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\begin{document}

\title{Incorporating IMERG Satellite Precipitation Uncertainty into Seasonal
and Peak Streamflow Predictions using the Hillslope Link Hydrological
Model}



\author[1]{Samantha Hartke}%
\author[1]{Daniel B Wright}%
\author[2]{Felipe Quintero}%
\author[3]{Aline S Falck}%
\affil[1]{University of Wisconsin-Madison}%
\affil[2]{University of Iowa}%
\affil[3]{National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
In global applications and data sparse regions, which comprise most of
the earth, hydrologic model-based flood monitoring relies on
precipitation data from satellite multisensor precipitation products or
numerical weather forecasts. However, these products often exhibit
substantial errors during the meteorological conditions that lead to
flooding, including extreme rainfall. The propagation of precipitation
forcing errors to predicted runoff and streamflow is scale-dependent and
requires an understanding of the autocorrelation structure of
precipitation errors, since error autocorrelation impacts the
accumulation of precipitation errors over space and time in hydrologic
models. Previous efforts to account for satellite precipitation
uncertainty in hydrologic models have demonstrated the potential for
improving streamflow estimates; however, these efforts use satellite
precipitation error models that rely heavily on ground reference data
such as rain gages or weather radar and do not characterize the
nonstationarity of precipitation error autocorrelation structures. This
work evaluates a new method, the Space-Time Rainfall Error and
Autocorrelation Model (STREAM), which stochastically generates possible
true precipitation fields, as input to the Hillslope Link Model to
generate ensemble streamflow estimates. Unlike previous error models,
STREAM represents the nonstationary and anisotropic autocorrelation
structure of satellite 2 precipitation error and does not use any ground
reference to do so. Ensemble streamflow predictions are compared with
streamflow generated using satellite precipitation fields as well as a
radar-gage precipitation dataset during peak flow events. Results
demonstrate that this approach to accounting for precipitation
uncertainty effectively characterizes the uncertainty in streamflow
estimates and reduces the error of predicted streamflow. Streamflow
ensembles forced by STREAM improve streamflow prediction nearly to the
level obtained using ground-reference forcing data across basin sizes.%
\end{abstract}%



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