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\begin{document}

\title{Comprehensive analysis of the NOAA National Water Model: A call for
heterogeneous formulations and diagnostic model selection}



\author[1]{J. Michael Johnson}%
\author[2]{Shiqi Fang}%
\author[2]{Arumugam Sankarasubramanian}%
\author[1]{Arash Modaresi Rad}%
\author[3]{Luciana Kindl da Cunha}%
\author[4]{Keith C Clarke}%
\author[5]{Amirhossein Mazrooei}%
\author[6]{Lilit Yeghiazarian}%
\affil[1]{Lynker}%
\affil[2]{North Carolina State University}%
\affil[3]{West Consultants}%
\affil[4]{University of California Santa Barbara}%
\affil[5]{National Center for Atmospheric Research, NCAR}%
\affil[6]{University of Cincinnati}%


\vspace{-1em}



  
  \date{June 16, 2024}


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\begin{abstract}
With an increasing number of continental-scale hydrologic models, the
ability to evaluate performance is key to understanding uncertainty in
prediction and making improvements to the model(s). In 2016, the NOAA
National Water Model (NWM) was put into operations to improve the
spatial and temporal resolution of hydrologic prediction in the U.S.
Here, we evaluate the NWM 2.0 historical streamflow record in natural
and controlled basins using the Nash Sutcliffe Efficiency metric
decomposed into relative error, conditional, and unconditional bias.
Each of these is evaluated in the contexts of categorized meteorologic,
landscape, and anthropogenic characteristics to assess model performance
and diagnose error types. Broadly speaking greater rainfall and snow
coverage leads to improved performance while larger potential
evapotranspiration (PET), aridity, and phase correlation reduce
performance. More rainfall and phase correlation reduce overall bias,
while increasing PET, aridity, snow coverage/fraction increase model
bias. With respect to landscape traits, more barren and agricultural
land yeild improved performance while more forest, shrubland, grassland
and imperviousness tend to decrease performance. Lastly, more barren and
herbaceous land tend to decrease bias, while greater imperviousness,
urban, forest, and shrubland cover increase bias. The insights gained
can help identify key hydrological factors in NWM predictions; enforce
the need for regionalized physics and modeling; and help develop hybid
post-processing methods to improve prediction. Finally, we demonstrate
how the NOAA Next Generation Water Resource Modeling Framework can help
reduce the structural bias through the application of heterogenous model
processes and highlight opportunities for ongoing development and
evaluation.%
\end{abstract}%



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\textbf{Hosted file}

\verb`954071_0_art_file_10613013_rhhcdt.docx`  available at \url{https://authorea.com/users/577649/articles/619945-comprehensive-analysis-of-the-noaa-national-water-model-a-call-for-heterogeneous-formulations-and-diagnostic-model-selection}

\textbf{Hosted file}

\verb`954071_0_supp_10613015_rqqcdt.docx`  available at \url{https://authorea.com/users/577649/articles/619945-comprehensive-analysis-of-the-noaa-national-water-model-a-call-for-heterogeneous-formulations-and-diagnostic-model-selection}

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