Climate change is a global scientific problem, and its impact on the spatiotemporal variations of precipitation has been a crucial research topic. Although previous studies have used different methods to evaluate the precipitation characteristics of the Huaihe River Basin, the time series were short and the few stations could not fully and accurately represent the precipitation characteristics. In this study, daily temperature and precipitation data were collected from 233 meteorological stations in the Huaihe River Basin from 1960–2020. The Mann–Kendall test was used to analyze the trend and significance of interannual and interseasonal scale changes in temperature and precipitation in the Basin, respectively. The correlation between temperature and precipitation was analyzed using the Pearson correlation coefficient method. The spatial distribution of the significance of temperature and precipitation changes and the spatial distribution of the correlation between temperature and precipitation in the basin were plotted. The temperature in the basin tended to increase on interannual and interseasonal scales, with more noticeable changes in spring and winter. Precipitation showed an overall decreasing trend but an increasing trend in localized areas in the south. A decreasing trend in the interseasonal variation scale was observed in spring, an increasing trend in winter, a decreasing trend in the northeastern region in summer, an increasing trend in the southwestern region, and an increasing trend in the northern and southern parts in autumn were observed. The correlation between average temperature and precipitation on interannual and interseasonal scales was analyzed using Pearson’s correlation coefficient method, and the annual average temperature and annual average precipitation in the Huaihe River basin were found to be negatively correlated, except for sporadic areas that showed extremely weakly positive correlations or no correlations.

The impact of global climate change on the temporal and spatial variations of precipitation is significant. In this study, daily temperature and precipitation data from 258 meteorological stations in the Haihe River Basin, for the period 1960–2020, were used to determine the trend and significance of temperature and precipitation changes at interannual and interseasonal scales. The Mann–Kendall test and Spearman’s correlation analysis were employed, and significant change trends and correlations were determined. At more than 90% of the selected stations, the results showed a significant increase in temperature, at both interannual and interseasonal scales, and the increasing trend was more significant in spring than in other seasons. Precipitation predominantly showed a decreasing trend at an interannual scale; however, the change trend was not significant. In terms of the interseasonal scale, the precipitation changes in spring and autumn showed an overall increasing trend, those in summer showed a 1:1 distribution ratio of increasing and decreasing trends, and those in winter showed an overall decreasing trend. Furthermore, the Spearman’s correlation analysis showed a negative correlation between temperature and precipitation in the entire Haihe River Basin, at both interannual and interseasonal scales; however, most of the correlations were weak.

Soil preferential flow is an essential process that affects the movement and relocation of soil water and solutes. This study was conducted on cropland in an arid and semi-arid area in Zhongning County, Ningxia. According to the different cracks, rain intensity, rainfall duration, and slope, there were three groups, and 17 dye tracer experiments were conducted in the field. We quantified the characteristics of soil preferential flow by investigating and analyzing the infiltration depth, dyeing area, saturation, runoff coefficient, and rainfall infiltration coefficient using the dye tracer method. The results showed that increasing the rainfall or irrigation intensity could promote the activation of the fracture channel as the preferential flow channel, which is advantageous to the preferential flow formation. The fractures dominated the formation of the preferential flow. The fractures slowed the formation of runoff, reduced the velocity of slope flow, reduced the flow of the slope, and increased the amount of soil water infiltration. These results have theoretical and practical significance for understanding soil water transportation, especially for agricultural irrigation management and improving cropland water use efficiency in arid and semi-arid areas.

The reasonable allocation of water resources using different optimization technologies has received extensive attention. However, not all optimization algorithms are suitable for solving this problem because of its complexity. In this study, we applied an ameliorative multi-objective gray wolf optimizer (AMOGWO) to the problem. For AMOGWO, which is based on the multi-objective gray wolf optimizer, we improved the distance control parameter calculation method, added crowding degree for the archive, and optimized the selection mechanism for leader wolves. Subsequently, AMOGWO was used to solve the multi-objective optimal allocation of water resources in Handan, China, for 2035, with the maximum economic benefit and minimum social water shortage used as objective functions. The optimal results obtained indicate a total water demand in Handan of 2740.43 × 10⁶ m³, total water distribution of 2442.23 × 10⁶ m³, and water shortage of 298.20 × 10⁶ m³, which is consistent with the principles of water resource utilization in Handan. Furthermore, comparison results indicate that AMOGWO has substantially enhanced convergence rates and precision compared to the non-dominated sorting genetic algorithm II and the multi-objective particle swarm optimization algorithm, demonstrating relatively high reliability and applicability. This study thus provides a new method for solving the multi-objective optimal allocation of water resources.

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

Intensifying urbanization and climate change have highlighted the growing role of low-impact development (LID) practices in urban rainwater management systems. However, there is still room for improvement to optimally deploy LID practices, especially under different confluence relationships. In this study, 36 scenarios were designed based on different rainfall conditions, LID practices, confluence relationships, and locations, which were analyzed using hybrid hydraulic and water quality modeling. The following key results were obtained: (1) Series II was the main confluence path in the study area. The greenbelt occupied a large share; accordingly, the control of waterlogging and non-point source pollution in series II was better in the designed rainfall scenarios. (2) In the designed rainfall scenarios, series I had the best mitigation effect on waterlogging and non-point source pollution, with 24.5%, 16.4%, and 15.2% lower values than those of the series II and Parallel scenarios. There were no significant differences among the three confluence relationships under extreme rainfall. (3) Among the different LID practices, bioretention cells contributed to the maximum reduction in pollution (29.91%). Green roofs and permeable pavement resulted in the maximum reductions in total runoff (27.99% and 22.94%, respectively), and permeable pavement also reduced pollution by 26.50%. These results suggest that the pavement at some waterlogging points should be replaced with permeable pavement to avoid the negative effects of future extreme rainfall.