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Miyun Reservoir plays a fundamental role in providing drinking water for Beijing Municipality. In this study, agricultural nitrogen (N) flow was analysed for water pollution mitigation in Miyun Reservoir watershed. A partial substance flow analysis (SFA) method was applied to evaluate the historic changes in N flow from 2001 to 2017. Based on this method, N flow, N losses, N accumulation, and nitrogen use efficiency (NUE) over 17 years were identified and quantified. The results showed that N input, N output, and N stock gradually decreased in this period. The decline of N input mostly resulted from the considerable decrease in chemical fertilizer use and livestock excrement application. With decreasing N input, the N stock also decreased over these years. The N losses generally decreased, except for 2009–2012, as a result of the substantial increase in harvested grains which generated even more straw for feeding or burning. In addition, the average NUE was as low as approximately 15% which may due to the huge straw storage and N losses. It is indicated that there was a great potential to decrease N input especially chemical fertilizer for mitigation of water pollution through analysis. Therefore, combined technology innovation with policy impact was proposed in Miyun County. Reducing chemical fertilizer, improving waste cycling, reducing ammonia volatilization, preventing erosion and runoff and improving peasant awareness of environmental protection were proposed as recommendations. The historic analysis of N flow could be helpful to understand the challenges of N pollution. The integrated policy and technology measures will be effective to achieve water quality control. Both will provide a reference for other countries or regions with the similar issues. [ABSTRACT FROM AUTHOR]

The community diversity of phytoplankton is quite high, and hundreds of phytoplankton species can coexist. Aggregating phytoplankton species into different phytoplankton functional groups (PFGs) can better assess and predict the potential structural and functional effects of aquatic ecosystems. The continuous interaction of key PFGs is the key cause of cyanobacterial blooms in drinking water supply systems, especially in shallow border reservoirs, but the reasons have not been understood. This study was carried out in Macau's two shallow border reservoirs, the Main Storage Reservoir (MSR) and Sai Pa Van Reservoir (SPVR). Then, phytoplankton samples from these reservoirs were collected monthly over a four-year period (from January 2011 to December 2014) to reveal key factors driving PFGs succession and their synthetic effects on the dominant species. The trophic state index (TSI) of TN and TP of the two reservoirs ranged from 36.25 to 83.95 and 8.32–56.13, respectively, indicating that the SPVR and MSR were between a eutrophic and hypereutrophic state. The phytoplankton analysis results showed that there were 14 dominant PFGs and 8 dominant PFGs in the MSR and SPVR, respectively. The group S 1 was always predominant in the SPVR during the succession of PFGs, while the succession of PFGs in the MSR were quite different, the PFGs could mainly be summarized as S 1 /S N /X 1 (2011) → X 1 /J/B/T B /P (2012, 2013) →S 1 /X 1 /J (2014). In addition, redundancy analysis (RDA) through stepwise discriminant analysis was used to analyze the dominant PFGs and abiotic factors in both reservoirs. The results indicated that the turbidity value of the reservoir was the key factor influencing groups S 1 , Lo and S N in the SPVR. However, the different PFGs were influenced by the turbidity, NH 3 –N, NO 3 –N and EC values of the reservoir in the MSR. The relationship between PFGs and environmental factors was described by stepwise regression analysis. The investigation of the different ecological responses between the interconnected reservoirs was useful for targeted water source protection. [ABSTRACT FROM AUTHOR]

Reservoir operation policies affect reservoir nitrogen (N) cycling and budgets through complex influences on physical and biogeochemical processes in water and sediment. However, the underlying influencing mechanisms, especially the impact of operations on sediment processes, remain unclear. This study combines a coupled hydrodynamic-eutrophication-sediment model and a reservoir operation model to clarify reservoir operation impact on N cycling processes in water and sediment as well as impact on stored and discharged water quality. Three typical reservoir operation policies (i.e., standard, hedging, and eco-friendly) were selected to create operation scenarios. The hydrodynamic-eutrophication-sediment model was used to simulate N cycling processes and N concentrations in stored and discharged water under each scenario. Using China's Danjiangkou Reservoir as a case study, results revealed that reservoir operation policies significantly affected spatiotemporal dynamics of N cycling processes and water quality. Specifically, reservoir sediment acted as a N source, contributing to annual net release of 9.5–16.1 Gg N. Higher water levels promoted sediment ammonium release through effects on reservoir submerged area and sediment temperature. Moreover, denitrification processes in water and sediment collectively contributed to annual N removal of 14.0–16.9 Gg N. Annual N removal was greater in the higher water level scenario despite its lower denitrification rate per unit area. Additionally, these processes and hydrodynamic conditions combine to affect N forms and concentrations. The N concentration in the lower water level scenario was generally higher, risking algal bloom outbreaks in the shallow-water zone. This study is intended to help guide reservoir operation policymaking to account for N management while also coordinating water quantity and quality targets. • Spatiotemporal dynamics in N cycling processes and water quality are assessed. • Higher storage levels can promote sediment ammonia release in deep reservoirs. • Denitrification in water and sediment significantly contributes to N removal. • Tradeoffs exist between the quantity and quality of water released to downstream. [ABSTRACT FROM AUTHOR]

High flow events (including both the high flow pulses and floods) exert many critical ecological functions in riverine ecosystems and their elimination will lead to severe ecological degradation. However, common hydropower operations are only required to legally maintain the minimum environmental flows (e-flows). The extra release of high flow events will result in revenue loss of hydropower producers. In this research, we propose an approach to assessing the eco-compensation standard which is necessary to compensate the loss of hydropower producers and promote the release of high flows to downstream rivers. The scenario tree approach is adopted for the generation of possible scenarios of flows and electricity prices to deal with the uncertainties of the two factors in deregulated electricity markets. A stochastic linear programming method is proposed to determine the maximum mean annual revenues under two e-flow provision scenarios, i.e., only sustaining the minimum e-flows (scenario 1) and sustaining both the minimum e-flows and a specified number of high flow events (scenario 2). The revenue difference under the two e-flow scenarios is set as the eco-compensation standard. A case study in the Wangkuai reservoir, China, demonstrates that the eco-compensation standard is obviously influenced by the installed hydropower generation capacity (IHGC) and the number of high flow events planned to be released. Moreover, the greatest standards may be not corresponding to a very large IHGC. [ABSTRACT FROM AUTHOR]

Inflow forecast is an important input for reservoir operation. Due to limited forecast techniques, the inflow forecast always has uncertainties (usually in the form of forecast errors). Handling inflow forecast uncertainties effectively is important for optimizing reservoir operation. The magnitude of this uncertainty depends on both the forecast lead times and the hydrological periods (i.e., wet season vs. dry season). Two-stage Bayesian stochastic dynamic programming (TBSDP) is an effective way to address the forecast uncertainties. However, current TBSDP methods pay more attention to the uncertainty differences caused by forecast lead times, neglecting the uncertainty differences in wet and dry seasons. Starting with a TBSDP model, we proposed a new two-stage and two-period version of the model (TTBSDP), which can not only consider the forecast uncertainty differences caused by forecast lead times, but also separately accounts for forecast uncertainty in wet and dry seasons. Because dealing separately with the uncertainties in the two periods increases the computational complexity, we further explored the conditions under which degree of forecast uncertainties this separation is necessary. We compared our new model's results (in terms of cumulative annual power generation) with the previous Bayesian stochastic dynamic programming models. With increasing forecast uncertainty in the first stage of the overall forecast horizon (in the wet season), the TTBSDP model produced superior results at higher uncertainty, with the most stable performance (the smallest variation of cumulative annual power generation when the forecast uncertainty coefficient increases). The proposed new model is benefit for hydropower operations when the magnitude of forecast uncertainty in the wet seasons is large. • A two-stage and two-period Bayesian stochastic dynamic programming model was developed. • The new model dealt separately with forecast uncertainties not only in two stages but also in two hydrological periods. • The TTBSDP model produced superior results at higher uncertainty, with the smallest variation of power generation. [ABSTRACT FROM AUTHOR]