Mon Aug 24, F. Flechtner
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Foroodi, Zahra, Amerian, Yazdan, and Mahbuby, Hany, 2026. Weighted downscaling of groundwater storage anomalies using random forests in Tehran region. Advances in Space Research, 78(5):4529–4547, doi:10.1016/j.asr.2026.06.077.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026AdSpR..78.4529F,
author = {{Foroodi}, Zahra and {Amerian}, Yazdan and {Mahbuby}, Hany},
title = "{Weighted downscaling of groundwater storage anomalies using random forests in Tehran region}",
journal = {Advances in Space Research},
keywords = {Groundwater storage, GRACE, Downscaling, Random forests},
year = 2026,
month = sep,
volume = {78},
number = {5},
pages = {4529-4547},
abstract = "{Gravity Recovery and Climate Experiment (GRACE) data have been
extensively used to monitor terrestrial water storage (TWS) and
groundwater storage (GWS). Estimating accessible groundwater is
a crucial for managing water consumption in arid and semi-arid
regions such as Tehran, while the coarse resolution of GRACE
satellites poses a limitation for estimating groundwater storage
anomalies (GWSA) at regional scale. Therefore, downscaling GWSA
is of significant importance. In this study, we estimated three
parameters{\textemdash}time lag, scale, and
shift{\textemdash}for each observation well and converted
groundwater level anomalies (GWLA) time series into GWSA time
series. We propose a novel weighted Random Forest (RF)-based
downscaling method that leverages an autoregressive (AR) model.
Since the decline in GWSA in Tehran, the capital of Iran, has
intensified over time, temporal factors such as year exert a
stronger influence than climatic variations such as reduced
precipitation, with precipitation ranking second in importance.
Therefore, both temporal and climate-related features were
simultaneously incorporated to effectively train the Random
Forest (RF) model. The RF model was trained in both weighted and
unweighted forms. In the unweighted case, the root mean square
error (RMSE) and R$^{2}$ were 2.4 cm and 0.94, respectively,
whereas in the weighted-case, they were 2.18 cm and 0.95,
respectively. The results demonstrate the high performance and
accuracy of the proposed method. Additionally, we estimated the
effective resolution using the variogram method and the mean
distance between wells, achieving an improved resolution of the
downscaled GWSA to 0.05{\textdegree}.}",
doi = {10.1016/j.asr.2026.06.077},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026AdSpR..78.4529F},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Aug 24, F. Flechtner