Urban development normally occurs at the urban-rural interface, often in a spider-like appearance known as 'urban sprawl'. Mixed urban-rural watersheds are likely to experience hydrologic alterations such as increased peak flow and pollutant loads, further intensified by climate change. The implementation of low-impact development (LID) controls and best management practices (BMPs) offers a promising approach to improve watershed resilience. However, determining the optimal type, size, and placement of these measures remains challenging due to multiple objectives and constraints. We present a multi-objective simulation-optimization tool that integrates a coupled modeling framework with an evolutionary algorithm to optimize the performance of LID and BMP strategies. The simulation framework sequentially couples the Storm Water Management Model (SWMM) and the Soil and Water Assessment Tool Plus (SWAT+), and links to a cost estimation module to identify cost-effective stormwater control measures (SCMs) that balance peak flow reduction and runoff volume reduction. This tool was applied to the Stroubles Creek watershed in Montgomery County, Virginia, to optimize SCMs across a mixed urban-rural landscape. The tool generated a set of Pareto-optimal strategies that illustrate trade-offs among peak flow reduction, volume reduction, and implementation cost, helping stakeholders make informed decisions based on regulations, budgets, and practical constraints.

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.

The fast-paced urbanisation has resulted in urban-rural transitional zones known as 'sprawl' that exhibit both urban and rural characteristics. This transformation significantly impacts water balance, leading to changes in surface runoff patterns in these mixed urban rural watersheds. The contrasting runoff generation patterns in urban and rural land uses pose significant challenges for accurately simulating runoff in these urban rural watersheds. To address these mixed watersheds, we developed an Integrated Environmental Modelling (IEM) approach to effectively represent complex hydrological behaviours in both urban and rural areas by integrating two widely used models: the Storm Water Management Model (SWMM) and the Soil and Water Assessment Tool Plus (SWAT+). The integrated model provides a means for coupling models, data storage, analysis, visualisation, and decision support systems. This framework links PYSWMM and PYSWAT+, offering automatic calibration of the integrated model in Python, which improves runoff simulation by considering distinct runoff generation patterns for each land use and the unique characteristics of each subcatchment. This study aims to improve our understanding of how runoff generation and streamflow behaviour vary across urban and rural areas by evaluating the performance of a coupled SWMM-SWAT+ model. A case study conducted in the urban-rural watershed of Stroubles Creek, Virginia, USA, demonstrated that the integrated model outperformed both standalone models in predicting streamflow during dry and wet periods. This study successfully shows that the SWMM-SWAT+ integration can effectively represent hydrological processes in mixed urban-rural watersheds, achieving higher accuracy than standalone models. The integrated model achieved higher accuracy than the standalone models, improving NSE from 0.51 (SWAT+) and 0.64 (SWMM) to 0.85, reducing RSR from 0.70 (SWAT+) and 0.60 (SWMM) to 0.39, and lowering PBIAS to 16.8%, compared to 37.87% (SWAT+) and -70.37% (SWMM), demonstrating its superior performance in streamflow simulation. These findings provide a practical and adaptable modelling framework for improving stormwater management, flood risk assessment, and sustainable water resources planning in transitional watersheds experiencing both urban and rural influences.

Communities increasingly face challenges from changing hydrological patterns caused by the combined effects of climate change and land use/land cover (LULC) change. However, the hydrological response of mixed land use watersheds to these combined effects remains poorly understood. To address this gap, we developed an automated platform to retrieve projected climate and LULC data and input them into the Storm Water Management Model (SWMM) and the Soil and Water Assessment Tool Plus (SWAT+). These models were coupled to assess the impacts of climate and LULC change on the Stroubles Creek watershed in Montgomery County, Virginia. Climate projections were obtained from CMIP6 and combined with LULC projections from the Integrated Climate and Land Use Scenarios (ICLUS) data source. The joint impacts of climate and LULC change were assessed over three 30-year periods, baseline (1981-2010), mid-century (2031-2060), and late-century (2071-2100), using projections from two Shared Socioeconomic Pathways (SSP245 and SSP585). Results indicate that temperatures are projected to increase under both scenarios in the mid- and late-century periods, with a maximum rise of nearly 9 °C by the late century under SSP585. The warming is most pronounced in winter, while spring and summer exhibit comparatively milder increases. Precipitation projections indicate a shift toward more intense rainfall events under most scenarios, with the exception of mid-century SSP585. In the mid-century SSP245 scenario, precipitation increases by up to 21.6 %. Expanding urban land use, together with these higher-intensity storms, leads to a sharp rise in urban peak flows, with 25-year flood magnitudes increasing by up to 100 % in the mid-century SSP245 scenario. In contrast, the decline in mean annual precipitation, coupled with the expansion of impervious surfaces, leads to substantial reductions in 7Q30 in urban areas, with decreases reaching nearly 45 % under the late-century SSP585 scenario. These findings underscore that urban areas are particularly vulnerable to the compounded effects of climate and land use change, facing amplified flood risks and reduced drought resilience compared to rural portions of the watershed.

Due to the recent improved availability of global and regional climate change (CC) models and associated data, the projected impact of CC on urban stormwater management is well documented. However, most studies are based on simplified design storm analysis and unit-area runoff models; evaluations of the long-term, continuous hydrologic response of extensive stormwater control measures (SCM) implementation under future CC scenarios are limited. Moreover, channel stability in response to CC is seldom evaluated due to the input data required to develop a long-term, continuous sediment transport model. The study objective was to evaluate the impact of CC on storm event-based flow regimes and channel stability in a small, urbanized catchment (0.9 km2) in Montgomery County, Maryland, USA. This study employed a previously developed sequential, hierarchical modeling approach, integrating a watershed-scale Storm Water Management Model (SWMM) with the Hydrologic Engineering Center River Analysis System (HEC-RAS) to achieve the study goal. Ensemble modeling results indicate that conclusions related to impacts on SCM performance drawn from simplified, unit area models are not supported by findings from dynamic, continuous simulations that consider the complexities of real urban catchments and SCM interactions. Despite a general decrease in the total rainfall amount of individual storm events for most storm events, there is a noted increase in intensity for nearly all future storm events compared to current climatic conditions. This change in storm event-based rainfall pattern is expected to drive the catchment-scale hydrology to a flashier regime in the future, which in turn is expected to increase the extent of channel erosion compared to the current climate condition. A multicriteria design approach considering the interplay of multiple SCMs and local sediment transport capacity is thus necessary to ensure channel stability under changing climate.

The occupation and use of increasingly impermeable urban land have made it difficult to infiltrate water and, consequently, increase the volume of runoff in different cities, which has required the development of bioretention techniques in the field of hydrology. The aim of this article is to define and apply criteria for the identification of areas for the construction of Bioretention systems for evaluations based on Geographic Information System indicators, considering the aspects of quantity and quality in urban drainage . The developed method allows to verify and compare changes in the surface of urban areas and their interference in the local environment , the mapping of land use and occupation to simplify procedures to define and prioritize areas for the construction of Bioretention systems, the use of resources from georeferenced bases to resolve eco-hydrological issues. The study develops technical bases for the use of a georeferencing tool to analyze areas with speed and consistency as a basis for decisions on the implementation of Bioretention systems

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

Nitrogen (N) has received attention as an indicator of water quality and pollution. However, there is still a lack of systematic research on the influence of temperature. An experiment was conducted with five tanks containing sediments from the Pearl River Delta region of China and distilled water to assess the release of N from sediments under controlled conditions; temperatures from 10 to 30 degrees C were assessed. Results show that the effect of temperature on N in the water column and sediment is nonlinear. NO3-N was affected at temperatures between 20 and 25 degrees C in shallow sediments (1-3 cm below the sediment-water interface) with rapid increase concentration, while NH4-N concentration in water column was decreased significantly with increased temperature between 10 and 15 degrees C. NO3-N was dominant at temperatures from 25 to 30 degrees C. However, the presence of NH4-N in a water body can inhibit its release, thus the relationship between the diffusive flux with temperature is not linear. The relationship between N diffusive flux at the sediment-water interface was described by Polynomial2D and Lorentz2D models.

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.

Communities are coping with changes in runoff quantity and quality, stemming mainly from changes in climate and land use/land cover (LULC); there is a need to identify the most adaptable strategies that improve community resilience. However, the joint impacts of climate and LULC change have rarely been assessed at local scales. To address these needs, we assessed the response of runoff and pollutant loads from Broad Run, a rapidly developing watershed in northern Virginia, to projected climate and LULC change. Climate data from two downscaled Global Climate Models (GCMs) were used to force an urban watershed model, the Storm Water Management Model (SWMM), while forecasts of LULC change were derived from the Chesapeake Bay Land Change Model (CBLCM). Two Representative Concentration Pathways (RCPs), 4.5 and 8.5, a historic baseline (1995–2020), and projected periods (2040–2065); and four LULC change scenarios designated agricultural conservation (AC), forest conservation (FC), growth management (GM), and historical trend (HT) were used to create a series of ensemble simulations of coupled LULC and climate change. Results indicated that, under RCP 8.5, annual precipitation is projected to increase substantially more than RCP 4.5. Projected LULC change resulted in a projected increase in imperviousness from 6.3% to 13.1%. Results indicated that climate change will likely increase the seasonal variability of runoff, Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) for both RCPs. The largest increase for a single LULC change (without climate change) scenario for runoff, TSS, TN, and TP was 32.6%, 33.4%, 31.6%, and 35.8%, respectively. which occurred with the HT scenario. Results of LULC change also indicated that more pollutant loads were associated with increased imperviousness from increased urban development and loss of deciduous forests and grasslands. The largest increase for climate and LULC change scenarios in runoff, TSS, TN, and TP was 67.6%, 66.7%, 63.4%, and 69.4%, respectively, which occurred with the RCP 8.5 and HT scenarios. Similar, but smaller increases were obtained for other scenarios, suggesting that climate and LULC change may be synergistic, likely undermining watershed restoration efforts. The results of our study also indicate that runoff, TSS, TN, and TP are expected to be more affected by changes in future LULC than by projected changes in climate. Our study can be used to inform watershed restoration efforts, urban planning, and environmental policy. The combined impact of climate and LULC change will likely generate increased runoff, and nutrients and sediment loading, indicating that robust mitigation strategies are needed for watershed restoration to succeed.

Ridge-furrow rainwater harvesting (RFRH) is known to be effective in controlling water loss and soil erosion, and increasing soil moisture and crop yield in semiarid regions. However, it can cause waterlogging, ridge overtopping, and harvest failure if not properly designed. A four consecutive-year field trial was carried out to assess the impacts of various slope gradients and tillage practices on soil moisture, runoff, sediment yield, associated soil nutrient losses, fodder yield, and water use efficiency (WUE) of alfalfa in the Loess Plateau, China. The trial adopted a split-plot design, taking slope gradient (5 degrees and 10 degrees) as main plot treatment and tillage practice (traditional tillage, open-ridging, and tied-ridging) as split plot treatment, from 2015 to 2018. There were greater variations in runoff, sediment yield, and associated soil nutrient losses than in fodder yield and WUE. Tied-ridging and open-ridging resulted in decreased runoff and reduced sediment transport and associated soil nutrient losses, and increased soil moisture, fodder yield, and WUE. Runoff, sediment transport, and associated soil nutrient losses inclined with increasing slope; while fodder yield and the WUE declined. For slopes of 5 degrees and 10 degrees, the average decrease in runoff, sediment yield, total nitrogen, total phosphorus, and organic matter losses for open ridging were 47.7-56.2%, 91.0-92.7%, 90.4-93.1%, 90.1-92.2%, and 88.9-90.2%, respectively, while these for tied-ridging were 62.4-68.4%, 94.5-96.4%, 93.6-95.5%, 93.9-95.9%, and 93.0-94.9%, over 4 years as compared with traditional tillage. The mean annual runoff, sediment, total nitrogen, total phosphorus, and organic matter losses for slopes of 10 degrees were 1.31, 1.96, 1.87, 1.99, and 1.64 times greater than the corresponding values for slopes of 5 degrees, respectively. The increase of fodder yield and WUE for open ridging and tied-ridging was 40.1-45.6% and 16.0-17.5%, and 3.66-5.99 and 1.35-2.50 kg ha(-1) mm(-1), respectively, for slopes of 5 degrees and 10 degrees. The average fodder yield and WUE for slopes of 5 degrees were 1.19 and 1.23 times greater than corresponding values for slopes of 10 degrees, respectively. The disparities in fodder yield and WUE of alfalfa between slopes of 5 degrees and 10 degrees were not clear in dry years, but were evident in wet years. Tied-ridging was recommended for water and soil conservation, while open-ridging was recommended for increase of fodder yield and WUE of alfalfa. The implementation of RFRH in crop production should be evaluated by the use of a model in accordance with climatic conditions, soil type, and plant species. (C) 2021 International Research and Training Center on Erosion and Sedimentation, China Water and Power Press, and China Institute of Water Resources and Hydropower Research. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

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.

Download a PDF of "Watershed Approach to Mitigating Hydrologic Impacts of Transportation Projects: Conduct of Research Report" by the National Academies of Sciences, Engineering, and Medicine for free.

Urban developments can result in higher runoff and nutrient loadings transported to downstream receiving waterbodies. While much effort has been made recently inwatershed restoration in the U.S., a lack of recent runoff quality data limits the prediction capability of urban watershed models. The objectives of this study was to fill an existing information gap on how rainfall and land uses interact and affect such loadings. This study instrumented six coastal urban catchments, each dominated by a single land use. We measured total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), total Kjeldahl Nitrogen (TKN), ortho-P, and nitrate concentrations in runoff from 30 storm events over one year from six urban land uses, namely commercial, industrial, transportation, open space, low density residential, and high density residential. Results indicated that the median event mean concentrations (EMCs) for TSS, TP, and TN were 30 (19-34), 0.31 (0.26-0.31), and 0.94 (0.73-1.25) mg L-1, respectively. TSS EMCs from the open space and industrial land uses were significantly greater than other land uses; there were positive correlations between TN concentrations and imperviousness and between TP concentrations and turf coverage. Both the amount and intensity of rainfall positively influenced TSS concentrations in runoff regardless of land use. Using the collected data, this study developed a generic equation for predicting the loading of a pollutant as a function of rainfall depth. This equation was verified by comparing its predictionswith the simulations of a sufficiently-calibratedwater quality model in terms of TSS, TP, and TN loadings from various land uses in another coastal catchment for a period of ten years. Average TSS, TN, and TP loadings from the catchment were estimated to be 0.86, 0.03, and 0.01 kg ha(-1) cm(-1), respectively. (C) 2021 Elsevier B.V. All rights reserved.