Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM - an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Stormwater infrastructure designers and operators rely heavily on the United States Environmental Protection Agency’s Storm Water Management Model (SWMM) to simulate stormwater and wastewater infrastructure performance. Since its inception in the late 1970s, improvements and extensions have been tested and evaluated rigorously to verify the accuracy of the model. As a continuation of this progress, the main objective of this study was to quantify how accurately SWMM simulates the hydrologic activity of low impact development (LID) storm control measures. Model performance was evaluated by quantitatively comparing empirical data to model results using a multievent, multiobjective calibration method. The calibration methodology utilized the PEST software, a Parameter ESTimation tool, to determine unmeasured hydrologic parameters for SWMM’s LID modules. The calibrated LID modules’ Nash–Sutcliffe efficiencies averaged 0.81; average percent bias (PBIAS) −9%; average ratio of root mean square error to standard deviation of measured values 0.485; average index of agreement 0.94; and the average volume error, simulated vs. observed, was +9%. SWMM accurately predicted the timing of peak flows, but usually underestimated their magnitudes by 10%. The average volume reduction, measured outflow volume divided by inflow volume, was 48%. We had more difficulty in calibrating one study, an infiltration trench, which identified a significant limitation of the current version of the SWMM LID module; it cannot simulate lateral exfiltration of water out of the storage layers of a LID storm control measure. This limitation is especially severe for a deep LIDs, such as infiltration trenches. Nevertheless, SWMM satisfactorily simulated the hydrologic performance of eight of the nine LID practices.

Stormwater management seeks to reduce runoff from rain or melted snow and improve water quality.Where it can absorb into soil, runoff is filtered and returns to streams, rivers, and aquifers, but in developed areas, precipitation often cannot soak into the ground because impervious surfaces (e.g., pavement, buildings), and already saturated soils can create excess runoff.This water, which can contain pollutants, then runs across urban surfaces and into storm drains, drainage ditches, and sewer systems.Stormwater runoff can cause flooding, erosion, infrastructure and habitat damage, and contamination (including combined and sanitary sewer overflows).In urban and developed areas, effective stormwater management that routes and detains stormwater helps to mitigate these impacts and improve water quality.

Water infrastructure simulation models, such as EPANET and SWMM, have played an important role in the development and dissemination of simulation technologies for water distribution, stormwater and sanitary sewer systems. These software packages have performed varying functions for different portions of the water infrastructure community. Municipalities, water utilities, and consultants use these packages to ensure adequate water

The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peer-reviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model's ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced-making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructure (GI) practices. The framework conceptualizes GI practices using blocks (spatial features) and connectors (interfaces) representing functional components of a GI. The blocks represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, or a generic storage zone) and water quality constituents including chemical constituents and particles. The hydraulic module can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models. The particle transport module is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed, bound to particles, and undergo user-defined transformations. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating urban GI performance.

CANARY Contamination event detection system CMMS Computerized maintenance management systems CWS Contamination warning system DHS Department of Homeland Security DSL Digital subscriber lines EDS Event detection system EPA U.S. Environmental Protection Agency GA Genetic algorithm GAO Government Accountability Office h Hours HMI Human–machine interface ICS Industrial control system IT Information technology LIMS Laboratory information management system LAN Local area network MCMC Marko chain Monte Carlo MILP Mixed integer linear program

The U.S. Environmental Protection Agency National Stormwater Calculator (NSWC) simplifies the task of estimating runoff through a straightforward simulation process based on the EPA Stormwater Management Model. The NSWC accesses localized climate and soil hydrology data, and options to experiment with low‐impact development (LID) features for parcels up to 5 ha in size. We discuss how the NSWC treats the urban hydrologic cycle and focus on the estimation uncertainty in soil hydrology and its impact on runoff simulation by comparing field‐measured soil hydrologic data from 12 cities to corresponding NSWC estimates in three case studies. The default NSWC hydraulic conductivity is 10.1 mm/h, which underestimates conductivity measurements for New Orleans, Louisiana (95 ± 27 mm/h) and overestimates that for Omaha, Nebraska (3.0 ± 1.0 mm/h). Across all cities, the NSWC prediction, on average, underestimated hydraulic conductivity by 10.5 mm/h compared to corresponding measured values. In evaluating how LID interact with soil hydrology and runoff response, we found direct hydrologic interaction with pre‐existing soil shows high sensitivity in runoff prediction, whereas LID isolated from soils show less impact. Simulations with LID on higher permeability soils indicate that nearly all of pre‐LID runoff is treated; while features interacting with less‐permeable soils treat only 50%. We highlight the NSWC as a screening‐level tool for site runoff dynamics and its suitability in stormwater management.

A water quality sampling model is developed that provides a quantitative basis for determining sampling locations and schedules. The deterioration of water quality in distribution systems is known to correlate strongly with water age. The sampling model developed here uses water age as the basis for determining sample "representativeness." A mixed integer linear programming formulation is described and an example application is developed. The application illustrates how sampling objectives can be parameterized within the model and how unmodeled issues effect the implementation of the sampling plans produced.

Many engineering and environmental problems that involve the determination of unknown system characteristics from observation data can be categorized as inverse problems. A common approach undertaken to solve such problems is the simulation-optimization approach where simulation models are coupled with optimization or search methods. Simulation-optimization approaches, particularly in environmental characterization involving natural systems, are computationally expensive due to the complex three-dimensional simulation models required to represent these systems and the large number of such simulations involved. Emerging grid computing environments (e.g., TeraGrid) show promise for improving the computational tractability of these approaches. However, harnessing grid resources for most computational applications is a nontrivial problem due to the complex hierarchy of heterogeneous and geographically distributed resources involved in a grid. This paper reports and discusses the development and evaluation of a grid-enabled simulation-optimization framework for solving environmental characterization problems. The framework is designed in a modular fashion that simplifies coupling with simulation model executables, allowing application of simulation-optimization approaches across problem domains. The framework architecture utilizes standard communications protocols and the message passing interface with an application programming interface to establish a connection between a centralized search application and simulation models running on TeraGrid resources. Sets of performance and scalability results for solving a groundwater source history reconstruction (SHR) problem are presented. The results show that for a given set of resources, parameters controlling the granularity at various levels of parallelism play an important role in the overall parallel performance. A production run for solving the SHR problem using three geographically distributed grid resources indicates that even in a cross-site grid environment a factor of 90 speedup is possible using 140 computer processors.

The design of sensor networks for monitoring contaminants in water distribution systems is currently an active area of research. Much of the effort has been directed at the contamination detection problem and the expression of public health protection objectives. Monitoring networks once they are in place, however, are likely to be used to gather monitoring data for source inversion as well. Thus, the design of these networks with the unique objectives associated with source inversion problems in mind is a necessity. Source inversion problems in water distribution systems are inherently underdetermined and exhibit solution nonuniqueness; and moreover, the structure of the errors associated with a solution are a function of monitoring observations. Optimal inverse experiment design is investigated as an approach for improving solution quality. The approach involves the selection of monitoring locations that are best suited to the generation of a well-conditioned source identification inverse problem. The monitoring design problem is formulated as a nonlinear combinatorial optimization problem and solved using a genetic algorithm. The monitoring designs generated exhibit an optimal substructure that may be exploited to develop more efficient methods of solution. An analysis is conducted to evaluate the source inversion performance of an optimized monitoring network relative to networks designed using different methods. The results of the analysis demonstrate that when the source identification problem is underdetermined, the number of monitoring sensors installed in the network is more important than the method used to locate them.

This paper describes experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires readily available computational resources of the grid for computational tractability. The solution framework developed by the authors uses a master—worker strategy for task distribution and a pool for task mapping. Solution and computational performance results are presented for groundwater source identification and release history reconstruction problems. They indicate that high-quality solutions and significant raw performance improvements were attained for a deployment of the solution framework on the TeraGrid.

A two-step approach is proposed to assist forensic investigation of possible source locations following a contaminant detection in drinking water systems. Typically this identification problem is ill posed as it has more unknowns than observations. First, linear algebra is employed to rule out potential contaminant injections. Second, an entropic-based Bayesian inversion technique, the minimum relative entropy method, solves for the remaining variables. This formulation allows for the less committed prior distribution with respect to unknown information and can include model uncertainties and measurement errors. The solution is a space-time contaminant concentration probability density function accounting for the various possible injections that may be the cause of the observed data. Besides, a probability measure quantifying the odds of being the actual location of contamination is assigned to each potential source. Effectiveness and features of the method are studied on two example networks.

Groundwater characterization involves the resolution of unknown system characteristics from observation data, and is often classified as an inverse problem. Inverse problems are difficult to solve due to natural ill-posedness and computational intractability. Here we adopt the use of a simulation–optimization approach that couples a numerical pollutant-transport simulation model with evolutionary search algorithms for solution of the inverse problem. In this approach, the numerical transport model is solved iteratively during the evolutionary search. This process can be computationally intensive since several hundreds to thousands of forward model evaluations are typically required for solution. Given the potential computational intractability of such a simulation–optimization approach, parallel computation is employed to ease and enable the solution of such problems. In this paper, several variations of a groundwater source identification problem is examined in terms of solution quality and computational performance. The computational experiments were performed on the TeraGrid cluster available at the National Center for Supercomputing Applications. The results demonstrate the performance of the parallel simulation–optimization approach in terms of solution quality and computational performance.

Conventional hydraulic models can be used to analyze operational scenarios for a variable‐speed pump (VSP) delivering target hydraulic characteristics of either the fixed hydraulic head or the desired pump flow. However, modelers must repeatedly adjust relative speed factors for achieving the target hydraulic performance, which can be a time‐consuming task. A new solution method has been developed for directly calculating the required VSP speed to provide a fixed hydraulic head. However, this method needs to be extended as a generic modeling approach for practical applications. This article presents an enhanced VSP solution method that extends the previously developed VSP hydraulic network solver to automatically calculate the relative pump‐speed coefficient for attaining the prescribed hydraulic head or pumping the preferred amount of flow. Using this improved method, engineers can model not only a single VSP but also multiple VSPs with rule‐based logic controls. This allows the target control head to be specified at any location or for the target flow to be specified for VSPs of different capacities. The extended method provides a flexible, robust modeling approach for engineers to analyze a variety of VSP configurations in water and wastewater collection systems.

This paper investigates groundwater system characterization problem, in this inverse problem the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optimization approach is a technique utilized to solve inverse problems by formulating them as an optimization model, where evolutionary algorithms are used to perform the search. In this approach, the PDE groundwater transport simulation model (simulation model) is solved iteratively during the evolutionary search, which in general can be computationally expensive. To overcome this limitation, high performance computing and grid computing are used to improve the simulation model efficiency. The parallel PDE groundwater transport simulation model is then coupled with an evolutionary computation search procedure to solve two instances of groundwater inverse problems. The results demonstrate the performance of the grid-enabled simulation-optimization approach in terms of solution quality and computational performance.

Monitoring network design for the detection of contaminants in water distribution systems is currently an active area of research. Much of the effort has been directed at the contamination detection sub-problem and the expression of public health protection objectives. Monitoring networks, once they are in place, however, are likely to be used to gather monitoring data for source inversion as well. Thus, the design of these networks with the unique objectives associated with source inversion in mind is a necessity. Source inversion problems in water distribution systems are inherently under-determined and exhibit solution non-uniqueness. Judicious monitoring design is one approach for addressing these difficulties. Herein, discrete linear inverse theory is applied to the monitoring sensor network design problem; in particular, a quantitative description of solution existence, uniqueness, stability, and resolution is developed using singular value decomposition.

The problem of identifying the locations (or network areas) that may be the source of a detected contamination event in drinking water distribution systems, is typically ill-posed and an infinite set of solutions is possible. For a conservative (or first order reactive) contaminant, the observed concentration behavior at any network node can be written as linear combination of contaminant injections at previous times. Linear algebra analysis is thus employed to account for prior information and pipe grouping to improve problem invertibility. Given the possible multiple solutions and presence of uncertainties, the minimum relative entropy method is suggested for solving the inverse problem. Thus, a probability density function rather than a specific contaminant injection value is found for each potential contamination source node. Several analytical results may be derived when this method is applied to linear systems. Moreover, this entropic formulation provides significant advantages when dealing with a prior bias in the estimated probability function, it may allow for the less committed prior distribution with respect to unknown information, and it does not introduce spurious structures that do not reflect the real physical system.