Streamflow Generation Processes and Stream Intermittency in a Mediterranean Basin: Insights From End‐Member Mixing Analysis
Bibliographic record
Abstract
ABSTRACT Climate change, land use, and resource exploitation increasingly threaten river hydrology and water quality, leading to scarcity. Non‐perennial or intermittent rivers are vulnerable systems characterised by highly variable flows. While recent research has advanced understanding of their natural variability, integrating the complex effects of multiple human impacts at the basin scale remains challenging. Aiming to improve this understanding and supporting effective management, this study uses end‐member mixing analysis (EMMA) to identify streamflow sources in an intermittent Mediterranean river network, based on hydrochemical data from 23 sites over 14 monthly surveys and potentiometric data from six shallow wells. The analysis identifies three main contributors to streamflow: headwater runoff, groundwater baseflow from aquifers and hillslopes and treated wastewater. Their relative contributions varied spatially and seasonally. Upstream of urban areas, headwater runoff dominated (71%), while in the central catchment, groundwater contributed 15%–75%. Downstream of industrial areas, contributions were more balanced: 35% runoff, 27% groundwater and 38% wastewater. Alluvial groundwater was more important in small upstream subbasins in terms of maintaining the connectivity of the streamflow, whereas groundwater contribution from deeper aquifers dominated in lower reaches. EMMA results also highlighted discrepancies with point potentiometric data, emphasising the limitations of local measurements for understanding basin‐scale hydrology. Under future climate scenarios, reduced headwater runoff and groundwater storage are expected to lower baseflow, increasing reliance on treated—but lower‐quality—wastewater to sustain streamflow. This highlights the need for long‐term monitoring and management, with a focus on protecting baseflow sources and improving wastewater quality to ensure water sustainability.
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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 | 0.000 | 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".