Due to complicated noise interference, seismic signals of high arch dam are of nonstationarity and a low signal‐to‐noise ratio (SNR) during acquisition process. The traditional denoising method may have filtered effective seismic signals of high arch dams. A self‐adaptive denoising method based on ensemble empirical mode decomposition (EEMD) combining wavelet threshold with singular spectrum analysis (SSA) is proposed in this paper. Based on the EEMD result for seismic signals of high arch dams, a continuous mean square error criterion is used to distinguish high‐frequency and low‐frequency components of the intrinsic mode functions (IMFs). Denoised high‐frequency IMF using wavelet threshold is reconstructed with low‐frequency components, and SSA is implemented for the reconstructed signal. Simulation signal denoising analysis indicates that the proposed method can significantly reduce mean square error under low SNR condition, and the overall denoising effect is superior to EEMD and EEMD‐Wavelet threshold denoising algorithms. Denoising analysis of measured seismic signals of high arch dams shows that the performance of denoised seismic signals using EEMD‐Wavelet‐SSA is obviously improved, and natural frequencies of the high arch dams can be effectively identified.

This study quantitatively assesses the risk of water shortage (WSR) in the Baiyangdian area due to the Inter-Basin Water Transfer (IBWT) project, focusing on the impact of water transfer on regional water security. The actual evapotranspiration (ETa) is calculated, and the correlation simulation using Archimedes’ Copula function is implemented in Python 3.7.1, with optimization using the sum of squares of deviations (OLS) and the AIC criterion. The joint distribution model between ETa and three water supply scenarios is constructed. Key findings include (1) ETa increased by 27.3% after water transfer, far exceeding the slight increase in water supply before the transfer; (2) various Archimedean Copulas effectively capture the dependence and joint probability distribution between water supply and ETa; (3) water shortage risk increased after water transfer, with rainfall and upstream water unable to alleviate the problem in Baiyangdian; and (4) cross-basin water transfer reduced risk, with a reduction of 8.90% in the total probability of three key water resource scheduling combinations. This study establishes a Copula-based framework for water shortage risk assessment, providing a scientific basis for water allocation strategies in ecologically sensitive areas affected by human activities.

The lower Yellow River is characterized by low water discharge and a high sediment load, resulting in a fragile aquatic ecosystem. It is important to develop a reasonable method of ecological flow calculation that can be applied to the water-scarce rivers like the Yellow River. In this paper, we selected the Huayuankou hydrological station in the lower Yellow River as our study site and assessed the ecological flow using several methodologies including the monthly frequency calculation method, the sediment transportation method, the habitat simulation method, and the improved annual distribution method. Based on the seasonal applicability of the four methods across months of the year, we established an ecological flow calculation method that considers the integrated ecological functions of the lower Yellow River. In this method, ecological flow in the lower Yellow River during the dry season (November to March) can be determined by using the improved annual distribution method, ecological flow in the fish spawning period (April to June) can be calculated using the habitat simulation method, and the ecological flow during the flood season (July to October) can be calculated using the sediment transportation method. The optimal ecological flow regime for the Huayuankou section was determined using the established method. The ecological flow regimes derived in our study ranged from 310 m³/s to 1532 m³/s. However, we also observed that the ecological flow has a relatively low assurance rate during the flood season in the lower Yellow River, with the assurance rate not exceeding 63%. This highlights the fact that more attention should be given in reservoir regulations to facilitating sediment transport downstream.

Soil salinization commonly prevails in global arid and semi-arid areas, shrinking farmland and endangering ecological, agricultural and social sustainability. Therefore, it is essential to develop effective strategies for salinized soil remediation. In this study, soil samples were collected from Nanliang Farm in Yinchuan, China. Compound microbial strains (CMS) and humic acid (HA) were selected as soil amendments. A total of eight treatments with different application rates of CMS and HA were set up in pot cultivation experiments, where oil sunflower was planted. The results showed that both amendments effectively elevated soil water content and chlorophyll content, as well as multiple physiological indices of sunflower. Meanwhile, they decreased soil total salinity, proline content and malondialdehyde (MDA) content. For single humic acid treatments, Treatment F1 achieved the optimal amelioration effect: it reduced soil total salinity by an average of 24.34%, and increased sunflower plant height, leaf area and aboveground fresh weight by 5.84%, 95.01% and 77.40%, respectively. Among the single CMS treatments, Treatment S3 performed best, with an average reduction of 31.04% in soil total salinity, and increases of 5.66%, 2.85% and 8.16% in plant height, leaf area and aboveground fresh weight correspondingly. Notably, among all eight groups, the control group CK1 exhibited the most prominent improvement effect, which was significantly superior to F1 and S3. This finding suggests that long-term application (one year or more) of CMS can produce an especially strong ameliorative effect on salinized soil.

Correctly assessing seepage amount of the regulated reservoir is vital to water resources efficlent utilization and engineering safety. A regulated reservoir in mudstone area without seepage control measures was used as the engineering prototype. Based on the mutual verification of field monitoring and numerical simulation, the estimation formula of the seepage amount of the regulated reservoir was determined. In addition, the sensitivity analysis of the factors affecting the seepage amount of the regulated reservoir was carried out, and the seepage prediction models were established based on the different optimization algorithms. The research results indicate that the regulated reservoir without lying geomembranes exists in a certain amount of seepage every day, accounting for about 7 to 10 % of the total reservoir capacity. The sensitivity ranking of influencing factors in descending order is as follows: reservoir bottom width, water depth, hydraulic conductivity, and saturated volumetric water content. The prediction accuracy of Bayesian regression is significantly better than that of traditional regression models under small training samples. This research approach provides a highly accurate and strongly robust solution to the small sample engineering prediction problem.