Title of article :
Impervious surfaces and sewer pipe effects on stormwater runoff temperature
Author/Authors :
F. Sabouri، نويسنده , , B. Gharabaghi، نويسنده , , A.A. Mahboubi، نويسنده , , E.A. McBean، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2013
Pages :
8
From page :
10
To page :
17
Abstract :
The warming effect of the impervious surfaces in urban catchment areas and the cooling effect of underground storm sewer pipes on stormwater runoff temperature are assessed. Four urban residential catchment areas in the Cities of Guelph and Kitchener, Ontario, Canada were evaluated using a combination of runoff monitoring and modelling. The stormwater level and water temperature were monitored at 10 min interval at the inlet of the stormwater management ponds for three summers 2009, 2010 and 2011. The warming effect of the ponds is also studied, however discussed in detail in a separate paper. An artificial neural network (ANN) model for stormwater temperature was trained and validated using monitoring data. Stormwater runoff temperature was most sensitive to event mean temperature of the rainfall (EMTR) with a normalized sensitivity coefficient (Sn) of 1.257. Subsequent levels of sensitivity corresponded to the longest sewer pipe length (LPL), maximum rainfall intensity (MI), percent impervious cover (IMP), rainfall depth (R), initial asphalt temperature (AspT), pipe network density (PND), and rainfall duration (D), respectively. Percent impervious cover of the catchment area (IMP) was the key parameter that represented the warming effect of the paved surfaces; sensitivity analysis showed IMP increase from 20% to 50% resulted in runoff temperature increase by 3 °C. The longest storm sewer pipe length (LPL) and the storm sewer pipe network density (PND) are the two key parameters that control the cooling effect of the underground sewer system; sensitivity analysis showed LPL increase from 345 to 966 m, resulted in runoff temperature drop by 2.5 °C.
Keywords :
Stormwater management , Thermal enrichment , Runoff temperature , Artificial neural network , Urbanization
Journal title :
Journal of Hydrology
Serial Year :
2013
Journal title :
Journal of Hydrology
Record number :
1095932
Link To Document :
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