Application of remote sensing information about land use – land cover in flood forecasting with the Xin'anjiang model
Bibliographic record
Abstract
Grid-based digital elevation data with a spatial resolution of 30 s of latitude or longitude, covering the Hanjiang River basin, were matched spatially with a 1 km × 1 km grid of land use – land cover data generated from remotely sensed data. The ratio of impervious area to subcatchment area (denoted IMP), a parameter in the Xin'anjiang model, can then be extracted directly from the land use – land cover data. Soil free water storage capacity (SM), a sensitive parameter in the Xin'anjiang model, can be obtained for the subcatchment by the relation between SM and the ratio of forest land area to subcatchment area. Thus, in the proposed semidistributed hydrological model, the spatial variability of land surface characteristics is taken into consideration. As a result, the physical meanings of model parameters are so clear that they can be extended from gauged catchments to ungauged catchments according to the land surface characteristics over the catchments. The accuracy of flood forecasting is improved as well. A case study of 24 flood events within the Baohe River, the upper tributary of the Hanjiang River, has shown that constructing the relationship between model parameters and land surface characteristics is an effective way to reduce errors in flood forecasting. In concrete terms, if the result computed by the semidistributed algorithm is compared with the result obtained by the lumped subcatchment algorithm, the Nash–Sutcliffe coefficients of 15 flood events increase, and the relative errors of 22 flood peaks decrease markedly. Also, the sensitivity of SM to flood peak discharge is greater than its sensitivity to the Nash–Sutcliffe coefficient. The semidistributed hydrological model is of practical value to flood forecasting and to the quantitative description of land use – land cover change related to the water cycle. We are convinced that this research is also helpful in the operation of a water supply system for the middle route of the large project involving water transfer from southern to northern China.
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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 | 0.000 | 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".