New functional topographic, lithology and climatic indices to define shallow groundwater systems in (un)gauged basins
Bibliographic record
Abstract
The dynamics of the rainfall-runoff processes are complex and variable both spatially and temporally. There is a rich literature on physical representation of streamflow generation processes, such as saturation excess overland flow, often at small scales. Yet, continental-scale estimations of the streamflow generation processes in zones with shallow groundwater systems are still poor. This has led to inability of earth system models or large-scale hydrologic models to correctly simulate stream flows at (un)gauged basins with high potential for the presence of saturation excess overland flow. Zones with shallow groundwater have a direct impact on the hydrologic response of rainfall events. Depending on the subsurface storage, climate signals and topography, they can enhance the overland flow, or act as a buffer zone to flatten the flood hydrographs. We have introduced new indices, inspired by the concept of hydrologic function, that include the interactions amongst climatic and geophysical characteristics (soil parameters, topography and lithology) to delineate zones of shallow groundwater over the United States and Canada. We have evaluated and tested the ability of these indices in locating high-resolution zones of shallow groundwater against in-situ observations of water table depth. The knowledge of the spatial pattern of shallow groundwater zones at (un)gauged basins allows an accurate inclusion of hydrologic connectivity in earth system models or large-scale hydrologic models, improving their prediction of stream peak flow. Furthermore, as a significant part of incoming precipitation is transformed to overland flow due to oversaturation, these datasets could be introduced as a useful indicator of areas with flood and erosion susceptibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".