Understanding soil organic carbon (SOC) at the aggregate level is crucial for soil health in secondary-salinized greenhouse development. Nevertheless, the specific patterns and contributions of ion content and microbial communities on SOC density fractions at the aggregate level remain unclear in secondary-salinized soil. We investigated variations in salts [electrical conductivity (EC) and ions] and microbial communities across various aggregate classes in both a 16-year-use greenhouse and open-field soils. We also examined SOC density fractions, including the light fraction (LF), the heavy fraction of particulate organic matter (POM), and mineral-associated organic matter (MOM) across different aggregates. The findings revealed that a lower Ca²⁺/K⁺ along with elevated EC levels (average 2.49 mS cm⁻¹) reduced the macroaggregate percentage in greenhouse compared to open-field conditions, with a lower EC of 0.58 mS cm⁻¹. Bacterial diversity and community composition exhibited no variation across different aggregate sizes at both sites. Conversely, fungal diversity and relative abundance (primarily dominated by Ascomycota of 78.50%) substantially increased in microaggregates (0.25 mm). Macroaggregates exhibited a higher proportion of LF and MOM (3.3–18.2%, 24.9–34.5%, and 2.9–4.0% for LF, MOM, and POM, respectively) than microaggregates. Correlation and redundancy analyses revealed that fungal diversity, particularly the relative abundance of Ascomycota in aggregates < 0.25 mm, significantly and positively influenced (p < 0.05) the proportion of MOM carbon in terms of the overall SOC (J-type). This study provides valuable insights into the distribution patterns of SOC within the secondary salt-affected soils.

Vegetation dynamics result from the interaction between human activities and climate change. Numerous studies have investigated the contributions of human activities and climate change to vegetation cover dynamics using statistical methods. However, these studies have not focused much on the spatially non-stationary effects of human activities on vegetation cover changes and future trends. Taking the Three Gorges Reservoir (TGR) area as the case study area, it was divided into 32 combinations by considering the spatially varying effects of five factors related to human activity and climate change, including gross domestic product (GDP), population, land use change, precipitation, and temperature. Regression in terms of pixels was then performed for each combination at the pixel scale. The result showed that from 2001 to 2020, the annual average normalized digital vegetation index (NDVI) in the TGR area exhibited an upward trend (slope = 0.0051, p < 0.01), with the mean NDVI increasing from 0.53 to 0.64. Compared with the regression with climate variables, the proposed model improved the ${\mathrm{R}}^2$ value from 0.2567 to 0.6484, with the p-value in the t-test reduced from 0.2579 to 0.0056. It indicated that changes in vegetation were dominated by human activities and climate change in 48.77% and 3.19% of the TGR area, respectively, and 43.70% of the vegetation coverage was dominated by both human activities and climate change. This study also predicted the future NDVI according to the shared socioeconomic pathways (SSPs) and representative concentration pathway (RCP) scenarios provided by the Intergovernmental Panel on Climate Change. It suggests that, assuming future regional policies are the same as the historical policies in the TGR, the SSP5–8.5 scenario would have the highest and fastest growth in average NDVI, with the average NDVI increasing from 0.68 to 0.89, because of the large increase in the GDP, lower population in this scenario, and adequate hydrothermal conditions.

Antecedent soil moisture determines how much storage remains in bioretention media before rainfall, but its relationship with runoff control is poorly quantified. Three wet seasons of monitoring were analyzed for two field-scale bioretention systems with contrasting media depths, runoff loading, and drainage boundaries. For each event, antecedent available storage was calculated from the initial soil moisture profile relative to field capacity and, separately, to the 95th percentile of peak water content at each depth. Event-activated storage was defined as the greatest synchronous increase in water stored across the monitored profile following rainfall onset. Both storage metrics were expressed as equivalent rainfall depths over the contributing roof area. Runoff volume reduction ranged from 62.3% to 100% in BR1 and from 43.3% to 100% in BR2. Median event-activated storage over the media area was 4.9 mm in BR1 and 19.1 mm in BR2. However, after conversion to the contributing roof area, median antecedent storage was larger in BR1 than in BR2 under both the field capacity reference (3.8 versus 0.6 mm) and the high water reference (11.0 versus 5.0 mm). Greater antecedent storage was associated with a lower probability of drainage activation in both systems and greater runoff volume reduction in BR1. In BR2, greater event-activated storage was associated with lower runoff volume reduction, indicating that stronger within-event media wetting did not necessarily correspond to greater facility-scale runoff retention. Dynamic storage should therefore be interpreted in relation to hydraulic loading, media configuration, and drainage pathways.