Impact of Climate Variability and Land Use Change on Surface Hydrological Processes in the Hilly-Gully Region of the Loess Plateau, China
Bibliographic record
Abstract
Abstract: Climate and land-use change are the two main driving forces that affect watershed hydrological processes. Separately assessing the impacts of climate and land use change on hydrology is important for water resource management. In this research, the SWAT (Soil and Water Assessment Tool) and statistical methods were employed to evaluate the effects of climate and land-use change on surface hydrology in the hilly-gully region of the Loess Plateau. The results showed that both the temperature and potential evapotranspiration (PET) had significant upward trends (p < 0.05), while the precipitation presented a slightly downward trend in the Yanhe watershed during 1982-2012. The contribution of precipitation to streamflow is concentrated in the flooding periods (from July to September), the average contribution rate of surface runoff on stream flow accounted for 55%, of which the flooding period accounted for 40%. With the 2.17% of slope farmland transformed to the forest and grassland, the average runoff coefficient decreased from 0.36 to 0.15 during 1982-2012. The impact of land use change on soil water content is mainly happened in the upstream stream, while the dominated factor converted to climate from northwest to southeast in the Yanhe watershed. The Evapotranspiration was more sensitive to land-use change than climate variability in all sub-basins, and increased by 209% with vegetation restoration in the Yanhe watershed. Therefore, the impacts of climate variation and land use change on surface hydrological processes were heterogeneity in different geographical regions, climate is the main factor to influence the runoff, while the land use is the dominated factor to evapotranspiration. The quantitative assessment the influence of climate variability and land-use change on hydrology can provide insight into the extent of land use/cover change on regional water balance, and develop appropriate watershed management strategies on the Loess Plateau. Keywords: climate shift, human activities, hydrological processes, SWAT, the Loess Plateau Funding: This study was funded by the National Natural Science Foundation of China (No. 41877078, 41371276), Key research and development project of Shaanxi Province (2020ZDLSF06-03-01), National Key Research and Development Program of China (No. 2017YFC0504703) and Knowledge Innovation Program of the Chinese Academy of Sciences (No. A315021615).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".