Regional estimation of parameters of a rainfall‐runoff model at ungauged watersheds using the “spatial” structures of the parameters within a canonical physiographic‐climatic space
Bibliographic record
Abstract
A regionalization scheme by which parameters of a continuous rainfall‐runoff model are estimated from physiographic and climatic watershed descriptors is presented. The approach makes use of the spatial structures displayed by the parameters within a physiographic‐climatic space defined on the basis of a canonical correlation analysis between model parameters and watershed descriptors. Traditionally, regionalization has been performed using a two‐step procedure of first estimating the model parameters in a set of subwatersheds independently and then establishing a relationship between the parameters thus estimated and a set of watershed descriptors. The approach presented in this paper follows a procedure by which the two steps are combined into one. The model is calibrated for the training subwatersheds with a dual objective of maximizing the model performance and achieving well‐defined spatial structures of the parameters within the physiographic‐climatic space. The model parameters in the subwatersheds that are not used for training are estimated from the optimum parameters obtained in the training set of subwatersheds using ordinary kriging within the physiographic‐climatic space. The performance of the model in these subwatersheds is comparable to the performance in the training set obtained using the optimum parameters estimated through model calibration. The results also indicate the possibility of extrapolation of the model parameters under a situation where some of the watershed descriptors lie slightly outside the range within which the training was done.
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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 | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".