Simulating the Upper St. Johns River for Extreme Events
Bibliographic record
Abstract
The St. Johns River is one of the most important river systems in Florida.The watershed begins in East Central Florida and discharges to the Atlantic Ocean east of Jacksonville, Florida.The watershed is susceptible to large rainfall events including tropical storms and hurricanes and is topographically flat such that flooding is a real concern.The Upper St. Johns River Basin (USJRB) encompasses an area of approximately 4 530 km 2 .USJRB mainly comprises marsh and agricultural land types including man-made storage areas used for flood control and environmental management, and includes numerous water control structures.The Middle St. Johns River Basin (MSJRB) is downstream of the USJRB and covers an area of approximately 3 100 km 2 .The land use in this region is dominated by more urbanized areas including parts of Orlando.Recently, researchers at the University of North Florida developed a preliminary HEC-HMS rainfall-runoff model of the USJRB and a portion of the MSJRB.The model domain covers roughly 5 200 km 2 and includes numerous subbasins.The model was calibrated and verified against observed data recorded between 2007 and 2011 and including a large tropical storm event.This new study expands the use and scope of the model by examining extreme rainfall events and resulting flows that may occur in the future under possible climate change scenarios.First, the model is used to simulate a hypothetical probable maximum precipitation (PMP) event.Then, the model is used to simulate the same PMP event but with future climate change forcing added.Lastly, the model is used to simulate two 100 y rainfall events occurring in the same week.This study provides the simulation results, compares them to historical flow data at key locations within the model domain, and then finishes with a discussion of water resources planning considerations for the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".