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The selection of sponge city facilities (e.g., pump, storage tank, wetland, or bioretention pond) to mitigate urban floods has been a crucial issue in China. This study aims to develop a conceptual flood-simulation model, which can take into account the effects of such facilities of a sponge city. Taking Jiangpu polder in Kunshan City as a case study, CityDrain3 was implemented to develop a baseline model and another three sponge polder models (pump only, storage tank only, pump, and storage tank). A sensitivity analysis was carried out to guarantee the robustness of the newly developed model. In the model application part, firstly, one-hour rainfall scenarios with different return periods (2a, 5a, 10a, 20a, 50a, 100a, with ‘a’ referring to a year) were employed as inputs to the conceptual baseline model. The growing trend of flood depth (from 12.69 mm to 17.16 mm) simulated by the baseline model under increased return periods (from 3a to 100a) demonstrated the feasibility of polder flood simulations using CityDrain3. Secondly, a one-hour rainfall scenario with a 10-year return period was employed on the baseline model and the three sponge polder models. The results showed that the effect rankings of the control strategies on the total flood volume, peak flow, flood yielding time, and the peak-flow occurrence time were comparable—combined strategies (pump and storage tank) > storage tank only > pump only. The conceptual, and hydrological model developed in this study can serve as a simulation tool for implementing a real-time urban storm water drainage control system in the Jiangpu polder. [ABSTRACT FROM AUTHOR]

Climate change is globally causing more intense meteorological phenomena. Our cities experience increased rainfall intensity, more intense heat waves, and prolonged droughts providing economic, social, health and environmental challenges. Combined with population growth and rapid urbanization, the increasing impact of climate change will make our cities more and more vulnerable, especially to urban flooding. In order to adapt our urban water systems to these challenges, the adoption of newly emerging water management strategies is required. The complexity and scale of this challenge calls for the integration of knowledge from different disciplines and collaborative approaches. The water sensitive cities principles provide guidance for developing new techniques, strategies, policies, and tools to improve the livability, sustainability, and resilience of cities. In this study, the DAnCE4Water modeling approach promoting the development of water sensitive cities was applied to Parma, an Italian town that has faced serious water issues in the last few years. The city, indeed, had to face the consequences of flooding several times, caused by the inadequacy of both the network of open channels and the sewerage network due to the urban expansion and climate change of the last 30 years. Through the model, the efficiency of decentralized technologies, such as green roofs and porous pavement, and their integration with the existing centralized combined sewer system was assessed under a range of urban development scenarios. The obtained results show that the adoption of an integrated approach, including soft engineering hydraulic strategies, consisting in the use of natural and sustainable solutions, can increase resilience to urban flooding. Further, the study shows that there is a critical need for strategic investment in solutions that will deliver long-term sustainable outcomes. [ABSTRACT FROM AUTHOR]

In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman's Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making. [ABSTRACT FROM AUTHOR]