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Dam impoundment causes significant phosphorus (P) retention in reservoirs, leading to the emergence of legacy P release as a water quality threat under external pollution load reduction measures. Reservoir operations impact sediment P release through their complex influence on hydrodynamic and biogeochemical processes, but applicatory reservoir operation strategies for legacy P management have rarely been proposed. This study introduces a reservoir operation optimization approach for balancing economic objectives (i.e., water supply and hydropower generation) and P release mitigation. A coupled hydrodynamic-eutrophication-sediment model based on the Environmental Fluid Dynamics Code platform is used to simulate sediment P release under multiple reservoir operation scenarios. The resultant simulation data are used to train a dynamic artificial neural network (ANN) to model the response of P release to reservoir operations. By coupling the ANN with a reservoir operation model, we develop a multi-objective optimization model to refine reservoir operations for a three-tier tradeoff (i.e., water supply, hydropower generation and P release mitigation). China's Danjiangkou Reservoir is chosen as a case study to generate optimal operation rules. The optimal operation scheme decreases multi-year averaged annual P release by 19% (from 6688 tons to 5436 tons) while inversely increasing annual economic loss caused by water supply shortages by 14% and decreasing hydropower generation by 5%. Pareto-optimal solutions are subsequently generated to analyze tradeoffs between water supply/hydropower generation and P release mitigation. This study demonstrates the efficiency of using reservoir operations for legacy P management and offers an optimization tool for reservoir operation decision-making processes.

The water resources carrying capacity (WRCC) is fundamental in aiding sustainable socioeconomic regional development, and increasing attention is being paid to WRCC forecasting. The challenge of WRCC forecasting lies in the complex characteristics of nonlinear multiple indicator time series data. To address this problem, this study proposed a feedforward neural network (FNN) based on normalization and error correction for WRCC forecasting. Firstly, units and values of multiple indicators for use in the WRCC were normalized simultaneously to allow the data to be treated as a single equivalent indicator. Thus, the high-dimensional forecasting model was simplified to a low-dimensional model. To improve the effectiveness of the model, an error correction method was then adopted to correct forecasted WRCC values according to similar corresponding sample values. Two simple types of structured FNN were used to address the overfitting problem, and the bipolar sigmoid function was used as the activation function of hidden nodes. The final calibrated model was expressed as an equation rather than a programming language, thus making it easier to use. Yantai, a city in Shandong Province, was selected as a case study to validate this proposed method. Results showed that the mean relative absolute errors of these two simplified FNN models are 1.23% and 1.18%, respectively. Compared to other models, this model has been shown to be feasible and simple to use for WRCC forecasting.

In this study, an environmental-friendly modeling system was developed and applied to an agriculture nonpoint source (AGNPS) management in Ulansuhai Nur watershed. In this system, water environmental capacity, credibility-based chance-constrained programming (CCCP), and AGNPS optimization models were integrated into a general modeling framework. It could be used to calculate water environmental capacity of total nitrogen and total phosphorus in Ulansuhai Nur watershed, which could consequentially provide input data for the developed AGNPS optimization model. Also, the inherent uncertainties in estimating water environmental capacities that can be expressed as possibilistic distributions were reflected and addressed based on computational results of three widely used methods. Such uncertainties were consequentially transferred to the proposed CCCP model based on the adoption of multiple credibility satisfactory levels, significantly facilitating objectivity reflection of decision alternatives. The developed modeling system was then applied to Ulansuhai Nur watershed of Inner Mongolia, a semi-arid river basin in northwestern China. Optimal strategies for AGNPS management in Ulansuhai Nur watershed were generated with consideration of the maximum total agricultural income under multiple policy scenarios. The results showed that the total agricultural income would increase with point source pollution being cut down, and would decrease with rising credibility levels, representing decreasing system violation risks. It was indicated that the higher of total nitrogen/phosphorus discharge being less than water environmental capacity of Ulansuhai Nur, the lower the total agriculture incomes. The proposed methods could help decision makers establish various production patterns with cost-effective agriculture nonpoint source management schemes in the basin of Ulansuhai Nur, and gain in-depth insights into the trade-offs between total agricultural incomes and system reliabilities.

Soil erosion risk assessment is an essential foundation for the planning and implementation of soil and water conservation projects. The commonality among existing studies is that they considered different indicators (e.g., rainfall and slope) in order to determine the soil erosion risk, however, the majority of studies in China neglect one important indicator, namely the slope aspect. It is widely accepted that the vegetation and distribution of rainfall differs according to the different slope aspects (such as sunny slope and shady slope) and these attributes will accordingly influence the soil erosion. Thus, existing studies neglecting this indicator cannot reflect the soil erosion well. To address this problem, a flexible soil erosion risk assessment method that supports decision makers in identifying priority areas in soil and water conservation planning was developed in the present study. Firstly, in order to verify the impact of the slope aspect on soil erosion, field investigations were conducted, and its impact on the characteristics of the community in the study area was analyzed. Secondly, six assessment indicators were selected, including slope gradient, precipitation, NDVI, land use, soil texture and slope aspect. Next, a developed multi-criteria decision analysis (MCDA) method based on the Choquet integral was adopted to assess the soil erosion risk. The MCDA method, combining objective data with subjective assessment based on Choquet integral, could solve the weight problem encountered when using the quantitative method. The parameters required can be modified according to the soil erosion types, assessment scales, and data availability. The synergistic and inhibitory effects among the soil erosion parameters were also considered in the assessment. Finally, the soil erosion risk results in the Xinshui River watershed revealed that more attention should be paid to the slope of farmland and grassland during the planning and management of soil and water conservation projects. The methodology used in the current study can support decision makers in planning and implementing soil and water conservation measures in regions with different erosion types.

Lake eutrophication is a serious environmental problem worldwide, caused by both natural processes and anthropogenic influences. For effective lake eutrophication management, a variety of models have been designed to investigate the complex interrelationship between physical, chemical and biological factors and processes in lakes. However, these models are inconvenient for predictions of lake eutrophication in practical application. To date, very few studies have focused on lake eutrophication in terms of anthropogenic influences (such as sewage emissions and agricultural practices), which are more readily regulated than natural processes. Effective lake eutrophication prediction models should base primarily on facile controlled or predictable indicators. Therefore, to meet this requirement, we designed simple predictive models to determine the interrelationship between the trophic states of lakes and nutrient inputs, which is a direct measurement to characterize anthropogenic influences. Lake Taihu (China) was used as a representative eutrophic water body to assess the accuracy of the proposed predictive models. Firstly, to comprehensively understand the role of nutrient input indicators (NIIs) during eutrophication, Pearson correlation coefficient analysis was conducted on 7 NIIs and 38 types of algae. Secondly, based on the NIIs identified with high correlation coefficients, we modified three commonly used growth models (Gompertz, logistic and Richards) to describe the lake's trophic state. A particle swarm optimization algorithm (PSOA) was used to optimize model variables. Results showed that the mean absolute percentage errors of the optimized Gompertz, logistic and the Richards models were 2.95%, 2.88% and 2.17%, respectively. Finally, these optimized models were used to predict lake eutrophication under several different nutrient input scenarios. Two adjustment scenarios revealed remarkably satisfied trophic states (including roughly oligotrophic and even ultra-oligotrophic) by the 2030s. Our results show that these established models are a simple way to support lake restoration projects by setting realistic and effective targets.