Use of an Efficient Proxy Solution for the Hillslope‐Storage Boussinesq Problem in Upscaling of Subsurface Stormflow
Bibliographic record
Abstract
Abstract The hillslope‐storage Boussinesq (hsB) equation is a modification of the one‐dimensional Boussinesq representation of saturated subsurface flow that accommodates the impact of converging and diverging flows resulting from a hillslope with nonconstant width. Here, a surrogate model, the hsB Proxy, is developed which closely emulates the results of high‐quality numerical solutions of the hsB to reproduce the drainage response of a wide range of wedge‐shaped hillslopes in response to a recharge time series at a fraction of the computational cost of a numerical solution and with minimal loss of fidelity. The efficacy of the Proxy in emulating the hsB response of a single hillslope and the aggregate response of a network of hillslopes is demonstrated. The Proxy applies to wedge‐shaped hillslopes with homogenous hydraulic conductivity under uniform recharge, with the expectation that these hillslopes may inform future investigations into the upscaling behavior that results in the known power‐law relationship between subsurface storage and outflow in a basin. The utility of the hsB as a tool for evaluating upscaling problems is demonstrated in two applications. First, the Proxy response is compared to the known basin‐scale recession behavior in the Panola Mountain Research Watershed, under both transient and steady‐state conditions. Second, the Proxy is applied to derive the single best effective hillslope in a basin. The Proxy is expected to be a useful tool for rapid simulation of subsurface flow from hillslopes for use in hydrological models and land surface schemes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".