terms of grassland type by assimilating NEP observations from eddy covariance (EC) stations. (2) Migrate the grassland type-scale model to discrete pixels within prohibited pastures, then fine-tune the model parameters with strong spatial divergence and sensitivity by assimilating remotely sensed NPP data on a per-pixel basis; (3) Use ensemble learning algorithms to construct spatial interpolation models driven by environmental factors for discrete parameters. The results confirmed that the calibrated CENTURY model has higher spatial generalization performance compared to model calibrated using previous parameterization scheme. Specifically, the RMSE (R²) for NEP simulations at all EC stations was reduced from 10.18 g C m² mo^-1 (0.58) to 7.58 g C m² mo^-1 (0.72). The CENTURY model was employed in the Selinco region to generate spatio-temporal datasets of grassland NEP from 1980 to 2020, incorporating various grazing intensity scenarios. The following conclusions were drawn from the spatio-temporal analyses. (1) Regardless of grazing scenarios, almost all grasslands functioned as carbon sinks, exhibiting a significant annual increase of net sinks. (2) The annual net sink decreased significantly with altitude. (3) Alpine meadows and alpine steppe were the main contributors to carbon sequestration because alpine meadows have high sequestration capacity and alpine steppe are widely distributed.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"366 ","pages":"Article 110471"},"PeriodicalIF":5.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563380","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":"Spring frost risk assessment on winter wheat in South Korea","authors":"Yean-Uk Kim ,&nbsp;Senthold Asseng ,&nbsp;Heidi Webber","doi":"10.1016/j.agrformet.2025.110484","DOIUrl":"10.1016/j.agrformet.2025.110484","url":null,"abstract":"<div><div>Spring frost remains a major climatic risk for winter wheat production. However, frost risk is often overlooked in climate change studies, especially those that rely on process-based crop models. This study assesses the spring frost risk for winter wheat in South Korea using observed trial data, a process-based crop model, and a large ensemble of climate data. Trial data from seven sites across South Korea suggest that the extreme yield loss in the 2019/20 season resulted from a combination of a warm winter, which accelerated phenology, and a cool April, which led to several frost events around heading. Projections with a calibrated DSSAT-Nwheat model and a large ensemble of climate data (HAPPI) suggest that the risk of yield loss due to spring frost will increase in the southern region of South Korea. However, this risk can be reduced by switching to later-maturing cultivars to avoid spring frost. In contrast, while the risk of yield loss due to spring frost in the central and northern regions is not expected to increase significantly, it will persist and can only be reduced by introducing frost-tolerant cultivars. Extending this analysis to include losses from other major stressors and linking it to socio-economic analyses will be needed for developing long-term strategies to boost wheat production, enhance self-sufficiency, and ensure food security.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"366 ","pages":"Article 110484"},"PeriodicalIF":5.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143546894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for estimating evapotranspiration by considering topographic effects in radiation over mountainous terrain","authors":"Yixiao Zhang ,&nbsp;Tao He ,&nbsp;Shunlin Liang ,&nbsp;Yichuan Ma ,&nbsp;Yunjun Yao","doi":"10.1016/j.agrformet.2025.110468","DOIUrl":"10.1016/j.agrformet.2025.110468","url":null,"abstract":"<div><div>Mountains are one of the hotspots of climate change, and their complex morphology makes the monitoring of water and energy fluxes extremely challenging. Evapotranspiration (<em>ET</em>) is a crucial component of the water cycle and energy budget and its accurate estimation is essential for water resources management and ecosystem protection in mountains. It has been reported that the topography substantially controls the distribution of <em>ET</em> in mountainous terrain. However, most of the existing models neglect the impact of topography, leading to considerable errors and deviations for <em>ET</em> simulation. To address this issue, this study proposed a terrain-extended <em>ET</em> model (TEEB) based on the principle of energy balance, which can be used for <em>ET</em> estimation over complex terrains. Given the substantial impact of topography on net shortwave radiation (<em>NSR</em>), the mountain radiative transfer scheme was employed for <em>NSR</em> estimation, the net radiation model considering topographic effects was then constructed. Soil heat flux and sensible heat flux were then estimated from net radiation. The proposed TEEB model was tested using data from seven eddy covariance (EC) flux towers and a multidimensional comparison was made with the most widely used Surface Energy Balance Algorithm for Land (SEBAL) model. Regarding the results, the simulation of the TEEB model had a high consistency with EC measurements, with a root-mean-square-error of 0.713 mm/d, and was significantly superior to the SEBAL model. Moreover, the spatial pattern of estimated <em>ET</em> with the TEEB model exhibited distinct topographic characteristics, such as the <em>ET</em> on shady slopes being much lower than on sunlit slopes. Meanwhile, topographic analyses revealed that <em>ET</em> estimates on shady slopes would be reduced by 46 % with a proper consideration of topographic effects. The TEEB model can improve the estimation accuracy of <em>ET</em> in mountains, and provide a useful reference for maintaining ecological balance and optimizing water resources management.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"366 ","pages":"Article 110468"},"PeriodicalIF":5.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the simulation of maize growth using WRF-Crop model based on data assimilation and local maize characteristics","authors":"Lun Bao ,&nbsp;Lingxue Yu ,&nbsp;Entao Yu ,&nbsp;Rongping Li ,&nbsp;Zhongquan Cai ,&nbsp;Jiaxin Yu ,&nbsp;Xuan Li","doi":"10.1016/j.agrformet.2025.110478","DOIUrl":"10.1016/j.agrformet.2025.110478","url":null,"abstract":"<div><div>Global climate change presents a significant challenge to the sustainable development goal of eradicating hunger. Accurate assessment or projection of crop yields is crucial for ensuring food security at both global and regional levels in a changing environment. However, traditional crop models may introduce significant uncertainties due to lack of the intensified feedbacks between crop vegetation and climate systems. In this study, we coupled dynamic crop model (Noah-MP-Crop) with the Weather Research and Forecasting (WRF) model (WRF-Crop) based on data assimilation and local maize characteristics to simulate dynamic maize growth and subsequent yield at Jilin Province, China. We utilized in-site phenological observation data to refine the model's cumulative growing degree days (GDDs), and employed leaf mass assimilation to enhance the accuracy of crop phenology cycles. Our findings suggest that refining the model's GDDs thresholds and incorporating data assimilation leads to better alignment with MODIS-observed Leaf area index (LAI), evapotranspiration (ET), and gross primary productivity (GPP), with a reduction in the mean absolute error of 41.2 %, 14.1 %, and 27.5 %, respectively. The in-site eddy covariance flux observation data on soil moisture (layer 1 R = 0.9) and GPP (R = 0.82) also support our results. With the improvement of the maize growth cycles, the adjusted WRF-Crop model exhibited significantly improved accuracy in simulating maize yield, averaging 10,140 kg/ha in Jilin Province. This represents an approximate 9.26 % increase in accuracy compared to the default model configuration. Therefore, the dynamic crop-coupled WRF-Crop model showcases substantial potential for regional crop yield estimation and predictions, featuring dynamic downscaling capabilities through the incorporation of interactions between crops and the atmosphere.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"365 ","pages":"Article 110478"},"PeriodicalIF":5.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529526","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":"Does high resolution in situ xylem and atmospheric vapor isotope data help improve modeled estimates of ecohydrological partitioning?","authors":"Christian Birkel ,&nbsp;Dörthe Tetzlaff ,&nbsp;Ann-Marie Ring ,&nbsp;Chris Soulsby","doi":"10.1016/j.agrformet.2025.110467","DOIUrl":"10.1016/j.agrformet.2025.110467","url":null,"abstract":"<div><div>Ecohydrological partitioning of rainfall into different sources of evaporated and transpired water is crucial to quantify water balance impacts from land cover change. However, resolving ecohydrological partitioning into component fluxes can be ambiguous and uncertain, even where detailed, small-scale measurements are available. To constrain ecohydrological fluxes at the scale of an individual tree in an urban setting, we combined hydrometeorological, sap flow, soil water and high-resolution in situ plant xylem and atmospheric vapor stable isotope measurements over the growing season from April to October 2022. These data were integrated with parsimonious tracer-aided conceptual modeling. The data helped isolate temporal patterns of shifting preferential fractionation in xylem and atmospheric vapor from δ¹⁸O to δ²H mainly depending on air temperature and relative humidity. Modeling high-resolution in situ isotope data revealed the dominant local influence of interception, soil evaporation and transpired water sources on atmospheric vapor particularly during dry periods, whereas wet periods were driven by more variable non-local moisture sources. Additionally, modeling tree water storage did not explain the highly variable and more depleted xylem isotope data compared to enriched and fractionated soil water. Despite volumetrically constrained (within transpiration measurement uncertainty bounds) ecohydrological partitioning, the atmospheric vapor isotope data showed that fine-scale variations of interception and soil evaporation vapor sources can have nuanced impacts on the atmospheric vapor mixture. The comparison of a more complex conceptualization of modeled soil storages (three soil storages) with a minimalist two-storage model indicated the notoriously difficult isotopic discrimination of root water uptake depths. Nonetheless, the combination of soil moisture, transpiration and high-resolution in situ isotope measurements with modeling helped enhance our understanding of plot-scale vegetation-mediated urban hydrological processes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"365 ","pages":"Article 110467"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can the eco-evolutionary optimality concept predict steady-state vegetation? An evaluation and comparison of four models","authors":"Dameng Zhang,&nbsp;Yuting Yang,&nbsp;Ajiao Chen","doi":"10.1016/j.agrformet.2025.110470","DOIUrl":"10.1016/j.agrformet.2025.110470","url":null,"abstract":"<div><div>The Eco-Evolutionary Optimality (EEO) theory posits that vegetation adopts specific growth strategies, co-evolving with the environment to achieve a steady state. The EEO models, by capturing the mechanistic interactions between vegetation and the environment while maintaining simplicity, hold promise in simulating vegetation at steady states. In this study, four EEO models (the Eagleson model, the Yang–Medlyn model, the VOM, and the P model) were selected for evaluation and comparison of their performance across 44 undisturbed flux sites globally. Overall, all four models effectively reproduced key variables such as fraction of vegetation cover, evapotranspiration, and gross primary production across most sites, with the Yang–Medlyn and P models demonstrating superior performance. Variability in model performance across different plant functional types was observed, with poorer performance generally noted at shrub sites, while forest and savanna sites exhibited better performance. Analysis across precipitation and temperature gradients revealed better model performance under wetter or warmer conditions. Furthermore, variations in model sensitivity to climate factors were evident, with outputs generally exhibiting higher sensitivity to precipitation and atmospheric CO₂ concentration compared to temperature and vapor pressure deficit. Sensitivity tended to be higher in arid regions compared to relatively humid regions. These findings underscore the capability of EEO models to simulate steady-state vegetation with minimal or no parameter calibration, demonstrating satisfactory performance across diverse environmental conditions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"365 ","pages":"Article 110470"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526959","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":"Inter-comparison of soybean models for the simulation of evapotranspiration in a humid continental climate","authors":"Evandro H. Figueiredo Moura da Silva ,&nbsp;Kritika Kothari ,&nbsp;Elizabeth Pattey ,&nbsp;Rafael Battisti ,&nbsp;Kenneth J. Boote ,&nbsp;Sotirios V. Archontoulis ,&nbsp;Santiago Vianna Cuadra ,&nbsp;Babacar Faye ,&nbsp;Brian Grant ,&nbsp;Gerrit Hoogenboom ,&nbsp;Qi Jing ,&nbsp;Fábio R. Marin ,&nbsp;Claas Nendel ,&nbsp;Budong Qian ,&nbsp;Ward Smith ,&nbsp;Amit Kumar Srivastava ,&nbsp;Kelly R. Thorp ,&nbsp;Nilson A. Vieira Junior ,&nbsp;Montserrat Salmerón","doi":"10.1016/j.agrformet.2025.110463","DOIUrl":"10.1016/j.agrformet.2025.110463","url":null,"abstract":"<div><div>Accurate simulation of evapotranspiration (ET) with crop models is essential for improving agricultural water management and yield forecasting. Few studies have evaluated multiple soybean [<em>Glycine</em> max (L.) Merr.] models for simulating ET under conditions of low evaporative demand that is characteristic for a warm-summer humid continental climate. Six soybean crop models, encompassing 15 different modeling approaches, were evaluated for ET simulation and compared against eddy covariance data collected over five growing seasons in Ottawa, Canada. Models were first calibrated with phenology, in-season growth, and yield data, followed by calibration with measured ET and soil water content (SWC) data during the second step. After initial calibration, simulated daily ET was higher on average than measured ET, particularly during full canopy cover (normalized bias, nBias = 17.1 to 49.2% depending on the model). Following the second calibration, simulated daily ET was closer to measured values, but bias remained (nBias = 5.9 to 52.1% during full canopy). The ensemble median reduced uncertainty in the simulation of daily ET compared to most models, but DNDC remained the top-ranking model (nRMSE = 0.7 mm <em>d</em>⁻¹, nBias = 11.2%). The MONICA model was most accurate simulating cumulative ET (RMSE = 39.9 mm, nBias = 11.3%), whereas the CROPGRO models excelled simulating SWC (RMSE= 0.04 to 0.05 m³ m⁻³, nBias = 0.10 to 0.9% depending on soil depth). This study was instrumental in evaluating the best ET methodologies and parameters in soybean models. However, there was bias across the models compared to measured eddy covariance ET in a humid environment. The results reveal the need to further investigate possible biases in ET estimates by eddy covariance over soybean canopies, and to review the role of night-time dew contributions to ET in process-based models.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"365 ","pages":"Article 110463"},"PeriodicalIF":5.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519901","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":"Integrating LUCC and forest aging to project and attribute subtropical forest NEP in Zhejiang Province under four SSP-RCP scenarios","authors":"Zihao Huang ,&nbsp;Xuejian Li ,&nbsp;Fangjie Mao ,&nbsp;Lei Huang ,&nbsp;Yinyin Zhao ,&nbsp;Meixuan Song ,&nbsp;Jiacong Yu ,&nbsp;Huaqiang Du","doi":"10.1016/j.agrformet.2025.110462","DOIUrl":"10.1016/j.agrformet.2025.110462","url":null,"abstract":"<div><div>Net ecosystem productivity (NEP) serves as a key indicator of the ecosystem carbon balance. However, the combined effects of various drivers, particularly land use/cover change (LUCC) and forest aging, on NEP remain uncertain, leading to uncertainties in regional and global future NEP simulations. This study integrated Future Land Uses Simulation (FLUS), System Dynamic (SD), and optimized Integrated Terrestrial Ecosystem Carbon-budget (InTEC) models to account for future LUCC and its induced changes in forest age structure into future forest NEP simulations. Taking the Zhejiang Province as the study area, we applied four SSP-RCP scenarios (i.e., SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) to simulate its subtropical forest NEP from 1980 to 2100. Our simulations indicate that the forests existing in 2020 will function as a carbon sink from 2020 to 2060 but will transition to a carbon source from 2060 to 2100, primarily due to the gradual aging of existing forests and the combined influences of climate and CO₂ changes. Nonetheless, after considering LUCC such as afforestation, the overall cumulative NEP will continue to increase after 2060. By 2100, cumulative forest carbon sinks from 2020 will reach 631.74 Tg C under SSP1–2.6, 681.75 Tg C under SSP2–4.5, 586.41 Tg C under SSP3–7.0, and 601.28 Tg C under SSP5–8.5. Among these contributions, aging forests existing in 2020 with climate and CO₂ changes account for 27.04 % to 63.30 % of cumulative NEP. Climate change exerts a negative impact ranging from -47.39 % to -14.39 %, while CO₂ fertilization has a positive contribution of 6.31 % to 73.79 %. Regarding LUCC, afforestation/reforestation contributes significantly, accounting for 43.66 % to 53.65 %, whereas deforestation has a negative impact of -22.77 % to -10.49 %. Additionally, continuous regeneration further supports NEP growth, contributing 12.85 % to 34.77 %. Finally, Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to elucidate the interactions between these factors. The analysis revealed that future LUCC has significant positive impacts on forest NEP whereas forest aging has significant negative impacts. These findings are crucial for understanding the future carbon cycle of subtropical forests and informing adaptation strategies in response to global climate change.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"365 ","pages":"Article 110462"},"PeriodicalIF":5.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The spatiotemporal variations in ecosystem photosynthetic quantum yield and their drivers","authors":"Liyao Yu ,&nbsp;Xiangzhong Luo ,&nbsp;Ruiying Zhao ,&nbsp;Tin W. Satriawan ,&nbsp;Jiaqi Tian","doi":"10.1016/j.agrformet.2025.110466","DOIUrl":"10.1016/j.agrformet.2025.110466","url":null,"abstract":"<div><div>The quantum yield (<em>α</em>) of photosynthesis represents the maximum light use efficiency (LUE) as indicated by the initial slope of photosynthetic light response curves. <em>α</em> is an important variable in LUE-based models which are widely used to simulate gross primary productivity (GPP) from regional to global scales. However, the spatiotemporal variations in <em>α</em> at the ecosystem scale remain elusive despite its importance. Here, we leveraged long-term eddy-covariance observations from 90 sites globally and examined the spatiotemporal variations in <em>α</em> and their drivers, using statistical and machine learning approaches. We found significant spatial variability in <em>α</em> across and within biomes, primarily driven by atmospheric vapor pressure deficit (VPD) and soil moisture variations. Meanwhile, the temporal changes in <em>α</em> are primarily driven by the negative effect of VPD, which weakens the positive effects of elevated CO₂ and leaf area index (LAI). Our results highlight the dominant role of VPD in controlling the spatiotemporal variations of <em>α</em> and the unneglectable impacts of soil moisture, CO₂, and LAI on <em>α</em>. These new results provide insights for improving the representation of <em>α</em> in LUE-based models for GPP simulations.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"365 ","pages":"Article 110466"},"PeriodicalIF":5.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}