Semi‐distributed modelling of basin hydrology with radar and gauged precipitation
Bibliographic record
Abstract
Abstract Even though gauged rainfall data generally provide accurate depth measurements, sparsely spaced, gauging stations cannot effectively account for the spatial variability of precipitation at basin scale. On the other hand, radar data such as the WSR‐88D stage III radar rainfall data can generally capture the spatial variability of rainfall fields, but tends to underestimate rainfall depth of stratiform storms, or both convective and stratiform storms if a storm is of low intensity. To take advantage of both the strength of radar data (mapping accurate spatial variability of rainfall) and that of gauge data (accurate depth measurements), the two data sets were merged together by the Statistical Objective Analysis (SOA) scheme. The event‐based hydrologic experiments using a semi‐distributed, physics‐based hydrologic model (distributed physically based hydrologic model using remote sensing, DPHM‐RS) revealed that WSR‐88D Stage III radar rainfall data simulated more accurate runoff hydrographs than gauged data for convective storms but less accurate runoff hydrograph for stratiform storms, because radars measured slightly more rainfall than gauges for convective storms, but substantially less rainfall for stratiform storms. However, after merging WSR‐88D stage III radar data with gauge data by SOA, the radar's underestimation of stratiform storm depth decreased substantially, but the adjustment could be counter productive for convective storms. Results show that rainfall spatial variability, depths, and hydrologic model resolution play a major role on the accuracy of simulated runoff volumes and peak flows. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 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".