Drought monitoring and prediction in climate vulnerable Pakistan: Integrating hydrologic and meteorologic perspectives
Bibliographic record
Abstract
Abstract. Effective drought monitoring, prediction and early warning systems are crucial for management of human activities associated with water use in a climate affected world. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus and Kabul rivers. Hence, impact of droughts manifested through water deficits in these catchments are strongly felt by downstream users. Use of different drought indicators is limited in Pakistan's operational drought monitoring system. Moreover, there is very limited prior literature that explores the use of multiple indicators for unearthing relationships between different drought types. This study aims to explore the relationship between meteorological and hydrological droughts in the Upper Indus catchments of Pakistan using the Standard Precipitation and Evaporation Index (SPEI) and the Standard Streamflow Index (SSI). Since there are no previous studies for the Indus that compare different distributions for SSI computation, we compare five distributions to adequately compute SSI values at catchment outlets. Our most crucial contribution in this study is analysis of seasonal cross-correlations and lagged cross-correlations between SSI and SPEI for the above-mentioned four catchments. The cross-correlation analysis shows strong lagged (with up to 2 lag months) cross-correlations between SPEI and SSI for Chenab, Jhelum and Kabul catchments in early Kharif months. These correlations may be used in operational drought monitoring and forecasting systems, and also in reservoir planning and operations (for Mangla resrvoir in Jhelum) in drought conditions. We strongly believe that the findings of this study can be used in future to collectively explore hydrological and meteorological drought perspectives in Pakistan and to successfully incorporate multiple indicators into operational drought management.
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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 | 0.000 | 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 | 0.000 |
| 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 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".