Journal of Water Resources Planning and Management

Abstract

Expanding sanitary sewer networks is vital for meeting the Sustainable Development Goals (SDGs) for urban areas. Uncertain future design conditions threaten this infrastructure expansion; notably, climate change has been described as the biggest threat to achieving the SDGs. However, the literature on how climate change can affect sanitation is sparse, especially in low- and middle-income countries, where infrastructure is most needed. We created a simple hydraulic model that quantitatively examines how the uncertainty-induced threats related to urbanization, stormwater inflow, and climate change might affect designed sanitary sewers. Our sanitary sewer model allows a designer or operator to estimate how urban development (e.g., population growth, water consumption, and expansion of impervious areas), stormwater infow, or changing rainfall patterns (e.g., climate change scenarios) would affect the performance of a sanitary sewer network. We identified that the fraction of stormwater inflow entering the sanitary sewers is the most significant threat to urban sewers. Applying the model to three case studies in Brazil revealed that the fraction of stormwater inflow in sanitary sewers is at least 1.8 times more influential than the other considered uncertainties. Stormwater inflow collected in the sanitary system leaves the network vulnerable to extreme rainfall events, consequently leaving the design guidelines vulnerable to climate change, which may compromise their ability to achieve the SDGs. Our model quantitatively demonstrates the essential role that minimizing inflow must play in ensuring climate resilient sanitation and in maximizing the utilization of sanitary sewers.

Abstract

Safely managing human waste is critical to the health and well-being of humans, especially in cities. Investments in resilient sanitation infrastructure can generate a significant return on investment in health-related benefits. However, if not accounted for, climate change effects can compromise the ability to provide safe sanitation. While 3.5 billion people still need sanitation infrastructure today, methods of expanding sewer systems amid climate change are unclear. In this paper, we provided a simple model that gives a designer or operator insights into the implications of how various sources of uncertainty (urbanization, stormwater inflow, and climate change) can affect the performance of sanitary sewers. Applying the proposed model to three regional capital cities in Brazil, we demonstrated that the biggest threat to urban sewers, even compared to urbanization and climate change, is the fraction of stormwater entering a sanitary sewer. When building or expanding sanitary sewers, inaccurate estimates of how much stormwater will enter the sanitary system can lead to sewage overflow, which might result in health, environmental, and economic hazards. Minimizing the fraction of stormwater that enters the sanitary system would increase the resilience of the sewer network against extreme rainfall events that would increase due to climate change.

As urbanization has accelerated, the coverage area of water distribution networks (WDNs) has been significantly increasing, making the management of large WDNs more difficult. To manage WDNs more accurately, water utilities split them into district metered areas (DMAs) of different sizes. However, the design of static DMAs has limitations in hydraulic performance during abnormal conditions in a WDN. To address this issue, first, this paper proposes a method for optimal DMA layout based on the combination of the self-organizing map (SOM) algorithm and the Leiden algorithm, achieving the optimal multilevel DMA layout and obtaining boundary pipes between DMAs. Then, the nondominated sorting genetic algorithm III (NSGAIII) method is employed to select the optimal arrangement of valves and flowmeters on boundary pipes from multiple perspectives, controlling the opening and closing of valves to achieve dynamic DMA partition. In the case of L-TOWN, the results show that the employed method reduces the average pressure variance by 15.6% compared with Louvain algorithms in the optimal multilevel DMA, which also effectively reduces bad connections between DMAs. Additionally, the obtained dynamic DMA layout exhibits excellent hydraulic performance. Under the two abnormal conditions, including fire events and increased water demand, the resilience index increased by 42% and 16%, respectively.

Wastewater surveillance has recently emerged as a valuable tool for environmental and public health monitoring. By analyzing the constituents and biomarkers present in wastewater, stakeholders can gather critical information regarding contamination events and disease outbreaks. However, little attention has been given to the crucial question of where to collect water quality samples or place water quality sensors to maximize the usefulness of wastewater surveillance data. To address this gap, this study introduces a novel framework for sensor placement (SP) optimization in sewer networks. The objective of the optimization is to maximize both the observability and reliability of source identification (SI) under different scenarios. To achieve this objective, a machine learning–based SI model was integrated within the SP optimization framework. The SI model features a multilayer perceptron neural network model that was trained to forecast concentrations at various sensor locations, which were then propagated into a genetic algorithm that finds the optimal sensor network design that maximizes SI performance. The capabilities of the SP framework were demonstrated in a case study featuring a real-life, midsize sewer network. The SP framework was applied to multiple scenarios, including optimal design of a sensor network comprising one or more sensors, as well as optimal extension of existing sensor networks. The results showed that a clear trade-off exists between the sensor network’s observability and reliability, highlighting the importance of considering both metrics for SP optimization. Overall, this study offers a practical approach for SP optimization to improve environmental and public health monitoring in a variety of contexts.

Although pressure regulation valves (PRVs) are commonly used for reducing leakage in water distribution networks (WDNs), in recent years, many studies have highlighted the opportunity of using pumps as turbines (PATs) so as to couple pressure reduction (and thus leakage reduction) with hydropower generation. Unlike in water transmission systems however, flow discharge in a WDN varies continuously, consequently requiring real-time control (RTC) of pressure. Nevertheless, RTC of a PAT in a WDN remains an open issue, due to the lack of information and knowledge about PAT operation in a real environment. Whereas previous experimental studies have described PAT installation with a frequency converter in which the power produced gets dissipated through electrical resistance, in the hydraulic regulation layout, impeller speed depends on the electrical grid. Consequently, this paper proposes a comprehensive algorithm to both control the pressure at the control node of a WDN and produce power through a PAT directly connected to the electrical grid. The operation of the prototype was considered under hydraulic regulation, demonstrated by several studies to represent the most effective trade-off between costs and benefits. Further testing by means of laboratory experiments designed to simulate the operation of a grid-connected PAT installed in a WDN effectively validated the robustness of the proposed method. The results proved the ability of the prototype and algorithm to couple hydropower generation with pressure control even when the flow varied suddenly.

The success of water system operation, maintenance, and management (OMM) critically depends on the local workforce. Extreme environmental conditions, limited financial resources, challenging supply chains, and increased technological requirements especially challenge the workforce to equitably and reliably deliver such OMM services in Alaska. To better understand these challenges, this paper presents a metasynthesis of the gray literature regarding water system management in Alaska, with a particular focus on workforce development and OMM regulation. This synthesis was conducted based on qualitatively coding 49 documents that were representative of the full corpus of 183 documents identified on this topic. While prior work tends to focus on a single regulatory level (national or state), this metasynthesis reveals important differences that occur between regulatory levels of government. More specifically, we find federal and state governments focus more on standardization (one size fits all), while regional and local governments focus more on situatedness (tailoring for every circumstance). This may have equity implications for water utilities in Alaska and other Arctic regions where national and state standards drastically differ and overlook local needs. We find that this theme of standardization versus situatedness may generalize to other state water systems, especially those with similar conditions as Alaska (such as Wyoming and Montana), as well as in other sectors in Alaska beyond water (such as environmental management, nursing, and aviation). Given the multilevel governance of water system OMM, these findings suggest that training materials and programs, certification processes, financial support, and policy decisions could be more effective if they consider the local context in which these water systems are situated more, especially when local conditions markedly differ from national norms. Such an approach may help better ensure more reliable and equitable access to safe drinking water in extreme settings such as those in Alaska and in the Arctic, more generally.

The propagation of pressure waves in water pipes is frequency dependent, which leads to these waves experiencing a frequency-dependent wave speed and attenuation, resulting in wave dissipation and dispersion. The effect is much more significant and complex in plastic pipes than in metal pipes, which makes most wave-based pipe condition assessment techniques ineffective for plastic pipes. In this paper, a new technique is developed to calibrate the frequency-dependent wave speed and attenuation for pressurized water pipes. Persistent hydraulic waves induced by a side-discharge valve are used as excitation. Pressure responses are measured using two pressure sensors, and a paired-impulse response function (paired-IRF) is determined through a deconvolution process. The transfer function between the two sensors is determined using the main spike in the paired-IRF trace, which contains the information on the wave propagation characteristics. The frequency-dependent wave speed and attenuation are then derived from the transfer function. The proposed new technique is validated by both numerical simulations and laboratory experiments. Three pipe configurations are considered in the experiments: (1) a high-density polyethylene (HDPE) pipe in the air; (2) an HDPE pipe buried in sand; and (3) a copper pipe in the air. The frequency-dependent wave speed and attenuation are calibrated for all three configurations and the results are distinctive from each other.

Abstract

The present work aims to investigate the effect of pipe roughness on the construction cost of sewer networks in steep terrains. To achieve this objective, a sensitivity analysis was performed, wherein a sewer network was designed with different pipe roughnesses and varying terrain slopes to simulate four distinct steep terrains. In addition, this study considers the effect of new pipes with very high roughnesses that have appeared on the market in recent years, evaluating their influence on sewer network design and construction cost in steep terrains. The sewer networks were designed using an existing methodology for an optimal design that includes drop manholes if required to meet all the hydraulic constraints. The effect of pipe roughness was tested in two sewer networks from distinct sectors of Bogotá, Colombia.

Practical Applications

This study has relevance when considering its ability to lead to the expansion of the sewer service, particularly in hilly regions. The use of higher roughness pipes as a result of this finding has a direct impact on reducing the need for drop manholes, leading to a significant reduction in associated costs. This reduction in costs is of paramount importance in the viability and execution of sewer projects in communities facing significant financial constraints. By minimizing costs, this approach not only optimizes project efficiency but also facilitates access to basic sanitation services in geographic areas previously considered difficult to address. Consequently, this research not only improves sewer systems but also contributes to community development.

Timely pipe break and burst detection and localization are important for proactive management of water distribution systems (WDSs). This paper presents a novel timing-based method for fast localization of hydraulic transient waves that may occur from a pipe break or burst event. The proposed technique deals with the arrival time difference from sensor pairs coupled with a new graph-based transient wave propagation mapping strategy to identify potential link (pipe) candidates for the transient event. The fusion of the wave arrival differences from all combinations of sensor pairs in the network is then used to more comprehensively locate the transient event source among the candidates. This novel integration of topological information avoids missing the correct candidate by identifying all possible solutions, and thus improves the reliability of the localization. Applications to numerical case studies and laboratory experiments show that the method can accurately locate the transient source even with uncertainties from measurement devices and address issues with previous conventional methods concerning detection reliability.

The surface area of Poyang Lake, the largest freshwater lake in China, has decreased substantially in recent decades due to multiple potential factors, including the change in inflow into the lake, flow rate regulation of the Three Gorges Reservoir (TGR), shifts in the Yangtze River stage-discharge relationship, and sand mining in the lake. Here, the daily surface area of Poyang Lake is estimated using Landsat image data and water-level measurements. Multiple regression models are then used to establish the relationship between the surface area and the predictor variables of inflow into the lake, Yangtze River water level, and cumulative sand mining. The contributions of each driving factor to the reduction of Poyang Lake’s surface area are quantified by scenario comparison method. The results reveal that the mean annual surface area of Poyang Lake decreased by $388{\mathrm{km}}^2$ (approximately 15% of the total surface area), from $\mathrm{2,585}{\mathrm{km}}^2$ in 1980–2002 to $\mathrm{2,197}{\mathrm{km}}^2$ in 2003–2016. In terms of overall contribution, sand mining is identified as the most important factor in the surface area reduction, followed by the shift in the Yangtze River stage-discharge relationship and the TGR flow rate regulation. In terms of monthly scale impacts, sand mining reduced the surface area by more than $260{\mathrm{km}}^2$ each month. The shift in the Yangtze River stage-discharge relationship reduced the surface area by $16–156{\mathrm{km}}^2$ during the dry season (November–May) of the Yangtze River and increased the surface area by $33–84{\mathrm{km}}^2$ during the flood season (June–October). The TGR flow rate regulation has the opposite effects: it reduced the surface area by $109–172{\mathrm{km}}^2$ during the impoundment period from September to October and increased it by $19–68{\mathrm{km}}^2$ during the release period from December to June.

Climate change is projected to increase the intensity and frequency of extremes in river basins around the world. Water infrastructure such as reservoirs are often used to buffer against these extremes, enabling a more reliable water supply for human uses like irrigation. Yet this can have negative impacts on the system’s ecological flows. In designing water infrastructure for human adaptation to climate change, it is important to consider whether the infrastructure is mitigating or exacerbating climate change impacts on ecological systems. Prior work has found that dams mitigate long-duration extremes but exacerbate short-duration extremes. In this study, we investigate whether reservoir operations can be designed to also yield beneficial climate adaptation outcomes for short-duration high and low flow extremes while still improving average socioeconomic and ecological objectives compared to uncontrolled conditions. We explore this research question in the Omo River Basin in Ethiopia, where controversy surrounding the socioecological impacts of recent and ongoing dam construction makes this a pressing issue to understand. Using multiobjective optimization of reservoir control rules, we are able to find several policies that can in fact mitigate the impact of climate change on annual maxima and annual 7-day minima. While we do see tradeoffs across reservoir operating policies that best preserve the distribution of these two statistics, we also find compromise policies that mitigate both of these extremes compared to uncontrolled conditions. This shows promise for the role dams can play in climate adaptation to short-duration flow extremes if their operations are designed with multiple objectives in mind.