Stormwater Quality Modelling Research Articles

Exploring granular filter media in sustainable drainage systems (SuDS) for stormwater pollutant adsorption: A pilot study

Granular filter media are integral to sustainable drainage systems (SuDS) for their efficiency in removing pollutants from urban runoff. This study focuses on understanding the filtration processes within these media by combining a pilot experimental study with a modeling approach. The experimental study involved characterizing the physical and hydraulic properties of various granular filter media materials, including sand, pea-gravel, gravel, and geotextile membranes. Three laboratory-scale stormwater filtration rigs were tested to evaluate the filter media's pollutant removal capacity and hydraulic performance. This work presents a phenomenological model that predicts the spatial variation in the concentrations of stormwater and urban runoff substances, specifically nitrate ions (NO3-), phosphate ions (PO43-), chemical oxygen demand (COD), and suspended solids, by studying their concentration profiles. The stormwater quality model was used to predict the concentration profiles for stormwater with an average inflow consisting of 2.9 mg/L nitrates, 3.4 mg/L phosphate ions, 225 mg/L COD, and 3.3 mg/L of suspended solids. The predicted outlet concentrations matched well with measured experimental data. The results showed that adding geotextile membranes to a granular filter significantly improves its ability to adsorb dissolved species for stormwater applications. This research highlights the importance of understanding the physical and hydraulic properties of granular filter media and their impact on stormwater pollutant removal efficiency. The developed model can assist in the design and optimization of stormwater treatment systems by predicting the performance of different filter media materials, allowing for informed decision-making and improved system functionality.

The uncertainity in stormwater quality modelling for temperate and tropical catchments

More research has been done on the uncertainty of stormwater quantity as compared to quality modelling. Although the washoff model has been in use for decades, there is a general lack of studies on the uncertainty associated with washoff modelling and how this uncertainty varies between catchments in different climate zones due to differences in rainfall conditions. This study investigated model structure uncertainty, in addition to gaining knowledge on model parameter uncertainty in water quality modelling of urban catchments located in two climate zones. Such a study not only adds to the body of knowledge but highlights the importance of distinguishing the difference in washoff behaviour, and their results, between catchments in different climate zones. Following an exhaustive search and assessment of data quality, the rainfall, discharge and water quality data sourced from 11 residential catchments, which include 72 events from temperate catchments and 59 from tropical catchments were analysed. Comparison of uncertainty between buildup-washoff model (BW) and washoff model (WO) results for particulate (Total Suspended Solids (TSS)), mixed (Total Phosphorus (TP), Total Nitrogen (TN)) and dissolved (ortho-Phosphate (OP), Ammonium (NH4-N)) phase water quality parameters were carried by using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Higher Average Relative Interval Length (ARIL) values obtained for tropical catchments showed that uncertainty in model prediction is higher than temperate catchments. This result is confirmed for both the BW and WO modelling approaches. The threshold criteria for obtaining behavioural set chosen as likelihood measure of Tr = 0.7 (based on the Nash-Sutcliffe coefficient) and η ≥ 70 % (proportion of observed data falling within the 90 % uncertainty band of the simulated data distribution over the total number of observations) are mostly satisfied for the particulate phase and least by the dissolved phase water quality parameters, providing further evidence that the structure of the models are not universally applicable, and that model results should be understood in the light of this difference. Comparisons between the BW and WO approaches showed similar levels of uncertainty, irrespective of climate. Although this is consistent with other studies reported in the literature, the antecedent dry period (ADP), a key parameter in the buildup process, was not significantly (p =.07) different between the temperate and tropical datasets. This is likely an artifact of sampling, especially for temperate catchments, where field data is collected during the rainy season, when ADP is limited and similar with catchment in the tropics.

Impact of rainfall characteristics on urban stormwater quality using data mining framework

Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics.

SWMM stormwater quality model developed to assess the performance of best management practices: a case study of the town of Jasper in Canada

Stormwater quality management is essential for the protection of aquatic life of receiving waterbodies. This study presents a unique case study, whereby a stormwater management model (SWMM) was developed to assess the performances of street sweeping and infiltration trenches to reduce total suspended solids (TSS) loads in the Town of Jasper located in Jasper National Park, AB, Canada. Two catchment models were developed for the Town using long-term meteorological data, as well as watershed and drainage system data provided by the Municipality of Jasper. The quality parameters were calibrated so that TSS loads at the outfalls were within a 20% error of monitoring data. The performance of proposed best management practices was assessed using single-event and continuous data simulations. The results indicated that a combined sweeping/trench control scenario would generate the highest TSS removal efficiency of up to 51%.

Examination of the exponential wash-off function using experimental data

The standard exponential wash-off function has been widely adopted in urban stormwater quality models and tested in many studies of sediment wash-off experiments. There is neither sufficient information on the wash-off coefficients for other types of pollutant nor a good mathematical interpretation that accounts for the effects of surface features on wash-off processes. This study assesses the standard exponential wash-off function using experimental data on the wash-off of various types of pollutant. In-situ wash-off experiments using simulated rainfalls were conducted in typical urban areas with various landuse. Experimental data from the literature were also analysed to support the examination. Mass balance requirement was always applied as a criterion in the experimental data analysis and wash-off model examination. The experimental results indicate that wash-off processes of organic pollutants (BOD5), nitrate (NO3), total oil and grease (TOG), total petroleum hydrocarbon (TPH) and coliform stochastically comply with the standard exponential function.

Understanding nutrient dynamics for effective stormwater treatment design

Current stormwater quality modelling tools lack robust mathematical replication of nutrient entrainment in runoff. This makes it challenging to design effective stormwater treatment systems such as nature based solutions with adequate resilience to future changes in nutrient inputs in urban environments. Consequently, poorly treated stormwater can be discharged into receiving waters, leading to nutrient enrichment and in turn, environmental and human health impacts. This study integrated empirically based with statistical modelling techniques to incorporate nutrient dynamics into commonly used Intensity-Frequency-Duration (IFD) distributions of design rainfall. Field based nutrient wash-off experiments were conducted to understand nutrient behaviour during a runoff event. New mathematical formulations were derived to describe the decay (wash-off) of nutrients. Rainfall intensity, duration and initially accumulated pollutant load exert positive influence on the decay of nitrogen and phosphorous, while organic carbon has a negative impact on phosphorus decay. It was also evident that nitrogen species would decay at a similar rate, while phosphorus species may decay at different rates. Compared to nitrogen species, phosphorous species were found more likely to be washed-off during a rainfall event. Using the mathematical formulations developed, wash-off of nitrogen and phosphorous was simulated for 435 very frequent and frequent/infrequent design rainfall events leading to the creation of Intensity-Frequency-Duration-Wash-off (IFDW) curves. Analysis of uncertainty associated with IFDW indicated that total phosphorous could be completely washed-off during most of the design rainfall events, while total nitrogen would only be completely washed-off by very few events that are rarer than 10 % AEP (annual exceedance probability). IFDW can act as a tool for supporting effective stormwater treatment design in order to promote sustainable stormwater management and reuse.

Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation

A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique. Use of ABC in automatic model calibration was undertaken for a combined urban hydrologic, hydraulic and stormwater quality model. The simulated runoff hydrograph and total suspended solid (TSS) pollutograph were compared with observed data for multiple events from three different catchments, and found to be within 95% confidence intervals of the simulated results. The R programmed model was validated by comparing simulated flow with similar commercially available modeling software, MIKE URBAN output determined using mean value of parameters obtained from the calibration exercise, and performed well by satisfying statistical criteria's such as coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME). The developed framework is useful for automatic calibration and uncertainty estimation using ABC approach in complex hydrologic, hydraulic and stormwater quality models with multi-input-output systems.

Conceptual Stormwater Quality Models by Alternative Linear and Non-linear Formulations: an Event-Based Approach

Conceptual Stormwater Quality Models by Alternative Linear and Non-linear Formulations: an Event-Based Approach

Effects of land use, climate, and imperviousness on urban stormwater quality: A meta-analysis

Many natural and anthropogenic factors cause degradation of urban stormwater quality, resulting in negative consequences to receiving waters. In order to improve water quality models at a variety of scales, accurate estimates of pollutant (nutrients, total suspended solids, and heavy metal) concentrations are needed using potential explanatory variables. To this end, a meta-analysis was performed on aggregated stormwater quality data from the published literature from 360 urban catchments worldwide to understand how urban land use and land cover (LULC), climate (i.e., Kӧppen-Geiger zone), and imperviousness (1) affect runoff quality, and (2) whether they are able to predict stormwater pollutant concentrations. Runoff pollutant concentrations were more influenced by LULC and climate than imperviousness. Differences in LULC significantly affected the generation of metals and some nitrogen species. Road, city center, and commercial LULCs generally produced the most elevated pollutant concentrations. Changes in climate zones resulted in significant differences in concentrations of nutrients and metals. Continental and arid climate zones produced runoff with the highest pollutant concentrations. Rainfall patterns seemed to have a more important role in affecting runoff quality than seasonal temperature. Differences in imperviousness only significantly affected chromium and nickel concentrations, although increased imperviousness led to slightly (not significantly) elevated concentrations of nutrients, suspended solids, and other heavy metals. Multiple linear regression models were created to predict the quality of urban runoff. Predictive equations were significant (p < 0.05) for 67% of the pollutants analyzed (ammonia, total Kjeldahl nitrogen, total nitrogen, total phosphorus, cadmium, nickel, lead, and zinc) suggesting that LULC, climate, and imperviousness are useful predictors of stormwater quality when local field monitoring or modeling is not practical. This study provides useful relationships to better inform urban stormwater quality models and regulations such as total maximum daily loads.

A coupled hydrodynamic and particle-tracking model for full-process simulation of nonpoint source pollutants

Nonpoint source (NPS) particulate pollutants are a major source of urban surface pollution. It is essential to better understand and model the dynamic process of NPS pollutants to inform the design of effective strategies for urban stormwater management and pollution control. This work presents a novel coupled hydrodynamic and particle-based model to simulate the full-process dynamics of NPS particulate pollutants from detachment, transport to deposition in urban areas. A particle-based approach is proposed to represent the physical processes of pollutant detachment and deposition. The transport of pollutant particles is simulated using a random-walk particle-tracking model, which can directly trace out the trajectories of individual particles and hence identify the pathways of pollutants. The new coupled hydrodynamic and particle-based stormwater quality model is successfully validated against several analytical and experimental test cases to demonstrate its capability in accurately simulating the full-process dynamics of NPS particulate pollutants in urban areas.

A Framework for Stormwater Quality Modelling under the Effects of Climate Change to Enhance Reuse

Water scarcity, which is exacerbated by climate change, is a major challenge to ensure human well-being. Therefore, it is equally important to protect conventional water resources from degradation and at the same time to identify cost-effective alternatives with a low carbon footprint. In this regard, stormwater plays a key role as it is a largely under-utilised resource for both, potable and non-potable use. However, stormwater carries substantial loads of pollutants to receiving waters such as rivers. Unfortunately, the lack of comprehensive stormwater quality modelling strategies, which account for the effects of climate change, constrains the formulation of effective measures to improve the quality of stormwater. Currently, there is a significant knowledge gap in the merging of stormwater quality modelling and climate modelling. This paper critically reviews current stormwater quality modelling approaches (quantity and quality) and the role of climate modelling outputs in stormwater quality modelling. This is followed by the presentation of a robust framework to integrate the impacts of climate change with stormwater quality models.

Regionalization of Default Parameters for Urban Stormwater Quality Models

AbstractAccurate parameterization of water quality routines within stormwater models can improve simulation, prediction of system behavior, and lead to more informed decision making. This work recognizes that factors controlling runoff concentrations and treatment in stormwater best management practices (BMPs) vary with region, assumes region is a proxy for these factors, and seeks to improve tool parameterization by (1) identifying a regionalization scheme that best explains variability in national datasets of runoff water quality and BMP performance, (2) generating region‐specific model parameter values, and (3) demonstrating how model output varies when using national vs. regional parameters. Of the four regionalization schemes tested, the National Climatic Data Center’s Regions best explained variance in pollutant runoff concentrations from the National Stormwater Quality Database, accounting for more data variability (1.1%) than the watershed’s land use (0.4%). For BMP performance extracted from the International Stormwater BMP Database, the United States (U.S.) Environmental Protection Agency’s Rain Zones explained the most variance (1.1%), which is one‐fifth of the variance explained by BMP type (5.0%). These results were used to generate regional parameter lookup tables for stormwater quality modeling. Test cases from the 100 most populous cites in the U.S. showed national parameters predicted BMP effluent concentrations that were 15% lower than the regional parameters for five pollutants.

Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring

Sponge city construction (SCC) in China, as a new concept and a practical application of low-impact development (LID), is gaining wide popularity. Modelling tools are widely used to evaluate the ecological benefits of SCC in stormwater pollution mitigation. However, the understanding of the robustness of water quality modelling with different LID design options is still limited due to the paucity of water quality data as well as the high cost of water quality data collection and model calibration. This study develops a new concept of ‘robustness’ measured by model calibration performances. It combines an automatic calibration technique with intensive field monitoring data to perform the robustness analysis of storm water quality modelling using the SWMM (Storm Water Management Model). One of the national pilot areas of SCC, Fenghuang Cheng, in Shenzhen, China, is selected as the study area. Five water quality variables (COD, NH3-N, TN, TP, and SS) and 13 types of LID/non-LID infrastructures are simulated using 37 rainfall events. The results show that the model performance is satisfactory for different water quality variables and LID types. Water quality modelling of greenbelts and rain gardens has the best performance, while the models of barrels and green roofs are not as robust as those of the other LID types. In urban runoff, three water quality parameters, namely, SS, TN and COD, are better captured by the SWMM models than NH3-N and TP. The modelling performance tends to be better under heavy rain and significant pollutant concentrations, denoting a potentially more stable and reliable design of infrastructures. This study helps to improve the current understanding of the feasibility and robustness of using the SWMM model in sponge city design.

Factors influencing the stormwater quality model of sewer networks and a case study of Louis Fargue urban catchment in Bordeaux, France.

Pollution caused by combined sewer overflows has become a global threat to the environment. Under this challenge, quality-based real-time control (RTC) is considered as an effective approach to minimize pollution through generating optimal operation strategies for the sewer infrastructure. To suit the fast computation requirement of RTC implementation, simplified quality models are required. However, due to the hydrological complexity, it is not easy to develop simplified quality models which are amenable to be used in real-time computations. Under this context, this paper contributes a preliminary analysis of influencing factors for the quality models of sewer networks in order to give supportive knowledge for both model development and application. Conceptual quality models which were proposed previously by the authors, with total suspended solids (TSS) as quality indicator, are used in this study. A clustering algorithm is used for exploratory analysis. Further analysis about the correlations between different factors and model performance is also carried out. The study and analysis are demonstrated on a real pilot based on the Louis Fargue urban catchment in Bordeaux. Conclusive results about the influencing factors, flow rate, rain intensity and pipe length, as well as their correlations with the TSS models are elaborated.

Impact of global warming on urban stormwater quality: From the perspective of an alternative water resource

Stormwater is a largely under-utilised resource available to alleviate water shortages in urban areas. However, stormwater needs treatment before use. Further, projected changes to rainfall patterns due to global warming will alter stormwater quality characteristics. This study projects potential changes to the primary source of stormwater degradation, namely, particulates that transport the largest fraction of toxic pollutants in stormwater. The results show that 1.5 °C and 2 °C warming can influence the increase in toxic pollutant loads (heavy metals) by more than 90% on urban surfaces and by nearly 50% in stormwater runoff. Stormwater quality will be further degraded under the upper limit of the projected changes to dry periods and precipitation in the context of Representative Concentration Pathway (RCP) 8.5. Compared to the present day, results indicate that cities in the future will face more challenging tasks in designing effective measures to address increasingly complex stormwater quality responses to different combinations of dry and wet weather conditions. Therefore, this study urges the need for achieving a paradigm shift in the accuracy of stormwater quality predictions by incorporating the effects of changing weather patterns into the current practice of stormwater quality modelling, and thereby to optimise urban stormwater management.

A modified wash-off function for stormwater suspended solids modelling

In urban areas, the classical exponential wash-off equation for modelling of stormwater suspended solids (SS) relates the washoff of solids to the overland flow rate, assuming that runoff is proportional to rainfall. However, in catchments where infiltration occurs through the pavement surface, this assumption may not be valid. Furthermore, many researchers have concluded that the effect of rain impaction, related to rain intensity, should be considered as a separate model parameter to runoff rate. To address this problem, a semi-deterministic stormwater quality model was developed which incorporated rain intensity directly in the exponential wash-off equation. The modified function was compared to the more classical wash-off equation using a dataset of 18 storm events sampled from a separate pipe and gully system in Dublin, Ireland. The modified wash-off equation showed a consistent improvement in the fit of observed SS fluxes at the outlet of the drainage system: the Nash-Sutcliffe index of modelled performance increased from 0.39 to 0.58 on average using the modified wash-off function. The form of the modified wash-off function allows for simple integration into many stormwater quality models currently in use, without the need for any additional model parameters.

A practical GIS-based hazard assessment framework for water quality in stormwater systems

Abstract A practical 4-stage-3-model framework is introduced that models and maps pollutant concentrations in, and potential hazards of, municipal stormwater point-source releases to surface water bodies. The framework consists of a stormwater model, a stormwater quality model, geographic information systems (GIS) mapping models, and a hazard assessment procedure based on event mean concentrations (EMC) and fuzzy logic. This framework was applied to project both outfall concentrations and environmental hazards of a degradable point-source pollutant, monochloramine, in stormwater effluents released to the North Saskatchewan River, Alberta, Canada, under different weather conditions. Monochloramine loads were over the permissible limits under dry weather flows throughout the basin, and only a design storm of a 10-year return period produced monochloramine concentrations below regulation discharge limits. An EMC-based hazard map was developed that revealed 25% of the study area to lie within the moderate to high-risk zones. EMC-based hazards were driven primarily by proximity to the system outlet, total pollutant mass and system layout. Next, a generalized, fuzzy-based monochloramine hazard map was developed, using monochloramine decay-inducing factors such as annual rainfall, land-use type, EMC values, ground slope, decay rates, property assessment values, drainage system proximity and density of impervious areas. This map showed 54% of the basin at moderate to high risk. The maps developed using the proposed 4-stage-3-model framework can help system operators to improve management of stormwater pollutants without time- and labor-intensive, case-by-case calculations and to focus mitigation measures on releases from specific “hot spot” locations in the system. The framework can be generalized to other chemical substances that impair stormwater quality through either point or non-point source pollution.

Physically-based urban stormwater quality modelling: An efficient approach for calibration and sensitivity analysis

Physically-based urban stormwater quality modelling is helpful for increasing the understanding of spatial-temporal dynamics of urban pollution, and for designing innovative management technologies. However, because of the high computational cost, calibration and validation of physically-based models is still challenging. In this context, this study aims to develop a new meta-model based framework for efficient calibration and sensitivity analysis of complex and computationally intensive physically-based models. The proposed approach is applied to the FullSWOF-HR model. According to the average rainfall intensity, 21 rainfall events are categorized into three groups, such as 9 light rains, 6 moderate rains and 6 heavy rains. After upscaling the original high-resolution model, 77 parameter nodes are selected by using the adaptive stochastic collocation method with sparse grids algorithm on the lower-resolution surrogate. 77 simulation runs are then performed with the original model for three representative rainfall events, respectively. The interpolating polynomials of the original models are hence generated. Once the meta-model is constructed, we performed the sensitivity analysis with the variance-based Sobol's method, the results of which are consistent with our previous studies. Calibration process of the meta-model is based on the Markov chain Monte Carlo method. The optimized parameters are verified with the original model and then validated for different rainfall events. These promising results show that the proposed meta-model based approach can efficiently perform sensitivity analysis and parameter optimization for complex physical stormwater quality models, and hence will be very helpful for spreading the detailed water quantity and quality modelling for urban water management issues.

Characteristics of Stormwater Quality in Singapore Catchments in 9 Different Types of Land Use

Stormwater quality is well known for its highly stochastic nature and not necessarily well explained by mechanistic urban build up and wash off models. Therefore, local empirical data (based on land use) are an essential compliment to statistical analyses of global data. This paper reports on a large-scale monitoring of the 12 key water quality parameters of suspended solids, nutrients, and heavy metals for stormwater runoff in urban discharges from nine urban land uses with varying sizes in Singapore. It was found that, in general, the average of the event mean concentrations for total nitrogen, total phosphorus, total organic carbon, total suspended solids (TSS), and phosphate in parkland land use were higher than the other eight studied land uses. Based on Pearson’s correlation analysis, significant correlation between pairs of water quality parameters was observed. Particularly, there was significant correlation between TSS and most of the other tested water quality parameters in all land uses. A pollutant data set from this study will assist in developing appropriate stormwater quality models, guide the establishment of stormwater treatment objectives and preliminary designs for Singapore catchments, as well as provide an essential complement to statistical analyses of global data for stormwater characteristics.

Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids – TSS, total phosphorus – TP and total nitrogen – TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less ‘traditional’ variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling.