Liao Ouyang

Percentile-based thresholds widely used in flash drought monitoring lack a clear physical basis, as the same percentile may correspond to substantially different soil moisture states under different soil and climatic conditions. This study constructed a framework based on the soil moisture loss rate function L(SM) to identify root-zone loss stages, estimate physically informed thresholds, and compare them with statistical percentile thresholds. Using ERA5-Land reanalysis data from 1950 to 2025, the framework combines non-parametric identification of L(SM) with piecewise linear fitting to characterize root-zone soil moisture loss stages and estimate physically informed thresholds in the Dongjiang River Basin, southeastern China. Two physically informed critical thresholds were identified: SM_WT, representing the wet–transitional boundary, and SM_TD, representing the transitional–dry boundary. Their relationships with statistical percentile thresholds and reference soil hydraulic parameters were then evaluated through spatial comparison and regression analysis. Results showed that root-zone L(SM) curves were overwhelmingly dominated by a wet–transitional–dry three-stage structure, accounting for 92.5% of all grid cells, while the gravitational drainage stage was absent at the root-zone scale. The basin-mean SM_WT and SM_TD were 0.36 and 0.27 m³/m³, respectively, with mean bootstrap 95% confidence interval widths of 0.03 and 0.02 m³/m³. The 40th and 20th percentile thresholds were fully nested between SM_WT and SM_TD, and their correlations with the physically informed thresholds reached 0.98–0.99. However, the statistical threshold window covered only approximately 55.6% of the physically informed transition zone, indicating weaker ability to characterize terminal agricultural drought severity than flash drought onset. The proposed framework provides physically informed reference thresholds for flash drought monitoring and supports regional adaptation of percentile-based methods.

Coastal sediment bacteria mediate essential biogeochemical cycles, yet their functional gene potential to anthropogenic stress gradients remains insufficiently understood. This study presents a comparative analysis of sediment bacterial communities and carbon, nitrogen, phosphorus, and sulfur (C/N/P/S) cycling genes between the west and east coasts of Shenzhen, China, a region exemplifying sharp contrasts between intensive urbanization and relatively pristine marine environments. We integrated 16S rRNA gene high-throughput sequencing with QMEC (Quantitative Microbial Element Cycling), a high-throughput quantitative PCR chip to profile both bacterial taxonomic composition and quantitatively measured gene abundances across C/N/P/S biogeochemical pathways. Western sediments, under strong terrestrial input, exhibited higher ammonium and organic carbon, while eastern sediments had higher salinity. Bacterial alpha diversity was significantly higher in the west, and community structure differed markedly between regions (ANOSIM R = 0.334, p = 0.001). Although dominant phyla (Pseudomonadota, Bacteroidota, and Planctomycetota) were shared, genus-level taxa diverged: Sulfurovum (sulfur cycling) was enriched in the west, while organic-degrading Burkholderia-Caballeronia-Paraburkholderia and Phaeodactylibacter prevailed in the east. Notably, functional gene abundance for all four biogeochemical cycles was an order of magnitude higher in western sediments, with denitrification genes dominating there and nitrification genes in the east. Redundancy analysis (RDA) and the Mantel test identified nitrate as the primary environmental driver of bacterial variation (r = 0.574, p = 0.001), alongside salinity, temperature, ammonium, and total organic carbon. These findings reveal distinct bacterial adaptations to contrasting anthropogenic pressures and demonstrate that sediment bacterial functional gene profiles can serve as sensitive indicators of ecosystem health, providing a quantitative microbiological basis for targeted pollution management in urbanized coastal zones.

Percentile-based thresholds widely used in flash drought monitoring lack a clear physical basis, as the same percentile may correspond to substantially different soil moisture states under different soil and climatic conditions. This study constucted a framework based on the soil moisture loss rate function L(SM) to identify root-zone loss stages, estimate physically informed thresholds, and compare them with statistical percentile thresholds. Using ERA5-Land reanalysis data from 1950 to 2024, the framework combines non-parametric identification of L(SM) with piecewise linear fitting to characterize root-zone soil moisture loss stages and estimate physically informed thresholds in the Dongjiang River Basin, southeastern China. Two physically informed critical thresholds were identified: SMWT, representing the wet–transitional boundary, and SMTD, representing the transitional–dry boundary. Their relationships with statistical percentile thresholds and reference soil hydraulic parameters were then evaluated through spatial comparison and regression analysis. Results showed that root-zone L(SM) curves were overwhelmingly dominated by a wet–transitional–dry three-stage structure, accounting for 92.5% of all grid cells, while the gravitational drainage stage was absent at the root-zone scale. The basin-mean SMWT and SMTD were 0.36 and 0.27 m³/m⁻³, respectively, with mean bootstrap 95% confidence interval widths of 0.03 and 0.02 m³/m⁻³. The 40th and 20th percentile thresholds were fully nested between SMWT and SMTD, and their correlations with the physically informed thresholds reached 0.98–0.99. However, the statistical threshold window covered only approximately 55.6% of the physically informed transition zone, indicating weaker ability to characterize terminal drought severity than flash drought onset. The proposed framework provides physically informed reference thresholds for flash drought monitoring and supports regional adaptation of percentile-based methods.

Under global climate change, frequent droughts threaten ecosystem functions, but how drought characteristics affect ecosystem resilience remains unclear. Focusing on the Dongjiang River Basin, China, we identified drought events at an 8-day scale from 2000–2024 using multi-source remote sensing and reanalysis data. The water use efficiency-based resilience index (R_de) was calculated, and a random forest model quantified the contributions of 21 potential driving factors. The model explained 68% of R_de variance (R² = 0.68, RMSE = 0.12). Downward shortwave radiation was the primary factor, followed by antecedent water use efficiency and soil moisture anomaly, with drought intensity and air temperature ranking fourth and fifth. All dominant factors exhibited nonlinear threshold effects: R_de decreased significantly after radiation exceeded ~110 W·m⁻²·(8d)⁻¹; R_de declined when standardized soil moisture anomaly fell below −2.0; and R_de increased sharply when drought intensity exceeded 12%. Drought intensity far outweighed duration and severity, establishing it as the key drought attribute. This study reveals the dominant drivers and their thresholds governing ecosystem resilience in the Dongjiang River Basin, providing quantifiable indicators for ecological drought early warning.

Based on 0.1° × 0.1° soil moisture reanalysis data from 1950 to 2024, combined with remote sensing ecological products such as Enhanced Vegetation Index (EVI) and gross primary productivity (GPP), this study systematically investigates the spatiotemporal evolution, transition process, and ecological responses of flash droughts and slowly evolving droughts (including seasonal and cross-seasonal droughts) in the Dongjiang River Basin of China. The results indicate the following: (1) The average occurrence frequencies of flash droughts, seasonal droughts, and cross-seasonal droughts within the basin were 4.1%, 7.8%, and 8.4%, respectively. (2) The vast majority of flash droughts (approximately 90.1%) further developed into longer-lasting, slowly evolving droughts, indicating that flash droughts serve as a critical precursor to persistent drought events. Moreover, winter was identified as the key season for the occurrence of flash droughts and their transition to slowly evolving droughts. (3) In terms of ecological response, droughts significantly suppressed vegetation growth, but ecosystem resilience exhibited notable differences: although flash droughts caused relatively mild initial suppression, they were accompanied by a severe lack of ecosystem resilience; in contrast, cross-seasonal droughts, despite inducing stronger suppression, were met with higher ecosystem resilience. This study underscores the importance of the early monitoring and warning of flash droughts, and the findings provide a scientific basis for drought risk management in humid basins.

Based on 0.1° × 0.1° soil moisture reanalysis data from 1950 to 2024, combined with remote sensing ecological products such as EVI and GPP, this study systematically investigates the spatiotemporal evolution, transition process, and ecological responses of flash droughts and slowly-evolving droughts (including seasonal and cross-seasonal droughts) in the Dongjiang River Basin of China. The results indicate that: (1) The average occurrence frequencies of flash droughts, seasonal droughts, and cross-seasonal droughts within the basin were 4.1%, 7.8%, and 8.4%, respectively. (2) The vast majority of flash droughts (approximately 90.1%) further developed into longer-lasting slowly-evolving droughts, indicating that flash droughts serve as a critical precursor to persistent drought events. Moreover, winter was identified as the key season for the occurrence of flash droughts and their transition to slowly-evolving droughts. (3) In terms of ecological response, droughts significantly suppressed vegetation growth but ecosystem resilience exhibited notable differences: although flash droughts caused relatively mild initial suppression, they were accompanied by a severe lack of ecosystem resilience; in contrast, cross-seasonal droughts, despite inducing stronger suppression, were met with higher ecosystem resilience. This study underscores the importance of early monitoring and warning of flash droughts, and the findings provide a scientific basis for drought risk management in humid basins.

Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought’s impact on plants. Chlorophyll, crucial for photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool for selecting a representative calibration dataset. By integrating MSD with partial least squares regression (PLSR) and the Cubist model, we aim to accurately predict chlorophyll content, focusing on key spectral bands within the ranges of 500–640 nm and 740–1100 nm. In the validation dataset, PLSR achieved a high determination coefficient (R²) of 0.70 and a low mean bias error (MBE) of 0.04 mg g⁻¹. The Cubist model performed even better, demonstrating an R² of 0.77 and an exceptionally low MBE of 0.01 mg g⁻¹. These results indicate that the MSD method serves as a tool for selecting a representative calibration dataset in leaves, and vis/NIR spectrometry combined with the MSD method is a promising alternative to traditional methods for quantifying chlorophyll content in various leaf types over various months. The technique is non-destructive, rapid, and consistent, making it an invaluable tool for assessing drought impacts on plant health and productivity.

The investigation of metabolic pathways and regulatory mechanisms in newly discovered species can offer valuable insights into the nitrogen removal function of heterotrophic nitrification–aerobic denitrification (HN-AD) bacteria. To investigate the nitrogen removal mechanism of a new genus, Delftia, we analyzed the complete genome, metabolic pathways, and the related genes of Delftia sp. B7. We further examined the nitrogen removal capacity of Delftia sp. B7 under various nitrogen sources and real wastewater. Our results demonstrate the presence of several genes in Delftia sp. B7, including narGHI, nasAB, nirK, nirS, nirBD, norBC, nosZ, nxrAB, gdhA, glnA, gltBD, amt, and nrt. These genes encode enzymes that facilitate ammonia assimilation, assimilatory nitrate reduction to nitrite, HN-AD, and dissimilatory nitrate reduction (DNRA) in Delftia sp. B7. Specifically, we propose an HN-AD pathway in Delftia sp. B7, NH₄⁺-N → NH₂OH → NO₂⁻-N → NO₃⁻-N → NO₂⁻-N → NO → N₂O → N₂, which accounts for the majority of nitrogen removal. Here, the transformation of NH₄⁺-N to NO₂⁻-N was achieved by unknown enzymes or by another pathway. When treating municipal wastewater, Delftia sp. B7 was able to remove 45.62 ± 1.29% of TN. These findings provide a theoretical basis for utilizing microbial resources to mitigate nitrogen contamination.

Nitrogen removal through heterotrophic nitrification–aerobic denitrification (HN–AD) has been acknowledged as one of the most efficient and cost-effective nitrogen removal processes. This study involved the isolation of a novel HN–AD bacterium (Rhodococcus sp. S2) from landfill leachate. Rhodococcus sp. S2 exhibited high nitrogen removal performance under aerobic conditions without the accumulation of nitrite as an intermediate. The maximum removal efficiencies for NH₄⁺-N, NO₂⁻-N, and NO₃⁻-N were found to be 99.97 ± 0.3%, 99.79 ± 0.14%, and 83.53 ± 0.31%, respectively. Additionally, Rhodococcus sp. S2 demonstrated simultaneous nitrifying and denitrifying capabilities and showed a preference for utilizing NH₄⁺-N in mixed nitrogen sources. The optimal nitrogen removal conditions for Rhodococcus sp. S2 were as follows: sodium acetate as a carbon source, a C/N ratio of 16, a shaking speed of 200 rpm, a pH of 9, and a temperature of 35 °C. Genome sequencing results revealed the presence of nitrate reductases (NarG), nitrate oxidoreductase (NxrA), and nitrite reductase (NirBD) in Rhodococcus sp. S2, providing further evidence of its HN–AD capability. In treating raw wastewater under the aforementioned experimental conditions, S2 achieved a maximum TN removal efficiency of 57.16 ± 0.52% with the addition of sodium acetate as a carbon source. These results suggest that Rhodococcus sp. S2 might be a promising candidate for wastewater nitrogen removal.

A novel malachite green molecularly imprinted membrane (MG-MIM) with specific selectivity for malachite green (MG) and leucomalachite green (LMG) was prepared using a hydrophobic glass fiber membrane as the polymer substrate, methyl violet as a template analog, 4-vinyl benzoic acid as the functional monomer, and ethyleneglycol dimethacrylate as the crosslinking agent. MG-MIM and non-imprinted membrane (NIM) were structurally characterized using scanning electron microscopy, surface area analyzer, Fourier-transform infrared spectrometer and synchronous thermal analyzer. The results showed that MG-MIM possessed a fluffier surface, porous and looser structure, and had good thermal stability. Adsorption properties of MG-MIM were investigated under optimal conditions, and adsorption equilibrium was reached in 20 min. The saturated adsorption capacities for MG and LMG were 24.25 ng·cm⁻² and 13.40 ng·cm⁻², and the maximum imprinting factors were 2.41 and 3.20, respectively. Issues such as “template leakage” and “embedding” were resolved. The specific recognition ability for the targets was good and the adsorption capacity was stable even after five cycles. The proposed method was successfully applied for the detection of MG and LMG in real samples, and it showed good linear correlation in the range of 0 to 10.0 μg·L⁻¹ (R² = 0.9991 and 0.9982), and high detection sensitivity (detection limits of MG and LMG of 0.005 μg/kg and 0.02 μg·kg⁻¹ in shrimp, and 0.005 μg/kg and 0.02 μg/kg in fish sample). The recoveries and relative standard deviations were in the range of 76.31–93.26% and 0.73–3.72%, respectively. The proposed method provides a simple, efficient and promising alternative for monitoring MG and LMG in aquatic products.