Upscaling Hillslope‐Scale Subsurface Flow to Inform Catchment‐Scale Recession Behavior
Bibliographic record
Abstract
Abstract The subsurface flow contribution to a basin hydrograph is often conceptualized as a single lumped reservoir parameterized by a power law function fit through the observed recession behavior of the basin. However, basins may be better represented as multiple independent subsurface hillslope reservoirs characterized with variable, transient recession behavior as a function of the time history of recharge. In this work, we unify the latter, more complex understanding of the subsurface with the large‐scale power law recession behavior convenient in hydrologic modeling applications. This unification is achieved by deriving upscaling relationships capable of converting simple metrics of basin hillslope topography into a signature basin response characterizing the subsurface‐flow‐induced recession from a single recharge event. Event superposition may then be used to simulate the application of a time series of recharge and a simple scaling relationship may be used to handle basins with homogeneous conductivity. The signature response is informed by numerical simulations of the hillslope‐storage Boussinesq equation for unconfined saturated flow through sloping hillslopes applied to 30 basins in the CAMELS database. The efficacy of the upscaled signature response in replicating numerical simulations in 50 CAMELS basins is demonstrated, as is the practical efficacy of the upscaling relationships for predicting transient recession in 17 of these basins. The upscaled signature response method thus provides a tool for hydrologic modelers to estimate transient recession parameters using the insights of subsurface flow modeling, although the predictions made by this method are unable to capture the full variability of observed recession behavior.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".