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Showing 3 results for Urban Flood
Flood Risk Assessment using Multi-Criteria Decision-Making Models (MCDM) and Data Mining Methods (Case Study: Shiraz District 4)
S. Ayoubi Ayoublu, M. Vafakhah, H.r. Pourghasemi,
Volume 26, Issue 3 (12-2022)
Abstract
Population growth, urbanization, and land use change have increased disastrous floods. Iran is also among the countries at high risk of floods. The latest examples of flood damage are the devastating floods of the spring of 2019 with significant mortality and financial losses in more than ten provinces of the country. The purpose of this study is to prepare an urban flood risk map of District 4 City Shiraz. The vulnerability of the region was made using PROMETHEE Ⅱ and COPRAS multi-criteria decision-making models and urban flood hazard zones were prepared by partial least squares regression (PLSR) and ridge regression (RR) models and a risk map was obtained by multiplying the vulnerability and hazard in ArcGIS software. The highest percentage of the study area in the PROMETHEE Ⅱ and COPRAS models belongs to the moderate class of vulnerability. The evaluation of the vulnerability models using Boolean logic and RMSE and MAPE statistics, showed that the COPRAS model provided better results than the PROMETHEE model. The results of partial least square regression (PLSR) and ridge regression (RR) models in flood risk modeling were analyzed by the Taylor diagram, which showed the superiority of the ridge regression (RR) model and the accuracy of this model in preparing urban flood hazard maps. The risk map of the study area indicated that 34% of the area (973 ha) is in the range of high and very high flood risk.

Bio-Retention Cells Performance in Restoring the Hydrologic Cycle of Highly Urbanized Catchments
M. Mehri, M. Hashemy, S. Javadi, M. Movahedinia,
Volume 27, Issue 3 (12-2023)
Abstract
Rapid urbanization is responsible for impervious area increases and more runoff generation in urbanized catchments. Higher runoff volume in urbanized catchments leads to higher flood risk. One of the methods of runoff management is low impact development (LID). Bio-retention cell (BRC) is one of the infiltration-based LID practices that allows restoring the pre-development hydrologic cycle. However, the overall hydrologic performance of BRCs can vary depending on different urban environments. In this study, the hydrologic performance of BRC in terms of runoff and flood reduction was investigated in a highly urbanized area in the east of Tehran, Iran. The SWMM model was used to evaluate the performance of BRC. The results showed that BRC for rainfall with a return period of 2 to 50 years reduced the total runoff volume by 76.2% to 70.2% and the peak discharge by 65.9% to 36.4%, respectively. Also, for rainfall with a return period of 2 to 50 years, BRC resulted in 15.2% to 27.5% infiltration of rainfall in the study area, respectively. This study demonstrates that BRC can help restore the natural hydrologic cycle of urbanized catchments by reducing runoff and increasing infiltration.

The Effect of the Sub-Basins Area and Methods of Calculating Concentration Time on the Simulation of Urban Runoff Volume Using SewerGEMS Software. (Case Study: Shahrekord)
M.a. Abdullahi, J. Abedi Koupai, M.m Matinzadeh,
Volume 28, Issue 3 (10-2024)
Abstract
Today, the problems related to floods and inundation have increased, particularly in urban areas due to climate change, global warming, and the change in precipitation from snow to rain. Therefore, there has also been an increasing focus on rainfall-runoff simulation models to manage, reduce, and solve these problems. This research utilized SewerGEMS software to explore different scenarios to evaluate the model's performance based on the number of sub-basins (2 and 8) and return periods (2 and 5 years). Additionally, four methods of calculating concentration time (SCSlag, Kirpich, Bransby Williams, and Carter) were compared to simulate flood hydrographs in Shahrekord city. The results indicated that increasing the return period from 2 to 5 years leads to an increase in peak discharge in all scenarios. Furthermore, based on the calculated continuity error, the Kirpich method is preferred to estimate the concentration-time in scenarios with more sub-basins and smaller areas. For the 2-year return period, a continuity error of 4% was calculated for the scenario with 2 sub-basins, while for the 5-year return period, the continuity error was 19%. On the other hand, the SCSlag method is preferred to estimate the concentration-time in scenarios with fewer sub-basins and larger areas. For the scenario with 8 sub-basins, a continuity error of 16% was calculated for the 2-year return period, and 11% for the 5-year return period.

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