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Salix matsudana is an important tree species for shelterbelt and barren hill afforestation in North China. Knowledge of the seasonal pattern of the stem radial growth (SRG) of S. matsudana and its response to environmental factors is crucial for understanding the tree growth and climate relationships in the increasingly warm and dry climate of North China. To this end, a field trial was conducted in a semi-arid area of North China, and the stem radial dynamics of eight sample trees at two sites (four trees per site) with different soil moisture regimes, as well as the meteorological and edaphic conditions, were monitored from May to October in 2016 and 2019. During the growing season, the daily mean air temperature was 1.69 °C higher in 2019 than in 2016, and the total precipitation was 179.2 mm lower in 2019 than in 2016. The results showed that the seasonal pattern of S. matsudana SRG was divided into the initial stage (stage 1), the rapid growth stage (stage 2), and ending stage (stage 3) in terms of the threshold of 0.02 mm day−1 (daily growth rate). The initiation of stage 2 in 2019 (day of year (DOY) 126) was 48 days earlier than that in 2016 (DOY 174). However, the date on which the maximum growth rate was reached occurred also 34 days earlier, indicating the co-occurrence of the positive effect of the higher temperature in 2019 than in 2016 on growth initiation in stage 1 as well as the negative consequences of the water deficit on tree growth in stage 2. Higher cumulative growth of S. matsudana at the high soil moisture site was monitored relative to the low soil moisture site throughout the trial, which could be attributed to differences in soil moisture and the initial stem size of the trees in 2016 and 2019, respectively. Temperature and soil moisture were the main factors restricting daily SRG, but the growth of S. matsudana subjected to prolonged summer water stress would be less significantly limited by soil moisture in stage 3. These results showed that the seasonal pattern of SRG of S. matsudana was closely related to the dynamics of the soil and climatic factors. Therefore, seasonal variation in growth-limiting factors should be considered when forecasting tree growth in response to climate change in North China.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO(2)flux, commonly though imprecisely termed soil respiration (R-S), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequencyR(S)measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measuredR(S), the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Seven treatments were implemented in a degraded steppe in north China to evaluate the effects of gap creation on two native species regeneration. Shoot gaps and root gaps (10cm, 20cm and 40cm in diameters), no gaps (control). Shoot gaps were formed by removing vegetation without restricting the re-growth of neighbor roots back into the gap.Root gaps were accomplished by using PVC pipes sunk in the soil of shoot gaps. Seedling growth performance after 90 days of growing were recorded for both species. Seedling emergence and survivorship of both species were greater in gaps than in controls.However, the gap size showed a significantly negative effect onAgropyron cristatum's survivorship.G rowth performance of Agropyron cristatumandStipa kryloviidiffer in their response to gap disturbance.Gaptreatments had positive effects on seedling growth(including seedling height, dry weight, and numbers of tillers andleaves) of Stipa krylovii, but had negative effects on seedling growth ofAgropyron cristatum.The two species have significantly different responses to gap disturbance. All results suggest that Stipa kryloviiis a gap-enhanced species, and Agropyron cristatumis not. Gap creation promoted the seedling establishment. Different species differ in their regeneration dynamics when the gap is available.

With aircraft structural safety becomes an increasingly issue, people start to use Structural Health Monitoring (SHM) technology to monitor the reliability of airframe structural materials. Fiber Bragg Grating (FBG) sensors are often used to monitor the composite materials due to their inherent advantages, but the gap between the FBG sensors' sampling rate and the damage monitoring signals' bandwidth has brought problem analyzing the ‘health condition’ of the airframe structure. To solve this problem, SHM technology, in conjunction with the reconstruction algorithms of Compressed Sensing (CS) theory, is expected to compensate the losing information of the signals sampled by FBG sensors and reconstruct the high frequency damage monitoring signals. In order to satisfy the applicable conditions of CS, this paper proposes an innovative method to convert a 1D signal to a 2D (2D) signal and has designed corresponding structurally random measurement matrix. Finally, the high frequency damage monitoring signal is reconstructed successfully and the relative error of the reconstruction is less than 30% under appropriate number of samples.

In recent decades, the FBG (Fiber Bragg Grating) sensors are widely used in SHM (Structural Health Monitoring) fields for the characteristics of its simple structure, tiny volume, light weight, anti-corrosion, and anti-electromagnetic interference. However, there are two defects that limit its application in some fields. Firstly, the low sampling frequency due to the induction principle provides a big challenge to the solver algorithm. Secondly, real-time online monitoring which requires quick and efficient signal processing for the long time series is the development direction of SHM and also needs progress in the fast solver algorithm. For these two reasons, we propose a novel method based on the PSD (Power Spectrum Density) to analyze the sampled data from FBG sensors in this paper and it is applied in an online crack initiation monitoring system and obtain some satisfactory results. A comparison with the preliminary research indicates the accuracy and rapidity of the proposed algorithm and points out the applicability in the real-time online monitoring system for crack initiation.

Correctly quantifying the relationships between soil respiration and environmental factors and their sources of variability is essential to predict future carbon fluxes and climate feedback. Soil water conditions and soil temperature strongly affect soil respiration and the dynamics of soil organic matter. Despite this, simulation of soil respiration (Rs) based on soil temperature (Ts) and soil volumetric water content (θ) must still be improved, as demonstrated by its discrepant model performance among different seasons. With the objective of gaining a further understanding of the relationships of Rs with Ts and θ and providing an improved model to simulate Rs variations, we measured hourly Rs, Ts and θ using the chamber technique in artificial smooth brome grassland for analysis. We began by dividing the four seasons of a year according to the daily mean air temperature, followed by representing the seasonal variation of Rs, Ts and θ. We found that Rs correlated significantly with Ts in an exponential relationship and with θ in a parabolic relationship seasonally, where the determination coefficient of the Rs-θ relationship was significantly larger than that of the Rs-Ts relationship. We also discovered that the shape of the Rs-θ relationship was seasonally dependent because the optimal θ and the width of the peak Rs around the optimal θ were seasonally specific. Finally, by considering seasonality, the combinational simulation model explained more variation of soil respiration. Thus, seasonality should be considered for more reliable model simulations of soil respiration. These findings are relevant for more accurate predictions and modelling of soil respiration, particularly in temperate artificial grasslands with a continental monsoon climate, where the ‘Birch effect’ strengthens seasonality, and these findings further our understanding of changes in the rates of soil carbon losses as artificial grassland is established.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO$_{2}$ flux, commonly though imprecisely termed soil respiration (R$_{S}$), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency R$_{S}$ measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R$_{S}$, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package., Global Change Biology, 26 (12), ISSN:1354-1013, ISSN:1365-2486