Groundwater surface mapping informs sources of catchment baseflow
Bibliographic record
Abstract
Abstract. Groundwater discharge is a major contributor to stream baseflow. Quantifying this flux is difficult, despite its considerable importance to water resource management and evaluation of the effects of groundwater extraction on streamflow. It is important to be able to differentiate between contributions to streamflow from regional groundwater discharge (more susceptible to groundwater extraction) compared to interflow processes (arguably less susceptible to groundwater extraction). Here we explore the use of groundwater surface mapping as an independent data set to constrain estimates of groundwater discharge to streamflow using traditional digital filter and tracer techniques. We developed groundwater surfaces from 88 monitoring bores using Kriging with external drift and for a subset of 33 bores with shallow screen depths. Baseflow estimates at the catchment outlet were made using the Eckhardt digital filter approach and tracer data mixing analysis using major ion signatures. Our groundwater mapping approach yielded two measures (percentage area intersecting the land surface and monthly change in saturated volume) that indicated that digital filter-derived baseflow significantly exceeded probable groundwater discharge during most months. Tracer analysis was not able to resolve contributions from ungauged tributary flows (sourced from either shallow flow paths, i.e. interflow and perched aquifer discharge, or regional groundwater discharge) and regional groundwater. Groundwater mapping was able to identify ungauged sub-catchments where regional groundwater discharge was too deep to contribute to tributary flow and thus where shallow flow paths dominated the tributary flow. Our results suggest that kriged groundwater surfaces provide a useful, empirical and independent data set for investigating sources of fluxes contributing to baseflow and identifying periods where baseflow analysis may overestimate groundwater discharge to streamflow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".