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Article

Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta

by
Wei Dai
1,2 and
Yang Tan
2,3,*
1
Research Institute for Urban Planning and Sustainability, Hangzhou City University, Hangzhou 310015, China
2
School of Spatial Planning and Design, Hangzhou City University, Hangzhou 310015, China
3
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(12), 3797; https://doi.org/10.3390/buildings14123797
Submission received: 23 October 2024 / Revised: 22 November 2024 / Accepted: 25 November 2024 / Published: 27 November 2024
(This article belongs to the Special Issue Resilient Urban and Architecture Design: Strategies for Low-Carbon and Climate-Adaptive Cities)
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Abstract

:
In the face of global climate change and rapid urbanization, the Pearl River Delta is confronted with frequent river floods and heavy rainfall, which leads to substantial economic losses and casualties. Enhancing the role of blue-green space in rain-flood resilience is crucial for mitigating such damages in this new era. Firstly, based on an analysis of the current status quo of blue-green space in the Pearl River Delta and the identification of potential areas at risk from rain and floods, this paper elucidates that resilient blue-green space in the Pearl River Delta should be guided by a systematic, bottom-line, and forward-looking orientation while considering spatial characteristics such as multi-scale network connectivity, redundancy and diversity/multi-functionality. Secondly, an optimization route is proposed based on steps of analysis of existing blue-green space, identification of inundated areas prone to rain and flood damage and optimization of blue-green spaces. Strategies for optimizing blue-green space are put forth including enhancing water corridor connectivity, optimizing ecological barriers and corridors, as well as constructing water gates to control hydrological flow direction. Simulation results demonstrate that under similar rain-flood disaster conditions, optimized blue-green space exhibits smaller sizes and lower depths of potential inundated areas compared to the original ones.

1. Introduction

Blue-green space encompasses various natural elements such as rivers, lakes, mulberry ponds, wetlands, woodlands, grasslands, and urban green spaces [1,2]. Over the course of its long evolutionary history, the Pearl River Delta (PRD) in China has developed a vast expanse of blue-green space characterized by large-scale coverage, high density and diverse composition. In fact, blue-green space area accounts for 74.8% of the region’s total land area of the PRD and is the most densely water-networked space in China [3]. Blue-green space is not only crucial for achieving rainstorm reduction and the purification of water, but also serves as the foundation for the provision of ecosystem services and promotion of biodiversity. It is of special importance to the rain-flood resilience of PRD in collaboration with grey infrastructure such as levees, dams, and municipal pipe networks. However, since the 1980s, the rapid urban development in the PRD has led to the degradation of forested areas, grasslands and mulberry, etc. [4,5,6], which has resulted in the fragmentation of large-scale interconnected blue-green space. Parts of blue-green space have been destroyed during events such as the fragmentation of high-value wetlands and the decrease of mangrove natural shoreline and beach land in the PRD. The fundamental role of blue-green space in mitigating rain-flood disturbance has long been weakened, which has led to the exacerbation of the diversity, frequency and destructiveness of rain and flood damages in the PRD.
Urbanization has caused cities within the PRD to be more susceptible to erosion caused by climate change compared to other regions [7,8,9]. Over the past three decades, the PRD has witnessed six significant rain-flood disasters occurring in 1994, 1998, 2008, 2010, 2018 and 2023. These calamities have resulted in extensive building collapses and widespread inundation of agricultural lands as well as human settlements [10,11,12]. For instance, during July 1994, river floods occurred in both Xijiang River and Beijiang River and led to an average inundation depth exceeding one meter in some streets of Zhongshan City, Guangzhou City and surrounding regions, as well as causing direct economic losses surpassing CNY two billion. In August 2010, an extended period of heavy precipitation triggered severe urban waterlogging across eight districts of Guangzhou City resulting in traffic congestion and shop inundation. In September 2018, Typhoon “Shanzhu” breached multiple dikes within the PRD causing extensive flooding of agricultural lands along with human settlements. In September 2023, cumulative precipitation exceeding a 600-mm depth was recorded along the southern coast of the PRD within a span of twenty-four hours [13].
In recent years, scholars from various countries have conducted studies on rain-flood disaster in respect to diverse topography, land use and surface runoff in delta areas. They have explored the hydrological response and basin resilience under climate change [14,15,16], have investigated the influence mechanism of land use on surface runoff [17,18,19] and have examined strategies for implementing blue-green infrastructure and restoration in rain and flood-prone regions [20,21,22,23]. For instance, Zaghloul et al. conducted analysis on long-term river flow trends in basins of Canada and concluded that the gradual increase in river flows observed in recent decades was likely to persist until the mid-century. Ogasawara et al. utilized the Budyko framework to evaluate the hydrological resilience of 26 basins in southeastern Brazil to climate change and the proposed correlation between hydrological response and basin resilience. Rehman et al. investigated the impact of land use on rainstorm runoff and developed an optimization and improvement method for blue-green infrastructure by integrating the NSGA-II algorithm. Focused on the PRD, research on rain-flood issues in this region primarily focuses on investigating characteristics [24], understanding mechanisms [25,26], analyzing spatio-temporal distribution patterns [27,28,29], assessing vulnerability levels [30,31,32,33], evaluating ecosystem services [34], employing multi-objective decision-making approaches [35] and developing spatial responsive strategies [36,37,38,39] for rain-flood issues. For instance, Chen et al. conducted an empirical study on the Hongshu Bay area in Shenzhen City based on the complex system theory. Xiong et al. analyzed changes in blue-green spaces during different periods using the cross-scale mapping method. Zhu et al. applied the HV-SS model to assess highly vulnerable areas prone to rain-flooding in the PRD. Lin et al. used the FLUS model to discuss the changes in inundation areas within Guangzhou City under future sea level rise scenarios. Lu et al. evaluated spatial planning schemes for enhancing rain-flood resilience on Lingshan Island in the PRD region based on the TOPSIS model. Dai et al. built a multi-level flood control and drainage model based on the concept of “river embankment flood control, surface runoff control and water level regulation”, and proposed an optimal pathway for resilient flood control and drainage combined with the characteristics of Hengli Island in Mingzhu Bay, Nansha District of the PRD.
In the face of the uncertainties posed by climate change and the risks associated with rain and flood events, establishing blue-green space for rain-flood resilience in the PRD serves as a fundamental component in constructing a system of rain and flood security. However, there is still a lack of in-depth research on the application of the concept of rain-flood resilience in the optimization of the blue-green spatial structure of the PRD. In light of this, considering the background of rain and flood damage and focusing on optimizing blue-green space in the PRD, this paper investigates three scientific research questions: (1) Based on the analysis of the current state of blue-green space, how should the potential distribution of future rain-flood prone inundation areas from multi-scenarios in the PRD be projected? (2) What are the key points of orientation for rain-flood resilient blue-green space optimization? (3) How can blue-green space for rain-flood resilience in the PRD be optimized?
The aim of this paper is to put forward the optimization strategies of blue-green spaces for rain-flood resilience based on the simulation of multi-scenario rain and flood damage in the PRD.

2. Overview of Blue-Green Space of the PRD

The PRD is formed through sediment accumulation brought by Xijiang River, Beijiang River, Dongjiang River and their tributary Tanjiang River. The west bank of the PRD is a fluvial-controlled delta formed through millennia of siltation and sedimentation, while the east bank of the PRD is a tidal-controlled delta influenced by Lingdingyang tides (Figure 1). The majority of elevation of the PRD is below 50 m (Figure 2a), with average gradient of 0.1–0.2‰ (Figure 2b). Soft soil is distributed in estuary (Figure 2c). Distance between major rivers ranges of the PRD from 0 to 80 km (Figure 2d). The PRD exhibits a typical subtropical monsoon climate characterized by average annual cumulative precipitation exceeding 1470 mm and extended duration of the rainy season. The Xijiang River and Beijiang River Basins have nearly 100 main waterways with a total length of 1600 km, and the average density of the river network is 0.81 km/km2. Dongjiang River Basin has five main waterways with a total length of 138 km, while the average density of the river network is 0.88 km/km2.
The northern, western and eastern areas of the PRD intersect ecological corridors, while the southern region comprises coastal shorelines, tidal flats and mangrove vegetation. The roots of mangrove plants have facilitated the deposition of silt at estuaries of the PRD, which had led to the formation of shoals and wetlands particularly in the Mipu area and the west bank of Shenzhen City. Due to the impact of rural development and urbanization over recent decades, there is an uneven distribution of vegetation coverage and surface water permeability in the PRD (Figure 2e,f).
The PRD demonstrates significant interactions between marine and continental processes that lead to noticeable scouring and siltation phenomena. Within the Lingdingyang area, a distinctive pattern known as “three beaches and two troughs” has emerged, consisting of the West Beach, West Trough, Middle Beach, East Trough and East Beach in sequential order from west to east. Over the past millennium, human activities such as levee construction, land reclamation and exploitation of marine resources have consistently driven the coastline of the PRD towards the South China Sea. These activities have impacted river runoff and sediment accumulation rates that increase hydrological flow velocity and accelerate sedimentation within the estuary of the PRD. The average annual rate of land formation is estimated to be 1.78~2.41 km2.

3. Identification of Potential Inundation Areas in the PRD Under Rain-Flood Disturbance

3.1. Data Sources

According to the monitoring records of 40 meteorological stations distributed throughout the PRD from 1954 to 2020, the average temperature in the PRD has increased by 0.45 °C over the past 70 years. Taking into account comprehensive data from hydrographic stations at key control sections of the PRD, official documents of Flood Control Planning of the Pearl River Basin, Flood Tide Surface Lines of the Lower Reaches of Xijiang and Beijiang Rivers and their Delta network River Design, China Sea Level Bulletin 2023, Water Problems in the Pearl River Delta under Climate Change and Countermeasures and Control Measures (Work Package 5) [40,41,42,43], as well as research results provided in references [9,26,33,44,45], it is predicted that the peak discharge for a return period of 200 years will be approximately between 53,900 and 54,500 m3/s for the Xijiang River, between 15,800 and 16,700 m3/s for the Beijiang River and between 12,000 and 12,800 m3/s for the Dongjiang River in the PRD. The precipitation depth with a return period of 200 years is estimated to range from 250 to 600 mm within a span of twenty-four hours (Figure 3). Additionally, the storm surge height with a return period of 200 years is projected to be between 2.62 and 2.94 m. Furthermore, the sea level rise in the Pearl River Estuary is forecasted to be 120~210 mm by 2100.

3.2. MIKE Flood Model and Calibration

This study applies MIKE Flood model to project potential inundation areas in the PRD under multi-scenarios of rain-flood disturbances. The MIKE Flood model has been widely used in the simulation of rain and flood damage because of its powerful function. For example, scholars such as Tansar [46], Hlodversdottir [47] and Ramteke [48] all applied the MIKE Flood model to simulate and analyze rain-flood affected areas. The MIKE Flood model is shown in Figure 4.
In order to improve the simulation accuracy, the key parameters of the MIKE Flood model were calibrated first in this study. The calibration process of the MIKE Flood model parameters is as follows:
Three observation points (O1, O2 and O3) situated in Guangzhou City, Foshan City and Shenzhen City for inundation depths data collection during typhoon-induced rainstorm in the PRD on 7–8 September 2023 are shown in Figure 1. Data about the inundation depth from three observation points are utilized to determine the key parameters of the MIKE Flood models. Site-specific interpolated data, including river network, drainage network, river section, elevation, land use and precipitation amount are input into the MIKE Flood model. The Manning Coefficient, which is one of the key parameters and represents resistance to water flow within both river channels and walls, is iteratively adjusted until satisfactory approximation that the difference between model output of inundation depths and actual measured inundation depths of observation points are small enough. In this study, when the value of the Manning Coefficient is adjusted to 0.036, the best fit results occur with a maximum difference of 8.62% and with a Nash–Sutcliffe Efficiency Coefficient of 0.744. This indicates that the MIKE Flood model has high reliability when applying it to the PRD. The Nash–Sutcliffe Efficiency Coefficient is calculated as follows:
E = 1 − ∑ t = 1 T   ( Q o t − Q m t ) 2 ∑ t = 1 T   ( Q o t − Q o ¯ ) 2
where E represents the Nash–Sutcliffe Efficiency Coefficient; Q o t represents the actual measured value of observation point at t time; Q m t represents the simulated value of the observation point at t time; and Q o ¯ represents the average of the actual measured value the at observation point.
When E approaches 1, it indicates that the MIKE Flood model is of very high quality and reliability. When E approaches 0, it suggests that the simulation results closely align with the average of the actual measured value at the observation point, thereby indicating its overall credibility.

3.3. Identification of Potential Inundation Areas

Based on the MIKE Flood model with calibrated key parameters, data are obtained, such as rain-flood disaster factors, such as a 200-year river flood, 200-year precipitation, 200-year storm surge and sea level rise in the Pearl River Estuary by 2100, along with environmental factors like land surface elevation, land slope and land cover. In respect of the above adjusted and calibrated MIKE Flood model, simulations are conducted under different disaster scenarios including river floods, urban waterlogging, storm surges and sea level rises, which are input to determine inundation depth within the PRD (Figure 5). The simulation results demonstrate that:
(1) High-risk areas caused by a 200-year river flood primarily concentrate on both sides of the middle and lower reaches of Xijiang River, Pearl River and Dongjiang River. They encompass the southern area of Guangzhou City, the northwestern area of Dongguan City, the southern area of Foshan City, and the northern area of Zhongshan City, such as the districts of Haizhu, Panyu, Nansha and many towns such as Mayong, Zhongtang, Shijie, Xiaolan and Nantou, etc.
(2) High-risk areas caused by 200 years of heavy precipitation are primarily concentrated in the central area of Guangzhou City, as well as the central and western areas of Dongguan, Shenzhen and Zhuhai City, such as districts of Tianhe, Haizhu, Yuexiu, Baoan, Nanshan, Guangming and most towns in the central and western areas of Dongguan City.
(3) High-risk areas caused by 200-year storm surge-induced inundation are primarily concentrated in the coastal areas of Guangzhou City and the western area of Shenzhen City such as districts of Nansha, Panyu, Bao’an, Nanshan and many districts of Hong Kong and Macau. Guangzhou, Dongguan and Shenzhen City fall within zones classified as high-risk or medium-risk.
(4) High-risk areas caused by sea level rise are primarily concentrated in the southern area of Guangzhou City, the northern area of Zhongshan City, the southeastern area of Foshan City and the northwestern area of Dongguan City. These encompass districts of Nansha, Panyu, Shunde, Chancheng and many towns such as Nantou, Dongfeng, Minzhong, Xiaolan Zhongtang and Mayong, etc.
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Figure 5. Potential inundation areas and corresponding depth in PRD under various types of rain-flood disasters: (a) River flood disaster scenario; (b) Urban waterlogging disaster scenario; (c) Storm surge scenario; (d) Sea level rise scenario.
Figure 5. Potential inundation areas and corresponding depth in PRD under various types of rain-flood disasters: (a) River flood disaster scenario; (b) Urban waterlogging disaster scenario; (c) Storm surge scenario; (d) Sea level rise scenario.
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The simulation results of the PRD under the above scenarios of rain and flood disasters including a 200-year river flood, 200 years of heavy precipitation, a 200-year storm surge, and sea level rise of the Pearl River Estuary by 2100 are superimposed equally. Potential inundation areas and corresponding depths in the PRD are illustrated in Figure 6. It is evident that the potential inundated areas in the PRD are extensive particularly within major cities such as Guangzhou, Shenzhen, Dongguan and Zhongshan. Inundation occurs primarily in districts of Tianhe, Huang, Zengcheng and some towns such as Zhongtang, Wangniudun, Mayong Xiaolan, Zhongtang and Mayong, etc. Conversely, inundation shows a multiple-pointed spatial distribution pattern. In light of continuous rural development and urbanization within the PRD, the existing capacity for rain-flood storage through blue-green spaces will be further diminished, which may result in an expansion of both the scope and degree of potential inundation within this region.

4. Key Points of Blue-Green Space Optimization for Rain-Flood Resilience

Resilience, derived from the Latin term “Resilio”, refers to the capacity of a system to recover after experiencing impact. Since its introduction by Holling, extensive studies have been conducted on engineering resilience, ecological resilience and evolutionary resilience, which demonstrate a deepening understanding of this phenomenon [49,50,51]. Although there is currently no universally accepted definition of resilience in academia, there is broad consensus that the core of resilience entails the abilities to withstand, recover from and adapt to inherent changes of uncertainty and unexpected events.
The optimization of blue-green space in deltas, as discussed in this paper from the perspective of rain-flood resilience, specifically refers to enhancing the coordination between blue-green spaces planning and municipal drainage infrastructure so as to exert control over inundation areas exceeding specified depths and to minimize the duration of inundation regression. In consideration of the current state of blue-green space in the PRD and potential inundation areas prone to various rain-flood scenarios, as well as taking into account previous studies on rain-flood resilience in deltas [38,39,52,53,54,55,56,57], it is argued that optimizing the blue-green space for rain-flood resilience in the PRD should be guided by a systematic, bottom-line and forward-looking orientation. The spatial characteristics of multi-scale network connectivity, redundancy, diversity/multi-functionality are highlighted.

4.1. Orientation of Rain-Flood Resilient Blue-Green Space Optimization

4.1.1. Systematic Orientation

Systematic orientation is crucial in understanding interdependence, mutual restrictions and interactions among spatial elements of blue-green space. After conducting comprehensive analysis of historical evolution, current status and potential change trends of topographic elevation and other natural elements in the PRD, it becomes imperative to clarify relationships between upper and lower reaches, above-ground and underground structures, as well as blue-green infrastructure and municipal drainage facilities. Moreover, utilizing natural structure units as a foundation of unit division can facilitate multi-scale and multi-dimensional cooperation among blue-green spatial elements. The layout and spatial form of blue-green space that is compatible with trans-scale water ecological processes can be recreated well by optimizing.

4.1.2. Bottom-Line Orientation

Maintaining bottom-line orientation is crucial in adhering to problem-oriented analysis and estimation of the probability and risk level of extreme rain-flood disasters occurring in the PRD from a multidimensional perspective, while identifying the relationship between thresholds and necessary constraints to improve rain-flood resilience. It is essential to strengthen top-down binding transmission for constructing both blue lines and green lines within the spatial framework of blue-green space to implement stringent and rigid control measures.

4.1.3. Forward-Looking Orientation

Forward-thinking is crucial in the face of uncertain disturbances. It is important to identify critical risk factors, assess risk levels, and determine risk impact on rain-flood disturbance in the PRD. To optimize blue-green spaces from a rain-flood resilience perspective, active, passive and collaborative approaches tailored to local conditions should be adopted. Furthermore, effective coordination between blue-green infrastructure, grey infrastructure and the surrounding environment must be ensured while prioritizing disaster resistance.

4.2. Focus of Rain-Flood Resilient Blue-Green Space Optimization

4.2.1. Connectivity

By implementing ecological corridors and blue-green infrastructure, connections among various elements of blue-green spaces, including water bodies, ecological patches, and stepping stones, must be established. This results in the creation of a multi-dimensional blue-green network through a combination of points, lines and surfaces at different levels. At the same time, on the basis of rain-flood detention areas, green river channels, infiltration systems, runoff management and structural infrastructure networking, the construction standards of facilities should be improved according to local conditions, and the flood control and drainage capacity of potential inundation risk areas should be improved. Through the multi-scale connectivity of blue-green spaces, strong connections are established between the various parts of the blue-green spaces to make the exchange of materials and energy smooth, thereby enhancing the contribution of blue-green spaces to rain-flood resilience.

4.2.2. Diversity and Multi-Functionality

A multi-level blue-green space is constructed by integrating corridors, points, lines and surfaces, which enables the natural storage, infiltration and purification of rain and flood water on site, thereby mitigating surface runoff volume and velocity. Through the diversity of blue-green spatial elements, the flexibility of element combination can be enhanced, and the adaptability of space to rain-flood disasters can be improved. The optimal allocation of blue-green space allows for the full utilization of its functions including preserving biodiversity, regulating microclimate and providing cultural leisure and aesthetic entertainment. The protection and restoration of blue-green space, such as rivers, lakes and wetlands, should be prioritized. These areas can be effectively utilized for both rain-flood disaster prevention, as well as serving as public landscapes for citizens to engage in exercise, entertainment and relaxation during peacetime.

4.2.3. Redundancy

Redundant blue-green space is essential for responding to inundation areas prone to rain-flood disasters, as they serve as backup modules for emergency use. For critical infrastructure and life support systems, appropriate margins and sufficient blue-green space for disaster relief should be reserved in a forward-looking manner to serve as emergency shelters and evacuation routes. By integrating nature-based solutions (NbS) with blue-green spaces as a central element, rain-flood storage and the drainage capacities of blue-green spaces can be enhanced through diverse approaches such as establishing natural floodplains, incorporating rainwater storage facilities and constructing surface rainwater channels.

5. Strategies of Optimizing Blue-Green Space for Rain-Flood Resilience in the PRD

After conducting comprehensive analyses of inundation areas, depicted in Figure 5 and Figure 6, as well as in references, it is evident that the primary problems of blue-green spaces in the PRD are as follows.
  • Urbanization and riverbed dredging have resulted in unreasonable water flow diversion at critical hydrological nodes in the upper reaches of the Beijiang River and Xijiang River, as the average division ratio has changed from approximately 7:3 in 1980 to 6:4 in 2020. The average water volume in the upper reaches of the Beijiang River in 2020 has increased by nearly 40% compared with 1980. The general rise of water levels in tributaries such as the Donghai Waterway, Dongping Waterway and Shunde Waterway have led to a significant increase in potential inundation areas in Guangzhou and Foshan City.
  • Over the past five decades, land reclamation in the Modao Estuary has resulted in issues of riverwater backflow and seawater intrusion, which has exacerbated flood pressure on Jiangmen City. The potential threat to Jiangmen City is posed by summer rain and flooding in the lower reaches of the Xijiang River.
  • The development of Nansha Port and the land reclamation of Wanqingsha, Hongqi Estuary and Heng Estuary have resulted in significant siltation in some key river sections of Nansha District. Consequently, there has been negative change in water volume of Hongqili Waterway from 1980 to 2020, which represents a decrease of −23.46%. This obstruction hampers smooth flooding and adversely affects the overall development of Nansha City.
Based on the current spatial pattern and the main problems of blue-green space, this paper aims to optimize blue-green space in systematic, bottom-line and forward-looking orientations as well as in relation to connective, redundant, diverse and multi-functional spatial characteristics. Considering the seasonal changes of hydrological systems and uncertainty of rain-flood risks, some following strategies for optimizing blue-green spaces are put forward.

5.1. Strengthening the Connectivity of Water Corridors

On the basis of existing rivers, some important potential water corridors are excavated and connected to natural river channels in order to mitigate rainstorm. Different surfaces and their environments have different resistance to new water corridors. The Minimum Cumulative Resistance Model (MCR) is a model that calculates the minimum cumulative resistance required during the movement from source to destination. The formula of the MCR model is as follows:
M C R i j = m i n ∑ j = n i = m D i j R i
R i = 1 5 ∑ q = 1 v R i q ∗ W q
where M C R i j represents the minimum cumulative resistance value of a potential water corridor from grid i to surface grid j; D i j represents the distance of the water corridor from grid i to grid j; R i represents the comprehensive resistance coefficient generated by grid i; R i q represents the 5-level assignment of resistance factor q for grid i. W q represents the weight of resistance factor q; and v represents the number of resistance factors. The specific conductive steps are as follows:
Firstly, large potential inundation blocks with a depth of more than 0.30 m and simultaneously with more than 50 km2 under multi-scenario rain and flood damage in the PRD are selected as the source area from the perspective of “source-sink-flow” (Figure 7). Secondly, resistance factors including elevation, slope, type of soil, distance from large rivers, vegetation cover index and surface water permeability are selected by assigning 5-level values (Table 1). Thirdly, based on expert scores and the analytic hierarchy process (AHP), the weights of resistance factors are determined: elevation (0.1), slope (0.15), type of soil (0.2), distance from large rivers (0.15), vegetation cover index (0.15), surface water permeability (0.1) and population density (0.15). Fourthly, based on formula (3) and combined with the natural breakpoint method, the resistance level surface for water corridors is formed (Figure 8). Fifthly, potential water corridors are developed based on the MCR model by utilizing ArcGIS (Figure 9). Sixthly, the comparative analyses of candidate Schemes 1 to 5 (Figure 10) are conducted by regarding their respective advantages and disadvantages (Table 2 and Table 3).
Based on the results of comparative analyses, the research put forward the following recommendations.
  • It is recommended to apply Scheme 3 in the AB section, which involves widening the waterway from point A’ to A based on the original Foshan Waterway A’B in order to serve as buffer for excessive water volume from both the Beijiang River and Pearl River, which can effectively mitigate rain-flood issues in Guangzhou City and Foshan City while enhancing spatial quality;
  • It is recommended to use the existing Nanhua Water Conservancy Hub to connect the lower reaches of Xijiang River and Tanjiang River in the CD Section, which aims to divert the water volume of Xijiang River, mitigate siltation in the Modao Estuary, and facilitate the development of Jiangmen’s north and west sides;
  • It is recommended to apply Scheme 5 in the EF section that can divert water volume of the Hongqili Waterway, alleviate the congestion of Hongqi Estuary, and increase the spatial quality of the Pearl Bay area, Nansha District.

5.2. Optimizing Ecological Barriers and Ecological Corridors

From the perspective of restoring high valuable wetlands and reducing fragmentation of the ecological patch, it is recommended to integrate green corridors and water bodies, which can mutually support one another to establish cohesive blue-green spaces for “water-green” integration and “city-green” integration (Figure 11).
  • The large ecological patches of Yunwu Mountain, Qingyun Mountain, Lianhua Mountain and Fengmao Mountain serve as the foundation for establishing three outer ecological barrier sources in the east, northwest, and southwest parts of the PRD;
  • Organic connectivity should be strengthened between the Xijiang Ecological Corridor, Beijiang Ecological Corridor, Dongjiang Ecological Corridor and Pearl River Ecological Corridor by strategically incorporating species habitat patches and diverse ecological stepping stones of various sizes and types, which will effectively extend the rain-flood control benefits of the outer ecological barrier to the urban circle;
  • The reclamation of high-value mulberry-based fish ponds in urban areas, such as mulberry gardens, should be reduced and the storage capacity of rain and flood water should be improved;
  • Ecological protection zones should be established in the South China Sea, including Mipu Wetland, Houhai Bay, Jiaoyi Bay, Qi’ao Island, Dahengqin Island and Huangmao Bay. Additionally, natural coastlines should be safeguarded while harnessing ocean power for artificial beach cultivation;
  • Measures such as river bank greening, ecological revetment and multi-functional park facilities should be adopted to create ecological environment with smooth river flow, continuous green trees and a beautiful landscape.
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Figure 11. Schematic diagram of the ecological barrier and ecological corridor.
Figure 11. Schematic diagram of the ecological barrier and ecological corridor.
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5.3. Controlling the Hydrological Flow Direction of Key Water Gate

  • In Figure 9, it is recommended to construct a water gate at point AB to regulate hydrological flow direction. When rain-flood pressure increases on both sides of Pearl River and Guangzhou City, opening the water gate at point B will redirect the hydrological flow from point B to point A, thereby reducing the potential inundation area in Guangzhou City. Similarly, when rain-flood pressure rises in the upper reaches of Beijiang River and Foshan City, opening the water gate at point A will divert the hydrological flow from point A to point B, which can effectively minimize potential inundation areas in Beijiang River and Foshan City.
  • It is recommended to add a water gate in conjunction with the Nanhua Water Conservancy Project. When rain-flood pressure of the Xijiang River and its tributaries increases, the water gate of point C is opened to smooth hydrological flow from point C to point D and it reduces the influence of the Xijiang River and its tributaries on Jiangmen and the lower reaches of Xijiang River. When the rain-flood pressure of the Xijiang River is not significant, the water gate at point C is closed to maintain a stable water level in the lower Xijiang River and its tributaries and preserve shipping capacity.

5.4. Simulation of Potential Inundation Areas in the PRD After Optimizing Blue-Green Space

Figure 12 illustrates simulation results which show the potential inundation area and corresponding depth in the central area of the PRD before and after implementation of strengthening the connectivity of water corridors. Table 4 is the comparative data of inundation areas in the central area of the PRD before and after the optimization of blue-green space. Results reveal that in the central area of the PRD, the size of 0.15 ~0.50 m inundation depth areas are largely reduced, which means that optimized blue-green space becomes more resilient against rain-flood disasters than originally.

6. Conclusions

Rain and flood damage has emerged as a significant disruptive factor in the future development of the PRD. Diverging from existing research that focused on rain and flood characteristics, mechanisms, spatio-temporal distribution patterns, vulnerability levels and multi-objective decision-making, this paper concentrates on optimizing blue-green space in the PRD using theory, methodology and strategy for enhancing rain-flood resilience.
  • Theory: Combining multi-scenario rain and flood damage with resilient blue-green space, this paper expounds that the optimization of blue-green space for rain and flood resilience should be guided by systematic, bottom-line and forward-looking orientation, as well as highlighted by spatial characteristics of connectivity, diversity/multi-functionality and redundancy.
  • Methodology: The steps of optimization of blue-green space for rain and flood resilience is followed as analysis of existing blue-green spaces, identification of prone inundated areas and optimization of blue-green spaces.
  • Strategy: This paper proposes strategies such as strengthening the connectivity of water corridors, optimizing ecological barriers and ecological corridors, as well as controlling the hydrological flow direction of key water gates.
  • Limitations: The Manning Coefficient of the MIKE Flood model is calibrated by using inundation data received from observation points to improve model effectiveness. However, given the continuous expansion of the application domain, it is needed to extend both the number of observation points and the length of observation time. The accuracy of the MIKE Flood model can be further improved by incorporating additional calibration parameters other than the Manning Coefficient. Moreover, there is still a certain level of subjectivity involved in determining the weights for resistance factors in using the MCR model.
The research results of this paper need to be combined with the planning documents of PRD Global Planning for the Pearl River Delta to form a national protection area of blue-green space. For instance, at the macro level, it is crucial to establish clear boundaries for protection of some important potential water corridors such as Scheme 3 and Scheme 5, and exploring the interconnections between the controlling elements in blue and green space from upper-level planning to lower-level planning is essential.
The purpose of this study is to provide reference for the blue-green spatial optimization of the PRD and other deltas.

Author Contributions

Conceptualization, W.D.; methodology, W.D.; validation, W.D. and Y.T.; formal analysis, W.D. and Y.T.; data curation, W.D.; writing—original draft preparation, W.D.; writing—review and editing, W.D. and Y.T.; visualization, Y.T.; supervision, Y.T.; project administration, W.D. and Y.T.; funding acquisition, W.D. and Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Zhejiang Provincial Natural Science Foundation of China, grant number LQ22E080016; Project of Zhejiang Emergency Management Department, grant number 2024YJ016; Zhejiang Provincial Social Science Federation Project, grant number 2024N087; Project of Hangzhou Agricultural and Social Development, grant number 20241029Y008; Project of Zhejiang Provincial Department of Culture and Tourism, grant number 2023KYYY036; Project of Zhejiang Provincial Social Science Federation, grant number 2025N068; Project of Zhejiang Province philosophy and social science planning, grant number 23NDJC402YBM; Project of Zhejiang Provincial Social Science Federation, grant number 2025B013.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The author declare no conflict of interest.

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Figure 1. Blue-green space of PRD.
Figure 1. Blue-green space of PRD.
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Figure 2. Blue-green spatial analysis of PRD: (a) Elevation; (b) Slope; (c) Type of Soil; (d) Distance from Large Rivers; (e) Vegetation Cover Index; (f) Surface Water Permeability.
Figure 2. Blue-green spatial analysis of PRD: (a) Elevation; (b) Slope; (c) Type of Soil; (d) Distance from Large Rivers; (e) Vegetation Cover Index; (f) Surface Water Permeability.
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Figure 3. The distribution of precipitation depth in 24 h with return period of 200 years in PRD.
Figure 3. The distribution of precipitation depth in 24 h with return period of 200 years in PRD.
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Figure 4. MIKE Flood model.
Figure 4. MIKE Flood model.
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Figure 6. Potential inundation areas and corresponding depth in PRD under combined rain-flood damage.
Figure 6. Potential inundation areas and corresponding depth in PRD under combined rain-flood damage.
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Figure 7. Distribution of potential inundation areas in PRD.
Figure 7. Distribution of potential inundation areas in PRD.
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Figure 8. Resistance surface of river corridor in PRD.
Figure 8. Resistance surface of river corridor in PRD.
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Figure 9. Potential main water corridor simulated based on MCR Model.
Figure 9. Potential main water corridor simulated based on MCR Model.
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Figure 10. Schematic diagram of potential water corridors schemes.
Figure 10. Schematic diagram of potential water corridors schemes.
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Figure 12. Potential inundation areas and corresponding depth in the central area of PRD: (a) Before optimizing blue-green space; (b) After optimizing blue-green space.
Figure 12. Potential inundation areas and corresponding depth in the central area of PRD: (a) Before optimizing blue-green space; (b) After optimizing blue-green space.
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Table 1. Resistance factor and its 5-level value.
Table 1. Resistance factor and its 5-level value.
Resistance FactorValue
54321
Elevation (m)Elevation > 10070 < Elevation ≤ 10040 < Elevation ≤ 7010 < Elevation ≤ 40Elevation ≤ 10
Slope (°)Slope > 3025 < Slope ≤ 3020 < Slope ≤ 2515 < Slope ≤ 20Slope ≤ 15
Type of soilNon-soft soilOther soft soilsSoft soil–tidal soilSoft soil–swamp soilSoft soil–alluvial soil
Distance to Large rivers (km)Distance > 5060 < Distance ≤ 5040 < Distance ≤ 3020 < Distance ≤ 20Distance ≤ 10
Vegetation cover indexIndex > 0.90.7 < Index ≤ 0.90.5 < Index ≤ 0.70.3 < Index ≤ 0.5Index ≤ 0.3
Surface water permeability (%)Permeability ≤ 1010 < Permeability ≤ 3030 < Permeability ≤ 5010 < Permeability ≤ 30Permeability > 90
Population density (person/km2)Density > 1000700 < Density ≤ 1000400 < Density ≤ 700100 < Density ≤ 400Density ≤ 100
Table 2. Comparative analyses of different schemes in AB segment.
Table 2. Comparative analyses of different schemes in AB segment.
Candidate Connecting CorridorAdvantagesDisadvantages
Scheme 1Shortest physical distance.
Gentle terrain which can guarantee stable hydrological flow after construction.		Pass through rural-urban junction.
Larger cost of newly developed corridor.
Scheme 2The corridor will not pass through main areas of Foshan City.
The corridor will go through most of the ecological areas, which can be combined with ecological shoreline to create a nature reserve.
There are many low-lying areas around and sufficient space reserve, which can be combined with low-lying areas to set up large-scale rain-flood inundation areas.		The corridor is relatively longer.
May affect some animal habitats.
Scheme 3On the basis of the original Foshan Waterway, A’B, A’ to A point will be widened and connected to increase the rain-flood discharge capacity.
The construction cost is lower.
New water system can promote the vitality of Foshan City to be further promoted.		The flood control standards of Foshan City need to be improved.
Table 3. Comparative analyses of different schemes in EF segment.
Table 3. Comparative analyses of different schemes in EF segment.
Candidate Connecting CorridorAdvantagesDisadvantages
Scheme 4The spatial distance is shorter and the con-struction cost is lower.
The present water system texture is rela-tively straighter, which is conductive to water system connection.		The water corridor passes through Changan and Shakeng Industrial Park; the water quality is likely to be polluted.
Scheme 5The water corridor will diverse the hydrological flow of Hongqi Waterway, alleviate the congestion of Hongqi Estuary, which can pose a positive impact on rural and urban development of Nansha Central area and Mingzhu Bay area.The water system texture is more tortuous
Table 4. Inundation information before and after blue-green space optimization.
Table 4. Inundation information before and after blue-green space optimization.
Inundation DepthInundation Area Accounts for Central Area of PRD (%)
Before Blue-Green Space OptimizationAfter Blue-Green Space Optimization
0.00–0.051.17.6
0.05–0.108.519.6
0.10–0.1514.828.3
0.15–0.3021.019.8
0.30–0.5054.624.7
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Dai, W.; Tan, Y. Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta. Buildings 2024, 14, 3797. https://doi.org/10.3390/buildings14123797

AMA Style

Dai W, Tan Y. Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta. Buildings. 2024; 14(12):3797. https://doi.org/10.3390/buildings14123797

Chicago/Turabian Style

Dai, Wei, and Yang Tan. 2024. "Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta" Buildings 14, no. 12: 3797. https://doi.org/10.3390/buildings14123797

APA Style

Dai, W., & Tan, Y. (2024). Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta. Buildings, 14(12), 3797. https://doi.org/10.3390/buildings14123797

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