Evaluation of the value of spatially improved SMAP soil moisture products in enhancing streamflow forecast skills
Bibliographic record
Abstract
Soil moisture (SM) measurements over large areas are vital for many operational applications such as flood forecasting, irrigation scheduling, and drought monitoring. Although obtaining SM over extensive areas was difficult until recently, the advent of satellite remote sensing technologies such as passive microwave satellites (e.g., SMOS and SMAP) opened a new way. Nevertheless, the utilization of SM products of these satellites is often impeded because of their coarse spatial resolution (i.e., about 40 km). A number of studies have been attempted to improve the coarse resolution satellite SM products via downscaling. However, despite of many downscaling efforts, subsequent use of downscaled satellite SM products for operational applications has not yet been fully explored. Thus, the objective of this study is to evaluate the value of SMAP SM in enhancing short-term streamflow forecast skills. The random forest machine learning technique was used to downscaled SMAP SM from 36 km to a range of resolutions from 1 to 9 km (i.e., 9, 3, and 1 km). Thereafter, a host of experiments were carried out to update a physically-based distributed hydrological model through direct ingestion of the original SMAP SM (e.g., 36 km), SMAP enhanced SM (i.e., 9 km), and downscaled SMAP SM at different spatial resolutions (e.g., 9, 3 and 1 km). A non-updated model was used as a benchmark for comparison. The result shows that the downscaled SMAP SM has presented better spatial detail than its corresponding native resolution and updating the model state with SMAP SM products (i.e., with the native and downscaled products) shows promising potential for improving short term flood forecasting. Finally, this will in turn helps in better water resources management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".