Continuous separation of land use and climate effects on the past and future water balance
Bibliographic record
Abstract
Understanding the combined and separate effects of climate and land use change on the water cycle is necessary to mitigate negative impacts. However, existing methodologies typically divide data into discrete (before and after) periods, implicitly representing climate and land use as step changes when in reality these changes are often gradual. Here, we introduce a new regression-based methodological framework designed to separate climate and land use effects on any hydrological flux of interest continuously through time, and estimate uncertainty in the contribution of these two drivers. We present two applications in the Yahara River watershed (Wisconsin, USA) demonstrating how our approach can be used to understand synergistic or antagonistic relationships between land use and climate in either the past or the future: (1) historical streamflow, baseflow, and quickflow in an urbanizing subwatershed; and (2) simulated future evapotranspiration, drainage, and direct runoff from a suite of contrasting climate and land use scenarios for the entire watershed. In the historical analysis, we show that ~60% of recent streamflow changes can be attributed to climate, with approximately equal contributions from quickflow and baseflow. However, our continuous method reveals that baseflow is significantly increasing through time, primarily due to land use change and potentially influenced by long-term increases in groundwater storage. In the simulation of future changes, we show that all components of the future water balance will respond more strongly to changes in climate than land use, with the largest potential land use effects on drainage. These results indicate that diverse land use change trajectories may counteract each other while the effects of climate are more homogeneous at watershed scales. Therefore, management opportunities to counteract climate change effects will likely be more effective at smaller spatial scales, where land use trajectories are unidirectional.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".