Towards developing a streamflow forecasting system for data-poor mountainous watershed: an approach using parameter transfer
Bibliographic record
Abstract
Studying the hydrological responses of the Indian Himalayan Region (IHR) is crucial given the rise in the frequency of floods and other natural disasters. The hydrological processes in this area are more complicated due to the extreme weather pattern and varied topography. Streamflow forecasting is made more difficult by the extremely low number of stream gauge stations and the absence of accurate stream flow data. The problem of lack of observational data in ungauged watersheds can be resolved by transferring model parameters from similar gauged basins (Regionalisation). According to the traditional regionalization procedures using rainfall-runoff models, donor and recipient catchments must be similar in a variety of ways, including slope, size, drainage pattern, area, etc. It is extremely difficult to locate a catchment with all those similarities. In this study, we use a fully distributed hydrological model WATFLOOD for developing a streamflow forecast of the Alakananda River basin where the stream flow observation is very limited for the calibration of the hydrological model. WATFLOOD is working based on Grouped Response Unit (GRU). The requirement that has to be satisfied for regionalization using the WATFLOOD model is that land cover classes of the ungauged watershed should be represented in the gauged watershed irrespective of their spatial distribution. Also, there should be as many as possible gauged sub-watersheds that represent each land cover class. We identified a similar watershed that has similar land cover classes and sufficient stream flow gauges to represent each of the land cover classes. The three-step calibration process of the WATFLOOD model for both river basins is carried out to transfer the parameters. The results of ongoing work will be presented at the conference.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".