The water-sediment regulation scheme (WSRS) imposed on dams throughout the Yellow River not only alleviates siltation in the downstream section but also alters the nutrient characteristics, which indirectly affects the enrichment of nutrients in the estuary. Nevertheless, the long-term changes in the nutrient contents and their causes in the lower Yellow River (LYR) remain unclear, and the nutrients characteristics during the years with and without WSRS have yet to be compared. Therefore, the purpose of this study was to explore the variations in the nutrient contents and limitations at the Lijin station on the LYR over the past decade, especially during the annual WSRS period, and to compare the water quality characteristics at Lijin between the years with and without WSRS. The results reveal that WSRS significantly changed the seasonal nutrient concentrations (nitrogen, phosphorus and silicon) at the Lijin station. The fluxes of these nutrients during WSRS (excluding 2016 and 2017) accounted for 11.64–40.63% of the total annual fluxes. The N concentration in the LYR was higher than that in some global rivers, while the concentrations of dissolved inorganic phosphorus (DIP) and dissolved silica (DSi) were lower than the average levels in other rivers. In addition, higher values of dissolved inorganic nitrogen (DIN), DSi and the Redfield ratio indicated that the growth of phytoplankton at the Lijin station was strongly restricted by P. However, during the 2 years without WSRS (2016 and 2017), the proportions of the nutrient fluxes in June were less than 66% of those in the WSRS period in other years. Additionally, there was a potential Si limitation in June in these 2 years. Furthermore, due to the occurrence of floods upstream of the Yellow River and the low-level operation of the Xiaolangdi Reservoir, the fluxes of nutrients during WSRS in 2018 were approximately 0.90–4.20 times those during the same period in 2009–2015 and 6.30–35.76 times those in June 2016 and June 2017. This study shows that WSRS effectively changes the nutrient balance in the LYR and provides a reference for the multi-objective collaborative optimization of WSRS to improve siltation and control flood in the LYR.

Little research has been done on the application of machine learning approaches to evaluating the damage level of river training structures on the Yangtze River. In this paper, two machine learning approaches to evaluating the damage level of spur dikes with tooth-shaped structures are proposed: a supervised support vector machine (SVM) model and an unsupervised model combining a Kohonen neural network with an SVM model (KNN-SVM). It was found that the supervised SVM model predicted the damage level of the validation samples with high accuracy, and the unsupervised data-mining KNN-SVM model agreed well with the empirical evaluation result. It is shown that both machine learning approaches could become effective tools to evaluate the damage level of spur dikes and other river training structures.

Highlights. The SWAT model was used under different scenarios for a typical agricultural area, North China Plain.The influences of land use/cover change on key

River migration and anthropogenic controls on hydrological processes may play important roles in estuarine system transformations and nutrient diffusion. We used a two-dimensional shallow water equation hydrodynamic water quality model to simulate total nitrogen (TN) transport under the situations of river migration and the “Water-Sediment Regulation Scheme” (WSRS). The results showed the following: (1) River migration changed the diffusion direction of high-TN-concentration water in the YRE from the east–west diffusion in 2009 to the north–south diffusion in 2019. (2) In the years the WSRS was active, the maximum diffusion distance of high-concentration-TN water is basically the same as that of the plume edge. In 2009 and 2019, it was 30 km in the southeast of the estuary and 26.5 km in the north. Concentrations of 0.5 mg/L and 1.05 mg/L in 2009 and 2019 can be used as the threshold for judging the farthest distance of diffusion. (3) In the years without the WSRS, the TN concentration in the YRE from June to July was generally lower than the same period in 2019, and the northward diffusion distance of high-concentration-TN water in 2017 was only 10% of that during the WSRS in 2019. (4) Runoff determines the diffusion range of TN in the YRE. The average runoff during the WSRS in 2019 was 6.88 times that of the same period in 2017, and the high concentration diffusion distance of TN in 2019 was 10 times that of 2017. Changes in estuary morphology determine the diffusion direction of nutrients. The results of this paper are helpful to further understand the nutrient diffusion law of estuaries and coasts under the influence of different factors, and to provide reference for the protection of water quality safety.