Improved methods to estimate the effective impervious area in urban catchments using rainfall-runoff data
Article
Ebrahimian, A, Wilson, BN, Gulliver, JS. (2016). Improved methods to estimate the effective impervious area in urban catchments using rainfall-runoff data
. JOURNAL OF HYDROLOGY, 536 109-118. 10.1016/j.jhydrol.2016.02.023
Ebrahimian, A, Wilson, BN, Gulliver, JS. (2016). Improved methods to estimate the effective impervious area in urban catchments using rainfall-runoff data
. JOURNAL OF HYDROLOGY, 536 109-118. 10.1016/j.jhydrol.2016.02.023
Impervious surfaces are useful indicators of the urbanization impacts on water resources. Effective impervious area (EIA), which is the portion of total impervious area (TIA) that is hydraulically connected to the drainage system, is a better catchment parameter in the determination of actual urban runoff. Development of reliable methods for quantifying EIA rather than TIA is currently one of the knowledge gaps in the rainfall-runoff modeling context. The objective of this study is to improve the rainfall-runoff data analysis method for estimating EIA fraction in urban catchments by eliminating the subjective part of the existing method and by reducing the uncertainty of EIA estimates. First, the theoretical framework is generalized using a general linear least square model and using a general criterion for categorizing runoff events. Issues with the existing method that reduce the precision of the EIA fraction estimates are then identified and discussed. Two improved methods, based on ordinary least square (OLS) and weighted least square (WLS) estimates, are proposed to address these issues. The proposed weighted least squares method is then applied to eleven urban catchments in Europe, Canada, and Australia. The results are compared to map measured directly connected impervious area (DCIA) and are shown to be consistent with DCIA values. In addition, both of the improved methods are applied to nine urban catchments in Minnesota, USA. Both methods were successful in removing the subjective component inherent in the analysis of rainfall-runoff data of the current method. The WLS method is more robust than the OLS method and generates results that are different and more precise than the OLS method in the presence of heteroscedastic residuals in our rainfall-runoff data.
