Pan evaporation (E_pan) is an important indicator of regional evaporation intensity and degree of drought. However, although more evaporation is expected under rising temperatures, the reverse trend has been observed in many parts of the world, known as the “pan evaporation paradox”. In this paper, the Haihe River Basin (HRB) is divided into six sub-regions using the Canopy and k-means (The process for partitioning an N-dimensional population into k sets on the basis of a sample is called “k-means”) to cluster 44 meteorological stations in the area. The interannual and seasonal trends and the significance of eight meteorological indicators, including average temperature, maximum temperature, minimum temperature, precipitation, relative humidity, sunshine duration, wind speed, and E_pan, were analyzed for 1961 to 2010 using the trend-free pre-whitening Mann-Kendall (TFPW-MK) test. Then, the correlation between meteorological elements and E_pan was analyzed using the Spearman correlation coefficient. Results show that the average temperature, maximum temperature, and minimum temperature of the HRB increased, while precipitation, relative humidity, sunshine duration, wind speed and E_pan exhibited a downward trend. The minimum temperature rose 2 and 1.5 times faster than the maximum temperature and average temperature, respectively. A significant reduction in sunshine duration was found to be the primary factor in the E_pan decrease, while declining wind speed was the secondary factor.

With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA). To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA) shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 10⁶ m³ or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.

Droughts often have a substantial impact on normal socio-economic activities and agricultural production. The Haihe River Basin, one of the primary food production areas in China, has become increasingly sensitive to alternating droughts and floods, and the sharp transitions between them, due to rapid economic development and population growth combined with climate change. In this study, we employ the self-organizing map (SOM) neural network method to perform a cluster analysis on 43 meteorological stations in the study area, dividing the basin into five sub-regions. Then daily precipitation data (1960–2015) are collected, and the number of continuous dry days is used as a drought index to investigate drought evolution trends. Lastly, the Pearson-III curve is used to analyze the first daily precipitation after different drought duration, and the relationships between precipitation intensity, drought duration, and interdecadal drought frequency are observed. The results demonstrate that under the climate warming of the Haihe River Basin, the frequency of droughts increases throughout the whole basin, while the droughts are of shorter duration, the probability of more intense first daily precipitation after droughts increases during the dry–wet transition. The research provides a useful reference for the planning and management of water resources in the Haihe River Basin.

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.

In order to solve the water crisis, it is important to optimize the allocation of water resources. In this paper, the Whale Optimization Algorithm (WOA) is applied to the optimal allocation of water resources in Xingtai with the goal of maximum economic benefit and minimum total water shortage. The results show that the total water demand of different water users in each district is 26.94×108m3, the total allocated water is 19.83×108m3, the total water shortage is 7.11×108m3, and the water shortage rate was 26.39%. The lack of water is mainly concentrated in the primary industry. The result of the solution reflects the principle of water supply order and water use equity, which is in line with the actual development and utilization of water resources in the study area. It also verifies the feasibility of the whale optimization algorithm, such as less parameter adjustment, faster convergence, and better global optimization ability when solving water resources optimization problems.

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.