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Study region: Yangtze River Delta core urban agglomeration, China Study focus: Traditional research on flood susceptibility assessment using machine learning often seeks to enhance model performance by increasing the number of input variables, which is impractical in regions with limited data availability. In this study, we constructed a variable system comprising 13 features for flood susceptibility assessment through machine learning techniques. A flexible framework, primarily incorporating methods for importance value calculation and repeated random sampling, were established to identify a minimal set of features that yield high-performance classifiers. Finally, the feasibility of the proposed framework was verified by comparing the classifier performances and flood susceptibility maps. New hydrological insights for the region: Results underscored the significance of features such as Land Use / Land Cover, Impervious Area, Normalized Difference Vegetation Index, Distance to Lake and Built-up Probability in model development. These five features proved sufficient to produce a classifier with Area Under the Curve (AUC) indices exceeding 0.9 for both training and testing data. Susceptibility maps generated using varying feature counts revealed that regions with limited vegetation cover and near lakes face higher flood susceptibility. The framework's feasibility and viability were confirmed by the excellent classifier performance (mean AUC > 0.9) with reduced features and the consistent outcomes of generated maps, offering theoretical and technical support for flooding research in data-constrained regions.

With global climate warming, the variability of climate and weather tends to increase driving the water resources available for vegetation become more uncertain. Therefore, there is still a debate as to how vegetation response to, and to which extent it is constrained to water availability. In this study, we analyzed the correlation between vegetation growth and water availability in the Northern Hemisphere from 1982 to 2018. The condition of vegetation growth was characterized by Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI), while the Standardized Precipitation Evapotranspiration Index (SPEI) represented water condition. Our results suggest that the water deficit stress on vegetation growth in the Northern Hemisphere caused by global warming may not be as severe as described in previous studies. Although there was an increasing trend in areas where vegetation growth was constrained by water deficit in the Northern Hemisphere, such trend was not significant; in contrast, there was a significant decreasing trend in areas where vegetation growth was constrained by surplus water. In addition, there was a spatial variability in water availability for vegetation growth at mid and high latitudes. We further found that the response time of vegetation to both water deficit and water surplus was increasing, indicating that the cumulative effect of water availability on vegetation growth was delaying, and that vegetation tends to be affected by climatic extreme events such as drought with longer time scales. Our results highlight the need to reconsider the response of vegetation activities to water availability, and shed light on a more divers reaction pattern of vegetation to water in the context of climate change.

Urban reservoirs contribute significantly to human survival and ecological balance. Machine learning-based remote sensing techniques for monitoring water quality parameters (WQPs) have gained increasing prominence in recent years. However, these techniques still face challenges such as inadequate band selection, weak machine learning model performance, and the limited retrieval of non-optical active parameters (NOAPs). This study focuses on an urban reservoir, utilizing unmanned aerial vehicle (UAV) multispectral remote sensing and ensemble machine learning (EML) methods to monitor optically active parameters (OAPs, including Chla and SD) and non-optically active parameters (including CODMn, TN, and TP), exploring spatial and temporal variations of WQPs. A framework of Feature Combination and Genetic Algorithm (FC-GA) is developed for feature band selection, along with two frameworks of EML models for WQP estimation. Results indicate FC-GA’s superiority over popular methods such as the Pearson correlation coefficient and recursive feature elimination, achieving higher performance with no multicollinearity between bands. The EML model demonstrates superior estimation capabilities for WQPs like Chla, SD, CODMn, and TP, with an R2 of 0.72–0.86 and an MRE of 7.57–42.06%. Notably, the EML model exhibits greater accuracy in estimating OAPs (MRE ≤ 19.35%) compared to NOAPs (MRE ≤ 42.06%). Furthermore, spatial and temporal distributions of WQPs reveal nitrogen and phosphorus nutrient pollution in the upstream head and downstream tail of the reservoir due to human activities. TP, TN, and Chla are lower in the dry season than in the rainy season, while clarity and CODMn are higher in the dry season than in the rainy season. This study proposes a novel approach to water quality monitoring, aiding in the identification of potential pollution sources and ecological management.

The type and content of organic acids have important influence on the quality of fruit wine. Proper amount of organic acids can give fruit wine soft taste, and has certain antibacterial effect. However, higher organic acid content causes the unpleasant sensation of sharp prickles, which seriously affected the sensory quality of fruit wine. Therefore, it is of great significance to master the flavor characteristics and acid reduction methods of organic acids in fruit wine brewing. The imporant organic acids in fruit wine are reviewed, and the physical, chemical, biological and emerging technologies for acid reduction and their application are emphatically discussed. The advantages and disadvantages of various acid reduction techniques are also compared, in order to provide reference for the production of high quality fruit wine.

The rheological property of asphalt is an important factor affecting the pavement performance of asphalt binder, and the fundamental reason for the change of asphalt rheological property is the strong evolution of asphalt material meso structure. However, the internal mechanism of rejuvenated asphalt mastic system is complex and its rules are difficult to grasp. Aiming to study the relationship between meso mechanical parameters and rheological parameters of rejuvenated asphalt mastic, the meso structure model of rejuvenated asphalt mastic was established and improved based on the discrete element method. Moreover, the meso parameters of the model were obtained by the objective function method, and then the influences of various factorswere studied to construct the mathematical constitutive model of rheological parameter modulus and meso mechanical parameters. Combing with the reliability of the improved Burgers model was verified based on the rheological test results of rejuvenated asphalt mastic. In addition, the virtual test of dynamic shear rheological dynamic frequency scanning was carried out on the asphalt mastic sample by particle flow software. By adjusting the mesomechanical parameters, the simulation results (complex shear modulus and phase angle)were consistent with the test results. This study clarified the relationship between mesomechanics and macro performance, and this model could be used to obtain the complex shear modulus of rejuvenated asphalt mastic under different types, filler-asphalt ratio and external force environments by adjusting particle flow, wall boundary and other conditions, which can greatly save the workload for the later research and provide a theoretical basis for production experiments.

Abstract Gross primary production is the basis of global carbon uptake. Gross primary production losses are often related to hydroclimatic extremes such as droughts and heatwaves, but the trend of such losses driven by hydroclimatic extremes remains unclear. Using observationally-constrained and process-based model data from 1982-2016, we show that drought-heat events, drought-cold events, droughts and heatwaves are the dominant drivers of gross primary production loss. Losses associated with these drivers increase in northern midlatitude ecosystem but decrease in pantropical ecosystems, thereby contributing to around 70% of the variability in total gross primary production losses. These asymmetric trends are caused by an increase in the magnitude of gross primary production losses in northern midlatitudes and by a decrease in the frequency of gross primary production loss events in pantropical ecosystems. Our results suggest that the pantropics may have become less vulnerable to hydroclimatic variability over recent decades whereas gross primary production losses and hydroclimatic extremes in northern midlatitudes have become more closely entangled.

Rainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socio-economic development. While individual rainfall or wind events have been widely studied, little attention has been devoted to studying the lead–lag relationship between rainfall and wind speed, particularly in coastal regions where strong dependence between rainfall and wind speed is expected. Taking China's coastline as the case study, this paper aims to explore the variation trends of wind speed and rainfall and reveal the relationships between rainfall events and wind speeds on days before and after rainfall occurrence, by using meteorological station data from 1960 to 2018. The results show that wind speed tended to decrease while rainfall showed a slight increase for most stations. The daily wind speed increased 2 days before rainfall occurrence and decreased after then, with the highest wind speed observed during rainfall onset regardless of rainfall amount. Moreover, heavier rainfall events are more likely to occur with higher wind speeds. The findings of this study potentially improve the understanding of the dependence of rainfall and wind speed, which could help rainfall or wind-related disaster mitigation. HIGHLIGHTS Dependence of daily rainfall and wind speed over China's coastline is investigated.; Daily wind speed increased 2 days before rainfall occurrence and decreased after then.; Heavier rainfall events are more likely to occur with higher wind speeds.;

Abstract Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast mapping of urban flood: a coupled model based on physical mechanisms was first constructed, a rainfall-inundation database was generated, and a hybrid flood mapping model was finally proposed using the multi-objective random forest (MORF) method. The results show that the coupled model had good reliability in modelling urban flood, and 48 rainfall-inundation scenarios were then specified. The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events. The spatial inundated depths predicted by the MORF model were close to those of the coupled model, with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951. The MORF model, however, achieved a computational speed of 200 times faster than the coupled model. The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model. Our research provides a novel approach to rapid urban flood mapping and flood early warning.

The direct and seasonally-lagged effects of compound weather and climate events in spring on vegetation productivity vary with latitude and can amplify the effects of individual weather events, according to observationally-constrained estimates and process-based terrestrial ecosystem models.

Study region: The Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Study focus: Using hourly rain gauge data and CMORPH data, we use the duration-dependent generalized extreme value (d-GEV) model and the scaling invariant GEV model inferred by the Bayesian hierarchical model to derive the intensity-duration-frequency IDF characteristics of extreme precipitation in GBA and adjust their uncertainties. New hydrological insights for the region: The GEV location and scale parameters of IDF curves in GBA show similar spatial distribution and the higher-resolution CMORPH can capture more local details than rain gauge data. Meanwhile, compared with the rain gauge data, CMORPH produces significantly lower rainfall intensity of storms with short durations, which leads to large uncertainties of IDF curves derived from CMORPH for the short-duration rainfall. Additionally, the uncertainties of IDF curves can be substantially reduced by using the scaling invariant model that was inferred by the Bayesian hierarchical model, compared with the ordinary d-GEV method. Therefore, the Bayesian inference is suggested to be adopted for regional estimation of IDF curves, especially for regions of limited sub-daily gauge data.

The ‘rural revitalization strategy’ is important to achieve sustainable development in the Three Gorges Reservoir Area (TGRA), the largest reservoir in China, located in the poverty alleviation region of the Qinling-Daba mountains, and characterized as mountainous and hilly dominated by backwards resettlements. Pig farming around the reservoir area is a key industry, accounting for 0.90% of the arable land area in the country, while the annual pig market represents a 1.37% share of the national figure. Here, 12 study sites were investigated on-site for understanding agricultural green development in the TGRA. We found two main prevailing models: one of ecological circulation (EC), based on animal husbandry and recycling. Of the 12 site, six cases of the ecological circulation model relied on pig husbandry, combined with cropping (grains/fruits/vegetables) by eco-industrial chains, such as pig-biogas-fruit (grain/vegetables), to prevent environmental pollution, while promoting agricultural economic growth by recycling fecal residues and wastewater (FSW) from pig-husbandry facilities to the fields. Our analysis predicted that a farm holding 10,000 pigs may save inorganic nitrogen and phosphorus fertilizers by as much as 74.36 and 11.15 ton·a−1, respectively. On the other hand, five cases of ecological models oriented towards agritourism provided tourists with high-quality ecological products while coordinating environment protection with economic development. In addition, 11 research points applied water and fertilizer integration system for the purpose of saving water. However, lack of sufficient supporting arable land made intensive pig farming a risk of ecological degradation. Green control technologies are rarely used leading to an increase in the type and amount of pesticides. Our study has theoretical and practical significance for decision-makers to promote agricultural cleaner production (ACP).

The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) often causes secondary bacterial infection in piglets, resulting in inflammatory lung injury and leading to high mortality rates and significant economic losses in the pig industry. Microvascular endothelial cells (MVECs) play a crucial role in the inflammatory response. Previous studies have shown that HP-PRRSV can infect porcine pulmonary MVECs and damage the endothelial glycocalyx. To further understand the role of pulmonary MVECs in the pathogenesis of HP-PRRSV and its secondary bacterial infection, in this study, cultured porcine pulmonary MVECs were stimulated with a HP-PRRSV HN strain and lipopolysaccharide (LPS). The changes in gene expression profiles were analyzed through transcriptome sequencing, and the differentially expressed genes were verified using qRT-PCR, Western blot, and ELISA. Furthermore, the effects on endothelial barrier function and regulation of neutrophil trans-endothelial migration were detected using the Transwell model. HP-PRRSV primarily induced differential expression of numerous genes associated with immune response, including IFIT2, IFIT3, VCAM1, ITGB4, and CCL5, whereas LPS triggered an inflammatory response involving IL6, IL16, CXCL8, CXCL14, and ITGA7. Compared to the individual effect of LPS, when given after HN-induced stimulation, it caused a greater number of changes in inflammatory molecules, such as VCAM1, IL1A, IL6, IL16, IL17D, CCL5, ITGAV, IGTB8, and TNFAIP3A, a more significant reduction in transendothelial electrical resistance, and higher increase in neutrophil transendothelial migration. In summary, these results suggest a synergistic effect of HP-PRRSV and LPS on the inflammatory response of porcine pulmonary MVECs. This study provides insights into the mechanism of severe lung injury caused by secondary bacterial infection following HP-PRRSV infection from the perspective of MVECs, emphasizing the vital role of pulmonary MVECs in HP-PRRSV infection.

Understanding the internal microstructure of silkworm cocoons is beneficial for developing biomaterials. In this study, both domesticated (Bombyx mori) and wild silkworm cocoons (Antheraea pernyi and Antheraea mylitta) were studied using the X-ray micro computed tomography (XCT), and their microstructural characteristics were analyzed quantitatively. It is discovered that fiber widths of the cocoons significantly increased from the inner to the outer layer. It is also detected that for the three cocoons in the core regions, unaffected by the surface condition, from the inner to the outer layer, fiber percentages show significant decrease initially, and then no obvious change, and the fibers in different layers are more aligned towards the short diameter of the cocoon, and Antheraea mylitta fibers display the highest fiber orientation. Fractal dimension of the domestic cocoon was significantly reduced, whereas those of the wild cocoons display no obvious change. The two wild cocoons show significantly higher maximum strength, initial modulus and breaking energy than the domesticated cocoons, and Antheraea pernyi performs better maximum strength and breaking energy than Antheraea mylitta. The tensile property is closely related to fiber percentage, fiber intersectional density and fractal dimension.

Powdery mildew is one of the severe diseases on common bean in Southwestern China, but the identity of the pathogen inciting this disease is unclear. The objective of this study was to identify the causal agent of common bean powdery mildew and to screen resistant cultivars. The pathogen was identified through morphological identification, molecular phylogenetic analysis, and pathogenicity tests. Resistance of common bean cultivars was evaluated by artificial inoculation at the seedling stage. The common bean powdery mildew isolate CBPM1 was obtained after pathogen isolation and purification. Morphological identification confirmed that the isolate CBPM1 belonged to the Oidium subgenus Pseudoidium and germinated Pseudoidium-type germ tubes. Molecular phylogenetic analysis showed that the isolate CBPM1 and Erysiphe vignae isolates from different hosts were clustered into a distinct group. The pathogenicity and host range tests revealed that the isolate CBPM1 was strongly pathogenic to common bean, multiflora bean, lablab bean, cowpea, and mung bean, but not to soybean, adzuki bean, pea, faba bean, chickpea, lentil, pumpkin, and cucumber. In addition, 54 common bean cultivars were identified for resistance to powdery mildew, and 15 were resistant or segregant. Based on the morphological, molecular and pathogenic characteristics, the causal agent of common bean powdery mildew was identified as E. vignae. This is the first time E. vignae has been confirmed on common bean. Cultivars with different resistance levels were screened, and these cultivars could be used for disease control or the breeding of new resistant cultivars.

Anthropogenic N inputs have become progressively more problematic and have profoundly affected the water quality in megacities throughout China. Thus, to design and implement appropriate megalopolis watershed management, it is important to understand the relationship between N inputs and exports and to identify the N pollution sources. To that end, in this work, the net anthropogenic N inputs (NANI) in Chengdu City were estimated based on statistical data collected between 1970 and 2019. N input fluxes and pollution sources were estimated through sample collection and field measurements that were performed between 2017 and 2019, while nitrate (NO3−) was identified using stable isotope and Bayesian model (SIAR) analysis. The NANI was found to be affected primarily by livestock and poultry consumption of N rich feed. Moreover, the N export fluxes and runoff showed a high degree of correlation. Notably, NO3− fluxes exhibited a significant increase over the course of the study period, such that, by 2019, the total N fluxes (18,883.85 N kg/km2) exceeded the NANI (17,093.87 N kg/km2). The results indicate that although livestock and poultry farming were the original primary sources of NANI, their contributions declined on an annual basis. Moreover, with the emphasis placed on point source management in Chengdu City, domestic sewage discharge has been significantly reduced. Therefore, N retention in groundwater is thought to be the factor driving the N flux increase. These findings are pivotal to solving the N pollution problem in megacities like Chengdu (China).

An in-pond raceway system (IPRS) is an effective intensive aquaculture practice for regions with high water consumption and limited land resources. Water flow and dissolved oxygen (DO) are important for sustainable aquaculture. Several innovations have been made in IPRS design and operation to increase water exchange and DO concentration; one of these is the aeration plug-flow device (APFD). The APFD is commonly used in China as the only power source for water recirculation in aquaculture ponds. Understanding of the hydrodynamics of the system is necessary to improve the design of the IPRS with APFD. To this end, we performed experimental studies on a model system. We measured three-dimensional velocity at various locations using an Acoustic Doppler Velocimeter. Velocity distribution and turbulence characteristics were assessed, and plug-flow characteristics were analysed. Two patterns of velocity and turbulence in horizontal sections were observed: near the APFD, the water flow was intensively pushed downstream and simultaneously recirculated; farther away, the reflux area gradually decreased and the velocity and turbulence distribution trended towards uniform. Secondary flows occurred in different directions, which improved the diffusion of materials and DO retention. The system is effectively self-circulating, and the plug-flow capability may be scaled up for commercial application.

Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates.

In recent decades, the severe drought across agricultural regions of China has had significant impact on agriculture. The standardized precipitation evapotranspiration index (SPEI) has been widely used for drought analyses; however, SPEI is prone to be affected by potential evapotranspiration (PET). We thus examined the correlations between soil moisture anomalies and the SPEI calculated by the Thornthwaite, Hargreaves, and Penman–Monteith (PM) equations to select the most suitable for drought research. Additionally, the Mann–Kendall and wavelet analysis were used to investigate drought trends and to analyze and the impact of atmospheric circulation on drought in China from 1961 to 2018. The results showed that (1) PET obtained from the PM equation is the most suitable for SPEI calculation; (2) there were significant wetting trends in Northern China and the whole Chinese mainland and most of the wetting mutation points occurred in the 1970s and 1980s and the significant inter-annual oscillations period in the Chinese mainland was 2–4 years; (3) the Chinese mainland and Northern China are strongly influenced by West Pacific Trade Wind, while Western Pacific Subtropical High Intensity and Pacific Subtropical High Area have primary impact on Southern China.

Anthropogenic activities have a tremendous impact on water ecosystems worldwide, especially in China. To quantitatively evaluate the hydrological alteration connected with aquatic lives and river ecological risks, we took the Beijiang River located in South China as the case study and used ecosurplus (defined as ecological carrying capacity exceeding ecological consumption)/ecodeficit (defined as ecological consumption exceeding carrying capacity) and Indicators of Hydrological Alterations to evaluate hydrological changes. The Ecologically Relevant Hydrologic Indicators were employed to select the key indices of Indicators of Hydrological Alterations, and the eco-environmental water demand calculation provide an effective way for the reservoir operation. Results showed that: (1) High flows contributed more to the ecodeficit, while low flows contributed more to the ecosurplus; (2) the ecodeficit in some parts of the river basin might exceed the ecosurplus after reservoir construction, especially along the main stream; and (3) the determination of eco-environmental water demand is a feasible way for improving the environment by controlling reservoirs. The current study can help guide the optimization of hydrological operation in the basin toward making the ecosystem healthier and has potential to further provide a reference for other basins in terms of hydrological alterations driven by anthropogenic activities.

Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960–2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960–1984, wider range of the three indices over SDR and ADR were detected for 1985–2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

With a high spatial resolution and wide coverage, satellite-based precipitation products have compensated for the shortcomings of traditional measuring methods based on rain gauge stations, such as the sparse and uneven distribution of rain gauge stations. However, the accuracy of satellite precipitation products is not high enough in some areas, and the causes of their errors are complicated. In order to better calibrate and apply the product’s data, relevant research on this kind of product is required. Accordingly, this study investigated the spatial error distribution and spatial influence factors of the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) post-process 3B42V7 (hereafter abbreviated as 3B42V7) data over mainland China. This study calculated accuracy indicators based on the 3B42V7 data and daily precipitation data from 797 rain gauge stations across mainland China over the time range of 1998−2012. Then, a clustering analysis was conducted based on the accuracy indicators. Moreover, the geographical detector (GD) was used to perform the error cause analysis of the 3B42V7. The main findings of this study are the following. (1) Within mainland China, the 3B42V7 data accuracy decreased gradually from the southeast coast to the northwest inland, and shows a similar distribution for precipitation. High values of systematic error (>1.0) is mainly concentrated in the southwest Tibetan Plateau, while high values of random error (>1.0) are mainly concentrated around the Tarim Basin. (2) Mainland China can be divided into three areas by the spectral clustering method. It is recommended that the 3B42V7 can be effectively used in Area I, while in Area III the product should be calibrated before use, and the product in Area II can be used after an applicability study. (3) The GD result shows that precipitation is the most important spatial factor among the seven factors influencing the spatial error distribution of the 3B42V7 data. The relationships between spatial factors are synergistic rather than individual when influencing the product’s accuracy.

Evapotranspiration is a vital component of the land surface process, thus, a more accurate estimate of evapotranspiration is of great significance to agricultural production, research on climate change, and other activities. In order to explore the spatiotemporal variation of evapotranspiration under global climate change in the Pearl River Basin (PRB), in China, this study conducted a simulation of actual evapotranspiration (ETa) during 1960−2014 based on the variable infiltration capacity (VIC) model with a high spatial resolution of 0.05°. The nonparametric Mann−Kendall (M−K) test and partial correlation analysis were used to examine the trends of ETa. The dominant climatic factors impacting on ETa were also examined. The results reveal that the annual ETa across the whole basin exhibited a slight but not significant increasing trend during the 1960−2014 period, whereas a significant decreasing trend was found during the 1960−1992 period. At the seasonal scale, the ETa showed a significant upward trend in summer and a significant downward trend in autumn. At the spatial scale, the ETa generally showed a decreasing, but not significant, trend in the middle and upper stream of the PRB, while in the downstream areas, especially in the Pearl River Delta and Dongjiang River Basin, it exhibited a significant increasing trend. The variation of the ETa was mainly associated with sunshine hours and average air pressure. The negative trend of the ETa in the PRB before 1992 may be due to the significant decrease in sunshine hours, while the increasing trend of the ETa after 1992 may be due to the recovery of sunshine hours and the significant decrease of air pressure. Additionally, we found that the “paradox” phenomenon detected by ETa mainly existed in the middle-upper area of the PRB during the period of 1960−1992.

Terrestrial net primary productivity (NPP) plays an essential role in the global carbon cycle as well as for climate change. However, in the past three decades, terrestrial ecosystems across mainland China suffered from frequent drought and, to date, the adverse impacts on NPP remain uncertain. This study explored the spatiotemporal features of NPP and discussed the influences of drought on NPP across mainland China from 1982 to 2015 using the Carnegie Ames Stanford Application (CASA) model and the standardized precipitation evapotranspiration index (SPEI). The obtained results indicate that: (1) The total annual NPP across mainland China showed an non-significantly increasing trend from 1982 to 2015, with annual increase of 0.025 Pg C; the spring NPP exhibited a significant increasing trend (0.031 Pg C year−1, p < 0.05) while the summer NPP showed a higher decreasing trend (0.019 Pg C year−1). (2) Most areas of mainland China were spatially dominated by a positive correlation between annual NPP and SPEI and a significant positive correlation was mainly observed for Northern China; specific to the nine sub-regions, annual NPP and SPEI shared similar temporal patterns with a significant positive relation in Northeastern China, Huang-Huai-Hai, Inner Mongolia, and the Gan-Xin Region. (3) During the five typical drought events, more than 23% areas of mainland China experienced drought ravage; the drought events generally caused about 30% of the NPP reduction in most of the sub-regions while the NPP in the Qinghai-Tibet Plateau Region generally decreased by about 10%.

Climate change and human activity are typically regarded as the two most important factors affecting runoff. Quantitative evaluation of the impact of climate change and human activity on runoff is important for the protection, planning, and management of water resources. This study assesses the contributions of climate change and human activity to runoff change in the Dongjiang River basin from 1960 to 2005 by using linear regression, the Soil and Water Assessment Tool (SWAT) hydrologic model, and the climate elasticity method. Results indicate that the annual temperature in the basin significantly increased, whereas the pan evaporation in the basin significantly decreased (95%). The natural period ranged from 1960 to 1990, and the affected period ranged from 1991 to 2005. The percentage of urban area during the natural period, which was 1.94, increased to 4.79 during the affected period. SWAT modeling of the Dongjiang River basin exhibited a reasonable and reliable performance. The impacts induced by human activity on runoff change were as follows: 39% in the upstream area, 13% in the midstream area, 77% in the downstream area, and 42% in the entire basin. The impacts of human activity on runoff change were greater in the downstream area than in either upstream and midstream areas. However, the contribution of climate change (58%) is slightly larger than that of human activity (42%) in the whole basin.

For probing the influence of kukoamine A on interleukin-1 beta-evoked damage of chondrocytes and its possible mechanism. Interleukin-1 beta was used to induce human knee joint chondrocytes to establish a cell injury model, and different concentrations of kukoamine A were used to treat chondrocytes. Following anti-microRNA-302b-3p introduction into chondrocytes, interleukin-1 beta (10 ng/ml) was employed for treating cells for 24 h. microRNA-NC and microRNA-302b-3p mimics were respectively transfected into chondrocytes and treated with kukoamine A (40 µmol/l) and interleukin-1 beta (10 ng/ml) for 24 h. Enzymelinked immunosorbent assay method was used for detecting the levels of interleukin-6, tumour necrosis factor alpha, and interferon gamma. Flow cytometry was used for detecting the rate of cell apoptosis. The quantitative reverse transcription polymerase chain reaction was utilized for detecting microRNA-302b-3p level. Western blot was employed for detecting Bcl-2 associated X, apoptosis and B-cell lymphoma 2 protein levels. Kukoamine A could reduce interleukin-6, tumour necrosis factor alpha, and interferon gamma levels in chondrocytes induced by interleukin-1 beta, and could reduce the rate of apoptosis and the protein level of Bcl-2 associated X, apoptosis, and could also reduce the level of micoRNA-302b-3p, whereas it promoted B-cell lymphoma 2 level dose-dependently. After anti-microRNA-302b-3p introduction, interleukin-6, tumour necrosis factor alpha, and interferon gamma were downregulated, the rate of apoptosis and the protein level of Bcl-2 associated X, apoptosis were declined, whereas B-cell lymphoma 2 level was elevated. Transfection of microRNA-302b-3p mimics reversed kukoamine A impact on inflammation and apoptosis in interleukin-1 betaevoked chondrocytes. Kukoamine A could inhibit cell inflammatory reaction and apoptosis by downregulating microRNA-302b-3p level, thereby reducing interleukin-1 beta induced chondrocyte damage. [ABSTRACT FROM AUTHOR]