Articles | Volume 30, issue 14
https://doi.org/10.5194/hess-30-4771-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-4771-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jul 2026
Research article |
| 28 Jul 2026
| 28 Jul 2026
Limits to GRACE-based groundwater storage monitoring in a 23 000 km2 coastal basin: evidence from the Lower Kutai Basin, Indonesia
Arifin
CORRESPONDING AUTHOR
Department of Geography, University College London, London, WC1E 6BT, UK
Department of Geology, Institut Teknologi Bandung, Bandung, 40132, Indonesia
Department of Groundwater Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia
Richard G. Taylor
Department of Geography, University College London, London, WC1E 6BT, UK
Department of Earth Sciences, University of Toronto, Toronto, Ontario, M5S 3B1, Canada
Mohammad Shamsudduha
Department of Risk and Disaster Reduction, University College London, London, WC1E 6BT, UK
Agus M. Ramdhan
Department of Geology, Institut Teknologi Bandung, Bandung, 40132, Indonesia
Department of Groundwater Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5625, https://doi.org/10.5194/egusphere-2025-5625, 2026
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An improved method is used to integrate satellite-derived terrestrial water storage and surface soil moisture observations into a hydrological model within the Brahmaputra River basin (South Asia). This integration leads to a more realistic representation of the water stored in the land, allowing us to better understand its changes in space and time, which is crucial in this basin due to its increasing water demand and vulnerability to extreme events due to climate change.
Anna Pazola, Mohammad Shamsudduha, Jon French, Alan M. MacDonald, Tamiru Abiye, Ibrahim Baba Goni, and Richard G. Taylor
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This study advances groundwater research using a high-resolution random forest model, revealing new recharge areas and spatial variability, mainly in humid regions. Limited data in rainy zones is a constraint for the model. Our findings underscore the promise of machine learning for large-scale groundwater modelling while further emphasizing the importance of data collection for robust results.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
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Short summary
We evaluate the limitations of Gravity Recovery and Climate Experiment (GRACE) satellite data for estimating groundwater storage changes (ΔGWS) in a small, data-scarce coastal basin in Indonesia, where ocean signal leakage and spatial-scale mismatch present major challenges. On average, only 42% of ΔGWS estimates can be plausibly derived from GRACE, indicating that, without expanded in situ monitoring, ΔGWS estimates in this setting will remain uncertain.
We evaluate the limitations of Gravity Recovery and Climate Experiment (GRACE) satellite data...
