Can runoff modeled at coarse resolution simulate floods at finer resolutions? A case study over the Ohio River Basin
Bibliographic record
Abstract
• The VIC model with a kinematic wave routing model reasonably simulates flood statistics and reproduces flood events. • Smaller catchments with dams have large errors, but neglecting dams does not impact model results in larger basins. • VIC-simulated runoff and the subsequent routed floods are insensitive to subdaily model timesteps but are sensitive to spatial resolution. Recent studies on flood-generating mechanisms have advanced understanding of the hydrologic processes that lead to riverine flooding. Flood modeling frameworks can play a role in furthering this research, but they can be computationally intensive. This study tests the ability to simulate floods using the widely used Variable Infiltration Capacity (VIC) land surface model with a kinematic wave routing model over the Ohio River basin, which is flood-prone and topographically variable. Using 200 USGS streamflow gauges, the model estimates the median annual maximum daily flow (AMF) with an average bias of 1.8% across the gauges and the 90th percentile AMF with an average bias of 6.2% for 1979–2022. Errors tend to be larger in flatter regions and in smaller basins with dams, highlighting the role dams play in reducing flood peaks in this basin. Model experiments show that the simulated AMF is not sensitive to the subdaily model timestep, but it is sensitive to the spatial resolution, with larger grid cells resulting in underestimating AMF. Overall, this modeling framework reproduces flooding across a range of land cover, topography, and drainage area, indicating it can be used in future studies to investigate flood generating mechanisms and flood risk estimation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".