A Novel Framework for Integrally Evaluating the Impacts of Climate Change and Human Activities on Water Yield Services from Both Local and Global Perspectives
Bibliographic record
Abstract
With global climate change and irrational human activities, regional water resource conflicts are becoming more and more pronounced. The availability of water resource in watersheds can be indicated by the water yield. Exploring the factors that influence the water yield is crucial in responding to climate change and protecting water resource. Previous research on the factors influencing the water yield has frequently adopted a macro-level perspective, which has failed to reflect the influencing mechanisms of changes at the local scale adequately. Therefore, this study proposes a novel framework for integrally evaluating the impacts of climate change and human activities on water yield services from both local and global perspectives. Taking Ganzhou City, the source of the Ganjiang River, as an example, the results show the following: (1) Ganzhou City had the largest water yield of 1307.29 mm in 2016, and the lowest was only 375.32 mm in 2011. The spatial distribution pattern was mainly affected by the surface environment, and the high-value water yield regions in the study area were predominantly located in urban areas with flat terrain. (2) At the local scale, regions where human activities contribute more than 80% accounted for 25% of the area. In comparison, the impact of climate change accounted for 0.95%. The contribution rate of human activities to the water yield in Ganzhou City was significantly greater than that of climate change. (3) At the global scale, the simulation results of four scenarios show that climate change contributed (>98%) to the water yield, which is significantly higher than human activities (<2%). This study puts forward pioneering views on the research of water yield driving forces and provides a valuable theoretical basis for water resource protection and ecological environment construction.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".