A novel method for cold-region streamflow hydrograph separation using GRACE satellite observations
Bibliographic record
Abstract
Streamflow hydrograph analysis has long been used for separating streamflow into baseflow and surface runoff components, providing critical information for studies in hydrology, climate and water resources. Issues with established methods include the lack of physics and arbitrary choice of separation parameters, problems in identifying snowmelt runoff, and limitations on watershed size and hydrogeological conditions. In this study, a Gravity Recovery and Climate Experiment (GRACE)-based model was developed to address these weaknesses and improve hydrograph separation. The model is physically based and requires no arbitrary choice of parameters. The new model was compared with six hydrograph separation methods provided with the U.S. Geological Survey Groundwater Toolbox. The results demonstrated improved estimates by the new model particularly in filtering out the bias of snowmelt runoff in baseflow estimate. This new model is specifically suitable for applications over large watersheds which is complementary to the traditional methods that are limited by watershed size. The output from the model also includes estimates for watershed hydraulic conductivity and drainable water storage, which are useful parameters in evaluating aquifer properties, calibrating and validating hydrological and climate models, and assessing regional water resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".