a data driven approach to bioretention cell performance: prediction and design

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ID: 217724
2013
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Abstract
Bioretention cells are an urban stormwater management technology used to address both water quality and quantity concerns. A lack of region-specific design guidelines has limited the widespread implementation of bioretention cells, particularly in cold climates. In this paper, experimental data are used to construct a multiple linear regression model to predict hydrological performance of bioretention cells. Nine different observed parameters are considered as candidates for regressors, of which inlet runoff volume and duration, and initial soil moisture were chosen. These three variables are used to construct six different regression models, which are tested against the observations. Statistical analysis showed that the amount of runoff captured by a bioretention cell can be successfully predicted by the inlet runoff volume and event duration. Historical data is then used to calculate runoff volume for a given duration, in different catchment types. This data is used in the regression model to predict bioretention cell performance. The results are then used to create a design tool which can assist in estimating bioretention cell size to meet different performance goals in southern Alberta. Examples on the functionality of the design tool are provided.
Reference Key
he2013watera Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jianxun He;Angus Chu;Caterina Valeo;Usman T. Khan
Journal Journal of food biochemistry
Year 2013
DOI
10.3390/w5010013
URL
http://www.mdpi.com/2073-4441/5/1/13
Keywords
hydrology low impact development urban runoff multiple linear regression bioretention

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