Coupled Optimization Of Urban Grey-green Infrastructure For Climate Adaptation

Sustainable planning and construction of urban stormwater management systems is a hot topic in the field of habitat construction and a major issue that needs to be addressed in the context of climate change and rapid urbanisation.Climate change has led to an increase in the intensity and frequency of extreme weather events,threatening traditional urban stormwater management systems.To mitigate urban flooding,distributed green infrastructure offers an innovative approach to surface runoff management,and its coupling strategy with grey infrastructure has the opportunity to provide an advanced solution to urban hydrological management and is in a position to become an important idea for the construction of sponge cities in China.However,most current integrated grey-green infrastructure strategies rely heavily on diachronic rainfall data,ignoring the long-term effects of non-stationary climate change.In addition,the optimisation of grey-green infrastructure coupling strategies is often based on the optimisation of a single infrastructure,which is less likely to achieve true grey-green coupling in the true sense.Therefore,this study aims to build a framework for coupled optimisation of grey-green infrastructure systems from macro to micro,and from history to the future,in order to provide more systematic,scientific and forward-looking theoretical and technical support for future urban stormwater management.This study takes the Guangdong,Hong Kong and Macao Greater Bay Area,one of the four major bay areas in the world,as the study site.Firstly,a regional rainfall forecast is carried out,and future rainfall forecasts are made for the Guangdong,Hong Kong and Macau Greater Bay Area with the aid of the latest global circulation model,and the spatial distribution characteristics of rainfall under different future scenarios(SSP1-2.6,SSP2-4.5and SSP5-8.5)are analysed through extreme indicator changes.In addition,this study breaks the traditional inter-decadal division of rainfall into five or ten years,and identifies the trend characteristics of future rainfall through the stage division method,realising a stage division that better reflects the non-stationary characteristics of climate change and establishing a more scientific long-duration scenario as a climate risk simulation tool.The study results show that the Guangdong-Hong Kong-Macao Greater Bay Area currently suffers from higher annual rainfall and more severe heavy precipitation days,mainly in the south and north.In the future,most parts of the Guangdong-Hong Kong-Macao Greater Bay Area(especially the central and southeastern regions)will face more frequent and intense rainfall events due to climate change,especially under the high emission scenario of SSP 5-8.5.Subsequently,Hengli Island Tip,a future financial centre located in the centre of the Guangdong-Hong Kong-Macao Bay Area with great development potential,was selected as a test site for the coupled climate adaptation and optimisation of grey-green infrastructure,to assess the climate adaptation of the current stormwater management system of the site and to carry out further optimisation studies.In the simulations of rainfall scenarios for short and different long term scenarios,it can be seen that the existing pipe system of the site still has limitations in the face of long time scenarios,especially in the simulations of different future scenarios,where the urban stormwater pipes demonstrate poorer runoff control than in the historical scenarios.In addition,the site’s current stormwater management is overly dependent on large diameter pipes,which leads to higher construction costs and more severe environmental impacts.Therefore,this study is based on the current state of the pipe network system and further optimisation studies are carried out.In this process the optimal grey infrastructure layout for different degrees of decentrality(i.e.different number of outlets)is first obtained by means of a graph theory-based air garden algorithm and a genetic algorithm.Subsequently,based on the optimal grey infrastructure layout for different degrees of decentrality,a series of grey-green infrastructure satisfying hydraulic reliability is generated using a genetic algorithm with minimum life cycle cost and minimum carbon emission as the optimisation objectives.In the evaluation of representative solutions in the solution set,this study found that the proportion of green infrastructure has a greater effect on the system’s stormwater management effectiveness compared to the degree of decentralisation of the pipe network.A greater proportion of green infrastructure can better direct,infiltrate and collect runoff,reduce the drainage pressure on stormwater pipes,and at the same time achieve pollutant removal at source,compensating for the shortcomings of the current stormwater management system.Finally,with the help of a multi-criteria decision-making tool,the study scores and ranks representative solutions in the optimisation solution set to obtain an optimal integrated grey-green infrastructure layout that can better balance life-cycle costs,carbon emissions,total runoff control effects and pollutant control effects under different future scenarios,and proposes a targeted grey-green infrastructure planning and design strategy in conjunction with the current site planning.The optimisation and decision-making framework proposed in this study can also provide a methodological reference for climate-resilient stormwater management planning in other cities,enhancing the foresight,systematicity and science of grey-green infrastructure construction in urban hydrological management.