A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Physical parameterization and sensitivity of urban hydrological models: Application to green roof systems
https://orcid.org/0000-0002-3890-5628
Abstract Rapid urbanization has emerged as the source of many adverse environmental effects and brings cities to a vulnerable situation under future climate challenges. Green roofs are proven to be an effective solution to alleviate these effects by field observations under a wide range of climate conditions. Recent advances in modeling urban land-atmosphere interactions provide a useful tool in capturing the dynamics of coupled transport of water and energy in urban conies, thus bridge the gap of modeling at city to regional scales. The performance of urban hydrological models depends heavily on the accurate determination of the input parameter space, where uncertainty is ubiquitous. In this paper, we use an advanced Monte Carlo approach, viz. the Subset Simulation, to quantify the sensitivity of urban hydrological modeling to parameter uncertainties. Results of the sensitivity analysis reveal that green roofs exhibit markedly different thermal and hydrological behavior as compared to conventional roofs, due to the modification of the surface energy portioning by well-irrigated vegetation. In addition, statistical predictions of critical responses of green roofs (extreme surface temperature, heat fluxes, etc.) have relatively weak dependence on climatic conditions. The statistical quantification of sensitivity provides guidance for future development of urban hydrological models with practical applications such as urban heat island mitigation.
Highlights We quantify the sensitivity of urban hydrological modeling. van Genuchten model for unsaturated soil hydraulics is more robust. Green roofs exhibit distinct thermal and hydrological behavior. The current numerical framework are scalable to city and regional studies.
Physical parameterization and sensitivity of urban hydrological models: Application to green roof systems
https://orcid.org/0000-0002-3890-5628
Abstract Rapid urbanization has emerged as the source of many adverse environmental effects and brings cities to a vulnerable situation under future climate challenges. Green roofs are proven to be an effective solution to alleviate these effects by field observations under a wide range of climate conditions. Recent advances in modeling urban land-atmosphere interactions provide a useful tool in capturing the dynamics of coupled transport of water and energy in urban conies, thus bridge the gap of modeling at city to regional scales. The performance of urban hydrological models depends heavily on the accurate determination of the input parameter space, where uncertainty is ubiquitous. In this paper, we use an advanced Monte Carlo approach, viz. the Subset Simulation, to quantify the sensitivity of urban hydrological modeling to parameter uncertainties. Results of the sensitivity analysis reveal that green roofs exhibit markedly different thermal and hydrological behavior as compared to conventional roofs, due to the modification of the surface energy portioning by well-irrigated vegetation. In addition, statistical predictions of critical responses of green roofs (extreme surface temperature, heat fluxes, etc.) have relatively weak dependence on climatic conditions. The statistical quantification of sensitivity provides guidance for future development of urban hydrological models with practical applications such as urban heat island mitigation.
Highlights We quantify the sensitivity of urban hydrological modeling. van Genuchten model for unsaturated soil hydraulics is more robust. Green roofs exhibit distinct thermal and hydrological behavior. The current numerical framework are scalable to city and regional studies.
Physical parameterization and sensitivity of urban hydrological models: Application to green roof systems
https://orcid.org/0000-0002-3890-5628
Abstract Rapid urbanization has emerged as the source of many adverse environmental effects and brings cities to a vulnerable situation under future climate challenges. Green roofs are proven to be an effective solution to alleviate these effects by field observations under a wide range of climate conditions. Recent advances in modeling urban land-atmosphere interactions provide a useful tool in capturing the dynamics of coupled transport of water and energy in urban conies, thus bridge the gap of modeling at city to regional scales. The performance of urban hydrological models depends heavily on the accurate determination of the input parameter space, where uncertainty is ubiquitous. In this paper, we use an advanced Monte Carlo approach, viz. the Subset Simulation, to quantify the sensitivity of urban hydrological modeling to parameter uncertainties. Results of the sensitivity analysis reveal that green roofs exhibit markedly different thermal and hydrological behavior as compared to conventional roofs, due to the modification of the surface energy portioning by well-irrigated vegetation. In addition, statistical predictions of critical responses of green roofs (extreme surface temperature, heat fluxes, etc.) have relatively weak dependence on climatic conditions. The statistical quantification of sensitivity provides guidance for future development of urban hydrological models with practical applications such as urban heat island mitigation.
Highlights We quantify the sensitivity of urban hydrological modeling. van Genuchten model for unsaturated soil hydraulics is more robust. Green roofs exhibit distinct thermal and hydrological behavior. The current numerical framework are scalable to city and regional studies.
Physical parameterization and sensitivity of urban hydrological models: Application to green roof systems
Yang, Jiachuan (author) / Wang, Zhi-Hua (author)
Building and Environment ; 75 ; 250-263
2014-02-15
14 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2014
Sensitivity-guided evaluation of the HBV hydrological model parameterization
| British Library Online Contents | 2013
Performance comparison of green roof hydrological models for full-scale field sites
| DOAJ | 2021
Parameterization of a Hydrological Model for a Large, Ungauged Urban Catchment
| DOAJ | 2016