Simulated Impacts of Nature-Based Solutions on Flooding in the Upper Illinois River Basin
Bibliographic record
Abstract
Simulation experiments in a high-resolution configuration of the Weather Research and Forecasting Model are used to test the hypothesis that land-surface vegetation inhibits propagation of rainstorms into the Upper Illinois River Basin (UIRB), thus decreasing associated total precipitation (Pt) and flooding. Two historical flood-generating rainstorms, representative of storm types in the UIRB, are selected and simulated: a mesoscale convective rainstorm in July 1996 and the remnant of the Hurricane Ike rainstorm in September 2008. For each rainstorm, three sensitivity experiments with differing land-surface vegetation configurations are simulated and compared with the reference experiment. Results show that vegetation changes inside the UIRB or inside a 1° belt around it caused an increase in basin-average Pt during the 2008 rainstorm. However, these same vegetation configurations caused a decrease in basin-average Pt in the 1996 rainstorm. The largest decrease, between 18% and 32%, occurred in the experiment with a belt of trees. The mechanism for this decrease in Pt is windspeed decrease associated with increase in surface roughness owing to vegetation with high vertical extent, which inhibits propagation of the rainstorm into the basin. These findings highlight a linkage between land-cover characteristics outside the UIRB and precipitation inside it associated with mesoscale convective storms. Since precipitation is the key driver of flooding, this linkage can be combined with other structural approaches toward developing a long-term flood mitigation strategy for the UIRB.
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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 | 0.001 | 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 | 0.000 |
| 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".