Application of a Conceptual Hydrological Model to Identify the Impacts of Green Roof Substrate Ageing on Detention Performance

The substrate within a green roof is subject to numerous natural processes throughout its intended design life. As such there is a need to identify the impacts these processes have on substrate hydrological performance over time. Presented is a conceptual hydrological green roof model that utilises non-linear reservoir routing techniques to parameterise detention processes into scale, k and exponent, n. The value of n can be fixed as it largely influenced by the roofs construction (roof slope, drainage length, etc.), thus reducing the model to a single parameter, k. Using observed rainfall/runoff data from test beds at The University of Sheffield values of k were identified for a series of 25 events over a period of 4 years. A rise in the mean value of k was observed for each year of the study, indicating a reduction in detention performance. A design storm exercise allows for the changes in detention performance to be quantified in commonly reported detention metrics

Temporal variations in the potential hydrological performance of extensive green roof systems

Existing literature provides contradictory information about variation in potential green roof hydrological performance over time. This study has evaluated a long-term hydrological monitoring record from a series of extensive green roof test beds to identify long-term evolutions and sub-annual (seasonal) variations in potential hydrological performance. Monitoring of nine differently-configured extensive green roof test beds took place over a period of 6 years in Sheffield, UK. Long-term evolutions and sub-annual trends in maximum potential retention performance were identified through physical monitoring of substrate field capacity over time. An independent evaluation of temporal variations in detention performance was undertaken through the fitting of reservoir-routing model parameters. Aggregation of the resulting retention and detention variations permitted the prediction of extensive green roof hydrological performance in response to a 1-in-30-year 1-h summer design storm for Sheffield, UK, which facilitated the comparison of multi and sub-annual hydrological performance variations. Sub-annual (seasonal) variation was found to be significantly greater than long-term evolution. Potential retention performance increased by up to 12% after 5-years, whilst the maximum sub-annual variation in potential retention was 27%. For vegetated roof configurations, a 4% long-term improvement was observed for detention performance, compared to a maximum 63% sub-annual variation. Consistent long-term reductions in detention performance were observed in unvegetated roof configurations, with a non-standard expanded-clay substrate experiencing a 45% reduction in peak attenuation over 5-years. Conventional roof configurations exhibit stable long-term hydrological performance, but are nonetheless subject to sub-annual variation

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

EvapotranspirationStormwater managementUrban green infrastructureBioretentionHydrological performanceSustainable Drainage Systems (SuDS

A longitudinal microcosm study on the effects of ageing on potential green roof hydrological performance

Green roofs contribute to stormwater management through the retention of rainfall and the detention of runoff. These processes are reasonably well understood, and runoffresponses can be accurately modelled given known system properties. The physical properties of the substrate are particularly relevant to the hydrological response. The substrate is a living biological system, whose properties may change over time. Two sizes of green roof microcosms (50 mm and 150 mm diameter) were observed over a 12-month period. Six system configurations were considered, with two contrasting substrates and three vegetation treatments. Multiple approaches were used to characterize the microcosms' physical and hydrological properties: standard physical tests, bespoke laboratory detention tests, and visualization of the substrate and the root systems using X-ray microtomography. Results suggests that both the substrates' maximum water holding capacity and its capacity to detain runofftend to increase with age. However, there were inconsistencies in the data and these are discussed within the paper. The noted increases were generally not statistically significant as a result of substrate heterogeneity. Notably, the observed differences after one year were relatively small when compared with differences resulting from original substrate compositions and seasonal changes reported elsewhere

Estimating evapotranspiration from commonly occurring urban plant species using porometry and canopy stomatal conductance

Evapotranspiration (ET) is a key moisture flux in both the urban stormwater management and the urban energy budgets. While there are established methods for estimating ET for agricultural crops, relatively little is known about ET rates associated with plants in urban Green Infrastructure settings. The aim of this study was to evaluate the feasibility of using porometry to estimate ET rates. Porometry provides an instantaneous measurement of leaf stomatal conductance. There are two challenges when estimating ET from porometry: converting from leaf stomatal conductance to leaf ET and scaling from leaf ET to canopy ET. Novel approaches to both challenges are proposed here. ET was measured from three commonly occurring urban plant species (Sedum spectabile, Bergenia cordifolia and Primula vulgaris) using a direct mass loss method. This data was used to evaluate the estimates made from porometry in a preliminary study (Sheffield, UK). The Porometry data captured expected trends in ET, with clear differences between the plant species and the reproducible decreasing rates of ET in response to reductions in soil moisture content

A case of Incontinentia Pigmenti associated with congenital absence of portal vein system and nodular regenerative hyperplasia

Congenital absence of portal vein system (CAPVS) is a rare condition in which portal perfusion is bypassed by portosystemic shunt leading to the development of portal hypertension (PH) or porto‐systemic encephalopathy (PSE). Visceral anomalies and liver cancer can be associated with CAPVS1.Thanks to the advances in imaging, the number of CAPVS cases detected has increased. Incontinentia Pigmenti (IP) (OMIM #308300) also represents a rare condition, characterized by skin, teeth, hair, nails, eyes and central nervous system alterations, due to mutations of NEMO/IKBKG gene. We report on the first case of IP associated with CAPVS and nodular regenerative hyperplasia (NRH) of the liver, in a patient with facial dysmorphisms and speech delay. Although rare, this finding may support the role of NEMO in liver homeostasis

Outcome Prediction of Consciousness Disorders in the Acute Stage Based on a Complementary Motor Behavioural Tool.

Attaining an accurate diagnosis in the acute phase for severely brain-damaged patients presenting Disorders of Consciousness (DOC) is crucial for prognostic validity; such a diagnosis determines further medical management, in terms of therapeutic choices and end-of-life decisions. However, DOC evaluation based on validated scales, such as the Revised Coma Recovery Scale (CRS-R), can lead to an underestimation of consciousness and to frequent misdiagnoses particularly in cases of cognitive motor dissociation due to other aetiologies. The purpose of this study is to determine the clinical signs that lead to a more accurate consciousness assessment allowing more reliable outcome prediction. From the Unit of Acute Neurorehabilitation (University Hospital, Lausanne, Switzerland) between 2011 and 2014, we enrolled 33 DOC patients with a DOC diagnosis according to the CRS-R that had been established within 28 days of brain damage. The first CRS-R assessment established the initial diagnosis of Unresponsive Wakefulness Syndrome (UWS) in 20 patients and a Minimally Consciousness State (MCS) in the remaining13 patients. We clinically evaluated the patients over time using the CRS-R scale and concurrently from the beginning with complementary clinical items of a new observational Motor Behaviour Tool (MBT). Primary endpoint was outcome at unit discharge distinguishing two main classes of patients (DOC patients having emerged from DOC and those remaining in DOC) and 6 subclasses detailing the outcome of UWS and MCS patients, respectively. Based on CRS-R and MBT scores assessed separately and jointly, statistical testing was performed in the acute phase using a non-parametric Mann-Whitney U test; longitudinal CRS-R data were modelled with a Generalized Linear Model. Fifty-five per cent of the UWS patients and 77% of the MCS patients had emerged from DOC. First, statistical prediction of the first CRS-R scores did not permit outcome differentiation between classes; longitudinal regression modelling of the CRS-R data identified distinct outcome evolution, but not earlier than 19 days. Second, the MBT yielded a significant outcome predictability in the acute phase (p<0.02, sensitivity>0.81). Third, a statistical comparison of the CRS-R subscales weighted by MBT became significantly predictive for DOC outcome (p<0.02). The association of MBT and CRS-R scoring improves significantly the evaluation of consciousness and the predictability of outcome in the acute phase. Subtle motor behaviour assessment provides accurate insight into the amount and the content of consciousness even in the case of cognitive motor dissociation

Evaluating different machine learning methods to simulate runoff from extensive green roofs

Green roofs are increasingly popular measures to permanently reduce or delay storm-water runoff. The main objective of the study was to examine the potential of using machine learning (ML) to simulate runoff from green roofs to estimate their hydrological performance. Four machine learning methods, artificial neural network (ANN), M5 model tree, long short-term memory (LSTM) and k nearest neighbour (kNN), were applied to simulate storm-water runoff from 16 extensive green roofs located in four Norwegian cities across different climatic zones. The potential of these ML methods for estimating green roof retention was assessed by comparing their simulations with a proven conceptual retention model. Furthermore, the transferability of ML models between the different green roofs in the study was tested to investigate the potential of using ML models as a tool for planning and design purposes. The ML models yielded low volumetric errors that were comparable with the conceptual retention models, which indicates good performance in estimating annual retention. The ML models yielded satisfactory modelling results (NSE >0.5) in most of the roofs, which indicates an ability to estimate green roof detention. The variations in ML models' performance between the cities was larger than between the different configurations, which was attributed to the different climatic characteristics between the four cities. Transferred ML models between cities with similar rainfall events characteristics (Bergen–Sandnes, Trondheim–Oslo) could yield satisfactory modelling performance (Nash–Sutcliffe efficiency NSE >0.5 and percentage bias |PBIAS| <25 %) in most cases. However, we recommend the use of the conceptual retention model over the transferred ML models, to estimate the retention of new green roofs, as it gives more accurate volume estimates. Follow-up studies are needed to explore the potential of ML models in estimating detention from higher temporal resolution datasets