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Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate: The Effect of Rainfall Variability and Subcatchment Parameterization

Version 2 2024-06-03, 22:50
Version 1 2023-04-13, 04:16
journal contribution
posted on 2024-06-03, 22:50 authored by Kim Irvine, Lloyd ChuaLloyd Chua, Mohammad Ashrafi, Ho Huu Loc, Song Ha Le
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.

History

Journal

Journal of Water Management Modeling

Volume

31

Article number

C496

Pagination

1-12

Location

Guelph, Ont.

ISSN

2292-6062

eISSN

2292-6062

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Computational Hydraulics International

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