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\begin{document}

\title{A multisite Stochastic Watershed Model (SWM) with intermittency for
regional low flow and flood risk analysis}



\author[1]{Zachary Paul Brodeur}%
\author[1]{Rohini S Gupta}%
\author[1]{Scott Steinschneider}%
\affil[1]{Cornell University}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Stochastic Watershed Models (SWMs) are an important innovation in
hydrologic modeling that propagate uncertainty into model predictions by
adding samples of model error to deterministic simulations. A growing
body of work shows that univariate SWMs effectively reduce bias in
hydrologic simulations, especially at the upper and lower flow
quantiles. This has important implications for short term forecasting
and the estimation of design events for long term planning. However, the
application of SWMs in a regional context across many sites is
underexplored. Streamflow across nearby sites is highly correlated, and
so too are hydrologic model errors. Further, in arid and semi-arid
regions streamflow can be intermittent, but SWMs rarely model zero flows
at one site, let alone correlated intermittency across sites. In this
technical note, we contribute a multisite SWM that captures univariate
attributes of model error (heteroscedasticity, autocorrelation,
non-normality, conditional bias), as well as multisite attributes of
model error (cross-correlated error magnitude and persistence). The SWM
also incorporates a multisite, auto-logistic regression model to account
for multisite persistence in streamflow intermittency. The model is
applied and tested in a case study that spans 14 watersheds in the
Sacramento, San Joaquin, and Tulare basins in California. We find that
the multisite SWM is able to better reproduce regional low and high flow
events and design statistics as compared to a single-site SWM applied
independently to all locations.%
\end{abstract}%



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