Forecasting Snowmelt-Induced Flooding Using GRACE Satellite Data: A Case Study for the Red River Watershed
Bibliographic record
Abstract
. Flood forecasting of the spring freshet for cold-region watersheds where the discharge is predominately governed by snowpack accumulation and melting remains a challenge. A cold-region flood forecasting model is developed, using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The model forecasts flood by simulating peak surface runoff from snowmelt and the corresponding baseflow from groundwater discharge. Surface runoff is predicted from snowmelt, using a temperature index model. Baseflow is predicted, using a first order differential equation model. Streamflow measurement is used for model calibration. The model was applied to the Red River watershed, a USA–Canada transboundary basin located in central North America. The predicted flood compares well with the observed values at a downstream hydrometric station (r = 0.95). The result also reveals a 2-week hysteresis between the maximum snowmelt and the peak streamflow observed at the station. The model is relatively simple and needs only GRACE and temperature inputs for flood forecasting. It can be readily applied to other cold-region basins after simple calibration and could be particularly useful in regions with minimal data. For potential flood warning, the model also has the advantage of a much longer lead time than most traditional flood forecasting approaches.
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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 | 0.000 |
| 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".