Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal
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Bibliographic record
Abstract
Abstract Heavy precipitation events are recurrently occurring in Nepal, affecting lives and properties every year, especially in the summer monsoon season (i.e., June‐September). We investigated an extreme (heavy) precipitation event of August 2014 over the West Rapti River (WRR) Basin, Nepal. First, we forced a rainfall‐runoff model with ground‐based (gauge) hourly rainfall data of nine stations. Second, we validated against hourly water level at an outlet of the WRR Basin. This study then evaluated the performance of different satellite‐based rainfall estimates (SREs) in capturing an extreme precipitation event. We considered the use of half‐hourly data of Integrated Multi‐satellite Retrievals for GPM (IMERG) (Early, Late, and Final versions), spatial resolution (10 km), and hourly data of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), spatial resolution (25 km), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS), spatial resolution (4 km). Also, we used 3 h data of Tropical Multi‐satellite Precipitation Analysis (TMPA) product real‐time (3B42RT), spatial resolution (25 km). In general, we find that all selected SREs depicted a similar pattern of extreme precipitation as shown by the gauge data on a daily scale. However, we find these products could not replicate precisely on a sub‐daily scale. Overall, IMERG and TMPA showed a better performance than PERSIANN and PERSIANN‐CCS. Finally, we corrected poor‐performed SREs with respect to gauge data and also filled data gaps of gauge rainfall using the information of good‐performed SREs. Our study reveals that there is a great challenge in local flood simulation employing SREs at high‐temporal resolution in Nepal.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it