A comprehensive ecological flow calculation for a small hydropower development river: A case study
Bibliographic record
Abstract
Abstract Small hydropower projects play a significant role in supplying clean energy and promoting economic development all around the world. The calculation of a river's ecological flow, that is, not only the quantity but also the quality of flowing water, in the watershed where small hydropower projects are located play an important role in scientifically guiding the discharge of small hydropower stations in the upper reaches of the river. By combining Gini coefficient with hydrological variation diagnosis, this study provided a novel approach to test the randomness of hydrological series and assess the evenness of runoff distribution. Furthermore, this study is the first attempt to calculate ecological flow for two separate periods (flood and low‐water periods), and thus is able to better reflect annual runoff variations. In the meantime, the Tennant method was improved by introducing of monthly runoff coefficient and median substitution, and integrating four other hydrological methods (empirical method, Qp method, annual distribution method and minimum monthly average measured runoff method) into the comprehensive calculation and analysis. The above methods were applied to the ecological base flow calculation for a small hydropower project in Dongjiang River basin of China. Through comprehensive and comparative analysis, better applicability was demonstrated by the improved Tennant method and the annual distribution method. The recommended values of ecological base flow for each month were derived at the range of 407–431 m3/s during the flood period and 179–239 m3/s during the low‐water period. This study demonstrated that the method could meet the ecological flow calculation requirements for river ecosystem and provide scientific basis for the delineation of ecological flow thresholds, and thereby ensure the healthy and sustainable development of small hydropower and the healthy river aquatic ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".