Riverine flood risk assessment with a combined model chain in southeastern China

Wang, Lihong; Cui, Shenghui; Tang, Jianxiong; Fang, Lei; Fang, Xuejuan; Shrestha, Sabita; Manandhar, Bikram; Huang, Jinliang; Nitivattananon, Vilas

doi:10.1016/j.ecolind.2023.110686

Cited by 1 publication

(1 citation statement)

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“…This model facilitated fine-scale simulation of the eco-hydrological storage processes in semi-enclosed floodplain wetlands [23]. Furthermore, Wang devised a comprehensive river flood risk model, enabling the derivation of exceedance probability loss (EPL) curves and expected annual damage (EAD) assessments under prevailing climate conditions [24].…”

Section: Introductionmentioning

confidence: 99%

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Jiang,

Xiong,

Yang

et al. 2023

Water

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Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

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Section: Introductionmentioning

confidence: 99%