Study on Potential Influence of Runoff on Observatory-Based Watershed in Japan
Bibliographic record
Abstract
Land use shows the interaction between human and nature, and its impacts especially on runoff have received global attention and required more research studies. However, watershed database is generally available only for very large scales and not yet adapted to small scales. This paper presents a methodology for watershed delineation and potential influence assessment of runoff on the small watersheds in Japan based on the locations of the official observatory point data. Flow direction, flow accumulation, snap pour point and watershed recognition were analyzed by using the D8 algorithm and 10 m × 10 m DEM data of Japan. Totally 3831 watersheds were delineated by defining snapping pour point distance of 50 m as the input value, and the nation-wide watershed database was established. According to the suggestion of Notification No. 521 from the Ministry of Land, Infrastructure and Transport of Japan and land use/land cover data from JAXA satellite Alos-2, comprehensive runoff coefficient (CRC) was calculated by the weighted average method and divided into 10 levels by the equal interval method using ArcGIS. The obtained watershed boundary lines were almost identical to the ridge lines in the mountain area which accounts for more than half of Japan. The CRC values in the biggest cities such as Tokyo and Osaka were the highest, indicating these cities would receive the highest impacts of runoff. These results can provide the technical support for the decision-making on watershed management. The creation of small-scale watershed database would provide basic materials for the subsequent research such as flood prevention, water shortage and biogeochemical cycle of the sustainable regional development.
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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 | 0.001 | 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".