Quantifying the hydrological responses to climate change in an intact forested small watershed in <scp>S</scp>outhern <scp>C</scp>hina
Bibliographic record
Abstract
Abstract Responses of hydrological processes to climate change are key components in the Intergovernmental Panel for Climate Change (IPCC) assessment. Understanding these responses is critical for developing appropriate mitigation and adaptation strategies for sustainable water resources management and protection of public safety. However, these responses are not well understood and little long‐term evidence exists. Herein, we show how climate change, specifically increased air temperature and storm intensity, can affect soil moisture dynamics and hydrological variables based on both long‐term observation and model simulations using the Soil and Water Assessment Tool (SWAT) in an intact forested watershed (the Dinghushan Biosphere Reserve) in Southern China. Our results show that, although total annual precipitation changed little from 1950 to 2009, soil moisture decreased significantly. A significant decline was also found in the monthly 7‐day low flow from 2000 to 2009. However, the maximum daily streamflow in the wet season and unconfined groundwater tables have significantly increased during the same 10‐year period. The significant decreasing trends on soil moisture and low flow variables suggest that the study watershed is moving towards drought‐like condition. Our analysis indicates that the intensification of rainfall storms and the increasing number of annual no‐rain days were responsible for the increasing chance of both droughts and floods. We conclude that climate change has indeed induced more extreme hydrological events (e.g. droughts and floods) in this watershed and perhaps other areas of Southern China. This study also demonstrated usefulness of our research methodology and its possible applications on quantifying the impacts of climate change on hydrology in any other watersheds where long‐term data are available and human disturbance is negligible.
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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 | 0.000 | 0.000 |
| 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".