Reservoir ecological operation by quantifying outflow disturbance to aquatic community dynamics

Abstract

Reservoir operation causes spatiotemporal variations in outflow, which influence the dynamics of downstream aquatic communities. However, empirical evidence of community responses to hydrological alteration remains limited for dam-regulated rivers. This study focused on quantifying the streamflow disturbance to multi-population dynamics in downstream of the China’s Danjiangkou Reservoir. First, the stochastic population dynamics model (PDM) was used to simulate aquatic community dynamics. Then, the flow–ecology relationship was established to identify community response to reservoir outflow. Third, two novel ecological indicators, stable time (ST) and coefficient of variation at stable time (CVST), were proposed to evaluate the resilience and resistance of multi-population systems, respectively. Finally, the reservoir operating rule curves were optimized by considering tradeoffs between socioeconomic and ecological objectives. The coevolution processes of multi-population systems (fish, phytoplankton, zooplankton, zoobenthos, and macrophytes) were simulated by stochastic PDMs. The population densities of stable states showed continuous downward trends with increasing degree of hydrological alteration for multi-population systems, and aquatic community systems could be destroyed when alteration reached its acceptable maximum. The greater the degree of hydrological alteration, the longer the recovery time from an unstable to a stable state, and the weaker resistance for each population system. The resilience and resistance of downstream multi-population systems were enhanced by optimizing reservoir outflow. The optimization results illustrated that the performances of the multiple objectives of water supply, hydropower generation, and ST were improved by 2.37%, 2.40%, and 2.67%, respectively, whereas the performance of CVST was the same as the conventional operation. The flow–ecology relationship provided an approach to quantify the impacts of reservoir outflow on an aquatic community, which is helpful in guiding ecological flow strategies.

River regulation effects on zooplankton community assembly: Implications for arid river restoration and conservation

“…During the warm humid season from June to October, water eutrophication is accelerated in temperate lakes, promoting an increase in pollution indicator species, such as rotifers, and decreasing the biodiversity and stability of zooplankton assemblages (Diniz et al, 2021; Zganec, 2012; Zhao et al, 2017). In this natural succession process, temporary flood pulses undoubtedly function as a high disturbance, resulting in nutrient washout, dissolved organic matter (DOM) input, species interactions, and community reassembly (de Souza et al, 2019; Wang et al, 2021). The cascade lake series along the 20 km section U was only connected during the PHF days by the ‘flood’ flow.…”

“…Undoubtedly, the rational timing and magnitude of water flow effectively promote the integrity and ecological functions of river ecosystems and can help in decision‐making for river restoration (Palmer & Ruhi, 2019). However, in most cases, water regulation projects consider hydro‐security, irrigation, and landscape outputs while undervaluing the water needs of aquatic species and the overall ecosystem equilibrium (Liu et al, 2013; Wang et al, 2021). In this study, man‐made PHF floods were scheduled in Spring and early Winter, immediately before and after the humid season from July to September.…”

“…In river systems, zooplankton generally disperse passively downstream (de Souza et al, 2019) but hydrodynamic turbulence depresses their feeding efficiency and viability, especially for large rotifers and crustaceans (Pécseli et al, 2015). The species composition and population sizes of zooplankton communities change frequently in river systems in response to river flux variations and water quality alterations (Wang et al, 2021).…”

“…DOM) input, species interactions, and community reassembly(de Souza et al, 2019;Wang et al, 2021). The cascade lake series along the 20 km section U was only connected during the PHF days by the 'flood' flow.…”

Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks

“…Driven by the increasing computational power of computers and the availability of big data, unsupervised deep learning methods exhibit superior analytical and learning capabilities compared to statistical models (Kratzert et al 2019, Sit et al 2020). Deep learning networks have achieved remarkable success in the field of Earth science, deriving extensive applications in addressing challenges such as spatial missing data (Wang et al 2021), spatial down-scaling, and improvement of the spatial precipitation prediction (Pan et al 2019). CNNs enable automatic feature learning from vast amounts of data and generalize them to unfamiliar domains of the same nature (Shin et al 2016).…”