Long-term groundwater recharge rates across India by in situ measurements
Bibliographic record
Abstract
Abstract. Groundwater recharge sustains groundwater discharge, including natural discharge through springs and the base flow to surface water as well as anthropogenic discharge through pumping wells. Here, for the first time, we compute long-term (1996–2015) groundwater recharge rates using data retrieved from several groundwater-level monitoring locations across India (3.3 million km2 area), the most groundwater-stressed region globally. Spatial variations in groundwater recharge rates (basin-wide mean: 17 to 960 mm yr−1) were estimated in the 22 major river basins across India. The extensive plains of the Indus–Ganges–Brahmaputra (IGB) river basins are subjected to prevalence of comparatively higher recharge. This is mainly attributed to occurrence of coarse sediments, higher rainfall, and intensive irrigation-linked groundwater-abstraction inducing recharge by increasing available groundwater storage and return flows. Lower recharge rates (<200 mm yr−1) in most of the central and southern study areas occur in cratonic, crystalline fractured aquifers. Estimated recharge rates have been compared favorably with field-scale recharge estimates (n=52) based on tracer (tritium) injection tests. Results show that precipitation rates do not significantly influence groundwater recharge in most of the river basins across India, indicating human influence in prevailing recharge rates. The spatial variability in recharge rates could provide critical input for policymakers to develop more sustainable groundwater management in India.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".