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\begin{document}

\title{Benefits of Using Higher Density Lower Reliability Weather Data from the
Global Historical Climatology Network (GHCN) Monitors for Watershed
Modelling}



\author[1]{Daniel Fuka}%
\author[1]{Roja K. Garna}%
\author[1]{Zachary Easton}%
\author[2]{Joshua Faulkner}%
\author[3]{Amy S. Collick}%
\affil[1]{Virginia Polytechnic Institute and State University Department of Biological Systems Engineering}%
\affil[2]{University of Vermont}%
\affil[3]{Morehead State University}%


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  \date{March 14, 2025}


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\begin{abstract}
Hydrological models require a complete and accurate time series of
weather inputs to adequately represent watershed-scale responses. The
Global Historical Climatology Network (GHCN) is the most comprehensive
ground-based global weather database and is often used in hydrological
modeling studies. Since higher density, lower reliability precipitation
measurements from private citizens collected by the Community
Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were
integrated into the GHCN, hydrological modelers in the U.S. have access
to a much greater amount of weather data. However, the benefit of using
CoCoRaHS data has not been assessed. The objectives of this work were to
develop a method for generating a complete weather data time series
based on the combination of data from multiple GHCN monitors and to
assess several methods for estimation of missing weather data. Weather
data from GHCN monitors located within a specific radius of a watershed
were obtained and interpolated using three estimation methods (Inverse
Distance Weighting (IDW), Inverse Distance and Elevation Weighting
(IDEW), and Closest Station), creating a seamless time-series of weather
observations. To evaluate the performance of the methodologies, weather
data obtained from each estimation method was used to force the Soil and
Water Assessment Tool (SWAT) models of 21 U.S. Department of
Agriculture-Conservation Effects Assessment Project watersheds in
different climate regions to simulate daily streamflow for 2010-2021.
Except for three watersheds, all SWAT models had Nash-Sutcliffe
Efficiency above 0.5, the ratio of the root mean square error to the
standard deviation of observations below 0.7, and percent bias from
-25\% to 25\% with a satisfactory performance rating. Overall, IDEW and
IDW performed similarly, and the Closest Station method resulted in the
poorest streamflow simulation. A comparison with published SWAT model
results further corroborated improved model performance using newly
combined GHCN data with all Closest Station, IDW, and IDEW methods.%
\end{abstract}%



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