To reveal the influence process of land use changes on runoff variation trends, this paper takes the Luojiang River of China as the study area, and the Soil and Water Assessment Tool (SWAT) model was constructed to quantitatively analyze the impact of different land uses on runoff formation in the watershed, and used the Cellular Automata-Markov (CA-Markov) model to predict future land use scenarios and runoff change trends. The results show that: (1) the SWAT model can simulate the runoff in the Luojiang River basin; (2) the runoff in the Luojiang River basin has a decreasing trend in recent 10 years, caused by the decrease of rainfall and runoff due to changes in land use; (3) the forecast shows that the land-use changes in the basin will lead to an increase in runoff coefficient in 2025. The increase of the runoff coefficient will bring some adverse effects, and relevant measures should be taken to increase the water storage capacity of urban areas. This study can help plan future management strategies for the study area land coverage and put forward a preventive plan for the possible adverse situation of runoff variation.

The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m3, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups.

This paper, based on daily rainfall erosivity model, ArcGIS, trend analysis and Kriging interpolation method, analyzed the spatial and temporal distribution characteristics of rainfall erosivity in the Luojiang River Basin of China, and then explored the influence relationship between land use change types and rainfall erosivity potential. The results showed the following: (1) from 1980 to 2019, the distribution range of multi-annual rainfall erosivity in the Luojiang River Basin was 14,674–15,227 MJ·mm/ (hm2·h), with an average value of 14,102 MJ·mm/(hm2·h), showing an overall increasing trend; (2) the spatial distribution of rainfall erosivity value tends to be consistent with the multi-year average rainfall, showing a decreasing trend from the middle to the periphery of the basin; (3) land use change is an important factor affecting the spatial and temporal distribution characteristic of rainfall erosivity value in the basin. The increase in rainfall erosivity will undoubtedly increase the potential of soil erosion. This study can provide theoretical reference for future basin land use planning and put forward preventive suggestions according to the distribution characteristics of rainfall erosivity.

The Latent Heat Flux (LE) is an important component of surface water heat transfer and hydrological cycle, and monitoring it is of great value for water resource management and crop water demand estimation. The Heihe River Basin has complex topography, which ensures better variable control in LE analysis. In this paper, the time series analysis and statistics of LE under different underlying surface conditions in summer were carried out by using the eddy correlation observation data in the Heihe River Basin, and the regression factors were analyzed. The results show that when the underlying surface types are greatly different, there are obvious differences in the daily distribution of LE, the daily variation trend of LE and the influencing factors. The range of diurnal distribution of LE in dune, Gobi and desert from −50 W/m2 to 100 W/m2. The diurnal LE distribution of vegetable fields, cornfields and wetlands were about 55% concentrated between −50 W/m2 and 100 W/m2. Temperature and carbon dioxide concentration (CO2) are the dominant factors affecting latent heat flux. Further analysis of temperature and CO2 is carried out by stepwise regression analysis, and multiple regression models are established. In terms of correlation and confidence, the results are better than the single factor fitting, which can better reflect the synergistic effect of temperature and CO2 on LE.