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Drought indices are hard to balance in terms of versatility (effectiveness for multiple types of drought), flexibility of timescales, and inclusivity (to what extent they include all physical processes). A lack of consistent source data increases the difficulty of quantifying drought. Here, we present a global monthly drought dataset with a spatial resolution of 0.25∘ from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index incorporating snow dynamics (SZIsnow), driven by systematic fields from an advanced data assimilation system. The proposed SZIsnow dataset includes different physical water–energy processes, especially snow processes. Our evaluation of the dataset demonstrates its ability to distinguish different types of drought across different timescales. Our assessment also indicates that the dataset adequately captures droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is particularly evident over snow-covered high-latitude (e.g., Arctic region) and high-altitude areas (e.g., Tibetan Plateau). We found that 59.66 % of Earth's land area exhibited a drying trend between 1948 and 2010, and the remaining 40.34 % exhibited a wetting trend. Our results also indicate that the SZIsnow dataset can be employed to capture the large-scale drought events that occurred across the world. Our analysis shows there were 525 drought events with an area larger than 500 000 km2 globally during the study period, of which 68.38 % had a duration longer than 6 months. Therefore, this new drought dataset is well suited to monitoring, assessing, and characterizing drought and can serve as a valuable resource for future drought studies. The database is available at http://doi.org/10.5281/zenodo.5627369 (Wu et al., 2021).

Background: Asthma is a common chronic respiratory disorder characterized by inflammation and remodeling of the airways. Aims: This study aimed to identify the inhibitory effects of Huatan Tongluo decoction (HTTLD) on airway inflammation and associated remodeling mechanisms. Methods: Mice were immunized with ovalbumin (OVA) for 8 weeks to generate chronic asthma mouse models (CAS), which were randomly divided into 4 groups administrated with pachyman, dexamethasone (DEX), HTTLD, and without anything (CAS model), while mice who administrated saline were assigned as the control group. Hematoxylin-eosin (H&E) and Masson trichrome were used to determine inflammatory infiltration and airway remodeling (fiber deposition). Inflammatory cytokines, including VEGF, PDGF, and TGF-β1, were analyzed using ELISA. The gene transcriptions and expressions of MMP-9, TIMP-1, VEGF, HIF-1α, NF-kB, and β-actin were evaluated using RT-PCR and Western blot, while the expression of p-Smad2/3 was determined by Western blot. Results: HTTLD inhibited inflammatory infiltration and airway remodeling (reducing airway wall thickness and decreasing fiber deposition) of lung tissues in the CAS mouse model. HTTLD markedly attenuated levels of TGF-β1, VEGF, and PDGF compared to those of mice in the CAS model group (p Conclusion: HTTLD inhibited inflammatory infiltration and airway remodeling in an OVA-induced chronic asthma mouse model by attenuating the TGF-β1/Smad2/3 signaling pathway and suppressing the oxidative stress-mediated NF-kB/HIF-1α/MMPs signaling pathway.

The implementation of large-scale vegetation restoration over the Chinese Loess Plateau has achieved clear improvements in vegetation fraction, as evidenced by large areas of slopes and plains being restored to grassland or forest. However, such large-scale vegetation restoration has altered land-atmosphere exchanges of water and energy, as the land surface characteristics have changed. These variations could affect regional climate, especially local precipitation. Quantitatively evaluating this feedback is an important scientific question in hydrometeorology. This study constructs a coupled land-atmosphere model incorporating vegetation dynamics, and analyzes the spatio-temporal changes of different land use types and land surface parameters over the Loess Plateau. By considering the impacts of vegetation restoration on the water-energy cycle and on land-atmosphere interactions, we quantified the feedback effect of vegetation restoration on local precipitation across the Loess Plateau, and discussed the important underlying processes. To achieve a quantitative evaluation, we designed two simulation experiments, comprising a real scenario with vegetation restoration and a hypothetical scenario without vegetation restoration. These enabled a comparison and analysis of the net impact of vegetation restoration on local precipitation. The results show that vegetation restoration had a positive effect on local precipitation over the Loess Plateau. Observations show that precipitation on the Loess Plateau increased significantly, at a rate of 7.84 mm yr−2, from 2000 to 2015. The simulations show that the contribution of large-scale vegetation restoration to the precipitation increase was about 37.4%, while external atmospheric circulation changes beyond the Loess Plateau contributed the other 62.6%. The average annual precipitation under the vegetation restoration scenario over the Loess Plateau was 12.4% higher than that under the scenario without vegetation restoration. The above research results have important theoretical and practical significance for the ecological protection and optimal development of the Loess Plateau, as well as the sustainable management of vegetation restoration.

Impact of climate change on sugarcane (Saccharum spp. interspecific hybrids) crop growth, development and productivity remains unclear globally. In China, the per capita available water resource amounts to only 1/4th of the global average. The limited water is one of the most significant constraints for sustainable agriculture development and crop productivity in China, and thus water-saving irrigation technology can reduce the seasonal water shortage in agricultural crop production, improve crop yield and quality, and enhance sustainable agricultural crop development. Keeping this in view, we conducted a field experiment at Guangxi sugarcane-growing hilly dry land with steep slope from 2017 to 2020 in plant and two ratoon crops. Results showed that bud germination (%), tillering rate, plant height and millable cane were not significantly different in spray and drip irrigation treatments. As compared to control, spray irrigation and drip irrigation (DI) remarkably increased the crop growth and yield. Drip irrigation produced a significantly higher cane production, consumed less irrigation water (65%) and provided maximum irrigation water use efficiency. Our study suggested that DI was far more effective in conserving water for irrigation and could be recommended and adopted in Guangxi and similar crop production regions.

The agro-pastoral ecotone of Northwestern China (APENC) is one of the major agricultural production areas in China and a region where climate change is evident. Maize is a widely cultivated crop in the APENC, but the potential impact of climate change on maize, and potential adaptation strategies in response to this, are poorly understood. In this study, we used the Cropping System Model (CSM)-CERES-Maize to evaluate the impacts of climate change on maize yield, as well as the feasibility of 2 adaptation strategies; namely, adjusting the planting date and supplying irrigation. CSM-CERES-Maize was driven by an ensemble of 20 global climate models under 2 Representative Concentration Pathways (RCPs: RCP4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). CSM-CERES-Maize performed well in simulating phenology, leaf area index (LAI), maize yield, and soil water dynamics. The results showed that irrigated maize yield would change by +3.9, -16.3, and -20.4% under the RCP4.5 scenario and +0.1, -31.2, and -53.1% under the RCP8.5 scenario in the 2030s, 2060s, and 2090s, respectively. Rainfed maize yield during the 2030s, 2060s, and 2090s would change by +21.7, +16.4, and +12.6% under the RCP4.5 scenario and +25.1, +4.8, and -12.3% under the RCP8.5 scenario, respectively. Evaluation of adaptation strategies suggests that delaying planting dates and supplying irrigation at the tasseling and grain filling stages are the best strategies to increase maize yield under climate change. These results will provide comprehensive information for local policymakers to combat the adverse impacts of climate change.

Polymer solubility in ionic liquids (ILs) cannot be predicted by the solubility parameter approach based on “like dissolves like” principle. According to Kamlet-Abraham-Taft (KAT) multiple polarity scale, ILs can be categorized as hydrogen-bond acidity or basicity ones. The experimental observations, that the acidity ILs easily dissolve the basicity polymers and basicity ILs dissolve acidity polymers, reflect the complementary nature of hydrogen-bonding interactions. A quantitative hydrogen-bonding analysis is proposed for predicting the solubility by taking the product of ΔαΔβ as an indicator of the competition between cross-association and self-association H-bonding, where Δα is the difference of acidity parameters between polymer and IL, and Δβ the difference of basicity. This solubility criterion has been validated by the solubility data of 19 polymers (11 acidity, and 8 basicity) in 11 ILs (7 acidity, and 4 basicity). These principles based on KAT parameters can apply to other systems dominated by hydrogen-bonding.

Polymer-tethered nanoparticles with different geometric shapes are very useful fillers of polymer nanocomposites. Herein, a universal approach for the fabrication of such nanoparticles with precisely controlled shape and composition is reported. By microphase separation of poly(3-(triethoxysilyl)propyl methacrylate)-block-polystyrene (PTEPM-b-PS) in the presence of oligomers, o-TEPM (oT) and/or o-S (oS), followed by cross-linking and dispersion in PS solvent, precisely tailored PS-grafted nanoparticles were prepared. These particles include those with varied shapes but identical PS shells, particles with varied core sizes but the same PS shell, and particles with fixed shapes but varied PS shells. These particles are ideal model nanofillers to study the dynamics and reinforced mechanism of polymer nanocomposites.

Thermal sublimation, a specific method to fabricate semiconductor nanowires, is an effective way to understand growth behavior as well. Utilizing a high-resolution transmission electron microscope (TEM) with in situ heating capability, the lattice-asymmetry-driven anisotropic sublimation behavior is demonstrated of wurtzite GaN: sublimation preferentially occurs along the [

From 1998 until now, the Chinese government has implemented numerous policies and programs, such as the Grain for Green Program, the Three-North Shelter Forest Program, and the Beijing-Tianjin Sand Control Program, to restore ecosystems and to improve environmental protection in the agro-pastoral ecotone of Northern China (APENC). However, it remains unclear how the large-scale vegetation restoration modulates the regional moisture cycle in the APENC. To fill this gap, we investigated the variations of observed precipitation and estimated evapotranspiration from 1995 to 2015. The evapotranspiration is estimated by the Priestley-Taylor Jet Propulsion Laboratory model with dynamic vegetation (DV). The precipitation recycling ratio calculated by the Dynamic Recycling Model is used to analyze the impacts of vegetation restoration on regional moisture recycling. Our results show that the precipitation and ET under the DV were significantly increased during the period of 1995-2015, with the increasing rate of 4.42 mm yr-1 and 2.13 mm yr-1, respectively. The precipitation recycling ratio was also significantly increased during the study period, showing positive feedback of vegetation restoration on precipitation. The atmospheric water budget analysis shows that vegetation restoration noticeably modifies the annual mean values of water transport terms in the regional water cycle, indicating an indirect effect on local precipitation. Our findings help better understand the impacts of land cover change on local water resources, which in turn supports local water resource management and decision making.

Profile soil moisture (SM) in mountainous areas are significant in water resources management and ecohydrological studies of downstream arid watersheds. Satellite products are useful in providing spatially distributed SM information, but only have limited penetration depth (e.g. top 5 cm). In contrast, in situ observations can provide multi-depth measurements, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using statistical methods, but this requires sufficient coupling strength among surface and subsurface SM. This study evaluates methods to calculate subsurface SM from surface SM and an application to the satellite SM product based on a SM observation network in the Qilian Mountains (China) established since 2013. First, we used cross-correlation to analyze the coupling strength among surface (0–10 cm) and subsurface (10–20, 20–30, 30–50, 50–70 cm, and profile of 0–70 cm) SM. Our results indicated an overall strong coupling among surface and subsurface SM in this study area. Afterwards, three different methods were tested to estimate subsurface SM from in-situ surface SM: the exponential filter (ExpF), artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods. The results showed that both ANN and ExpF methods were able to provide accurate estimates of subsurface soil moisture at 10–20 cm, 20–30 cm, and for the profile of 0–70 cm using surface (0–10 cm) soil moisture only. Specifically, the ANN method had the lowest estimation error (RSR) of 0.42, 0.62 and 0.49 for depths of 15 and 25 cm and profile SM, respectively, while the ExpF method best captured the temporal variation of subsurface soil moisture. Furthermore, it could be shown that the performance of the profile SM estimation was not significantly lower with using an area-generalized Topt (optimum T) compared to the station-specific Topt. In a final step, the ExpF method was applied to the satellite SM product (Soil Moisture Active Passive Level 3: SMAP_L3) to estimate profile SM, and the resulting profile SM was compared to in situ observations. The results showed that the ExpF method was able to estimate profile SM from SMAP_L3 surface products with reasonable accuracy (median R of 0.718). It was also found that the combination of ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in the mountainous areas in comparison to the SMAP_L4 root zone product. Overall, it was concluded that the ExpF method is able to estimate profile SM using SMAP surface products in the Qilian Mountains.

Soil hydraulic properties (SHPs) and soil moisture (SM) are fundamental to describing and predicting water and energy cycles at the land surface, and for regulating evapotranspiration, infiltration and runoff. However, information about these soil properties from existing datasets is often scarce and inaccurate for high and cold mountainous areas such as the Qinghai-Tibet Plateau (QTP), which hampers our understanding of hydrological and energy cycle processes over large mountainous areas like the QTP. Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 SM monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a SHP and SM dataset for a high and cold mountainous area, Northeastern QTP. We used this dataset to explore the large-scale spatial and temporal variability of SHPs and of SM across the study area. Our evaluation of several existing SHP datasets, SM datasets derived from remote-sensing, reanalysis and data assimilation, showed that SHPs (soil texture, bulk density, and soil saturated hydraulic conductivity) in these datasets are biased, and do not capture the spatial variability recorded in the in-situ observations. When comparing with the in-situ SM observations, the SM product derived from remote-sensing was more reliable than the SM product derived from reanalysis data (which had a higher bias), and than the data assimilation product (which did not capture SM temporal variability). The in situ observation dataset presented here provides unique and important information about the SHP variability and long-term SM trends at a large-scale, high and cold mountainous area, and thus offers opportunity for further understanding of water cycle and energy exchange processes over the QTP.

Objective. Gastrodin is a main medicinal component of traditional Chinese medicine (TCM) Gastrodia elata Blume (G. elata), presenting the potential for the treatment of attention-deficit/hyperactivity disorder (ADHD). However, the underlying targets and action mechanisms of the treatment have not been identified. Methods. The gastrodin-related microarray dataset GSE85871 was obtained from the GEO database and analyzed by GEO2R to obtain differentially expressed genes (DEGs). Subsequently, the targets of gastrodin were supplemented by the Encyclopedia of Traditional Chinese Medicine (ETCM), PubChem, STITCH, and SwissTargetPrediction databases. ADHD-associated genes were collected from six available disease databases (i.e., TTD, DrugBank, OMIM, PharmGKB, GAD, and KEGG DISEASE). The potential targets of gastrodin during ADHD treatment were obtained by mapping gastrodin-related targets with ADHD genes, and their protein–protein interaction (PPI) relationship was constructed by the STRING database. The GO function and KEGG pathway enrichment analyses were performed using the ClueGO plug-in in the Cytoscape software and DAVID database, respectively. Finally, the binding affinity between gastrodin and important targets was verified by molecular docking. Results. A total of 460 gastrodin-related DEGs were identified from GSE85871, and 124 known gastrodin targets were supplemented from 4 databases, including ETCM. A total of 440 genes were collected from the above 6 disease databases, and 267 ADHD-relevant genes were obtained after duplicate removal. Through mapping the 584 gastrodin targets to the 267 ADHD genes, 16 potential therapeutic targets were obtained, among which the important ones were DRD2, DRD4, CHRNA3, CYP1A1, TNF, IL6, and KCNJ3. The enrichment analysis results indicated that 16 potential targets were involved in 25 biological processes (e.g., dopamine (DA) transport) and 22 molecular functions (e.g., postsynaptic neurotransmitter receptor activity), which were mainly localized at excitatory synapses. The neuroactive ligand-receptor interaction, cholinergic synapse, and dopaminergic synapse might be the core pathways of gastrodin in ADHD treatment. Through molecular docking, it was preliminarily verified that gastrodin showed good binding activity to seven important targets and formed stable binding conformations. Conclusions. Gastrodin might exert an anti-ADHD effect by upgrading the dopaminergic system and central cholinergic system, inhibiting the inflammatory response and GIRK channel, and exerting a synergistic effect with other drugs on ADHD. For this reason, gastrodin should be considered a multitarget drug for ADHD treatment.

We report planar AlN/GaN resonant tunneling diodes (RTDs) fabricated using a nitrogen ion implantation isolation process on silicon substrates. The active area of AlN/GaN RTDs is defined by nitrogen ion implantation. A planar RTD consists of two different-sized RTDs connected in series, where the larger one acts as a resistor and the performance of the planar RTD is determined by the smaller one. Repeatable and hysteresis-free negative differential resistances without degradation are observed through 500 continuous bidirectional voltage sweeps. The peak current density is 15.5 kA/cm2. The RTD exhibits stable negative differential resistance (NDR), with the peak-to-valley current ratio varying from 1.39 to 1.28 as the temperature increases from 77 to 295 K. This practicable and reproducible ion implantation process is compatible with silicon fabrication technology. It, thus, provides a feasible method for device design of GaN-based RTDs and facilitates the implementation of complex monolithic microwave integrated circuits based on planar III-nitride RTDs on large-size silicon wafers.

Drought indices are hard to balance in terms of versatility (effectiveness for multiple types of drought), flexibility of timescales, and inclusivity (to what extent they include all physical processes). A lack of consistent source data increases the difficulty of quantifying drought. Here, we present a global monthly drought dataset from 1948 to 2010 based on a multitype and multiscalar drought index, the standardized moisture anomaly index incorporating snow dynamics (SZIsnow), driven by systematic fields from an advanced data assimilation system. The proposed SZIsnow dataset includes different physical water‒energy processes, especially snow processes. Our evaluation of the dataset demonstrates its ability to distinguish different types of drought across different timescales. Our assessment also indicates that the dataset adequately captures droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is particularly evident over snow-covered high-latitude (e.g., Arctic region) and high-altitude areas (e.g., Tibetan Plateau). We found that 59.66 % of Earth's land area exhibited a drying trend between 1948 and 2010, and the remaining 40.34 % exhibited a wetting trend. Our results also show that the SZIsnow dataset successfully captured the large-scale drought events that occurred across the world; there were 525 drought events with an area larger than 500,000 square kilometers globally during the study period, of which nearly 70 % had a duration longer than 6 months. Therefore, this new drought dataset is well suited to monitoring, assessing, and characterizing drought, and can serve as a valuable resource for future drought studies.

3D printing, also known as additive manufacturing, is a technology that uses a variety of adhesive materials such as powdered metal or plastic to construct objects based on digital models. Recently, 3D printing technology has been combined with digital medicine, materials science, cytology, and other multidisciplinary fields, especially in the field of orthopedic built-in objects. The development of advanced 3D printing materials continues to meet the needs of clinical precision medicine and customize the most suitable prosthesis for everyone to improve service life and satisfaction. This article introduces the development of 3D printing technology and different types of materials. We also discuss the shortcomings of 3D printing technology and the current challenges, including the poor bionics of 3D printing products, lack of ideal bioinks, product safety, and lack of market supervision. We also prospect the future development trends of 3D printing.

Multi-frequency multi-bit programmable amplitude modulation (AM) of spoof surface plasmon polaritons (SPPs) is realized at millimeter wave frequencies with interdigital split-ring resonators (SRRs) and In-Ga-Zn-O (IGZO) Schottky diodes. Periodic SRRs on a metal line guide both SRR mode and spoof SPP mode, the former of which rejects the spoof SPP propagation at the SRR resonant frequencies. To actively modulate the amplitude of spoof SPPs, IGZO Schottky diodes are fabricated in the SRR gaps, which continuously re-configure SRRs to metallic loops by applying bias. Interdigital gaps are designed in SRRs to increase the capacitance, thus red shifting the resonant frequencies, which significantly broadens the operation bandwidth of multi-frequency AM. Thus, cascading different kinds of interdigital SRRs with Schottky diodes enables multi-frequency multi-bit AM programmable. As a demonstration, a dual-frequency device was fabricated and characterized, which achieved significant multi-bit AM from −12.5 to −6.2 dB at 34.7 GHz and from −26 to −8.5 dB at 50 GHz independently and showed programmable capability.

Active metamaterials (MTMs) are artificially engineered structures with tunable and exceptional properties that are absent in natural materials. Recently, InGaZnO (IGZO), a widely used semiconductor for large-area and flexible display backplane drivers, has gained interest for active control of MTMs due to its large-area uniformity, ease of thin film deposition, and low cost. In this paper, IGZO Schottky barrier diodes (SBDs) are proposed to reconfigure electric-field-coupled inductor-capacitor (ELC) MTMs and actively control terahertz (THz) waves for the first time. The SBDs are designed to bridge the capacitors of the ELC resonators so that the average conductivity within the capacitor gap can be modulated by bias voltage while keeping the capacitance value almost unchanged. To precisely simulate this mechanism, two U-shaped resistive sheet models beside the gap are built for IGZO SBD in 3-D simulation for maintaining the same capacitance and resonant frequency. Furthermore, a device with 14400 MTM cells is fabricated and characterized using frequency-domain spectroscopy. The measured transmission shows a continuous modulation from -14.2 to -9.4 dB at 0.39 THz, which corresponds to a modulation depth of 14.3%. This work paves a new way for active THz MTMs using industrial compatible thin-film technology.

We report repeatable AlN/GaN resonant tunneling diodes (RTDs) grown on a silicon substrate by plasma-assisted molecular-beam epitaxy. The RTDs exhibit stable negative differential resistance without hysteresis at room temperature, where no degradation is observed even after 500 continuous bidirectional sweeps. The peak-to-valley current ratio is 1.36, and the peak current density is 24.38 kA/cm2. When the temperature is changed from 77 to 475 K, the peak current remains almost unchanged and the valley current increases gradually, resulting in a reduced peak-to-valley current ratio from 1.59 to 1.07. Our work softens the material quality constraints on realizing the room-temperature repeatable negative differential resistance and paves the way to low-cost III-nitride-based monolithic and hybrid microwave integrated circuits on large-size silicon wafers.

Drought indices do not always provide the most relevant information for water resources management as most of them neglect the role of snow in the land surface water balance. In this study, a physically based drought index, the Standardized Moisture Anomaly Index (SZI), was modified and improved by incorporating the effects of snow dynamics for drought characterization at multiple time scales. The new version of the SZI, called SZIsnow, includes snow in both the water supply and demand in drought characterization by using the water‐energy budgets from the Global Land Data Assimilation Systems product. We compared and evaluated the performance of SZIsnow and SZI in drought identification globally across various time scales using observed multicategory drought evidences from several sources. Results show that the SZIsnow agrees better with the observed changes in hydrological and agricultural droughts than the SZI, particularly over basins with high snow accumulation. Furthermore, the SZIsnow is more consistent with the residual water‐energy ratio than the SZI over snow‐influenced regions. Overall, the SZIsnow can be either a complement or an improvement over the SZI for identifying, monitoring, and characterizing hydrological and agricultural droughts at various scales (e.g., 1–48 months) over high‐latitude and high‐elevation regions that receive snow., Key Points A global multi‐scalar drought index (SZIsnow) was developed to improve the drought characterization by incorporating snow processesThe SZIsnow generally performs better than the SZI for monitoring multi‐category droughts by considering snow dynamicsThe SZIsnow incorporates water‐energy budgets to address the influence of snow on both the water supply and demand in drought assessment

The Three-North Shelterbelt Project (TNSP) is one of the largest ecological restoration projects of the world. Although its important role in regulating the ecosystem of northern China has been acknowledged, how this project affects the surface water-energy balance deserves further evaluation. This study characterizes spatiotemporal variation of climate variables and vegetation coverage/density over the Three-North Region (TNR) of China using multiple datasets since the implementation of the TNSP. Of particular importance is that effects of vegetation restored during the TNSP on surface water-energy budget are examined for the study domain. Our results show that accompanied by a significant enhancement of vegetation coverage and density in the TNR, its annual air temperature and precipitation have increased 1.46 °C and 89.1 mm, respectively from 1982 to 2015. We find such rise in the air temperature and precipitation plays a positive role in the TNR’s vegetation restoration in a sense that the vegetation dynamics show positive correlation with its regional air temperature and precipitation, while more sensitive to precipitation. We also find carbon sequestration of the TNR increases at the cost of greater water consumption through evapotranspiration since the TNSP commences. In some arid regions, revegetation accelerates the water deficit due to an excessive rate of restoring vegetation; yet, no substantial water imbalance occurs as a result of enhanced precipitation and water use efficiency during this period. Although the solar radiation increases with decreases in surface albedo over the last few decades, our results do not reveal an appreciably increasing trend in the land surface temperature of the TNR. This is because the improved vegetation can assimilate CO2 (mitigating greenhouse gas emissions) and transpire water by photosynthesis, thereby increasing latent heat flux and reducing the warming effect. This study highlights a mixed consequence of the TNSP by inducing both positive and negative effects on the surface water-energy balance over the TNR.

(Zr47Cu44Al9)100−xNbx (x = 0, 1, 2, 3, and 5 at.%) bulk metallic glasses (BMGs) were prepared by copper mould casting. The influence of Nb addition on glass-forming ability (GFA), thermal stability and mechanical properties of ZrCuAl alloys was studied. The GFA of Zr47Cu44Al9 BMGs is enhanced obviously after the addition of Nb. The critical size of completely amorphous sample increases from 4 mm for Zr47Cu44Al9 to 10 mm for (Zr47Cu44Al9)98Nb2 alloy. The average activation energy of crystallization is 505 kJ/mol for (Zr47Cu44Al9)98Nb2 BMG, which is larger than that of Zr47Cu44Al9 BMG with 443 kJ/mol. The fracture strength and plasticity are sensitive to the Nb content. (Zr47Cu44Al9)98Nb2 BMG exhibits high fracture strength of 2397 MPa and prominent plastic strain of 13.2%, and multiple shear bands with high density were observed. Furthermore, the possible mechanisms involved in obtaining high GFA and large plasticity were explored.

Understanding the dynamic response of soil moisture to rainfall is critical for hydrological modelling in arid and semi-arid basins. However, little is known about rainfall-related soil moisture dynamics in arid high-altitude mountainous areas due to the absence of long-term high-resolution soil moisture observations. In this study, we investigated the dynamic response processes of profile soil moisture using data from a soil moisture monitoring network in the Qilian Mountains established in 2013 covering altitudes from 2,000–5,000 m a.s.l. To investigate the effects of different land covers on soil moisture response, we selected data from eight soil moisture stations with the same soil textural class and slope, but different land covers (scrubland, meadow, high coverage grassland (HCG), medium coverage grassland (MCG) and barren land). Several indices were evaluated to quantitatively describe soil moisture dynamics during the growing seasons of 2014–2016 based on soil wetting events. In addition, HYDRUS-1D simulations were used to further analyze the effect of land cover on soil moisture dynamics. Our results showed that soil moisture response amplitudes along profile are similar under MCG and barren land, but significantly different under scrubland, meadow and HCG. The rate of soil moisture increment decreased significantly with depth for all land covers, except for the HCG. The temporal pattern of soil moisture increase was highly variable along the soil profiles depending on land cover type. In particular, the difference of response time between the adjacent layers varied from negative values to 280 h with depth. Preferential flow occurred mostly in soils covered by scrubland. Water transferability was higher in deeply rooted soil. Furthermore, sensitivity analysis indicated that soil hydraulic properties are key factors in regulating profile soil wetting events. Our results show that the soil moisture response indices are useful to quantitatively characterize patterns in profile soil moisture dynamics, and provide new insights into the soil moisture profile wetting process (e.g. occurrence of preferential flow etc.), which helps for effective model parameterization and validation, in turn improving hydrological modelling in arid high-altitude mountainous areas.

Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter (EnKF) and Simple Biosphere Model (SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation, and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the dee-per water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.