Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probabilistic Stormwater Runoff and Water Quality Modeling of a Highway in Suburban Maryland
The U.S. EPA Storm Water Management Model (SWMM) was used to simulate upland runoff production and the subsequent performance of a downstream, ponded infiltration basin installed adjacent to a highway in suburban Maryland. The SWMM’s performance was evaluated with a unique, rich suite of in situ flow and water quality observations. The availability of these in situ observations creates a novel opportunity to explore the performance of SWMM across small scales in space and time. In order to systematically explore the ability of SWMM to leverage these observations, an automatic Monte Carlo–based calibration framework was developed and a multiparameter sensitivity analysis was conducted. As expected, the calibrated model showed better skill in terms of reproducing water quantity observations relative to water quality observations. An uncertainty analysis showed model predictions (flow and water quality) were consistent in the sense that the model was able to encapsulate the observations between 5 and 95% confidence intervals. Example code for use by other researchers to employ the techniques discussed in this paper is made publicly available with this manuscript.
Probabilistic Stormwater Runoff and Water Quality Modeling of a Highway in Suburban Maryland
The U.S. EPA Storm Water Management Model (SWMM) was used to simulate upland runoff production and the subsequent performance of a downstream, ponded infiltration basin installed adjacent to a highway in suburban Maryland. The SWMM’s performance was evaluated with a unique, rich suite of in situ flow and water quality observations. The availability of these in situ observations creates a novel opportunity to explore the performance of SWMM across small scales in space and time. In order to systematically explore the ability of SWMM to leverage these observations, an automatic Monte Carlo–based calibration framework was developed and a multiparameter sensitivity analysis was conducted. As expected, the calibrated model showed better skill in terms of reproducing water quantity observations relative to water quality observations. An uncertainty analysis showed model predictions (flow and water quality) were consistent in the sense that the model was able to encapsulate the observations between 5 and 95% confidence intervals. Example code for use by other researchers to employ the techniques discussed in this paper is made publicly available with this manuscript.
Probabilistic Stormwater Runoff and Water Quality Modeling of a Highway in Suburban Maryland
Wang, Jing (Autor:in) / Forman, Barton A. (Autor:in) / Davis, Allen P. (Autor:in)
08.12.2017
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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