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\begin{document}

\title{Developing and Testing a Long Short-Term Memory Stream Temperature Model
in Daily and Continental Scale}



\author[1]{Farshid Rahmani}%
\author[2]{Samantha Oliver}%
\author[3]{Wenyu Ouyang}%
\author[4]{Alison Appling}%
\author[1]{Kathryn Lawson}%
\author[1]{Chaopeng Shen}%
\affil[1]{Pennsylvania State University Main Campus}%
\affil[2]{USGS}%
\affil[3]{DUT Dalian University of Technology}%
\affil[4]{USGS Integrated Information Dissemination Division}%


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  \date{June 08, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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