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The frequency analysis method is commonly used to calculate design floods. Under the double challenge of the non‐stationary situation under the changing environment and the inadequate length of flood series, developing a new method to integrate the historical extraordinary floods into the non‐stationary frequency analysis is essential. First, the Multi‐Model Ensemble projections of temperature and precipitation based on Global Climate Model outputs were employed to drive the Soil & Water Assessment Tool hydrological model for runoff simulation. Then, the Integrated Time‐Varying Moment (ITVM) model was developed to re‐analyze the design floods based on the Pearson‐III distribution. The calibrated SWAT model can satisfactorily simulate the rainfall‐runoff relationship in the Yalong River basin. The developed ITVM model is effective to conduct the design flood frequency analysis to cope with the problems of insufficient length and non‐stationarity of the flood series. The design flood values of Maidilong station show an obvious increase, with variations of 6.5%–9.4%, 2.9%–12.3%, and 16%–33.7% for SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. The significant increase of low frequencies (p = 0.2%, p = 0.1%) floods, especially for SSP5‐8.5 scenario, requires more attention, as the increased floods may exceed the discharge capacity of the reservoir determined at the design stage. [ABSTRACT FROM AUTHOR]

Combined organic and inorganic fertilization can improve soil fertility in coal mine reclamation areas. However, the contribution of the bacterial community (especially its occurrence patterns) to soil physicochemical properties and enzyme activity needs further evaluation. The objective of this research was to clarify the bacterial community diversity, composition, and intraspecific interactions in response to combined organic and inorganic fertilizer application in coal mine reclamation areas in the Loess Hilly Region, China. Maize rhizosphere soil samples were collected under four fertilization regimes (CK, no fertilization control; NPK, compound inorganic fertilizer; M, organic fertilizer; and NPKM, combined organic and inorganic fertilization) in a 10-year field experiment in Gujiao city, Shanxi Province. Bacterial communities were characterized using high-throughput sequencing of the 16S rRNA gene V3–V4 region. A cross-treatment Spearman correlation network was constructed to explore the bacterial co-occurrence patterns. Compared with CK, NPK, M, and NPKM decreased the pH by 0.59%, 2.27%, and 0.12%; increased the soil organic carbon by 11.25%, 11.69%, and 27.05%; and significantly decreased the bacterial Shannon diversity by 3.68%, 0.14%, and 3.54%, respectively. Compared with CK, NPKM significantly increased sucrase, urease, and alkaline phosphatase activities (p < 0.05). Critically, oligotrophic Acidobacteria were significantly more abundant in CK than in the other treatments. Gemmatimonadetes were more abundant in NPK and M, and Actinobacteria, Bacteroidota, and Patescibacteria were more abundant in NPKM. In addition, network analysis revealed that the keystone taxa in the different fertilization treatments belonged to different network modules and were significantly correlated with soil nutrient content and enzyme activity. Simultaneously, the Actinobacteria enriched in NPKM formed specific clusters through strong symbiosis, and there were significant positive correlations among sucrase, urease, and alkaline phosphatase. In summary, long-term combined organic and inorganic fertilization improved maize rhizosphere soil fertility by regulating enzyme activity, bacterial community composition, and bacterial species interactions in coal mine reclamation areas in the Loess Hilly Region. [ABSTRACT FROM AUTHOR]

Baseflow is an essential component of runoff, which is the dominant water resource for the dry season. To better manage water resources, it is vital to investigate the links between the multiple influencing factors and the baseflow for better prediction in light of global changes. Previous studies have seldom separated these influencing factors in the analysis, making it difficult to determine their effect on the baseflow. In this study, based on the analysis datasets generated by the Soil and Water Assessment Tool (SWAT) model, the control single variables, correlation analysis, and multiple linear regression (MRL) methods were firstly combined to analyze the influences of the chosen factors (land use, topography, and soil type) on the baseflow. The findings revealed that the ability of precipitation to replenish the baseflow was better in areas with a higher slope. The ability of precipitation to recharge the baseflow for different land uses was ranked as "forest land > grass land > agricultural land > urban land"; land use factors should be added to the baseflow prediction equation. The hydrological group is the main property of soil affecting the baseflow recharge. A regression model established using publicly acquired remote sensing data had a good performance (R2 = 0.84) on baseflow prediction on an annual scale. As a result of this information, relevant government officials and environmentalists may better manage water supplies in drought years. In addition, this regression model frame has the potential to be used for a baseflow inquiry inside an ungauged zone for a better ecological assessment. [ABSTRACT FROM AUTHOR]