Cemented sand and gravel (CSG) material is a new type of dam material developed on the basis of roller compacted concrete, hardfill, and ultra-poor cementing materials. Its main feature is a wide range of sources of aggregate (aggregate is not screened but by simply removing the large particles it can be fully graded on the dam filling) and low amounts of cementitious materials per unit volume. This dam construction material is not only economical and practical, but also green and environmentally friendly. There are many factors affecting the mechanical properties of CSG materials, such as aggregate gradation, sand ratio, water content, water–binder ratio, fly ash content, admixture content, etc. Based on the existing research results of the team, this paper focuses on the influence of fly ash content on the compressive strength of CSG materials. Through a large number of laboratory measured data, we found: (1) The compressive strength law of materials at different ages; the compressive strength of CSG material at age 90 d is generally 10%~30% higher than that at 28 d, and it is proposed that 90 d or 180 d strength should be used as the design strength in the design of CSG material dam; (2) There is an optimal value of fly ash content in CSG materials: when the fly ash content is 50% of the total amount of cementitious materials (cement + fly ash), the fly ash content is defined as the optimal content, and the test data are verified by regression analysis. The discovery of an ‘optimal dosage’ of fly ash provides an important reference for the design and construction of CSG dams.

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R²) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R² and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R² and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

With the rapid development of economy and society, the contradiction between supply and demand of water resources is increasing. Efficient utilization and allocation of limited water resources are one of the main means to solve the above contradictions. In this paper, the multidimensional joint distribution of natural streamflow series in reservoirs is constructed by introducing the mixed Copula function, and the probability of wet and dry encounters between natural streamflow is analyzed. Luan River is located in the northeastern part of Hebei Province, China, taking the group of Panjiakou Reservoir, Douhe Reservoir and Yuqiao Reservoir in the downstream of Luan River Basin as an example, the probabilities of synchronous and asynchronous abundance and depletion of inflow from the reservoirs are calculated. The results show that the probability of natural streamflow series between reservoirs is 61.14% for asynchronous wetness and dryness, which has certain mutual compensation ability. Therefore, it is necessary to minimize the risk of water supply security in Tianjin, Tangshan and other cities, and strengthen the optimal joint water supply scheduling of reservoirs. The research results are reasonable and reliable, which can provide reference for water supply operation of other basins.

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management.

To overcome the shortcomings of traditional concrete coatings, such as high roughness and poor frost resistance, this study developed and evaluated a new hydrophobic coating—modified polyurea hydrophobic coating (MPHC). MPHC features strong adhesion, high hydrophobicity and excellent durability. The coating performance was evaluated through contact angle measurement, tensile bond strength test, and assessment of environmental durability under several aging conditions including immersion, heat resistance and freeze–thaw cycles. The experimental results showed that the surface contact angle of MPHC reached 131.2°, demonstrating strong hydrophobicity. After durability testing, there was no significant decrease in contact angle and bond strength, confirming the robustness of the coating. The coating combines a “dual structure” formed by polydimethylsiloxane and microsilica powder, thereby creating a hydrophobic rough surface. This structure minimizes the fluid–solid interface area and adhesion, thereby enhancing drag reduction performance. In drag reduction tests on channel model linings, compared with ordinary concrete, MPHC reduced the roughness coefficient by 10.0–11.6%, and by 7.4–7.5% compared with ordinary polyurea coatings. The outstanding hydrophobicity, durability and drag reduction performance of MPHC make it a promising solution for improving the water conveyance efficiency of concrete-lined channels.

Water and thermal resources are changing significantly because of climate change, further affecting important crops, such as grains, worldwide. Previous studies on climate change trends and their impacts on grain yield were mainly conducted on a single time scale, with few studies conducted on multiple time scales. Therefore, here, climate data and grain yield statistics from 1978–2021 in the Henan Province were used to assess how water and thermal changes impact grain yield on multiple time scales. Water and thermal variation were analyzed using the least squares method, Mann-Kendall method, and wavelet analysis method, and grain yield impacts were analyzed using gray correlation method. Results showed increasing trends for ≥0 °C and ≥10 °C accumulated temperature and precipitation, with decreased precipitation in spring. The lowest daily minimum temperature increase was 2–3 times the highest daily maximum temperature. Additionally, grain yield fluctuations were caused by climate change. Climate change affected grain yield on all time scales, fluctuating more in autumn than in summer, which was mainly due to changes in temperature followed by precipitation and extreme precipitation. This study provides a scientific basis for the maintenance of food security under climate change.