World Environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

Water distribution systems (WDS) are critical to delivering clean, healthy water at the required quantities and qualities. They are exposed to different kinds of attacks, both physical and cyber, as well as accidental contamination events. In the past years, several events occurred in which contaminants polluted the drinking water. Most of the cases were accidental, but there had also been criminal acts involved. Placing water quality monitoring sensors in designated locations throughout the WDS is considered one of the most effective protection methods for coping with deliberate or occasional water quality contamination intrusions. Sensors, which comprise the water quality monitoring system, communicate over a cyberinfrastructure layer (via radio, cellular, satellite, or wire) and, as such, are exposed to cyber-attacks. Traditionally, sensor placement methodologies included objective functions such as the polluted volume of water consumed or the time for the first detection of possible pollution. However, so far, water quality sensor placement algorithms and tools have not considered cases where the sensor network itself is attacked or malfunctions in a way that part of its components is deactivated. In this study, we propose to develop and demonstrate a methodology for evaluating the impact of a cyber-attack on a sensor network compromising part of its functionality. Our proof-of-concept, using a simple network and straightforward cyber-attack scenario, has revealed potential in analyzing the performance of sensor networks under attack and can provide valuable information for decision-makers in the water utilities and regulators.

Urban drainage networks (UDNs) ensure protection of humans from harmful impacts of nature as well as nature from negative anthropogenic impacts. These networks are under pressure both internally (structural failures) and externally (functional failures) and hence require efficient modelling strategies for their management and maintenance. Most of literature on pipe failure modelling in UDNs focuses on single pipe failure analysis, while there are very few studies that target multiple pipe failure scenarios while considering cascading failure. One reason is that systematically evaluating different combinations of multiple pipe failure using conventional hydrodynamic modelling methods is computationally very expensive. Considering this research gap, a novel graph-based method is presented in this study for evaluating multiple pipe failure scenarios in UDNs. The presented method evaluates the overall flooding impact on the system when different combinations of pipes fail together. The number of possible combinations increases tremendously with increasing number of pipes failing. Graph-based method is used to find the most critical failure combinations. The results show that the proposed graph-based methodology overestimates flooding volumes as compared to hydrodynamic modelling results but can still be used reliably to recognize the critical combinations. In terms of computational time, the graph-based methodology is much more efficient as it is a non-iterative method and hence a lot of pipe failure combinations can be evaluated in a reasonable time. Furthermore, a hybrid graph and hydrodynamic approach is also discussed, combining the accuracy of hydrodynamic modelling with efficient computational time of graph method.

Non-powered dams (NPDs) are dams that do not include hydraulic turbine (hydropower) equipment. Currently, there are more than 80,000 such dams in the United States, which provide a variety of non-energy benefits, including flood control, water supply, navigation, and recreation. Approximately 500 of these NPDs are identified as having the potential to add hydropower generation (totaling up to a capacity of more than 8,200 MW). A large share of investment costs and environmental impacts of dam construction have already been incurred at these NPDs. Hence, adding power to the existing dam structure is hypothesized to be achieved at a lower cost, with less risk, and a shorter timeframe than the development required for new dam construction. The abundance of NPDs, the associated environmental favorability, and cost advantages, combined with the reliability, predictability, and dispatchability of hydropower, make NPDs a strong candidate in the nation’s renewable energy portfolio. To assess the NPD to hydropower conversion potential, in this study, we developed a GIS-based multi-criterial decision analysis tool, which allows users to rank these NPDs based on the grid, community, industry, and environmental impacts (i.e., GCIE impacts). This web-based interactive tool (developed using open-source Python and JavaScript) lets the user choose from a wide range of features to define each of the GCIE impact scores through a user-friendly graphical user interface. These features are related to dam operation, hydropower generation opportunity, power market economy, social vulnerability and risk, proximity to critical infrastructure and energy generating facilities, environmental concerns (air, water, and critical habitat), and exposure to natural hazards. The overall priority score of NPDs is calculated based on user-defined weights for each of the GCIE impact scores. Besides ranking NPDs, the tool can also be used to estimate the energy-storage feasibility (battery, hydrogen, and pump-storage hydropower) at each of the potential sites.

Globally, landslides are one of the most prominent natural catastrophes. Extreme precipitation, such as cloudbursts, is a primary factor causing a landslide in the Indian Himalayan region. By implementing the best engineering measures, identifying potential locations vulnerable to landslides using hazard zonation mapping may play an important role in reducing their occurrences by implementing appropriate engineering practices. This study uses satellite images and a geographic information system (GIS) to prepare a landslide hazard zonation map for the Alaknanda River (AR) basin in Uttarakhand (India). The river basin elevation ranges between 600 and 7,800 m above mean sea level with a mean slope of 31.5 degrees. This river basin has experienced several cloudbursts and flash flood events in the past that initiated landslides causing significant loss of life and property. This study uses the analytical hierarchy process (AHP) to assess the basin’s susceptibility to landslides, rock falls, and mudslides. The contributing factors considered in this study include elevation, slope, aspect, curvature, proximity to rivers and lineament, soil, geomorphology, land use, land cover, and rainfall. The past-affected locations are identified and validated from the landslide inventory of Uttarakhand. The results are classified into five qualitative tiers: very high, high, moderate, low, and very low hazard zones. It is observed that about 8.1% of the AR basin area comprising 65 villages is in a very high hazard zone, while 14.7% area consisting of 241 villages is in a high hazard zone, followed by 26.3% area comprising 350 villages, 32.2% area covering 468 villages, and 18.6% areas surrounding 169 villages lying in the moderate, low, and very low hazard zones, respectively. It is hoped that such mapping will help decision-makers in risk management, disaster response, and effective emergency services planning.

The largest source of uncertainty in a flow frequency analysis often comes from not having enough data. Reducing the uncertainty in the estimated annual exceedance probability of a flood requires incorporation of as much information into the flow frequency analysis as reasonably practical. Opportunities to reduce uncertainty include spatial (trading space for time), temporal (historical information), and causal (rainfall-runoff modeling) information expansion. The full spectrum of information includes at-site systematic and historical data, regional skew information, estimates of flood quantiles derived from a regional regression analysis, and estimates of flood quantiles derived from a rainfall-runoff model using regional precipitation-frequency. Bulletin 17C can accommodate much of the available information, but the current procedures cannot seamlessly combine all types of information into a single flow frequency analysis. These limitations can be overcome by adopting a Bayesian analysis framework. In collaboration with the Engineer Research and Development Center (ERDC), the US Army Corps of Engineers Dam and Levee Safety Programs have adopted the Bayesian framework and developed supporting software. The Bayesian framework effortlessly combines all types of information in a single and more formal probabilistic model. Bayesian estimation methods have less bias and greater efficiency than the traditional method of moments. This leads to greater confidence in flow frequency curves. Bayesian estimation methods provide more flexibility by supporting additional distributions other than log-Pearson type III, such as the generalized extreme value and generalized Pareto distributions. This allows a hydrologist to adapt their analysis to accommodate unique situations. Complementing a flow frequency analysis with all the available temporal, spatial, and causal information is the gold standard. Adopting a Bayesian framework provides a pathway to achieve the recommendations in Bulletin 17C and advance the state of practice in flow frequency analysis.

Within the city of Los Angeles, the Hyperion Water Reclamation Plant (HWRP), operated by LA Sanitation & Environment (LASAN) with an average dry weather capacity of 450 mg/day, has gone through upgrades and phases to protect the environment through sustainable practices while meeting stringent permit regulations. Separately from the proper primary and secondary water treatment, HWRP is currently applying an advanced system with the goal to be 100% recycled water by 2035. One such upgrade is the new Headworks odor control biotrickling filter (BTF) system. This upgrade will replace the existing five (5) chemical wet scrubbers, three (3) carbon towers, and two (2) chemical storage tanks. In its place, four (4) BTFs and eight (8) carbon scrubber tanks have been designed to remove hydrogen sulfide (H₂S) from the influent and Headworks foul air stream. Starting on December 21, 2021, the first BTF unit was put in operation with the other three BTF units following shortly thereafter. Results have been outstanding with an average of over 99% H₂S removal occurring in the BTF process and design air flow rate of 66,000 scfm. Currently, temporary carbon towers are being used until the permanent carbon towers can be placed and put into operation. It is estimated that sometime in Q1 of 2023 that the permanent carbon towers will be installed and in operation. The performance data and operations and maintenance (O&M) challenges at HWRP will be discussed, and the total project cost is just over $14 million. The performance data, air emissions measures, O&M challenges, and future BTF advancements will be presented for LASAN’s new technology BTF odor control systems.

In recent years the use of sulfonamides (synthetic antibiotics) has expanded largely, to the point where large quantities of the drug reach the environment through human and animal excretions and wastewater. Most of the drug is released into the environment in its original form, which remains for long periods causing bioaccumulation and posing a threat to environmental pollution. It is very important to use improved soil components in the stormwater management facilities that can promote pollutant trapping. This study aims to evaluate the adsorption efficiency of sulfamethazine using wood-derived biochar. The wood biochar used in this study was from Wakefield Biochar. The sulfonamide used in this study was sulfamethazine, commonly used in animal husbandry. The solution was added into vials containing biochar, and filtrated samples were analyzed for sulfamethazine to evaluate the immobilization of this substance. Variations in concentrations, time, and pH were used to evaluate the adsorption characteristics.

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018–2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices.

Physical flow models are used to study parameters affecting the erodibility of rock in dam unlined spillways. Among the existing physical models, the parameters of joint orientation and of protrusion are the most studied. However, other rock mass parameters have an important contribution in the erosion mechanism, but they were given less attention in existing research, or when studied, mixed results were obtained. In addition, most of the models simulate rock mass considering a single block, which cannot simulate possible interactions between blocks. To address this problem, a scale model that simulates an existing spillway was built in a UQAC laboratory. This model is a 1:40 scale of the existing spillway and reproduces hydraulic conditions of the prototype. The physical model makes it possible to vary several rock mass parameters, such as joint orientation, joint opening, protrusion, block volume, as well as joint roughness, to evaluate their impact in the rock erosion process.

Industrial wastewater often contains considerable amounts of toxic pollutants that would endanger public health and the environment. In developing countries, these toxins are often discharged into natural ecosystems without pretreatment as it requires costly treatment processes, which causes long-term harmful socioeconomic impacts. Employing wastewater treatment plants using physical, biological, and chemical methods to clean the wastewater is considered by many nations the answer to the environmental crises. The treated water could be used for targeting the irrigation systems in its majority, as it is biologically acceptable for that specific use, which economizes the use of freshwater sources for municipal use specifically. This study presents a novel method for fabricating an efficient adsorbent sheet for wastewater treatment. The sheets are fabricated by combining sugarcane bagasse pulp as a scaffold with commercial, naturally activated carbon and bimetallic-prepared adsorbents. Fava beans and algae biomass are utilized in the production of activated carbon because of their high carbon contents, availability, and low cost. The prepared composite sheets are synthesized and investigated for several pollutants’ removal such as methyl orange, crystal violet dyes, and chromium heavy metals. These pollutants are selected due to the high discharge amount and toxic effect on aquatic life. FT-IR and SEM analyses are used to characterize the samples. To determine the mechanism of adsorption, the intra-particle diffuse, pseudo-first-order, and pseudo-second-order kinetic models are used to test the experimental data. All the prepared sheets can retain the pollutants, with the best removal efficiency of 96.24% for methyl orange adsorption onto the bio-composite mixed sheet. For methyl orange, the error values and correlation coefficient R² of 0.971 and 0.951 shows that the Temkin isotherm and pseudo-first-order kinetic model, respectively, are capable of providing the highest goodness of fit for the experimental data. The results of the isotherms and kinetics parameter sets provided valuable proof that the adsorption of methyl orange onto the bio-composite sheet is an endothermic phenomenon involving both chemical and physical adsorption.

Pastures with grazing livestock and further agricultural production can have unwanted impacts on water bodies. Livestock can cause erosion to embankments and sediment transport into surface water and cause contamination of nutrients to the water through fecal matter and urine. Fertility practices for maintaining high-quality pastures could potentially runoff into water sources causing an increase in nutrients, disrupting the dissolved oxygen concentration levels of the water. Catalpa Creek in Oktibbeha County, Mississippi, is not exempt from these potential harmful impacts caused by agriculture practices in bordering pastures. Water quality standards defined by the Mississippi Department of Environmental Quality relate that dissolved oxygen concentrations (DO) shall be maintained at a daily average of not less than 5.0 mg/L; and the normal pH of water shall be between 6.0 and 9.0. In order to identify temporal and spatial variability of water quality in a tributary of the Catalpa Creek receiving runoff from a livestock production field, biweekly stream water samples and water quality measurements are collected. The measurements include DO, temperature, pH, turbidity, salinity, total dissolved solids, specific conductivity, velocity of flow, width of water column, and depth of water column. Samples collected from the creek were taken to the lab where they were analyzed for total suspended solids, total nitrogen, and total phosphorous concentrations. These samples were collected and analyzed from August 2021 to August 2022. Results from the study suggest that additional level of protection is needed to guarantee the accomplishment of the designated use of the water body, which is fish and wildlife, and the standard for water quality related by the environmental authority in Mississippi.

Electrical resistivity survey has proven to be an effective tool for groundwater exploration and has been widely embraced to help reduce the drilling of unsuccessful wells. Currently, information from electrical resistivity survey is often used in locating points for drilling, but it does not give indication of the yield of the borehole. The lack of this information therefore sometimes results in the drilling of dry and marginal wells. This study therefore looks at the possibility of using resistivity data, which is readily available from electrical resistivity surveys for groundwater exploration, for estimating the yield of yet-to-be-drilled borehole. The study was limited to the Garu-Tempane and Bawku West Districts. Secondary data on vertical electrical sounding (VES) and drill logs for 49 boreholes in the selected districts were used. The thicknesses, apparent resistivities, longitudinal conductance, and transverse resistance of the various subsurface layers of the boreholes were determined from drill logs and VES data. Correlations between borehole yields and the third-layer apparent resistivity, longitudinal conductance, and transverse resistance were then investigated to develop regression models for estimation of the borehole yields. The results showed that the third layer is fractured and contributes significantly to borehole yields in the area; hence, the fractured subsurface layer is of primary interest to be considered in groundwater exploration and estimating potential borehole yield from VES data. The results obtained further indicated that apparent resistivity, longitudinal conductance, and transverse resistance had good exponential and positive linear relationships with borehole yield.

Of all the natural resources available to humankind, water holds a prominent place, particularly because of its importance for human livelihood. Savelugu District in northern Ghana is characterized by unpredictable rainfall patterns with periodic and perennial water shortages. The distance people travel to fetch water and the person-hours spent in search for water affect productivity, economic livelihood, and health and education benefits. Provision of potable water supply to these communities is expected to bring not only health and education benefits but also increase in sanitation and hygiene practices. Static water levels (SWLs) of 19 wells in the study area were collected, analyzed, and compared to the initial SWLs measured when the wells were immediately drilled and constructed. The SWL data was subjected to paired samples T-test (with α = 0.05). From the results, there was significant difference in the SWL immediately after drilling and construction (µ = 12.15, σ = 7.50) and SWL after at least 10 years (µ = 17.81, σ = 10.29); t (18) = –3.7, P = 0.002. Lowered groundwater levels were recorded in all wells measured. This can lead to drying up of some of the wells whose difference between the current SWL and well depth is close. There must be strong advocacy, development, and implementation of IWRM plans to help address the problem of inadequate WASH in the study area.

Open loop geothermal energy systems (GES) extract groundwater through an extraction well, pass the water through a heat exchanger to remove or add heat, then reinject the cooled or heated water into the aquifer. Because the reinjected water has a different temperature than the ambient groundwater, reinjection creates a thermal plume that travels downgradient and can be harmful to downstream water users or ecosystems. The heat from the thermal plume dissipates as the plume travels through the aquifer, so after sufficiently long distances have been traveled, the water has returned to the ambient temperature of the aquifer. Thus, the sites chosen for GES should be located sufficiently far from temperature-sensitive ecosystems. We propose the adjoint method for identifying allowable locations of GES. With the adjoint method, information is propagated upgradient from the location of the ecosystem, such that only one adjoint simulation is required to evaluate the appropriateness of any location as a site for GES. The application of the adjoint method in this context requires the assumption that the water extraction and reinjection have a minimal influence on the flow field. In this work, the errors introduced by that assumption are evaluated, and a correction is developed to reduce the errors introduced by that assumption. The approach is applied and tested for a field site in Finland, where the ambient groundwater velocity is very low and therefore the extraction and reinjection of water at the GES substantially alters the flow field.

We strongly recommend EWRI members visit the London Water & Steam Museum. This presentation includes many slides of this outstanding museum near the Kew tube station. London Museum of Water & Steam features artefacts and interactive exhibits on water. On October 26, 2018, David Gilbert and Jerry Rogers toured the London Museum of Water & Steam, located at Green Dragon Lane, Brentford, London, TW8 0EN near Kew station. Kew Bridge Pumping Station was originally opened in 1838 by the Grand Junction Waterworks Company. In 1999, the United Kingdom government’s Department for Culture, Media, and Sport described Kew Bridge Pumping Station as “the most important historic site of the water supply industry in Britain.” The heart of the museum showcases a majestic collection of steam pumping engines, including engines from Corynwall, as well as rotative engines. There are many excellent London water supply and treatment exhibits also. Due to COVID-19, the planned International Historic Civil Engineering Landmark plaque ceremony of July 26, 2020, for the 200-year-old Union Chain Suspension Bridge at Berwick-upon-Tweed was cancelled. Note the book: Samuel Brown and Union Chain Bridge: Gordon Miller, Friends of the Union Bridge, 306 pp, 135 photographs, 15.5 GBP. A tour of the Paxton Estate (Paxton Trust), a historic house at Paxton, Berwickshire, was planned to be a part of the plaque ceremony. There is discussion of having a modified plaque ceremony in the spring of 2023 (specific date to be determined) possibly before the May 2023 EWRI Congress in Henderson, Nevada.

In the literature of the history of the arid American West, names of prominent figures like Frederick Haynes Newell, Arthur De Wint Foote, Hiram Chittenden, William Hammond Hall, and Elwood Mead often appear. These men—engineers all—have rightly been the focus of much research and analysis for the impact that their work had on the development of the region’s water resources. Less known, but of comparable influence, is Edwin Shelton Nettleton: Chief Engineer on the early irrigation works of Greeley Colony, leading Consulting Engineer, Colorado State Engineer, and Federal investigator on questions of water use in the West. The most extensive previous treatment of Nettleton is a brief biography in Water: Colorado’s Real Gold by Richard Stenzel and Tom Cech. This paper follows the course of Nettleton’s life with particular emphasis on the most influential aspects of his engineering career.

Successful calibration of model components is vital for numerical hydraulic analysis. However, model calibration is a challenging task, especially when there are hydraulic structures within the simulation domain. This paper presents two such HEC-RAS modeling case studies on the Kissimmee River Restoration Project. The first case study discusses the modeling of S65A complex focusing on the backwater effects due to restoration of the canal and structure systems. The 2D model covers about 10-mile reach of the C-38 Canal system including the flood plain and hydraulic control structures (spillway, culverts, ship lock, weirs, levees) in South Florida. Accurate estimation of the discharge capacity of each hydraulic structure is critical for the model calibration. However, the presence of several hydraulic water control structures in the simulation domain complicates the process owing to potentially high number of calibration combinations of structure discharge parameters and sub-domain roughness coefficients in the system. Instead of the tedious trial-and-error approach, this paper applied CFD modeling to directly determine the discharge coefficients for stage-discharge relationships embedded in 2D HEC-RAS model. The second case study illustrates the use of CFD to improve the calibration of 2D HEC-RAS model for emergency modeling of S69 Weir and adjacent hydraulic features. The approach presented herein provides the technical advantage of obtaining CFD-based discharge coefficients for water control structures and bypass the traditional calibration process of the structure’s conveyance capacity. Subsequently, the 2D hydraulic model calibration can be accomplished focusing on calibration of the roughness of the flood plain and canal system.

Flood mitigation and planning is one of the primary objectives for the US Army Corps of Engineers’ (USACE) Civil Works Mission. Toward meeting the needs of the flood control mission, USACE relies on HEC-RAS for inland flood inundation mapping for standard or routine events. Recently, there has been an interest in the investigation of HEC-RAS to perform more complex hydrodynamic simulations such as those that occur in estuaries and bays. Hydraulic modeling of geophysical flows in estuaries and bays is complex. This complexity is caused not only by the complex bathymetry but also the interwoven flow pathways that are common in tidal wet- and marshlands. These complexities are not easy to translate to inland hydraulic simulation codes such as HEC-RAS; therefore, there is a need to investigate and document the ability of HEC-RAS to accurately, efficiently, and robustly simulate these complex flows. The behavior of estuaries and bays is often governed by the type of tides they experience. For example, the tides on the Gulf Coast are micro-tidal, the East Coast is meso tidal, and the West Coast experiences meso to macro tides. To have confidence that a numerical model can successfully simulate these tidal conditions and associated wetland hydrodynamic processes requires the application of the model to all tidal ranges. This study will apply the release version of HEC-RAS version 5.0.7 to the simulation of tidal movements in San Francisco Bay, California, and will seek to show what HEC-RAS is capable of in a large bay and what deficiencies it has by comparing simulated data with observed data from the NOAA Ports database.

To keep the port of Port Arthur operational, a maintenance dredging is required every six months to remove sediment deposition caused by tidal, river flows, and currents, or bank erosion from the nearby due to wake wash. The objective of this study is to investigate the cause of the sediment deposition to better manage the dredging events at the port of Port Arthur, Texas. The historical bathymetrical data of the port is analyzed using geographic information system (GIS) tools to demonstrate that the sediment deposition in the waterway is mainly caused by hydraulic condition changes, for example, flooding or drought, although wake wash erosion occurs along the eastern bank. A one-dimensional unsteady hydrodynamic and sediment transport model using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) is developed using the available bathymetry data, flow conditions, and dredging data during Hurricane Harvey. The advection and dispersion mass transport equation are applied to calculate the total sediment load, which accounts for both cohesive and noncohesive sediment transport processes. The results show that the total sediment deposition is fairly close to the field sediment estimated with the bathymetrical data. The sensitivity analysis indicates that sediment grain size distribution is the key parameter for better model prediction. In addition, both dispersed particle setting velocity and floc settling velocity are applied depending on the sediment concentration. In conclusion, the HEC-RAS model can be a useful tool to assess the sediment deposition in the waterway to better manage future dredging events.

Due to its robustness, versatility, and open-source, EPA’s Storm Water Management Model (SWMM) has become one of the most popular applications used for urban hydraulics analysis nowadays. One reason that explains the model popularity is the ability to predict flow in complex stormwater systems with reasonable accuracy in realistic inflow conditions. However, in highly dynamic conditions associated with extreme inflows, recent research indicated that SWMM accuracy can be improved with spatial accuracy and smaller time steps. Yet, even among approaches to representing stormwater systems using spatial discretization, there are alternative approaches regarding pressurization algorithms that are not fully understood. The aim of this work is to evaluate how modeled results of piezometric head and flow hydrograph in a stormwater tunnel are affected by varying discretization and pressurization algorithms in SWMM. Comparisons with a combination of three different spatial discretization (link-node and two different spatial discretizations), three different algorithms to solve pressurized flow (EXTRAN, original SLOT, and a custom SLOT with pressure wave celerity of 250 m/s), and 10 different time step discretizations with Hydraulic Analysis for Sewer and Tunnels (HAST), a model specifically designed to simulate surges in tunnels, are presented. These alternative solutions are placed on a real-world stormwater tunnel subjected to a rain event. SWMM’s outputs are evaluated using flow continuity errors and Nash-Sutcliffe normalized statistic. Results show that in relation to the Richmond Tunnel, the DxD 10 spatial discretization proved to be the most reliable, especially when combined with the SWMM standard Preissmann slot algorithm and a time step factor ranging from 0.01 to 1 from the time step calculated using Courant number. The EXTRAN algorithm only showed good results when combined with DxD 10 spatial discretization and time step factor of 1. The DxD 05 discretization presented acceptable results when used with SWMM standard Preissmann slot algorithm and time step factor of 0.01 and 0.02. Link-node setups did not show satisfactory results. Furthermore, this study demonstrates that ensuring small flow continuity errors is not enough to achieve representative flow rates using SWMM, highlighting that another parameter should be taken into account to evaluate numerical oscillation.

The studies on dam breaches due to overtopping have recently increased as breaches have had devastating impacts on urban and rural communities. The ability to predict and understand the breaching process is critical for flood-damage mitigation, emergency planning, and evacuation. Experiments on dam breaching due to overtopping were run on non-cohesive model embankments of heights varying between 10 cm and 45 cm. Experiments were performed in a 1-m-wide and 1.2-m-high flume in the Hydraulics Laboratory at the University of South Carolina. The dams were built in 5-cm lifts with equal compaction. Slopes equal to 3H:1V were cut to create the final dam shape. A pump provided a constant discharge, and an overflow weir was used to maintain a constant water level in the upstream reservoir. A rectangular pilot channel of 2.5 cm in width and 1.9 cm in depth was used to initiate the breach over the center of the dam. Preliminary results show that failure timing depends on dam height.

The study develops the framework for simulating rainfall-runoff in an ungauged watershed using satellite precipitation products and develops a floodplain map. The combined effects of urbanization and climate change have led to an increase in flooding incidents during the past decades. For policymakers and system managers to make the watershed resilient during extreme flooding events, where even minor creeks are generating urban flooding and whose watershed has no meteorological station, the precise prediction of the flood-prone zone is essential. Rainfall and runoff data sets are essential for hydrologic and hydraulic analysis to develop flood-prone zones. This work applies satellite precipitation products that are extracted with the aid of Python code in a hydrologic model to develop runoff and makes use of generated runoff in a hydraulic model to develop a flood-prone zone in an ungauged watershed. To simulate and model interactions between rainfall, runoff, and the extent of the flood zone in the proposed study region, the Hydrologic Engineering Center-River Analysis System (HEC-RAS) and Hydrologic Modeling System (HEC-HMS) models are used. The first phase involves setting up and calibrating the HEC-HMS model using a satellite precipitation product. In the second phase, three simulation types are run in the calibrated HEC-HMS model with 25, 50, and 100 years of rainfall return periods to produce the hydrograph during the corresponding precipitation occurrences. Finally, a 1-D HEC-RAS model using the obtained peak hydrograph is used to create a flood inundation map for the relevant flooding episodes. The created flood map shows the impacted area during various return periods of flooding occurrences as well as a quantified depiction of inundation extent percentage (IE%) by comparing the values of the specified return periods.

It is important to study the bed evolution in artificial channel confluence as flooding in them causes morphological changes. In this study, a detailed three-dimensional (3D) numerical simulation is conducted in a right-angled channel confluence to investigate the complex fluid-sediment interaction. Important features of the bed like scour hole, bank-attached bar, and flow separation zone in the confluence hydrodynamic zone (CHZ) are investigated for their evolutions by using the CFD software FLOW 3D. Governing equations used are 3D Reynolds-averaged Navier-Stokes (RANS) equations along with the continuity equation for water flow, K-ε model for turbulence closure, volume of fluid (VOF) equation for free surface tracking, and Meyer-Peter-Muller (MPM) equation for bed-load transport rate. Simulated results for water surface profiles, velocity profiles, and bed profiles at different time periods are obtained for steady flows at inlets. Effects of bed materials on the scour depth indicate that as the grain size increases, the maximum scour depth downstream of the confluence decreases. However, there is no effect on the location of the maximum scour depth. Similar trends are observed in the simulations for the bank-attached bar. The finding of the present study may be used in the design of artificial channel confluences.

The present study discusses and analyses the flow resistance parameters and stage-discharge relationships in compound channels with different bed roughnesses. The shear forces acting on the channel beds and walls develop an analytical solution of depth-averaged velocity. In addition, an expression for non-dimensional coefficient, integration constants, and analytical solution to compute the composite roughness are also derived. Experiments were conducted in compound channels. The model was applied to compute composite roughness from the present experimental study and two other experimental data in the literature. The key parameters friction factor, composite roughness, roughness height, non-dimensional coefficient, and secondary flow term are discussed in the model application to different data sets. The results indicate that the composite roughness and friction factor are the key flow resistance parameters that can influence the depth-averaged velocity and boundary shear stress distributions. The result shows that, in a rough bed, the boundary shear stress in the floodplain was significantly higher in a heterogeneous bed as compared to a smooth bed. The non-dimensional coefficient and secondary flow term for rough bed are high at the interfaces between the main channel and floodplains due to velocity differences and developed shear layers between the main channel and floodplain. The mean relative errors of the present model and existing models were also discussed. It is concluded that the present analytical solution gives a good prediction of the flow resistance parameters, stage-discharge relationships, depth-averaged velocity, and boundary shear stress distributions when compared with experimental data with different bed roughnesses.

The Pakke River is a mountainous river that flows through the Pakke Kesang District of Arunachal Pradesh, India, and is a tributary of the mighty Brahmaputra River. During the monsoon season, it transports a huge amount of sediments. In addition, the rising of the riverbed due to the deposition of sediment in the main channel causes overflooding and meandering of the river. This meandering of the Pakke River toward the left bank has flooded and damaged several agricultural lands, rural settlements, and nearby areas of Seijosa town. HEC-RAS 6.3 is used to simulate one-dimensional hydrodynamic flood modeling to estimate discharge, velocities, and other important parameters for the return periods of 25 years, 50 years, and 100 years. Identification of the vulnerable areas by generating flood inundation maps of the study location is another objective of this study. This study will be useful in determining appropriate preventive and mitigation measures in the region. Being situated in a remote location in northeast India there are hardly any studies available of this kind for this river.

Embankment failures can result in considerable property damage and fatalities. The most common cause of embankment failure is overtopping due to high water level in the river. The embankment failure process is complex because of rapid changes in flow and breach geometry. Accurate predictions of breach evolution help improving emergency preparedness and evacuation plans. In this work, a two-dimensional numerical model is developed and applied to simulate a real-life dam failure by overtopping during the historic October 2015 flood in South Carolina. The depth-averaged fluid mass and momentum conservation equations, known as shallow water equations, along with the sediment mass conservation equation are solved numerically. A unique feature of the model is the ability to simulate discrete slumping of breach sidewalls. The model captures the failure stages and estimates breach characteristics including breach dimensions and the breach hydrograph during the unsteady and steady phases of the failure. The comparison between model results and field observation shows a satisfactory agreement between the observed and simulated final breach-top width as well as the maximum eroded breach depth.

Shallow water equations (SWE) are the governing equations for the open channel flow. The numerical solution is widely considered the most effective approach for solving the SWE in the past few decades. However, numerical solutions are inefficient and need to compromise many aspects, such as order of scheme accuracy, Courant numbers, boundness, etc. In recent years, deep learning (DL) has been one of the rapidly rising techniques that have been widely used in the engineering field. DL models can bridge approximation relations between input and output variables by conducting multiple elementary operations constructed by artificial neural networks. Many researchers achieved success in hydrology and hydraulic problems by using DL models. However, there are still some drawbacks to the previous DL models. These DL models are often purely empirical and not constrained by real physics, which may cause a larger prediction error when test conditions are not included in the training data set. Besides, training this model requires big data, which is mostly expensive in hydrology and hydraulic problems. In this paper, we will introduce a novel and data-free neural network framework that can solve the SWE. The architecture of the framework will be demonstrated in detail, and the framework can be applied to any SWE problems. Additionally, we employed a numerical solver, HEC-RAS, as reference to verify the solution accuracy. As a result, this framework shows great agreement with numerical solutions.

Flow through a small pond outlet structure was modeled using the 3D volume-of-fluid method. Turbulent effects were included using the K omega SST model, and rough wall functions were used to model the regions near the solid-fluid interface. The purpose of the model was to develop a stage-discharge curve for the structure. The outlet structure includes a trash rack with horizontal bars, the hydraulic effects of which traditional 1D or 2D models cannot account for. The geometry of the structure was determined using a combination of build plans, LiDAR data, and field measurements. The results of the model were compared to the stage-discharge curve calculated using weir equations. Modeling of the flow was performed using the OpenFOAM InterFoam solver. The study demonstrates a practical method to determine stage-discharge curves for hydraulic structures, including geometry generation and meshing.

Climate change is a crucial global issue that exacerbates water stress which directly and extensively impacts society, and the environment, including extreme climate events (droughts and floods). The purpose of this study is to assess the rainfall variability under climate change using observed rainfall data over five states of south India (Karnataka, Andhra Pradesh, Tamil Nadu, Telangana, and Kerala). These south Indian states always got attention due to water challenges such as water scarcity, water conflict, and natural disasters (floods and droughts). The major part of south India receives its annual rainfall from the southwestern monsoon that falls between June and September. In this study, interquartile range (IQR) and precipitation concentration index (PCI) were applied to evaluate the rainfall variability. Additionally, the standardization precipitation index (SPI), a meteorological drought indicator, was presented to estimate the drought characteristics. Annual rainfall variability and trend were estimated using Mann-Kendall and Sen’s slope. The results show that 28.5% of stations exhibit extreme drought characteristics, whereas most of the stations experience mild drought and wet events. Moreover, drought magnitudes and intensity were observed much higher in the southern part of south India. Furthermore, it was found that most of the southern states exhibit a monotonic trend of rainfall.

Forecasting the El Niño-Southern Oscillation (ENSO) is one of the most challenging tasks because ENSO events show large differences in their amplitude, temporal evolution, and spatial pattern. This research discusses a deep neural network model that integrates convolutional short-term and long-term memory (ConvLSTM) to forecast the patterns of sea surface temperature that represent the evolution of ENSO over different time horizons with simultaneous space-time sequences in the Niño 3.4 and Niño 1+2 regions. Monthly gridded sea surface temperature and anomaly index data were used in El Niño regions for the period 1854–2021. In general, the methodology includes the sea surface temperature (SST) normalization of the tropical Pacific, the creation and selection of the architecture of the deep neural network model, and the training and validation. Furthermore, the forecast and evaluation of the SST and SST anomalies in space-time with the El Niño of 1982/1983 and 1997/1998 and with different time horizons for the test stage were developed. The results show a good model performance with ENSO data for El Niño 1982/1983 (training) and El Niño 1997/1998 (validation) in space and time, and with climatic indices for regions 3.4 and 1+2. The model performance was evaluated using statistical metrics of observed and forecasted data over six months in the equatorial Pacific Ocean (latitude 10°S–10°N and longitude 140°E–80°W). In general, all the metrics used to evaluate the accuracy and forecast of the model show a range from good to excellent for both El Niño regions in the six months of forecast.

Climate change and its adverse environmental impacts are major concerns for Small Island Developing States (SIDS), such as St. Lucia. In particular, the annual atmospheric temperature and rainfall continue to receive a great deal of attention in the Caribbean and worldwide. Subsequently, looking at the spatiotemporal elements of meteorological factors with regard to the evolving environment, especially in countries where tourism and agriculture activities are dominating, is essential to evaluate climate-prompted changes and propose feasible adaptation techniques. The present study attempts to examine and provide findings on the long-term changes and fluctuation in rainfall and atmospheric temperature over the island of St. Lucia using data for the last three decades (1990–2020). The problem was analyzed using soft computing technique gene expression programming and a nonparametric statistical trend analysis technique of Mann Kendall (MK) and Sen’s slope tests. The detailed analysis indicated that there was no significant trend of rainfall on the island over the last 30 years. Additionally, the GEP analysis indicated that during the next 10 years the country will experience drought-like instances of low rainfall volumes. Further analysis showed that the island’s mean temperature will increase at a rate of 0.032°C per year. Subsequently, due to the island becoming warmer with little to no increase in rainfall, the island would require in-depth planning in the area of energy consumption as well as mitigation measures due to the potential impact of increased storm activities that can be correlated with these climatic variabilities.

Located in the southern tip of Lake Michigan, northwest Indiana has many large industries with busy I-80/94 interstate corridor. The historic dunes national park is located in this region. Lake, Porter, and LaPorte counties are in the northwest Indiana, and most of the watersheds drain into Lake Michigan. Long-term trends in precipitation-based climate indices were performed using Mann Kendall test as well as by clustering the regional data using Self-Organizing Maps (SOM) by considering 113 years of historic daily precipitation data. Trends in maximum one-day precipitation (RX1), maximum five-day precipitation (RX5), consecutive dry days (CDD), and consecutive wet days (CWD) were examined for this region along with monthly trends. Further, relative entropy (RE) and seasonality ratio (SR) were also examined for the changes in the distribution of precipitation. By definition, RE explains the uniformity of precipitation in an annual scale. If it is close to zero, the precipitation is well distributed throughout the year. If it is close to 8.515, then single event contributes to the entire annual sum. Regional behavior was analyzed by performing data clusters using SOM. An insight about these changing trends was very crucial in water resources and water quality management and mitigating floods. This comprehensive study indicates statistically significant increasing annual rainfall trends in all the three counties of this region. Precipitation in April, July, and August months showed statistically significant increasing trends in Lake and Porter counties. While RX1 and RX5 did not show any significant trends, RE showed a decreasing tendency in the recent time period.

Hydrological extreme events are exacerbated by global climate change. The root cause of climate change is mainly attributed to increased emissions of green house gases (GHGs) and increased fossil fuel use. Although Pakistan contributes nominal in GHGs emissions, it is one of the most vulnerable countries facing climate-related disasters. These disasters include heat waves, weather abnormalities, and hydrological extremes such as drought and flood events. Frequent and unprecedented rainfall caused fluvial 2010 and pluvial 2022 devastating floods in Pakistan. Anthropogenic activities in natural floodways have multifold the extremities of floods. This study compares two gridded precipitation data sets: Climate Prediction Center MORPHing technique (CMorph) and Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) with high temporal (half-hourly) and spatial resolution (8–10 km) and the use of Direct Rainfall Modeling (DRM) or Rain on Grid (ROG) approach to reproduce pluvial flood 2022 in lower Indus basin in Sindh, Pakistan. Results indicate that gridded precipitation and DRM approach can be used for the modeling of pluvial floods (urban and flash floods) for probabilistic flood mapping. Both the precipitation data sets were able to reproduce the pluvial flood 2022 with reasonable accuracy. However, IMERG precipitation data was able to produce a more realistic flood extent as compared to CMorph when both were compared with Synthetic Aperture Radar (SAR) extracted flood extent. The estimated stream flow or gridded precipitation forecast can be directly used as rain on grid in HEC-RAS to produce flood hazard maps. The study also highlights the possibility of utilizing the Global Forecast System (GFS), Weather Research Forecasting (WRF) predictions for flood forecasting, and early warning systems that can be developed for a country like Pakistan. The study can be useful for government departments, National Disaster and Management Authority (NDMA), and other agencies involved in flood fighting and flood prevention to make an early and well-informed decision before the disaster strike, and thus can reduce the loss of life and property. The methodology can be useful in transforming flood management from reactive to proactive.

Regional flood frequency analysis (RFFA) is a widely recognized technique for estimating reliable flood quantiles at ungauged sites or to augment the data available at gauged sites. The primary step in RFFA is regionalization, that is, the process of identifying hydrologically similar regions for spatial pooling. Artificial intelligence techniques such as distance-based clustering algorithms are most commonly adopted for the delineation of homogeneous regions, using the Euclidean distance metric by default. This study aims to understand the performance of regionalization techniques under different choices of distance measures, and how such sensitivity varies across clustering techniques used. Leave-one-out cross-validation (LOOCV) analysis is performed to compare estimated flood quantiles with at-site values. The distance measures considered are squared Euclidean, cityblock, cosine, correlation, Minkowski, and Mahalanobis distances, while two clustering algorithms are chosen—the conventional fuzzy C-means (FCM) approach and an advanced machine learning algorithm called the random forest classifier (RFC). The Nash-Sutcliffe efficiency (NSE) is used as a performance evaluation statistic to evaluate the accuracy of the estimated flood quantiles at ungauged sites. For illustration of regionalization and prediction in ungauged basins, naturalized flows are considered at 243 catchments in the State of Indiana, United States. It is found that in general the algorithms are sensitive to the choice of distance measure, with the Mahalanobis and cosine distances performing poorly both for region formation and subsequently for flood quantile estimation at ungauged sites. However, choice of a more suitable clustering algorithm is found to overcome such limitations with the RFC showing improved performance (NSE > 0.73 across all distance metrics), even with the Mahalanobis distance. Therefore, this study concludes that the choice of clustering algorithm is the more important parameter in RFFA for robust and reliable estimation of design floods.

The Chesapeake Bay (CB) is the largest estuary in the United States, and a large bolide crashed into it 35 million years ago. This study analyzed observations from seven pairs of closely spaced tide gauges (TG) and GPS stations around the CB to simulate relative sea level rise (RSLR) since the 20th century. Outcrops or subcrops are pre-Cretaceous (pre-C), Cretaceous (C), Tertiary (T), and Quaternary (Q) from the Northwest to the Southeast in the CB coastal plain. RSLR at TG is assumed to be the sum of paired GPS-detected land subsidence (LS) and absolute sea level rise (ASLR) in this paper. Before 1992 in the 20th century, TG Washington, DC, located in the pre-C outcrop/subcrop zone appears to have RSLR and LS rates of (2.68, 1.58) mm/year; TG Baltimore in the C zone (3.0, 1.9) mm/year; TGs Annapolis, Cambridge, and Solomon Island in the T zone (3.39, 2.24) mm/year, (3.45, 2.34) mm/year, and (3.75, 2.66) mm/year, respectively; and TGs Kiptopeke and Yorktown in the Q zone (4.05, 2.95) mm/year and (3.06, 1.96) mm/year, respectively. The LS rate increases from the pre-C through Q zones except the Yorktown station impacted by the crater; the ASLR before 1992 in the 20th century in the CB area is in the range of 1.10 mm/year–1.15 mm/year by removing LS from RSLR at above seven TG locations.

A major impact of climate change is on the occurrence of extreme precipitation events leading to disastrous consequences for both riverine and urban flooding. Therefore, mitigation and adaptation to extreme events have received considerable attention from researchers and policy makers around the world. The present research describes the simulation of extreme precipitation events at a rainfall station in New Delhi, the capital city of India, using an improved K-nearest neighbor model. The intent is to assess the vulnerability of the city to future extreme precipitation events under changing climatic conditions. A distinguishing feature of the K-nearest neighbor model used herein is that it can simulate, through perturbation of the historical data, extreme events that are significantly more severe than observed in the historical record. An 800-year simulation was carried out to simulate three extreme events, namely, total precipitation in extreme events, duration of wet spells, and duration of dry spells. Analysis of the simulation results clearly indicated that several extreme events not seen in historical record were simulated by the model. A precipitation event with a total precipitation of 715 mm was simulated by the model compared to a historical precipitation event with a total precipitation of 544 mm. A dry spell of 201 days was simulated by the model compared to a corresponding historical value of 175 days. The model simulated an extreme wet spell of 56 days compared to an extreme wet spell of 47 days in the historical record. The K-NN model employed herein can be potentially utilized for the design of urban drainage system to cope with extreme precipitation events under changing climatic conditions.

The rate at which sea level is rising in recent years due to global warming has become a growing concern, most especially as it affects coastal areas of the world. The devastating impact of sea level rise (SLR) on coastal communities, ranging from coastal beach erosion, nuisance high tide flooding, and saltwater pollution of low-lying farmlands to loss of tidal wetlands is leading to a decline in social and economic activities especially in coastal areas. According to the National Oceanic and Atmospheric Administration (NOAA), 40% of the US population living on the coast is inevitably vulnerable to SLR. Therefore, the objective of this study is to project relative sea level rise (RSLR) for Anne Arundel County and to estimate the contribution of land subsidence to RSLR at this location. To project RSLR for Anne Arundel County, this study combines global mean sea level rise (GMSLR) scenarios with local land subsidence measured at GPS LOYF station in Annapolis, Anne Arundel County, Maryland. Current quadratic trend of RSLR in Anne Arundel County projects that by 2100, RSLR for the county will be approximately 1.2 m forecasting from 1992, which is 86% and 174% of the GMSLR intermediate-high and intermediate-low scenarios, respectively. Land subsidence significantly contributed to RSLR in the 20th century; however, since 2001 absolute sea level rise (ASLR) driven by climate change has significantly contributed to RSLR in this location. The results in this paper suggest considering the intermediate-high RSLR scenario for planning and decision-making in Anne Arundel County, Maryland, in relation to SLR.

There exists an urgent need to assess the possible impacts of climate change on the design storm for improving the design of urban water infrastructure in the context of a changing climate. This design storm is commonly estimated from the intensity-duration-frequency (IDF) relations at the location of interest. Consequently, the derivation of IDF relations in the climate change context for a given location has been recognized as one of the most challenging tasks in current engineering practice. The main challenge is how to establish the linkages between the climate projections given by global/regional climate models at global/regional scales and the observed extreme rainfalls at a given local site or at many sites concurrently over an urban catchment area. If these linkages could be established, then the projected climate change conditions given by climate models could be used to predict the resulting changes of local extreme rainfalls and related runoff characteristics. Hence, innovative downscaling approaches are needed in the modeling of extreme rainfall processes over a wide range of temporal and spatial scales and given the high uncertainty in climate projections by different climate models. Therefore, the overall objective of the present paper is to provide an overview of some recent progress and shortcomings in the modeling of extreme rainfall processes in a changing climate from both theoretical and practical viewpoints. Another focus of this paper is to introduce the recently published technical guide by the Canadian Standards Association to provide some guidance to water professionals in Canada on how to consider the climate change information in the design of urban water infrastructure.

Coastal populations are facing increasing environmental stress from coastal hazards including sea level rise, increasing tidal ranges, and storm surges from hurricanes. The East and Gulf Coasts of the United States (US) are projected to face high rates of sea level rise and include many of the US’ largest urban populations. This study proposes modelling land-use change and coastal change between 1996 and 2019 to project the impacts of intensifying coastal hazards on the US Gulf and East Coast populations and to estimate how coastal populations are growing or retreating from high-risk areas. The primary objective is to develop a multifaceted spatial-temporal (MuST) framework to model coastal change through land-use projections and thorough analysis of the indicators of coastal urban growth or retreat. While urban growth models exist, one that presents an interdisciplinary evaluation of potential growth and retreat due to geographic factors and coastal hazards has not been released. This study proposes modelling urban growth using geospatial metrics including topographic slope, topographic elevation, distance to existing urban areas, distance to existing roads, and distance to the coast. The model will also use historic hurricane data, including storm track and footprint for named storms between 1996 and 2019 and the associated flood claims data from Federal Emergency Management Agency (FEMA), to account for existing impacts from coastal storms. Additionally, climate change data including sea level rise projections and future tidal ranges will be incorporated to project the impacts of future coastal hazards on urban expansion over the next 30 years (2020–2050). The basis of the urban growth model compares land-use change between 1996 and 2019 to complete a geospatial analysis of both the areas shifting from rural (agricultural, forest, wetlands) to urban, indicating change in population data from 2000 to 2020, to evaluate coastal retreat or abandonment over the next 30 years. It is expected that slow or no growth may indicate retreat from coastal areas, while urbanization and increasing population will indicate a shift towards coastal areas and growth.

The Watershed Environmental Hydrology Hydro-Climate Model (WEHY-HCM) allows the simulation of runoff and groundwater flows at a watershed scale using various atmospheric inputs, particularly the precipitation field over a watershed. High-resolution precipitation data is available by dynamical downscaling of global reanalysis data, and it might contribute to better representations of variations in the flow discharge in the target watershed. This study investigated the impacts of precipitation inputs on the simulated flow discharge at the Cottonwood Creek Watershed in the Sacramento River basin. As the first step, the Cottonwood Creek Watershed WEHY-HCM was developed considering the calibration period from 1997 to 1999 and dynamically downscaled atmospheric data from Climate Forecast System Reanalysis (CFSR). Then, with two different precipitation inputs, WEHY-HCM discharge simulations were compared: WEHY-CFSR, where the precipitation input was obtained by downscaling of CFSR; and WEHY-PRISM, where the precipitation input comes from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). The simulations indicated that WEHY-PRISM showed better accuracy for higher flows but tended to overestimate the flows. On the other hand, WEHY-CFSR simulation tended to be more equilibrated in lower flows and had a satisfactory performance for the validation period.

This case study describes the methodology adopted in designing and analyzing a rural water supply network in Cameroon using EPANET and the inferences made from the analysis results. EPANET is a sophisticated water modeling tool which was used to model a small rural water supply network. This case study aims to lay out the instructions to model a simple network in order to assist non-governmental organizations (NGOs) and volunteers involved in similar rural water supply projects. The focus is on the assumptions, their justifications, and various restrictions in designing such a network and sharing the information we gathered while doing this project to aid novice learners. Our case study system on the ground struggles with low pressure, with two taps providing water only when the tank is full. The analysis results were used to suggest possible changes in this project to improve water pressure and velocity and reduce costs. Specifically, the comparison indicated that best results are achieved with relatively larger main water pipes and smaller distribution pipes.

Development of irrigation schedules that lead to efficient use of water is imperative. For this, crop experiments were conducted in the humid sub-tropical climate region of Western Uttar Pradesh, India, where a plant-based index, crop water stress index (CWSI), has been derived for a crop season of wheat crop. Four irrigation treatments, two equipped with drip irrigation and two with flood irrigation, of 25% MAD (drip), 50% MAD (drip), 50% MAD (flood), and a farmer’s field replication (flood) were experimented in the study. MAD is maximum allowable deficit that represents the soil moisture depletion at which irrigation was applied. Research-grade infrared thermometers were used for field measurements of crop canopy temperature. The empirical approach of CWSI is applied to formulate lower and upper baselines utilizing the air temperature humidity parameters and CWSI values for wheat crop for the pre-heading and post-heading stages are derived. Mean CWSI values for irrigation treatments are found out as 0.03, 0.11, 0.29, and 0.31 for 25% MAD (drip), 50% (drip), 50% (flood), and a farmer’s field replication (flood), respectively, during the crop period with the higher values of CWSI representing more crop water stress. The highest water use efficiency (WUE) of 26.9 kg/ha-mm is observed for 50% MAD (drip) irrigation treatment with a mean CWSI value of 0.11. The farmer’s field replication, which corresponds to the lowest WUE, has by far the highest value of mean CWSI during the post-heading stage. The use of a plant-based index rather than the conventional soil moisture method can be a more effective method to determine water-efficient irrigation schedules.

Controlled field experiments on wheat crop were conducted at Shiv Nadar Institution of Eminence, Greater Noida, India, during 2021–2022 crop season in sandy-loam soil type in humid sub-tropical climate zone. The aim was to assess water saving irrigation treatment and amount of water saving for wheat crop by combining drip irrigation with regulated deficit irrigation. The experimental setup consists of four irrigation treatments with four replications of each irrigation treatment in which soil moisture values are regularly measured. Irrigation is supplied to the four treatments at different levels of soil moisture depletion, thus varying the timing and amount of irrigation. Drip irrigation system was used in two irrigation treatments, while flood irrigation was used in the remaining two treatments. The soil moisture depletion levels, referred to here as maximum allowable deficit (MAD), for the three irrigation treatments were 25% MAD (drip), 50% MAD (drip), and 50% MAD (flood). In the fourth irrigation treatment (the farmer’s field), irrigation is applied based on the recommendations of a local farmer, without considering the soil moisture measurements. The highest value of grain yield was recorded for 50% MAD (drip), very closely followed by 25% MAD (drip). The irrigation crop water productivity value was highest for 50% MAD (drip) treatment and lowest for the farmer’s field treatment. The crop productivity value of 50% MAD (drip) treatment is found 98%, 35%, and 28% higher than the farmer’s field, 50% MAD (flood), 25% MAD (drip) irrigation treatments, respectively. The results show vast potential of drip irrigation system when combined with regulated deficit irrigation in water saving for wheat crop in sandy-loam soil.

The uncertainty associated with traditional evapotranspiration (ET) based irrigation scheduling contributes to over- or underwater application. The lack of reliability in ET-based irrigation scheduling is motivating researchers to develop on-site soil moisture-based irrigation scheduling. While soil moisture can be considered a direct measurement of available water in a soil column and hence the required amount of water to be applied to a field, the resource constraints always limit the number of soil moisture sensors to be installed in an agricultural field. Hence, the simplification and linearization of the soil type and stratification are assumed. The gain due to soil moisture-based irrigation can be diminished due to this oversimplification. Moreover, soil spatial and temporal stability may lead to unidentified over- or underwater applied zones. This study set up a numerical model to simulate soil type and potential sprinkler locations to investigate the combined effect of vertical and horizontal hydraulic conductivity on soil moisture response. A series of numerical experiments have been conducted by considering different combinations of soil types and potential sprinkler locations. Lower pressure in the root zone causes lower soil moisture with potential to pull more water. As expected, more saturated area is found under silty clay loam than under any other soil type.

Rainfed agriculture in the Southeastern United States is vulnerable to hydrological extremes, especially droughts. The characteristics of a drought event (duration, severity, intensity, and recurrence interval) have varying impacts on crop yields. As the climate changes, drought characteristics and associated impacts are expected to change, thus threatening crop productivity and in turn local and state economies. Understanding the risks that drought poses to agriculture is a critical step in mitigating the effect of climate change. However, the literature on the effect of drought characteristics on crop yield in the Southeastern United States is limited, hence the need to elucidate how drought affects yield in the region. In this study, we examine and compare the univariate relationships between the return period of drought severities on the yield of four major crops, namely corn, cotton, peanuts, and soybeans. We extract yearly drought duration and severities from the standardized precipitation and evapotranspiration index (SPEI) and compute their return periods. We investigate if a linear relationship exists between return periods and crop yield variability and to what extent this relationship can explain differences in yield. Preliminary results indicate return period of drought severities does explain loss in crop yield. But this pattern was not observed across all crop types or across all states.

Photovoltiac generation is an intriguing alternative power source for pressurized irrigation system pumps. Two different on-farm power generation applications using photovoltaic panel systems have been identified: (1) grid-tied, where pumping power consumption is offset on annual basis, and (2) off-grid, with a sole-sourced, direct connection to an on-farm solar generation facility. The authors recently reviewed proposed system designs for both grid-tied and off-grid photovoltaic generation system applications. The reviews resulted in several key findings: (1) that some vendors marketing on-farm photovoltaic systems for irrigation pumps are unwilling or incapable of producing potential generation performance, (2) several key photovoltaic design considerations can impact subsequent operation of the irrigation system, and (3) preliminary design guidelines have been developed. This paper presents an overview of the available options and the results of the proposed design reviews. A discussion of engineering guidelines and key considerations will also be provided for the design and integration of photovoltaic generation systems with pressurized on-farm irrigation systems.

Irrigation districts in California are required to provide volumetric water accounting for all customers. Fresno Irrigation District (FID) requested that ITRC examine the accuracy and feasibility of portable methods of measuring irrigation district turnout flow rates to meet volumetric water accounting requirements. The intent was to recommend a measurement method that provides accurate flow measurement while requiring minimal modification/cost for implementation with low head loss and a simple measurement technique. All components were located on the upstream side of a turnout to minimize modification. To determine the best method to accurately measure flow rate with a portable velocity meter on the upstream side of an irrigation district turnout, ITRC fabricated and tested multiple conditioning devices and tested various established velocity measurement methods. Initial iterations of the conditioning device were fabricated by ITRC, while the final device was commercially fabricated. All devices were tested at ITRC’s NIST-traceable testing facility. The final device was installed and tested for an irrigation season in FID. The final device (a cone-shaped conditioning device) was combined with a four-point velocity/area measurement and provided reasonable flow rate accuracy. However, ITRC has recommendations for improvement in future iterations.

The paper provides research for applying the theoretical, system, and calculation bases for modeling pollutant and fertilizer migration processes in irrigation zones. Modern models and approaches currently used to solve practical problems of the zone of active water exchange are considered, and a discussion of strategies for their application and suggestions for ways to improve existing models and methods used to solve practical migration problems are provided. A typical scheme and a mathematical model of migration processes are considered for the hydrogeological conditions of southern Ukraine. Examples of calculations are given for lateral pollution (which comes from the channel located on the left border of the aquifer) and simultaneous pollution coming from the channel and the territory of the irrigated massif, considering infiltration feeding. Calculation data show that under the adopted scheme and the values of the given parameters, the concentrations under the irrigation array and in the adjacent territories monotonously increase until equilibrium is reached according to the rules of mixing. This approach can be used to substantiate engineering recommendations for the construction of protective structures to reduce the negative impact of the described processes on the irrigation conditions of the adjacent territories.

Rural Alaskan water systems present unique challenges, which differ from water systems in the contiguous United States. For instance, while prior literature is focused on piped water systems, water provision in rural Alaska is a multifaceted endeavor, whereby water is delivered not just via piped but also hauled and traditional methods. This study seeks to understand whether there are different sets of preferences attached to each of these water delivery methods. This paper uses qualitative interviews with 40 end users in the YK Delta to identify relevant preferences across each of these systems based upon technical, economic, and social considerations. In terms of overall preferences, initial findings indicate those who use hauled systems are the most vocal of their desire for piped systems, not just for themselves but also for their entire community. In terms of socially driven preferences, while those who prefer hauled (and secondarily piped) methods, most worry about water appearance, while those preferring traditional methods most worry about taste of chlorine. In terms of technical-driven preferences, hauled and piped users are most concerned about freezing system components (and secondarily about pipe contamination). In terms of economic-driven preferences, while piped and hauled users tend to note their systems are expensive (some saying legitimately so), traditional users tend to note their systems as more affordable; this suggests cost is a differentiator in system choice. Identifying factors that differentiate end user preferences will help utilities, planners, and decision-makers better select systems that align with the needs and interests of local communities in rural Alaska.

Traditionally, state water plans focused on supply development to address future water needs. More recently, demand management strategies, conservation, alternative supply development such as reuse, aquifer storage, and public and stakeholder involvement have emerged in state water plans. This paper examines on a content basis the extent to which 17 recent western state water plans have incorporated climate change considerations and impacts on future water supply and demand. It employs a five-category coding scale for categorizing how each state water plan treats climate change. The scale ranges from a (1) no or limited discussion to (5) evidence of implementation of specific adaptation actions. The most recent water plan for each state was analyzed and assigned a value according to the scale. With the exception of California, Colorado, and Oregon that have mechanisms in place for implementing climate change adaptations, western state water plans acknowledge climate change, but many lack sufficient recognition of its potential impacts to their state water resources.

Lead is a toxic metal that can be harmful to human health even at low exposure levels. For homes that draw water from community water systems, the most significant source of lead in the water comes is typically lead service lines or lead pipes that connect homes to the water main. Households that are exposed to high water lead levels (WLLs) should be identified rapidly to mitigate exposure through preventative measures. Laboratory testing of water samples across a community is expensive, and at-home water chemistry test kits are affordable and report water quality parameters, including copper, iron, pH, and lead. At-home chemistry tests, however, are subject to high levels of error and do not reliably identify WLLs. This research explores the use of machine learning approaches to improve the use of at-home water chemistry test results and classify WLLs at households. Water chemistry parameters, such as iron, copper, and pH, and observations of tap water quality, including odor, taste, and discoloration, are correlated with the presence of lead and can be used to predict WLLs in a statistical or machine learning approach. Data were collected by citizen scientists, who used at-home water chemistry test kits and reported household characteristics, plumbing type, build year, water quality observations, and at-home water chemistry reports through a citizen science project, Crowd the Tap. Machine learning methods, including Bayesian Belief Networks models, were applied to use variables reported by participants to classify WLLs above lead thresholds. This research explores and couples machine learning and citizen science methods to predict WLLs in tap water and prioritize laboratory testing of households in a community with high risk of lead.

Cost benefit analysis (CBA), a conventional method of assessing the cost and benefits of proposed investments, is often associated with uncertainty due to a wide range of factors that hinder the accurate projection of costs and benefits into the future. Using a case study of the Metolong Dam in Lesotho, this paper explores uncertainty in the economic analysis of projects under climate change via a bottom-up approach to economic evaluation. Streamflow is assessed as measurable metric of climate uncertainty upon which sensitivity analysis is performed, to assess the impact of climate uncertainty on the outflow of the Metolong Dam, which supports production at a textile factory. Although climate change was not identified as a near-term threat with GCM projections estimating precipitation changes to be within ±10% of current annual totals and less than 2°C rise in temperature over historical values, uncertainty in water allocations and rates of reservoir release proved consequential when combined with climate change. Following a vulnerability analysis, the project was found to be robust to approximately 10% of the considered future scenarios. The project is judged to have low climate risk, but high uncertainty relative to other factors, so flexible adaptation strategies that provide incremental robustness to the project are recommended to avoid infrastructure redundancy and resource waste.

Rapid urbanization in China and many other countries brought renewed attention to urban water supply and demand management, as evidenced in many cities in the Yangtze River Delta (YRD) region of China, the economic hub of the country. To optimize urban water supply to meet growing demand for many small and mid-sized cities in the YRD, we develop an optimization-based integrated urban water planning model, which consists of water demand growth prediction and water supply infrastructure capacity optimization. The demand growth forecasting module projects into the future for the domestic, industrial, and public water demands. The water supply capacity expansion module minimizes the total net present value of the water supply infrastructure expansion, construction, and water diversion costs over the forecasting period, focusing on the size and timing of construction of new, or expansion of existing reservoirs or diverting water from other sources to satisfy forecasted water demand. With the Haining City in Zhejiang Province as a case study, we forecasted future urban water demand out to 2050 and provided optimal schedule and size of expanding the capacity of existing water supply reservoirs, which can satisfy growing water demand at the minimal level of economic cost. Given the representativeness of Haining as a mid-sized city in the YRD, this study also provides useful insights for urban water planning of other cities in the region and beyond.

The purpose of this study is to discuss how assessing climate change impacts on water availability can drive new adaption links between different water uses (e.g., irrigation, urban and industrial water supply, energy generation). This is a case study for the Tietê River basin, located in southeastern Brazil, the most populated region in the country. The study quantified how water availability behaves under two climate change scenarios (SSP-RCP 2-4.5 and SSP-RCP 3-7.0), in two time horizons (20:30 and 20:50) using the outputs of four global circulation models. All model data were bias-corrected, and the flow catchment was measured by a distributed hydrologic model. An in-depth analysis on the operation of hydropower plants, the main source of electricity generation in Brazil, was conducted. Promissão power plant’s operation was simulated using an optimizer model. Results shows that total mean yearly amount of water is expected to increase in 2030 and 2050 (11% and 8%, respectively, under SSP-RCP 2-4.5 scenario; and 16% and 14%, respectively, under SSP-RCP 3-7.0 scenario). However, dry and wet seasons tend to become more extreme, and water availability is more concentrated in the rainy season (up to 26% in February and 27% in March and April combined). Climate models also point to increases up to 93% in peak flows. In terms of power output, results point to an increase in energy fluctuation and in spilled flows and a higher risk of damage to nearby infrastructure, crop fields, and cities placed along the river.

In the early 1900s, stormwater or urban runoff is often conveyed by concrete infrastructure to prevent surface flooding or stormwater ponding in front of vertical or horizontal infrastructure in the urban cities. It has caused many existing natural streams to be buried or replaced with stormwater pipe and concrete channel that has significantly impacted the water quality, aquatic ecosystem, flooding, and existing nature in the urban cities. Currently, the existing stream in urban cities is deeply incised, channel bank vertical exposed and utilities (sanitary sewer lines, gas, and water line), flows rarely extend to the floodplains, and no base flow to protect aquatic habitats. This unstable condition will continue to degrade without restoration. The city of Baltimore implemented urban stream restoration projects that have improved water quality and aquatic ecosystem and protect the existing utility infrastructure to meet National Pollution Discharge Elimination System’s (NPDES) municipal separate storm sewer system (MS4) permit under the federal Clean Water Act requirements. Using a natural stream restoration approach by removing the concrete channel, daylighting stormwater pipe, reconnecting the floodplain, use of boulder types of structures to stabilize the stream banks and reduce the energy of the stream system. This natural stream restoration design provided a stable stream system supporting a biologically diverse aquatic and riparian ecosystem, as well as providing a beautiful amenity to the communities. In addition, the stream restoration projects would provide the nitrogen, phosphorus, and sediment reduction into the nearest watershed and meet the Chesapeake Bay total maximum daily load goal.

As climate change and population growth exert more pressure on urban water systems, new water management strategies are needed. Water micro-trading is a novel economic strategy that aims to reduce urban water demand. In micro-trading, a household acts as a prosumer and collects surplus rainwater through rainwater harvesting. Prosumer households can trade rainwater with another consumer household that exerts an outdoor water demand. The traded water is transferred via an underground aquifer without the need for additional citywide infrastructure. New research is needed to quantify the effect of micro-trading on urban water supply and water table height. This research uses a data set from Perth, Australia, which contains 30-min water demand data for 36 households that use a variety of sources including rainwater and mains water, which is water provided through centralized drinking water infrastructure. These households are modeled as agents in an agent-based model (ABM). Agent actions include harvesting rainwater, trading water, and using water. The ABM is simulated to generate water consumption and groundwater contributions over a nine-month period. Water trading results are used as input to a groundwater model developed with MODFLOW to assess the effects on groundwater tables and sustainability. Preliminary results indicate that roughly 8% of consumer outdoor water demand can be offset using traded water. Research results will evaluate the impacts on water tables in the Greater Perth area. Results of this study demonstrate the effectiveness of micro-trading as a new water saving strategy.

An increase in air temperature caused by global climate change has increased the moisture-holding capacity of the atmosphere. This resulted in an intensification of precipitation magnitude and frequency. As a result of this, areas within the Midwest have seen several episodes of floods caused by such precipitation events. Engineers and scientists have consistently improved the hydrologic and hydraulic techniques to quantify the severity of flood events. The rain-on-mesh approach is a new advancement in the rainfall-runoff modeling technique that enables the performing of simultaneous hydrologic and hydraulic computations. This helps in increasing the modeling details while minimizing errors and model complexity. Rain-on-mesh model often known as the rain-on-grid model is spatially detailed by discretization of the entire model into two-dimensional (2D) finite elements also known as 2D cells or 2D computation cells. The hydrologic losses and runoff are calculated for each 2D cell and are further routed through downstream 2D cells for evaluating spatially varied flood depths and velocities. The current research will test the applicability of the depth-averaged Hydrologic Engineering Center’s River Analysis System (HEC-RAS) 2D rain-on-mesh model, to evaluate the rainfall-runoff upstream of the East Branch Du Page River at Bolingbrook, IL. The watershed has an area of approximately 75.8 square miles and has observed multiple flood warnings in the past. A spatially detailed, yet simplified, 2D model for evaluating the flood magnitude and extent can be a beneficial tool for the community and stakeholders.

Artificial sports fields are becoming more common to increase usage and decrease maintenance cost. Modern artificial sports fields use rubber crumb to cushion impact. The Auckland Council Parks Department commenced investment of NZ$85 million to convert 37 sports fields to artificial turf. Seddon Fields and Michael Avenue Reserve sports fields were converted to artificial turf in 2013. In New Zealand, rubber crumb is sourced from discarded tyres. However, tyre wear is a principal source of zinc contamination in the region. An initial mass balance model of rubber crumb was undertaken to assess the order of magnitude of rubber crumb likely discharged from the fields. Previous estimates of zinc sources, contribution and harbour modelling of zinc accumulation in Motions Creek and the Meola Reef area of the Waitemata Harbour are compared to the new source of zinc from Seddon Fields’ rubber crumb run-off. Initial estimates suggest that rubber crumb run-off from sports fields constitutes a small, yet appreciable, source of zinc that merits further assessment regarding local contaminant loads and potential methods to decrease rubber crumb reaching waterways. A potential sustainable reuse of discarded tyres may be exacerbating contamination of Auckland’s waterways.

Large cities like New Orleans have embraced green infrastructure (GI) strategies to help absorb, delay, and treat stormwater to reduce flood risk and improve environmental resilience. The Gentilly Resilience District, for example, is the city’s first-ever resilience district focusing on projects that reduce flood risk and support the area’s recovery and revitalization. The University of New Orleans (UNO) Lakefront campus is located within the area along Lake Pontchartrain’s north shore. It is an ideal location for this campus-wide case study. UNO investigators are developing a cost-effective network of water-level sensors for real-time monitoring and calibration of a Storm Water Management Model (SWMM) for the campus and will implement this tool to study the effectiveness of GI systems. Currently, SWMM calibration for large-scale site-specific simulation is cost-prohibitive because observed subsurface drainage flow data is needed in multiple locations during the same event. Investigators are testing an inexpensive unit that includes a microcontroller, Wi-Fi antennae, battery pack within a waterproof junction box, and an adjustable linkage to a Maxbotix ultrasonic distance sensor mounted inside the manhole. It is installed simultaneously with a Teledyne area-velocity meter recording observed water level for data correlation to provide improvement in the ultrasonic sensor’s corresponding measurement estimates. The Wi-Fi antennae allow communication through the cloud-based IoT Thingspeak open data platform, allowing real-time sampling rate adjustment for an expected storm event and display of adjusted water-level readings. The SWMM model can be used to design/assess low impact development (LID) storage structures with respect to volume, peak flow, and timing and provide a comparison to the required permitting of peaks and volumes for evaluating the long-term impacts of LID structures. The development of this system will allow the designers investigate real-time flood potential, assess the condition of the stormwater drainage system, and the efficiency of the LID alternatives. This paper describes the development process for the sensor network and the SWMM model currently underway.

In the field of stormwater management, multiple studies have been conducted on bioretention efficiency in removing common stormwater pollutants (e.g., oil and grease, heavy metals, and nutrients). However, its application in removing microplastics is still in preliminary stages. This study aims to fill a clear knowledge gap regarding the removal efficiency of microplastic particles in bioretention soil media. Laboratory experiments were designed to evaluate and analyze the effects of bioretention media characteristics with various stormwater parameters on microplastic removal efficiency using fluorescent microscopy to detect particles after simulated storm events.

Roadside soil amendments, like compost and biochar, are used to mitigate the impact of stormwater runoff on the transport of pollutants, like nitrogen, to waterways. However, biochar and compost may leach nitrogen, and the leaching mechanism could be exacerbated by climate change-driven increases to storm volume and antecedent dry period (ADP). Despite this, neither amendment has been evaluated under these climate change scenarios. To properly implement these materials, it is crucial to characterize leachate from biochar-amended and compost-amended soils. We compared nitrogen concentrations in outflow from biochar-amended and compost-amended roadside soils following a series of simulated large volume storm events and a series of intermittent storm events with increasing ADPs. First, columns of roadside soil and biochar (30% by volume and 4% by weight) or compost (30% by volume) were irrigated to simulate either a 1 in storm event or 1 year-6 h storm events for Virginia. Then, columns underwent a series of 1 year-6 h storm events with increasing ADPs. Outflow was analyzed for total dissolved nitrogen (TDN). Columns were routinely weighed to gravimetrically track soil moisture. Results indicated that compost-amended soils consistently had the highest effluent TDN concentrations in large storm events and increased ADPs. 30% Biochar by volume consistently had the lowest effluent TDN concentrations in both storm series. All amendments improved soil moisture with 30% biochar by volume having the most significant effect on soil moisture. These results suggest that 30% biochar by volume may be suitable for use as a roadside soil amendment, especially under these increasingly common climate scenarios.

Waterways and water bodies worldwide are impacted by anthropogenic litter (hereafter “litter”), generated from non-point sources. This study analyzes litter loads across land uses within three watersheds: Ballona Creek, Los Angeles River, and San Diego River. Using extensive litter collection data from small drainages (approximately 572 locations, from 0.05 to 8.5 ha), we estimated model parameters used in a numerical stormwater simulation model, PCSWMM, to simulate the buildup and subsequent washoff (load) of litter from the land surface. To date, no study has used litter washoff data to develop these parameters and apply them on a regional scale. Reported litter washoff data for Ballona Creek and the Los Angeles River from a 2002–2004 monitoring study were used to plot washed off litter per given storm events against preceding dry days for each land use. We used an exponential buildup function to model buildup, growing with respect to time, and assuming a maximum buildup value from observed data. Washoff parameters were set so that the only washoff variable was the runoff rate per unit area, excluding parameter variation based on the sediment transport theory such as particle diameter. The parameters for the maximum buildup possible and the buildup rate constant were used in PCSWMM models for Ballona Creek and the Los Angeles River. An average maximum buildup per land use was assumed to be the average value for maximum washoff, which occurred when there was the longest dry period between storms. The buildup rate constants varied between land uses, ranging from 0.023 to 0.089 per day. Estimates for litter loading for average annual rainfall were lower in the Ballona Creek Watershed (2.5 kg/ha) than in the Los Angeles River Watershed and Lower San Diego River Watershed (both approximately 8 kg/ha). Standardized buildup and washoff parameters for litter loading need to be further validated to accurately model litter loading on a larger or regional scale.

Urbanization is growing worldwide, and it is expected that two-thirds of the world’s population will be in urban areas by 2050. In India, by 2050, the urban population will cross the rural population. Gurugram, a satellite city in the National Capital Region of India, is one of the few significantly growing secondary cities. Urban flooding in this city has become a big challenge as it hinders various activities and has brought life to a standstill in recent times. The increased population density, imperviousness, reduced drain capacities, and climate change have caused increased stress in terms of extreme precipitation. The complexities involved in the problem are random and unplanned urbanization and the lack of real-time hydrological monitoring. This study highlights the need for increasing the sponginess in terms of low-impact development (LID) and holistic strategic planning for urban flood management. The study proposes a holistic plan for improving the natural Badshahpur and Nazafgarh drains through desilting and maintenance before each rainy season. This also highlights the importance of local depressions or ponds in the nearby areas to act as a sponge for the city by providing detention and retention to the stormwater runoff. It is expected that the experience of this case study will help the decision-makers do strategic planning to manage urban flooding more efficiently and effectively.

Flooding in highly urbanized coastal communities leaves an adverse impact on socially vulnerable populations as they receive fewer benefits from emerging flood reduction strategies. Green stormwater infrastructure (GSI) can be a sustainable flood resilience approach for vulnerable communities as it can provide nature-based solutions to reduce flood depths and improve runoff water quality. This ongoing study aims at planning GSI strategies and evaluating their performance in the vulnerable communities in the coastal city of Houston, Texas, to mitigate the existing flood risk. Flood exposure in the Kashmere Gardens and Trinity/Houston Gardens neighborhoods of Houston is undeniably higher because of years of environmental injustice, inadequate stormwater infrastructure, non-strategic local planning, and anticipated sea-level rise due to climate change. In addition, releasing untreated industrial wastes into flooding water has given rise to water quality concerns as the toxic chemicals in the flood water enter Galveston Bay downstream through Hunting Bayou posing a severe threat to the fisheries. The potential of a range of GSI practices, including bio-retention cells, permeable pavements, green roofs, rain barrels, etc., are being evaluated using the Storm Water Management Model (SWMM) for the critical sites near the Hunting Bayou in terms of lowering the runoff quantity and pollutant loading for continuous simulation. Results reveal that multiple GSIs within a site perform better than a single low-impact development option. The study results will assist policymakers and other stakeholders to understand the real-time impact of GSI measures and make informed decisions and investments to solve flooding issues in the Gardens neighborhoods.

University of New Orleans investigators are developing a cost-effective network of flow monitoring sensors to calibrate a campus Storm Water Management Model (SWMM). The pre-calibrated model is used as a preliminary tool to measure and study the effectiveness of GI design alternatives on the campus for the 10 year/24 h design event. A geographic information system (GIS) that facilitates large-scale, site-specific, low impact development (LID) modeling in SWMM is essential in simplifying and defining model development for the LID system. Several SWMM parameters, such as pervious/impervious surface areas, sub-catchment width, and surface and subsurface drainage slope, among others, are a function of onsite attributes such as infiltration rate, depression storage, roughness, flow length, etc. With GIS processing, these attributes can be more precisely defined. The LID-GIS becomes a valuable pre-processing input tool for SWMM model development and site LID analysis by properly delineating GIS layers and assigning attributes specific to LID modeling. This study conducts a preliminary evaluation of the effectiveness of LID features using a SWMM model of the UNO Lakefront campus. Five LID systems are designed as an exercise to test the preliminary model and identify nodes of interest for hydraulic analysis. At each node of interest, the effectiveness of the LID at reducing peak flow, mean total inflow, total inflow volume, and delayed time to peak was tested using the paired t-test for LID effectiveness and the Nash-Sutcliffe efficiency coefficient (NSE) for evaluation of statistical significance of differences between the baseline model and the LID implementation. One example LID implementation with the improvement of a vegetative swale and the addition of impervious pavement parking results in a simulated reduction of 17% mean peak flow, 6% mean total inflow, and 6% mean total volume. Results like these are then used to evaluate and recommend specific LID design modification alternatives.

Rain gardens are recent best management practices adopted for mitigating urban floods. However, its design parameters lack a rational basis. The present study employs conventional and machine learning algorithms to predict the rain garden’s infiltration characteristics. Four small rain gardens were constructed in the hydraulics laboratory of the Civil Engineering Department, NIT Kurukshetra, Haryana, India, to perform experiments on perennial flower plants. The study includes three conventional models (Kostiakov model, Philips model, and multi-linear regression) and two machine learning algorithms [multivariate adaptive regression spline (MARS) and group method of data handling (GMDH)] for prophecies the infiltration rate of rain gardens. Air temperature, soil water content, water inflow rate to a rain garden, number of plants, plant height, and time elapsed in various water level drops of accumulated water in a rain garden are input parameters, and infiltration rate as output parameter has been used for modeling. Seventy-five percent of the experimental data were used for training purposes, and the rest for validation. The performance of conventional and machine learning algorithm models was assessed using the statistical coefficients viz. correlation (CC), Nash-Sutcliffe efficiency (NSE), and root mean square error (RMSE). It is observed that the performance of the MARS is the best among all the models. For training data set values of CC, RMSE, and NSE are obtained as 0.998, 0.129 cm/h, and 0.996, respectively. And for validation, data set values of CC, RMSE, and NSE are obtained as 0.977, 0.141 cm/h, and 0.948. The study outcome will be useful in the accurate assessment of the infiltration rate of the rain garden.

A combined sewer system (CSS) collects rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. The volume of wastewater can sometimes exceed the system capacity during heavy rainfall events. When this occurs, untreated stormwater and wastewater discharge directly to nearby streams, rivers, and other water bodies. This would threaten public health and the environment, contributing to drinking water contamination and other concerns. Minimizing sewer overflows requires an optimization method that can provide an optimal sequence of decision variables at control gates. Conventional strategies use classical optimization algorithms, such as genetic algorithms and pattern search, to find the optimal sequence of decision variables. However, these conventional frameworks are very time-consuming, and it is almost impossible to achieve near real-time optimal control. This paper presents a faster optimization framework by using a new optimal control tool: reinforcement learning. The environment (flow modeler) used in this paper is the numerical model: Environmental Protection Agency’s Storm Water Management Model (EPA SWMM) to ensure the accuracy of environment response. The reward function is constructed based on the calculated water depth and overflow rate from SWMM. The process keeps minimizing the reward function to obtain the optimal flow release sequence at each controlled orifice gate. The combined sewer system (CSS) of the Puritan-Fenkell 7-mile facility in Detroit, MI, is chosen as the case study.

Low-impact developments (LID) and best management practices (BMP) are methods that were devised to address hydrology and water quality concerns in urban and rural environments. BMPs are designed to remediate a predetermined drainage area and volumetric flow of stormwater runoff. BMP allows the water to either evapotranspiration or infiltrate into the ground allowing the water to reenter the water cycle at its own pace, recharging the groundwater and preventing flooding. The plants in the BMP provide the water treatment by absorbing, sequestering, vaporizing, or metabolizing the pollutants out of the water. The purpose of this study is to explore methodology for maintaining the long-term effectiveness of a BMP structure. There are many factors to determine the long-term effectiveness of a BMP such as the maintenance performed on the structure, the health of the plant life, the age of the structure, climate conditions, and public knowledge on the purpose of BMPs. After a while, the plants in the BMP will begin to die off, weeds and other invasive plant species crowd out the native plants in the structure, and the bed will fill with silt and trash. Part of the study is to determine how these factors impact performance and determine the critical threshold for addressing these problems. To establish a BMP performance index, it is necessary to first determine a benchmark for performance. The effectiveness of a BMP decreases over time and the amount of stormwater remediation decreases. The decrease in effectiveness over time can be charted and used to determine a maintenance schedule and important optimization milestones.

There are many opinions surrounding stormwater utility fees (SUFs). The issue is no longer just an environmental one; it is also a matter of political interest. The fact is that something pressing must be done about it because stormwater management programs (SMP) do not have enough money to execute and maintain their programs as it is. The municipalities in the United States need dedicated funding for the SMP. A solution to this would be to implement SUFs. It needs to be implemented equitably, and carefully, explaining to payers what they are paying. In addition, the program must teach people how important this fee is and show that they are paying for better quality water. When the city has more runoff, it consequently has more pollution and more diseases as well. This fee will prevent floods, erosion, and even dead zones in water streams. It will create a safe budget to invest in SMP straightaway and also in the future anticipating and planning for climate change implications. The execution of SUFs must be clear enough to everybody. It needs to be able to answer questions such as how will the municipalities measure every rooftop and impervious surface in the city? How much will the effort cost? Can people know if their property was measured correctly? As a result, the municipalities will have greater confidence in the population, and it is one of our major goals to make the community trust the SUFs.

The use of artificial turf is gaining popularity for residential lawns and commercial applications due to its low maintenance cost and water conservation potential, in addition to its more commonly used applications in sports fields. Artificial turf can have high installation costs and also absorb more heat compared to natural grass contributing to heat hazards. This paper provides a review of thermal characteristics of artificial turf, a comparison between artificial turf and natural turf, and the health and environmental impacts of artificial turf. This review was conducted with 26 peer-reviewed articles, which were searched using the keywords artificial turf, synthetic grass, artificial grass, urban, thermal, temperature, health impact, environmental impact, and heat. Among the reviewed papers, eight have discussed the thermal characteristics and energy balance and surface temperature of artificial turf. Thirteen papers have discussed the health and environmental impacts of artificial turf. Five papers have discussed the comparison between artificial turf and natural turf. Studies on artificial turf would be helpful for urban planners to understand their utility, impacts, and trade-offs.

In recent years, severe climate changes have led to extremely hot weather to severe rainfalls resulting in flooding and disruption of transportation infrastructure. For example, in the event of flooding, pavements deteriorate faster due to the presence of water on the pavement and hence require more frequent maintenance. Although there have been noticeable changes in weather patterns, the pavement design inputs have not been improved or updated to reflect the weather changes. However, there is noteworthy progress in the research world that is working to assign weight to climate conditions and pavement performance and hence changes to pavement design standards for long sustainable transportation infrastructure. As part of this research review paper, the status of the research and approaches being used to study climate change impacts on pavement performance are identified; hence, a framework for future research and pavement design and maintenance standards can be established. The paper also elaborated on findings related to sources of climate data being used and approaches used to format the climate data to use with standard pavement design, and analysis tools are discussed. The findings presented in this paper are expected to benefit researchers, public agencies, and engineers to set future research direction, design, and operation of pavement-related transportation infrastructure.

Urban water utilities are increasingly grappling with the threat of drinking water scarcity amid intensifying extreme weather events and continued population growth. Despite decades of success in improving residential indoor water-use efficiency through low-flow water fixtures and appliances, outdoor water use may still represent up to 65% of total residential water use in many cities throughout the western United States and Texas. Up to half of water used outdoors is wasted because of inefficient sprinkler systems and overwatering. As cities in arid and semi-arid regions have sought to curtail outdoor water use amid multidecadal drought, many in humid climates have engaged with consumers to water their landscapes efficiently. Since 2015, the city of College Station, Texas, has implemented BVWaterSmart, a campaign for water conservation providing free weekly lawn watering recommendations to residents who subscribe. A network of eight rain gauges enables these recommendations to be furnished to households at the neighborhood level, capturing significant differences in irrigation requirements. Here, we examine the impact of this targeted behavioral intervention on outdoor water-use efficiency for nearly 300 single-family households using monthly water billing data and landscape water needs estimated using daily precipitation and evapotranspiration. Preliminary results suggest that irrigation efficiency improved for up to two years post-intervention, marked by a reduction in irrigation relative to the water budget and decreases in over-irrigation volume and frequency. Lessons gleaned from this study can help water utilities elsewhere implement a similar behavioral intervention program to promote water conservation through efficient use.

Promoting household adoption of water conservation behaviors (WCBs) has emerged as a vital strategic response to address water scarcity. Nevertheless, it is imperative to recognize that acceptance of residential WCBs relies on households’ voluntary engagement, as are interventions designed to encourage households to become involved in such actions. Meanwhile, environmental psychology plays a key role in understanding individuals’ WCBs and exploring the psychosocial determinants underlying these behaviors. Furthermore, the spread of technology and behavior among a population are known as a complex adaptive system (CAS), and agent-based modeling (ABM) offers a comprehensive and practical approach to studying CASs. Hence, the main aim of the present research is to introduce a novel, hybrid agent-based framework for simulating the adoption of residential WCBs, in which an extended model of the theory of planned behavior acts as household agents’ behavioral rule for adopting water-efficiency behaviors (WEBs). The framework is also grounded in various theories rooted in psychological and social sciences. Moreover, the presented framework integrates ABM and structural equation modeling approaches. Although this study mainly focuses on WEBs, the framework includes a mechanism to update agents’ water use based on their adoption level of water curtailment behaviors. Thus, the presented framework allows for simultaneously but separately exploring the preferred level of both types of WCBs among household agents. Finally, total water consumption is systematically calculated based on agents’ aggregated behavior.

With low rainfall and high evaporation in arid and semi-arid regions, existing water resources are under high pressure. Rainwater harvesting (RWH) systems are gaining popularity as a low-cost, nature-based solution to water scarcity and securing water. In this paper, 18 research papers were searched using keywords performance evaluation, rainwater harvesting, water scarcity, water demand, and runoff. These papers have been reviewed for state of science about the performance evaluation of rainwater harvesting structures in arid and semi-arid regions. We have found that the effectiveness of RWH systems depends on their capacity, location, type, and operation and maintenance. The reliability indices should determine the performance of the RWH systems based on their capacity to store excess runoff rather than based on their capacity to secure water demand. Evaluation methodologies are also inconsistent with the use of multi-criteria decision analysis methods, such as defining criteria and their weights.

In the process of developing a smart city framework, sensor data are crucial to enable cities and communities to make informed decisions on future plans. Involving community-based organizations and residents is an integral component of this process to ensure equity and accessibility of data. This study aims to develop a sociotechnical network to (1) identify vulnerability zones; (2) measure data on flooding, air, and water quality; and (3) inform community members and decision-makers through a data dashboard. A small coastal town in the Texas Coastal Bend region is utilized as a case study. Methodologically, this study utilizes participatory action research to frame a mixed-methods approach toward developing a data dashboard. This research project is a practical guide for engaged scholars in the social sciences, engineering, and urban design fields. The outcomes include recommendations for the engaged community and provide a data-dashbord targeting academic and non-academic audiences, residents, and decision-makers.

Water age is the time taking the water to travel through a distribution system and reach the consumer. Generally, there is a trade-off between water pressure and water age in a water distribution system—higher pressure results in higher flow velocity, which often means shorter travelling time for the water, while lower pressure leads to slower flow and thus higher water age. Low pressure is a desired objective in a distribution system, as it reduces the physical stress on its components and minimizes water losses in an event of a leak. Low water age is a desired objective as well, as increased age is regarded as low water quality. Therefore, the two objectives compete with one another. The problem of trying to minimize both water pressure and age is a common problem in water distribution system design and management. This paper introduces an algorithm for pressure reducing valve (PRV) placement for reducing water age in water distribution systems. The algorithm is based on graph-theory elements and uses EPANET 2.2 for simulation and analysis. The method is demonstrated on a small-scale example, and the results present relatively significant improvements in respect to water age.

Ensuring consistent and high-level water quality is paramount for water utilities to meet health requirements and attain customer satisfaction. To this end, water utilities need to constantly surveil all relevant water quality parameters, for example, chlorine-concentration, as well as to optimally control dosage rates in their drinking water distribution systems (DWDSs). Simulation models coupling DWDS hydraulics and water quality have been well established and highly accurate. However, they are computationally very expensive such that optimization of control parameters may only be possible to a very limited extent. In this work, we are proposing the use of a lightweight, machine learning-based surrogate model for the coupled simulation of hydraulic and water quality parameters that may serve to reduce simulation times and render optimization of control parameters more efficient. The baseline model is system-specific and learns to predict the steady-state hydraulic and water quality state simultaneously based on common inputs to a DWDS model, that is, water demands and dosage rates at reservoir levels. Results indicate good prediction capabilities of the surrogate model with R² values greater than 0.98 as well as error rates below 0.01% for hydraulic parameters and below 1% for water quality parameters. Some slight spatial trends in the prediction error’s variance are identified for hydraulics as well as for water quality parameters.

Water distribution networks (WDNs) are a vital component of urban water infrastructure. They transport water from production sites (sources) to spatially distributed consumers (sinks). Multiobjective optimization procedures are often used to minimize construction costs and at the same time maximize the resilience of such systems, which is usually a very computationally expensive task. Recently, highly efficient approaches based on complex network analysis (CNA) have been developed to solve this task more computationally efficiently. With CNA, very large WDNs can be optimized, considering network topology and demand distribution (using, e.g., demand edge betweenness centrality). However, existing CNA approaches do not consider network topography (i.e., height differences between sources and sinks). Comparing design solutions based on CNA with those found by evolutionary algorithms shows that the least-cost CNA design cannot compete with the latter. In this work, a hybrid approach is developed, where low-cost design CNA solutions are evaluated with a hydraulic solver (Epanet2), and subsequently the demand edge betweenness centrality distribution is iteratively altered for nodes with pressure deficits. This enhanced CNA-based optimization is tested on two different large case studies from the literature and shows promising results (2% cost increase). These solutions were obtained using significantly less computational effort (at least factor 1,000 faster), enabling solving very large WDN optimization problems (>150,000 decision variables).

Traditionally, the rehabilitation and/or upgrade of water distribution networks (WDNs) are performed with only one perspective with the aim to bring a long-term reliable system to supply the consumers. On the contrary, such systems are not isolated but interdependent with other adjacent infrastructure and networks, including urban drainage and road networks. This means that the performance and rehabilitation schemes of different networks could have direct or indirect impacts on each other, requiring coordinated infrastructure management strategies to be adopted. For example, the pipe replacement in a WDN could interrupt a traffic flow on a co-located street, or a flood could prevent water utility staff from accessing the section of a WDN pipe, thus making maintenance infeasible. Furthermore, if different infrastructural activities (operation, maintenance, rehabilitation, and upgrading) are not planned in an integrated way, there will be a high risk of redundant activities in the same location, imposing unnecessary costs. This implies that in the decision-making process for the rehabilitation of a WDN, the status and plans of other correlated urban systems should be considered simultaneously. In this study, the single and multi-utility rehabilitation of a simplified real-world WDN are dynamically planned, as a part of asset management process, with the aid of a multiobjective optimization engine in which the objectives are utility practice cost and outcome reliability measures. Then the results are compared and discussed in terms of the pros and cons of the two mentioned approaches.

Water quality is one of the most important factors in the design of water distribution systems (WDS). To maintain the conditions of water age, residual chlorine, and other quality characteristics, the ability to adjust hydraulic conditions using control elements is necessary. Although there are different methodologies for the analysis of water quality dynamics, the continued development and application of new modeling alternatives and methodologies to more efficiently represent water quality dynamics in WDS are needed to provide efficient tools for managing water quality in real time. The ability to provide water quality information in real-time would provide benefits under normal operating conditions and increase capabilities for fast responses in an unexpected pollution event, whether intentional or unintentional. Therefore, being able to use these new methodologies will enhance the capacity to develop and use adequate models to analyze different alternatives to quickly solve and control problems like the spread of a contaminant in a network. This study presents an initial analysis of water quality input-output data necessary for black-box modeling performed using the EPANET-MATLAB toolkit with respect to an existing WDS network, Bogotá, Colombia. The methodology is based on (1) analysis of model input and output variables, (2) numerical simulation of network water quality in the aforementioned software, and (3) data analytics comparing historical vs. simulated conditions for multiple scenarios. The purpose of this methodology is to estimate the sensitivity of existing models compared with new scenarios, created from different initial conditions for data analysis previous to the use of a black-box model.

Power and water networks are massive critical infrastructure systems that are strongly coupled through the power transmitted from the power network to the water network, yet they are commonly operated independently. The resemblance between the two systems is high: a power network comprises a system of power stations, power transmission, and power distribution to individual customers, while a water network likely consists of water sources, regional pipelines, and a distribution system for supplying its end users. The physics of both systems are governed by Kirchhoff’s laws of continuity of flow/current at nodes and energy pressure/voltage along paths. Smart joint operation of water and power networks has the potential of saving water, saving energy, and reducing environmental impact. Several studies have dealt with the problem. However, modeling can still be significantly improved, and many different research directions can be explored. The complexity of managing both systems independently differs according to the adopted assumptions and can be as easy as solving a linear programming (LP) problem or can end up as a challenging large-scale mixed integer nonlinear problem (MINLP). The objective of this study is to formulate and solve a conjunctive water-power flow (OWPF) problem for optimizing both systems simultaneously. The formulation and solution scheme will be demonstrated on a small yet general layout of a coupled power-water network.

Water distribution systems (WDSs) deliver clean, safe drinking water to consumers, providing an essential service to constituents. WDSs are increasingly at risk of contamination due to aging infrastructure and intentional acts that are possible through cyber-physical vulnerabilities. Identifying the source of a contamination event is challenging due to limited system-wide water quality monitoring and non-uniqueness present in solving inverse problems to identify source characteristics. In addition, changes in the expected demand patterns that are caused by, for example, social distancing during a pandemic, adoption of water conservation behaviors, or use of decentralized water sources can change the anticipated propagation of contaminant plumes in a network. This research develops a computational framework to characterize contamination sources using machine learning (ML) techniques and simulate water demands and human exposure to a contaminant using agent-based modeling (ABM). An ABM framework is developed to simulate demand changes during the COVID-19 pandemic. The ABM simulates population movement dynamics, transmission of COVID-19 within a community, decisions to social distance, and changes in demands that occur due to social distancing decisions. The ABM is coupled with a hydraulic simulation model, which calculates flows in the network to simulate the movement of a contaminant plume in the network for several contamination event scenarios. ML algorithms are applied to determine the location of source nodes. Research results demonstrate that ML using random forests can identify source nodes based on inline and mobile sensor data. Sensitivity analysis is conducted to explore the number of mobile sensors that are needed to accurately identify the source node. Rapidly identifying contamination source nodes can increase the speed of response to a contamination event, reducing the impact to the community and increasing the resiliency of WDSs during periods of changing demands.

A skeletonized water distribution system (WDS) model plays an important role in decision making process for its design and operations in planning phase. Given energy is the main driving force to operate a WDS efficiently and with the recent hikes in energy price, the investigation of energy flow in WDS is much more important than ever before. While a skeletonized network is easy to handle and takes less amount of time to run, it should represent the real-world scenario including the demand and hydraulics as accurately as possible. Failure to represent real-world scenario can mislead design and operational decisions including energy consumptions and delivery pressure. The purpose of this paper is to investigate how the energy flow in a WDS varies with the extent of the skeletonized network. Keeping the total demand constant for a network and the spatial distribution of the demand within a range in the skeletonized networks, how the energy consumption at the pump station, energy loss in the pipe, and the energy storage in the tank vary with the size of the skeletonized networks are investigated in this study. Energy balance model has been set up for each skeletonized model to observe the impact on energy flow for a series of models. Since the number of pipes and nodes can impact hydraulics, which eventually impact the energy flow, the paper considers two types of networks: branch and loop water distribution systems. The study has been applied to two hypothetical literature networks. Due to skeletonization, the flow pattern in the loop system changes more than that in the branch system causing higher percentage change of frictional energy loss in the loop system. The energy consumption or loss per unit demand per node in the skeletonized networks is found almost linear for the branch system. However, this parameter changes non-linearly in the skeletonized networks for the loop system.

Lead leaching from water service pipelines is one of the major problems faced by water utilities. This study investigated how major water quality parameters affected lead leaching from lead pipes taken from a drinking water system. The water parameters included hardness, alkalinity, pH, and chloramine. Each tested lead pipe section was placed in a sealed beaker filled with the tested water. The study was conducted over a period of six months. The control water quality had hardness of 107 mg/L CaCO₃, alkalinity of 38 mg/L CaCO₃, pH of 9.45, and chloramine of 2.29 mg/L. One water quality parameter was adjusted for each of the tested conditions. Study results revealed that the water quality parameters affecting lead leaching the most in descending order were alkalinity decreased, chloramine decreased, pH decreased, alkalinity increased, and hardness; decreased alkalinity resulted in 1,630 ppb of lead concentration that was 28 times higher than that in the control sample; decreased chloramine resulted in approximately 300 ppb increase in lead concentration. Findings from this study could benefit water utilities to develop appropriate treatment and flushing strategies to assist in the control of lead leaching.

Water distribution pipes convey clean drinking water to billions of end users around the globe. Unexpected pipe breaks can lead to several challenges, including reduced fire-fighting capability and contamination. Recent studies have developed machine learning models to predict pipe break status. Because the number of pipes that have never experienced breaks generally outnumbers broken pipes, pipe break data sets are inherently imbalanced. For this reason, different approaches for data preparation might yield different model performances. This study explored the impact of different data preparation strategies on the performance of pipe break status prediction for a case study water distribution system. The system had 55,561 pipes and 28,815 breaks recorded between 1970 and 2019. Because ANN, decision trees, XGBoost, and logistic regression were shown to perform well for similar models they were applied to all model variations. Four areas of variation were explored: (1) break type, (2) break period, (3) data splitting, and (4) data sampling. Firstly, data sets with first breaks and all breaks were compared. Secondly, break status was aggregated into one-year or five-year periods. Thirdly, random split of data and split by time period were compared. Fourthly, no sampling and undersampling of non-broken pipes was compared. In all cases, the last 10 years of data was excluded from training to ensure comparability of test results. All models were evaluated with F1 scores and AUC. Findings indicated that random data sampling and undersampling lead to skewed results. Future studies should consistently evaluate models based on selected time periods to better reflect real model performance.

Post-treatment drinking water is far from sterile, so microbial regrowth occurs, and the distribution pipes behave like a dynamic ecological niche for microbes. Unwarranted microbial regrowth within distribution pipes can lead to quality issues and health concerns. The application of computer-based tools adept at mechanistically simulating the dynamics of heterotrophic bacteria within distribution pipes is a practical approach to safeguarding biological stability during drinking water distribution systems (DWDS) operation. The imperfect understanding of most of the stochastic processes related to the heterotrophic bacterial dynamics and the natural randomness of the system constraints make mechanistic modeling complicated. Within the context of the current state of the art, the questions on the level of detailing in describing the processes within the pipe domain for mechanistically describing the microbiological quality with a certain acceptable level of accuracy are still unanswered. In this regard, we attempt to answer these questions and overcome the limitations of the state of the art to develop a practically relevant modeling tool for DWDS management. The specific objectives of this study are to (1) develop stochastic mechanistic models considering the three-level uncertainties—model parameters (kinetic rate constants), model coefficients (dispersion coefficient), and state variables (source quality characteristics); (2) understand the level of complexity that needs to be integrated into modeling to explain the water quality dynamics correctly; and (3) present the applicability of the developed models for simulating microbiological quality fluctuations in a real-world DWDS.

Conventional water infrastructure, particularly with a centralized water resources and supply systems, has provided reliable water services. However, recent water crises, such as the severe droughts in Texas and California in 2022 and the water outage in Jackson, Mississippi, from a pump failure in the city’s water treatment plant, highlight the challenges of dealing with extreme and uncertain disruptions. To address the uncertainties, various systems in the fields of computer networking, ecosystems, financial asset management, and armed forces have employed diversification and decentralization strategies. In this context, this study presents the configuration and operation of water distribution system (WDS) composed of decentralized sub-networks including diverse water sources (e.g., rainwater and reclaimed water) as a resilient water system. First, this study built a lab-scale physical model of a diversified and decentralized WDN and demonstrated its operational and physical failures under various disruption scenarios (e.g., intentional pump-shutoff and pipe leakage). Then, using a quantifiable resilience measure, the resilience of the diversified and decentralized WDN was compared with that of a centralized WDN (a single water source) in its configuration and operation. The results showed that the diversified and decentralized WDN has the higher resilience effects than the traditional WDNs and discussed the practical implementation of the diversified and decentralized WDN to improve the resilience of current water infrastructure systems. The findings will provide engineering insights into how the diversification and decentralization strategies are incorporated into the current water systems to deal with uncertain and extreme disruptions with resilience.

To ensure high-quality water in water distribution systems (WDS), disinfectant, generally chlorine, is injected into the system. However, this chlorine limits should be kept within acceptable levels. The higher limit is enforced to control the disinfection by-products, whereas lower limit is enforced to ensure that the water is organic contaminant free. The chlorine reaction rate within the pipe systems varies depending on the type of pipe, age, water PH level, temperature, and many other factors. This hinders the accurate water quality modelling in water distribution systems which in turn affects the optimal amount of booster injection rate. To minimize the risk of the uncertainty in chlorine reaction rate, current work suggests a robust counterpart reformulation of the optimal booster chlorination scheduling problem considering the rate of reaction of chlorination as uncertain. The proposed reformulation is tested on a benchmark WDS, and the results are compared with deterministic case. The results indicated that with an increase in the protection level the injection mass increased and with very large protection levels, the formulation resulted in no solution. The proposed reformulation of the traditional approach can be applied to any WDS and obtain optimal scheduling with appropriate protection levels.

The goal of water distribution systems (WDSs) is to supply water to consumers. Pumping and distributing water is an energy-intensive process, and it is also subject to different constraints such as demand satisfaction, water quality, pressure boundaries, etc. As such, finding an operation policy in which all constraints are satisfied while energy costs are minimized is a primary objective for water utilities. While many studies tackled the WDS optimal operation problem, most of them used deterministic approaches where all the problem parameters were assumed to be known. Recently, there is a growing interest in decision-making under uncertainty. One promising approach for utilizing optimization under uncertainty is robust optimization (RO) which assures a robust (feasible) solution to any realization of the uncertain parameters, within predefined bounds. In addition, RO does not depend on assuming the existence of probability density functions and is based on tractable formulations that converge to the global optimum. This study suggests utilizing RO to optimize the operation of WDSs under two types of uncertainties: consumers demand uncertainty and pumping cost uncertainty. The method is illustrated on a small illustrative network, showing how these uncertainties impact the operation policies.

A water distribution network (WDN) costs more than 60% of the total cost of a water supply system. It is indeed crucial to optimize a WDN for its economical design. Due to rapid urbanization and population growth, a particular portion of the existing network must be reorganized or extended to new zones. For the purpose of optimal reorganization, the new zone or the portion of the existing network can be identified as the sub-network. The optimal design of the sub-network demands high computational effort as the entire network needs to be simulated multiple times. To improve computational efficiency, the sub-network is separated from the large network, and the remaining network is replaced with its equivalent network with a minimum number of elements using the Thevenin theorem. The theorem can replace an extensive network with its equivalent, having a single reservoir connected in series with an equivalent pipe. The optimal design of the sub-network uses the derived Thevenin equivalent network connected with the sub-network. As the overall network size becomes considerably small, the computational effort also reduces exponentially. The probability of getting the most optimal solution is also improved as the search space is reduced. The proposed methodology is demonstrated with the help of a realistic WDN, and computational efficiency is investigated for various sets of pipe diameters. The network reduction method is found to be enormously helpful for hydraulic engineers in designing and optimizing a sub-network for reorganization and extension.

A sufficient dose of disinfectant needs to be applied to maintain a minimum residual throughout drinking water distribution systems (WDSs). Yet, excessive dosing of chlorine-based disinfectants leads to the formation of hazardous disinfection byproducts. Several frameworks have been proposed in previous literature to optimize chlorine dosing schedules by minimizing the total dose while maintaining evenly distributed residuals throughout the WDS. Many of these studies relied on evolutionary algorithms (EAs), such as the genetic algorithm (GA) and particle swarm optimization (PSO). EAs are known to require numerous evaluations of computationally expensive water quality (WQ) models and typically feature many parameters that require careful tuning. Recently, Bayesian optimization (BO) has been proposed as an alternative to EAs for the optimization of water quality in WDSs. To speed up convergence, BO builds a probabilistic surrogate model (e.g., Gaussian process) in place of the original black-box model and then leverages the predictions to explicitly control the exploration/exploitation trade-off. Yet, it is still unclear how BO’s performance compares against EA’s for the optimization of WDSs. This study aims to fill this knowledge gap by conducting a systematic comparison between the performance of BO, GA, and PSO for the optimization of chlorine dosage schedules. To that end, a comprehensive sensitivity analysis is conducted on each optimization approach to understand how different optimization parameters influence their performance. The results revealed that BO requires significantly fewer evaluations than GA and PSO to converge to high-quality solutions. On the other hand, GA displayed lower sensitivity to the change in the optimization parameters compared to BO and PSO.

A pipe or isolation valve failure in an urban water distribution network (WDN) causes service interruption to the water users at or downstream of the failure(s). Its severity impact can be reduced by identifying and prioritizing such failure for repair purposes. This study suggests a prioritization model for the pipe and valve rehabilitation and replacement in a WDN based on the socio-economic, physical parameters and the failure impact on the system’s performance. The importance of these parameters is assessed using the analytic hierarchy process (AHP). The ArcGIS and WaterGEMS software are used for the hydraulic simulation and segment identification of the WDN. A real WDN of Dire Dawa City in Ethiopia is considered to illustrate the proposed methodology. Based on the expert’s opinion and the AHP analysis in the Dire Dawa City WDN, system performance, socio-economic, and physical parameters may be given relative weightage of 0.43, 0.14, and 0.43, respectively. Further, it is noticed that the water supply shortfall is the most important parameter among the various system performance parameters. The age of the pipe/valves is the most critical physical parameter, and the location of the important facilities near the failure is the most critical socio-economic parameter. Each of the pipes and valves is assessed for the prioritization score, and based on such score, pipes and valves are prioritized for the repair/replacement purposes. The case study result found that the pipes (P110, P112, and P107) and valves (V19, V29, and V3) are the most critical having the highest PS. It is hoped that such study will help the decision-makers in making decisions for the efficient and effective operation and maintenance of a WDN.

Lacking required information is a challenge for the analysis of water distribution networks (WDNs). This missing data often encompasses network information such as the location and attributes of pipes. Therefore, an automatic reconstruction approach is needed to rebuild the missing data of WDNs with limited available information. To address this issue, a novel data reconstruction method is developed in this work based on complex network theory and using graph metrics. As a showcase with a complete data set, scenarios with missing diameter information are systematically created and successively reconstructed based on graph characteristics of available diameters and neighboring pipes. To validate our approach, a WDN is used as the case study, where information about pipe diameters is gradually deleted to increase the data gap. Afterward, the structural (i.e., diameter) and hydraulic (i.e., pressure) features of the reconstructed networks are compared with the complete data set of that WDN. The results show that a significant part of unknown diameters can be successfully reconstructed with high accuracy until the 50% data gap. Our framework can significantly assist water companies/urban planners who commonly face incomplete data within their models.

Increasing global populations, changing water consumption patterns, and climate change are continuously putting a strain on available water resources. Identifying these changes in consumption patterns and future demand requirements through water demand forecasting plays a key role in the allocation, planning, and management of urban water resources and infrastructure. In the last decades, advances in statistics, machine learning, and artificial intelligence techniques have shown promising results in predicting future water demands. This study aims to forecast hourly water demand, while factoring the effects of other exogenous features on consumption patterns using machine learning techniques for Telford Borough, a predominantly residential town in Pennsylvania, US. In this study, the predictive abilities of the support vector machine and the random forest regression models have been compared in forecasting water demand using past demand, and temporal and climatic features. The results demonstrated the practicability of applying machine learning solutions to water management with random forest being the best model, and temporal features being the most important in the exploratory variables. The authors conclude that these predictive models can be used for near real-time forecasting to guide the allocation and planning decisions of water resources.

The Aqualibrium Water Competition is widely used as a teaching and outreach tool at institutions around the world. In a teaching environment, students are given the Aqualibrium problem in advance and tasked with finding a solution using the Epanet modelling package. They then work in groups to build and optimise their designs in the laboratory. In 2021, the authors embarked on a programme to develop new applications for the Aqualibrium water competition equipment to better integrate laboratory exercises into the course material. Several new experiments were developed, including pipe friction losses, pipes in series and parallel, and pumped systems. The experiments lend themselves to simple, but accurate measurements. Initial tests have shown that realistic values for the pipe friction factor (or roughness) and pump working point are obtained, thus reinforcing the theoretical concepts in a hands-on way.

Emerging contaminants (ECs) are natural or manufactured chemical compounds that are hard to remove through water treatment; hence, they accumulate in the environment. Such contaminants have already been detected in wastewater, aquatic environments, and water distribution systems (WDSs). Consequently, researchers are developing sensors tailored explicitly to new contaminants. By combining those new sensors with real-time simulation models, digital twins are within reach to assess system-wide water quality. In the future, such twins will build the cornerstone for early warning or real-time control systems concerning these new pollutants. However, realistic simulation tools competent enough to create such digital twins are lacking, mainly because of two reasons: (1) hydraulic models are unable to account for the spatiotemporal dynamics of customer demand, and (2) water quality models are not equipped to simulate the fate and transport of ECs. Our work aims to close this gap by proposing a novel way to model water quality that combines realistic water demand models (i.e., by using the stochastic water demand end-use model, pySIMDEUM), hydraulic solvers (i.e., using the object-oriented Python NETwork analysis tool, OOPNET), and water quality solvers (i.e., using EPyT-C). Extending the state-of-the-art for hydraulic and water quality modeling by incorporating the water demand stochasticity and the uncertainties associated with the imperfect understanding of the formation and transmission of ECs in WDSs is expected to advance the digital twins technology for detecting ECs’ formation and evaluating health-related exposure risks in WDSs.

Many researchers and engineers working with water distribution systems have the need to model, simulate, analyze, and visualize these systems. Several open-source tools, such as EPANET and the Python package WNTR, allow users to perform extended period simulations and analysis of water distribution system models under a range of different conditions. However, while these tools provide basic visualization functionality, they are limited in their flexibility and capabilities. Here, we present VisWaterNet, a Python package that enables clear and customizable visualization options specifically catered toward water distribution network models. VisWaterNet provides users with straightforward functions to plot simulation results and intrinsic characteristics of system nodes (e.g., pressure and demand) and links (e.g., flow rate and pipe diameter) by customizing the size and color of network elements. The package allows for the visualization of single time step simulation data as well as data summarized over extended periods. Users may specify if data should be represented in continuous fashion with a color bar or as discrete values with an accompanying legend, and can also plot categorical data that may be generated or imported independently. Furthermore, this package includes a function to produce animations that display changes of network properties, such as nodal demand or link head loss, over time. VisWaterNet includes scripts that highlight its functionalities, as well as applications that demonstrate how functions can be applied in conjunction to create illuminating plots. For example, the package can be used to display possible sensor placement locations, highlight the concentration of a chemical species throughout the network, or present statistical summaries of model simulation outputs. The result of our work is a streamlined, user-friendly package that facilitates the creation of neat, comprehensible plots to aid researchers and practitioners in communicating results of applications of design and analysis.

Water distribution systems (WDSs) were studied intensively in the last decades. The research concerning WDS is varied and covers many aspects of the WDS lifecycle such as design, operation, water quality, sensor placement, leak detection, demand forecasting, and more. A common approach in all areas of research on WDSs is to explore within predefined boundaries of the problem and partially represent anything beyond these boundaries. For example, consumers that consume water from the network and use it, a direct outcome of this action is that the sewage collection system gets its input. The two systems are inherently related to each other but analyzed and operated separately. More examples of such boundaries are the connection between WDSs and electricity grids, where electrical prices are usually treated as inputs to WDS problems, but the costs of electricity production and transmission are not considered. Moreover, WDS storage tanks can be used for regulating loads in the electricity grid. Another example is the state (volume, levels, and quality) of water sources such as aquifers and surface waters. These are usually studied from a hydrological perspective, but WDSs and natural water resources are subject to mutual interconnections. These examples (and others) point out that WDSs are not isolated systems; they are integrated with other natural and infrastructure systems. Accordingly, the decision making related to the different aspects of WDSs should consider the full complexity that exists in real systems, which is broader than the WDS itself. This study elaborates on several opportunities to expand the existing boundaries of WDS problems, thus opening new research areas in WDS management.

Lake Erie is the smallest and shallowest of the Great Lakes, and approximately 12 million people live in the watershed that includes 17 metropolitan areas. Lake Erie is the significant resource for drinking water; during the 1960s water quality issues became a concern, and Lake Erie was perceived to be “dying.” By the late 1960s, Canadian and American regulatory agencies agreed that limiting phosphorus loads was the key to controlling excessive algal growth and that a coordinated lake wide approach was necessary. This resulted in open lake phosphorus concentrations declined, by more than 62% of the total phosphorus load during 1968–1981; these decreases were attributed to the phosphorus abatement program on municipal sources, restriction of phosphate concentrations in detergents, and no-till farming. However, since 2003, in-lake concentrations of soluble reactive phosphorus and overall phytoplankton biomass (often dominated by the HAB genus Microcystis) have increased, while annual total phosphorus loading to the lake has remained below the 11-kilotonne level as mandated. Agricultural nonpoint sources of soluble reactive phosphorus have increased and contribute a larger proportion of total phosphorus loads to the Lake Erie. Further, nitrogen has been identified as potentially capable of limiting growth of freshwater cyanobacterial blooms, and it is suggested nitrogen loads to the lake should be controlled. Currently, the governors of Ohio and Michigan and the premier of Ontario committed to reducing phosphorus loadings to Lake Erie by 40%. This history of nutrient discharge to the Lake Erie and regulatory requirements are elucidated in the paper.

Groundwater contamination is a severe problem that deteriorates ecosystems, human health, and plant/animal life. Assessment and modeling of groundwater quality is a possible solution to tackle this problem. In this study, 449 groundwater samples during the year 2018 in Haryana State, India, were analyzed for 13 water quality parameters such as pH, electrical conductivity (EC), total hardness (TH), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), carbonate (CO₃^2–) and bicarbonate (HCO₃^–), nitrate (NO₃^–), chloride (Cl^–), sulfate (SO₄^2–), and fluoride (F^–). Three machine learning techniques, say generalized linear model (GLM), distributed random trees (DRF), and extremely random trees (XRT), were applied to estimate the water quality index (WQI) for drinking purposes. The prediction performances of these three models are determined by using four error metrics, namely, coefficient of determination (R²), root mean square error (RMSE), maximum absolute error (MAE), and root mean squared logarithmic error (RMSLE). The GLM model has shown maximum accuracy (R² = 0.999964, RMSE = 0.759963, MAE = 0.525975, and RMSLE = 0.005606) and is the best prediction model for estimating WQI as compared to DRF and XRT models. Further, the WQI results suggested that approximately 53% of the groundwater samples fall under the excellent to a good category for drinking. For a better assessment of these 13 water quality parameters, the spatial distribution map has also been plotted by using ArcGIS. The expected results will contribute to the effective management of groundwater worldwide.

Indiana Department of Environmental Management (IDEM) does daily water samples in southern Lake Michigan beaches daily during regular beachgoer season. This data is used to close the beaches for E. coli spikes. One-day lag data are used for beach closures on a given day. This research is an effort to create a model with a recent tool to predict E. coli classes using other physical, chemical, and site conditions so that it becomes a tool to avoid one-day time lag. In 2019, during regular beachgoer season, data samples were collected and documented to develop an artificial neural network model. Different combinations were tried, and the best model was identified after cross validation. BRNN (Bayesian regularization neural network) was selected as the best model. This model used 13 input data to predict E. coli classes with a successful prediction of 80%–90% in training, testing, and validation data sets.

Wastewater surveillance has received significant attention in recent years due to the valuable environmental and epidemiological information it reveals. Yet, limited attempts have been made in the previous literature to determine the best locations for sample collection. In this study, we are introducing a model to solve the sensor placement (SP) problem that considers source identification (SI) as the main criterion for sensor placement. The objective of this study is to maximize the ability of the sensor design to identify the characteristics of the injection source (i.e., the injection location and concentration) of any given species of interest. To that end, we developed a python-based model that couples an SI model with a greedy algorithm to determine the best locations to place water quality sensors. The developed SP model was demonstrated on a benchmark, mid-sized sewer network. The study results revealed that a clear tradeoff exists between the overall identifiability and the detection reliability of the sensor design. In general, placing the sensors in the downstream end of the network resulted in higher overall identifiability, while better detection reliability was achieved by the sensors placed in the upstream section of the network.

Pollutant source identification is essential to manage river health in the long run. The Yamuna River is the main tributary of the Ganges in India’s northern plains. Its water quality has declined substantially due to rapid urbanisation and various anthropogenic activities. This study focuses on the identification of major sources of pollution in the upper Yamuna basin. To study the impact of anthropogenic activities, land use land cover (LULC) analysis is carried out. It is noticed that settlements are the most prevalent form of LULC, expanding by 13% since 2017 during the five years. The river’s BOD, total and faecal coliform, and heavy metal concentrations are significantly higher in the built-up areas. A data set of 15 hydro-chemical parameters is used, and the principal component analysis (PCA) is performed to identify the pollutant sources in the area. The PCA reveals that heavy metals are the primary contributor to pollution in the region. It is concluded that industrial waste, agricultural runoff, and certain geogenic contaminants are the leading sources of pollution threatening the upper Yamuna River’s ecosystem.

Southern Green Bay is a designated area of concern (AOC) by EPA due to high land-based pollution, hypoxia, and degradation of water quality. Restoration of Green Bay ecosystem requires detailed understanding of its physical patterns. Based on assumption that the influence of river loadings is local, most computational studies have not considered the effect of tributary inflows on hydrodynamics of the bay. This study examines the effect of variability in tributary inflows and the related lake intrusions on the circulation and thermal regime in lower Green Bay (area south of Chambers Island in Green Bay). Results of this hydrodynamic modeling indicate the significance of tributary inflows on the circulation and thermal regime, especially near the mouth of the rivers and throughout the bay. Then summertime circulation and temperature fields of the bay are compared for two cases with/without tributary discharges into the bay. Results show that there is less mass exchange and heat flux between lower and upper Green Bay in the idealistic case of no tributary inflows. Results of this study on the effects of tributary flows and water exchange between Lake Michigan-Green Bay can improve our understanding of the dynamics of the bay and help future estuarine rehabilitation activities.

Salinity or TDS-related issues are a major cause of economic loss due to their devastating impact on agricultural production. The Colorado River Basin (CRB) Salinity Control Act requires the delivery of water by the US to Mexico from the Colorado River with an average maximum TDS concentration of 115 ± 30 mg/L in addition to the annual average salinity of the river at the Imperial Dam. However, traditional TDS measurements are costly and labor-intensive, and there is a need to estimate changes in TDS along the river system in the CRB. This study developed models for the estimations of TDS using remote sensing data and machine learning (ML) models. The ML models used in the study are categorized as standalone and hybrid ML models. Standalone models used include linear, random forest, and extreme gradient boosting regressors. The hybrid ML models include bagging, stacking, and adaptive boosting regressors. The reflectance data from 10-m resolution Sentinel-2A/B and the ground-truth TDS data obtained from the United States Geological Survey are used for the study. The remote sensing data was processed in the Google Earth Engine (GEE) platform. The GEE platform provides an interface to use JavaScript libraries and serves as a toolbox for visualization and geospatial analysis. The model provides a cost-effective approach for the estimation of TDS. For standalone ML algorithms, the analysis produced average Wilmot’s index of agreement (WI) and mean absolute relative error (MARE) of 0.62 and 0.36, respectively. The ensemble ML algorithms produced average WI and MARE of 0.65 and 0.45, respectively. From the results and data processing methods attempted, the ensemble ML methods have not shown better performance compared to the standalone ML models using the model evaluation metrics.

The study explains the dynamics of air-water stratified flow in the branched pipe with different diameters and restricted ventilation. The air-circulation process in a reduced-length hydraulic model with realistic diameters is investigated for the comparatively low water filling case. For this purpose, the Manning formula is used to determine the water-flow-rate-dependent normal depth in the mildly sloped circular pipes, which are both closely connected to the large-diameter pipe collector. The fluids (air and water) can be coupled in the sewer pipe not only by pressure but also due to drag, and thus, the stratified flow in the water collection system can reveal different type dynamics. The non-mixing flow conditions with the more-or-less stable interface between the air and water are used to demonstrate numerically the airflow patterns through manholes during the water-filling process. Sewer airflow analysis is usually needed to perform an odor study, and the present work gives some insight to the complexity of airflow dynamics in a poorly vented pipe. This study analyzes the air-circulation process qualitatively in the large-diameter pipe to explain the air inflow and outflow situations, which may be relevant for the outdoor air quality analysis in the urban environment. For this purpose, the 3D numerical model, representing the two-phase flow of immiscible fluids, is used. The numerical model flow regime is chosen to be turbulent, and the air and water phases are considered to be incompressible, isothermal, and Newtonian fluids. Interface tracking for the air-water flow is resolved by applying the volume of fluid method. The air exchange that is associated with the branched pipe stratified-flow regimes is presented, that is, unsteady and uniform flow conditions. The case study demonstrates the ability of the advanced numerical modeling tool to predict air-water interfacial drag and associated air exchange of the collector that is ventilated through three venting towers.

In recent years, produced water recycling and reuse has been one of the most discussed topics in water treatment in the oil and gas industry. As with conventional wastewater treatment, some important processes of produced water treatment include filtration, solid content settling, oxidation, disinfection, and storage. As such, mixing efficiency is one of the important aspects when designing a treatment and recycling system. With the help of computational fluid dynamic (CFD) methods, the design and optimization of such systems becomes feasible by accurately simulating the hydraulic, mixing, and settling characteristics of a flow field. Through CFD analysis and system configuration optimization, the efficiency of the treatment process can be improved, which results in a reduction in operation costs. Artificial neural network (ANN) based metamodel is a mathematical simulation of a complex multi-dimensional system. To train such a model, a properly selected large-scale data set is required. However, these data would be both time and resource consuming to obtain with conventional physical experiments. With the help of CFD simulations, the data for the training process becomes easier to get from the virtual parametric study. As a set of mathematical equations, the developed ANN can provide a relative accurate prediction in a much shorter time, which will be a useful tool for the practical applications. This study explores the use of CFD modeling to simulate the mixing process in one of the most common water containing devices in Permian basins: above-ground storage tanks (AST). The first part of this study details how CFD, coupled with design of experiments methods (DOE) and post-processing automation, can quickly understand the relative importance and cross-correlations of various design elements to the mixing quality. The second part of the study introduces an artificial intelligence algorithm to the workflow to carry out an optimization of the AST tank design.

Waste stabilization ponds (WSPs) are recommended to treat wastewater in small communities, which make up to 85% of the cities in the US. Thus, a novel design, the pond-in-pond (PIP), was proposed to promote an efficient and sustainable nature-based solution for wastewater treatment and agricultural reuse. This presentation summarizes the previous studies on this design, its current state-of-the-art, and the future research under development. The PIP consists of a deeper anaerobic pit, surrounded by berms, that outflow into a larger pond. Monitoring of existing systems’ data shows how the PIP treatment system outperforms a traditional pond system in terms of both consistent effluents and increased organic removal for similar-sized units so that it can be sustainably applied to cropland. Currently, the most up-to-date research on this technology focused on the pond’s hydrodynamics, using computational fluid dynamics (CFD). For comparison purposes, two equally sized ponds were modeled with a design flow of 1 MGD (0.034 m³/s). The particle settling was significantly higher on the PIP compared to the traditional pond configuration. Since most of the solids settled in the pit, this portion was analyzed comparing different length-to-width ratios (LWRs), 1:3, 1:1, and 3:1, and number of outflowing sides. The CFD simulations demonstrated that the 1:1 shaped pit was the most appropriate design for the anaerobic pit, outperforming the other LWR in all aspects. Furthermore, a preliminary analysis of ponds’ hydraulic performance as a function of its length-to-width ratio resulted in 3:1 to 4:1 for the outer pond portion of the PIP being the optimal LWRs. Future analysis will focus on how to improve overall organic matter removal, and preliminary research points to the LWR as the main factor in pond’s efficiency.

Flash floods can have devastating effects on life and property. Flash flood susceptibility is a measurement of the likelihood at which flash floods can occur in an area based on the local terrain, geographical, hydrological, meteorological, and anthropogenic factors. There have been efforts to improve the overall state of flash flood susceptibility assessment through modeling methods. In this article, we present the current state of literature on flash flood susceptibility modeling (FFSM) derived from remote sensing sources and assembled through the utilization of machine learning algorithms. The approach is based on a literature review consisting of an analysis of 26 peer-reviewed articles searched using the keywords remote sensing, flash flood, susceptibility, detection, potential, prediction, forecast, deep learning, machine learning, and artificial intelligence. Examples of ML algorithms include random forest and extreme gradient boosting. RS sources include satellite and aerial imagery. This review shows that FFSM derived through ML algorithms is accurate and reliable in determining susceptibility to flash flooding. Through a combination of multiple ML algorithms, a higher accuracy and reliability of FFSM can be observed. Common conditioning factors for model development involve relief slope, elevation, and basin size. Flash flood susceptibility modeling can be performed for any study area, causing it to be an effective tool in mitigating the effects of flash flooding. It is possible to augment current flash flood warning systems with the use of flash flood susceptibility modeling derived from machine learning algorithms with input data from remote sensing sources. This can aid future development or awareness of flooding hazards for urban areas.

Rainwater drainage is one of the essential components of traffic safety. Rainwater drainage structures are costly, and it needs continuous maintenance. Existing separate stormwater inlets absorb water non-evenly. Climate changes and the wear of drainage infrastructure need to be improved in the systems, even in highly developed countries. Such problems also will be present in areas with extensive damage to infrastructure, for example, from military action, as in Russia’s military invasion of Ukraine. The most natural city-sponge concept is the better and cheaper solution to resolve issues with rainwater drainage. The proposed concept uses sponge facilities, for example, rain gardens, permeable pavements, green roofs, etc. The authors in this paper proposed a new solution for even and quick water absorption from roads: rain-garden bands, which also dampen cars’ energy during road accidents. In this paper, the authors describe their proposed two- or three-level roads with elevated bike lanes and sidewalks, damping the energy of water falling to the lower level avoiding soil erosion, and allowing wider plant assortment in an urban landscape, and describe the design for the proposed solution. Materials and methods used in the proposed solution are as follows: a mathematical model of falling water to calculate the size of the impingement plate. The purpose and goal of implementing the proposed solution in urban are to achieve a completely secure and dry road in the city-sponge concept using rain-garden bands adapted to creating multilevel road designs. A result of the research on the matter described in this paper is the possibility of creating safe roads with rapid and stable rainwater drainage without creating complex and expensive engineering structures.

Heavy monsoon in the upper and lower Indus Basin has resulted in frequent flooding in Pakistan. Major factors causing floods include climate change, extreme rainfall, and urbanization. The 2010 and 2022 floods in Pakistan have left millions of people homeless, claimed thousands of lives, and resulted in huge economic losses. Efforts have been made to identify potential remote sensing techniques used to monitor and assess the floods in Pakistan. This study reviews the literature on the various remote sensing techniques for flood extent mapping, flood monitoring, flood hazards, exposure, and damages, as well as identifying flood resilience. This review consists of 24 peer-reviewed articles published during the 2002–2022 period, searched using keywords flood, remote sensing, and Pakistan. A few examples of remote sensing techniques used for flood studies include inundation mapping, delineating flood extent, and supervised classification using maximum likelihood to detect LULC change. Various remote sensing data sets are used to monitor and assess floods such as OLI, ETM, ETM+ (Landsat), MODIS (TERRA and AQUA), and DEM (SRTM). This review provides the remote sensing techniques used in the literature to monitor, assess, and manage disastrous situations. The findings are helpful in improving the strategies used by disaster management authorities to control future flood conditions.

Fourteen leading watershed models simulating hydrology, erosion, and transport/fate of sediment and pollutants were critically reviewed by the EWRI Total Maximum Daily Load (TMDL) Analysis and Modeling Task Committee. The review was published in Chapter 2 of the ASCE Manual of Practice (MOP) 150 “TMDL Development and Implementation—Models, Methods, and Resources,” to provide guidance in selecting models. To further advance watershed modeling, this paper has looked into some critical aspects of watershed models, specifically, spatial discretization such as lumped, semi-distributed, or fully distributed; temporal resolution in preserving the most dynamics of the physical processes; simulating best management practices; computing probability and confidence level of model outputs; incorporating computational efficient numerical solutions; and simulating constitutes of emerging concerns such as PCB and PFAS. The applicability of the models beyond TMDL, such as holistic watershed management including the consideration of climate change impacts, is discussed in the context of advancing successful and comprehensive watershed modeling.

High-intensity and short-duration monsoon rainfall can induce severe flash flood in arid region, especially in Arizona. As climate becomes warmer, climate models predicted more intensified precipitation in arid region, so flood frequency and magnitude will increase. Hydrologic modeling system (HEC-HMS) is the most commonly used rainfall and runoff model incorporated the start-of-art knowledge in hydrology, hydraulics, and sediment transport. This study applied the HEC-HMS model to predict surface runoff from Lucky Hills watershed in Walnut Gulch in southeastern Arizona. The watershed is ungagged and located in a remote mountain area. To explore the applicability of the HMS model, we applied the model to a gaged watershed within the Walnut Gulch experimental watershed. The model was used to simulate multiple precipitation events, and parameters were optimized to match the observed surface runoff. We obtained the relatively large rainfall from Data Access Project of Southwest Watershed Research Center (SWRC) and used SCS distribution function to generate the rainfall hyetograph. Multiple grids were tested until the errors between two simulations’ convergence to nearly zero. This study illustrated the finer delineation for HMS model with observation data from SWRC improves the simulated results.

Urban stormwater systems have faced flood management challenges from the extremity of catastrophic losses from flooding events in recent years. In this regard, various modeling approaches have been introduced to explore flood control options by evaluating flood risk and impacts, early forecasting flooding events, and planning emergency and recovery options (e.g., evacuation). However, decision-making can suggest different flood control options depending on the modeling strategies since the variety of flood models has historically led to intense debate on the “appropriate” approach for hydrologic modeling, with discussion focusing on the suitability of process parameterizations, data limitations and uncertainty, and computational limitations on hydrologic analysis. Thus, the selection of an appropriate modeling approach is a challenge. Therefore, this study compares rainfall-runoff simulation models to extreme flooding events in an urban watershed. This study selected hydrologic, hydraulic, and data-driven models, that is, HEC-HMS, PCSWMM, and random forest, applied to a real urban watershed. The models were developed using the meteorological, hydrologic, and spatial data obtained from the United States Geological Survey (USGS) website and extracted with the ARC-GIS tool. The model performance was evaluated using statistical error measures such as standard deviation ratio (RSR), and Nash-Sutcliffe efficiency coefficient (NSE). The results discussed the strengths and weaknesses of the three different models to evaluate rainfall-runoff relationships under extreme flooding events. The discussions in this study will be able to encourage flood managers to improve current rainfall-runoff simulation models in an integrated way to resiliently cope with extreme flooding events.

The Natural Resources Conservation Service’s (NRCS) curve number method is a popular approach for estimating discharges from rainfall data. It makes use of a dimensionless parameter—called the curve number—that is obtained from soil, antecedent moisture, and land use conditions. This parameter is traditionally taken to be a constant watershed characteristic; however, in this paper, the validity of this assumption is questioned by generating event curve numbers from a data set of 1,393 rainfall-runoff events from 61 watersheds in Texas, with each watershed having at least 10 rainfall-runoff events. For a given event, the accumulated precipitation was obtained from the rainfall data, and the accumulated depth of runoff was obtained by dividing the volume of direct runoff by the area of the watershed. From the accumulated precipitation and runoff depth, the curve number was obtained using the NRCS’s curve number method. The standard deviations of the curve numbers for each watershed were calculated, and it was found that 24 watersheds had standard deviations of the calculated curve numbers greater than 10; 35 watersheds, between 5 and 10; and only two watersheds had standard deviations of less than five. Moreover, the maximum, minimum, and average standard deviations considering all watersheds were 18, 4, and 10, respectively. The fact that these values are not small indicates that the curve number is not a constant watershed characteristic; rather, it varies from event to event—although the subject of what standard deviation is considered small may be subject to debate.

The natural hydrology and runoff water quality of a watershed change due to the conversion of pervious surfaces into impervious surfaces. This article reviews the effects of urbanization on watershed hydrology and runoff water quality. The approach of this study is based on a literature review focusing on urbanization and its impacts on watershed hydrology and runoff water quality. The literature review consists of an analysis of 107 peer-reviewed articles published from 1976 to 2022. The search used keywords such as land use, land cover, hydrology, and water quality. Most of these studies observed an increase in urbanization. The hydrologic effects of urbanization discussed in the studies include an increased surface runoff and a decreased groundwater flow and evapotranspiration rate. These areas need a detailed characterization to model the changes and their effects. Therefore, watershed hydrologic modeling techniques and numerical models are discussed in this review. Sediment and pollutant loadings depend on the type of land use and land cover changes of a watershed. In the case of urbanization, building roofs, parking lots, and roads serve as host surfaces, where pollutants accumulate and wash off during a storm. The changes in sediment and pollutant loadings are also well documented in the studies reviewed. An overview of the methods used by researchers to model these pollutants is provided. The study offers insight into the modeling tools and techniques used to estimate the impacts of urbanization on watershed hydrology and runoff water quality.

Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surge hazard is critical for coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storm of interest and assign a probability to its likelihood. The framework leverages the joint probability method with response surfaces (JPM-RS) for probabilistic hazard characterization, a storm surge machine learning model, and an SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on high-fidelity storm surge simulations provided by the US Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the United States and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard in two areas: one is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid hazard assessment that accounts for future sea-level rise.

The ASCE Manual of Practice (MOP) 150, “Total Maximum Daily Load (TMDL) Development and Implementation: Models, Methods, and Resources,” was written by the ASCE-EWRI TMDL Analysis and Modeling Task Committee. Chapter 12 of the MOP, “Modeling for Total Maximum Daily Load Implementation,” discusses state-of-the-practice on uses of models in TMDL implementation planning. The committee is currently expanding its work. In this paper, an overview of green infrastructure (GI) and modeling of those for TMDL implementation and reviews of specific models, including the USEPA’s GI Modeling Toolkit and the Landscape and Green Infrastructure Design (L-GrlD) model for designing GI strategies with flexibility to support stakeholder engagement and decision support tools for stormwater management that can advance the TMDL implementation state-of-the-practice, are presented. Non-traditional approaches are presented with cost-effective implementation while broadening the scope of TMDL to combine with other similar watershed management efforts for greater synergy. Designing of TMDL implementation measures (e.g., BMPs), including engaging stakeholders and managing multiple co-benefits and diverse costs, is discussed as those are crucial steps for the success of TMDLs. Emerging technologies, for example, remote sensing, are discussed with an example from the perspective of efficacy of the implementation measures in the long run.