Stormwater hydrographs simulated for different structures of urban drainage network: dendritic and looped sewer networks

Organized Oral Session 16. Linking Data and Theory in Dendritic Ecological Networks: from Ecological Problems to Rapid Understanding

Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

The core of comprehensive treatment effect of water environment in river basin lies in pipe network system. The blockage of urban drainage pipe network is one of the main reasons affecting the healthy operation of urban drainage network system. So the intelligent analysis method of urban drainage pipe network blockage diagnosis can quickly find the problems of urban drainage pipe network and improve the operation and maintenance efficiency of urban drainage pipe network. In this paper, through the analysis of rainfall data, rainfall and sewage pipe network flow variation regular pattern, the change range, the essay obtains the rain and sewage pipe network blockage diagnosis threshold. Combined with the urban intelligent drainage pipe network system, through the customized setting of pipe network blockage threshold, the rapid diagnosis and early warning of the blockage problem of the drainage pipe network are realized, and the healthy operation of the urban drainage network is guaranteed.

Habitat network connectivity influences colonization dynamics, species invasions, and biodiversity patterns. Recent theoretical work suggests dendritic networks, such as those found in rivers, alter expectations regarding colonization and dispersal dynamics compared with other network types. As many native and non-native species are spreading along river networks, this may have important ecological implications. However, experimental studies testing the effects of network structure on colonization and diversity patterns are scarce. Up to now, experimental studies have only considered networks where sites are connected with small corridors, or dispersal was experimentally controlled, which eliminates possible effects of species interactions on colonization dynamics. Here, we tested the effect of network connectivity and species interactions on colonization dynamics using continuous linear and dendritic (i.e., river-like) networks, which allow for active dispersal. We used a set of six protist species and one rotifer species in linear and dendritic microcosm networks. At the start of the experiment, we introduced species, either singularly or as a community within the networks. Species subsequently actively colonized the networks. We periodically measured densities of species throughout the networks over 2 weeks to track community dynamics, colonization, and diversity patterns. We found that colonization of dendritic networks was faster compared with colonization of linear networks, which resulted in higher local mean species richness in dendritic networks. Initially, community similarity was also greater in dendritic networks compared with linear networks, but this effect vanished over time. The presence of species interactions increased community evenness over time, compared with extrapolations from single-species setups. Our experimental findings confirm previous theoretical work and show that network connectivity, species-specific dispersal ability, and species interactions greatly influence the dispersal and colonization of dendritic networks. We argue that these factors need to be considered in empirical studies, where effects of network connectivity on colonization patterns have been largely underestimated.

Urban drainage systems are an essential component of urban infrastructure that plays a critical role in managing stormwater and preventing flooding in urban areas. With the changing climate and urbanization, the challenges faced by urban drainage systems are becoming increasingly complex. Additionally, aging infrastructure further exacerbates the problem, creating anurban sewage line that must be made more resilient. Redundancy is just a fundamental characteristic of such a robust urban drainage network. Redundancy in urban drainage systems can help to ensure that the system continues to function during extreme weather events or emergencies, reducing the likelihood of flooding and damage. However, the exact locations where redundancy should be increased and its contribution to resilience are not well understood. In recent years, several studies have focused on developing frameworks for optimising urban drainage structures which account pipeline redundancy.One similar research presented a paradigm for constructing the ideal network layout for urban drainage infrastructure, which considers pipeline redundancy under consideration. The original architecture and structure of the urban drainage network was developed using emperor penguin optimizer algorithms and graph theory in the research. Complicated system modelling was done to find extra water pathways or redundancy which might well be implemented to boost resistance. The suggested approach has been utilised to the test region in Dongying City, Shandong Province, China, and its findings revealed even under rainfall above the design specification, the entire overflow capacity of such urban drainage network including pipeline redundancy significantly decreased about 20-30%, compared to the network without pipeline redundancies. The interest in creating optimization algorithms had also increased recently that can be used to design and manage urban infrastructure systems. One such algorithm is the Emperor Penguin Optimization (EPO) algorithm. EPO was a recently developed swarm-based optimization An algorithm which simulates Emperor penguins behaviour in their search for food in Antarctica. The algorithm has shown promising results in solving complex optimization problems in different fields, including engineering, computer science, and management. The EPO algorithm's key features include an emperor search strategy, local search, and randomization, enabling that to efficiently and successfully examine the search process. The algorithm's emperor penguin search strategy enables it to dynamically adjust the search parameters based on the problem's characteristics and progress. The local search feature allows it to escape local optima and explore the search space further. Finally, the randomization feature adds stochasticity to the search process, helping to ensure that the algorithm can avoid getting stuck in a sub-optimal solution. In this article, we aim to explore the potential of EPO as a tool for optimizing Urban drainage solutions which take into account pipeline redundancy. We will start by reviewing the existing Literature upon that optimization in urban drainage facilities, particularly the application of particle swarm optimization, genetic algorithms, and ant colony enhancement. The EPO algorithm will next be described in full including its working principle, key features, and the steps involved in applying it to an optimization problem. Finally, we will present a case study that applies the EPO technique was developed to optimise the network model of such an urban drainage infrastructure taking into account pipeline redundancies, and compare the results with other optimization algorithms. Through this, we aim to demonstrate the potential of EPO as a powerful tool for designing and managing urban infrastructure systems, particularly for enhancing the resilience of urban drainage systems. The study will provide valuable insights into the optimal design of urban drainage technologies which take into account pipeline redundancy, helping policymakers and urban planners make informed decisions about improvingthe adaptability of urban drainage networks. The findings can contribute to the development of sustainable and resilient urban infrastructure systems, which are essential for ensuring the well-being and prosperity of urban residents.

Landscape structure is an important determinant of spatial variation in population and community processes. Theoretical research commonly assumes that extinction and recolonisation can be modelled without considering population dynamics, and ignores the latter in metapopulation models. However, the scales at which landscapes must be actively managed are generally commensurate with scales of population dynamics, suggesting the importance of developing models of (meta-)population response that explicitly consider population dynamics. It has been previously demonstrated that habitat network structure affects the responses of metapopulations in two-dimensional habitat networks typical of terrestrial systems. However, other types of habitat networks have largely been ignored. In particular, river systems follow a rigid hierarchical branching structure to form a Dendritic Ecological Network (DEN), which differs fundamentally from terrestrial systems because habitat patches are only ever linked by a single path. It has been hypothesized that this unique structure must have important implications for resident populations and communities. We developed a theoretical method to relate both population abundance and two different measures of population diversity to landscape structure. We simulated lattice and dendritic networks of differing complexity, representing open terrestrial landscapes with no particular constraints on patterns of connectivity, and river networks constrained to bifurcating connectivity patterns, respectively. We used two different measures to quantify structural complexity. Landscapes were simulated as directed graphs, with nodes representing individual habitat patches defined by a habitat quality or carrying capacity. Patches were connected by directed graph edges, with asymmetrical edge weights quantifying the probability of or-ganisms being able to move in each direction between pairs of nodes. Density dependent population processes were modelled stochastically within individual patch-level populations. Results were affected both by the way in which structural complexity was represented, and also by the way in which diversity was quantified. Nevertheless, both the type and the complexity of the habitat network affected population abundance and diversity. Complex lattice and dendritic networks supported the highest and lowest abundances, respectively. As complexity was reduced, network-scale carrying capacities of the two network types converged. The results for diversity were quite different. Diversity was almost unaffected by structural complexity in dendritic networks, and was higher than for any lattice network. Within lattice networks, simple structures had the highest diversity. Simple lattice networks are structurally similar to complex dendritic networks, except for the occasional presence of loops and multiple paths. These features appear to have profound effects on network-scale abundance and diversity. These results show that the unique structure of dendritic ecological networks do indeed have unique implications for resident organisms. They suggest that highly branched riverine systems are more able to foster diverse ecosystems that less branched equivalents and terrestrial landscapes. They also call into question the predominate use of metapopulation models that ignore population processes when studying the effects of habitat network structure.

Understanding the consequences of spatial structure on ecological dynamics is a central theme in ecology. Recently, research has recognised the relevance of river and river‐analogue network structures, because these systems are not only highly diverse but also rapidly changing due to habitat modifications or species invasions.Much of the previous work on ecological and evolutionary dynamics in metapopulations and metacommunities in dendritic river networks has been either using comparative approaches or was purely theoretical. However, the use of microcosm experiments provides the unique opportunity to study large‐scale questions in a causal and experimental framework.We conducted replicated microcosm experiments, in which we manipulated the spatially explicit network configuration of a landscape and addressed how linear versus dendritic connectivity affects population dynamics, specifically the spatial distribution of population densities, and movement behaviour of the protist model organismTetrahymena pyriformis. We tracked population densities and individual‐level movement behaviour of thousands of individuals over time.At the end of the experiment, we found more variable population densities between patches in dendritic networks compared to linear networks, as predicted by theory. Specifically, in dendritic networks, population densities were higher at nodes that connected to headwaters compared to the headwaters themselves and to more central nodes in the network. These differences follow theoretical predictions and emerged from the different network topologies per se. These differences in population densities emerged despite weakly density‐dependent movement.We show that differences in network structure alone can cause characteristic spatial variation in population densities. While such differences have been postulated by theoretical work and are the underlying precondition for differential dispersal evolution in heterogeneous networks, our results may be the first experimental demonstration thereof. Furthermore, these population‐level dynamics may affect extinction risks and can upscale to previously shown metacommunity level diversity dynamics. Given that many species in natural river systems exhibit strong spatiotemporal patterns in population densities, our work suggests that abundance patterns should not only be addressed from a local environmental perspective, but may be the outcome of processes that are inherently driven by the respective habitat network structure.

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

Landscape connectivity structure, specifically the dendritic network structure of rivers, is expected to influence community diversity dynamics by altering dispersal patterns, and subsequently the unfolding of species interactions. However, previous comparative and experimental work on dendritic metacommunities has studied diversity mostly from an equilibrium perspective. Here we investigated the effect of dendritic versus linear network structure on local (α‐diversity), among (β‐diversity) and total (γ‐diversity) temporal species community diversity dynamics. Using a combination of microcosm experiments, which allowed for active dispersal of 14 protists and a rotifer species, and numerical analyses, we demonstrate the general importance of spatial network configuration and basic life history tradeoffs as driving factors of different diversity patterns in linear and dendritic systems. We experimentally found that community diversity patterns were shaped by the interaction of dispersal within the networks and local species interactions. Specifically, α‐diversity remained higher in dendritic networks over time, especially at highly connected sites. β‐diversity was initially greater in linear networks, due to increased dispersal limitation, but became more similar to β‐diversity in dendritic networks over time. Comparing the experimental results with a neutral metacommunity model we found that dispersal and network connectivity alone may, to a large extent, explain α‐ and β‐diversity dynamics. However, additional mechanisms, such as variation in carrying capacity and competition–colonization tradeoffs, were needed in the model to capture the detailed temporal diversity dynamics of the experiments, such as a general decline in γ‐diversity and long‐term dynamics in α‐diversity.

How does network structure and complexity in river systems affect population abundance and persistence?

Based on the one-dimensional channel conservancy model, this paper considers the factor of urban pipe network drainage, and couples the pipe network model with the one-dimensional channel conservancy model to fully analyse the interaction between the channel water level and the drainage pipe network displacement. Taking a coastal city as an example, the model simulation results show that when the water level of the river is high, it will cause the drainage of the pipe network; the lower part of the terrain will become waterlogged because of the aging of the urban pipe and rainstorm. It is impossible to effectively solve the problem of flood control and drainage by widening the river channel. It is necessary to adopt the coordinated improvement of the pipe network and the river channel to achieve effective targets.

With the development of the simulative theory and calculation method of drainage pipe network, urban drainage pipe network model gradually become an indispensable part of the urban drainage system management. The characteristics of modern drainage pipelines,the content of drainage pipelines system model, the problems of combining GIS with drainage pipelines and how to solve them are briefly described in this paper.

In drainage engineering design, particularly, in analysis, check and hydraulic calculation of drainage pipe network, recognition and acquisition of drainage pipe network is the first and foremost task (Li et al., 2007).To implement the auto-generation of drainage pipe network drawing, this paper applies the graph theory to model the composition and data structure of a drainage pipe network. An effective method is proposed to automatically generate the directed drainage pipe network given a sink. The Breadth-first Search algorithm, Incidence-edge calculation algorithm and Transpose-graph algorithm from Boost Graph Library (BGL) are applied to implement the algorithm for the proposed method. The method provides greater automation and more efficiency but requires less inputs in the process of obtaining the practical drainage pipe network than other commonly used software do. It also lays a foundation for drainage pipe network analysis and related to hydraulic calculations. The accuracy and efficiency of the method is verified by the engineering practices described in this paper.

Spatial dynamics can promote persistence of strongly interacting predators and prey. Theory predicts that spatial predator-prey systems are prone to long transients, meaning that the dynamics leading to persistence or extinction manifest over hundreds of generations. Furthermore, the form and duration of transients may be altered by spatial network structure. Few empirical studies have examined the importance of transients in spatial food webs, especially in a network context, due to the difficulty in collecting the large scale and long-term data required. We examined predator-prey dynamics in protist microcosms using three experimental spatial structures: isolated, river-like dendritic networks and regular lattice networks. Densities and patterns of occupancy were followed for both predators and prey over a time scale that equates to >100 predator and >500 prey generations. We found that predators persisted in dendritic and lattice networks whereas they went extinct in the isolated treatment. The dynamics leading to predator persistence played out over long transients with three distinct phases. The transient phases showed differences between dendritic and lattice structures, as did underlying patterns of occupancy. Spatial dynamics differed among organisms in different trophic positions. Predators showed higher local persistence in more connected bottles while prey showed this in more spatially isolated ones. Predictions based on spatial patterns of connectivity derived from metapopulation theory explained predator occupancy, while prey occupancy was better explained by predator occupancy. Our results strongly support the hypothesized role of spatial dynamics in promoting persistence in food webs, but that the dynamics ultimately leading to persistence may occur with long transients which in turn may be influenced by spatial network structure and trophic interactions.

Meierdiercks, Katherine L., James A. Smith, Mary Lynn Baeck, and Andrew J. Miller, 2010. Analyses of Urban Drainage Network Structure and Its Impact on Hydrologic Response. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00465.xAbstract: Urban flood studies have linked the severity of flooding to the percent imperviousness or land use classifications of a watershed, but relatively little attention has been given to the impact of urban drainage networks on hydrologic response. The drainage network, which can include storm pipes, surface channels, street gutters, and stormwater management ponds, is examined in the Dead Run watershed (14.3 km2). Comprehensive digital representations of the urban drainage network in Dead Run were developed and provide a key observational resource for analyses of urban drainage networks and their impact on hydrologic response. Analyses in this study focus on three headwater subbasins with drainage areas ranging from 1.3 to 1.9 km2 and that exhibit striking contrasts in their patterns and history of development. It is shown that the drainage networks of the three subbasins, like natural river networks, exhibit characteristic structures and that these features play critical roles in determining urban hydrologic response. Hydrologic modeling analyses utilize the Environmental Protection Agency’s Stormwater Management Model (SWMM), which provides a flexible platform for examining the impacts of drainage network structure on hydrologic response. Results of SWMM modeling analyses suggest that drainage density and presence of stormwater ponds impact peak discharge more significantly in the Dead Run subbasins than the percent impervious or land use type of the subbasins.