Soil moisture tendencies into the next century for the conterminous United States
Bibliographic record
Abstract
A monthly snowpack and soil‐moisture‐accounting model is formulated for application to each of the climate divisions of the conterminous United States for use in climate impacts assessment studies. Statistical downscaling and bias adjustment components complement the model for the assimilation of large‐scale global climate model precipitation and temperature. The model produces monthly streamflow that is broadly consistent with observed streamflow from several drainage basins in the United States for the period 1931–1998. Simulated historical soil moisture fields reproduce several features of the available observed soil moisture in the Midwest. The simulations produce large‐scale coherent seasonal patterns of soil moisture field moments over the conterminous United States, with high soil moisture means over divisions in the Ohio Valley, the northeastern United States, and the Pacific Northwest, and with pronounced low means in most of the western U.S. climate divisions. Characteristically low field standard deviations are produced for the Ohio Valley and northeastern United States, the Pacific Northwest in winter, and the southwestern United States in summer. Differences in extreme standardized anomalies of soil moisture over the historical record possess high values (2.5–3) in the central United States, where the available water capacity of the soils is high. Application of the methodology for future periods using output from the Canadian coupled global climate model (CGCM1) shows that for at least the first few decades of the 21st century, somewhat drier‐than‐present soil conditions are projected, with highest drying trends found in the southeastern United States. The soil moisture deficits in most areas are of the same order of magnitude as the soil moisture field standard deviations arising from historical natural variability. Brumbelow and Georgakakos [this issue] study the implications for crop yield in the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".