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 quality 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 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° at the reservoir's near-surface (1.0 m) for 7-day ahead forecasts and 1.62° for 16-day ahead forecasts. The RMSE of forecasted water temperatures at the near-sediments (8.0 m) was 0.87° for 7-day forecasts and 1.20° 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.

中文翻译：

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近期迭代预测系统可成功预测储层流体动力学并实时划分不确定性

由于人类活动，淡水生态系统正经历更大的变化，因此需要新的工具来预测未来的水质。作为响应，我们开发并部署了实时迭代水温预测系统（FLARE：预测湖泊和水库生态系统）。FLARE包括：无线传输数据的水质和气象传感器，使用传感器观测值更新流体力学模型的预测并校准模型参数的数据同化算法以及基于集合的预测算法以生成包括不确定性的预测。重要的是，FLARE可以量化不同不确定性源（驱动程序数据，初始条件，模型过程和参数）对每天多个深度的水温预报的贡献。在包括分层和混合热条件的475天期间，我们将FLARE应用到了落水河水库（美国弗吉尼亚州温顿），这是一种饮用水源。在此期间内，提前7天预测的水库附近地表（1.0 m）每日预测水温的均方根误差（RMSE）为1.13°，而提前16天的预测水温的均方根误差（RMSE）为1.62°。7天预报的近水量（8.0 m）水温的均方根误差为0.87°，16天预报为1.20°。FLARE成功地连续两年提前4-14天成功预测了秋季营业额的开始。不确定性划分将气象驱动程序数据作为大多数深度和热条件预报中不确定性的主要来源，但夏季附近的沉积物除外，当模型过程的不确定性占主导地位时。总体而言，FLARE为湖泊和水库水质预测提供了一个开源系统，以改善实时管理。