Virtual Stormwater Management Planning in the 21st Century

Phillips, Brett C; Thompson, Geoffrey B.

doi:10.1061/40644(2002)147

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…In order to prioritize the implementation of stormwater treatment measures, urban waterway managers need to be able to predict and assess their performance, both individually and in various combinations. There is thus a strong demand for predictive models that can be applied across a range of locations and conditions, to predict the general characteristics of stormwater and performance of a range of stormwater treatment measures (Phillips and Thompson 2002). It is practically impossible to analyze each and every storm event, but one can predict the pollutant concentration using the regression models (Arora and Reddy 2014).…”

Section: Stormwater Quality Assessment Through Modelingmentioning

confidence: 99%

Conceptualizing a decentralized stormwater treatment system for an urbanized city with improper stormwater drainage facilities

Arora

Reddy

2014

Int. J. Environ. Sci. Technol.

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Urbanization involving replacement of previous areas with impervious surfaces and drainage channel modifications results in increased runoff volume leading to flooding and also in deteriorating the quality of the stormwater runoff. An effective and efficient stormwater management is needed in the urban areas for mitigating the impacts and overcoming the problems associated with the urban stormwater. An attempt has been made in the present study to investigate the relationships between stormwater quality and five different land uses for the Patiala city of Punjab State. Stormwater samples collected from these locations, between April 2010 and March 2011, were analyzed for various organic and inorganic water quality parameters and heavy metals. Inter-parameter relationships studied provided vital information in deciding upon the treatment units most likely to be suitable for that particular sub-watershed. Multiple regression modeling helped in assessment of water quality parameters and predicting the quality well before actually analyzing the parameters and thus aided in developing treatment schemes suitable for taking care of the stormwater quality even under worst set of conditions. The outcomes of the research study effectively contributed toward conceptualizing a decentralized stormwater treatment system that would be capable of coping with the stormwater runoff-associated problems faced by the city and can serve as a model for other underdeveloped and developing cities having improper stormwater management facilities.

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Section: Stormwater Quality Assessment Through Modelingmentioning

confidence: 99%

Conceptualizing a decentralized stormwater treatment system for an urbanized city with improper stormwater drainage facilities

Arora

Reddy

2014

Int. J. Environ. Sci. Technol.

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Development of Multiple Linear Regression Models for Predicting the Stormwater Quality of Urban Sub-Watersheds

Arora

Reddy

2013

Bull Environ Contam Toxicol

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Stormwater management at urban sub-watershed level has been envisioned to include stormwater collection, treatment, and disposal of treated stormwater through groundwater recharging. Sizing, operation and control of the stormwater management systems require information on the quantities and characteristics of the stormwater generated. Stormwater characteristics depend upon dry spell between two successive rainfall events, intensity of rainfall and watershed characteristics. However, sampling and analysis of stormwater, spanning only few rainfall events, provides insufficient information on the characteristics. An attempt has been made in the present study to assess the stormwater characteristics through regression modeling. Stormwater of five sub-watersheds of Patiala city were sampled and analyzed. The results obtained were related with the antecedent dry periods and with the intensity of the rainfall event through regression modeling. Obtained regression models were used to assess the stormwater quality for various antecedent dry periods and rainfall event intensities.

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