The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance.

While research on the hydrologic impact of different types of stormwater control measures (SCMs) is extensive, little research exists linking urbanization, widespread implementation of SCMs and channel stability in headwater streams. This study evaluated whether the unified stormwater sizing criteria (USSC) regulations in the state of Maryland, USA, which require the use of both end‐of‐pipe and distributed, small‐scale SCMs, protect channel stability. To achieve this goal, a coupled hierarchical modelling approach utilizing the Storm Water Management Model (SWMM) and the Hydrologic Engineering Center River Analysis System 6.3 (HEC‐RAS) was developed to predict changes in streamflow and sediment transport dynamics in a first‐order gravel‐bed, riffle‐pool channel. Storm event discretization revealed that 88% of observed storm events during the 16 years (2004–2020) had durations less than 18 h and that the greatest peak flows resulted from storm events with durations less than 24 h. HEC‐RAS simulation results also showed that both channel degradation and aggradation, as high as 1.2 m, will likely occur due to regulations which require the use of 24 h duration design storms with a target stormwater detention time rather than bed material sediment transport limits. Overall, this study provides valuable insights into the complex interactions between SCM practises, flow regimes and sediment transport dynamics in heavily urbanized watersheds. It is recommended that SCMs be designed using a continuous simulation model with at least 10 years of continuous rainfall data. Furthermore, to protect channel stability, the SCM design goal should focus on maintaining pre‐development sediment transport regimes across a range of flows.

Urbanization changes land cover through the expansion of impermeable surfaces, leading to a significant rise in runoff, sediment, and nutrient loading. The quality of stormwater is related to land use and is highly variable. Currently, stormwater is predominantly described through watershed models that rely minimally, if at all, on field monitoring data. The simple event mean concentration (EMC) wash-off approach by land use is a common method for estimating urban runoff loads. However, a major drawback of the EMC approach is it assumes concentration remains constant across events for a specific land use. Build-up/wash-off equations have been formulated to consider variations in concentration between events. However, several equation parameters are challenging to estimate, making them difficult to use. We conducted a monitoring and modeling study and investigated the impact of land use on stormwater quantity and quality and optimized and investigated the build-up/wash-off parameters for three homogenous urban land uses to estimate nutrients (nitrogen and phosphorus) and sediment loads. Stormwater from commercial, medium-density residential, and transportation land uses was sampled using automatic samplers during storm events, and water quality was characterized for a variety of them for 14 months. Analysis of stormwater samples included assessments for total nitrogen, total phosphorus, and total suspended solids. Results showed that medium-density residential land use had the highest median total nitrogen and total phosphorus event mean concentrations and commercial had the highest median total suspended solids EMCs. Water quality parameters (or build-up/wash-off parameters) exhibited significant variation between land uses, confirming that land use is a key determinant of stormwater quality. The median particle size for each land use was less than 150 μm, indicating that the most common particle size in stormwater was a very fine sand or smaller. This small size should be considered by stakeholders in the design of stormwater treatment systems.

Sunlight plays a key role in the nutrient cycle within streams. Streams are often piped to accommodate urban residential or commercial development for buildings, roads, and parking. This results in altered exposure to sunlight, air, and soil, subsequently affecting the growth of aquatic vegetation, reducing reaeration, and thus impairing the water quality and ecological health of streams. While the effects of urbanization on urban streams, including changing flow regimes, stream bank and bed erosion, and degraded water quality, are well understood, the effects of piping streams on dissolved oxygen (DO) concentrations, fish habitats, reaeration, photosynthesis, and respiration rates are not. We addressed this research gap by assessing the effects of stream piping on DO concentrations before and after a 565-m piped section of Stroubles Creek in Blacksburg, VA, for several days during the summer of 2021. Results indicate that the DO level decreased by approximately 18.5

The Storm Water Management Model (SWMM) is an urban watershed model for simulating urban runoff quantity and quality. Although SWMM has been widely used, little to no research has explored the effect of fitting the same SWMM dataset to multiple hydrologic conditions. This paper assesses the impact of best-fit parameter estimates on the hydrologic responses of dry and wet hydroclimatic conditions, evaluates parameter uncertainty, and identifies the physical processes that govern parameter changes during dry and wet conditions. We compared the ability of SWMM to predict flows when independently calibrated to a dry and a wet year, respectively, using an automatic calibration program for SWMM, OSTRICH-SWMM (Optimization Software Toolkit for Research Involving Computational Heuristics). The model developed using SWMM calibrated to a wet year performed better during the model assessment period. The best fit estimates of SWMM parameters differed significantly between dry and wet years. For instance, Manning's roughness coefficient for overland flow was higher in a dry year, as less runoff meant less flow depth on surfaces. Some parameters, e.g., % effective imperviousness, exhibited an expanded posterior probability distribution, increasing the uncertainty of the parameter estimate. However, other parameters, such as Manning's roughness coefficient for streams were the least sensitive and well-defined as the width of their posterior distribution only spanned a relatively narrow range which did not change drastically between the two cases. The robust assessment conducted in this study demonstrated that the hydrologic behavior of SWMM was different between models developed and calibrated for dry and wet hydroclimatic conditions. The different optimal parameter values suggest that although the processes and mechanisms driving hydrologic processes in dry and wet years are similar, their magnitude differs greatly. This study highlights that precipitation patterns affect the selection of optimal parameter values and the water budgets differed markedly between dry and wet years, as expected. The results of this study can assist urban watershed planners in selecting the most appropriate model to use for prediction purposes or when there is a paucity of data available for model calibration. This study recommends changing SWMM model parameters on an annual basis when projecting the effects of new conditions outside of current experience (i.e., land use and climate change) on urban runoff quantity and quality.

Estimating pollutant loads from developed watersheds is vitally important to reduce nonpoint source pollution from urban areas, as a key tool in meeting water quality goals is the implementation of Stormwater Control Measures (SCMs). SCMs are selected and sized based on influent pollutant loads. A common method used to estimate pollutant loads in urban runoff is the Event Mean Concentration (EMC) method. In this study, we develop and apply data-driven models using Random Forest (RF), a machine learning approach, to predict Total Nitrogen (TN), Total Phosphorus (TP), Total Suspended Solids (TSS), and Ortho-Phosphorus (Ortho-P) EMCs in urban runoff. The parameters considered in this study were climatological characteristics (i.e., Antecedent Dry Period or ADP, Precipitation Depth or P, Duration or D, and Intensity or I) and catchment characteristics including land use-related parameters including Imperviousness or Imp, Saturated Hydraulic Conductivity or Ksat, and Available Water Capacity or AWC), and site-specific parameters including Slope (S), and Catchment Size (A). Stormwater quality data for this study were obtained from the National Stormwater Quality Database (NSQD), which is the largest repository of stormwater quality data in the U.S. Results demonstrate that land use-related characteristics (i.e., Imp, Ksat, and AWC) were the most effective variables for predicting all EMCs. For TP, TSS, and Ortho-P, site-specific characteristics (S and A) had a greater effect than climatological characteristics (i.e., ADP, P, D, and I). However, for TN, climatological characteristics had a greater effect than site-specific characteristics (S and A). In addition, for TN, TP, and TSS, precipitation characteristics (P, D, and I) were found to be more effective parameters for estimating EMCs than ADP. This study highlights the most influential parameters affecting EMCs which can be used by stakeholders and SCMs designers to improve estimates of nutrients and sediment EMCs. The selection and design of the highest performing SCMs is essential in achieving effective treatment of stormwater, attaining water quality goals, and protecting downstream waterbodies.

The Storm Water Management Model (SWMM) is a widely used urban watershed model. We compared the ability of SWMM to predict flows when independently calibrated to a dry and wet year, respectively, using OSTRICH-SWMM. The model developed using SWMM calibrated to a wet year performed better in the model assessment period. The water budgets differed markedly between the dry and wet years. The best fit estimates of SWMM parameters differed significantly between dry and wet years. For instance, Manning’s roughness coefficient for overland flow was higher in a dry year, as less runoff meant less flow on already wetted surfaces. Some parameters, e.g., % effective imperviousness, exhibited an expanded posterior probability distribution, increasing uncertainty of the parameter estimate. However, other parameters, such as Manning’s roughness coefficient for streams were well-defined. These changes in parameter sets for dry and wet hydroclimatic conditions affect the hydrological response of an urban watershed.

Urbanization increases runoff, sediment, and nutrient loadings downstream, causing flooding, eutrophication, and harmful algal blooms. Stormwater control measures (SCMs) are used to address these concerns and are designed based on inflow loads. Thus, estimating nutrient and sediment loads is important for meeting restoration objectives. Pollutants accumulate on surfaces during dry periods, making Event Mean Concentration (EMC) a function of antecedent dry period (ADP). An EMC results from wash-off of accumulated pollutants from catchment surface during runoff events. However, several studies found little to no correlation between constituent concentrations in stormwater and ADP. The objective of this study is to verify this finding and discover which climatological or catchment characteristics most significantly affect stormwater quality. Stormwater quality data were obtained from the National Stormwater Quality Database (NSQD), which is the largest data repository of stormwater quality data in the U.S. Bayesian Network Structure Learner (BNSL) was used to assess the relationships between catchment characteristics, climatological information, and stormwater quality for selected land uses. Given the optimal BN structure, it was determined which parameters most affect stormwater quality EMCs. The results demonstrate that both catchment and rain characteristics affected stormwater quality EMCs. Among catchment characteristics, land use (LU) was the most important factor and catchment size was the least. Precipitation depth (P) and duration (D) affected Total Phosphorus (TP), Total Nitrogen (TN), and Total Suspended Solids (TSS). This indicated that it is likely that P and D had a greater influence on stormwater quality more than ADP. P, D, and ADP affected the dissolved constituents of TN (i.e. NO2-N/NO3-N) and TP (i.e. Ortho-P). Compared to other factors (i.e. P and D), the effect of ADP on TSS was negligible. Stormwater quality EMCs related to nitrogen were not affected by catchment slope (S). However, TSS and Ortho-P were influenced by S.

The USEPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) is one of the most widely used numerical models to simulate urban runoff and drainage. A typical SWMM project has hundreds or thousands of sub-catchments and more than 20 parameters associated with six different physical processes for each sub-catchment. Consequently, model calibration is a challenging task. In this study, SWMM was integrated with the Optimization Software Tool for Research Involving Computational Heuristics (OSTRICH) to perform single- and multi-objective automatic calibration. The newly developed OSTRICH-SWMM is an open-source tool with dozens of parallelized optimization algorithms. A catchment in Buffalo, NY was selected as a case study and was calibrated according to two competing criteria: (1) minimizing errors in simulated peak flow, and (2) minimizing errors in total flow volume. The Pareto front for the case study was obtained using a multi-objective calibration algorithm and this allowed for evaluating tradeoffs between the peak flow and total volume criteria. The results demonstrate that OSTRICH-SWMM is a promising tool for automatic calibration of SWMM models.

Identifying spawning grounds of Asian carp is important for determining the reproductive front of invasive populations. Ichthyoplankton monitoring along the Illinois Waterway (IWW) has provided information on abundances of Asian carp eggs in the IWW's navigation pools. Post-fertilization times derived from egg development stages and water temperatures can be used to estimate spawning times of Asian carp eggs, but estimating how far these eggs have drifted requires information on river hydraulics. A Fluvial Egg Drift Simulator (FluEgg) program was designed to predict the drift of Asian carp eggs in the riverine environment with egg growth considered. This paper presents a reverse-time particle tracking (RTPT) algorithm for back-casting the spawning location of eggs from their collection site. The RTPT algorithm was implemented as a module in FluEgg. The new version of FluEgg was coupled with an unsteady hydrodynamic model of the IWW to predict the spawning locations for 530 eggs that were collected in June 2015. The results indicate that tailwater sections below the Locks and Dams (L&Ds) in each navigation pool appear to be preferred spawning locations for Silver Carp (a species of Asian carp). From the data analyzed, the most upstream spawning location for the June 2015 spawning period was in the upper Marseilles navigation pool, downstream of the Dresden Island L&D. The RTPT algorithm can efficiently estimate spawning locations for multiple egg samples.