Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
Bibliographic record
Abstract
Abstract. Hydrological drought is one of the main hydroclimatic hazards worldwide, affecting water availability, ecosystems, and socioeconomic activities. This phenomenon is commonly characterized by the Standardized Streamflow Index (SSI), which is widely used because of its straightforward formulation and calculation. Nevertheless, there is limited understanding of what the SSI actually reveals about how climate anomalies propagate through the terrestrial water cycle. To find possible explanations, we implemented the Structure for Unifying Multiple Modeling Alternatives (SUMMA) coupled with the mizuRoute routing model in six hydroclimatically different case study basins located on the western slopes of the extratropical Andes and examined correlations between the SSI (computed from the models for 1-, 3-, and 6-month timescales) and potential explanatory variables – including precipitation and simulated catchment-scale storages – aggregated at different timescales. Additionally, we analyzed the impacts of adopting commonly used timescales on propagation analyses of specific drought events – from meteorological to soil moisture and hydrological drought – with focus on their duration and intensity. The results reveal that the choice of timescale for the SSI has larger effects on correlations with explanatory variables in rainfall-dominated regimes compared to snowmelt-driven basins, especially when simulated fluxes and storages are aggregated to timescales longer than 9 months. In all the basins analyzed, the strongest relationships (Spearman rank correlation values over 0.7) were obtained when using 6-month timescales to compute the SSI and 9–12 months to compute the explanatory variables, excepting aquifer storage in snowmelt-driven basins. Finally, the results show that the trajectories of drought propagation obtained with the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI), and the SSI may change drastically with the selection of timescale. Overall, this study highlights the need for caution when selecting standardized drought indices and associated timescales, since their choice impacts event characterizations, monitoring, and propagation analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| 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 teacher head, 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".