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IntroductionSoil respiration (SR), the release of carbon dioxide (CO2) from soil due to the decomposition of organic matter and root respiration, is an important indicator for understanding agricultural carbon cycling and assessing anthropogenic impacts on the environment. Hyperspectral remote sensing offers a potential rapid, non-destructive approach for monitoring in agriculture. However, it remains uncertain whether hyperspectral remote sensing can provide an accurate and efficient method for estimating SR rate in croplands, particularly across different maize growth stages of under varying drought conditions.MethodsIn the study, we investigated the potential of combining hyperspectral remote sensing data with machine learning model (ML) to quantify SR rate in croplands. A drought field experiment was conducted, and SR and hyperspectral imagery were collected during four maize growth stages: Jointing Stage (JS), Tasseling Stage (TS), Flowering Stage (FS), and Grain Filling Stage (GFS). We compared the performance of traditional multiple linear regression (MLR) with that of an ML model (extreme gradient boosting, XGBoost), in simulating SR rate across these four growth stages.ResultsOur findings demonstrated that the simulation of the XGBoost model, utilizing soil temperature (Ts) and hyperspectral data, outperformed the MLR model. Across different growth stages, the SR simulated by the XGBoost model (R2 = 0.8103) was more reliable than that of the MLR model (R2 = 0.7451). The XGBoost model can also effectively capture the impact of drought treatments on SR.DiscussionThe XGBoost model’s tree-based structure allows it to effectively capture complex interactions and nonlinear patterns within variables, while its high sensitivity to changes in SR rates under drought conditions makes it more reliable for modeling SR across different growth stages compared to the linear-based MLR model. This study highlights the great promise of ML combined with hyperspectral imaging in predicting SR rate in croplands, which will help guide future agricultural management and environmental informatics.

Abstract Microbial processes are crucial in producing and oxidizing biological methane (CH4) in natural wetlands. Therefore, modeling methanogenesis and methanotrophy is advantageous for accurately projecting CH4 cycling. Utilizing the CLM‐Microbe model, which explicitly represents the growth and death of methanogens and methanotrophs, we demonstrate that genome‐enabled model parameterization improves model performance in four natural wetlands. Compared to the default model parameterization against CH4 flux, genomic‐enabled model parameterization added another contain on microbial biomass, notably enhancing the precision of simulated CH4 flux. Specifically, the coefficient of determination (R2) increased from 0.45 to 0.74 for Sanjiang Plain, from 0.78 to 0.89 for Changbai Mountain, and from 0.35 to 0.54 for Sallie's Fen, respectively. A drop in R2 was observed for the Dajiuhu nature wetland, primarily caused by scatter data points. Theil's coefficient (U) and model efficiency (ME) confirmed the model performance from default parameterization to genome‐enabled model parameterization. Compared with the model solely calibrated to surface CH4 flux, additional constraints of functional gene data led to better CH4 seasonality; meanwhile, genome‐enabled model parameterization established more robust associations between simulated CH4 production rates and environmental factors. Sensitivity analysis underscored the pivotal role of microbial physiology in governing CH4 flux. This genome‐enabled model parameterization offers a valuable promise to integrate fast‐cumulating genomic data with CH4 models to better understand microbial roles in CH4 in the era of climate change.

Irrigation can increase crop yields and could be a key climate adaptation strategy. At present, under the background of increasing food demand and continuous expansion of irrigation cropland, there still uncertainties about the soil carbon dynamics under the change of irrigation water volume and irrigated area in view of large-scale spatial heterogeneity. Therefore, this paper uses space-for-time + meta-analysis and a two-step methodology based on the residual trend analysis to quantitatively analyze the relationship between soil organic carbon (SOC) and soil respiration (Rs) in response to fluctuations in irrigation water volume and irrigated land extents. Here we show that the irrigation water volume within 100–1000 mm had a negative impact on SOC, and the impact was correlated with the irrigation water volume. Different levels of irrigation water manifest distinct effects on SOC content across varying soil depths. When irrigation water quantities are less than 700 mm, the impact on SOC content in the 0–30 cm depth layer surpasses that in the 30–200 cm depth layer. Conversely, when irrigation water quantities equal or exceed 700 mm, this pattern is reversed. The overall impact of irrigation on SOC stock at a depth of 0–200 cm was −14.88±6.7%. Tillage, planting intensity, topography, and soil type within irrigated cropland all exert variable impacts on SOC content. Whether these influences are deleterious or beneficial hinges predominantly upon the balance between the augmentation of SOC stock due to heightened carbon inputs from crops and the reduction of SOC through alterations in microbial activity. Mann-Kendall trend analysis showed that from 2000 to 2015, the overall Rs of cropland showed an increasing trend, with an increase rate of 3.67 g/m2/year. The increase of global Rs is mainly driven by climate change factors (temperature, precipitation and solar radiation), while the decrease of Rs in a small number of areas is mainly driven by management practices (fertilizer nitrogen, irrigation, and tillage). Our study further quantifies the impact of irrigation on soil carbon dynamics, thereby offering potential pathways and data support for the advancement of sustainable agriculture.

Abstract Recent studies have reported worldwide vegetation suppression in response to increasing atmospheric vapor pressure deficit (VPD). Here, we integrate multisource datasets to show that increasing VPD caused by warming alone does not suppress vegetation growth in northern peatlands. A site-level manipulation experiment and a multiple-site synthesis find a neutral impact of rising VPD on vegetation growth; regional analysis manifests a strong declining gradient of VPD suppression impacts from sparsely distributed peatland to densely distributed peatland. The major mechanism adopted by plants in response to rising VPD is the “open” water-use strategy, where stomatal regulation is relaxed to maximize carbon uptake. These unique surface characteristics evolve in the wet soil‒air environment in the northern peatlands. The neutral VPD impacts observed in northern peatlands contrast with the vegetation suppression reported in global nonpeatland areas under rising VPD caused by concurrent warming and decreasing relative humidity, suggesting model improvement for representing VPD impacts in northern peatlands remains necessary.

Tree-ring carbon (C) isotope composition (δ13C) varies depending on photosynthetic capacity and stomatal conductance, and it is therefore intrinsically associated with vegetation productivity. Still, very little is known about the relationship between tree-ring δ13C values and forest gross primary productivity (GPP). Here, we investigated relationships between tree-ring δ13C, tree-ring width index (RWI) and ecosystem-level GPP in a Korean pine (Pinus koraiensis)-dominated old-growth temperate forest in northeastern China. Specifically, we generated chronologies of RWI and δ13C from early-wood (δ13CEW) and late-wood (δ13CLW) of Korean pines, and we built a 17-year continuous ecosystem-level GPP series using eddy covariance measurements, in which a GPP model was used for gap filling. RWI showed vague relationships with tree-ring δ13C and whole-forest GPP, and it was insensitive to interannual variations in climate at the study site. By contrast, both δ13CEW and δ13CLW showed significant relationships with climate variables and GPP. This suggests that isotopic signals of photosynthetic C uptake were imprinted on the formation of tree rings and that tree-ring δ13C of Korean pine is a better proxy for forest GPP than RWI. In addition, tree-ring δ13C of Korean pines was positively correlated with GPP, with an especially close relationship between δ13CEW and GPP of the current year. This implies that photosynthetic activity, rather than stomatal conductance, was the main driver of tree-ring δ13C signals of Korean pine and it was mainly influenced by the climate conditions of the current year, probably because of a limited use of previously fixed C during early-wood formation. Our findings demonstrate the great potential of tree-ring δ13C for reconstructing GPP timeseries at the centennial scale for forest ecosystems, and they could help to constrain parameters in terrestrial C cycle models to improve predictions of C fluxes.

Terrestrial ecosystems are a critical carbon dioxide (CO2) sink for achieving carbon (C) neutrality before 2060 in China. Here, we used the Coupled Model Intercomparison Project phase 6 (CMIP6) model outputs to quantify the spatiotemporal patterns of net ecosystem productivity (NEP) and its major environmental controls, as well as the dominant C pools for NEP during 1900–2100. We found that (a) according to CMIP6 NEP spatiotemporal characteristics, terrestrial ecosystems in China sequestered 0.310 ± 0.058 Pg C·year–1 during 1980–2014 and will act as a strong C sink [from 0.515 ± 0.075 Pg C·year–1 under Shared Socioeconomic Pathway 126 (SSP126) to 0.631 ± 0.089 Pg C·year–1 under SSP585] in the future (2015–2100). Pronounced turning points were found for the temporal trends of NEP during the historical (1984) and under 4 future emissions scenarios (2057 for SSP126, 2053 for SSP245, 2038 for SSP370, and 2044 for SSP585). (b) The positive effect of temperature on NEP appears to weaken after each turning point of future scenarios. (c) The enlarged vegetation C pool size dominates the growing terrestrial ecosystem C storage. The CMIP6 projection shows that the total C storage in Chinese terrestrial ecosystems increases continuously and peaks in the 2040s to 2050s under each scenario. Future afforestation in the northeast, southeast, and southwestern regions, as well as soil C pool management in the northwest and middle north regions, will greatly contribute to achieving C neutrality in China, particularly under low emission scenario (SSP126).

Wetlands account for up to 70% of the natural source of methane (CH4) in terrestrial ecosystems on a global scale. Soil microbes are the ultimate producers and biological consumers of CH4 in wetlands. Therefore, simulating microbial mechanisms of CH4 production and consumptionwould improve the predictability of CH4 flux in wetland ecosystems. In this study, we applied a microbial-explicit model, the CLM-Microbe, to simulate CH4 flux in three major natural wetlands in northeastern China. The CLM-Microbe model was able to capture the seasonal variation of gross primary productivity (GPP), dissolved organic carbon (DOC), and CH4 flux. The CLM-Microbe model explained more than 40% of the variation in GPP and CH4 flux across sites. Marsh wetlands had higher CH4 flux than mountain peatlands. Ebullition dominated the CH4 transport pathway in all three wetlands. The methanogenesis dominates while methanotroph makes a minor contribution to the CH4 flux, making all wetlands a CH4 source. Sensitivity analysis indicated that microbial growth and death rates are the key factors governing CH4 emission and vegetation physiological properties (flnr) and maintenance respiration predominate GPP variation. Explicitly simulating microbial processes allows genomic information to be incorporated, laying a foundation for better predicting CH4 dynamics under the changing environment.

Wetland conversion to cropland substantially suppresses methane (CH4) emissions due to the strong suppression of methanogenesis, which consists of various pathways. In this study, we evaluated the cultivation impacts on four predominant CH4 production pathways, including acetate, carbon dioxide (CO2), methylamines, and methanol, in a wetland and cultivated cropland in northeastern China. The results showed significant suppression of CH4 production potential and the abundance of genes for all four methanogenic pathways in cropland. The consistency between CH4 production and methanogenesis genes indicates the robustness of genomic genes in analyzing methanogenesis. The suppression effects varied across seasons and along soil profiles, most evident in spring and 0 to 30 cm layers. The acetate pathway accounted for 55% in wetland vs. 70% in the cropland of all functional genes for CH4 production; while the other three pathways were stronger in response to cultivation, which presented as stronger suppressions in both abundance of functional genes (declines are 52% of CO2 pathway, 68% of methanol pathway, and 62% of methylamines pathway, vs. 19% of acetate pathway) and their percentages in four pathways (from 20 to 15% for CO2, 15 to 9% for methylamines, and 10 to 6% for methanol pathway vs. 55 to 70% for acetate pathway). The structural equation models showed that substrate availability was most correlated with CH4 production potential in the wetland, while the positive correlations of acetate, CO2, and methylamine pathways with CH4 production potential were significant in the cropland. The quantitative responses of four CH4 production pathways to land conversion reported in this study provide benchmark information for validating the CH4 model in simulating CH4 cycling under land use and land cover change.

Forest canopy closure affects snow processes by changing the redistribution of snowfall, snow interception, accumulation, sublimation, and melt. However, how the forest closure impacts snow processes at different periods has not been well explored. We conducted 3-year measurements of snow density and depth and carried out snow process calculations (i.e., interception, sublimation, and snowmelt) from 2018 to 2021 in four mixed forests with different canopy closures and an open site in the Changbai Mountains, northeast China. We found that the snow density of the five study sites varied greatly (0.14–0.45 g/cm3). The snow depth (SD) at four mixed forests sites was smaller than that of the nearby open site. The SD decreased as the forest canopy closure increased. Additionally, the forest interception effect increased with the canopy closure and decreased as the snowfall intensity increased. The total interception efficiency of the four mixed forests in normal snow years changed from 34% to 73% and increased with forest canopy closure. The averaged sublimation rate (Ss) and snowmelt rate (Sr) of the four mixed forests varied during different periods of snow process. The Ss was 0.1–0.4 mm/day during the accumulation period and 0.2–1.0 mm/day during the ablation period, and the Sr was 1.5–10.5 mm/day during the ablation period. There was a good correlation between Ss, or Sr, and canopy closure, but interannual variation was observed in the correlation. The mean values of the effect of the four mixed forests on understory SWE (snow water equivalent) over the 3 years ranged from −45% to −65%. Moreover, the impact effect was correlated with the forest canopy closure and enhanced with the canopy closure. This study provided more scientific information for studies of snow cover response to forest management.

Abstract Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low‐centered polygon (LCP) center, LCP transition, LCP rim, high‐centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM‐Microbe model against static‐chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low‐elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high‐elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2 + H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH4 fluxes. The model underestimated the EC‐measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.

Soil water dynamic is considered an important process for water resource and plantation management in Horqin Sand Land, northern China. In this study, soil water content simulated by the SWMS-2D model was used to systematically analyse soil water dynamics and explore the relationship between soil water and rainfall among micro-landforms (i.e., top, upslope, midslope, toeslope, and bottomland) and 0-200 cm soil depths during the growing season of 2013 and 2015. The results showed that soil water dynamics in 0-20 cm depths were closely linked to rainfall patterns, whereas soil water content in 20-80 cm depths illustrated a slight decline in addition to fluctuations caused by rainfall. At the top position, the soil water content in different ranges of depths (20-40 and 80-200 cm) was near the wilting point, and hence some branches, and even entire plants exhibited diebacks. At the upslope or midslope positions, the soil water content in 20-80 or 80-200 cm depths was higher than at the top position. Soil water content was higher at the toeslope and bottomland positions than at other micro-landforms, and deep caliche layers had a positive feedback effect on shrub establishment. Soil water recharge by rainfall was closely related to rainfall intensity and micro-landforms. Only rainfalls > 20 mm significantly increased water content in > 40 cm soil depths, but deeper water recharge occurred at the toeslope position. A linear equation was fitted to the relationship between soil water and antecedent rainfall, and the slopes and R2 of the equations were different among micro-landforms and soil depths. The linear equations generally fitted well in 0-20 and 20-40 cm depths at the top, upslope, midslope, and toeslope positions (R2 value of about 0.60), with soil water in 0-20 cm depths showing greater responses to rainfall (average slope of 0.189). In 20-40 cm depths, the response was larger at the toeslope position, with a slope of 0.137. In 40-80 cm depths, a good linear fit with a slope of 0.041 was only recorded at the toeslope position. This study provides a soil water basis for ecological restoration in similar regions.

Introduction Disruptive behaviour disorders are common among children and adolescents, with negative impacts on the youths, their families and society. Although multiple psychosocial treatments are effective in decreasing the symptoms of disruptive behaviour disorders, comprehensive evidence regarding the comparative efficacy and acceptability between these treatments is still lacking. Therefore, we propose a systematic review and network meta-analysis, integrating both direct and indirect comparisons to obtain a hierarchy of treatment efficacy and acceptability.Methods and analysis The present protocol will be reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. Ten databases, including Web of Science, PubMed, PsycINFO, MEDLINE, APA PsycArticles, Psychology and Behavioral Sciences Collection, OpenDissertations, The Cochrane Library, Embase and CINAHL, will be searched from inception for randomised controlled trials of psychosocial treatments for children and adolescents with disruptive behaviour disorders, without restrictions on language, publication year and status. The primary outcomes will be efficacy at post-treatment (severity of disruptive behaviour disorders at post-treatment) and acceptability (dropout rate for any reason) of psychosocial treatments. The secondary outcomes will involve efficacy at follow-up, severity of internalising problems and improvement of social functioning. Two authors will independently conduct the study selection and data extraction, assess the risk of bias using the revised Cochrane Collaboration’s Risk of Bias tool and evaluate the quality of the evidence using the Grading of Recommendations Assessment, Development and Evaluation framework to network meta-analysis. We will perform Bayesian network meta-analyses with a random effects model. Subgroup and sensitivity analyses will be performed to evaluate the robustness of the findings.Ethics and dissemination The research does not require ethical approval. Results are planned to be published in journals or presented at conferences. The network meta-analysis will provide information on a hierarchy of treatment efficacy and acceptability and help make a clinical treatment choice.PROSPERO registration number CRD42020197448.

Permafrost soils represent an enormous carbon (C) pool that is highly vulnerable to climate warming. We used the model output ensemble of the Coupled Model Intercomparison Project Phase 6 to estimate the C storage in soil, litter, and vegetation in the current extent of northern Asian permafrost during 1900–2100. The contemporary (1995–2014) C storage was estimated to be 368.1 ± 82.5 Pg C for the full column depth of the soil, 13.3 ± 4.6 Pg C in litter, and 22.2 ± 3.2 Pg C in vegetation biomass, while these C storage levels are projected to decline by 3.9 Pg C (1.1%) in soils, increase of 0.03 Pg C (0.2%) in litter, and increase by 21.1 Pg C (95.3%) in vegetation biomass by the end of the 21st century under SSP585. The total C storage was dominated by warming-induced vegetation growth. Partial correlation analysis showed that surface air temperature (TAS), soil liquid water, and soil mineral nitrogen (SMN) dominated the soil and vegetation C pools, while SMN controlled litter C during the historical period. Under future scenarios, TAS and SMN dominated the changes of soil and litter C, while TAS determined the vegetation C increase. The growing soil C loss with warming indicates positive C-climate feedback in soils; this warming-induced acceleration of soil C loss was canceled out by the enhanced vegetation C accumulation, leading to a strong C sink in the 21st century.

A shelterbelt is an important measure to protect farmland and increase crop yield. However, how a shelterbelt structure affects crop yield is still unclear due to the difficulties accessing sufficient data from traditional field observations. To address this problem, we developed an innovative framework to estimate the shelterbelt structure and crop yield profile at a regional scale based on Google Earth and Sentinel-2 data. Using this method, we quantified the impact of the shelterbelt structure on the corn yield at 302 shelterbelts in the Northeast Plain of China. Generally, the corn yield increased (by 2.41% on average) within a distance of 1.2–15 times the tree height from the shelterbelt. Such an effect was particularly prominent within a distance of two to five times the tree height, where the corn yield was significantly increased by up to 4.63%. The structure of the shelterbelt has a significant effect on the magnitude of increase in yield of the surrounding corn. The increment of corn yields with high-, medium-high-, medium- and low-width-gap grade shelterbelt were 2.01%, 2.21%, 1.99%, and 0.91%, respectively. The medium-high grade shelterbelt achieved the largest yield increase effect. The location of the farmland relative to the shelterbelt also affected the yield, with a yield increase of 2.39% on the leeward side and 1.89% on the windward side, but it did not change the relationship between the yield increase effect and the shelterbelt structure. Our findings highlight the optimal shelterbelt structure for increasing corn yield, providing practical guidance on the design and management of farmland shelterbelts for maximizing yield.

The net ecosystem CO2 exchange (NEE) is a critical parameter for quantifying terrestrial ecosystems and their contributions to the ongoing climate change. The accumulation of ecological data is calling for more advanced quantitative approaches for assisting NEE prediction. In this study, we applied two widely used machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to build models for simulating NEE in major biomes based on the FLUXNET dataset. Both models accurately predicted NEE in all biomes, while XGBoost had higher computational efficiency (6~62 times faster than RF). Among environmental variables, net solar radiation, soil water content, and soil temperature are the most important variables, while precipitation and wind speed are less important variables in simulating temporal variations of site-level NEE as shown by both models. Both models perform consistently well for extreme climate conditions. Extreme heat and dryness led to much worse model performance in grassland (extreme heat: R2 = 0.66~0.71, normal: R2 = 0.78~0.81; extreme dryness: R2 = 0.14~0.30, normal: R2 = 0.54~0.55), but the impact on forest is less (extreme heat: R2 = 0.50~0.78, normal: R2 = 0.59~0.87; extreme dryness: R2 = 0.86~0.90, normal: R2 = 0.81~0.85). Extreme wet condition did not change model performance in forest ecosystems (with R2 changing −0.03~0.03 compared with normal) but led to substantial reduction in model performance in cropland (with R2 decreasing 0.20~0.27 compared with normal). Extreme cold condition did not lead to much changes in model performance in forest and woody savannas (with R2 decreasing 0.01~0.08 and 0.09 compared with normal, respectively). Our study showed that both models need training samples at daily timesteps of >2.5 years to reach a good model performance and >5.4 years of daily samples to reach an optimal model performance. In summary, both RF and XGBoost are applicable machine learning algorithms for predicting ecosystem NEE, and XGBoost algorithm is more feasible than RF in terms of accuracy and efficiency.

Atmospheric nitrogen (N) deposition has been found to significantly affect plant growth and physiological performance in terrestrial ecosystems. Many individual studies have investigated how N addition influences plant functional traits, however these investigations have usually been limited to a single species, and thereby do not allow derivation of general patterns or underlying mechanisms. We synthesized data from 56 papers and conducted a meta-analysis to assess the general responses of 15 variables related to leaf economics, gas exchange, and hydraulic traits to N addition among 61 woody plant species, primarily from temperate and subtropical regions. Results showed that under N addition, leaf area index (+10.3%), foliar N content (+7.3%), intrinsic water-use efficiency (+3.1%) and net photosynthetic rate (+16.1%) significantly increased, while specific leaf area, stomatal conductance, and transpiration rate did not change. For plant hydraulics, N addition significantly increased vessel diameter (+7.0%), hydraulic conductance in stems/shoots (+6.7%), and water potential corresponding to 50% loss of hydraulic conductivity (P50, +21.5%; i.e., P50 became less negative), while water potential in leaves (−6.7%) decreased (became more negative). N addition had little effect on vessel density, hydraulic conductance in leaves and roots, or water potential in stems/shoots. N addition had greater effects on gymnosperms than angiosperms and ammonium nitrate fertilization had larger effects than fertilization with urea, and high levels of N addition affected more traits than low levels. Our results demonstrate that N addition has coupled effects on both carbon and water dynamics of woody plants. Increased leaf N, likely fixed in photosynthetic enzymes and pigments leads to higher photosynthesis and water use efficiency, which may increase leaf growth, as reflected in LAI results. These changes appear to have downstream effects on hydraulic function through increases in vessel diameter, which leads to higher hydraulic conductance, but lower water potential and increased vulnerability to embolism. Overall, our results suggest that N addition will shift plant function along a tradeoff between C and hydraulic economies by enhancing C uptake while simultaneously increasing the risk of hydraulic dysfunction.

Previous studies with different experimental methods have demonstrated that photosynthesis significantly influences soil respiration (RS). To compare the experimental results of different methods, RS after girdling and defoliation was measured in five-year-old seedlings of Fraxinus mandshurica from June to September. Girdling and defoliation significantly reduced RS by 33% and 25% within 4 days, and 40% and 32% within the entire treatment period, respectively. The differential response of RS to girdling and defoliation was a result of the over-compensation for RS after girdling and redistribution of stored carbon after defoliation. No significant effect on RS was observed between girdling and defoliation treatment, while the soluble sugar content in fine roots was higher in defoliation than in girdling treatment, indicating that defoliation had less compensation effect for RS after interrupting photosynthates supply. We confirm the close coupling of RS with photosynthesis and recommend defoliation for further studies to estimate the effect of photosynthesis on RS.

Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product over high latitude areas beyond the TRMM satellites latitude band (38°NS). This study attempts to statistically assess TMPA V7 data over the region beyond 40°NS using data obtained from numerous weather stations in 1998-2012. Comparative analysis at three timescales (daily, monthly and annual scale) indicates that adoption of a monthly adjustment significantly improved correlation at a larger timescale increasing from 0.63 to 0.95; TMPA data always exhibits a slight overestimation that is most serious at a daily scale (the absolute bias is 103.54%). Moreover, the performance of TMPA data varies across all seasons. Generally, TMPA data performs best in summer, but worst in winter, which is likely to be associated with the effects of snow/ice-covered surfaces and shortcomings of precipitation retrieval algorithms. Temporal and spatial analysis of accuracy indices suggest that the performance of TMPA data has gradually improved and has benefited from upgrades; the data are more reliable in humid areas than in arid regions. Special attention should be paid to its application in arid areas and in winter with poor scores of accuracy indices. Also, it is clear that the calibration can significantly improve precipitation estimates, the overestimation by TMPA in TRMM-covered area is about a third as much as that in no-TRMM area for monthly and annual precipitation. The systematic evaluation of TMPA over mid-high latitudes provides a broader understanding of satellite-based precipitation estimates, and these data are important for the rational application of TMPA methods in climatic and hydrological research.

Leaf respiration is an important component of carbon exchange in terrestrial ecosystems, and estimates of leaf respiration directly affect the accuracy of ecosystem carbon budgets. Leaf respiration is inhibited by light; therefore, gross primary production (GPP) will be overestimated if the reduction in leaf respiration by light is ignored. However, few studies have quantified GPP overestimation with respect to the degree of light inhibition in forest ecosystems. To determine the effect of light inhibition of leaf respiration on GPP estimation, we assessed the variation in leaf respiration of seedlings of the dominant tree species in an old mixed temperate forest with different photosynthetically active radiation levels using the Laisk method. Canopy respiration was estimated by combining the effect of light inhibition on leaf respiration of these species with within-canopy radiation. Leaf respiration decreased exponentially with an increase in light intensity. Canopy respiration and GPP were overestimated by approximately 20.4% and 4.6%, respectively, when leaf respiration reduction in light was ignored compared with the values obtained when light inhibition of leaf respiration was considered. This study indicates that accurate estimates of daytime ecosystem respiration are needed for the accurate evaluation of carbon budgets in temperate forests. In addition, this study provides a valuable approach to accurately estimate GPP by considering leaf respiration reduction in light in other ecosystems.