Optimum redesign of runoff harvesting channels to reduce vulnerability and increase reliability against climate change

Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability.

In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario.

This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement.

The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network.