Development of a comprehensive framework for quantifying the collective and individual influence of climate change and human activities on hydrological regimes
Bibliographic record
Abstract
Previous research rarely identified the synergistic effects of climate change and human activities on hydrological regimes, thus leading to an incomplete attribution of hydrological regime changes. To address this issue, this paper presents a complete approach for accurately quantifying these impacts. The framework integrates various methodologies, including the runoff abrupt change point judgment method for distinguishing pre- and post-impact periods, The Soil and Water Assessment Tool (SWAT) is used to isolate runoff processes influenced solely by climate change, the linear separation method for identifying runoff processes affected solely by human activities, and the Indicator of Hydrologic Alteration (IHA) for quantifying changes in hydrological regimes. Application of this framework to the Ganjiang River Basin (GRB) unveils significant alterations in hydrological regimes. This has resulted in a significant decline in aquatic organism species compared to the prior-impact period levels. Notably, climate change and human activities exert opposing effects on minimum flow indicators and flow pulse indicators in the GRB. Moreover, these factors exhibit a substantial synergistic effect on numerous indicators, resulting in hydrological regimes combined influenced by climate change and human activities being considerably different with the cumulative impact on hydrological regimes solely influenced by climate change and those solely influenced by human activities. Consequently, in future water management, it is crucial to recognize the positive role of prudent human activities in mitigating the adverse impacts of climate change.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".