Understanding Drivers of Baseflow Changes and Their Role in Hydrological Droughts
Bibliographic record
Abstract
Hydrological droughts are often viewed through the immediate lens of atmospheric droughts, driven by precipitation deficits and evaporative demand. However, these droughts can be exacerbated by the long-term impacts of baseflow changes, which alter groundwater-fed streamflow critical for sustaining hydrological systems during prolonged dry periods. This study employs a global dataset of 7,138 catchments and the PCMCI+ causal discovery algorithm to unravel the spatiotemporal drivers of baseflow changes and their relationship with hydrological drought severity. We identify key climatic controls—precipitation, evaporative demand, and snow fraction—and quantify their influence across diverse climate zones. Precipitation emerges as the dominant driver globally (58.3% of catchments), while evaporative demand and snow fraction govern baseflows in tropical and polar regions, respectively. By mapping concurrent spatial occurrence in baseflow and hydrological drought, we delineate zones of critical risk where these processes overlap, exacerbating vulnerability to extremes. This study advances our understanding of spatiotemporal extremes and offers insights for improving the modeling and management of compound hydroclimatic events under climate change.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".