Rapid and Accurate Estimates of Streamflow Depletion Caused by Groundwater Pumping Using Analytical Depletion Functions
Bibliographic record
Abstract
Abstract Reductions in streamflow due to groundwater pumping (“streamflow depletion”) can negatively impact water users and aquatic ecosystems but are challenging to estimate due to the time and expertise required to develop numerical models often used for water management. Here we develop analytical depletion functions , which are simpler approaches consisting of (i) stream proximity criteria, which determine the stream segments impacted by a well; (ii) a depletion apportionment equation, which distributes depletion among impacted stream segments; and (iii) an analytical model to estimate streamflow depletion in each segment. We evaluate 50 analytical depletion functions via comparison to an archetypal numerical model and find that analytical depletion functions predict streamflow depletion more accurately than analytical models alone. The choice of a depletion apportionment equation has the largest impact on analytical depletion function performance and equations that consider stream network geometry perform best. The best‐performing analytical depletion function combines stream proximity criteria which expand through time to account for the increasing size of the capture zone; a web squared depletion apportionment equation, which considers stream geometry; and the Hunt analytical model, which includes streambed resistance to flow. This analytical depletion function correctly identifies the stream segment most affected by a well > 70% of the time with mean absolute error < 15% of predicted depletion and performs best for wells in relatively flat settings within ~3 km of streams. Our results indicate that analytical depletion functions may be useful water management decision support tools in locations where calibrated numerical models are not available.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".