Integrating Remote Sensing and Machine Learning for Groundwater
Withdrawal Estimation in Arizona
Abstract
Groundwater is the largest source of Earth’s liquid freshwater and plays
a critical role in global food security. With the rising global demand
for drinking water and increased agricultural production, overuse of
groundwater resources is a major concern. Because groundwater
withdrawals are not monitored in most regions with the highest use,
methods are needed to monitor withdrawals at a scale suitable for
implementing sustainable management practices. In this study, we combine
publicly available datasets into a machine learning framework for
estimating groundwater withdrawals over the state of Arizona. This
extends a previous study in which we estimated groundwater withdrawals
in Kansas, where the climatic conditions and aquifer characteristics are
significantly different. Datasets used in our model include
energy-balance (SSEBop) and crop coefficient evapotranspiration
estimates, precipitation(PRISM), and land-use (USDA-NASS Cropland Data
Layer), and a watershed stress metric. Random forests, a widely popular
machine learning algorithm, are employed for predicting groundwater
withdrawals from 2002-2018 at 5 km spatial resolution. We used in-situ
groundwater withdrawals available over the Arizona Active Management
Area (AMA) and Irrigation Non-Expansion Area (INA) from 2002-2010 for
training and 2011-2018 for validating the model respectively. The
results show high training (R2 ≈ 0.98) and good testing (R2 ≈ 0.82)
scores with low normalized mean absolute error ≈ 0.28 and root mean
square error ≈ 1.28 for the AMA/INA region. Using this method, we are
able to spatially extend estimates of groundwater withdrawals to the
whole state of Arizona. We also observed that land subsidence in Arizona
is predominantly occurring in areas having high yearly groundwater
withdrawals of at least 100 mm per unit area. Our model shows promising
results in sub-humid and semi-arid (Kansas) and arid regions (Arizona),
which proves the robustness and extensibility of our integrated approach
combining remote sensing and machine learning into a holistic,
automated, and fully-reproducible workflow. The success of this method
indicates that it could be extended to areas with more limited
groundwater withdrawal data under different climatic conditions and
aquifer properties.