A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow
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Bibliographic record
Abstract
Ascertaining the spatiotemporal accuracy of precipitation is a challenge for hydrologists and planners for flood protection measures. The objective of this study was to compare streamflow simulations using rain gauge and radar data from a watershed in Southern Ontario, Canada, using the Hydrologic Engineering Center’s event-based distributed Hydrologic Modeling System (HEC-HMS). The model was run using the curve number (CN) and the Green and Ampt infiltration methods. The results show that the streamflow simulated with rain gauge data compared better with the observed streamflow than the streamflow simulated using radar data. However, when the Mean Field Bias (MFB) corrections were applied, the quality of the streamflow results obtained from radar rainfall data improved. The results showed no significant difference between the simulated streamflow using the SCS and the Green and Ampt infiltration approach. However, the SCS method is reasonably more appropriate for modeling the runoff at the sub-basin-scale than the Green and Ampt infiltration approach. With the SCS method, the simulated and observed runoff amount obtained using rain gauge rainfall showed an R2 value of 0.88 and 0.78 for MFB-corrected radar and 0.75 for radar only. For the Green and Ampt modeling option, the R2 value for the simulated and observed runoff amounts were 0.87 with rain gauge, 0.66 with radar only, and 0.68 with MFB-corrected radar rainfall inputs. The NSE values for rain gauge input ranged from 0.65 to 0.35. Overall, three values were less than 0.5 for streamflow for both the methods. For seven radar rainfall events, the NSE was greater than 0.5, with a range of very good to satisfactory. The analysis of RSR showed a very good comparison of stream flow using the SCS curve number method and Green and Ampt method using different rainfall inputs. Only one value, the 2 November 2003 event, was above 0.7 for rain gauge-based streamflow. The other RSR values were in the range of “very good”. Overall, the study showed better results for the simulated runoff with the MFB-corrected radar rainfall when compared with the simulations obtained using radar rainfall only. Therefore, MFB-corrected radar could be explored as a substitute rainfall source.
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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 | 0.001 | 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 | 0.001 |
| 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