Automating nested watershed delineation over the world considering endorheic basins and islands
Bibliographic record
Abstract
Global-scale nested watershed datasets are foundational for hydrological studies. However, their construction is not yet fully automated, making the process labor-intensive and time-consuming, primarily due to the lack of algorithms capable of encoding endorheic watersheds and islands. This study developed an innovative approach to automating the global delineation of nested watersheds accounting for endorheic basins and islands based on the widely used Pfafstetter coding system. Key advancements include combining adjacent endorheic watersheds into collections for systematic coding and developing automatic hierarchical delineation procedures for islands. This approach makes the delineation of endorheic basins and islands easy, reproducible, and more reasonable. Utilizing this approach, we developed a global nested watershed dataset, named NWEI (Nested Watershed dataset considering Endorheic basins and Islands), based on the Multi-Error-Removed Improved-Terrain (MERIT) Hydro datasets. Compared with the watershed boundaries reported from the Global Runoff Data Center (GRDC), 93% of the 4,301 watersheds globally exhibited relative area errors below 0.05. In comparison to the state-of-the-art HydroBASINS dataset, NWEI demonstrated improved detail and accuracy in delineated watershed boundaries. The automated nature of the proposed approach facilitated the rapid updating of NWEI using newer Digital Elevation Model (DEM) datasets.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".