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Effective urban land-use re-planning and the strategic arrangement of drainage pipe networks can significantly enhance urban flood defense capacity. Aimed at reducing the potential risks of urban flooding, this paper presents a straightforward and efficient approach to an urban distributed runoff model (UDRM). The model is developed to quantify the discharge and water depth within urban drainage pipe networks under varying rainfall intensities and land-use scenarios. The Nash efficiency coefficient of UDRM exceeds 0.9, which indicates its high computational efficiency and potential benefit in predicting urban flooding. The prediction of drainage conditions under both current and re-planned land-use types is achieved by adopting different flood recurrence intervals. The findings reveal that the re-planned land-use strategies could effectively diminish flood risk upstream of the drainage pipe network across 20-year and 50-year flood recurrence intervals. However, in the case of extreme rainfall events (a 100-year flood recurrence), the re-planned land-use approach fell short of fulfilling the requirements necessary for flood disaster mitigation. In these instances, the adoption of larger-diameter drainage pipes becomes an essential requisite to satisfy drainage needs. Accordingly, the proposed UDRM effectively combines land-use information with pipeline data to give practical suggestions for pipeline modification and land-use optimization to combat urban floods. Therefore, this methodology warrants further promotion in the field of urban re-planning.

Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, >0.70). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.

Recent studies have demonstrated the effectiveness of low impact development (LID) in preventing urban flooding in urban catchments. Majority of the past research focuses on the overall effects of LID on urban flood reduction in various configurations. However, how urban drainage system (UDS) performance changes at spatial scale under LID effectiveness within urban catchment is rarely explored. This study evaluates performance of UDS under different spatial placement strategies of LID to understand how urban flood dynamics of drainage system changes at catchment and local-scales. A practical UDS in China was chosen as a case study and divided into three sections (upstream, center, and downstream), with a combination of four LID practices installed on one of these sections or the entire catchment under six different rainfall scenarios and five different setting scales. An evaluation of individual LID practices demonstrated bioretention cell takes first place, followed by rain garden and green roof, and permeable pavement ranked at last place based on their overall performances. Results also confirmed the significant impact of the placement location of LID on UDS performance. Uniform placement strategy proves to be the best among four strategies because of the maximum potential for flood mitigation and improvement of UDS performance. Other investigated spatial placement strategies have approximately similar performances but are relatively poorer compared to the uniform strategy. Furthermore, the placement of LID facilities nearer to the flooded locations maximizes the benefits in terms of flood reduction and also reduces probability of transferring hydraulic load to other parts of UDS.

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP – Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM – the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.

Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.

Urban drainage network models (UDNMs) have been widely used to facilitate flood management. Typically, a UDNM is developed using data from Geographic Information Systems (GIS), and hence it consists of many short pipes and connection nodes or manholes. To improve modeling efficiency, a GIS‐based model is generally skeletonized by removing many elements. However, there has been surprisingly a lack of knowledge on to what extent such skeletonization can affect the model's simulation accuracy, resulting in uncertainty in flood risk estimation. This paper makes the first attempt to quantitatively evaluate multidimensional impacts of different skeletonization levels on hydraulic properties of UDNMs. This goal is achieved by a new evaluation framework comprising of eight existing and new metrics that make use of hydrographs, main pipe hydraulics and flood distribution properties. A real‐life UDNM is used to illustrate the new framework under various rainfall conditions and different skeletonization levels. The new framework is also used to compare the performance of two compensation methods in mitigating impacts caused by model skeletonization. Results obtained show that: (a) model skeletonization can significantly affect the magnitude of peak flow at the outfall, with a maximum overestimation of up to 20%, (b) hydraulics in main pipes can also be affected by model skeletonization with the maximum flow increasing up to 35%, and (c) model skeletonization may significantly alter the flood distribution properties which has been largely ignored in past studies. These findings provide guidance for UDNM skeletonization, where their associated impacts should be aware in engineering practice. Key Points A comprehensive framework is proposed to quantitatively evaluates the skeletonization impacts on urban drainage network model predictions A new metric is developed to simultaneously account for flood volume and spatial range when assessing the model skeletonization errors Proposed framework is applied to assess two existing compensation methods for the mitigation of simulation errors induced by skeletonization

The tidal jacking effect is a crucial factor exacerbating waterlogging in coastal cities, but its mechanism is complex and difficult to quantify. In this study, a comprehensive framework is established to explore how tidal jacking exacerbates waterlogging. The framework includes three components: hydrodynamic simulations of urban waterlogging combing rainfall and tide levels, analysis of the drainage system to reveal how tidal jacking impedes water flow and exacerbates waterlogging, and quantification of changes in flooded buildings to assess the impact of waterlogging hazards. Taking the Liede River Basin in Guangzhou, China, as a case study, the results show that tide levels intensify waterlogging through a series of cascading processes: jacking of drainage outfalls, impeded pipeline drainage, pipe overflow, and eventually surface waterlogging. When the drainage system encounters tidal jacking, the number and duration of jacked outfalls increase, extending the duration of full pipes. This leads to a 9%–43% increase in pipe overflow and a 4%–27% expansion of the waterlogging area. River overflow exceeds pipe overflow under tidal jacking. Tidal jacking changes the proportion of areas with different waterlogging risk levels, concentrating higher risk downstream. Tidal jacking also causes differential increase in losses among different building types. This study provides essential insights into how tide level exacerbates waterlogging and offers crucial evidence for mitigating waterlogging hazards. Plain Language Summary Urban waterlogging refers to the accumulation of water caused by excessive rainfall that exceeds the drainage capacity of urban drainage systems. This issue affects many cities globally, resulting in substantial social and economic losses while posing significant threats to public safety. In coastal cities, beyond the impact of rainfall, tides can further exacerbate waterlogging. Existing studies largely focus on predicting tide level variations or analyzing waterlogging outcomes but rarely explore how tide levels intensify urban waterlogging. To bridge this research gap, we propose a novel framework. This framework quantifies the physical processes by which tide levels exacerbate waterlogging through a series of hydraulic indicators. Furthermore, it assesses the impact of tide levels on flooded building losses. The framework is adaptable and can be broadly applied to waterlogging studies in other coastal cities. Key Points A new framework considering the hydraulic characteristics of the drainage system is developed to reveal how tide exacerbates waterlogging Multi‐scenario analyses of tide levels and rainfall reveal that tidal jacking notably alters the spatial distribution of waterlogging Under the tidal jacking effect, differences in loss increments are driven by the number of flooded buildings rather than building types

In the last few years, some regions of the Mediterranean area have witnessed a progressive increase in extreme events, such as urban and flash floods, as a response to the increasingly frequent and severe extreme rainfall events, which are often exacerbated by the ever-growing urbanization. In such a context, the urban drainage systems may not be sufficient to convey the rainwater, thus increasing the risk deriving from the occurrence of such events. This study focuses on a particularly intense urban flood that occurred in Palermo (Italy) on 15 July 2020; it represents a typical pluvial flood due to extreme rainfall on a complex urban area that many cities have experienced in recent years, especially in the Mediterranean region. A conceptual hydrological model and a 2D hydraulic model, particularly suitable for simulations in a very complex urban context, have been used to simulate the event. Results have been qualitatively validated by means of crowdsourced information and satellite images. The experience of Palermo, which has highlighted the urgent need for a shift in the way stormwater in urban settlements is managed, can be assumed to be a paradigm for modeling pluvial floods in complex urban areas under extreme rainfall conditions. Although the approaches and the related policies cannot be identical for all cities, the modeling framework used here to assess the impacts of the event under study and some conclusive remarks could be easily transferred to other, different urban contexts.

Sustainable urban drainage systems are multi-functional nature-based solutions that can facilitate flood management in urban catchments while improving stormwater runoff quality. Traditionally, the evaluation of the performance of sustainable drainage infrastructure has been limited to a narrow set of design objectives to simplify their implementation and decision-making process. In this study, the spatial design of sustainable urban drainage systems is optimized considering five objective functions, including minimization of flood volume, flood duration, average peak runoff, total suspended solids, and capital cost. This allows selecting an ensemble of admissible portfolios that best trade-off capital costs and the other important urban drainage services. The impact of the average surface slope of the urban catchment on the optimal design solutions is discussed in terms of spatial distribution of sustainable drainage types. Results show that different subcatchment slopes result in non-uniform distributional designs of sustainable urban drainage systems, with higher capital costs and larger surface areas of green assets associated with steeper slopes. This has two implications. First, urban areas with different surface slopes should not have a one-size-fits-all design policy. Second, spatial equality must be taken into account when applying optimization models to urban subcatchments with different surface slopes to avoid unequal distribution of environmental and human health co-benefits associated with green drainage infrastructure.

The rise in smart water technologies has introduced new cybersecurity vulnerabilities for water infrastructures. However, the implications of cyber‐physical attacks on the systems like urban drainage systems remain underexplored. This research delves into this gap, introducing a method to quantify flood risks in the face of cyber‐physical threats. We apply this approach to a smart stormwater system—a real‐time controlled network of pond‐conduit configurations, fitted with water level detectors and gate regulators. Our focus is on a specific cyber‐physical threat: false data injection (FDI). In FDI attacks, adversaries introduce deceptive data that mimics legitimate system noises, evading detection. Our risk assessment incorporates factors like sensor noises and weather prediction uncertainties. Findings reveal that FDIs can amplify flood risks by feeding the control system false data, leading to erroneous outflow directives. Notably, FDI attacks can reshape flood risk dynamics across different storm intensities, accentuating flood risks during less severe but more frequent storms. This study offers valuable insights for strategizing investments in smart stormwater systems, keeping cyber‐physical threats in perspective. Furthermore, our risk quantification method can be extended to other water system networks, such as irrigation channels and multi‐reservoir systems, aiding in cyber‐defense planning. Key Points We proposed a mathematical framework for evaluating flood risks of cyber‐physical attacks in a smart stormwater system False data injection can maliciously increase inflow and reduce the outflow of a targeted detention pond in a smart stormwater system Additional flood risks caused by false data injection are higher with smaller, more frequent storms