Exploring the utility of the downscaled SMAP soil moisture products in improving streamflow simulation
Bibliographic record
Abstract
The Susquehanna and upper Susquehanna watersheds in the Northeastern of the United States of America (USA) This study explored the utility of the Soil Moisture Active Passive (SMAP) soil moisture downscaled to a range of spatial resolutions for improving ensemble streamflow simulations. The SMAP level 3 soil moisture product with spatial resolution of roughly 40 km was downscaled to a range of spatial resolutions including 1, 3 and 9 km over the Susquehanna and upper Susquehanna watersheds. A set of experiments was conducted through direct insertion of the downscaled SMAP soil moisture into a physically-based distributed hydrological model. The updating of the model with the original and downscaled SMAP surface soil moisture markedly improved the accuracy of the ensemble streamflow simulations with the CRPSS and NRMSE values in the range of 0.10–0.17 and 0.79–0.85, respectively when compared to the non-updated model for the Susquehanna watershed. In addition, the ensemble spread was reduced, and the ensemble mean compares well with the observed streamflow. The 1 km downscaled SMAP soil moisture showed the highest accuracy in improving streamflow simulation with the CRPSS and NRMSE value of 0.21 and 0.72, respectively for the Upper Susquehanna watershed, whereas for the Susquehanna watershed downscaled SMAP at 9 km adequately improved the accuracy of the ensemble streamflow simulations with the CRPSS and NRMSE value of 0.17 and 0.80, respectively. Besides the top layer of the model, updating the second layer of the model with the vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the model.
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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 | 0.000 | 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".