An integrated framework for the comprehensive evaluation of low impact development strategies

The impacts of urbanization on water quality, hydrology, society, and the environment can be minimized through low impact development (LID) practices in urban areas. This study has evaluated the performances of seven different LID scenarios including stand-alone and different combinations of green roof (GR), bioretention cells (BC), permeable pavement (PP), and infiltration trench (IT) in the Ayamama watershed, which is one of the most densely urbanized areas in Istanbul. Stormwater Management Model (SWMM) was used to obtain the performances of LID scenarios in quantitative (i.e., volume reduction and peak runoff reduction) and qualitative (i.e., Total Suspended Sediment, Chemical Oxygen Demand, Total Nitrate, Total Phosphate reductions) manner. To calibrate the SWMM model, the Parameter EStimation Tool (PEST) was integrated for sensitivity analysis and parameter optimization. A focus group discussion (FGD) was performed to identify the criteria and LID scenarios applicable to the study area. 16 criteria were determined as suitable, based on three dimensions of sustainability such as social, economic, and environmental. The criteria were evaluated in compliance with the fuzzy analytical hierarchy process (AHP) method before performing technique for order preference by similarity to ideal solution (TOPSIS) for a comprehensive assessment of LID scenarios. The results showed that community resistance, operation feasibility, and quantitative benefits were the most significant criteria for LID scenario selection in social, economic, and environmental aspects, respectively. The integrated evaluation showed that the impacts of urban flooding can be reduced significantly with the combination of GR and BC. Thus, this study provides an integrated and sustainable solution to the topic based on the PEST-SWMM-fuzzy AHP-TOPSIS framework. Furthermore, the developed framework could assist decision-makers and governmental authorities to designate optimal LID scenarios.