Soil moisture tendencies into the next century for the conterminous United States
Bibliographic record
Abstract
A monthly snow-pack 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 data. Simulations of the formulated model driven by precipitation and temperature for the period 1931-1998 produce streamflows that are broadly consistent with observed data from several drainage basins in the US. 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 US, with high soil moisture means over divisions in the Ohio Valley, the northeastern US and the Pacific Northwest, and with pronounced low means in most of the western US climate divisions. Characteristically low field-standard- deviations are produced for the Ohio Valley and northeastern US, and the Pacific Northwest in winter, and the southwestern US in summer. Differences in extreme standardized anomalies of soil moisture over the historical record range possess high values (2.5 - 3) in the central US where the available water capacity of the soils is high. An application of the model to exemplify the methodology for determining projected US monthly soil moisture fields under control and greenhouse gas forcing is also documented. Climate simulations of the coupled global climate model from the Canadian Centre for Climate Modeling and Analysis were used for these sensitivity examples. The climatology of the control-run soil moisture fields reproduces several characteristic features of the historical soil moisture climatology. Simulations with forcing by a 1% greenhouse-gas- increase scenario show that for at least the first few decades of the 21 st Century somewhat drier-than-present soil conditions are projected, with highest drying trends found in the southeastern US. The soil moisture deficits in most areas are of the same order of magnitude as the soil moisture field-standard- deviations aris ing from historical natural variability. In a companion paper (Brumbelow and A. Georgakakos, 2000), the monthly soil moisture fields for the historical, control and greenhouse-gas-increase runs are used to initialize a site-specific daily crop yield model at the start of the growing season. Assessments of potential impacts of climate variability and trends on irrigation requirements and crop yield across the conterminous US are made.
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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 | 0.001 |
| 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) | 0.001 | 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 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".