Evapotranspiration (ET) is a critical process in the interaction between the terrestrial climate system and vegetation. In recent years, ET has undergone significant changes in the Jiziwan region of the Yellow River Basin, primarily due to the implementation of ecological restoration programs and the dual impacts of climate change. As a result, hydrological cycle processes have been profoundly affected, making it crucial to accurately capture trends in ET and its components, as well as to identify the key drivers of these changes. In this study, we first systematically analyzed the dynamic evolution of ET and its components in the Jiziwan of the Yellow River area between 1982 and 2018 from the perspective of land use change. To achieve accurate ET simulations, we introduced a multiple linear regression algorithm and quantitatively evaluated the specific contributions of five climate factors, including precipitation, temperature, wind speed, specific humidity, and radiation, as well as the normalized difference vegetation index (NDVI), a vegetation factor, to ET and its components. On this basis, we explored the combined influence mechanism of climate change and vegetation change on ET in detail. The results revealed that the structure of ET in the Jiziwan of the Yellow River area has changed significantly and that vegetation evapotranspiration has gradually replaced soil evaporation, occupies a dominant position, and has become the main component of ET in this area. Among the many factors affecting ET, the contribution of climate change is the most significant, with an average contribution rate of approximately 59%. Moreover, the influence of human activities on total ET and its components is also high. The factors that had the greatest impact on total ET, soil evaporation, and vegetation transpiration were precipitation, radiation, and the NDVI, respectively. In terms of spatial distribution, the eastern part of Jiziwan was more significantly affected by environmental changes, and the trends of the ET changes were more dramatic. This study not only enhances our scientific understanding of the changes in ET and their driving mechanisms in the Jiziwan area of the Yellow River but also provides a solid scientific foundation for the development of water resource management and ecological restoration strategies in the region.

Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 10⁸ m³. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 10⁸ m³. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system.

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Karst topography comprises a fragile ecological environment with a significant potential for carbon sequestration. It is characterized by severe rocky desertification, particularly in China’s karst fault basin. Therefore, there is a crucial need to scientifically evaluate the variations in carbon storage over time and space in this area to ensure effective land space planning and regional ecological security, especially considering the dual carbon target. Using land use data (1985–2020) from the karst fault basin in Southwest China, the study employed the InVEST model to evaluate temporal and spatial variations in carbon storage. A time span of 35 years was examined, and predictions regarding carbon storage in 2050 were formulated under three different conditions: natural evolution, ecological protection, and cultivated land protection. These predictions were based on natural, social, and economic driving factors. The results revealed a fluctuating downward trend in regards to carbon storage in the study area from 1985 to 2020, with a total decrease of 2.1 × 10⁶ t. After 2000, there has been significant improvement in the dynamic degree of land use for forest land, grassland, and construction land compared to the levels before 2000. Additionally, many land use types with high carbon density transitioned into those with lower carbon density. Spatially, the carbon density in the karst fault basin was higher in the north and lower in the central and southern basins. At the county spatial scale, except for the northern and central parts of the study area, there was a decrease in total carbon storage in the remaining counties. By 2050, under the ecological protection scenario, total carbon storage is projected to increase by approximately 6 × 10⁶ t, whereas under the natural evolution and cultivated land protection scenarios, it is expected to decrease by 2 × 10⁶ t and 3 × 10⁶ t, respectively. Specifically, under the natural evolution scenario, only five counties will experience an increase in carbon storage, while the other counties will witness a decrease. The findings of this study offer a scientific basis for enhancing ecosystem carbon services through land management practices and the control of rocky desertification in the karst fault basin. They can inform decision-making processes regarding carbon sequestration, ecosystem restoration, and sustainable land use planning in the region.

Based on the regional disaster system theory, this study constructed a comprehensive flood risk indicator system for small reservoirs, covering the entire flood disaster process from three dimensions: hazard, vulnerability, and exposure. The analytic hierarchy process (AHP) and entropy weight method (EW) were used to determine indicator weights, and a risk assessment was conducted for small reservoirs in Huangshan City, Anhui Province, China. The results indicate that most reservoirs exhibit medium–low overall risk, yet distinct localized high-risk zones exist. High-economic-density areas such as Tunxi District, the central–eastern parts of Huangshan District, and the central and eastern parts of Qimen County have become high-risk clusters due to prominent exposure indicators (numbers of villages and medical facilities). Reservoirs in western and northern regions exhibit higher hazard levels, primarily driven by rainfall and catchment areas. Dam height and reservoir capacity are the main factors affecting vulnerability, with no significant spatial clustering for high-vulnerability reservoirs. Through the decoupling of three-dimensional indicators, this study reveals the differentiated driving mechanisms of “hazard–vulnerability–exposure,” providing a scientific basis for optimizing flood control engineering (e.g., reservoir capacity expansion, dam reinforcement) and risk zoning management (e.g., emergency evacuation planning in high-exposure areas) for small reservoirs.

Droughts present persistent and severe challenges to the security of regional water supplies, particularly in arid and semiarid regions such as northern China. Traditional reservoir operation models that prioritize water supply reliability or economic efficiency often fail to adequately address the risks posed by extreme drought events. In this study, we develop a novel risk-informed multiobjective reservoir operation model that incorporates three key performance indicators: reliability, resilience, and vulnerability (RRV). This model aims to improve drought response and enhance the overall stability of the water supply system. It is applied to a multisource water supply system composed of the Nierji Reservoir and various water-user sectors. Unlike traditional models, this approach explicitly balances the trade-offs among supply reliability, recovery capability, and water shortage during drought periods. Comparative analyses with conventional strategies (CSs) under both a six-year consecutive dry period and a representative single dry year demonstrate the superior performance of the RRV-based model in drought management. Specifically, the model reduces the average supply disruption duration from 8–10 to 4–6 ten-day intervals, increases water supply reliability to 90%, decreases the maximum single-event shortage depth to 22 × 10⁶ m³, and lowers the average water shortage to 221 × 10⁶ m³. Agricultural water shortages are reduced, although slight increases occur in other sectors. The results highlight resilience as the most influential objective in the model, and its inclusion or exclusion can be adjusted based on different drought response priorities. This study presents a novel and adaptive framework for reservoir operation under drought conditions, offering practical implications for improving the resilience and efficiency of regional water resource systems in the context of climate change.