In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

When designing a fish collection system for fishpass facilities, considering the approach–avoidance behavior of fish under different flow velocities and light colors, is essential to ensure a good fishpass efficiency. In this study, a generalized physical model for a fish collection system entrance, including the fish collection system channel, the fish luring channel, and the mainstream channel was designed. Grass carp, a representative fish of “four major Chinese carps”, was selected as the research object, and the approach–avoidance behavior of 660 juvenile grass carps (8–12cm), under different flow velocity and light color at a water temperature of 28 ± 1 °C, were investigated. Three general indicators that reflect the fish-luring ability of a fish collection system were proposed, including the optimal flow velocity at the fish collection system entrance, the optimal ratio between the flow velocities at the entrances of the fish luring channel and the fish collection system channel, and the optimal light colors for approach–avoidance behaviors of the fish. Results indicate that (1) there was an optimal flow velocity (approximately 0.3 m/s) at the fish collection system entrance; (2) there existed an optimal ratio (approximately 2.3:1) between the flow velocities at the entrances of the fish luring channel and the fish collection system channel; (3) there were different approach–avoidance behaviors of the fish to various light colors, and the percentages of successful migration of the juvenile grass carps were 0.4%, 0.57%, 0.88%, and 1.43% of that obtained under natural light, when red, white, green, and blue light were used, respectively, at the fish collection system entrance, indicating that the juvenile grass carps would avoid the red light while approaching the blue light. The three proposed general indicators are the keys in the design of a fish collection system entrance, for successful migration of grass carps. The generalized physical model and the experimental devices and methods will provide important references for studying a fish collection system entrance for other fish species.

In fishway design, the combination of fish swimming behaviors and suitable fishway hydraulic characteristics increases the fish passage efficiency. In this study, the most representative grass carp among the “four major Chinese carps” was selected. Under conditions similar to the time period for feeding migration, juvenile grass carps were targeted to study the swimming characteristic indicators (i.e., critical and burst swimming speeds) and swimming behaviors that were closely associated with fishway hydraulic design using the incremental water velocity method in a homemade test water tank. (1) The study results reveal that both the absolute critical (U_crit) and burst (U_burst) swimming speeds increased linearly with increasing body length and both the relative critical (U’_crit) and burst (U’burst) swimming speeds decreased linearly with increasing body length. There existed a quantitative relationship between U_burst and U_crit, which could facilitate the fishway hydraulic design. (2) This study analyzed the effects of water velocity changes on fish swimming behaviors and proposed a classification method for four fish swimming behaviors—swimming freely, staying, dashing at a long distance, and dashing at a short distance—of tested fish during the process of adapting to water velocity changes interspersed with one another. The entire swimming process under the incremental water velocity was divided into four stages. (3) This study suggests that the maximum water velocity of the mainstream in a fishway using grass carp as the major passage fish should not exceed 52–60% U_burst at stage 1. For the high-water velocity areas of a fishway, such as vertical slots and orifices, the optimal water velocity should not be higher than 76–79% U_burst at stage 2 and should absolutely not exceed 90–96% U_burst at stage 3.

The hydropower system in the Upper Yellow River (UYR), one of the largest hydropower bases in China, plays a vital role in the energy structure of the Qinghai Power Grid. Due to management difficulties, there is still considerable room for improvement in the joint operation of this system. This paper presents a general LINGO-based integrated framework to study the operation of the UYR hydropower system. The framework is easy to use for operators with little experience in mathematical modeling, takes full advantage of LINGO’s capabilities (such as its solving capacity and multi-threading ability), and packs its three layers (the user layer, the coordination layer, and the base layer) together into an integrated solution that is robust and efficient and represents an effective tool for data/scenario management and analysis. The framework is general and can be easily transferred to other hydropower systems with minimal effort, and it can be extended as the base layer is enriched. The multi-objective model that represents the trade-off between power quantity (i.e., maximum energy production) and power reliability (i.e., firm output) of hydropower operation has been formulated. With equivalent transformations, the optimization problem can be solved by the nonlinear programming (NLP) solvers embedded in the LINGO software, such as the General Solver, the Multi-start Solver, and the Global Solver. Both simulation and optimization are performed to verify the model’s accuracy and to evaluate the operation of the UYR hydropower system. A total of 13 hydropower plants currently in operation are involved, including two pivotal storage reservoirs on the Yellow River, which are the Longyangxia Reservoir and the Liujiaxia Reservoir. Historical hydrological data from multiple years (2000–2010) are provided as input to the model for analysis. The results are as follows. 1) Assuming that the reservoirs are all in operation (in fact, some reservoirs were not operational or did not collect all of the relevant data during the study period), the energy production is estimated as 267.7, 357.5, and 358.3×10⁸ KWh for the Qinghai Power Grid during dry, normal, and wet years, respectively. 2) Assuming that the hydropower system is operated jointly, the firm output can reach 3110 MW (reliability of 100%) and 3510 MW (reliability of 90%). Moreover, a decrease in energy production from the Longyangxia Reservoir can bring about a very large increase in firm output from the hydropower system. 3) The maximum energy production can reach 297.7, 363.9, and 411.4×10⁸ KWh during dry, normal, and wet years, respectively. The trade-off curve between maximum energy production and firm output is also provided for reference.