Compound Flooding in a Non-Stationary World: A Primer for Practice
Bibliographic record
Abstract
Compound flooding conditions present a significant challenge for civil engineers in their pursuit to design for and maintain the integrity of a structure’s entire life cycle. Coupled with non-stationary processes due to a changing climate and land use change, risk is a moving target. Through the support of ASCE’s Task Committee on Compound Flooding, the Hydroclimatology Engineering Adaptation (HYDEA) sub-committee is developing a Manual of Practice (MOP) to provide a synthesis of available tools and methods of best practice for civil engineers designing for compound flooding conditions. This paper presents a primer for practicing civil engineers on this work. Hydrodynamic process-based models such as rainfall-runoff, riverine, and coastal modeling, as well as statistical models including multivariate statistical models, will be addressed. In addition, the importance of linking statistical and process-based models and their various approaches is identified. The MOP also discusses addressing nonstationarity due to changing local and regional conditions and tools to assess risk and uncertainty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".