This study developed four nonlinearity strength indicators for water quality responses based on cross sample entropy, Fourier transformation, non-sequence counting, and adjusted R² according to typical nonlinear load reduction-water quality responses suggested by previous studies. All the indicators were applied on typical numerical water quality simulation samples. Based on the calculation, the four indicators were compared with each other to provide suggestions on how to use them to detect the nonlinearity and measure the nonlinearity strength. Results show some overlaps among the four indicators, however, they are not interchangeable. The four indicators suggest seasonal differences, peak changes, short-term water quality deterioration, and averaged water quality changes respectively. After providing suggestions on how to use the four indicators to detect nonlinearity of water quality responses, this study further discusses the limitations on the nonlinearity definition and potential applications of the four indicators. This study will contribute to understanding, distinguishing, and analyzing the type of nonlinear water quality responses.

A hydrological simulation model was developed for the Dashetai watershed in the Inner Mongolia Autonomous Region with LSPC (Loading Simulation Program in C++). Two global sensitivity analysis (GSA) methods, Morris and Sobol, were applied to identify hydrological sensitive parameters and sensitive surface landuse types. The impacts of two GSA methods and model output measures on sensitivity analysis results were also evaluated. The main conclusions are as follows. 1) The model has a good fit for daily and monthly runoff simulation. The R² of the simulated value and the observed value is greater than 0.6, and NSE between them is greater than 0.5, which indicates that LSPC model is suitable for hydrological simulation of inland arid and semiarid areas. 2) The two GSA methods have impacts on the identification of sensitive parameters and the ranking of sensitive indexes; while the impact of the two measurement methods of MAE and MSE is mainly reflected in the identification of sensitive parameters. 3) The sensitive hydrological parameters are lower zone nominal storage (LZSN) and active groundwater evapotranspiration (AGWETP), and the sensitive underlying landuse types are grassland, farmland, forest and water. All have close relationships with precipitation and landuse types in the study area.

A three-dimensional hydrodynamic-water quality model of Lake Dianchi was used to simulate the dynamic process of lake hydrodynamics and water quality, identify the characteristics of dissolved oxygen (Do) in the lake water. Combined with the setting of the external load reduction scenario, the effects of dissolved oxygen on the endogenous release of sediment and the cycle of nitrogen and phosphorus were explored. The results are conckyded. 1) The severe hypoxia in Lake Dianchi from June to September is caused by a combination of algae outbreaks and obstructed oxygen transport in the water. 2) Dissolved oxygen regulates the release of nitrogen and phosphorus nutrients in the sediment, and then significantly affects the concentration of nitrogen and phosphorus in the bottom water. 3) The improvement of the hypoxia condition at the bottom of the lake has high requirements on the reduction of external load. 4) The dissolved oxygen concentration at the phosphorus absorption-release equilibrium point of the sediment is about 3.3 mg/L, and the change in nitrogen concentration in Lake Dianchi is more susceptible to external load.