Enhanced resilience in urban stormwater management through model predictive control and optimal layout schemes under extreme rainfall events.

Optimizing the layout of urban stormwater management systems is an effective method for mitigating the risk of urban flooding under extreme storms. However, traditional approaches that consider only economic costs or annual runoff control rates cannot dynamically respond to the uncertainties of extreme weather, making it difficult to completely avoid large accumulations of water and flooding in a short period. This study proposes an integrated method combining system layout optimization and Model Predictive Control(MPC)to enhance the system's resilience and effectiveness in flood control. An optimization framework was initially built to identify optimal system layouts, balancing annual average life cycle cost (AALCC) and resilience index. The MPC was then applied to the optimal layout selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, aiming to alleviate inundation cost-effectively. The adaptability of MPC to varying sets of control horizons and its efficacy in managing the hydrograph and flood dynamics of urban drainage system were examined. Conducted in Yubei, Chongqing, this study revealed patterns in optimal layout fronts among various extreme design rainfalls, showing that peak position rate and return period significantly influence system resilience. The contribution of MPC to the optimal system layout was particularly notable, resulting in improved instantaneous and overall flood mitigation. The application of MPC increased the resilience index by an average of 0.0485 and offered cost savings of 0.0514 million yuan in AALCC. Besides, our findings highlighted the importance of selecting an optimal set of control horizons for MPC, which could reduce maximum flood depth from 0.43m to 0.19m and decrease conduit peak flow by up to 14% at a flood-prone downstream location.

Bark and biochar in horizontal flow filters effectively remove microplastics from stormwater.

Organic materials such as bark and biochar can be effective filter materials to treat stormwater. However, the efficiency of such filters in retaining microplastics (MPs) - an emerging stormwater pollutant - has not been sufficiently studied. This study investigated the removal and transport of a mixture of MPs commonly associated with stormwater. Different MP types (polyamide, polyethylene, polypropylene, and polystyrene) were mixed into the initial 2 cm material of horizontal bark and biochar filters of 25, 50, and 100 cm lengths. The MP types consisted of spherical and fragmented shapes in size ranges of 25-900 µm. The filters were subjected to a water flow of 5 mL/min for one week, and the total effluents were analyzed for MPs by µFTIR imaging. To gain a deeper insight, one 100 cm bark filter replica was split into 10 cm segments, and MPs in each segment were extracted and counted. The results showed that MPs were retained effectively, >97%, in all biochar and bark filters. However, MPs were detected in all effluents regardless of filter length. Effluent concentrations of 5-750 MP/L and 35-355 MP/L were measured in bark and biochar effluents, respectively, with >91% of the MP counts consisting of small-sized (25 µm) polyamide spherical particles. Combining all data, a decrease in average MP concentration was noticed with longer filters, likely attributed to channeling in a 25 and 50-cm filter. The analyses of MPs in the bark media revealed that most MPs were retained in the 0-10 cm segment but that some MPs were transported further, with 19% of polyamide retained in the 80-90 cm segment. Overall, this study shows promising results for bark and biochar filters to retain MPs, while highlighting the importance of systematic packing of filters to reduce MP emissions to the environment from polluted stormwater.

Performance and uncertainty assessment of green roofs for urban flood reduction in a high-density catchment in Ahmedabad, India.

Urban flooding poses a significant challenge to the rapidly growing Indian cities. Low-impact development strategies such as green roofs have shown the potential to reduce urban flooding. However, their performance assessment significantly varies across different studies. Therefore, the study's primary objective is to evaluate green roofs in the Indian context. For this evaluation, the green roofs are assessed based on building-level implementation scenarios for a high-density urban area in India for 25%,50%, and 75% application rates and different rainfall intensities (2,3 and 4-h duration and 2,5,10 and 25-year frequencies). Secondly, to probe the variations in the green roof performance across studies, uncertainty contributions to the runoff reduction from different parameters are quantified. The results show that green roofs can reduce up to 62% of flood volume and 24% of runoff. However, they are reasonably effective only beyond 25% application rates. Further, rainfall intensity contributes the most to the uncertainty of runoff reduction from green roofs. This uncertainty assessment implies that localized evaluation of green roofs depending on local rainfall conditions is required for city-wide policy planning. The study has a significant contribution to building confidence in the ability of green roofs to reduce urban floods in the context of developing countries like India.

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.

Urban Stormwater Phosphorus Export Control: Comparing Traditional and Low-impact Development Best Management Practices.

Data from the International Stormwater Best Management Practices (BMP) Database were used to compare the phosphorus (P) control performance of six categories of stormwater BMPs representing traditional systems (stormwater pond, wetland basin, and detention basin) and low-impact development (LID) systems (bioretention cell, grass swale, and grass strip). Machine learning (ML) models were trained to predict the reduction or enrichment factors of surface runoff concentrations and loadings of total P (TP) and soluble reactive P (SRP) for the different categories of BMP systems. Relative to traditional BMPs, LIDs generally enriched TP and SRP concentrations in stormwater surface outflow and yielded poorer P runoff load control. The SRP concentration reduction and enrichment factors of LIDs also tended to be more sensitive to variations in climate and watershed characteristics. That is, LIDs were more likely to enrich surface runoff SRP concentrations in drier climates, when inflow SRP concentrations were low, and for watersheds exhibiting high impervious land cover. Overall, our results imply that stormwater BMPs do not universally attenuate urban P export and that preferentially implementing LIDs over traditional BMPs may increase TP and SRP export to receiving freshwater bodies, hence magnifying eutrophication risks.

Understanding the hydrological performance of green and grey roofs during winter in cold climate regions.

Green and grey roofs have emerged as promising and sustainable measures for effectively managing stormwater in urban catchments. However, there is a gap in the literature in understanding and modelling the hydrological performance of these roofs during winter and snow-covered periods in cold climate regions. The present study attempted to address this gap by validating the use of a snow module in simulating the dynamics of snow accumulation and melting of green and grey roofs. Then, the validated model was used to identify and separate the different events that occur in winter (melt only, rainfall only, rain-on-snow) to assess the hydrological performance of six different configurations of green and grey roofs in Trondheim, Norway. The snow module accurately simulated snow accumulation and melting of green and grey roofs. The results showed that rain-on-snow events in winter have longer duration compared to other events including rainfall events in summer. Consequently, rain-on-snow events yield a higher amount of inflow to the roofs compared to rainfall events in summer, despite summer events having higher intensities. The retention and detention performances of green and grey roofs were found to be lowest for rain-on-snow events compared to other types of events, but still yielding significantly lower peak runoffs when compared to standard black roofs. The decrease in retention and detention performances in winter were attributed to the long duration of events, accumulation effect of snow, freezing of roof surface layers, and reduction of evapotranspiration. The study highlights the importance of considering winter conditions in the design of green and grey roofs in cold climates to enhance stormwater management.

Determining the most appropriate stormwater management strategies within the development of urban landscape infrastructure using the TOPSIS method: applications to Rize city.

In this study, the application of multi-criteria decision-making (MCDM) methods in determining the most appropriate stormwater management strategy is examined using different areas in Rize. The determination of the most appropriate stormwater management practices for the Rize coastal park and Güneysu-Rize connection highway with TOPSIS is presented in detail within this study. In this context, commonly used applications suitable for urban areas are discussed. The criteria and their weights used for the evaluation of the selected applications were determined by consulting expert opinions from leading researchers. The most suitable applications in different scenarios such as changes in the cost or the amount of precipitation for Rize coastal park and Güneysu-Rize connection road were determined by the TOPSIS method. The TOPSIS analyses' ranking of the ideal solutions matches the results of the SWMM simulations one to one. SWMM results confirm that the outcomes of TOPSIS are the alternatives that provide maximum decrease in surface runoff.

Green infrastructure layout based on a dynamic operation feature of drainage systems.

Increasingly frequent urban floods strain the traditional grey infrastructure, overwhelming the capacity of drainage networks and causing challenges in managing stormwater. The heavy precipitation leads to flooding and damage to drainage systems. Consequently, efficient mitigation strategies for flooding have been researched deeply. Green infrastructure (GI) has proved to be effective in responding the increasing risk of flood and alleviate pressure on drainage systems. However, as the primary infrastructure of stormwater management, there is still a lack of attention to the dynamic operation feature of urban sewer systems during precipitation events. To fill this gap, we proposed a novel approach that integrates hydraulic characteristics and the topological structure of a sewer network system. This approach aims to identify influential nodes, which contribute to the connectivity of the sewer network amidst dynamic changes in inflow during precipitation events. Furthermore, we adopted rain barrels to serve as exemplars of GI, and 14 GI layout schemes are produced based on the different ranks of influential nodes. Implementing GI measures on both poorly performing and well-performing nodes can yield distinct benefits in mitigating node flooding. This approach provides a new perspective for stormwater management, establishing effective synergy between GI and the drainage system.

Spatial allocation of bioretention cells considering interaction with shallow groundwater: A simulation-optimization approach.

Green infrastructure (GI), as one type of ecological stormwater management practices, can potentially alleviate water problems and deliver a wide range of environmental benefits in urban areas. GIs are often planned and designed to reduce runoff and mitigate pollution. However, the influence of GI on groundwater hydrology and that of shallow groundwater on GI performance was seldom considered. This study utilized a calibrated surface-subsurface hydrological model, i.e., Storm Water Management Model coupled with USGS's modular hydrologic model (SWMM-MODFLOW) to consider the interaction between GI and groundwater into the process of GI planning. The optimal implementation ratio, aggregation level and upstream-downstream location of bioretention cells (BC, one type of GI) under different planning objectives and hydrogeologic conditions was explored. The consideration of groundwater management exerted a significant impact on the optimal spatial allocation of BCs. The results showed that when groundwater management was more concerned than runoff control, BCs were recommended to be allocated more apart from each other and more upstream in the catchment because more-distributed and upstream BCs can result in lower groundwater table rise which is beneficial. BCs were overall recommended to be allocated in areas of deeper groundwater tables, coarser soils, and flatter topographies. However, the spatial features of BCs are related to each other, the choice of them are affected by various hydrogeologic factors simultaneously. The exact location of BCs should be determined by considering the trade-off between runoff control efficiency and groundwater impact. The findings obtained in this study can provide guidance on GI planning in shallow groundwater areas.

Activated carbon amendment of sand in the base of a permeable pavement reduces total nitrogen and nitrate leaching.

Urban runoff from impermeable surfaces contains various pollutants. Stormwater samples were collected for one year from car parks on the campus of Newcastle University, located in northeast England, to monitor seasonal variation in stormwater properties and leachate quality following stormwater percolation through pilot-scale, outdoor permeable pavements. The pilot study compared an innovative 'pollution munching' permeable pavement with 2 % activated carbon (AC) amendment in the sand base with a conventional, un-amended sand base permeable pavement. Faecal coliforms were detected in stormwater at an average value of 3.75 ± 0.79 log10 CFUs per 100 mL. The permeable pavements without and with AC had mean log removal values of 0.81 ± 0.35 and 0.70 ± 0.35 for these faecal bacteria. The absence of genetic markers for human host associated Bacteroides (HF183) in eleven out of twelve stormwater samples showed that the faecal bacteria were mainly from animal sources. 16S rRNA gene sequencing results confirmed the presence of nitrifying bacteria from the genera Nitrosomonas, Nitrobacter, Nitrosococcus, Nitrospira, and Nitrosospira in stormwater. Nitrification and nitrate leaching was more notable for the conventional permeable pavement and may pose a groundwater pollution risk. Two percent AC amendment of the sand base reduced nitrate and total nitrogen leaching significantly compared with the conventional permeable pavement, by 57 ± 15 % and 40 ± 20 %, respectively. The AC amendment also resulted in significantly reduced Cu and DOC leaching, and lesser accumulation of PAHs by passive samplers embedded in the permeable pavement base. Hydraulic tests showed that the AC amended base layer still met the design specifications for permeable pavements, making it a promising proposition for pollution reduction in Sustainable Drainage Systems (SuDS).

Modeling the effect of land use change to design a suitable low impact development (LID) system to control surface water pollutants.

Growth in urbanization has led to increased impervious surfaces, exacerbating flood risks and water quality degradation. This study investigated the impact of land use change and Low-Impact Development (LID) systems on urban runoff quality and quantity in the second region of Tehran. Pioneering an innovative approach, the integration of the Land Change Modeler (LCM) with the Stormwater Management Model (SWMM) signifies a paradigm shift in urban water management. Combined with other hydrological models, this new approach provides a comprehensive method for assessing the future effectiveness of LID practices. The Event Mean Concentration Method (EMC) was used in this study to measure Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Phosphorus (TP), and Zinc (Zn) in urban runoff from five land uses. Results pinpointed transportation land uses as the primary source of pollutants. Using LCM, the study forecasted a surge in urban runoff pollutants by 2030, particularly in the Northwest area of the region due to anticipated land use shifts towards commercial and residential land uses. Model results showed an 11 % increase in TSS over a decade, highlighting the importance of land use change in runoff quality. The study used three types of LIDs to reduce contaminants in dense urban areas. Assessing the impact of LID scenarios on runoff pollutants using SWMM revealed that the bio-retention cell had the best performance, reducing TSS by 20.92 %, and the vegetative swale had the worst performance, reducing TSS by 8.43 %. The study also concluded that combining LIDs would be more effective than using them separately. The results of this study suggest that LID systems can be an effective way to reduce urban runoff pollutants and improve water quality in the second region of Tehran. However, more research is needed to optimize the design and placement of LID systems in different urban areas.

Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning.

Green infrastructure is often proposed to complement conventional urban stormwater management systems that are stressed by extreme storms and expanding impervious surfaces. Established hydrological and hydraulic models inform stormwater engineering but are time- and data-intensive or aspatial, rendering them inadequate for rapid exploration of solutions. Simple spreadsheet models support quick site plan assessments but cannot adequately represent spatial interactions beyond a site. The present study builds on the Landscape Green Infrastructure Design (L-GrID) Model, a process-based spatial model that enables rapid development and exploration of green infrastructure scenarios to mitigate neighborhood flooding. We first explored how well L-GrID could replicate flooding reports in a neighborhood in Chicago, Illinois, USA, to evaluate its potential for green infrastructure planning. Although not meant for prediction, L-GrID was able to replicate the flooding reported and helped identify strategies for flood control. Once evaluated for this neighborhood, we extended the model to include water quality through the representation of dispersion and settling mechanisms for two pollutant surrogates-total nitrogen and total suspended solids. With the extended model, Landscape Green Infrastructure Design Model-Water Quality (L-GrID-WQ), we examined benefits, costs, and tradeoffs for different green infrastructure strategies. Bioswales were slightly more effective than other green infrastructure types in reducing flooding extent and downstream runoff and pollution, through increased infiltration and settling capacity. Permeable pavers followed in effectiveness and are suggested where spatial constraints may limit the installation of bioswales. Although green infrastructure supports both flooding and pollution control, small tradeoffs between these functions emerged across spatial layouts: strategies based on only curb-cuts better controlled pollution, while layouts that followed the path of water flow better controlled flooding. By illuminating such tradeoffs, L-GrID-WQ can support green infrastructure planning that prioritizes unique concerns in different areas of a landscape.

Exploring the adoption of water quality trading as an alternative stormwater regulatory compliance strategy for land development projects: A case study for Roanoke, Virginia.

Urban stormwater runoff is a significant source of nutrient pollution that is very costly to treat. Water quality trading (WQT) is a market-based strategy that can be used to lower the costs associated with meeting stormwater quality regulations. While many WQT programs have experienced low participation, Virginia's program has seen high participation due to the inclusion of land developers and other regulated stormwater dischargers. However, the extent to which WQT is used as a compliance option by regulated stormwater dischargers is not well understood, particularly when compared with the adoption of traditional compliance options. To address this knowledge gap, we collated a novel dataset comprising site characteristics and stormwater compliance methods for all development projects in the City of Roanoke, Virginia from December 2015 to March 2022. We analyzed this dataset to characterize the adoption of nutrient offset credits and other compliance methods being used, including best management practices (BMPs) and improved land covers associated with reduced nutrient export. Results show that credits are the preferred compliance option in Roanoke and were used as the only treatment compliance method for 59% of projects with treatment requirements. Projects using credits corresponded with a lower median disturbed area (1.36 acres) and lower median nutrient load reduction requirement (0.69 pounds of total phosphorus per year) compared with other compliance methods. Furthermore, we found that 58% of the projects that used credits achieved stormwater quantity compliance using methods other than implementing stormwater control devices. By mapping buyers and sellers of credits, we found that all credit sellers are downstream of the development projects. We discuss how this downstream trading could be a cause for concern, as part of a larger discussion of the advantages of tracking stormwater compliance methods, drawing on Roanoke as a case study.

Identifying potential uses for green roof discharge based on its physical-chemical-microbiological quality.

Green roofs are promising tools in sustainable urban planning, offering benefits such as stormwater management, energy savings, aesthetic appeal, and recreational spaces. They play a crucial role in creating sustainable and resilient cities, providing both environmental and economic advantages. Despite these benefits, concerns persist about their impact on water quality, especially for non-potable use, as conflicting results are found in the literature. This study presents a comparative analysis of the quantity and quality of water drained from an extensive green roof against an adjacent conventional rooftop made of fiber-cement tiles in subtropical Brazil. Over a 14-month period, the water drained from both roofs was evaluated based on physical (turbidity, apparent color, true color, electrical conductivity, total solids, total dissolved solids, suspended solids), chemical (pH, phosphate, total nitrogen, nitrate, nitrite, chlorides, sulfates, and BOD), microbiological (total coliforms and E. coli), and metal (copper, iron, zinc, lead, and chrome) concentration parameters. The discharge from the green roof was 40% lower than its counterpart measured at the control roof, while the water quality from both roofs was quite similar. However, the green roof acted as source of chlorides, electrical conductivity, color, BOD, total hardness, E. coli, phosphate, sulfate, and turbidity. On the other side, the green roof neutralized the slightly acidic character of rainwater, showcasing its potential to mitigate the effects of acid rain. The study's results underscored that the water discharged from the green roof generally aligned with non-potable standards mandated by both Brazilian and international regulations. However, the findings emphasized the imperative need for pre-treatment of the green roof discharge before its utilization, specifically adjusting parameters like turbidity, BOD, total coliforms, and E. coli, which were identified as crucial to ensure water safety and compliance with non-potable use standards.

Biochar and vegetation effects on discharge water quality from organic-substrate green roofs.

Green roofs have been increasingly used to improve stormwater management, but poor vegetation performance on roof systems, varying with vegetation type, can degrade discharge quality. Biochar has been suggested as an effective substrate additive for green roofs to improve plant performance and discharge quality. However, research on the effects of biochar and vegetation on discharge quality in the long term is lacking and the underlying mechanisms involved are unclear. We examined the effects of biochar amendment and vegetation on discharge quality on organic-substrate green roofs with pre-grown sedum mats and direct-seeded native plants for three years and investigated the key factors influencing discharge quality. Sedum mats reduced the leaching of nutrients and particulate matter by 6-64% relative to native plants, largely due to the higher initial vegetation cover of the former. Biochar addition to sedum mat green roofs resulted in the best integrated water quality due to enhanced plant cover and sorption effects. Structural equation modeling revealed that nutrient leaching was primarily influenced by rainfall depth, time, vegetation cover, and substrate pH. Although biochar-amended sedum mats showed better discharge quality from organic-substrate green roofs, additional ecosystem services may be provided by native plants, suggesting future research to optimize plant composition and cover and biochar properties for sustainable green roofs.

A comparative analysis of methods and tools for low impact development (LID) site selection.

The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods.

Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems.

In the field of engineering systems-particularly in underground drilling and green stormwater management-real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid architecture, uniquely specialized for multi-task real-time predictions. Building on advancements in attention mechanisms and sequence modeling, our model integrates the core strengths of LSTM and Transformer architectures, offering a superior alternative to traditional predictive models. Further enriched with online learning, our architecture dynamically adapts to variable operational conditions and continuously incorporates new field data. Utilizing knowledge distillation techniques, we efficiently transfer insights from larger, pretrained networks, thereby achieving high predictive accuracy without sacrificing computational resources. Rigorous experiments on sector-specific engineering datasets validate the robustness and effectiveness of our approach. Notably, our model exhibits clear advantages over existing methods in terms of predictive accuracy, real-time adaptability, and computational efficiency. This work contributes a pioneering predictive framework for targeted engineering applications, offering actionable insights into.

Soil, climate, and landscape drivers of base cation concentrations in green stormwater infrastructure soils.

Deicing practices and infrastructure weathering can impact plants, soil, and water quality through the input and transport of base cations. Base cation accumulation in green stormwater infrastructure (GSI) soils has the potential to decrease soil infiltration rates and plant water uptake or to promote leaching of metals and nutrients. To understand base cation retention in GSI soils and its drivers, we sampled 14 GSI soils of different age, contributing areas, and infiltration areas, across 3 years. We hypothesized that soil, climate, and landscape drivers explain the spatial and temporal variability of GSI soil base cation concentrations. Sodium (Na), Calcium (Ca), and Magnesium (Mg) concentrations in GSI soils were higher than in reference soils, while Ca and Mg were similar to an urban floodplain soil. Neither the contributing area, contributing impervious area, nor their ratios to infiltration area predicted base cation concentrations. Age predicted the spatial variability of Potassium (K) concentrations. Ca and Mg were moderately predicted by sand and silt, while clay predicted Mg, and sand predicted K. However, no soil characteristics predicted Na concentrations. A subset of sites had elevated Na in Fall 2019, which followed a winter with many freezing events and higher-than-average deicer salt application. K in sites with elevated Na was lower than in non-elevated sites, suggesting that transient spikes of Na driven by deicer salt decreased the ability of GSI soils to accumulate K. These findings demonstrate the large variability of GSI soil base cation concentrations and the relative importance of soil, climate, and landscape drivers of base cation dynamics. High variability in GSI soil data is commonly observed and further research is needed to reduce uncertainties for modeling studies and design. Improved understanding of how GSI soil properties evolve over time, and their relation to GSI performance, will benefit GSI design and maintenance practices.

Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems.

Evapotranspiration is a key hydrological process for reducing stormwater runoff in bioretention systems, regardless of their physical configuration. Understanding the volumes of stormwater that can be returned to the atmosphere via evapotranspiration is, therefore, a key consideration in the design of any bioretention system. This study establishes the evapotranspiration dynamics of three common, structurally different, bioretention vegetation treatments (an Amenity Grass mix, and mono-cultures of Deschampsia cespitosa and Iris sibirica) compared with an un-vegetated control using lab-scale column experiments. Via continuous mass and moisture loss data, observed evapotranspiration rates were compared with those predicted by the FAO-56 Penman-Monteith model for five 14-day dry periods during Spring 2021, Summer 2021, and Spring 2022. Soil moisture reductions over the 14-day trials led to reduced rates of evapotranspiration. This necessitated the use of a soil moisture extraction function alongside a crop coefficient to represent actual evapotranspiration from FAO-56 Penman-Monteith reference evapotranspiration estimates. Crop coefficients (Kc) varied between 0.65 and 2.91, with a value of 1.0 identified as a recommended default value in the absence of treatment-specific empirical data. A continuous hydrological model with Kc=1.0 and a loading ratio of 10:1 showed that evapotranspiration could account for between 1 and 12% of the annual water budget for a bioretention system located in the UK and Ireland, increasing to a maximum of 35% when using the highest Kc observed (2.91).

Urban stormwater quality: A review of methods for continuous field monitoring.

Urban stormwater is contaminated by a wide range of substances whose concentrations vary greatly between locations, as well as between and during rain events. This literature review evaluates advantages and limitations of current methods for using continuous water quality monitoring for stormwater characterization and control. High-temporal-resolution measurements have been used to improve the understanding of stormwater quality dynamics and pollutant pathways, facilitate the performance evaluation of stormwater control measures and improve operation of the urban drainage system with real-time control. However, most sensors used to study stormwater were developed for either centralized water treatment or natural water contexts and adaptation is necessary. At present, the primary application of interest in stormwater - characterization of pollutant concentrations - can only be achieved through the use of indirect measurements with site-specific relationships of pollutants to basic physical-chemical parameters. In addition, various problems arise in the field context, associated with intermittent or variable flow rates, the accumulation of debris and sediment, adverse conditions for electrical equipment and human factors. Obtaining reliable continuous stormwater quality data requires the adoption of best practices, including the calibration and regular maintenance of sensors, verification of data and accounting for the considerable uncertainties in data; however, the literature review showed that improvement is needed among the scientific community in implementing and documenting these practices.