Department Urban Water Management

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m.
 

Urban floods will continue to be an alarming issue worldwide due to climate change and urban expansion. The costly and less environmentally friendly grey infrastructure is not always the most adequate solution to resolve urban pluvial flooding issues. The combination of grey and blue-green infrastructures, also called hybrid infrastructure, has been considered a promising solution for urban stormwater management. Existing approaches for identifying suitable hybrid solutions frequently rely on global multi-objective optimization algorithms. We developed a pre-screening method that decomposes a drainage network into clusters of pipes connected to sub-catchments, based on pipe hydraulic characteristic that allows for the impact of infrastructure combinations (blue-green and grey) to be mapped. Four impact matrices are proposed to map the total, local, upstream, and downstream flood reduction of all possible blue-green, grey, and hybrid solutions. Using an urban catchment in Guangzhou (China) as a case study, results showed that such an exercise could identify prime candidate locations for blue-green and grey infrastructure while filtering out ineffective locations for flood reduction. Furthermore, the impact matrices enabled the identification of flood zones where blue-green infrastructure could handle flood mitigation without the need of local grey infrastructure upgrades. As such, they are not only useful for quick screening of suitable interventions for each flooded zone, but can also potentially serve as a priori knowledge before diving into the data and computationally expensive process of finding the most effective flood mitigation solutions.

The impacts of climate change are becoming more widespread across the world, with hydro-meteorological extreme events on the rise, causing severe threats to nature and communities. Increasing trends in the frequency and intensity of floods and landslides have been projected by climate models. This necessitates the development of more effective measures such as nature-based solutions (NBS) which can complement grey infrastructures. Recent studies have identified knowledge gaps and limitations in existing research and tools that aid in spatial planning for the implementation of large-scale NBS and proposed new methodologies for the spatial allocation of large-scale NBS for flood risk reduction. This work presents a novel method for mapping the suitability of NBS addressing geo-hydrological hazards such as shallow landslides, debris flow, and rockfall, which are typically caused due to slope instability. This methodology incorporates landslide susceptibility mapping, and was used to create a toolbox ESRI ArcGIS environment to aid decision-makers in the planning and implementation of large-scale NBS. The spatial allocation toolbox was applied to the case study Portofino promontory, Liguria region, Italy, and 70% of the area was found to be highly susceptible to landslides. The produced suitability maps show that 41%, 33%, and 65% of the study area is suitable for the restoration of terraces, bio-engineering, and vegetative measures such as NBS for landslide risk reduction.

RRain-induced surface runoff and seasons lead to short- to medium-term anomalies in combined storm- and wastewater flows and temperatures, and influence treatment processes in wastewater resource recovery facilities (WRRF). Additionally, the implementation of decentralized heat recovery (HR) technologies for energy reuse in buildings affect energy-related processes across the urban water cycle and WRRFs heat inflows. However, quantitative insights on thermal-hydraulic dynamics in sewers at network scale and across different scales are very rare.
To enhance the understanding of thermal-hydraulic dynamics and the water-energy nexus across the urban water cycle we present a modular framework that couples thermal-hydraulic processes: i) on the surface, ii) in the public sewer network, iii) in households (including in-building HR systems), and iv) in lateral connections. We validate the proposed framework using field measurements at full network scale, present modelling results of extended time periods to illustrate the effect of seasons and precipitation events simultaneously, and quantify the impact of decentralized HR devices on thermal-hydraulics.
Simulation results suggest that the presented framework can predict temperature dynamics consistently all year long including short- to long-term variability of in-sewer temperature. The study provides quantitative evidence that the impact of household HR technologies on WRRF inflow heat budgets is reduced by approximately 20% during wet-weather periods in comparison to dry-weather conditions. The presented framework has potential to support multiple research initiatives that will improve the understanding of the water-energy nexus, pollutant dispersion and degradation, and support maintenance campaigns at network scale.

The process of urbanization can alter the local climate to the point that it threatens citizens’ well-being by creating heat-related hazards. The construction of Blue-Green Infrastructure (BGI) can improve the regulation of surface energy exchange processes and address this problem. However, the time needed for a BGI to deliver a stable cooling performance, referred to here as the Cooling Establishment Time (CET), is poorly understood and quantified in the literature and dependent on environmental, design and maintenance factors. Here, we analyze the feasibility of using satellite data to derive the CET for different BGIs across the city of Zurich, Switzerland. Results showed that remote sensing can quantify the land surface temperature impact of BGIs and assist in estimating their CET. BGI with trees or climbing plants required a longer CET (seven to ten years) before any notable shift in surface temperatures were visible, while grasses or artificial irrigated systems led to shorter CETs (one to three years). These results allow us to better account for BGI cooling establishment when planning for areas that need urgent action under warming climates. This work supports evidence-based urban greenery planning and design towards cooling our increasingly warming cities in a timely manner.

Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the frequency, magnitude, and impact of these events are expected to increase. Pluvial floods can occur in urban areas within minutes. A fast and reliable rainfall detection system should thus be implemented in flood-prone cities to warn the public of upcoming floods and save lives and reduce damage. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.

Transitioning urban drainage systems to serve water-smart societies requires the involvement of different disciplines and stakeholders. However, stakeholders have different visions and needs from the transitioning process (e.g in terms of financing, policy adaptation and system management) these also vary between regions and countries. Identifying such different needs for stakeholders is necessary to propose practical adaptation strategies. Therefore, evidence of needs as reflected in policy papers and legislation in seven European countries was collected. Knowledgeable individuals in the urban drainage community were asked about their visions. Results show that whilst there is consensus on the challenges, visions on how to transition are diverse, indicating that more interaction between the different stakeholder groups is required to develop consensus. Additionally, organisational and legislative structures often slow down the necessary change processes.

We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustworthy methods for urban flood management have become increasingly important due to higher rainfall intensity caused by climate change, the expansion of cities, and changes in land use. While physically based flood models can provide reliable forecasts for water depth at every location of a catchment, their high computational burden is hindering their application to large urban areas at high spatial resolution. While deep learning models have been used to address this issue, a disadvantage is that they are often perceived as "black-box" models and are overconfident about their predictions, therefore decreasing their reliability. Our deep learning model learns the underlying phenomena a priori from simulated hydrodynamic data, obviating the need for manual parameter setting for every new rainfall event at test time. The only inputs needed at the test time are a rainfall forecast and parameters of the terrain such as a digital elevation model to predict the maximum water depth with uncertainty estimates for complete rainfall events. We validate the accuracy and generalisation capabilities of our approach through experiments on a dataset consisting of catchments within Switzerland and Portugal and 18 rainfall patterns. Our method produces flood hazard maps at 1 m resolution and achieves mean absolute errors as low as 21 cm for extreme flood cases with water above 1 m. Most importantly, we demonstrate that our approach is able to provide an uncertainty estimate for every water depth within the predicted hazard map, thus increasing the model's trustworthiness during flooding events.

Data-driven and machine learning models have recently received increasing interest to resolve the computational speed challenge faced by various physically-based simulations. A few studies have explored the application of these models to develop new, and fast, applications for fluvial and pluvial flood prediction, extent mapping, and flood susceptibility assessment. However, most studies have focused on model development for specific catchment areas, drainage networks or gauge stations. Hence, their results cannot be directly reused to other contexts unless extra data are available and the models are further trained. This study explores the generalizability potential of convolutional neural networks (CNNs) as flood prediction models. The study proposes a CNN-based model that can be reused in different catchment areas with different topography once the model is trained. The study investigates two options, patch- and resizing-based options, to process catchment areas of different sizes and different shapes. The results showed that the CNN-based model predicts accurately on "unseen" catchment areas with significantly less computational time when compared to physically-based models. The obtained results also suggest that the patch-based option is more effective than the resizing-based option in terms of prediction accuracy. In addition, all experiments have shown that the prediction of flow velocity is more accurate than water depth, suggesting that the water accumulation is more sensitive to global elevation information than flow velocity.

Understanding the negative effects of widespread implementation of optimal water efficient solutions may have on existing centralised sewer systems is still limited – one of these effects is the accumulation of solids in sewer pipes. Predicting these effects requires setting up and simulating complex detailed hydraulic sewer network models. Often, precise details of the sewer network layout and diurnal patterns of the wastewater flows are not available, limiting the applicability of using model predictions for such phenomena. In this study, the applicability of supervised machine learning (ML) algorithms for the development of a simplified surrogate model to predict solid accumulation in sewer pipes was investigated. A large number of highly variable sewer networks were synthetically generated and used to produce results that can be generalizable within the limitations of the current study. A hydrodynamic sewer model was set up and simulated for each synthetic sewer network and various scenarios in which different water-efficient solutions were considered. Simulation results indicated that the most impacts are expected to occur in the upstream part of the sewer networks, and that with 50% reduction in (waste-)water flows, 3–20% more pipes are expected to accumulate solids. It was further found that ML algorithms can be used to successfully predict locations of solids accumulation in sewer pipes without using hydrodynamic models. A simple tool based on the findings of this study, sparing the need to conduct complex hydraulic simulations, was developed. It allows the user to enter a set of pipe characteristics and the proportion of flow that is reduced due to the implementation of water efficient solutions, and it predicts whether the pipe will accumulate solids or not. The study results and the proposed ML algorithms can support the implementation of optimal water-efficient solutions that will promote designing and managing the water sensitive cities of the future.

Urban drainage systems have developed way beyond the traditional piped combined or separate sewer systems. Many 'new' systems are being introduced, ranging from stormwater infiltration facilities to green roofs. However, the widely advocated blue-green infrastructures are typically overlooked by asset managers, which will very likely have detrimental effects on their performance, service life, and wider adoption. In this paper, the working group on Urban Drainage Asset Management (UDAM - https://udam.home.blog/) of the IWA and IAHR Joint Committee on Urban Drainage discusses whether the state-of-the-art knowledge based on conventional sewer asset management is sufficient to develop asset management for blue-green infrastructures (BGIs). The discussion is structured around the five preconditions for effective control and asset management. Results show that asset management for BGIs is still underdeveloped due to a lack of monitoring techniques covering the broad range of BGI benefits and performance indicators, inspection techniques covering relevant failure mechanisms and models describing these mechanisms, maintenance and rehabilitation options, and sufficient support tools to aid inhabitants in the operation and maintenance of their individual BGIs such as green roofs or vegetated swales.

Synthetic design storms are often used to plan new drainage systems or assess flood impacts on infrastructure. To simulate extreme rainfall events under climate change, design storms can be modified to match a different return frequency of extreme rainfall events as well as a modified temporal distribution of rainfall intensities. However, the same magnitude of change to the rainfall intensities is often applied in space. Several hydrological applications are limited by this. Climate change impacts on urban pluvial floods, for example, require the use of 2D design storms (rainfall fields) at sub-kilometer and sub-hourly scales. Recent kilometer scale climate models, also known as convection-permitting climate models (CPM), provide rainfall outputs at a high spatial resolution, although rainfall simulations are still restricted to a limited number of climate scenarios and time periods. We nevertheless explored the potential use of rainfall data obtained from these models for hydrological flood impact studies by introducing a method of spatial quantile mapping (SQM). To demonstrate the new methodology, we extracted high-resolution rainfall simulations from a CPM for four domains representing different urban areas in Switzerland. Extreme storms that are plausible under the present climate conditions were simulated with a 2D stochastic rainfall model. Based on the CPM-informed stochastically generated rainfall fields, we modified the design storms to fit the future climate scenario using three different methods: the SQM, a uniform quantile mapping, and a uniform adjustment based on a rainfall–temperature relationship. Throughout all storms, the temporal distribution of rainfall was the same. Using a flood model, we assessed the impact of different rainfall adjustment methods on urban flooding. Significant differences were found in the flood water depths and areas between the three methods. In general, the SQM method results in a higher flood impact than the storms that were modified otherwise. The results suggest that spatial storm profiles may need to be re-adjusted when assessing flood impacts.

Reflected in the growing body of literature, urban heat mitigation is increasingly relevant as cities experience extreme heat, exacerbated by climate change and rapid urbanisation. Most studies focus on urban–rural temperature differences, known as the Urban Heat Island, which does not provide insight into urban heat dynamics. Here, we synthesise current knowledge on spatio-temporal variations of heat sources and sinks, showing that a targeted and absolute understanding of urban heat dynamics rather than an urban–rural comparison should be encouraged. We discuss mechanisms of heat sinks for microclimate control, provide a clear classification of Blue Green Systems and evaluate current knowledge of their effectiveness in urban heat mitigation. We consider planning and optimisation aspects of Blue Green Infrastructure (greenery and water bodies/features), interactions with hard surfaces and practices that ensure space and water availability. Blue Green Systems can positively affect urban microclimates, especially when strategically planned to achieve synergies. Effectiveness is governed by their dominant cooling mechanisms that show diurnal and seasonal variability and depend upon background climatic conditions and characteristics of surrounding urban areas. Situationally appropriate combination of various types of Blue Green Systems and their connectivity increases heat mitigation potential while providing multiple ecosystem services but requires further research.

In many parts of the world, small towns are experiencing high levels of population growth and development. However, there is little understanding of how urban growth in these regional towns will impact urban runoff. We used the case study of Wangaratta, located in South-East Australia, between 2006 and 2016, to investigate land cover changes and their impacts on urban runoff discharge. Detailed spatio-temporal analysis (including neighbourhood composition analysis and supervised classification of aerial imagery) identified that population, land use and land cover changes in Wangaratta, although subtle, were mostly driven by residential growth in the outskirts of the town, where there were large increases in impervious surface area. Overall, the urban growth was minimal. However, in spite of these small changes, a sub-catchment only SWMM model showed that the increase in impervious surface area nevertheless resulted in a statistically significant increase in total runoff across the town. Particularly, this increase was most pronounced for frequent and shorter storms. The analysis of urban development pattern changes coupled with urban hydrological modelling indicated that land cover changes in regional towns, especially when analysed in detail, may result in hydrological changes in the urban region (likely to be exacerbated in coming years by changing climate) and that adaptation efforts will need to adopt a variety of approaches in both existing and growth zones. Our findings highlight the necessity of detailed fine-scale analyses in small towns as even subtle changes will have substantial future implications and robust planning and adaptation decisions are even more important when compared to larger cities due to the greater economic constraints that small towns face and their important relationship with the surrounding hinterlands.

Thermal-hydraulic considerations in urban drainage networks are essential to utilise available heat capacities from waste- and stormwater. However, available models are either too detailed or too coarse; fully coupled thermal-hydrodynamic modelling tools are lacking. To predict efficiently water-energy dynamics across an entire urban drainage network, we suggest the SWMM-HEAT model, which extends the EPA-StormWater Management Model with a heat-balance component. This enables conducting more advanced thermal-hydrodynamic simulation at full network scale than currently possible. We demonstrate the usefulness of the approach by predicting temperature dynamics in two independent real-world cases under dry weather conditions. We furthermore screen the sensitivity of the model parameters to guide the choice of suitable parameters in future studies. Comparison with measurements suggest that the model predicts temperature dynamics adequately, with RSR values ranging between 0.71 and 1.1. The results of our study show that modelled in-sewer wastewater temperatures are particularly sensitive to soil and headspace temperature, and headspace humidity. Simulation runs are generally fast; a five-day period simulation at high temporal resolution of a network with 415 nodes during dry weather was completed in a few minutes. Future work should assess the performance of the model for different applications and perform a more comprehensive sensitivity analysis under more scenarios. To facilitate the efficient estimation of available heat budgets in sewer networks and the integration into urban planning, the SWMM-HEAT code is made publicly available.

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image-to-image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data-driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood-safe urban layout planning.

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.

Sustainable urban drainage systems (SuDS) help in stormwater management by reducing runoff volume, increasing runoff concentration time and thereby improving the drainage system capacity. This study investigated the potential and cost-effectiveness of SuDS in reducing combined sewer overflows (CSOs). We simulated the performance of four SuDS techniques (bioretention cell, permeable pavement, rain barrel and green roof) at incremental levels of spatial coverage for a small urban catchment with a combined sewer system. We also used an Analytic Hierarchy Process (AHP) considering end-point CSO, land use, imperviousness, slope and elevation criteria to identify priority areas for SuDS deployment. Results show that CSO volume attenuation ranged a maximum of 50-99% for the catchment, depending on the deployment strategy and underlying mechanisms of each technology. We also found that deployment of SuDS in AHP-selected sub-catchments improved CSO reduction only for rain barrels and green roofs, but not for bioretention cells and permeable pavements. SuDS were also a cost-effective retrofit option: for a 40% volume reduction, the SuDS cost, at most, 25% of the equivalent cost required for a large CSO tank. Outcomes of this study demonstrate the efficacy of SuDS in controlling CSOs, adding yet another tangible benefit to their increasingly recognised multi-functionality.

The Portuguese discovered the uninhabited Azores archipelago in 1427 and started to settle it during the fifteenth century. Angra, located on Terceira Island, soon became the most important Azorean city, leading to rapid urban development. This article investigates the overlooked role of streams in the rise of Angra as a pivotal Atlantic urban centre. Through the intersection of historical and flood modelling methods, it makes a unique contribution to our understanding of Angra's urban morphologic development, highlighting the potential of applying urban flood modelling to analysis of the rise of coastal urban settlement.

Recurring combined sewer overflows (CSOs) can have a significant impact on the ecological condition of receiving water bodies. There are several structural measures, like adding retention basins and switching to low impact development solutions, that have been proposed to reduce the number of sewage overflows. Besides, several flow control strategies have been discussed in scientific literature that take advantage of the space within urban drainage networks, which is assumed to be adequate, for temporary storage. The adequacy of such storage space, however, is not a universally valid assumption as a large fraction of drainage networks frequently operate close to their design discharge. In this paper, we investigate the efficacy of flow control for a space-constrained drainage network. We employ a low-cost, heuristic real time control strategy with the use of flow control devices (FCDs) and available in-sewer space to reduce the magnitude of CSOs. We consider the performance of the proposed control strategy and discuss the effect of FCD location on CSO reduction. Our results, based on over 300 rainfall-event simulations, show that the flow control strategy using limited sewer capacity is more efficient during relatively small rainfall events, where the CSO is large enough to enable reduction using the chosen control rules. The CSO is reduced, to varying degrees, for around 80% of rainfall events with peak intensity between 10 and 20 mm h⁻¹. For larger rainfall events, the flow control is more unstable in response to abrupt water release during control operation, which seems to be unavoidable because of the water accumulation effect and the transition to pressurized pipe flow in space-constrained networks. We also found that the flow control performance is highly sensitive to the FCD location – as it depends on the interplay of the peak rainfall intensity and the water level condition immediately upstream of the FCD. The efficacy of a location for flow control is determined by the unfilled capacity (i.e., effective in-sewer storage potential) in the pipe upstream of the FCD during the rainfall peak; furthermore, the location also has to be resistant to the water accumulation effect. Using our analysis, we substantiate two anticipated caveats to flow control strategies when the storage space is limited in a drainage network: diminished performance in CSO reduction and the appearance of additional control-related challenges, which are otherwise mitigated in more spacious networks.

Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named _DEEPFLOOD, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with ≈11 cm root mean square error.

Urban flooding poses an increasing threat to citizens and property and has become a global challenge. The primary cause of urban flooding is rapid urbanization, which is outpacing infrastructure development and drainage system improvements. This paper utilizes a verified flood model that couples hydrological and hydrodynamic processes to investigate the flood response under rainfall events with different patterns. A sponge city pilot area in China is taken as a case study. Drainage system performance, surface inundation patterns, and relation between them are investigated by carrying out statistical analysis and Pearson correlation analysis, respectively. Results show that with increases in the rainfall recurrence interval (RRI) and peak position ratio (PPR), drainage system performance indicators (DSPIs) generally become worse, and the total inundation area (TIA) and surface inundation areas of larger depths become larger. The response time exhibits varied patterns but generally decreases with increasing recurrence intervals and peak ratios. For surface inundation, the recurrence interval presents an increasingly significant role compared with the peak ratio as rainfall becomes heavier. Compared with other DSPIs, the volume of overflowed manholes (VOMs) shows a delayed response to rainfall and has a stronger relationship with the inundation areas with greater water depths, which indicates a possible solution of utilizing manhole storage for water management and flood control. Analyzing the response of drainage systems and flood patterns to different rainfall events using a model coupling hydrological and hydrodynamic processes comprehensively reveals the specific problems of the study area and possible innovative solutions. The analytical method and knowledge gained can help support urban flood management in China and around the world.

Urban pluvial flood models need to be calibrated with data from actual flood events in order to validate and improve model performance. Due to the lack of conventional sensor solutions, alternative sources of data such as citizen science, social media, and surveillance cameras have been proposed in literature. Some of the methods proposed boast high scalability but without an on-site survey, they can only provide proxy measurements for physical flooding variables (such as water level). In this study, the potential value of such proxy measurements was evaluated by calibrating an urban pluvial flood model with data from experimental flood events conducted in a 25 × 25 m facility, monitored with surveillance cameras and conventional sensors in parallel. Both ideal proxy data and actual image-based proxy measurements with noise were tested, and the effects of measurement location and measurement noise were investigated separately. The results with error-free proxy data confirm the theoretic potential of such measurements, as in half of the calibration configurations tested, ideal proxy data increases model performance by at least 70% compared to sensor data. However, image-based proxy data can contain complex correlated errors, which have a complex and predominantly negative effect on performance.

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation, where we will experience a data demand, i.e. a “pull” of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.

Comprehensive management of urban drainage network infrastructure is essential for sustaining the operation of these systems despite stresses from component deterioration, urban densification, and a predicted intensification of rainfall events. In this context, up-to-date and accurate urban drainage network data is key. However, such data is often absent, outdated, or incomplete. In this study, a new approach to localize manhole covers and storm drains, using deep learning to mine publicly available street-level images, is presented, tested, and assessed. Thus, the time-consuming and costly acquisition of the location of these system components can be avoided. The approach is evaluated using 5,000 high-resolution panoramas covering 500 km of public roads in Switzerland. The object detection approach proposed shows good performance and an improvement over state of the art image-based urban drainage infrastructure component detection. While the geographical localization of the detected objects still contains errors, the accuracy achieved is nevertheless sufficient for some applications, e.g. flood risk assessment.

In the event of a flood, being able to build accurate flood level maps is essential for supporting emergency plan operations. In order to build such maps, it is important to collect observations from the disaster area. Social media platforms can be useful sources of information in this case, as people located in the flood area tend to share text and pictures depicting the current situation. Developing an effective and fully automatized method able to retrieve data from social media and extract useful information in real-time is crucial for a quick and proper response to these catastrophic events. In this paper, we propose a method to quantify flood-water from images gathered from social media. If no prior information about the zone where the picture was taken is available, one possible way to estimate the flood level consists of assessing how much the objects appearing in the image are submerged in water. There are various factors that make this task difficult: i) the precise size of the objects appearing in the image might not be known; ii) flood-water appearing in different zones of the image scene might have different height; iii) objects may be only partially visible as they can be submerged in water. In order to solve these problems, we propose a method that first locates selected classes of objects whose sizes are approximately known, then, it leverages this property to estimate the water level. To prove the validity of this approach, we first build a flood-water image dataset, then we use it to train a deep learning model. We finally show the ability of our trained model to recognize objects and at the same time predict correctly flood-water level.

Motivation and objective
Urban pluvial flooding is an issue of increasingly critical importance due to climate change and urbanization. However, the numerical models used for flood forecasting and risk mitigation suffer from a pronounced lack of monitoring data, which affects model accuracy. Monitoring data are necessary so the models, which contain undefined parameters, can be calibrated and validated against real flood events. In particular, it is important that the models are able to reproduce flood behavior in and around buildings, where the most damage is caused. However, conventional flow and water level sensors reach their limits in public spaces like streets due to irregular topography, moving obstacles, and the risk of vandalism. It has been suggested that surveillance cameras and social media could provide the necessary surface flooding data at a fraction of the cost of conventional sensors. The objective of this thesis is to explore how trend-like data can be extracted from surveillance footage and assimilated to boost the reliability of urban pluvial flood models. [...]

Motivation und Zielsetzung
Urbane regenbedingte Überschwemmungen sind aufgrund des Klimawandels und der Urbanisierung von immer größerer Bedeutung, aber den numerischen Modellen zur Hochwasservorhersage und Risikominderung mangelt es an Überwachungsdaten, was die Modellgenauigkeit einschränkt. Überwachungsdaten sind notwendig, damit die Modelle, welche unsichere Parameter enthalten, gegen echte Hochwasserereignisse kalibriert und validiert werden können. Insbesondere ist es wichtig, dass die Modelle das Überschwemmungsverhalten in und um Gebäude herum nachbilden können, wo die meisten Schäden verursacht werden. Herkömmliche Strömungs- und Wasserstandsensoren stoßen jedoch im öffentlichen Raum, wie Straßen, aufgrund unregelmäßiger Topographie, beweglicher Hindernisse und der Gefahr von Vandalismus an ihre Grenzen. Deswegen gibt es Interesse für alternative Datenquellen wie Überwachungskameras und soziale Medien, aus denen Oberflächenabflussdaten zu einem Bruchteil der Kosten herkömmlicher Sensoren gewonnen werden könnte. Das Ziel dieser Arbeit ist es, zu untersuchen, wie trendartige Daten aus Überwachungsbildern extrahiert und assimiliert werden können, um die Zuverlässigkeit von Überschwemmungsmodellen zu erhöhen. [...]

In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods.

Urban water systems are being redefined for a digital age, promising substantial advantages for service users and providers, and for society as a whole. However, beside the much-discussed benefits of smart urban water systems in future smart cities, the transition will also bring unique challenges, particularly with respect to privacy and cybersecurity. A preemptive delineation of these risks and providing appropriate recommendations will help to guide digital transformations of urban water towards sustainable solutions. Faced with emerging risks of digitalization, urban water management needs to look beyond technology while recognizing the central and multifaceted role of research.
In smart city initiatives, urban water systems remain largely out of the digitalization spotlight compared to other types of infrastructure. Oftentimes, the management and planning of urban water systems still follow traditional, steel and concrete-based approaches that treat used water as waste and not as a resource [1]. The need for innovation, however, is significant: the infrastructure, operation, and maintenance cost of urban water systems around the world already ranges in the hundred billion dollars annually and water-related stresses are expected to continue rising. Digitalization could not only increase the flexibility and effectiveness of existing urban water systems, but could also allow for the provisions of new services to society. The envisioned transformation, as outlined for example in the literature [2–4], entails novel data collection and transmission techniques, analytical methods, models, and automation. For example, smart water meter data can be combined with noise loggers to detect and roughly locate leaks in water mains. Together with automated control infrastructure, pressure can be regulated to reduce water losses [4]. By this and numerous other smart solutions, not only water distribution but also sewers and wastewater treatment will be transformed. In particular, digitalization facilitates large-scale implementation of disruptive technologies like decentralized wastewater treatment [5] and direct potable reuse, which will help cities to cope with climate change and urbanization.
However, we argue that as the benefits of smart urban water systems are increasingly explored and showcased in literature, it is time to also give attention to potential risks that might emerge. Recurring data breaches and the growing use of cyber-attacks for geopolitical ends are a reminder that the digitalization of society indeed carries risks. Given the critical role of water services in society and within the water-energy-food nexus, these risks must be identified and managed pro-actively. In the following, we raise awareness and suggest ways forward in approaching the unique privacy and security issues that accompany the digitalization of urban water systems. Finally, we highlight the central role of researchers in assessing and mitigating the potential risks of the smart urban water solutions they propose.

Multi-criteria decision models have been recently implemented in many urban flood risk management schemes due to the increasing demand of low-cost and reliable solutions to prevent and control overflow from sewer systems. Based on specific targets, these decision models encompass multiple reward (or objective) functions according to socio-economic requirements, vulnerability assessments and physical constraints on existing sewer systems. In this study, we present a simple algorithm to generalize reward functions aimed at locating the best sites to install in-sewer flow control devices, and thus controlling sewer overflow. The algorithm uses the mathematical structure of reward functions previously established, and adjust them based on hydraulic simulation results of two real sewer networks. Our results indicate that a single generalized reward function can efficiently identify strategic locations to guarantee (i) maximum in-sewer storage, (ii) minimum impact of possible flow control device failures and (iii) maximum flow discharge reduction upstream flood-prone urban areas.

Mit kurzen Videosequenzen von Überwachungskameras und Smartphones können Oberflächengeschwindigkeiten in Abwasserkanälen gemessen werden. Eingesetzt wird dabei die bildbasierte Methode namens Surface Structure Image Velocimetry, kurz SSIV. Die resultierenden Ergebnisse stimmen gut mit Ultraschall-Referenzmessungen überein und unterstreichen das Potenzial der innovativen Methode.

Messungen von Oberflächenabflüssen werden selten im urbanen Raum durchgeführt, weil traditionelle Messgeräte zu teuer sind oder direkt über oder innerhalb des Fliessgewässers installiert werden müssen. Deswegen sind Daten über Überschwemmungen im urbanen Raum nur selten vorhanden, was die Kalibrierung und Validierung von Überflutungsmodellen erschwert. In dieser Studie wird ein Ansatz vorgestellt, bei dem marktübliche Überwachungskameras zur Durchflussmessung eingesetzt werden.

Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.

When defining the variables to predict sewer failure and therefore optimise sewer systems maintenance, it is important to identify the ones that most significantly influence the quality of the predictions or to define the smallest number of variables that is sufficient to obtain accurate predictions. In this study, three different statistical variable selection algorithms are applied for the first time to identify the most important variables for sewer failure prediction: the mutual information indicator, the out-of-bag samples concept, based on the random forest algorithm, and the stepwise search approach. The methods were applied to a real data-set that consists of the categorisation of sewer condition and associated physical characteristics. The mutual information and the stepwise search methods provided good predictions while those obtained using out-of-bag samples based on random forest were somewhat different, justified by the lack of robustness to imbalanced class distributions.

Ein künstliches neuronales Netzwerk wurde darauf trainiert, Schachtdeckel und Einlaufschächte in sehr hochauflösenden Google Street View-Panoramen zu detektieren. Die Standorte der detektierten Objekte werden anschliessend anhand der Panorama-Metadaten und Annahmen über die Datenerhebung sowie das umgebende Gelände approximiert. Durch die Nutzung bereits gesammelter Daten ist der Ansatz kostengünstig und in Regionen, in denen Google Street View verfügbar ist, leicht umsetzbar.

Oberflächenabflüsse im urbanen Raum werden selten gemessen und fast nie für die Validierung von Siedlungsentwässerungmodellen verwendet. In dieser Studie wird vorgeführt, wie die Fliessgeschwindigkeit von Oberflächenabflüssen ausschließlich mit Videomaterial von Überwachungskameras und ohne Zusatz von Tracking-Partikeln ermittelt werden kann.

The activation of available in-sewer storage volume has been suggested as a low-cost flood and combined sewer overflow mitigation measure. However, it is currently unknown what the attributes for suitable objective functions to identify the best location for flow control devices are and the impact of those attributes on the results. In this study, we present a novel location model and efficient algorithm to identify the best location(s) to install flow limiters. The model is a screening tool that does not require hydraulic simulations but rather considers steady state instead of simplistic static flow conditions. It also maximises in-sewer storage according to different reward functions that also considers the potential impact of flow control device failure. We demonstrate its usefulness on two real sewer networks, for which an in-sewer storage potential of approximately 2,000 m³ and 500 m³ was estimated with five flow control devices installed.

Newly available, more detailed and accurate elevation data sets, such as Digital Elevation Models (DEMs) generated on the basis of imagery from terrestrial LiDAR (Light Detection and Ranging) systems or Unmanned Aerial Vehicles (UAVs), can be used to improve flood-model input data and consequently increase the accuracy of the flood modelling results. This paper presents the first application of the MBlend merging method and assesses the impact of combining different DEMs on flood modelling results. It was demonstrated that different raster merging methods can have different and substantial impacts on these results. In addition to the influence associated with the method used to merge the original DEMs, the magnitude of the impact also depends on (i) the systematic horizontal and vertical differences of the DEMs, and (ii) the orientation between the DEM boundary and the terrain slope. The greater water depth and flow velocity differences between the flood modelling results obtained using the reference DEM and the merged DEMs ranged from −9.845 to 0.002 m, and from 0.003 to 0.024 m s⁻¹ respectively; these differences can have a significant impact on flood hazard estimates. In most of the cases investigated in this study, the differences from the reference DEM results were smaller for the MBlend method than for the results of the two conventional methods. This study highlighted the importance of DEM merging when conducting flood modelling and provided hints on the best DEM merging methods to use.

Physically-based models are important tools for evaluating the hydraulic behaviour of urban drainage systems and, more specifically, assessing flood risk. While it is well known that such models should be calibrated and validated with monitoring data, overland runoff information is seldom available for this purpose. This study investigates the potential of using surveillance camera footage to measure surface flow velocity thanks to an LSPIV-based method called Surface Structure Image Velocimetry (SSIV). Seven real-scale experiments conducted in a specialized flood training facility were used to test the SSIV method under varied and challenging conditions. SSIV performance was evaluated by benchmarking bulk (mean) velocity against that measured by a conventional sensor array. In the best conditions tested, SSIV and conventional flow sensors differed by only 1.7% (0.1% standard deviation). While the method proved sensitive to light conditions, our results suggest that infrared lighting could be used to increase measurement consistency. Our study concludes that for measuring overland flow velocity in urban areas, surveillance and traffic cameras can be considered as a low-maintenance and easy-to-install alternative to conventional sensor systems.

Hexagonal segmentations of space have long been known for their advantages vis à vis squared partitions in discretizing spatial variables, be it natural phenomena or human-related features. However, readily available and easy-to-use tools to manipulate and interact with hexagonal rasters remain widely unavailable today. This article presents a first step to enable the use of hexagonal rasters in the GIS field. A format to encode cartographical hexagonal meshes as simple ASCII files is specified through a context-free grammar. Named HexASCII, this file format provides a simple means of storing and sharing such rasters. A set of simple tools based on the HexASCII format is presented, allowing their creation and basic interaction with traditional GIS software.

r.mblend is an implementation of the MBlend method for merging Digital Elevation Models (DEMs). This method produces smooth transitions between contiguous DEMs of different spatial resolution, for instance, when acquired by different sensors. r.mblend is coded on the Python API provided by the Geographic Resources Analysis Support System (GRASS), being fully integrated in that GIS software. It introduces improvements to the original method and provides the user with various parameters to fine tune the merging procedure. This article showcases the main differences between r.mblend and two conventional DEM merge methods: Cover and Average.

Sewer and drainage infrastructure are often not as well catalogued as they should be, considering the immense investment they represent. In this work, we present a fully automatic framework for localizing sewer inlets from image clouds captured from an unmanned aerial vehicle (UAV). The framework exploits the high image overlap of UAV imaging surveys with a multiview approach to improve detection performance. The framework uses a Viola–Jones classifier trained to detect sewer inlets in aerial images with a ground sampling distance of 3–3.5 cm/pixel. The detections are then projected into three-dimensional space where they are clustered and reclassified to discard false positives. The method is evaluated by cross-validating results from an image cloud of 252 UAV images captured over a 0.57-km² study area with 228 sewer inlets. Compared to an equivalent single-view detector, the multiview approach improves both recall and precision, increasing average precision from 0.65 to 0.73. The source code and case study data are publicly available for reuse.

System identification, sensitivity analysis, optimization and control, require a large number of model evaluations. Accurate simulators are too slow for these applications. Fast emulators provide a solution to this efficiency demand, sacrificing unneeded accuracy for speed. There are many strategies for developing emulators but selecting one remains subjective. Herein we compare the performance of two kinds of emulators: mechanistic emulators that use knowledge of the simulator's equations, and purely data-driven emulators using matrix factorization. We borrow simulators from urban water management, because more stringent performance criteria on water utilities have made emulation a crucial tool within this field. Results suggest that naive data-driven emulation outperforms mechanistic emulation. We discuss scenarios in which mechanistic emulation seems favorable for extrapolation in time and dealing with sparse and unevenly sampled data. We also point to advances in Machine Learning that have not permeated yet into the environmental science community.

Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.

The advantages of hexagonal meshes over squared grids in discretising spatial variables have been known for long. Notwithstanding, the raster data formats used in geo-spatial disciplines to this purpose are still today almost exclusively reliant on squared grids. The HexASCII file format is a core element in an attempt to introduce hexagonal rasters to mainstream GIS, defining a simple vehicle to store and share such data structures. This article describes the hex-utils tool-kit, an Application Programming Interface (API) and a set of command line tools enabling the use of HexASCII rasters. Basic operations are supported: creation of new hexagonal rasters from different inputs and transformation into file formats readable by desktop GIS programmes. The API sets a framework for the development of urther functionality.

The data sets described in this paper provide a basis for developing and testing new methods for monitoring and modelling urban pluvial flash floods. Pluvial flash floods are a growing hazard to property and inhabitants’ well-being in urban areas. However, the lack of appropriate data collection methods is often cited as an impediment for reliable flood modelling, thereby hindering the improvement of flood risk mapping and early warning systems. The potential of surveillance infrastructure and social media is starting to draw attention for this purpose. In the floodX project, 22 controlled urban flash floods were generated in a flood response training facility and monitored with state-of-the-art sensors as well as standard surveillance cameras. With these data, it is possible to explore the use of video data and computer vision for urban flood monitoring and modelling. The floodX project stands out as the largest documented flood experiment of its kind, providing both conventional measurements and video data in parallel and at high temporal resolution. The data set used in this paper is available at https://doi.org/10.5281/zenodo.830513.

Failure prediction plays an important role in the management of urban water systems infrastructures. An accurate description of the deterioration of urban drainage systems is essential for optimal investment and rehabilitation planning. In the study presented in this paper, a new method to predict sewer pipe failure based on robust decision trees is proposed. Five other different stochastic failure prediction models – the non-homogeneous Poisson process, the zero-inflated non-homogeneous Poisson process, classical decision tress (CART and Random Forest algorithms), the Weibull accelerated lifetime model and the linear extended Yule process – are also implemented and explored in order to identify models that combine good failure prediction results with robustness. The six models were tested on the asset register and pipe failure register of a large US wastewater utility; only pipe blockage failures were considered in this study. The linear extended Yule process and the zero-inflated non-homogeneous Poisson process presented the overall best results throughout the models’ comparison, showing a good ability to detect pipes with high likelihood of blockage failure. Decision trees based on robust random forests only detected pipes with high likelihood of failure when considering a short-term prediction window; the accuracy of the predictions was one of the best when using the robust decision tree model. The Weibull accelerated lifetime model provided some of the best medium-term predictions but performed less well for shorter prediction windows.

Tree roots cause significant and costly damage to sewer systems. However, the contributions made by tree and sewer characteristics to sewer system damage are not yet well understood. Previous research has proved inconclusive and there is a lack of agreement on the importance of the variables involved, which means that the results cannot be extrapolated globally. This study presents a methodology to improve the understanding of tree root-sewer conflicts through a geostatistical analysis of a tree inventory and CCTV video fault reports. The methodology is applied to a 11 km² case study in Bogotá, Colombia. The analysis used tree and sewer pipe georeferenced data, principal component analysis, and linear, Logit, and Poisson regression models. The proposed methodology identifies the pipe characteristics and the tree species most responsible for the root intrusion events into sewers. As expected, larger trees are prone to cause more pipe damage; therefore tree-pipe distance is a relevant parameter control for reducing potential deterioration. Cumulative precipitation and pipe length showed no effect on observed root intrusion events, and pipes made from brick had higher odds to present root intrusion in contrast with concrete pipes. The presented methodology uses readily available information and software, and can be modified depending on the requirements of each particular case study. This approach will therefore allow for more efficient use of costly, site-specific pipe investigations, and is especially useful for cases where there is a general lack of knowledge regarding the characteristics that favour the negative interactions between pipes and tree roots.

Urban floods are becoming more frequent. Thus, the European Union published the Directive 2007/60/CE to mitigate costs related with this phenomenon and to impose limits on the concentration of pollutants in pluvial water. The purpose of this study was to present the modelling of drainage systems in flood situations, based on the software of the United States Environmental Protection Agency (EPA), the Storm Water Management Model (SWMM), in which the study case is to analyze a unitary urban drainage system of Zona Central catchment in Coimbra, Portugal. By using the Automatic Overland Flow Delineation (AOFD) methodology for the generation of a superficial drainage network and implementing it in the SWMM, it was obtained a dual drainage model that allows the flow analysis in flood conditions, including flow control between the surface and collectors network and also modeling of water quality at the surface. This model allows quantifying the pollution load at the surface, relatively to the parameter of total suspended solids, for any extreme rainfall event.

Water supply networks have to be designed while taking into account the long-term future water demand. Given the difficulties in estimating the future water demand, a flexible approach for planning water supply systems was implemented. The flexibility in engineering design approach (de Neufville and Scholtes, 2011) is a procedure for designing systems in the face of an uncertain future. This approach was applied to a fictitious town, which is expected to expand in the near future and will need to extend its water supply network to the new areas.
To model the uncertain future water demand, 1000 scenarios were generated by randomly projecting the population of the town. A decision rule, which governs when the network must be modified, was defined based on the pressure that is required at the nodes of the network. Four different design alternatives were tested: the deterministic design, which is completely built in advance based on one single scenario, and three flexible designs, which consist in expanding the network as the water demand changes.
The design alternatives were simulated using the hydraulic solver EPANET and were evaluated in terms of costs, using the specific net present value, and in terms of performance based on the pressure and residence time of water in the network. The results showed that the flexible design alternatives provide a significant decrease in the specific costs compared to the deterministic design, thus demonstrating the value of flexible designs. By evaluating the designs over a range of water demand scenarios, different results were obtained compared to the case where only one scenario was used. This showed that the flexibility in engineering design approach is a helpful tool for designing water supply networks in the context of an uncertain future.

The hydraulic behavior and the capacity of sewer systems are assessed through hydrodynamic modeling. They are widely used in drainage planning, although their parameters are mostly estimated and the models are only checked for general plausibility. The Master‘s Thesis assessed the effect of calibration on the model reliability on the basis of a case study, namely Fehraltorf.
Fehraltorf is located in the agglomeration of Zurich and Winterthur and is inhabited by 6’292 people. Most of the village of Russikon and Fehraltorf are connected to the WWTP of Fehraltorf. The DWF amounts up to 66 l/s, whereas the residual flow Q347 of the main receiving water (Luppmen) is 72 l/s. The infiltration of the groundwater is 30.1 l/s, as a significant share of the drainage system was built below the average groundwater table.
During the thesis the conversion of the Mike Urban model to SWMM was revised and further adjustments were implemented. The data for input and calibration was chosen depending on its reliability and further processed if necessary. Rain events were selected and split for calibration and validation. Parameters known to be changed in the calibrations were selected and in the sensitivity analysis the most sensitive, and thus, important parameters were determined. Then, the calibration was conducted manually as well as automatically with simulated annealing. The effect of calibration was assessed for the calibration and validation events visually and with various objective functions, viz. Nash-Sutcliffe Efficiency, total volume, peak flow and time to peak. The hydraulic performance of the sewer system was tested by simulating a 10-year design rainfall event in the uncalibrated and the calibrated model.
For the simulations, the data of the rain gauge on the WWTP had to be discarded because it showed high deviations to the ones installed by EAWAG. The rainfall data and flow data (of the FloDars) collected by EAWAG were assessed to be reliable, but as the measurements were installed recently, only four rain events were available for calibration and validation. The objective functions could be raised to a satisfying level in the manual calibration and were further improved in the automatic calibration. The validation confirmed that the reliability of the model could be increased with calibration. Furthermore, the assessment of the sewer system revealed that there are less capacity limits in the calibrated model then in the uncalibrated one, but further adjustments are needed due to numerical instabilities.
Even though the validation suggests that manual as well as automatic calibration enhance the reliability of the model because of improved goodness of fit assessed by different objective functions, further input data and reliable calibration data is needed to perform the calibration once more.
After all, calibration is strongly recommended by the author of this thesis to not only improve parameter values which were only estimated in the uncalibrated models but also to detect systematic errors in the data and the model structure.

Precise and detailed digital elevation models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM, such as airplane light detection and ranging (lidar) DEMs and point and contour maps, remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of unmanned aerial vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions. In addition, we compared the best-quality UAV DEM to a conventional lidar-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to lidar-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g. buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional lidar-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local, and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.

Digital Elevation Models (DEMs) are used to represent the terrain in applications such as, for example, overland flow modelling or viewshed analysis. DEMs generated from digitising contour lines or obtained by LiDAR or satellite data are now widely available. However, in some cases, the area of study is covered by more than one of the available elevation data sets. In these cases the relevant DEMs may need to be merged. The merged DEM must retain the most accurate elevation information available while generating consistent slopes and aspects. In this paper we present a thorough analysis of three conventional grid-based DEM merging methods that are available in commercial GIS software. These methods are evaluated for their applicability in merging DEMs and, based on evaluation results, a method for improving the merging of grid-based DEMs is proposed. DEMs generated by the proposed method, called MBlend, showed significant improvements when compared to DEMs produced by the three conventional methods in terms of elevation, slope and aspect accuracy, ensuring also smooth elevation transitions between the original DEMs. The results produced by the improved method are highly relevant different applications in terrain analysis, e.g., visibility, or spotting irregularities in landforms and for modelling terrain phenomena, such as overland flow.

iGPI, the National Initiative for Infrastructure Asset Management is a Portuguese collaborative project led by LNEC (National Civil Engineering Laboratory, Portugal) through which 19 water utilities develop their own infrastructure asset management (IAM) systems and plans in a joint training and capacitation programme. Technical assistance to the participating utilities is ensured by LNEC, IST (Technical University of Lisbon) and Addition, a software development company. The water utilities get collective as well as one-on-one support and specific training. They benefit from networking with the other utilities in a common and simultaneous process, with similar difficulties and challenges, leading to an effective sharing of solutions. The developed products, including training materials, templates and guidelines for developing strategic and tactical IAM plans, are available to the general public. This project has greatly contributed to the establishment of reference methodologies and standards for IAM planning, in a range of utilities of widely diverse size and context, effectively defining an accepted best practice. This paper discusses the project's format and its advantages, and goes on to describe the main outcomes, including selected cases and final products.

This review describes and compares statistical failure models for water distribution pipes in a systematic way and from a unified perspective. The way the comparison is structured provides the information needed by scientists and practitioners to choose a suitable failure model for their specific needs.
The models are presented in a novel framework consisting of: 1) Clarification of model assumptions. The models originally formulated in different mathematical forms are all presented as failure rate. This enables to see differences and similarities across the models. Furthermore, we present a new conceptual failure rate that an optimal model would represent and to which the failure rate of each model can be compared. 2) Specification of the detailed data assumptions required for unbiased model calibration covering the structure and completeness of the data. 3) Presentation of the different types of probabilistic predictions available for each model.
Nine different models and their variations or further developments are presented in this review. For every model an overview of its applications published in scientific journals and the available software implementations is provided.
The unified view provides guidance to model selection. Furthermore, the model comparison presented herein enables to identify areas where further research is needed.

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km²) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions.

Modelling rainfall–runoff in urban areas is increasingly applied to support flood risk assessment, particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the catchment area as model input.
While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increases as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data are often unavailable. Modern unmanned aerial vehicles (UAVs) allow one to acquire high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements and obtaining a degree of detail tailored for the purpose of the study.
In this study, we investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and of using this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is proposed and evaluated in a state-of-the-art urban drainage modelling exercise. In a real-life case study (Lucerne, Switzerland), we compare imperviousness maps generated using a fixed-wing consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their overall accuracy, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyse the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and runoff volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet.
We show that imperviousness maps generated from UAV images processed with modern classification methods achieve an accuracy comparable to standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on predicted surface runoff and pipe flows, when traditional workflows are used. We expect that they will have a substantial influence when more detailed modelling approaches are employed to characterize land use and to predict surface runoff. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility of flexibly acquiring up-to-date aerial images at a quality compared with off-the-shelf image products and a competitive price at the same time. We believe that in the future, urban drainage models representing a higher degree of spatial detail will fully benefit from the strengths of UAV imagery.

Vor dem Hintergrund von zunehmender Urbanisierung, Klimawandel und alternder Infrastrukturen gewinnt die Analyse und Bewertung von Überflutungen im urbanen Raum aufgrund von Starkregenereignissen zunehmend an Bedeutung. Dazu sind Modelle erforderlich, die das Niederschlag-Abflussverhalten in einem angemessenen zeitlichen und räumlichen Detail beschreiben. Neben Daten zur Menge und Verteilung des Niederschlags gehen in die Modellierung auch Oberflächeneigenschaften ein, insbesondere der Versiegelungsgrad und die Flächennutzung. In dieser Studie untersuchen wir erstmals die Möglichkeit, hochauflösende Landnutzungskarten städtischer Gebiete aus Drohnen(UAV)-Bildern abzuleiten und die Eignung solcher Karten als Datengrundlage für Modellanwendungen in der Siedlungswasserwirtschaft. Wir zeigen, dass die aus UAV-Bildern (automatisch) abgeleiteten Karten zum Versiegelungsgrad hinsichtlich ihrer Genauigkeit mit Karten auf Basis konventioneller Luftbilder vergleichbar sind. Darüber hinaus zeigt sich in unseren Experimenten, dass sich die Genauigkeit der Versiegelungskarten bedeutend steigern lässt, indem man diskriminative Lernverfahren in Verbindung mit Bildinformation aus einer grösseren Nachbarschaft benutzt.