Improving the Representation of Subsurface Water Movement in Land Models
Bibliographic record
Abstract
Abstract Land models are increasingly used and preferred in terrestrial hydrological prediction applications. One reason for selecting land models over simpler models is that their physically based backbone enables wider application under different conditions. This study evaluates the temporal variability in streamflow simulations in land models. Specifically, we evaluate how the subsurface structure and model parameters control the partitioning of water into different flow paths and the temporal variability in streamflow. Moreover, we use a suite of model diagnostics, typically not used in the land modeling community to clarify model weaknesses and identify a path toward model improvement. Our analyses show that the typical land model structure, and their functions for moisture movement between soil layers (an approximation of Richards equation), has a distinctive signature where flashy runoff is superimposed on slow recessions. This hampers the application of land models in simulating flashier basins and headwater catchments where floods are generated. We demonstrate the added value of the preferential flow in the model simulation by including macropores in both a toy model and the Variable Infiltration Capacity model. We argue that including preferential flow in land models is essential to enable their use for multiple applications across a myriad of temporal and spatial scales.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".