Quantification of Ecohydrological Sensitivities and Their Influencing Factors at the Seasonal Scale
Bibliographic record
Abstract
Abstract. Ecohydrological sensitivity is defined as the response intensity of streamflow to per unit vegetation change. Understanding of ecohydrological sensitivity and its influencing factors is important for managing water supply, reducing water-related hazards and ensuring aquatic functions by vegetation management. However, this topic has rarely been examined. In this study, 14 large watersheds across various environmental gradients in China were selected to quantify ecohydrological sensitivities at the seasonal scale and to examine their influencing factors such as climate, vegetation, topography, soil and landscape. Based on the variables identified by correlation analysis and factor analysis, the prediction models of seasonal ecohydrological sensitivity were constructed to test their utilities for the design of watershed management and protection strategies. Our key findings were: (1) ecohydrological sensitivities were more sensitive in dry conditions than in wet conditions, for example, 1 % LAI (leaf area index) change averagely induced 5.05 % and 1.96 % change in dry and wet season streamflows, respectively; (2) seasonal ecohydrological sensitivities were highly variable across the study watersheds with different climate condition, dominant soil type and hydrological regime; and (3) the dry season ecohydrological sensitivity was mostly determined by topography (slope, slope length, valley depth, downslope distance gradient), soil (topsoil organic carbon, topsoil bulk density) and vegetation (LAI), while the wet season ecohydrological sensitivity was mainly controlled by soil (topsoil available water holding capacity), landscape (edge density) and vegetation (leaf area index). Our study provided a useful and practical framework to assess and predict ecohydrological sensitivities at the seasonal scale. We expect that ecohydrological sensitivity prediction models can be applied to ungauged watersheds or watersheds with limited hydrological data to help decision makers and watershed managers to effectively manage hydrological impacts through vegetation restoration programs. We conclude that ecohydrological sensitivities at the seasonal scale were varied by climate, vegetation and watershed property, and their understanding can greatly support management of hydrological risks and protection of aquatic functions.
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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 | 0.001 | 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 | 0.001 |
| 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".