Agricultural and Forest Meteorology最新文献

{"title":"Growth of European beech across altitudinal and climatic gradients: Experiences from Slovenia","authors":"Andrej Bončina,&nbsp;Vasilije Trifković,&nbsp;Matija Klopčič,&nbsp;Živa Bončina","doi":"10.1016/j.agrformet.2025.110806","DOIUrl":"10.1016/j.agrformet.2025.110806","url":null,"abstract":"<div><div>Studies on the long-term growth dynamics of tree species under global warming have yielded varying or even contradictory results, including for European beech. We studied the response of the basal area growth of European beech to 30-year climate averages (mean temperature, annual precipitation sum and mean diurnal range) across an altitudinal range of 78–1629 m a.s.l. Our analysis was based on an extensive dataset of 331,965 beech trees from 54,403 plots under diverse site and stand conditions. We found a non-linear response of beech basal area growth to temperature, precipitation sum and elevation. Separate analyses conducted for 400 m elevation belts revealed significant differences in growth responses. A unimodal response to temperature was observed along the entire elevation gradient; however, in the lowest and the highest elevation belts, the relationship was linear, negative in the lowest, and positive in the highest. Across the entire elevation range, growth showed a plateaued unimodal relationship with annual precipitation, while at elevations ≤400 m a.s.l., a positive linear response was observed. Significant differences in growth responses between stand canopies were also observed, with dominant trees being more sensitive to most predictors. Our results suggest that changes in growth rate due to rising temperatures should be interpreted relative to the current mean temperature. The varying responses of stand canopies to climatic variables, and the predominant impact of tree and stand variables on growth rate, underscore the importance of considering forest stand dynamics in climate-growth studies.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110806"},"PeriodicalIF":5.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931060","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":"Modeling the impact of climate warming on tomato phenology","authors":"Shakeel Ahmad ,&nbsp;Ghulam Abbas ,&nbsp;Zartash Fatima ,&nbsp;Sajjad Hussain ,&nbsp;Muhammad Ali Khan ,&nbsp;Mukhtar Ahmed ,&nbsp;Carol Jo Wilkerson ,&nbsp;Gerrit Hoogenboom","doi":"10.1016/j.agrformet.2025.110825","DOIUrl":"10.1016/j.agrformet.2025.110825","url":null,"abstract":"<div><div>Climate warming is a worldwide phenomenon that impacts all sectors of life from local to global levels. Agriculture has also been severely impacted by climate warming in terms of crop phenology, productivity, and produce quality. So far research on the impact of climate warming on tomato phenology has been not published either from Pakistan or elsewhere in the world. The first objective of this study was to assess the effect of thermal trends on the temporal and spatial variation on tomato phenology from 1980 through 2023. The second objective was to apply a dynamic crop growth model, CSM-CROGRPO-Tomato, for determining the influence of climate warming, variety selection, and management practices on tomato phenology. The overall goal was to devise adaptation strategies for tomato to mitigate the potential impact of climate warming in Punjab, Pakistan. The study was conducted at 18 locations in farmers’ fields in Punjab, Pakistan, from 1980 to 2023 by using observed phenological data. The results showed that at all study sites the phenological stages were delayed. There was an average delay of 7.5 days per decade for planting, 7.2 days per decade for emergence, 4.7 days per decade for anthesis, and 3.1 days per decade for maturity. The duration of phenological phases was on average reduced by 3.3 days per decade for planting-anthesis, 2.1 days per decade for anthesis-maturity, and 5.4 days per decade for planting-maturity. Local weather station observations showed that the average air temperature increased by 2.0 °C from 1980-2023. Phenology had a statistically significant but negative correlation with the increase in temperature. Using standard cultivars across sites and years, simulated phenology was advanced due to the increase in temperature as compared to observed phenology. These findings revealed that during the last four and half decades, adaptation approaches, such as a delay in planting and growing newer varieties that require more thermal time or growing degree days, have been adopted by tomato farmers to mitigate the impact of climate warming on tomato phenology.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110825"},"PeriodicalIF":5.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144931027","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":"Decadal variation in soil respiration in a Sitka spruce plantation in central Ireland and its temperature sensitivity","authors":"Junliang Zou ,&nbsp;Xinmei Liu ,&nbsp;Kieran Mc Kevitt ,&nbsp;Erica Cacciotti ,&nbsp;Giuseppe Benanti ,&nbsp;Matthew Saunders ,&nbsp;Brian Tobin ,&nbsp;Bruce Osborne","doi":"10.1016/j.agrformet.2025.110828","DOIUrl":"10.1016/j.agrformet.2025.110828","url":null,"abstract":"<div><div>Soil respiration (Rs) is an important part of the terrestrial ecosystem carbon cycle and one of the important contributors to increasing atmospheric carbon dioxide concentrations. However, few studies have examined the effect of stand age on Rs and its temperature sensitivity (Q₁₀) or their associated drivers in plantation forests. This study reports on measurements of Rs, soil water content (SWC), soil temperature (ST) and other environmental variables in Sitka spruce plantation in central Ireland from 2004 to 2016. Our results showed that the annual average Rs ranged from 1.46–2.81 μmol m^-2 s^-1, while Q₁₀ varied from 3.69–6.75. Rs generally decreased as the stand aged, although it first increased and then decreased during the early stages of stand development (Y1-Y3). As stand age increases, ST rose while SWC and Q₁₀ showed the opposite trend. The cumulative emissions of carbon dioxide first increased and then decreased with stand age. Variations in Rs showed a clear seasonal pattern, with the highest values in summer and the lowest in winter and were positively correlated with ST and negatively correlated with SWC. Q₁₀ decreased with increasing ST while high or low SWC reduced Q₁₀. The magnitude of the effect of projected increases in ST and reductions in water availability due to climate change on annual carbon budgets will depend on their relative impacts during different periods of the year. A combination of the expected higher temperatures and reduced soil water contents as projected for the future will, however, reduce Q₁₀. Whilst the planting of Sitka spruce is considered to be an extremely important aspect of increasing the terrestrial carbon sink, Q₁₀ may show a decreasing response to temperature through thermal acclimation, so that the rate of soil carbon sequestration could potentially slow down or remain constant in the future.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110828"},"PeriodicalIF":5.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926297","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 the cropping system have a substantial influence on greenhouse gas emissions? Dynamics and key factors-based insights from an empirical study of China","authors":"Jiongxin Chen ,&nbsp;Jianjun Zhang ,&nbsp;Shu Fu ,&nbsp;Sen Liang ,&nbsp;Ke Wang","doi":"10.1016/j.agrformet.2025.110805","DOIUrl":"10.1016/j.agrformet.2025.110805","url":null,"abstract":"<div><div>Different cropping systems exhibit significant variations in greenhouse gas (GHG) emissions. Understanding the GHG emission performance of these systems is conducive to the scientific allocation of emission reduction responsibilities. However, comprehensive investigations into the spatiotemporal dynamics and underlying causes of GHG emissions in major cropping systems across entire regions remain limited. Based on the classification of seven cropping systems in China, this study calculates their GHG emissions from 2000 to 2020. The Logarithmic Mean Divisia Index (LMDI) method is employed to decompose the factors driving changes in GHG emissions from various cropping systems at the provincial level. The results indicate that double rice and single rice were the largest contributors to GHG emissions in staple food production, accounting for over half of the total emissions. Notably, GHG emissions from double rice have decreased significantly, particularly in South China, where reductions exceeded 30 %. The agricultural economy, planting structure, land area per unit of agricultural GDP (AGDP), and agricultural labor structure are the dominant factors influencing fluctuations in GHG emissions from multiple cropping systems, exhibiting distinct spatiotemporal heterogeneity. This study identifies spatial differences in GHG emissions and their influencing factors among different cropping systems, highlighting the need for region-specific management strategies. The findings provide valuable decision-making support for formulating targeted GHG emission control strategies in regional food production.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110805"},"PeriodicalIF":5.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926298","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":"Unlock genotype-environment-management interaction via field phenotypic insights for multi-scale prediction of winter rapeseed flowering in the Yangtze River Basin","authors":"Chufeng Wang ,&nbsp;Jian Zhang ,&nbsp;Jie Kuai ,&nbsp;Jing Xie ,&nbsp;Wei Wu ,&nbsp;Shuijin Hua ,&nbsp;Mingli Yan ,&nbsp;Hai Du ,&nbsp;Ni Ma ,&nbsp;Liangzhi You","doi":"10.1016/j.agrformet.2025.110788","DOIUrl":"10.1016/j.agrformet.2025.110788","url":null,"abstract":"<div><div>Crop yields are significantly impacted by adverse climatic events during flowering. Accurately predicting flowering periods is crucial for optimizing strategies to enhance crop yields. Previous studies used crop models to predict flowering periods, challenging due to limited sowing date data and generalizability across different cultivars and environment. In this study, plot experiments and high-throughput field phenotypes were coupled to determine the impact of genotype–environment–management interaction (G × E × M) on the flowering period of winter rapeseed in the Yangtze River Basin. The findings indicated that the pre-winter leaf area index adeptly indicated the impact of sowing dates on flowering period. The leaf color during winter distinguished the genotype effects, and the cumulative temperature between 50 and 60 days after the winter solstice (WS) was identified as the pivotal climate factor. The predictive indicators for the flowering period were referenced to the time point of the WS, alleviating the constraints of uncertain sowing dates. A combination of these indicators could be used to predict the flowering period in 24 winter rapeseed cultivars with an error of &lt; 4 days at experimental plots across the Yangtze River Basin. Notably, the accuracy of flowering prediction model was validated on an actual farmland in Jingzhou City, aligning well with the observed flowering dynamics from satellite data. To extend the utility of the model to regional scales, distribution maps of the flowering period were generated using a linear regression model that correlated post-winter cumulative temperature with the flowering period, considering a 2.0 °C warming level by 2050 across the entire Yangtze River Basin. Results show higher temperatures or lower cumulative solar radiation during the flowering period will appear in many regions in the Yangtze River Basin. The findings of this study hold promise for aiding region-specific crop cultivation and breeding in the future.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110788"},"PeriodicalIF":5.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921113","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":"Incorporating plant access to groundwater in global root zone storage capacity estimate","authors":"Shuping Du ,&nbsp;Shanhu Jiang ,&nbsp;Liliang Ren ,&nbsp;Hao Cui ,&nbsp;Yongwei Zhu ,&nbsp;Miao He ,&nbsp;Chong-Yu Xu","doi":"10.1016/j.agrformet.2025.110807","DOIUrl":"10.1016/j.agrformet.2025.110807","url":null,"abstract":"<div><div>Plant-available groundwater water is well-documented. However, the impact of groundwater flux on root zone storage capacity (S_r), the maximum water storage within the subsurface root zone available for plant transpiration, remains poorly understood. In this study, we present a more conservative, lower-bound global estimate of S_r, incorporating groundwater for the first time into the deficit-based calculation of S_r using a novel conceptual method that accounts for groundwater contribution fraction. Our findings reveal widespread plant reliance on groundwater, with a mean use of at least 20 mm, equivalent to a water volume of 1700 km³. In western United States, this use can exceed 100 mm. Distinct spatial patterns in S_r emerge globally, with higher values in mountainous forests and lower values in boreal grasslands. Comparisons with observed rooting depths confirm that the deficit-based method, when incorporating groundwater, effectively predicts underground root traits. Biotic and abiotic factors critically influence S_r values, with irrigation and topographic convergence exacerbating this reduction. Groundwater-dependent ecosystems rely heavily on root-zone water storage, utilizing an average of 2151 km³ of water. Despite inherent uncertainties in input data, our study provides the first systematic evaluation of groundwater’s role in shaping S_r estimates, offering key insights for water resource management and ecosystem sustainability.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110807"},"PeriodicalIF":5.7,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918882","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":"Estimation of wheat LAI in different leaf layers through LiDAR canopy vertical light distribution parameters and UAV vegetation indices","authors":"Dongwei Han ,&nbsp;Shaolong Zhu ,&nbsp;Muhammad Zain ,&nbsp;Weijun Zhang ,&nbsp;Guanshuo Yang ,&nbsp;Lili Zhang ,&nbsp;Binqian Sun ,&nbsp;Yuanyuan Zhao ,&nbsp;Zhaosheng Yao ,&nbsp;Tao Liu ,&nbsp;Chengming Sun","doi":"10.1016/j.agrformet.2025.110827","DOIUrl":"10.1016/j.agrformet.2025.110827","url":null,"abstract":"<div><div>Different leaf layer area index (LAI) is a key indicator that describes the progress of crop canopies and photosynthetic potential in crops. Presently, remote sensing-based methods mainly focus on estimating total LAI of the canopy, with less research on estimating LAI of different leaf layers within the canopy. A new method for estimating LAI was proposed based on laser radar estimation of canopy vertical light distribution (CVLD), combined with multi-spectral (MS) unmanned aerial vehicle acquisition of vegetation indices (VIs). We constructed different canopy structures of winter wheat by combining different plant types varieties, planting densities and nitrogen application levels. The VIs and LAI values of different leaf layers at 0 day after anthesis (0 DAA), 20 DAA and 30 DAA and canopy vertical point cloud distribution (CVPCD) and CVLD data were measured. Firstly, the correlation between CVLD and LAI at different leaf layers was analyzed, then a model for estimating LAI based on measured CVLD and VIs at each stage was established. Finally, using CVPCD to estimate CVLD accurately enabled the construction of a secondary estimation model for LAI at different leaf layers. The results showed that (1) the model based on the measured CVLD and VIs could accurately estimate LAI of each leaf layer. The coefficient of determination (R²) of the model was between 0.77-0.96. (2) The correlation coefficient between CVPCD and CVLD ranged from 0.58 to 0.87 and using different features of CVPCD could accurately estimate CVLD. (3) The R², RMSE, and MAE of the LAI estimation models for each leaf layer based on the predicted CVLD combined with VIs ranged from 0.36 to 0.92, 0.06 to 0.76, and 0.02 to 0.60 respectively. This method achieved efficient non-destructive estimation of LAI in various leaf layers in winter wheat plants while providing a new perspective for studying CVLD.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110827"},"PeriodicalIF":5.7,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918883","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":"Enhancing carbon flux estimation in a crop growth model by integrating UAS-derived leaf area index","authors":"Xuerui Guo ,&nbsp;Bagher Bayat ,&nbsp;Jordan Steven Bates ,&nbsp;Michael Herbst ,&nbsp;Marius Schmidt ,&nbsp;Harry Vereecken ,&nbsp;Carsten Montzka","doi":"10.1016/j.agrformet.2025.110776","DOIUrl":"10.1016/j.agrformet.2025.110776","url":null,"abstract":"<div><div>Accurate estimation of agroecosystem carbon fluxes is essential for assessing cropland sustainability and climate resilience. This study integrates Leaf Area Index (LAI) retrieval from Radiative Transfer Model (RTM) inversion into AgroC, an agroecosystem model, from Unmanned Aerial System (UAS) platform to enhance carbon fluxes estimates, including Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Total Ecosystem Respiration (TER). By replacing the internally developed LAI in the AgroC model with interpolated LAI time series derived from UAS, improved spatiotemporal representativeness of agroecosystem carbon fluxes is observed under both the Farquhar-von Caemmerer-Berry (FvCB) and the Light Use Efficiency (LUE) photosynthesis approaches. Temporally, the highest GPP accuracy was achieved by the AgroC_FvCB model integrated with UAS-derived LAI (RMSE = 3.19 gC m⁻² d⁻¹, KGE = 0.89), while the best NEE estimation was obtained with the AgroC_LUE model integrated with UAS-derived LAI (RMSE = 2.10 gC m⁻² d⁻¹, KGE = 0.89). Spatially, the superior performance of the AgroC_FvCB model in integrating UAS-derived LAI enabled high-resolution (1 m) mapping of GPP and NEE, effectively capturing within-field spatial variations in a winter wheat field. The daily Pearson correlation coefficient <span><math><mrow><mo>(</mo><mi>r</mi><mo>)</mo></mrow></math></span> overtime ranged from 0.16 in non-vegetated areas to 0.94 in vegetated zones for GPP, and up to 0.88 for NEE. Despite the advantages taking physical basis in RTM inversion for LAI retrieval and biochemical constraints considered in FvCB approach, the limitation in TER improvement requires further investigation to refine RTM-AgroC coupling for cropland carbon fluxes modelling using UAS platforms.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110776"},"PeriodicalIF":5.7,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916467","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 remote sensing and mechanistic model for spatial evaluation of shelterbelt porosity and windbreak effectiveness in agricultural landscapes","authors":"Xing Zhang ,&nbsp;Bolin Fu ,&nbsp;Rongxin Deng ,&nbsp;Ying Li ,&nbsp;Jingwen Li ,&nbsp;Jianwu Jiang ,&nbsp;Jing Tang","doi":"10.1016/j.agrformet.2025.110822","DOIUrl":"10.1016/j.agrformet.2025.110822","url":null,"abstract":"<div><div>Shelterbelts serve critical functions in protecting agricultural ecosystems through soil erosion mitigation, wind damage reduction, and enhancement of farming system resilience. However, traditional evaluations of windbreak effectiveness have predominantly focused on localized zones close to shelterbelts, overlooking spatial heterogeneity and impeding landscape-level assessments. To fill this gap, this study first explored the relationship between shelterbelt structural parameters and remote sensing pixel, and further improved the method for extracting shelterbelt width by pixel decomposition. Then, we examined the influence of key shelterbelt parameters on porosity, and developed a mechanistic model to quantify shelterbelt porosity. We calculated the friction coefficient of shelterbelts and constructed a windbreak speed attenuation model within spatial computation domain based on porosity. Finally, by integrating regional prevailing wind direction and farmland distribution, we proposed the Windbreak Effectiveness Index (WEI) to assess shelterbelt protection in agricultural landscapes. The principal findings are summarized as follows: (1) The dimidiate pixel model reliably estimated shelterbelt fractional coverage across different ages (R² = 0.764, RMSE = 0.151), while the improved width extraction method demonstrated strong alignment with field measurements (R² = 0.758, RMSE = 2.12 m, MAE = 1.78 m) with minimal directional bias. (2) The developed mechanistic porosity model accurately characterized structural complexity of shelterbelt (R² = 0.775, RMSE = 0.066). Remote sensing data effectively captured porosity spatial variations (R² = 0.759, RMSE = 0.071) to extract shelterbelt horizontal structural parameters. (3) The friction coefficient effectively quantified wind speed attenuation near shelterbelts (R² = 0.628, RMSE = 0.080). The proposed WEI demonstrated to be a robust index for spatially revealing windbreak performance, with optimal windbreak effectiveness reaching 53.13 % in the study area. This research proposes a novel spatial framework for evaluating windbreak effectiveness of shelterbelt, offering actionable methods for optimizing shelterbelt design and management across agricultural landscapes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110822"},"PeriodicalIF":5.7,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913817","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":"Biases in radiative flux observations due to precipitation across the Arctic forest-tundra ecotone","authors":"Taeho Kim ,&nbsp;Wenbo Zhou ,&nbsp;Vinh Ngoc Tran ,&nbsp;Liujing Zhang ,&nbsp;Jingfeng Wang ,&nbsp;Modi Zhu ,&nbsp;Aleksey Y. Sheshukov ,&nbsp;Tianqi Zhang ,&nbsp;Desheng Liu ,&nbsp;Valeriy S. Mazepa ,&nbsp;Alexandr A. Sokolov ,&nbsp;Victor V. Valdayskikh ,&nbsp;Valeriy Y. Ivanov","doi":"10.1016/j.agrformet.2025.110814","DOIUrl":"10.1016/j.agrformet.2025.110814","url":null,"abstract":"<div><div>Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"374 ","pages":"Article 110814"},"PeriodicalIF":5.7,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908841","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}