The influence of substrate and vegetation on extensive green roof hydrological performance

The objective of this research was to investigate the hydrological processes occurring in extensive green roof systems through data collected during a continuous monitoring programme of different green roof configurations. Nine green roof test beds (TB) which vary systematically in their substrate composition and vegetation options have been monitored since April 2010 at the University of Sheffield, UK. Three green roof substrates were tested: two commercial substrates manufactured by Alumasc – Heather with Lavender (HLS) and Sedum Carpet (SCS) Substrate were considered alongside a Lightweight Expanded Clay Aggregate (LECA)-based substrate. Three vegetation treatments have been tested: a drought tolerant specie (sedum), a meadow flower mixture and a no vegetation option. Per event retention performance varied depending on the initial water content within the substrate and the characteristics of the rainfall event. Consistent behaviour was observed among the tested green roof configurations with respect to per event retention. Greater retention was associated with HLS and SCS substrates when compared with LECA. Vegetated configurations showed consistently higher retention performance. Sedum vegetation resulted in higher retention performance than Meadow Flower. This was particularly evident on the LECA substrate

Internal fluctuations in green roof substrate moisture content during storm events: Monitored data and model simulations

Understanding how the moisture content in a green roof substrate varies during a storm event is essential for accurately modelling runoff detention. In this paper, a green roof test bed installed with moisture probes at three depths was used to understand how moisture content varies during storms. Detailed studies were conducted on five selected storm events. Physical characterisation tests and field-data based calibrations were performed to acquire the model parameters. Two alternative detention models, based on Reservoir Routing and Richard’s Equation, were validated against the measured green roof runoff and temporary moisture storage data. Once the moisture content exceeds local field capacity, its response at different depths occurs simultaneously during storms, although the recorded data indicate a vertical gradient in the absolute values of local field capacity. Both Reservoir Routing and Richard’s Equation can provide reasonable estimations of the runoff and the vertical moisture content profiles, although Richard’s Equation exhibited stronger vertical water content gradients than were observed in practise. The vertical water content profile is not sensitive to the soil water release curve, although the hydraulic conductivity function influences both the vertical water content profile and runoff rate. The modelled results are highly sensitive to the bottom boundary condition, with a constant suction head boundary condition providing a more suitable option than a free drainage boundary condition or a seepage boundary condition

The importance of unsaturated hydraulic conductivity measurements for green roof detention modelling

Characterising the unsaturated hydraulic conductivity of a green roof substrate is essential for accurately modelling runoff detention in response to rainfall events. In this paper, the unsaturated hydraulic conductivities for four representative green roof substrates were determined in an infiltration column using steady state and transient techniques. The conventional Durner-Mualem Hydraulic Conductivity Function (HCF) model, for which parameters were calibrated based on the measured Soil Water Release Curve (SWRC) data, was shown to provide a poor fit to the experimental data. A new three-segment HCF was, therefore, proposed to fit measured unsaturated hydraulic conductivity data. Detention tests were carried out on 100 mm and 200 mm deep substrates using four simulated storm events. The runoff and moisture content data collected during the detention tests was used to validate the HCFs using the Richards Equation. The new three-segment HCF resulted in simulated runoff and moisture content profiles that closely matched the measured data (with mean Rt2= 0.754 for modelled runoff), in contrast to predictions made using the conventional Durner-Mualem model (with mean Rt2=0.409 for modelled runoff). It was also demonstrated that further simplification of the HCF to a function defined by moisture content at just two points – the saturated hydraulic conductivity and at an unsaturated hydraulic conductivity of 0.1 cm/min – provides a model that is fit-for-purpose for green roof runoff estimation (with mean Rt2=0.629 for modelled runoff)

Quantifying the performance of dual-use rainwater harvesting systems

Rainwater harvesting systems in urban settings are increasingly relied upon to mitigate pluvial flooding on top of providing an additional water supply. Alternative designs have been proposed to support their dual use. Stormwater management performance is typically evaluated through long-term averages. However, long-term assessment is not aligned with the goal of attenuating the impacts of short duration high-intensity rainfall events. This paper contributes a framework for evaluating the dual-use performance of design alternatives. The framework incorporates a set of stormwater management metrics that provides a robust characterisation of performance during significant rainfall events. To the usual long-term volumetric retention metric, we add: 1) metrics that represent the total volume and duration above predevelopment (greenfield) runoff rates; and 2) robust peak outflow rate and retention efficiencies based on the long-term median of a representative sample of significant rainfall events. Our multi-criteria performance visualisations of alternative dual-use designs highlight the importance of carefully designing the forecast-based controlled release mechanisms built into active systems. This work has direct implications for design guidance standards, which we discuss

The feasibility of domestic raintanks contributing to community-oriented urban flood resilience

This interdisciplinary study investigates the technical and social feasibility of developing a domestic raintank programme to increase urban flood resilience. Hydrological modelling of different types of tank was used to determine the advantages and disadvantages of different models in controlling runoff. Qualitative socio-cultural interviews with local people revealed that raintanks were broadly acceptable to the local community. However, interviews with representatives from flood authorities suggest that resource constraints and technocratic industry norms focused on physical flood risk mitigate against consideration of a raintank programme. Our research suggests that there are transformative advantages to a more community-oriented approach to flood resilience, particularly the potential to change the relationship between the public and flood authorities away from a traditional model that pictures the former as passive, towards a process of mutual learning and two-way communication. Our research illustrates that this is not merely a matter of ‘good practice’, but a shift that can produce new practical solutions that a technical perspective alone cannot reveal

Independent Validation of the SWMM Green Roof Module

Green roofs are a popular Sustainable Drainage Systems (SuDS) technology. They provide multiple benefits, amongst which the retention of rainfall and detention of runoff are of particular interest to stormwater engineers. The hydrological performance of green roofs has been represented in various models, including the Storm Water Management Model (SWMM). The latest version of SWMM includes a new LID green roof module, which makes it possible to model the hydrological performance of a green roof by directly defining the physical parameters of a green roof’s three layers. However, to date, no study has validated the capability of this module for representing the hydrological performance of an extensive green roof in response to actual rainfall events. In this study, data from a previously-monitored extensive green roof test bed has been utilised to validate the SWMM green roof module for both long-term (173 events over a year) and short-term (per-event) simulations. With only 0.357% difference between measured and modelled annual retention, the uncalibrated model provided good estimates of total annual retention, but the modelled runoff depths deviated significantly from the measured data at certain times (particularly during summer) in the year. Retention results improved (with the difference between modelled and measured annual retention decreasing to 0.169% and the Nash-Sutcliffe Model Efficiency (NSME) coefficient for per-event rainfall depth reaching 0.948) when reductions in actual evapotranspiration due to reduced substrate moisture availability during prolonged dry conditions were used to provide revised estimates of monthly ET. However, this aspect of the model’s performance is ultimately limited by the failure to account for the influence of substrate moisture on actual ET rates. With significant differences existing between measured and simulated runoff and NSME coefficients of below 0.5, the uncalibrated model failed to provide reasonable predictions of the green roof’s detention performance, although this was significantly improved through calibration. To precisely model the hydrological behaviour of an extensive green roof with a plastic board drainage layer, some of the modelling structures in SWMM green roof module require further refinement

Deconvolving Smooth Residence Time Distributions from Raw Solute Transport Data

A residence time distribution (RTD) provides a complete model of longitudinal mixing effects that can be robustly derived from experimental solute transport data. Maximum entropy deconvolution has been shown to recover RTDs from preprocessed laboratory data. However, data preprocessing is time consuming and may introduce errors. Assuming data were recorded using sensors with a linear response, it should be possible to deconvolve raw data without preprocessing. This paper uses synthetically generated raw data to demonstrate that the quality of the deconvolved RTD remains satisfactory when preprocessing steps involving data cropping or calibration are skipped. Provided noise levels are relatively low, filtering steps may also be omitted. However, a rough subtraction of background concentration is recommended as a minimal preprocessing step. Deconvolved RTDs often include small-scale fluctuations that are inconsistent with a well-mixed fully turbulent system. These are believed to be associated with oversampling and/or unsuitable interpolation functions used in the maximum entropy deconvolution process. This paper describes a new interpolation function—linear interpolation with an automatic moving average (LAMA)—and demonstrates that, in combination with fewer sample points (e.g., 20), it enables smoother RTDs to be generated. The two improvements, to deconvolve raw data and to generate smoother RTDs, have been validated with experimental data. Raw solute transport traces collected from a river were deconvolved after background subtraction. The deconvolved RTDs compare favorably with those generated from the more traditional advection-dispersion equation (ADE) and aggregated dead zone (ADZ) models, but provide more detail of mixing processes. A laboratory manhole solute transport data set was deconvolved with and without preprocessing using 40 sample points and linear interpolation. The raw data were also deconvolved using 20 sample points and LAMA interpolation. The two sets of RTDs deconvolved from the raw data show the same mixing trends as those deconvolved from preprocessed data. However, those deconvolved with LAMA interpolation and 20 sample points are significantly smoother

Implementing treat-to-target urate-lowering therapy during hospitalisations for gout flares.

OBJECTIVES: To evaluate a strategy designed to optimise care and increase uptake of urate-lowering therapy (ULT) during hospitalisations for gout flares. METHODS: We conducted a prospective cohort study to evaluate a strategy that combined optimal in-hospital gout management with a nurse-led, follow-up appointment, followed by handover to primary care. Outcomes, including ULT initiation, urate target attainment, and re-hospitalisation rates, were compared between patients hospitalised for flares in the 12 months post-implementation and a retrospective cohort of hospitalised patients from 12 months pre-implementation. RESULTS: 119 and 108 patients, respectively, were hospitalised for gout flares in the 12 months pre- and post-implementation. For patients with 6-month follow-up data available (n = 94 and n = 97, respectively), the proportion newly initiated on ULT increased from 49.2% pre-implementation to 92.3% post-implementation (age/sex-adjusted odds ratio (aOR) 11.5; 95% confidence interval (CI) 4.36-30.5; p < 0.001). After implementation, more patients achieved a serum urate ≤360 micromol/L within 6 months of discharge (10.6% pre-implementation vs. 26.8% post-implementation; aOR 3.04; 95% CI 1.36-6.78; p = 0.007). The proportion of patients re-hospitalised for flares was 14.9% pre-implementation vs. 9.3% post-implementation (aOR 0.53, 95% CI 0.22 to 1.32; p = 0.18). CONCLUSION: Over 90% of patients were initiated on ULT after implementing a strategy to optimise hospital gout care. Despite increased initiation of ULT during flares, recurrent hospitalisations were not more frequent following implementation. Significant relative improvements in urate target attainment were observed post-implementation; however, for the majority of hospitalised gout patients to achieve urate targets, closer primary-secondary care integration is still needed

Comparing cost-effectiveness of surface water flood management interventions in a UK catchment

This is the final published version. Available from Wiley via the DOI in this record.Despite significant consequences caused by recent events, surface water flooding has historically been of lower priority relative to fluvial and coastal risks in UK flood management. Legislation and research proposes a variety of innovative interventions to address this; however, widespread application of these remains a challenge due to a number of institutional, economic, and technical barriers. This research applies a framework capable of fast and high-resolution assessment of intervention cost-effectiveness as an opportunity to improve available evidence and encourage uptake of interventions through analysing permutations of type, scale, and distribution in urban catchments. Fast assessment of many scenarios is achieved using a cellular automata flood model and a simplified representation of interventions. Conventional and green strategies are examined across a range of design standard and high-magnitude rainfall events in an urban catchment. Results indicate high-volume rainwater capture interventions demonstrate a significant reduction in estimated annual damage costs, and localised surface water drainage interventions exhibit high cost-effectiveness for damage reduction. Analysis of performance across a wide range of return periods enhances available evidence for option comparison decision support and provides a basis for future resilience assessment of interventions.Engineering and Physical Sciences Research Council (EPSRC