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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RC1: 'Comment on egusphere-2025-2941', Juergen Kusche, 02 Aug 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
- Title: I don’t think the authors really succeed in „reconciling GRACE to piezometry“. This should be reflected in the title.
- The authors discuss the challenges of the GRACE residual approach, e.g. we need to know the soil moisture contribution. But we also need to know the contribution by surface water storage variability, water levels in wetlands and rivers, lakes and artificial reservoirs. The authors are aware of this. In my understanding, wetlands cover a significant share of land in Indonesia including the LKB. We need a quantitative discussion of the error that could be introduced here. WaterGAP may not be very good at this – WaterGAP does not simulate level changes in reservoirs, for example. And I suggest that the authors look into ways of quantifying this contribution, e.g. from remote sensing and/or radar altimetry
- Similar for hydrology, in my understanding in Indonesia peatlands are abundant and peatland hydrology may not be well represented in GLDAS or WaterGAP. What is the anticipated error? Or isn't this the case in the LKB?
- Similar, the Makassar and Sulawesi Straits are part of an ocean region that experiences above-average variability and sea level rise. Ocean signals are reduced in the standard GRACE data products but we know that the MPI reference ocean model which is forced by the atmosphere only does not capture all mass signals, and in coastal areas this may introduce a significant error irrespectively what mascon products were used. Put in simple words, the GRACE data may include real ocean mass change at least on the seasonal timescale here that could be misinterpreted as a groundwater signal. This is an error source that is not relevant for the average hydrological inland basin, but for Indonesia it may be very well. I’m missing a quantitative discussion here.
- The authors should also try to estimate the error in the piezometric analysis introduced by not knowing the local yield factor. They could take a range of possible yield factors from the hydrogeophysics maps or from publications and do a best/worst-case assessment.
- The comparison of the GRACE product error with three mascon solutions appears not very robust. I would very much recommend that the authors consider at least one product based on spherical harmonics. It is an unproven claim that mascon solutions are better or more suitable to coastal regions. They are easy to apply but this is not the same as being more appropriate or having less errors.
- I’m missing a map of the aquifer systems in the LKB, in particular are these aquifers extending under the sea? The dashed line in Fig. 1 suggests this. Would coastal groundwater withdrawal then cause storage changes in the marine part of the aquifer? Would that be expected to become visible in GRACE as well? Isn’t this suppressed by the mascon approach? These are just ideas, I am not an expert in this regions. Again, most GRACE groundwater studies need not worry about this, but the region here is particularly challenging.
- There are GRACE-assimilating hydrology model runs from NASA/Goddard and from the University of Bonn, Germany, and these provide the groundwater storage change at resolution between 30 and 50 km. Why don’t the authors look into these data sets or add them to their ensembles?
- The authors mention several times that poor GRACE data or poor corrections in the residual approach lead to arithmetic problems. This is true, but an arithmetic problem is just a symptom that the data are poor. In other words, even if no arithmetic problem occurs this may be just by chance, and we should not trust the data. I think this needs to be made clear in a scientific paper.
- Potential instrumental problems of the piezometric sensors should be discussed. I liked the part on the correlation between these data and the rainfall data, as it tells about the sensitivity. But this instigates trust for the short timescales only. What about the seasonal and interannual timescales, are there biases to be expected?
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
Citation: https://doi.org/10.5194/egusphere-2025-2941-RC1
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