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\begin{document}

\title{Advancing Remote Sensing and Machine Learning-Driven Frameworks for
Groundwater Withdrawal Estimation in Arizona: Linking Land Subsidence to
Groundwater Withdrawals}



\author[1]{Sayantan Majumdar}%
\author[1]{Ryan Smith}%
\author[2]{Brian Conway}%
\author[3]{V Lakshmi}%
\affil[1]{Colorado State University}%
\affil[2]{Arizona Department of Water Resources}%
\affil[3]{University of Virginia}%


\vspace{-1em}



  
  \date{June 15, 2024}


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\begin{abstract}
Groundwater plays a crucial role in sustaining global food security but
is being over-exploited in many basins of the world. Despite its
importance and finite availability, local-scale monitoring of
groundwater withdrawals required for sustainable water management
practices is not carried out in most countries, including the United
States. In this study, we combine publicly available datasets into a
machine learning framework for estimating groundwater withdrawals over
the state of Arizona. Here we include evapotranspiration, precipitation,
crop coefficients, land use, well density, and watershed stress metrics
for our predictions. We employ random forests to predict groundwater
withdrawals from 2002-2020 at a 2 km spatial resolution using in-situ
groundwater withdrawal data available for Arizona Active Management
Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002-2009 for
training and 2010-2020 for validating the model respectively. The
results show high training (R\textsuperscript{2}[?] 0.86) and good testing
(R\textsuperscript{2}[?] 0.69) scores with normalized mean absolute error
(NMAE) [?] 0.64 and normalized root mean square error (NRMSE) [?] 2.36 for
the AMA/INA region. Using this method, we spatially extrapolate the
existing groundwater withdrawal estimates to the entire state and
observe the co-occurrence of both groundwater withdrawals and land
subsidence in South-Central and Southern Arizona. Our model predicts
groundwater withdrawals in regions where production wells are present on
agricultural lands and subsidence is observed from Interferometric
Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By
performing a comparative analysis over these regions using the predicted
groundwater withdrawals and InSAR-based land subsidence estimates, we
observe a varying degree of subsidence for similar volumes of
withdrawals in different basins. The performance of our model on
validation datasets and its favorable comparison with independent water
use proxies such as InSAR demonstrate the effectiveness and
extensibility of our combined remote sensing and machine learning-based
approach.%
\end{abstract}%



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