As climate change intensifies drought severity and frequency, the stability of large-scale afforestation programs hinges on understanding the growth limits and drought-response patterns of key tree species. Robinia pseudoacacia is a critical species for ecological restoration in northern China, yet its nonlinear growth thresholds and drought-response patterns across hydro-climatic gradients remain poorly quantified. Here, we combined a regional dendrochronological network of 24 sites spanning a semi-arid to humid gradient with boosted regression trees (BRT) and partial least squares path modeling (PLS-PM) to disentangle the drivers of radial growth and drought resilience. Our results revealed divergent drought-response patterns across the gradient. Populations in arid sites exhibited a descriptive “elastic compensation” pattern, characterized by low resistance but strong post-drought recovery, whereas populations in humid sites showed a more conservative growth-maintenance pattern with higher resistance (Rt) and moderate recovery (Rc). BRT and threshold analyses identified critical thresholds for growth decline: The upper thermal threshold was substantially higher in arid sites (22.90 °C) than in humid sites (17.21 °C), indicating a region-specific shift in growth-temperature sensitivity. Furthermore, a distinct vapor pressure deficit (VPD) threshold of 0.55 kPa was detected in arid regions, above which radial growth declined within the observed data range. PLS-PM further revealed region-specific pathway structures: In arid zones, radial growth was positively associated with climate indicators and atmospheric drought and negatively associated with soil chemical properties, whereas in humid zones, climate was linked to radial growth mainly through atmospheric- and soil-drought pathways. These findings challenge uniform management approaches and suggest that future afforestation planning should consider hydrothermal thresholds and region-specific drought-response patterns to improve risk assessment for R. pseudoacacia plantations under a warming and drying climate.

Forest ecosystem photosynthesis is primarily driven by hydrothermal conditions. However, the effects of hydrothermal integration and synergy on carbon exchange across forest ecosystems are still not clear. We examined the divergence of carbon exchange over 16 forest ecosystems in eastern China. To explore the controls of hydrothermal change on gross primary productivity (GPP), ecosystem respiration (Re), and net ecosystem productivity (NEP), we developed two indices for hydrothermal integration (TP) and hydrothermal synergy (D) based on the copula function. Compared with traditional indices such as the water and thermal product index (K), aridity index (AI), and standardized precipitation evapotranspiration index (SPEI), TP and D demonstrated higher sensitivity and applicability in capturing seasonal and spatial variations in hydrothermal conditions. Vapor pressure deficit (VPD), soil water content (SWC), and AI responded nonlinearly to TP and D, with coordinated hydrothermal conditions enhancing SWC and uncoordinated or scarce conditions increasing drought risk. TP and D explained over 80% of the variability in GPP, Re, and NEP, which better captured hydrothermal controls on carbon exchange than temperature and precipitation alone. Carbon fluxes peaked at TP approximate to 1 and D slightly above 0, indicating that moderately water-dominated hydrothermal synergy provided optimal conditions for photosynthesis and respiration. Random forest analysis revealed that SWC was the primary driver of GPP, Re, and NEP, followed by D for GPP and NEP, indicating that forest carbon exchange is mainly regulated by soil water availability and atmospheric hydrothermal synergy. This study clarifies how hydrothermal conditions impact on carbon exchange in forest ecosystems and provides insights into assessing forest responses to climate change.

Currently, most research focuses on changes in forest productivity and evapotranspiration, while relatively less attention has been paid to water and carbon use efficiency and their future trends. This knowledge gap hinders a comprehensive understanding of the dynamic processes and interactions between water and carbon cycles within forest ecosystems. This study aimed to calibrate the parameters of the Biome-BGC model using net primary productivity (NPP) derived from tree-ring data of three plantations. The model was employed to simulate and predict the trends of NPP, evapotranspiration (ET), carbon use efficiency (CUE), and water use efficiency (WUE) under different climate scenarios, analyzing their consistent responses to climate change. The results showed that the simulated NPP (NPPs) from the Biome-BGC model showed a highly significant correlation with the measured NPP (NPPm) in mature plantations, with the regression slope close to 1:1, indicating the model’s accuracy in simulating ecological variables of mature plantations. Under three climate scenarios, the NPPs, ETs, and WUEs of Mongolian pine were significantly higher than the baseline, while CUEs decreased. For black locust and Chinese fir, NPPs, ETs, and CUEs significantly decreased, while WUEs exhibited complex changes, with black locust showing a significant increase in WUEs under the RCP2.6 scenario. The impacts of all four variables were more pronounced in the far future compared to the near future. For the same species, the responses of NPP, ET, and water-carbon use efficiency to climate change were generally consistent across most sites, though some divergence occurred due to local environmental conditions. In the future, CUEs is predicted to decrease across all sites and scenarios, suggesting that carbon consumption through respiration will exceed carbon fixation through photosynthesis, potentially exacerbating future climate warming via negative feedback. Clustering results revealed that as climate change intensifies, the three plantations developed distinct climate response patterns, although consistency within the same species was not absolute. The simulation and prediction results of this study provide valuable scientific insights for the sustainable management of plantations.

This study investigated three widespread plantation species in China: Mongolian pine (Pinus sylvestris var. mongolica), Black locust (Robinia pseudoacacia), and Chinese fir (Cunninghamia lanceolata). Utilizing tree-ring data from stands of varying densities across 18 sites, and integrating the competition index (CI) with the standardized precipitation evapotranspiration index (SPEI3), We quantified the relative contributions of competition and climate to radial growth. We further elucidated how competition modulates the drought resilience of plantation forests across different species. Our results revealed distinct species-specific responses to climatic factors: the growth of Mongolian pine is primarily temperature-limited, Black locust by precipitation, and Chinese fir co-limited by both water and temperature. Competition strongly affects the relationship between climate and growth, leading to a partial decoupling of growth from climatic drivers. Additionally, competition reduces the resistance (Rt), recovery (Rc), and resilience (Rs) of all three species, with the effect being most pronounced under high competition intensity. This indicates that competition undermines the drought resilience of plantation forests. Specifically, for Mongolian pine under high-density (H-D), Rt shows a significant negative correlation with drought intensity, while Rc increases as drought severity intensifies. In contrast, Black locust exhibits little sensitivity of resilience to drought intensity. For Chinese fir, Rt consistently decreases with increasing drought intensity across all density levels, highlighting its high sensitivity to drought stress. Competition also substantially alters the trade-off between drought resistance and recovery capacity: trees in H-D stands require more resources to resume growth, placing them at a greater disadvantage, particularly Chinese fir. Collectively, this study reveals species-specific drought response mechanisms under interactive competition and climate stressors, offering a scientific foundation for adaptive management to enhance plantation resilience under climate change.

Intercropping with legume forages is recognized as an effective strategy for enhancing nitrogen levels in agroforestry, while mowing may influence nitrogen fixation capacity and yield. This study investigated the rooting, nitrogen fixation, nutritive value, and yield of alfalfa (Medicago sativa L.) under intercropping and varying mowing frequencies (CK, 2, and 3) from 2021 to 2023, using walnut (Juglans regia L.) and alfalfa as experimental subjects. The results indicated that intercropping suppressed root growth, whereas increased mowing frequency stimulated root development in the topsoil (0–20 cm). Specifically, the average root length density, root surface area, and root volume from the twice- and thrice-mowed treatments increased by 18.26, 17.45, and 4.15%, respectively, in comparison to the control. The δ15N values of the intercropped alfalfa were significantly lower than those of the monocropped alfalfa (p < 0.05), with the δ15N values of the mowing-thrice treatment increasing by an average of 38.61% compared to the control. Intercropping suppressed alfalfa yield but did not affect the total nitrogen content in the leaves or the nutritive value, and all mowing treatments resulted in land equivalent ratios (LERs) greater than 1. Furthermore, increased mowing frequency enhanced both the nutritive value and yield of alfalfa. Our study suggests that intercropping with walnut can improve biological nitrogen fixation in alfalfa, and that adopting a mowing-thrice regime can optimize yield and nutritive value.

Growing evidence has revealed δD offsets in soil-plant water systems, contradicting the long-held hypothesis of isotopic non-fractionation during plant water absorption. We aimed to determine whether δD offsets exist for plants in walnut (Juglans regia L.) -alfalfa (Medicago sativa L.) intercropping system and to explore the effects of intercropping and plant species on δD offsets. We conducted an analysis of the isotopic signatures of soil and plant water, calculated δD offsets utilizing the soil water excess (SW-excess) equation across varying seasons from 2021 to 2023 in walnut-alfalfa intercropping, monocropping walnut, and monocropping alfalfa systems. The findings revealed that all plant systems exhibited δD offsets with distinct seasonal variations. Specifically, walnuts and alfalfa displayed noteworthy positive δD offsets during dry-1 season (1.43 and 2.98‰) and substantial negative δD offsets during the rainy season (-8.80 and -12.25‰). Additionally, walnut exhibited negative δD offsets (-7.53‰) in dry-2 season, while alfalfa showed no δD offsets. The δD offset patterns in intercropped and monocropped plants remained consistent across different seasons, with no significant disparities between them. Correlation analyses indicated that walnut δD offsets were negatively associated with air humidity and soil moisture content, yet positively correlated with saturated vapor pressure difference; whereas, alfalfa δD offsets were notably negatively correlated with air temperature and relative humidity. Our findings suggest that δD offset may be a prevalent phenomenon, unaffected by intercropping patterns but significantly influenced by plant species. Compared to walnuts, alfalfa displayed higher sensitivity to climatic factors, resulting in a more pronounced offset magnitude.

Meteorological observation is a fundamental basis for weather forecasting and early warning,climate prediction,and field scientific observation and research.And surface meteorological observation,as an important component of meteorological observation,stands out for its adherence to unified meteorological observation standards,featuring better spatial comparability.This is also what distinguishes it from microclimate observations.Henan Xiaolangdi Forest Ecosystem National Observation and Research Station(referred to as Xiaolangdi Station)is located in the transitional zone between the second and third steps of China's landforms in the southern Taihang Mountains.Situated in a key ecological area of the Yellow River,it experiences a warm temperate sub-humid monsoon climate with abundant biodiversity.This dataset consists of daily meteorological data from 2018 to 2020,processed and quality-controlled,based on raw data collected from the surface meteorological observation field at Xiaolangdi Station.The dataset covers 17 key meteorological indicators:mean temperature,maximum temperature and minimum temperature,relative humidity,mean wind speed and maximum wind speed,net radiation and direct radiation,air pressure,precipitation,and multi-layer soil temperature at depth of 080 cm.It aims to provide background information for addressing climate change,advancing ecological civilization and the construction of a beautiful China.Furthermore,it can provide support data-driven strategies for ecological protection and high-quality development in the Yellow River Basin.

Vegetation phenology serves as an important indicator for climate change and plays a crucial role in affecting the terrestrial water, energy, and carbon cycles. The green chromatic coordinate (GCC) obtained from digital repeat photographs has been widely applied in estimating phenology from the perspective of greenness, while the performance of satellite derived GCC is not well understood. We used flux tower GPP from seven deciduous broadleaf forest (DBF) and three grassland (GRA) sites over the Northern Hemisphere. The aim was to compare phenological events with GCC (obtained from digital repeat photographs and satellite remote sensing (GCCMODIS)) and the enhanced vegetation index (EVI). Meanwhile, we also explored the performance of these three indices in simulating GPP utilizing the light use efficiency (LUE) model at the DBF and GRA sites. Phenology retrieved by GCC, GCCMODIS, and EVI was all significantly correlated with GPP-estimated values at all sites (P < 0.001). It indicates the comparable performance of GCC, GCCMODIS, and EVI in estimating phenological events. The RMSE values between the GPP and three indices-estimated phenological events revealed that the three indices excelled in estimating the start of growing season (SOS) compared to the end of growing season (EOS) and the length of growing season (GSL). In terms of GPP estimation performance, the R2 values of GCCMODIS and EVI-estimated GPP increased by 2 % and 1 %, respectively, compared to GCC-simulated GPP. Meanwhile, the RMSE values for GCCMODIS and EVI reduced by 0.08, and the bias values were reduced by 0.06 and 0.12, respectively. This study showed that GCC obtained from satellite remote sensing data could be utilized as an effective tool in extracting phenology and has a great potential to estimate GPP, at least across the DBF and GRA regions.

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In recent years, PM2.5 pollution has become a most important source of air pollution. Prolonged exposure to high PM2.5 concentrations can give rise to severe health issues. Negative air ion (NAI) is an important indicator for measuring air quality, which is collectively known as the 'air vitamin'. However, the intricate and fluctuating meteorological conditions and vegetation types result in numerous uncertainties in the correlation between PM2.5 and NAI. In this study, we collected data on NAI, PM2.5, and meteorological elements through positioning observation during the period of June to September in 2019 and 2020 under the condition of relatively constant leaf area in Quercus variabilis forest, a typical forest in warm temperate zones. We investigated the spatiotemporal variation of PM2.5 and NAI under consistent meteorological conditions, established the correlation between PM2.5 and NAI, and explicated the impact mechanism of PM2.5 on NAI in natural conditions. The results showed that NAI decreased exponentially with the increases in natural PM2.5, with a significant negative correlation (y=1148.79x-0.123). The decrease rates of NAI in PM2.5 concentrations of 0-20, 20-40, 40-80, 80-100 and 100-120 μg·m-3 were 40.1%, 36.2%, 9.4%, 2.4%, 5.1% and 6.8%, respectively. Results of the sensitivity analysis showed that the PM2.5 concentration range of 0-40 μg·m-3 was the sensitive range that affected NAI. Our findings could provide a scientific basis for better understanding the response mechanisms of NAI to environmental factors.

Mowing is used as a management practice in tree-grass agroforestry, but the water relationships between trees and grasses have been divergent. Here, we aim to reveal the effect of mowing frequency on the interspecific water relationships. Walnut (Juglans regia L.) was intercropped with alfalfa (Medicago sativa L.) from 2021 to 2022, and the responses of soil moisture, plant roots and moisture sources, water use efficiency (WUE) and water consumption to different mowing frequencies (once, twice and thrice) were investigated. The soil moisture did not respond significantly to the mowing frequency; mowing significantly increased the root length density of alfalfa in all soil layers, especially in the 0–20 cm layer; mowing twice and thrice affected the water sources of walnut and alfalfa, and it increased the proportion of walnut trees that used deep soil water (up to 64.60

Evapotranspiration is a key link in the water cycle of terrestrial ecosystems, and the partitioning of evapotranspiration is a prerequisite for diagnosing vegetation growth and water use strategies. In this study, we used double-layer eddy covariance (DLEC) measurements within and above the canopy of poplar plantations to divide evapotranspiration into transpiration and evaporation during the growing season. We diagnosed the coupling state of airflows in the canopy vertical layer and found that the daytime coupling state at the half-hourly scale can mask nighttime decoupling. Furthermore, we investigated the daytime and nighttime vertical layer airflow coupling states separately and quantified the effects of coupling states on the DLEC of resolved transpiration. The partitioning results of the DLEC method were taken as the standard after the airflow coupling test. Then, the performance and accuracy of evapotranspiration partitioning for the modified relaxed eddy accumulation (MREA), the conditional eddy covariance (CEC), and the flux variance similarity (FVS) with DLEC were compared. Transpiration calculated from MREA showed the best agreement with DLEC, and the other methods showed different degrees of underestimation (1:1 slope = 0.64–0.83). Evaporation calculated from FVS showed the best agreement with DLEC, while CEC and FVS made an overestimation of more than 26% (1:1 slope = 1.26–1.99), but MREA made an underestimation from 5% to 35% (1:1 slope = 0.65–0.95). The correlation coefficients between DLEC and MREA for transpiration were 0.95–0.97 with RMSEs of 15.52–17.04 W m−2, and those between DLEC and FVS for transpiration were 0.73–0.78 with RMSEs of 10–21.26 W m−2 at the daily half-hourly scale. A detailed comparison of the differences between DLEC and evapotranspiration partitioning methods from high-frequency eddy covariance data under the condition of canopy vertical layer airflow mixing provides knowledge about the consistency of results for evapotranspiration partitioning in poplar plantation forests.

This research aimed to assess the impact of various moisture treatments on the nitrogen fixation and nutritive value of alfalfa seedlings. Potted alfalfa plants were served as the experimental material, and four moisture treatments were conducted (30

China ranks first globally in planted forest area,which plays an important role in mitigating climate change and enhancing regional carbon cycling.The South Taihang Mountain region is an integral part of the key ecological zones along the Yellow River in China.Studying carbon flux observations in plantations in this region is of great significance for understanding the impact mechanism of climate change on forest carbon sinks in the key ecological zones along the Yellow River.Quercus variabilis is one of the most widely distributed natural tree species in China,and also a primary tree species for afforestation in mountainous regions with extremely high economic and ecological value.This dataset is the daily carbon flux data product from the Quercus variabilis plantation ecosystem accumulated by Henan Xiaolangdi Forest Ecosystem National Observation and Research Station from 2011 to 2020.The dataset covers the data on net ecosystem carbon exchange(NEE),ecosystem respiration(RE),and gross ecosystem primary productivity(GPP).The data processing follows the standard quality control system of ChinaFLUX.This dataset can provide support for analyzing the response and mechanism of plantations in this region to global climate change,and it can also serve as a foundation for managing carbon sinks of plantations in the key ecological zones along the Yellow River.

Aims Under the background of global warming,the plantation of Pinus tabuliformis is highly sensitive to climate change.However,the impacts of climate change on the radial growth of the earlywood and latewood are still less understood.Therefore,it is important for predicting the productivity and vegetation dynamics of the plantations to understand the responses of radial growth of earlywood and latewood to climate change. Methods Based on dendrochronology,we established the standard chronology of earlywood and latewood tree rings from five sampling sites in the northern and northwest China.Furthermore,we analyzed the relationship between annual ring width index and climate variables,and investigated the relative influence of climate variables on the growth of P.tabuliformis. Important findings Air temperature showed a significant increasing trend from 1980 to 2020,and regional climate was becoming warmer and drier.The ring widths of P.tabuliformis at Kangle(KL),Tianshui(TS)and Lingshou(LS)were higher than those at Xunyi(XY)and Chunhua(CH).Compared with CH and XY,the total ring width and earlywood width at TS,KL and LS showed a smaller decreased trend.The chronology of earlywood of P.tabuliformis showed positive correlations with precipitation in last September and during the pre-growing season of current year.The latewood largely showed a positive correlation with air temperature throughout the whole year.The radical growth in P.tabuliformis was positively correlated with averaged air temperature and maximum air temperature from March to April,especially in LS,KL and TS.It is indicated that the radial growth of earlywood and latewood was significantly correlated with climate factors during the growing season.The responses of radial growth to climate variables between earlywood and latewood were different.The relative influence of air temperature on the width change of the latewood increased by 21.89％,8.63％,3.31％and 7.25％compared to the earlywood in LS,CH,TS and KL,respectively.The latewood was more sensitive to air temperature than earlywood.Therefore,considering the difference in response to climate change between early and late wood chronology is helpful for improving the quality of regional climate reconstruction in the future.

Plantations have great potential for carbon sequestration and play a vital role in the water cycle. However, it is challenging to accurately estimate the carbon and water fluxes of plantations, and the impact of biophysical drivers on the coupling of carbon and water fluxes is not well understood. Thus, we modified the phenology module of the Biome-BGC model and optimized the parameters with the aim of simulating the gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) of a warm-temperate plantation in northern China from 2009 to 2020. Photosynthetically active radiation (PAR) showed significant positive cor-relations on GPP and WUE during the first stage of the growing season (S1: from early April to late July). Active accumulated temperature (Taa) mainly controlled the changes in GPP and ET during the second stage (S2: be-tween the end of July and early November). Throughout the growing season, soil water content dominated daily GPP and WUE, whereas Taa regulated ET. The optimized Biome-BGC model performed better than the original model in simulating GPP and ET. Compared with the values simulated by the original model, root mean square error decreased by 7.89 % and 15.97 % for the simulated GPP and ET, respectively, while the determination coefficient increased from 0.77 to 0.81 for simulated GPP and from 0.51 to 0.62 for simulated ET. The results of this study demonstrated that the optimized model more accurately assessed carbon sequestration and water consumption in plantations.

Aims Water use efficiency(WUE)is an important indicator for understanding the carbon and water cycles and coupling mechanisms in terrestrial ecosystems.Inherent water use efficiency(IWUE)is a more suitable indicator than WUE for analyzing the carbon-water coupling mechanism of ecosystems on a daily scale.Here,we aimed to investigate the mechanism of water and carbon fluxes and their responses to drought in a planted forest ecosystem. Methods We carried out an in-situ observation on the water and carbon fluxes and environmental factors in a Quercus variabilis plantation using eddy covariance techniques and the micrometeorological observation system.The effects of biophysical factors on the gross primary productivity(GPP),evapotranspiration(ET),and IWUE during 2021-2022 were analyzed. Important findings GPP,ET,and IWUE showed obvious seasonal variations.GPP and ET in the wet year were 7.9％and 21.0％higher than those in the normal year,respectively,whereas IWUE in the wet year was 21.4％lower than that in the normal year.Vapor pressure deficit(VPD)was the main factor affecting GPP in the normal year,and net radiation(Rn)was the primarily factor limiting GPP in the wet year.ET was mainly determined by Rn in both normal and wet years.Relative extractable soil water(REW)was the main factor regulating IWUE in the normal year,whereas leaf area index(LAI)was the main factor controlling IWUE in the wet year.Environmental factors regulated carbon and water fluxes by affecting canopy conductance,and consequently impacting IWUE.The occurrence of soil drought significantly increased IWUE.The response of GPP and ET to REW showed a time lag of 1 month,while the response of IWUE to REW had no lag.

The impact of extreme weather events on carbon fluxes and water-use efficiency (WUE) in revegetated areas under water-limited conditions is poorly understood. We analyzed changes in carbon fluxes and WUE over three years of eddy-covariance measurements in a Pinus tabuliformis plantation in Northeast China to investigate carbon fluxes and WUE responses to drought events at different time scales. Mean annual net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Re) were -368.48, 1042.42, and 673.94 g C m - 2 , respectively. Drought events increased NEE, as GPP was more sensitive to water stress than Re at different growing stages. Mean annual WUE was 2.46 g C kg -1 H 2 O, and plant phenology played a key role in WUE responses to drought. Water stress had negative and positive effects on daily WUE at the early and late growing stages, respectively, and daily WUE was generally insensitive to drought at the mid growing stage. A lagged effect existed in the carbon fluxes and WUE dynamics after drought events at various time scales. Water stress at the early growing stage was more important than that at other growing stages on annual carbon sequestration and WUE, as it dominated canopy growth in the current year. The annual mean normalized difference vegetation index controlled interannual variations in carbon fluxes and WUE in the plantation. Our findings contribute to the prediction of possible changes in carbon and water fluxes under climate warming in the afforested areas of Northeast China.