Stormwater Monitoring Research Articles

A Compact, Low-Cost, and Low-Power Turbidity Sensor for Continuous In Situ Stormwater Monitoring.

Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist, their high cost prohibits their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor that is designed specifically for long-term stormwater in-field monitoring. Its low cost (USD 23.50) enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. By testing the sensor in the lab with standard turbidity solutions, the proposed low-cost turbidity sensor demonstrated a strong linear correlation between a low-cost sensor and a commercial hand-held turbidimeter. In the field, the low-cost sensor measurements were statistically significantly correlated to a standard high-cost commercial turbidity sensor. Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing that both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the GreenSpan sensor.

Data assimilation for urban stormwater and water quality simulations using deep reinforcement learning

Hydrological models have been used to understand the transportation of water quantity and quality in drainage systems, and the stormwater management model (SWMM) is one of the most widely-used models for runoff quantity and quality simulations in urban areas. Although significant efforts have been made to identify the appropriate input parameters of the SWMM model in various watersheds, it is difficult to reflect the variability of the real environment using a fixed input parameter. Data assimilation (DA) is a compatibility method that improves prediction accuracy by merging observations and simulation outputs. It is useful to reduce the forecast error from temporal transferability with information interaction between the model output and observation data. However, conventional DA approaches cannot completely overcome their unrealistic assumptions such as linearity, normality, and error covariances. To address these challenges, we used deep reinforcement learning (RL) to develop an automatic assimilation model that interactively optimizes the SWMM parameters in a real environment (SWMM-RL). In the SWMM-RL model, the agent is trained by rewarding and/or punishing the action (modulated SWMM input parameter) in real time according to the state changes. The model was constructed to minimize the error of runoff and pollutant load simulation of suspended solids (SS), total nitrogen (TN), and total phosphorus (TP) in each stormwater monitoring event. The results demonstrated that the SWMM-RL model primarily outperformed the fixed input parameter SWMM model (SWMM-PS) in simulating the runoff and the three different pollutant loads (the median value of Nash–Sutcliffe efficiency (NSE) for 10 stormwater events was increased by 0.11, 0.31, 0.36, and 0.07 for runoff, SS, TN, and TP, respectively). The SWMM-PS has disadvantages in simulating low rainfall events because of the high sensitivity of runoff peaks to heavy rainfall conditions. Furthermore, the effects of extreme rainfall events were estimated using the SWMM-RL model. This study showed how SWMM-RL combined with the DA method increased forecast accuracy by providing sensitivity and temporal transitions of input parameters.

A Relational Data Model for Advancing Stormwater Infrastructure Management

To date, the knowledge benefits that can result from the growing abundance of measured stormwater data have yet to be fully realized within the industry. Obstacles due to data format and storage, retrieval, and quality control have limited the size and impact of accessing known data, resulting in data that are often siloed by project or objective, which inhibits advancements in our understanding, as well as decision making and information sharing across municipalities. Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI)’s observations data model (ODM) is a relational data model designed to organize disparate types of data; however, it does not provide avenues for the storage of stormwater monitoring–specific metadata. To facilitate stormwater infrastructure data analysis, the Villanova stormwater infrastructure data model (SIDM), constructed comparably to ODM1, was created by adding and removing tables from the ODM1 structure, along with other modifications to enable efficient data access. The novel data model presented here facilitates a comprehensive spatiotemporal analysis specific to stormwater systems to overcome traditional stormwater data siloes and harness the benefits of stormwater data through efficient avenues. The data model structure of the SIDM enables stormwater-specific data to be managed efficiently not only to provide data regarding function and performance but to advance the science around stormwater planning and management, as well as knowledge sharing across researchers, utilities, and municipalities. In this paper, we describe the logic of the design, benefits of the data model, and applications to specific green stormwater infrastructure installations. This has the potential to not only provide novel insight but open the door for applications of modeling advancement in physics-based models and beyond to artificial intelligence, data analysis, and data-informed decision making in the planning, design, operation, and maintenance of stormwater systems.

Ecological risk assessment of trace elements accumulated in stormwater ponds within industrial areas

Stormwater ponds can provide flood protection and efficiently treat stormwater using sedimentation. As the ponds also host aquatic biota and attract wildlife, there is a growing concern that the sediment bound pollutants negatively affect aquatic organisms and the surrounding ecosystem. In this study, we used three methods to assess the accumulation and the potential ecological risk of 13 different heavy metals and metalloids (e.g. trace elements) including both elements that are frequently monitored and some which are rarely monitored in sediment from 5 stormwater ponds located within catchments with predominately industrial activities. Ecological risk for organisms in the older ponds was observed for both commonly (e.g. Cd, Cu, Zn) and seldom (e.g. Ag, Sb) monitored trace elements. The 3 methods ranked the degree of contamination similarly. We show that methods usually used for sediment quality assessment in aquatic ecosystems can also be used for screening the potential risk of other trace elements in stormwater ponds and may consequently be useful in stormwater monitoring and management. Our study also highlights the importance of establishing background conditions when conducting ecological risk assessment of sediment in stormwater ponds.

Modeling for Mitigating Storm Water Unban Flooding and Water Quality Issues by Using Small Serial Dams: A Case Study of the City of San Angelo

<p>The City of San Angelo has been imposed on urban flooding and no-point source pollution and develop storm water monitoring and modeling project for managing these storm water issues. This study focuses on the stormwater peak flow reduction and water pollutant improvement by using small serial retention structures. The storm water data collected are utilized to verify storm water and event mean concentration in SWMM model. The verified SWMM that has range from 0.6 to 0.8 of coefficient of determination is modeled to evaluate small serial dams for reducing peak flow and water quality loading. Small serial dams explain the 26%~55.3% peak flow reduction and 53.2%~93.7% water pollutant removal percent. Sensitivity analysis results for three kinds of orifice sizes provide that smaller size increases the hydraulic retention and reduces the peak flow than other bigger size while the bigger size shows effective water pollutant reduction than small size.</p>

A Low-Cost Water Depth and Electrical Conductivity Sensor for Detecting Inputs into Urban Stormwater Networks.

High-resolution data collection of the urban stormwater network is crucial for future asset management and illicit discharge detection, but often too expensive as sensors and ongoing frequent maintenance works are not affordable. We developed an integrated water depth, electrical conductivity (EC), and temperature sensor that is inexpensive (USD 25), low power, and easily implemented in urban drainage networks. Our low-cost sensor reliably measures the rate-of-change of water level without any re-calibration by comparing with industry-standard instruments such as HACH and HORIBA’s probes. To overcome the observed drift of level sensors, we developed an automated re-calibration approach, which significantly improved its accuracy. For applications like monitoring stormwater drains, such an approach will make higher-resolution sensing feasible from the budget control considerations, since the regular sensor re-calibration will no longer be required. For other applications like monitoring wetlands or wastewater networks, a manual re-calibration every two weeks is required to limit the sensor’s inaccuracies to ±10 mm. Apart from only being used as a calibrator for the level sensor, the conductivity sensor in this study adequately monitored EC between 0 and 10 mS/cm with a 17% relative uncertainty, which is sufficient for stormwater monitoring, especially for real-time detection of poor stormwater quality inputs. Overall, our proposed sensor can be rapidly and densely deployed in the urban drainage network for revolutionised high-density monitoring that cannot be achieved before with high-end loggers and sensors.

Quantifying the Effects of Residential Infill Redevelopment on Urban Stormwater Quality in Denver, Colorado

Stormwater quality in three urban watersheds in Denver that have been undergoing rapid infill redevelopment for about a decade was evaluated. Sampling was conducted over 18 months, considering 15 storms. Results: (1) The first-flush effect was observed for nutrients and total suspended solids (TSS) but not for total dissolved solids (TDS), conductivity, pH, and fecal indicator bacteria; (2) though no significant differences on event mean concentration (EMC) values were found among the three basins, local-scale EMCs were higher than traditional city-wide standards, particularly some metals and nutrients, most likely because of the significantly higher imperviousness of the studied urban basins compared to city averages; (3) peak rainfall intensity and total rainfall depth showed significant but weak correlations with some nutrients and metals, and TDS; (4) antecedent dry period were not correlated with water quality, except for phosphorus and lead; (5) contrary to what was expected, total coliforms and Escherichia coli were not correlated with TSS; and (6) no significant correlations between water quality and land-use or zoning categories were found. It was concluded that locally focused stormwater monitoring can aid data-driven decision-making by city planners where redevelopment is occurring at local “neighborhood scales”, particularly for the implementation and management related to green infrastructure and water-quality regulations.

Advances in Urban Stormwater Management in Japan: A Review

The series of annual flood disasters that struck Japan in recent years pose challenges to urban stormwater management. Japan has been implementing nation-wide hydrometeorological observation through a dense network of rain gauges. Since the recent decade, ground radars have been deployed to observe heavy rainfall with high spatiotemporal resolution as a countermeasure. While commercial software is popular in designing stormwater drainage systems, several integrated urban flood models have been developed domestically and are applicable in stormwater management. A paradigm shift with the rise of Internet of Things (IoT) provides an inexperienced opportunity in hydrological observation, and has been implemented for monitoring sewer network conditions. Despite this broad scope of research works and technological innovations, such advancement is not internationally recognized yet. The present study aims to review the development and role of science and technology in stormwater management in Japan, focusing specifically on rainfall observation, integrated urban flood modelling, and emerging technologies for stormwater monitoring. In addition, the possible future direction of stormwater management is envisioned. Considering the series of record-breaking rainfall events that struck Japan, we will have to face more severe challenges in urban flood management alongside the impact of global climate change. As compared to structural measures, which are subject to budgetary constraints, the relative importance of non-structural measures is increasing; therefore, effective application of numerical modeling techniques is required. A common weakness of the urban flood modeling framework is the limited availability of observations in sewer networks, which can be relaxed by emerging IoT based observations. The fusion of IoT based observations with an integrated urban flood modeling technique appears to the emerging technology for stormwater management.

An Investigation of Stormwater Quality Variation within an Industry Sector Using the Self-Reported Data Collected under the Stormwater Monitoring Program

Storm runoff pollutants are among the major sources of surface water impairments, globally. Despite several monitoring programs and guidance on stormwater management practices, there are many streams still impaired by urban runoff. This study evaluates an industry sector’s pollutant discharge characteristics using the self-reported data collected under Tennessee Multi Sector Permit program. The stormwater pollutant discharge characteristics were analyzed from 2014 to 2018 for an industry sector involving twelve facilities in West Tennessee, USA. The data analysis revealed the presence of both organic and inorganic contaminants in stormwater samples collected at all twelve industrial facilities, with the most common metals being magnesium, copper, and aluminum. The principal component analysis (PCA) was applied to better understand the correlation between water quality parameters, their origins, and seasonal variations. Furthermore, the water quality indexes (WQIs) were calculated to evaluate the stormwater quality variations among studied facilities and seasons. The results demonstrated slight variations in stormwater WQIs among the studied facilities ranging from “Bad” to “Medium” quality. The lowest seasonal average WQI was found for spring compared to the other seasons. Certain limitations associated with the self-reported nature of data were identified to inform the decision makers regarding the required future changes.

Urban Erosion Potential Risk Mapping with GIS

With increased regulatory focus on eroded sediment and its bound pollutants, methods are needed to predict areas with high erosive potential (EP) in urbanized areas. Using EP to prioritize urban areas for maintenance, implementation of Stormwater Control Measures (SCMs), stream restoration or monitoring is crucial. This study utilizes commonly available geospatial layers in conjunction with a computational procedure for prioritizing the contribution of site specific- and transport-erosion to compute relative EP risk throughout a target urban watershed.  Factors that contribute to erosion were evaluated: local cell slope, soil erodibility, land cover, runoff volume, distance and slope to nearest stormwater conveyance point along a surface flow travel path. A case study of the developed methodology was performed on a 1.6 square kilometer urban watershed in Blacksburg, VA, to generate EP risk maps. Results of the study indicate areas of erosive potential within the target watershed and provide a methodology for creating erosion potential risk maps for use by MS4 planners, engineers and other individuals that manage erosion control programs.

Uncertainty-based parameter estimation for urban pollutant buildup and washoff simulation using a multiple pattern inverse modeling approach

Reliable model parameter estimation for stormwater runoff water quality modeling has always been a challenge, especially in ungauged areas. Insufficient local observations can increase parameter uncertainty, leading to modeling outputs of limited credibility. This paper proposes an event-based multiple pattern inverse modeling approach to estimate regional-scale parameters characterizing the buildup and washoff processes in urban watersheds. The approach explicitly accounts for system uncertainties by simultaneously using a wide range of storm events. A genetic algorithm was employed to solve the inverse models. The solution populations that minimize the error between the observed and predicted values were then filtered into distinct parameter groups using a K-means clustering algorithm. Each identified parameter group may be used to represent the buildup and washoff processes.The proposed approach was applied for the New England region (United States) using two study sites with impervious land cover: Massachusetts and New Hampshire. Stormwater monitoring data of total nitrogen (TN) and total phosphorus (TP) in small to medium size rainfall events were used to identify representative parameter sets for the region. Of the 21 identified candidate parameter combinations, 13 were found to have acceptable Root Mean Square Error for both TN and TP.The 13 parameter sets were grouped into four distinct patterns to represent different potential buildup and washoff mechanisms. The results demonstrate that the approach is capable of estimating robust sets of buildup and washoff parameters at a regional scale. These can be used in a continuous simulation model to predict TN and TP loads in ungauged areas in the region.

Diffuse pollution monitoring and modelling of small urban watershed in Brazil Cerrado.

The stormwater runoff carries different pollutants that can reduce the quality of receiving waters due to diffuse pollutant loads. This research was aimed at evaluating the concentration of pollutants in stormwater and the application of SWMM (Storm Water Management Model) to an urban catchment in Lake Paranoá watershed to carry out the simulation of flow discharge with the hydraulic model, and subsequently to estimate the loads conveyed to the lake in ordinary events of precipitation. This study was carried out based on rainfall and runoff monitoring during events. It was confirmed that this model's results fit well in simulation of this type of watershed, leading to high value of the Nash-Sutcliffe coefficient after calibration but, as expected, precipitation distribution is a very important factor for calibration. Concerning water quality, it was observed that the event mean concentration values of suspended solids and chemical oxygen demand were high, indicating that the diffuse pollution is an important source of pollution of the receiving waters. The monitoring and modelling of stormwater are essential to identify diffuse pollution discharge, in searching for a sustainable solution.

Hydrology and water quality of isolated wetlands: Stormflow changes along two episodic flowpaths

The Dougherty Plain in southwest Georgia is a flat, karstic, depressional-landscape dominated by irrigated and dry-land agriculture devoted to row-crops and pasture with interspersed wetlands and forests. Stormwater runoff rarely discharges into perennial rivers and streams, except during large storms that induce hydrologic connectivity between fields, wetlands, and streams (event return period is less than one per year).We report the hydrologic and water-quality effects of a 173-mm rainfall event that generated three weeks (Feb 15 to Mar 9, 2014) of continuous flows through and between three normally isolated wetlands. A suite of water-quality parameters (physical, nutrients, and pathogen indicators) was monitored daily from offsite (agricultural) and onsite (forested) sources at two sites along one flowpath and five sites along a second at the Joseph W Jones Ecologic Research Center at Ichauway.Decreasing sediment, nutrient, and pathogen concentrations were observed as water moved across the forested landscapes with embedded wetlands. Two physical parameters (specific conductance and turbidity) were strongly-to-moderately correlated (r >0.8, 0.5, respectively) with laboratory-measured parameters (e.g., nutrients, suspended solids, pathogens), which suggest their utility for routine stormwater monitoring and prioritizing sample collection for laboratory analyses at this site.

Don't Throw the Baby Out with the Bathwater—Sample Collection and Processing Issues Associated with Particulate Solids in Stormwater

Particulate solids are the most common constituent examined during stormwater monitoring projects. They have also been used as the basis of many regulatory pro…

Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff

While the urban runoff are increasingly being studied as a source of fecal indicator bacteria (FIB), less is known about the occurrence of FIB in watershed with mixed land use and ongoing land use and land cover (LULC) change. In this study, Escherichia coli (EC) and fecal streptococcus (FS) were monitored from 2012 to 2013 in agricultural, mixed and urban LULC and analyzed according to the most probable number (MPN). Pearson correlation was used to determine the relationship between FIB and environmental parameters (physicochemical and hydrometeorological). Multiple linear regressions (MLR) were used to identify the significant parameters that affect the FIB concentrations and to predict the response of FIB in LULC change. Overall, the FIB concentrations were higher in urban LULC (EC=3.33–7.39; FS=3.30–7.36log10MPN/100mL) possibly because of runoff from commercial market and 100% impervious cover (IC). Also, during early-summer season; this reflects a greater persistence and growth rate of FIB in a warmer environment. During intra-event, however, the FIB concentrations varied according to site condition. Anthropogenic activities and IC influenced the correlation between the FIB concentrations and environmental parameters. Stormwater temperature (TEMP), turbidity, and TSS positively correlated with the FIB concentrations (p>0.01), since IC increased, implying an accumulation of bacterial sources in urban activities. TEMP, BOD5, turbidity, TSS, and antecedent dry days (ADD) were the most significant explanatory variables for FIB as determined in MLR, possibly because they promoted the FIB growth and survival. The model confirmed the FIB concentrations: EC (R2=0.71–0.85; NSE=0.72–0.86) and FS (R2=0.65–0.83; NSE=0.66–0.84) are predicted to increase due to urbanization. Therefore, these findings will help in stormwater monitoring strategies, designing the best management practice for FIB removal and as input data for stormwater models.

Towards effective monitoring of urban stormwater for better design and management

Towards effective monitoring of urban stormwater for better design and management

Land-use based stormwater pollutant load estimation and monitoring system design

Accurate quantification of the amount, quality and sources of urban stormwater runoff are a prerequisite for effective stormwater management. The goal of this study was to estimate city-scale stormwater pollutant export, examine the effect of data sources on export estimates, and develop an optimization method for stormwater monitoring. The results were presented for the city of Lahti in southern Finland, where site mean pollutant concentrations (SMCs), annual pollutant loads, and monitoring guidelines for specific land use types were determined for total suspended solids (TSS), nutrients (total nitrogen TN and total phosphorus TP), chemical oxygen demand (COD), and metals (Pb, Zn, Cu, Cr, and Ni). Depending on the data source, differences between estimated pollutant exports ranged from -89% to 215%. Lack of reference data for every urban land use type and differences in land use classification schemes were identified as key factors limiting the accuracy of pollutant load estimation.

E. coli and turbidity attenuation during urban stormwater recycling via Aquifer Storage and Recovery in a brackish limestone aquifer

Aquifer Storage and Recovery (ASR) has been used for decades as a technique to store harvested urban stormwater in aquifers for subsequent reuse. However, stormwater recycling via ASR may also provide a means of improving the quality of the recharged water through passage and storage in the aquifer, thereby allowing improved prediction and management of recovered water quality. In this study, water quality data were evaluated from four stormwater ASR sites operating for up to 14 years in Salisbury, South Australia. Stormwater was injected and stored in a confined carbonate aquifer that initially contained brackish water. Long term operational monitoring of stormwater recycling via ASR was used to quantify the changes in salinity, turbidity and Escherichia coli concentrations. At all sites, E. coli concentrations in recovered water was 2–3log10 (90 to 99%) less than injected water. Positive detections of E. coli were found >98% injected water samples in comparison with ∼20% of recovered water samples from all four sites. Turbidity reduction was >50% for three of the ASR sites. Reductions could not be explained through dilution alone and are the result of bacteria retention and inactivation in the aquifer. The magnitude of E. coli removal, and to a lesser degree turbidity reduction, highlights the potential for integration with engineered treatments.

Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: Experimental and modeling approach

Currently, continued urbanization and development result in an increase of impervious areas and surface runoff including pollutants. Also one of the greatest issues in pollutant emissions is the first flush effect (FFE), which implies a greater discharge rate of pollutant mass in the early part in the storm. Low impact development (LID) practices have been mentioned as a promising strategy to control urban stormwater runoff and pollution in the urban ecosystem. However, this requires many experimental and modeling efforts to test LID characteristics and propose an adequate guideline for optimizing LID management. In this study, we propose a novel methodology to optimize the sizes of different types of LID by conducting intensive stormwater monitoring and numerical modeling in a commercial site in Korea. The methodology proposed optimizes LID size in an attempt to moderate FFE on a receiving waterbody. Thereby, the main objective of the optimization is to minimize mass first flush (MFF), which is an indicator for quantifying FFE. The optimal sizes of 6 different LIDs ranged from 1.2 mm to 3.0 mm in terms of runoff depths, which significantly moderate the FFE. We hope that the new proposed methodology can be instructive for establishing LID strategies to mitigate FFE.

Characterization and source identification of stormwater runoff in tropical urban catchments

The characteristics of urban stormwater pollution in the tropics are still poorly understood. This issue is crucial to the tropical environment because its rainfall and runoff generation processes are so different from temperate regions. In this regard, a stormwater monitoring program was carried out at three urban catchments (e.g. residential, commercial and industrial) in the southern part of Peninsular Malaysia. A total of 51 storm events were collected at these three catchments. Samples were analyzed for total suspended solids, 5-day biochemical oxygen demand, chemical oxygen demand (COD), oil and grease, nitrate nitrogen, nitrite nitrogen, ammonia nitrogen (NH3-N), soluble reactive phosphorus and total phosphorus. Principal component analysis (PCA) and hierarchical cluster analysis were used to interpret the stormwater quality data for pattern recognition and identification of possible sources. The most likely sources of stormwater pollutants at the residential catchment were from surface soil and leachate of fertilizer from domestic lawns and gardens, whereas the most likely sources for the commercial catchment were from discharges of food waste and washing detergent. In the industrial catchment, the major sources of pollutants were discharges from workshops and factories. The PCA factors further revealed that COD and NH3-N were the major pollutants influencing the runoff quality in all three catchments.