Environmental flow envelopes: quantifying ecosystem-threatening flow alterations
Bibliographic record
Abstract
The benefits of harnessing rivers into human use should not come with a disproportionate expense on the Earth system. Especially, freshwater ecosystems suffer greatly from direct and indirect human impacts, such as excessive water withdrawals and climate change, which are expected to only increase in the near future. Here, we aim for quantifying the extent and degree of considerable flow alterations that threaten the well-being of freshwater ecosystems, across the world. At the global scale, the ecological status of river systems is often assessed using global hydrological models (GHMs) and hydrological environmental flow (EF) methods. These suffer from substantial uncertainties: 1) the GHMs parameterised with variable climate forcings may give highly dispersed discharge estimates and 2) individual hydrological EF methods capture ecosystem water needs poorly. We tackle these sources of uncertainty by introducing a novel methodology: environmental flow envelopes (EFEs). The EFE is an envelope of safe discharge variability between a lower and an upper bound, defined at the sub-basin scale in monthly time resolution. It is based on pre-industrial (1801-1860) discharge and a large ensemble of EF methods, GHMs, and climate forcings, using ISI-MIP2b data. Using the EFE, we can simultaneously assess the frequency and severity of ecosystem-threatening flow alterations. Comparing post-industrial (1976-2005) discharge to the EFEs, discharge in 32.7% of the total 3860 sub-basins, covering 28.4% of the global landmass, violates the EFE during more than 10% of all months across four GHMs. These violations are considered as severe threats to freshwater ecosystems. The most impacted regions include areas with high anthropogenic pressure, such as the Middle East, India, Eastern Asia, and Middle America. The violations clearly concentrate on the EFE lower bound during low or intermediate flow seasons. Discharge in 61.4% of sub-basins violates the EFE during more than 10% of low flow season months, average violation being 47.5% below the safe limit denoted by EFE lower bound. Indications of significantly increased flows by violations of the EFE upper bound are fewer and further apart, as well as lower bound violations during high flow season. Although fractional discharge allocations alone cannot fully capture the ecosystem water needs, this study is a step towards less uncertainty in global EF assessments. The introduced method provides a novel, globally robust way of estimating ecosystem water needs at the sub-basin scale. The results of this study underline the importance of the low flow season, during which EFE violations are the most prevalent. While only preliminary evidence of significantly increased flows emerges in relatively few areas, the EFE upper bound would benefit from further research. The EFE methodology can be used for exploring macro-regional areas where anthropogenic flow alteration threatens freshwater ecosystems the most. However, case-specific studies incorporating factors beyond quantitative flow only are required for practical implications.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".