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\title{Toward Field Scale Groundwater Withdrawals in the Western U.S. using Remote Sensing and Climate Data}



\author[1]{Sayantan Majumdar}%
\author[1]{Thomas Ott}%
\author[1]{Justin Huntington}%
\author[2,3]{Ryan Smith}%
\author[3]{Venkataraman Lakshmi}%
\affil[1]{Desert Research Institute}%
\affil[2]{Colorado State University}%
\affil[3]{University of Virginia}%


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  \date{May 18, 2024}


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\begin{abstract}
In the Western U.S., the combination of increased and projected
droughts, rising irrigation water demands, and population growth is
expected to intensify groundwater consumption leading to adverse
consequences like land subsidence and aquifer depletion. Despite the
urgent need to address these challenges, there is limited local-scale
monitoring of groundwater withdrawals in most of the groundwater basins
in this region. Understanding the volume of groundwater being withdrawn
is indispensable for implementing sustainable solutions to tackle water
security issues. Hence, developing reliable and efficient groundwater
withdrawal monitoring solutions is critical to address the pressing
water management concerns in this region. The existing methods for
estimating groundwater withdrawals are either costly and time-consuming,
or they cannot generate dependable predictions at the scales required
for effective local management. While our earlier works on integrating
remote sensing and machine learning techniques to estimate gridded (1-5
km) groundwater use in Kansas, Arizona, and the Mississippi Alluvial
Plain have been successful, field-scale estimation is still a challenge.
Here, we use statistical and machine learning-based approaches to relate
field-scale groundwater withdrawals with remote sensing-derived
datasets, e.g., Landsat evapotranspiration (ET), downscaled SMAP surface
soil moisture, and other hydrometeorological datasets for several
Western U.S. states. We apply and test our approach by estimating and
comparing groundwater pumping measurements at field- and regional-scales
for multiple groundwater basins. Preliminary results using linear
regression and machine learning-based approaches in Nevada and Arizona
show promise (R\textsuperscript{2} of 0.5 to 0.7), additional in-situ
pumping data actively being compiled will likely improve the models.
While there are clear opportunities for model improvements, modeled
withdrawal estimates are likely more accurate than common water right
duties and potential crop ET-based estimates. We aim to enable water
resource and user communities better understand water use, water budgets
and support field-scale management practices for metered and unmetered
groundwater basins.%
\end{abstract}%



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