4.7 Article

MASR: A novel monitoring method coupled with interpretation platform for near-term management in thermal stratified reservoirs

期刊

JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 323, 期 -, 页码 -

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2022.116172

关键词

Stratified reservoirs; Near -term management; Reservoir operation; Wave -drive platform; Real time interpretation

资金

  1. Natural Science Foundation of Tianjin [21JCQNJC00440]
  2. Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Research) [IWHR-SKL-KF202205]
  3. SKL [HESS-2117]
  4. National Natural Science Foundation of China [51609166, 42006168]
  5. Research Program of China Three Gorges Projects Development Co., Ltd. [JG/18011B]
  6. Tianjin Research Innovation Project for Postgraduate Students [2021YJSS038]
  7. Key Research and Development Program of Tianjin [20YFZCSN01040]

向作者/读者索取更多资源

Good water quality is crucial to public health and ecological security of reservoirs. This study developed a management assistant for stratified reservoirs, which includes a wave-driven monitoring platform and interpretation platform. The assistant demonstrated a strong capacity for capturing water quality dynamics and aided in designing withdrawal schemes and controlling algae blooms.
Good water quality is critical to public health and aquatic ecological security of global reservoirs. In stratified reservoirs, increasing near-term management demands foster extremely high monitoring and forecasting needs. In this study, a management assistant for stratified reservoirs (MASR) was developed, including a wave-driven monitoring platform and interpretation platform for multiple reservoir water quality variables. The wavedriven platform can adapt to the characteristics of water level changes and transmit the monitoring data through a mobile network to the reservoir manager, which are processed by the interpretation platform in real time for near-term management. After several months of application, MASR monitored 1237 groups of valid profile water quality data with an acceptable error, which showed a strong capacity for capturing the water quality dynamics in a stratified reservoir. The effective identification of thermal stratification structures and anoxic zones can help managers to design withdrawal schemes for reservoirs. Moreover, the prediction of algae state based on the back propagation (BP) neural network provided the basis for making operation plans to proactively control algae blooms. Our study provides an economical and convenient solution for stratified reservoirs to address near-term management issues.

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