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Developing and Testing a Long Short-Term Memory Stream Temperature Model in Daily and Continental Scale
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  • Farshid Rahmani,
  • Samantha Oliver,
  • Wenyu Ouyang,
  • Alison Appling,
  • Kathryn Lawson,
  • Chaopeng Shen
Farshid Rahmani
Pennsylvania State University Main Campus

Corresponding Author:[email protected]

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Samantha Oliver
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Wenyu Ouyang
DUT Dalian University of Technology
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Alison Appling
USGS Integrated Information Dissemination Division
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Kathryn Lawson
Pennsylvania State University Main Campus
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Chaopeng Shen
Pennsylvania State University Main Campus
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Stream water temperature (T) is a variable of critical importance and decision-making relevance to aquatic ecosystems, energy production, and human’s interaction with the river system. Here, we propose a basin-centric stream water temperature model based on the long short-term memory (LSTM) model trained over hundreds of basins over continental United States, providing a first continental-scale benchmark on this problem. This model was fed by atmospheric forcing data, static catchment attributes and optionally observed or simulated discharge data. The model achieved a high performance, delivering a high median root-mean-squared-error (RMSE) for the groups with extensive, intermediate and scarce temperature measurements, respectively. The median Nash Sutcliffe model efficiency coefficients were above 0.97 for all groups and above 0.91 after air temperature was subtracted, showing the model to capture most of the temporal dynamics. Reservoirs have a substantial impact on the pattern of water temperature and negative influence the model performance. The median RMSE was 0.69 and 0.99 for sites without major dams and with major dams, respectively, in groups with data availability larger than 90%. Additional experiments showed that observed or simulated streamflow data is useful as an input for basins without major dams but may increase prediction bias otherwise. Our results suggest a strong mapping exists between basin-averaged forcings variables and attributes and water temperature, but local measurements can strongly improve the model. This work provides the first benchmark and significant insights for future effort. However, challenges remain for basins with large dams which can be targeted in the future when more information of withdrawal timing and water ponding time were accessible.