the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jul 2025
Groundwater storage dynamics and climate variability in the Lower Kutai Basin of Indonesia: reconciling GRACE ΔGWS to piezometry
Abstract. Groundwater is considered a climate-resilient source of freshwater yet its long-term response to climate variability remains poorly understood in environments with limited ground-based monitoring networks. In the Lower Kutai Basin (LKB) where Indonesia’s new capital (Nusantara) is under development, we examine evidence from Gravity Recovery and Climate Experiment (GRACE) satellite data, global-scale models, precipitation records, and in situ piezometric observations to investigate groundwater storage changes (ΔGWS) over the last two decades. GRACE-derived terrestrial water storage anomalies (ΔTWS) exhibit strong seasonal and interannual variability that are dominated by changes in soil moisture storage (ΔSMS). Statistical analyses reveal low to moderate correlations (r: -0.30 to -0.56) between ΔTWS, ΔSMS, ΔGWS and the El Niño-Southern Oscillation (ENSO), particularly during the 2015–2016 El Niño when ΔTWS declined at a rate of 3.8 cm/month. Downscaled ΔTWS (0.25° and 0.5°) are strongly correlated (r = 0.85 to 1) to ΔTWS at coarser spatial scales (3° mascon and the entire Borneo) despite GRACE’s native spatial resolution limitations. As a residual parameter, GRACE ΔGWS is subject to arithmetic uncertainties that arise primarily from uncertainty in GRACE products and simulated storage components. Across the 36 realizations employed in this study, ~30 % of the GRACE-derived ΔGWS estimates per realization are physically implausible, exhibiting positive values during dry periods and vice versa; main sources of uncertainty derive from estimates of ΔSMS and surface water storage anomalies (ΔSWS) in this tropical, data-sparse environment. Despite these limitations, plausible GRACE ΔGWS values generally align with groundwater-level dynamics and trends observed from available piezometric data. High-frequency (hourly) groundwater-level observations indicate that episodic, high-intensity rainfall events (>90th percentile) disproportionately contribute to groundwater recharge.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-2941', Juergen Kusche, 02 Aug 2025
- AC1: 'Reply on RC1', Arifin Arifin, 20 Sep 2025
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RC2: 'Comment on egusphere-2025-2941', Anonymous Referee #2, 24 Oct 2025
Issues with scale: Have the authors thought about GRACEs coarse footprint? The basin, measuring about 23,000 km², is significantly less than GRACEs native resolution of around 90,000 to 300,000 km². Why is there the implication that interpolating GRACE to 0.25°–0.5° would add new detail? The ms explains that the "downscaled" delTWS on 0.25°–0.5° does not significantly change the results, since it correlates strongly with r≈0.85–1 with the initial 3° mascon and whole-Borneo signal. That is, the authors are basically looking at the same broad signal. Can we discuss whether this adds to our knowledge, or could this section be reworded or revised for simplicity? Could explain why these finer grids matter, as they are not independent signals.
Data and uncertainty: The delGWS estimates are based on the subtraction of soil and surface water models from GRACE TWS and thus inherit all related uncertainties. You are using several GLDAS models and WGHM and reporting their differences. But it would be helpful to discuss the unreliability of those in this situation. For example, you noticed that one of the GLDAS runoff datasets was "implausible," and you discarded it – this says that model results can not be completely trusted. Did you compare GLDAS soil moisture or WGHM surface water with any local observations? If that is the scenario, it would be helpful if this could be mentioned. Your technique for producing 36 GWS "realizations" through combinations of GRACE and model ensembles is resourceful. Nevertheless, the selection to eliminate some 30% of them as "implausible". Have you thought about reducing model biases instead? You are correct to have noted that these arithmetic anomalies mainly appear when delTWS is smaller than delSMS plus delSWS in the wet months, or the reverse during the dry months. Overlooking those months might affect your trends. It would be useful to have a numerical estimate of the effect of the filtering on the results. You show that removing the outliers improves the TWS–GWS correlation. It might be helpful to provide error estimates for delGWS or to note that the "mean plausible" series is just one model of noisy data? Assumptions of the model: Along these lines, it should be pointed out that GLDAS and WGHM dont account for groundwater pumping.
There appear to be signs that some wells are experiencing decline due to abstraction, but the GWS calculations dont account for this. If water pumping is stopped from the basin (and not simply recharging local surface water), GRACE would find a deficit. But the average GRACE DEL GWS trend indicates a very gentle positive change, even when one well went significantly deeper and had a local decrease. How do you resolve that? The conclusion blames the discrepancy on "withdrawals," but it would be nice to supply some quantification or at least investigate whether the pumping is too localized to be seen in GRACE. Are there other factors to take into account, such as groundwater draining from the basin by rivers or recharge assumptions?
ENSO correlations: The moderate values of delTWS/delSMS versus ENSO indices seem to make sense and are in line with results from other studies, but care should be taken with the short time series. Did you test for statistical significance or account for autocorrelation? Has it been possible for you to investigate correlating detrended or deseasoned data? It might improve your case if you think about stripping the seasonal cycle before associating it with ENSO. In some way or other, the physical explanation works out: large El Niño events dry the basin, which in turn amplifies the GRACE delGWS drops. But it is really the soil moisture changes that are having an effect on TWS or it seems that delGWS is much less sensitive? It may be beneficial to emphasize how the soils and floodplains contribute significantly to the GRACE signal here.
Piezometer comparisons: I do like the attempt to incorporate groundwater wells, but please be careful. In the first place, matching GRACE delGWS (in cm over the whole basin) with a small set of point measurements is of course an approximation. Your conclusion shows a "moderate" correlation , which is reasonable. Claiming, however, that the time series "align with groundwater-level" (Abstract) goes too far. For example, GRACE indicates a small upward trend in general, while the deep well has a downward trend. How are the authors confirming that these are actually deep observation wells? Furthermore, head to storage conversion demands a certain yield values, which has not been discussed. Thus, the comparison is qualitatively of the rise/fall type instead of being concerned with quantitative volume. Please the range of specific yield you are assuming, or flagging this as a possible source of uncertainty? without knowing aquifer properties, its not easy to relate a GRACE-derived cm change to an observed m depth change.
Monitoring groundwater: Filtering for the minimum daily level is an able strategy and seems to track recharge. As a reader, I am interested in the strength of that: can pump-off intervals be incorrectly interpreted, or can the lowest daily level nevertheless be influenced by slower pumping rates? Can you indicate how well these loggers are calibrated? A mention of data quality control, would be helpful.
Clarity and flow: The writing is generally easy to follow, although there are spots where its a bit dense. The methods section is very thorough, although it could do with more obvious labelling. It may be a good idea to divide the GRACE discussion from the GLDAS/WGHM. Some of the sentences are also very long – try breaking them up to make them easier to read, especially in the introduction and methods sections.
This paper shows a worthy effort in tackling a difficult problem, however, I am concerned about the 23,000 km² study area. The authors have used a wide range of data and monitoring methods. I would recommend significant revision, moderating any very assertive conclusions, especially about delGWS match with wells etc, and being forthright about the large uncertainties involved. I look forward to reading a revised version.
Citation: https://doi.org/10.5194/egusphere-2025-2941-RC2 - AC2: 'Reply on RC2', Arifin Arifin, 31 Oct 2025
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Arifin et al look at three different ways of measuring groundwater storage dynamics in the Lower Kutai Basin (LKB) of Indonesia: the GRACE budget residual approach, data from the existing (few) local piezometric sensors, and the possibility to use the sensors from pumping wells. The study is motivated by the fact that Indonesia’s new capital Nusantera is under development in the LKB which will lead to increased pressure on water resources.
The main message of the paper seems to me that currently neither of the three approaches can provide a reliable assessment of groundwater resource variability, and the government should think about rolling out a comprehensive measuring network. In my understanding the region is simply too small to be resolved well in GRACE (as the authors know very well), and there are too few in-situ sensors. The authors suggest that about 30% of their GRACE-derived dGWS ensemble timeseries are not usable since they provide unphysical results, and they discuss many timeseries with correlations about 0.3 as „weakly correlated“ – I think given that timeseries here are not very long on climate timescales we can say that such low correlation means nearly uncorrelated.
On balance, my judgement is that the topic is very relevant and there seems a pressing need to improve the monitoring system, however the quantitative evidence that is presented is a bit weak and the logic is not straightforward. It is clear that the region is not well suited for a thorough assessment of the GRACE data, or the GRACE residual approach for groundwater, since the basin is too small, the GRACE data are affected by ocean signals, and there are too few in-situ sensors. I am missing a discussion of related papers that discuss bigger inland regions of similar hydrogeophysics but better monitoring network with a similar approach. If I am right about the authors‘ intention, I am missing a much more extended discussion of how a monitoring network could look like, how many piezometers or observation wells would be needed, how could they be distributed to connect to the GRACE data. Without this I feel the paper misses a bit its mark.
Further issues
Overall, the error budgeting needs more detail and quantification. This is, as I said earlier, partly a consequence of the fact that the LKB region is a particularly challenging one for GRACE. That also means if the authors succeed to make their case, this could be a breakthrough in the application of GRACE data, so it is really worth to dig deeper. At the moment, results appear somewhat inconclusive and the message is not too clear. I suggest that the study logic – what is the underlying hypothesis, what exactly do we expect from GRACE at such small scales, why looking at the piezo-rainfall correlation, why looking at ENSO – is explained right at the start.
Best regards, Jürgen Kusche