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\begin{document}

\title{Effect of Remotely Sensed Vegetation in Hydrology and Water Quality
Predictions: New Evidence from Large-scale Watershed Modeling}



\author[1]{I Luk Kim}%
\author[2]{Adnan Rajib}%
\author[3]{Heather Golden}%
\author[3]{Charles Lane}%
\author[4]{Sujay Kumar}%
\affil[1]{Purdue University}%
\affil[2]{Texas A\&M University Kingsville}%
\affil[3]{US Environmental Protection Agency, Office of Research \& Development}%
\affil[4]{NASA Goddard Space Flight Center}%


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  \date{May 27, 2024}


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\begin{abstract}
Traditional watershed modeling often overlooks the role of vegetation
dynamics. While past studies indicate possible improved hydrologic
predictions by increasing the physical realism of vegetation dynamics in
process-based models, there has been little quantitative evidence to
support similar improvements in water quality predictions. To fill this
knowledge-gap, we recently applied a modified Soil and Water Assessment
Tool (SWAT) to quantify the extent of improvements that the assimilation
of remotely sensed Leaf Area Index (LAI) would convey to streamflow,
soil moisture, and nitrate load simulations across a 16,860 km2
agricultural watershed in the midwestern United States. We modified the
SWAT source code to directly insert spatially distributed and temporally
continuous LAI estimates from Moderate Resolution Imaging
Spectroradiometer (MODIS). Compared to a ``basic'' traditional model
with limited spatial information, our LAI assimilation model (i)
significantly improved daily streamflow simulations during medium-to-low
flow conditions, (ii) provided realistic spatial distributions of
growing season soil moisture, and (iii) substantially reproduced the
long-term observed variability of daily nitrate loads. Further analysis
revealed that assimilation of MODIS LAI data corrected the model's LAI
overestimation tendency, which led to a proportionally increased
rootzone soil moisture and decreased plant nitrogen uptake. With these
new findings, our study confirms that assimilation of MODIS LAI data in
watershed models can effectively improve both hydrology and water
quality predictions.%
\end{abstract}%



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