Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate: The Effect of Rainfall Variability and Subcatchment Parameterization
Bibliographic record
Abstract
Urbanization continues to increase in countries with tropical climates and this trend, combined with the likely increasing frequency of extreme rainfall events due to a changing climate, places such development at risk and in need of resiliency assessment. Conceptual models to assess runoff dynamics can be an important component of resiliency assessment, but there are comparatively less data to calibrate these models than are available in the global north. As such, there also is less information with respect to the drivers of model uncertainty and sensitivity. To address this gap in knowledge, we summarize the calibration results of PCSWMM for subcatchment areas in a tropical climate study catchment for which there are substantial rainfall and runoff data. Subsequently, we used the calibrated model to evaluate the impact that rain gauge density may have on runoff estimates. We also investigated the sensitivity of PCSWMM peak flow and total volume estimates to physical subcatchment parameters other than rainfall. With between 38 and 87 events captured for each monitoring station, the NSE, r2, and ISE ratings varied, but generally were in the respective ranges 0.7–0.8, 0.79–0.85, and good–excellent. It can be concluded that PCSWMM performed well in representing the tropical storm events. The rainfall pattern in the study catchment exhibited considerable spatial variability, both annually and seasonally, with annual rainfall increasing from 2063 mm near the coast to 3100 mm less than 17 km further inland. While the model was sensitive to %imperviousness, subcatchment width, impervious Manning’s n, and, to a lesser extent, various surface storage and infiltration parameters, the spatial variability of rainfall had the greatest impact on model uncertainty.
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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 | 0.002 | 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".