Effects of reservoir operation methods on downstream ecological disturbance and economic benefits

• Published in: River research and applications 2019-09, Vol.35 (7), p.955-965
• Main Authors: Liu, Hongrui, Yin, Xin‐An, Xu, Zhihao, Cai, Yanpeng, Yang, Wei
• Format: Article
• Language: English
• Subjects: Annual; Annual runoff; Aquatic ecosystems; Bayesian analysis; Coefficients; Computer applications; discrete number of storage states; Disturbance; Downstream effects; Dynamic programming; dynamic programming models; Ecological effects; Ecological monitoring; Economic benefits; Economic models; Economics; Ecosystem disturbance; Ecosystem integrity; Ecosystems; Electric power generation; flow regime alteration; Impact analysis; Mathematical models; Natural flow; Policies; Probability theory; Ratios; Reservoir management; Reservoir operation; Reservoir storage; riverine ecosystem; Runoff; Stochasticity
• ISSN: 1535-1459; 1535-1467
• DOI: 10.1002/rra.3485

The natural flow regime can sustain the ecological integrity of riverine ecosystems. Different reservoir operation polices differ in their effects on the degree of alteration of natural flow regimes. Dynamic programming plays an important role in developing operation policies. When using dynamic programming models to develop operation policies, the discrete number of storage states (DNSS), which is a key factor affecting the reservoirs operation policies, is always been determined based on computational efficiency and economic benefits. Little consideration has been given to the ecological disturbance caused by different DNSS‐based operation policies. To analyze the impact of DNSS, we built a deterministic dynamic programming model to explore the relationship among DNSS, the flow regime alteration (ecological disturbance), and the cumulative annual power generation (economic benefits) by setting a range of DNSS scenarios. We used three reservoirs with different storage coefficients (ratios of usable storage to annual average runoff) as examples and used the range of variability approach to assess the ecological disturbance under these scenarios. We compared the results with those of a stochastic dynamic programming (SDP) model and a Bayesian SDP (BSDP) model. We found that when DNSS is low, increasing DNSS improves economic benefits but causes a more severe ecological disturbance; when DNSS is high, increasing DNSS improves the economic benefits only slightly, without exacerbating the ecological disturbance; for a given DNSS, the BSDP model provides higher economic benefits than the SDP model and a similar disturbance of the riverine ecosystem; and larger reservoirs more often cause more severe disturbance of riverine ecosystems because monthly mean flows and annual extreme flows change more drastically. Our results will help to protect the riverine ecosystems and improve economic benefits if reservoir operation managers consider DNSS using dynamic programming models.