Numerical behavior of a coupled surface/subsurface, flow/transport hydrological model
Bibliographic record
Abstract
Integrated surface/subsurface hydrological models (ISSHMs) are by now widely used in research and applied hydrology. While most studies have so far focused on water flow alone, ISSHMs that include also solute transport are beginning to get attention (e.g., Scudeler et al., 2016, doi:10.5194/hess-20-4061-2016; Gatel et al., 2019, doi:10.1016/j.envsoft.2018.12.006). Numerous numerical challenges are associated with these "doubly coupled" systems: correct treatment of surface boundary conditions and other mass and flux exchange terms; appropriate time stepping schemes across subsystems that are characterized by different dynamic time scales and often also widely different numerical discretization approaches; performance assessments that can be highly sensitive to the response variables of interest; and so on. We will illustrate some of these challenges via test case simulations of an experimental hillslope using the CATHY (CATchment HYdrology) model (Camporese et al., 2010, doi:10.1029/2008WR007536; Weill et al., 2011, doi:10.1016/j.advwatres.2010.10.001). The boundary condition-based coupling strategy used in this model (Putti and Paniconi, 2004, doi:10.1016/S0167-5648(04)80152-7) has been shown to be mathematically rigorous and mass-conservative for the flow model (Sochala et al., 2009, doi:10.1016/j.cma.2009.02.024). The convergence-based time step adaptation strategy used for the nonlinear flow equation (Paniconi and Putti, 1994, doi:10.1029/94WR02046) is likewise thoroughly tested (e.g., D'Haese et al., 2007, doi:10.1002/fld.1369) and widely used. Nonetheless, these schemes, and analogous approaches used in other ISSHMs, need to be adapted and thoroughly tested for coupled systems that include solute transport.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".