Identifying effects of local and nonlocal factors of soil water storage using cyclical correlation analysis
Bibliographic record
Abstract
Abstract There are various factors governing the spatial and temporal variability of soil water storage including soil properties, topography and vegetation. Some factors act locally, whereas others act nonlocally, which means that a factor measured at one location has effect on soil water storage at another location. The objective of this study was to examine the effects of local and nonlocal controls of soil water storage in a hummocky landscape using cyclical correlation analysis. Soil water storage, soil properties and terrain indices were measured along a 128‐point transect of 576 m long from the semiarid, hummocky, prairie pothole region of North America. There are large coefficients of determination ( r 2 ) between soil water storage and sand content ( r 2 = 0.32–0.53), organic carbon content ( r 2 = 0.22–0.56), depth to carbonate layer ( r 2 = 0.13–0.63), wetness index ( r 2 = 0.25–0.45) and other variables at the measurement scale at different times, indicating strong local effects from these variables. The correlation coefficients were also calculated by physically shifting the spatial series of soil water storage with respect to that of controlling factors. The shifting improves the correlation between the spatial series, and the length of shifting indicated the difference in the response of soil water to its controlling factors. For example, the value of r 2 increased more than eightfold ( r 2 = 0.47–0.64) after shifting the spatial series of soil water storage by 54 m, almost equal to the average length of existing slope, compared with the very weak correlation ( r 2 = 0.02–0.08) at the measurement scale. This indicated the nonlocal effect from the relative elevation. The identification of nonlocal effects from factors improves the prediction of soil water storage. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 |
| 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".