Fri Apr 10, F. Flechtner
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Rostami Khalaj, Mohammad, Noor, Hamzeh, and Arjmand Sharif, Mohmood, 2026. A machine learning framework for downscaling and predicting groundwater levels using random forest and satellite data: a case study in Iran. Hydrogeology Journal, .
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026HydJ..tmp...26R,
author = {{Rostami Khalaj}, Mohammad and {Noor}, Hamzeh and {Arjmand Sharif}, Mohmood},
title = "{A machine learning framework for downscaling and predicting groundwater levels using random forest and satellite data: a case study in Iran}",
journal = {Hydrogeology Journal},
keywords = {Groundwater management, Downscaling, Remote sensing, Random forest, Iran},
year = 2026,
month = feb,
abstract = "{Groundwater is a critical resource in Iran's border regions, where
surface-water scarcity has intensified reliance on subsurface
reserves, leading to overextraction and rapid depletion.
Sustainable management in these arid areas demands high-
resolution, continuous data, yet field-based monitoring remains
limited by cost and logistical challenges. Satellite remote
sensing, particularly the GRACE mission, provides essential
large-scale terrestrial water storage anomaly (TWSA) estimates
but suffers from coarse spatial resolution that constrains local
applications. This study introduces a machine learning-based
framework to downscale GRACE-derived terrestrial water storage
anomaly and simulate groundwater level change at a finer
resolution of 0.25{\textdegree}
(\raisebox{-0.5ex}\textasciitilde 25 km). A random forest model
was applied to refine GRACE data from 1 to 0.25{\textdegree}
resolution using predictors such as precipitation,
evapotranspiration, land surface temperature, and vegetation
indices. The downscaled dataset, combined with ancillary
hydrological variables, supported the development of a second
random forest model for monthly groundwater level change
prediction, validated against in situ piezometric data. Results
indicated strong model performance, with R$^{2}$ values of 0.90
and 0.74 for training and testing phases, respectively,
confirming the framework's ability to capture groundwater
fluctuations across diverse aquifers. The study highlights the
potential of integrating downscaled satellite observations with
machine learning to enhance groundwater assessment and support
data-driven management in water-stressed environments.}",
doi = {10.1007/s10040-026-03015-4},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026HydJ..tmp...26R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Fri Apr 10, F. Flechtner