Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026138 |
A near‐term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time | |
R. Quinn Thomas; Renato J. Figueiredo; Vahid Daneshmand; Bethany J. Bookout; Laura K. Puckett; Cayelan C. Carey | |
2020-09-01 | |
发表期刊 | Water Resources Research |
出版年 | 2020 |
英文摘要 | Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real‐time iterative water temperature forecasting system (FLARE – Forecasting Lake And Reservoir Ecosystems). FLARE is composed of: water temperature and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble‐based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475‐day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean squared error (RMSE) of daily forecasted water temperatures was 1.13°C at the reservoir’s near‐surface (1.0 m) for 7‐day ahead forecasts and 1.62°C for 16‐day ahead forecasts. The RMSE of forecasted water temperatures at the near‐sediments (8.0 m) was 0.87°C for 7‐day forecasts and 1.20°C for 16‐day forecasts. FLARE successfully predicted the onset of fall turnover 4‐14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near‐sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open‐source system for lake and reservoir water quality forecasting to improve real‐time management. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/293078 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | R. Quinn Thomas,Renato J. Figueiredo,Vahid Daneshmand,等. A near‐term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time[J]. Water Resources Research,2020. |
APA | R. Quinn Thomas,Renato J. Figueiredo,Vahid Daneshmand,Bethany J. Bookout,Laura K. Puckett,&Cayelan C. Carey.(2020).A near‐term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time.Water Resources Research. |
MLA | R. Quinn Thomas,et al."A near‐term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time".Water Resources Research (2020). |
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