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

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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Main Authors: Irvine, Kim N., Chua, Lloyd H.C., Ashrafi, Mohammad, Loc, Ho Huu, Ha, Le Song
Format: Article/Letter to editor biblioteca
Language:English
Subjects:Life Science,
Online Access:https://research.wur.nl/en/publications/drivers-of-model-uncertainty-for-urban-runoff-in-a-tropical-clima
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spelling dig-wur-nl-wurpubs-6284772024-08-28 Irvine, Kim N. Chua, Lloyd H.C. Ashrafi, Mohammad Loc, Ho Huu Ha, Le Song Article/Letter to editor Journal of Water Management Modeling 31 (2023) ISSN: 2292-6062 Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization 2023 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. en text/html https://research.wur.nl/en/publications/drivers-of-model-uncertainty-for-urban-runoff-in-a-tropical-clima 10.14796/JWMM.C496 https://edepot.wur.nl/654058 Life Science https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research
institution WUR NL
collection DSpace
country Países bajos
countrycode NL
component Bibliográfico
access En linea
databasecode dig-wur-nl
tag biblioteca
region Europa del Oeste
libraryname WUR Library Netherlands
language English
topic Life Science
Life Science
spellingShingle Life Science
Life Science
Irvine, Kim N.
Chua, Lloyd H.C.
Ashrafi, Mohammad
Loc, Ho Huu
Ha, Le Song
Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization
description 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.
format Article/Letter to editor
topic_facet Life Science
author Irvine, Kim N.
Chua, Lloyd H.C.
Ashrafi, Mohammad
Loc, Ho Huu
Ha, Le Song
author_facet Irvine, Kim N.
Chua, Lloyd H.C.
Ashrafi, Mohammad
Loc, Ho Huu
Ha, Le Song
author_sort Irvine, Kim N.
title Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization
title_short Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization
title_full Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization
title_fullStr Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization
title_full_unstemmed Drivers of Model Uncertainty for Urban Runoff in a Tropical Climate : The Effect of Rainfall Variability and Subcatchment Parameterization
title_sort drivers of model uncertainty for urban runoff in a tropical climate : the effect of rainfall variability and subcatchment parameterization
url https://research.wur.nl/en/publications/drivers-of-model-uncertainty-for-urban-runoff-in-a-tropical-clima
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