River Damming Reduces Wetland Function in Regulating Flow
Bibliographic record
Abstract
The global scale wetland loss or degeneration triggers the assessment of how their function provisions are likely to change under different management scenarios. However, how and to what extent river damming can modify the hydrological function of wetlands remain largely unknown. In this study, we apply a distributed hydrological modeling platform for a larger river basin in Northeast China with a paired modeling scenario: (1) modeling with no dam present (i.e., under natural conditions), and (2) modeling with dam present (i.e., under disturbed conditions). The overarching goal of the study is to quantify the effect of damming on wetland hydrological processes. The modeling study demonstrates that river damming can alter the wetland effect on daily flow by significantly reducing flow under low flow conditions but slightly increasing flow under high flow conditions. Damming can impact the wetland function in alleviating floods with a 7% decrease when compared to the natural conditions without a dam. Consequently, the supporting effect of wetlands on baseflow is weakened substantially by damming regulation. These results indicate that river damming can impair flow regulation functions of downstream wetlands, and therefore, new flow regulation and optimization strategies for achieving complementary hydrological functions of wetlands and reservoirs are important to maximize basin resilience to hydrological extremes under climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".