Exploration of coupled surface–subsurface hydrological model responses and challenges through catchment- and hillslope-scale examples
Bibliographic record
Abstract
Selected runs with a physics-based model of surface water–groundwater interactions are used to examine in detail some numerical challenges and surprising behaviors that result from discretization, nested solution schemes, coupling, boundary condition, and other factors. Regardless of the spatial scale of the model domain (field, hillslope, catchment, …), the processes that are simulated by this class of integrated models can exhibit widely varying dynamics within and across the different subsystems comprising the land surface, the unsaturated zone, and deep groundwater formations. The presence of heterogeneities, nonlinearities, and complex boundary conditions can exacerbate numerical difficulties in resolving exchange fluxes across subsystems and lead to unexpected or undesired results, including localized numerical oscillations and an upper bound on adaptive time stepping. The need for accurate tracking of surface–subsurface exchanges and for better control of aspect ratio and mesh distortion can also influence and constrain spatial and temporal discretization choices. Finally, model performance assessments can be highly sensitive to the response variables of interest. We will illustrate some of these issues via test case simulations at large (13.66 km catchment transect) and small (450 m 2 hillslope) spatial scales, run at time scales from 10 days to hundreds of years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".