Comparing Streamflow Depletion Estimation Approaches in a Heavily Stressed, Conjunctively Managed Aquifer
Bibliographic record
Abstract
Abstract Estimating reductions in streamflow caused by groundwater pumping (“streamflow depletion”) is critical for conjunctive groundwater‐surface water management. Streamflow depletion can be quantified using analytical models, which have low data requirements but many simplifying assumptions, or numerical models, which represent physical processes more realistically but have high data, effort, and expertise requirements. Analytical depletion functions are a new tool that address some of the limitations of analytical models, but to date have only been evaluated in limited hydrogeological settings. Here, we compare eight different analytical depletion functions to streamflow depletion estimates from a calibrated MODFLOW numerical model used for conjunctive water management in the heavily stressed Republican River region of the High Plains Aquifer (USA). We find mostly strong agreement between the analytical depletion functions and the numerical model, though analytical depletion function estimates of depletion are lower for wells close to surface water features in high transmissivity settings. Compared to previous work, there is little variability among the eight analytical depletion functions, indicating that function formulation plays a minor role in this domain. Agreement between the modeling approaches is strongly influenced by hydrostratigraphic parameters (i.e., aquifer storage and transmissivity), suggesting accurate subsurface data are essential to estimating streamflow depletion regardless of modeling approach. Additionally, agreement between the two approaches is insensitive to pumping rate, confirming a key assumption of analytical models. Overall, analytical depletion functions provide comparable estimates of streamflow depletion to numerical models at a fraction of the time and data requirements.
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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 | 0.001 |
| 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".