Climate and agricultural land use change impacts on streamflow in the upper midwestern United States
Bibliographic record
Abstract
Abstract Increased streamflow and its associated impacts on water quality have frequently been linked to changes in land use and land cover (LULC) such as tile drainage, cultivation of prairies, and increased adoption of soybeans (Glycine max) in modern day cropping systems. This study evaluated the relative importance of changes in precipitation and LULC on streamflow in 29 Hydrologic Unit Code 008 watersheds in the upper midwestern United States. The evaluation was done by statistically testing the changes in slope and intercept of the relationships between ln(annual streamflow) versus annual precipitation for the periods prior to 1975 (prechange period) and after 1976 (postchange period). A significant shift either in slope or intercept of these relationships was assumed to be an indication of LULC changes whereas a lack of significant shift suggested a single relationship driven by precipitation. All 29 watersheds showed no statistical difference in slope or intercept of the relationships between the two periods. However, a simpler model that kept the slope constant for the two periods showed a slight upward shift in the intercept value for 10 watersheds in the postchange period. A comparison of 5 year moving averages also revealed that the increased streamflows in the postchange period are mainly due to an increase in precipitation. Minimal or the lack of LULC change impact on streamflow results from comparable evapotranspiration in the two time periods. We also show how incorrect assumptions in previously published studies minimized precipitation change impacts and heightened the LULC change impacts on streamflows.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".