Application of PCSWMM to Explore Possible Climate Change Impacts on Surface Flooding in a Peri-Urban Area of Pathumthani, Thailand
Bibliographic record
Abstract
Under climate change scenarios, many urban areas in Southeast Asia may become increasingly susceptible to localized flooding due to greater rainfall extremes. This study focused on Rattanakosin Village, Thailand, a peri-urban area near Bangkok. Rainfall data from Don Mueang International Airport showed 2000 was similar to the 30 y norm, 1980-2011, and therefore was used as the baseline against which climate change scenarios were compared. PCSWMM, run at hourly increments with the 2000 rainfall, suggested 11 nodes in the village would flood for >24 h, with an annual flood volume of 367 200 000 L. The hourly synthetic rainfall time series for this area, generated by linking the ECHAM4 General Circulation Model with the PRECIS Regional Climate Model under the IPCC emission scenario B2, were run through PCSWMM for the years 2021, 2016, and 2091. PCSWMM results showed the number of nodes flooded for >24 h increased by 3 over the base case scenario and annual flood volume progressively increased from 370 554 000 L to 483 060 000 L between 2021 and 2091. The annual flood volume in 2091 was similar to that generated by simply increasing the 2000 rainfall by between 10% and 20%. Increases in evaporation also were explored using PCSWMM, but compared to the changes in rainfall, evaporation had a smaller impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".