Groundwater storage dynamics and climate variability in the Lower Kutai Basin of Indonesia: reconciling GRACE ΔGWS to piezometry
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".