Green Roof Model Research Articles

Comparative Evaluation of Evapotranspiration and Optimization Schemes for Green Roof Runoff Simulations Using HYDRUS-1D

The use of green roofs, a low-impact development practice, can be an effective means of reducing direct runoff in urban centers. Green roof modeling can enable efficient design by preliminarily grasping the behavior of the green roof system according to specific configurations. In this study, we aimed to find appropriate evapotranspiration and parameter optimization schemes for HYDRUS-1D, a commonly used modeling tool for green roofs. Comparative studies of this sort in the context of green roof runoff modeling have not been conducted previously and are important in guiding users to overcome the difficulties of choosing the right numerical schemes for an accurate prediction of runoff from a green roof. As a study site, the Portland Building Ecoroof in Portland, Oregon, USA, was chosen, as green roof configurations and observed data for climate and runoff were available. From the simulation results of the runoff volume, the Blaney–Criddle method, which was considered an alternative, was found to be appropriate for calculating evapotranspiration from a green roof (R2 = 0.82) relative to the Hargreaves method built in HYDRUS-1D (R2 = 0.46). In addition, this study showed that the optimization method using the harmony search algorithm, which was proposed as an alternative optimizer, was better (R2 = 0.95) than that of the HYDRUS-1D’s own optimization module (R2 = 0.82) in calibrating HYDRUS-1D for green roof runoff. The findings are thought to be useful in guiding modelers who are considering using HYDRUS-1D for green roof runoff simulations.

Eco-friendly green roof from biodegradable substrate for stormwater quality improvement

Currently, there is a significant surge of interest in green roof technology for construction buildings due to its numerous environmental benefits, such as stormwater management, energy efficiency, and enhanced urban biodiversity. However, the issue of potential pollutant release from green roof substrates into runoff water, causing water pollution, needs to be addressed. To tackle this concern, a lab-scale green roof model was assessed, utilizing a biodegradable substrate made from banana peels and eggshell waste (organic fertilizer). Three models were tested: a conventional green roof (control), a green roof with chemical fertilizer, and a green roof with organic fertilizer. Various water quality parameters, including pH, total suspended solids (TSS), nitrogen, phosphorus, and potassium (NPK), and chemical oxygen demand (COD), were evaluated. The results demonstrated the effectiveness of organic fertilizer in reducing TSS and COD levels, where the eco-friendly green roof with biodegradable substrate exhibited an impressive performance, achieving a higher COD removal percentage (78%) compared to the green roof with chemical fertilizer (50%). The utilization of organic fertilizer led to an enhancement in the quality of stormwater runoff, resulting in NPK removal percentages ranging from 17% to 25%. Additionally, the organic fertilizer fostered healthier vegetation growth, leading to a greater number of leaves compared to the chemical fertilizer. These findings highlight the potential of eco-friendly green roofs as a sustainable and effective tool for stormwater management, provided suitable substrate materials are employed.

Water retention and runoff quality of a wildflower meadow green roof with different drainage layers

Extreme meteorological and hydrological phenomena, including high air temperatures and rainstorms, are becoming increasingly dangerous, causing floods and inundations, as well as long periods without precipitation, which lead to droughts. Green roofs may be one of the possible measures providing solutions to these problems. Rainfall, runoff and water quality data from three different intensive green roof models covered with wildflower meadows (WFs) over 20 months have been analysed to establish the extent to which the type of drainage layer affects hydrological performance. The field experiment was conducted at the Warsaw University of Life Sciences Water Centre park from November 2019 to November 2021. The monitoring of the quality and quantity of runoff was carried out on three models of green roofs incorporating wildflower meadows with drainage layers of 2 cm (WF 1) and 4 cm (WF 2) of polypropylene mat, as well as 6 cm of chalcedony (WF 3), in an urbanized area under moderate climate conditions. The model with the 4 cm polypropylene mat drainage layer retained approx. 6 % more rainwater compared to the model with the one made of chalcedony, and 4 % more than the one with the 2 cm polypropylene mat. Phosphates were detected in most of the leachates from all the wildflower-covered green roof models at 0 ÷ 0.459 mg PO4-P/L, 0 ÷ 0.402 mg PO4-P/L, and 0÷0.360 mg PO4-P/L for WF 1, WF 2 and WF 3. This may suggest that the type of drainage layer was not an important source of phosphates in the leachates.

Analyses of the Effectiveness of Different Media Depths and Plant Treatments on Green Roof Rainfall Retention Capability under Various Rainfall Patterns

Green roofs have been used to reduce rainfall runoff by altering hydrological processes through plant interception and retention as well as detention within the green roof system. Green roof media depth, substrate type, plant type and density, regional climatic conditions, rainfall patterns, and roof slope all impact runoff retention. To better understand the impacts of media depth (10, 15, and 20 cm), plant (planted and non-planted), and rainfall pattern (low, medium, and high) on rainfall retention, we analyzed data collected between September 2005 and June 2008 from 24 green roof models (61 cm × 61 cm) for growing and non-growing seasons. Our results showed that a planted green roof has greater rainfall retention capability than a non-planted green roof for all media depths. Interestingly, a non-planted green roof system with a 10 cm media depth retained greater rainfall than a planted green roof during both growing and non-growing periods. Retention capability decreased with increasing rainfall amounts for both planted and non-planted green roofs and seasons (growing and non-growing). The 15 cm media depth green roof retained significantly greater rainfall depth than the 20 cm models during medium (0.64 to 2.54 cm) and high (>2.54 cm) rainfall events for the growing season but not during the non-growing season. The study provides insight into the interactive effects of media depth, rainfall amount, plant presence, and seasons on green roof performance. The results will be helpful for designing economical and effective green roof systems.

Modeling green roofs in tropical housing to support micro-scale food security

Research on green roofs in tropical residential buildings has the opportunity to be a substitute for productive green spaces. This research implements several vegetable plants as building roof covering elements, i.e., peanut, eggplant, chili, and tomato. Experimental research was applied jointly for six months on four types of vegetables on a green roof covering an area of sixteen square meters. The green roof model consists of a green roof model on concrete and corrugated zinc through a floating technique as a roof model engineering. The aim of this research is to examine the roof as a substitute for green space in buildings. The results showed that the four types of vegetables could grow well on both roof models, both types of green roof have the opportunity to be implemented in residential areas, resulting in fruit that could be consumed on a household micro scale.

Sensitivity analysis and weather condition effects on hygrothermal performance of green roof models characterized by recycled and artificial materials’ properties

This study conducted a sensitivity analysis and assessed the effects of long-term weather conditions on green roof models, including recycled and artificial materials. Climate conditions can affect the hygrothermal performance of green roof materials, but this important issue has hardly been evaluated for drainage and substrate layers made of recycled and artificial materials. Climate change makes it unclear how well green roofs will perform hygrothermally. Moreover, the heat flux sensitivity to the thickness and physical characteristics of green roofs with artificial and recycled materials has received less attention. This study applied three weather scenarios on green roof models with artificial and recycled materials: the beginning, middle, and end of the 21st century. As per the results, at the beginning and middle of the 21st century, substrate layers' water content was roughly nine times more than the drainage layers'. At the end of the 21st century, the comparable difference was 6.5 times larger. During the summer and the beginning of autumn, the green roofs’ thermal performance with recycled and artificial materials was improved until the end of the 21st century. The entire parameter change demonstrated the scatter of thermal conductivity, density, and thickness effectively influenced the dispersion of heat flux for the green roof layers. Also, the scatter of density was more effective in heat flux dispersion for substrate layer than drainage layer.

An improvement in drainage flow equation of EPA SWMM green roof module.

Green roofs are among the important green infrastructures which decrease the negative impacts of urbanization. In this study, the green roof modeling capability of U.S. Environmental Protection Agency Storm Water Management Model (EPA SWMM) is evaluated and the modeling methods used in EPA SWMM, which need improvements, are determined. In particular, the drainage flow, which is originally calculated with Manning's equation in SWMM, is improved by introducing the drainage mat void thickness and adapting a new value for the exponential coefficient of depth in the equation. In addition, the capability of the percolation equation is further investigated, i.e., the value of the coefficient of the exponential function of soil moisture content that considers the percolation through the soil layer is determined for different substrates using the existing unsaturated hydraulic conductivity and soil moisture content data from steady-state tests. A comparison of the improved equation of drainage flow results with the existing experimental data shows that the improved equation represents the drainage flow more accurately than the one used in EPA SWMM.

Energy consumption for space cooling and heating depending on flat roof structures renovation. Case study of the Healthcare center Nis

Since the reduction of energy consumption and efficient energy use, particularly in the building stock, is the priority for contemporary societies, the ways of how to improve the existing buildings should be examined. Flat roofs, as a part of the existing building thermal envelope, are recognized as a field of intervention for improving efficient energy use for space cooling and heating. The topic of the paper is to determine to what extent different improved roof structures affect energy consumption intended to achieve thermal comfort. Comparative analysis of four roof structures was conducted for the building of Healthcare center Nis in Serbia, one improved non-walkable flat roof structure and three green roof systems. Urbancsape extensive green roof system was used in all tested green roof models: non-walkable extensive green roof, walkable extensive green roof, and extensive and intensive green roof systems within walkable terraces. DesignBuilder software was used for energy modeling. The obtained results indicate a slight decrease in energy consumption for building models with green roofs compared to the building model with the improved non-walkable roof structure, along the cooling period, by approximately 1,5%, which is correlated with previous studies in similar conditions. On the other hand, the reduction of energy consumption over the heating period was negligible (less than 1%). Considering the results and predominant usage of commercial Urbanscape extensive green roof system in all green roof models, being characterized by small thickness (10.64 cm) and light structure, and which is predicted to be installed over the already well-insulated roof, the system?s role as an additional thermal mass was confirmed.

A simplified model for analyzing rainwater retention performance and irrigation management of green roofs with an inclusion of water storage layer

Rainwater retention and water content in green roofs are primarily influenced by structural configurations (i.e., soil layer, vegetation layer, and water storage layer) and climatic factors (i.e., rainfall and evapotranspiration (ET)). Based on the principle of water balance, this study proposes a conceptual model for simulating water flow in green roofs with water storage layers. Three green roof model experiments were conducted from August 1st, 2020 to July 31st, 2021 for calibrating and verifying the conceptual model. The proposed model was solved iteratively using a newly developed program in Visual Basic. The results showed that the conceptual model can capture the dynamic variations in the rainwater retention and water content of green roofs well. The average Nash-Sutcliffe efficiency coefficient is 0.65 and the average error is 6%. The annual rainwater retention capacity (RRC) of green roofs in the perennial rainy climate model was on average 28% higher than that in the seasonal rainy climate model. At the expense of water stress, high ET plants significantly increased the annual RRC of green roofs at a low level. As the water storage layer depth increased from zero to 150 mm, the annual RRC of green roofs increased by 41%, and the water stress decreased by 49%. Compared with an increase in water holding capacity and soil depth, the response of the annual RRC and water stress of green roofs for increasing water storage layer depth is much greater. As per climate of Southern China region, the water storage layer depth of 100 mm is found to obtain optimal rainwater retention and irrigation management in green roof with similar soil thickness (100 mm).

Experimental and numerical investigation on hydrological characteristics of extensive green roofs under the influence of rainstorms.

Green roof rainwater retention, peak runoff reduction, and runoff time delay are considered important hydrological performance indicators for assessing management of urban stormwater. In this study, simulated rainfall experiments were conducted on three green roof models with different water storage layer depths. The numerical model was established using Hydrus-1D program, and the sensitivity of main parameters, the hydrological response of green roofs with a water storage layer, and water storage on the soil surface were analyzed. In addition to the saturated water content of the soil, the depth of the green roof water storage layer is the most sensitive parameter to rainwater retention and initial drainage time. During the simulated rainfall experiment, the 25-mm-deep water storage layer (WSL-25) increased the rainwater retention capacity (RRC) by 46%. For a 20-year return period corresponding to South China region, the RRC of green roofs with WSL-25 increased by 31% compared with that without a water storage layer. The initial drainage time was delayed by 50min, and the peak drainage rate was reduced by 89%. In this case, a 100-mm soil layer, a 50-mm water storage layer, and a 50mm maximum surface water storage depth were considered the optimal structural configurations of green roofs. This shows that water storage on the soil surface and bottom water storage layer were equally important for improving RRC, reducing peak drainage and delaying drainage time of green roofs.

Influence of water storage and plant crop factor on green roof retention and plant drought stress

Green roofs can reduce stormwater runoff with deeper substrates providing greater storage for water retention and evapotranspiration (ET) regenerating storage capacity between rainfall events. In green roof models, ET can be estimated using species-specific plant crop factors (Kc), which characterize water use under non-limiting conditions. We manipulated Kc by altering plant density in a glasshouse experiment under well-watered conditions. We determined Kc of green roof plants growing in pots with different substrate depths (150 mm and 300 mm) and plant densities (0, 1, 2 and 4 plants per pot). We then analyzed the influence of storage and Kc on retention and drought stress using a water-balance model, with a 30-year climate scenario for Melbourne, Australia. We hypothesized that greater planting density and substrate depth would result in proportionally greater ET and therefore higher Kc (glasshouse experiment) and that this would improve retention and reduce drought stress (rainfall simulation). Contrary to our hypotheses, cumulative ET increased by only 38–48% with increased substrate depth and by only 28–38% with increased plant density, despite large increases in plant biomass (67–150%) and growth. Kc values ranged from 1.9–2.2 and 2.7–3.8 for shallower and deeper substrates, respectively. Due to these very high crop factors, our water balance model showed very high annual rainfall retention (97.5%). However, higher Kc and storage only increased rainfall retention by 3–5% and resulted greater drought stress. Plants in deeper substrates experienced 14–29 more days of drought stress, as these plants depleted substrate moisture more efficiently (i.e., had a higher Kc) compared with shallow substrates. These findings suggest that improvements in rainfall retention for green roofs with deeper substrates or higher plant densities are small relative to the increased risk of plant drought stress. The lowest planting density was optimal for improving rainfall retention and reducing plant drought stress.

Towards improving the calibration practice of conceptual hydrological models of extensive green roofs

Conceptual rainfall-runoff models (CRRMs) can be used as a design tool for green roofs due to their simplicity and acceptable accuracy. This study showed how the uncertainty of CRRM parameters could be reduced by changing the calibration practice, which can enhance the interpretation and identifiability of CRRM parameters. A CRRM was developed and tested on a dataset of 14 extensive green roofs located in four Norwegian cities with different climatic conditions. Two calibration schemes were compared: a common scheme using runoff data as a basis for calibration (single-objective), and a scheme combining runoff and soil moisture data for the calibration (multi-objective scheme). The results confirmed the ability of the CRRM to simulate runoff from extensive green roofs across multiple climatic zones and different roof configurations (Kling Gupta Efficiency > 0.75). The multi-objective calibration scheme was found to reduce the uncertainty of the CRRM parameters, especially the storage parameters, enhancing the physical interpretation of parameter values. The study attempted to give guidelines to estimate parameters of the CRRM which can be used by practitioners for new roof configurations under different climatic conditions.

Insights into green roof modeling using SWMM LID controls for detention-based designs

Rainfall–runoff responses were observed in a laboratory environment using a rainfall simulator and a 7.43 m2 green roof cassette equipped with weighing lysimeters. SWMM LID controls were developed for various green roof profile configurations based on the physical properties of the composite materials. Unknown parameters affecting the drainage layer were adjusted in calibration. The cassette was modeled both as a typical Green Roof LID control using Manning’s equation at the drainage layer and a Bioretention LID control using an orifice equation in the drainage layer. Key parameters from a sensitivity analysis that were not directly measured were Manning’s roughness of the drainage layer, the drainage coefficient at the orifice, and the conductivity slope (HCO). The hydraulics of roof drains were considered by varying the width of the drain outlet from 0.25 m–1.22 m. During calibration and validation of multiple events, SWMM modeling resulted in a good fit compared to observed results (Nash–Sutcliffe model efficiency coefficient values of 0.70–0.89). Key limitations of SWMM green roof modeling are discussed with suggested improvements for future consideration.

Dynamic simulation and parametric analysis of green roofing in buildings

Enhancing energy efficiency and performance of the buildings and improving the air quality are acquisition during the cooling and heating seasons. According to the 2010 report by General Electricity Company of Libya, electrical energy consumption by residential sector account for approximately 39% of total demand in Libya. The green roof technologies in buildings are one of the main methods for using energy economically and reducing CO2 emission. The economic parameters such as the vegetation thickness and its thermal conductivity for the green roof, the electrical energy cost, coefficient of performance and EER all affect CO2 emission and total cost of the buildings. This study focuses on the impact of these parameters on the application of green roofs as well as degerming the optimum vegetation thickness and thermal conductivity of vegetation layer. Tripoli's degree days were used as one of methods to achieve these calculations or the results. The green roof and mathematical models were performed to find energy savings, and payback, and CO2 reductions. This study focuses on investigating these parameters that affect the green roof for external building based on life-cycle cost analysis (an economic model). As a result, the total cost increased from 6.7 to 23.05 $/m2; while CO2 emission decreased from 2.5 to 1.52 kg/m2/years with increasing the electricity costs which includes the effects of Interest rate on CO2 emission and total cost. Total cost decreased from 16.7 to 14.9 $/m2; while CO2 emission increased from 1.85 to 1.94 kg/m2/years with increasing the Interest. An increase in COP (Coefficient of Performance) and EER (Energy Efficiency Ratio) causes the CO2 emission to decrease from 2.2 to 1.51 kg/m2/years and from 2.08 to 1.37 kg/m2/year and total cost to decrease from 18.3 to 12.9 $/m2 and from 17.4 to 11.76 $/m2. The total cost and CO2 emission are increased from 13.7 to 16.5 $/m2 and from 0.34 to 1.74 kg/m2/years with increasing the thermal conductivity of vegetation. And the effects of degree day on CO2 emission and total cost, the total cost and CO2 emission are decreased from 18.6 to 14.67 $/m2 and from 2.45 to 1.55 kg/m2/years with increasing the degree day.

The effect of dynamic albedos of plant canopy on thermal performance of rooftop greenery: A case study in Singapore

A field experiment was conducted to study the albedo variations of plant canopy with time for two local species in Singapore. It was found that diurnal albedo variations of plant canopy showed a “M” shape on clear day and did not vary significantly on cloudy day. The maximum fluctuation range of hourly albedo was up to 0.085 and 0.015 on typical clear day and cloudy day, respectively. A dynamic canopy radiation transfer model was introduced in order to predict canopy albedo and was validated against measured data. The results show that the maximum relative error of predicted canopy albedo is less than 10%. Sensitivity analysis based on this model shows that leaf area index, leaf angle, albedo and transmissivity of leaves have significant effect on canopy albedo. Comparing the simulated results of thermal performance between green roof model with and without considering dynamic canopy albedo, it can be found that the dynamic canopy radiation model could further improve the predictive performance of green roof model. Finally, thermal performance of green roof was simulated with and without considering dynamic canopy albedo under different scenarios on typical clear day. The results indicate that significant error of simulation results may occur without considering the variation of canopy albedo caused by plant parameters, especially at noon period when solar radiation is strongest. The findings drawn from this study benefit to improve the accuracy of thermal performance simulation of green roof, and also provide some reference for plant selection in terms of thermal performance.

Simulation of humidity and temperature distribution in green roof with pozzolana as drainage layer: Influence of outdoor seasonal weather conditions and internal ceiling temperature

The outdoor seasonal weather conditions can influence the insulation performance of extensive green roof. In addition, the thermal behavior of extensive green roof can be affected by the thickness of its layers including substrate and drainage layer. On the other hand, the replacement of polyethylene modular panel with porous aggregates as drainage layer can affect the water retention capacity of green roof. Therefore, in this study, the green roofs with pozzolana (porous volcanic gravel) as drainage layer under constant and variable inside temperatures were modeled and subjected to the weather conditions of winter and summer to assess the humidity and temperature variations in the depth of the systems. The results showed that there was a decrease in temperature through the depth of the green roof system for the winter period, while the reverse occurred for the relative humidity. During the winter period, the green roof model with the 10-cm substrate and 8-cm pozzolana was recommended to be used. However, the model with the 8-cm substrate and 6-cm drainage layer had the best insulation performance, particularly for the summer period. Moreover, the internal ceiling temperature fluctuation of system under variable inside temperature was higher than that under constant inside temperature.

Modelling thermal and humidity transfers within green roof systems: effect of rubber crumbs and volcanic gravel

ABSTRACT The use of recycled materials and porous aggregates such as rubber crumbs and volcanic gravel (pozzolana) for the drainage layer can lead to improving thermal behaviour of the green roofs. On the other hand, the thermal performances of green roofs can be affected by the thickness of substrate and drainage layer. Therefore, the main objective of this study was to adapt modelling characteristics for different thicknesses of substrate and drainage layers used under constant and variable temperatures and solar radiation: specific rubber crumbs and volcanic gravel behaviour has been modelled. The simultaneous heat and moisture transfers within the green roof were estimated as well. According to the results, the 9 cm substrate was recommended to be used for the green roofs, once the thickness of drainage layer was 4 cm. Moreover, the optimum thickness of pozzolana and rubber crumbs as drainage layer was 6 and 7 cm, respectively, once the thickness of the substrate was kept constant (5 cm). By increasing the thickness of substrate and drainage layers, the fluctuation of internal ceiling temperature in the green roof models with the presence of humidity decreased, but not as much as that in the green roof models without the presence of humidity.

Impact of the Hydrogel Amendment and the Dry Period Duration on the Green Roof Retention Capacity

Abstract Climate changes as well as the urbanisation and economic development influence the characteristics of the stormwater runoff in the cities. The sealing of drainage basin surface leads to an increase of the runoff intensity, thereby decreasing the rainwater infiltration. This situation can lead to the risk of flooding in urban areas. Therefore, especially in great cities there is a need for application of such solutions that will support the operation of the sewage systems. The examples of such solutions are, among others, the green roofs. The paper presents the results of investigation of the water retention capacity of 4 green roof models containing following growing media: (1) the typical green roof substrate without any amendments, (2) the substrate with addition of about 1 % by weight of hydrogel (the cross-linked potassium polyacrylate), (3) the substrate containing about 0.25 % by weight of hydrogel, (4) the substrate with addition of expanded clay and perlite. The models were not vegetated in order to investigate only the water retention capacity of drainage elements and substrates. The water retention capacity of green roof models was investigated in the laboratory conditions with use of artificial precipitations simulated after diverse antecedent dry weather periods (ADWP) amounting to: 1, 2, 5, 7, and 12 days. The intensities of artificial precipitations were relatively high and ranged from 1.14 to 1.27 mm/min, whereas their durations ranged from 7.75 to 12.56 min. These values of intensities and durations corresponded to the design rainfall intensities calculated using Blaszczyk’s equation for annual rain depth equal to 600 mm and the return periods ranged from 5 to 15 years. The obtained results indicate that the water retention capacity of green roof models, expressed as the volumes (or depths) of rainwater retained within their structures, increases with an increase of ADWP. Results indicate that the relation between ADWP and the amount of water retained in the layers of green roofs in the case of relatively short antecedent dry weather periods provided for the analysis (from 1 to 7 days) may be approximately linear. The results of the one-way ANOVA indicate that in the case of all models there is a statistically significant difference between the values of retention depth for specified ADWP (p < 0.001). During more than half of simulated precipitations, especially in the case of longer ADWPs lasting 5, 7, and 12 days the best water retention capacity had Model 3, with substrate containing about 0.25 % by weight of hydrogel. On the other hand, the results show that the weakest retention capacity had Model 2 (with substrate containing 1 % by weight of hydrogel). In the case of longer ADWPs (lasting 7 and 12 days) relatively weak water retention capacity had Model 4 (with substrate containing the addition of expanded clay and perlite). It can be concluded that too large amount of hydrogel added to the substrate can have an unfavourable impact on the water retention capacity of green roofs.

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

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

A rainwater control optimization design approach for airports based on a self-organizing feature map neural network model

To address the problems of high overflow rate of pipe network inspection well and low drainage efficiency, a rainwater control optimization design approach based on a self-organizing feature map neural network model (SOFM) was proposed in this paper. These problems are caused by low precision parameter design in various rainwater control measures such as the diameter of the rainwater pipe network and the green roof area ratio. This system is to be combined with the newly built rainwater pipe control optimization design project of China International Airport in Daxing District of Beijing, China. Through the optimization adjustment of the pipe network parameters such as the diameter of the rainwater pipe network, the slope of the pipeline, and the green infrastructure (GI) parameters such as the sinking green area and the green roof area, reasonable control of airport rainfall and the construction of sustainable drainage systems can be achieved. This research indicates that compared with the result of the drainage design under the initial value of the parameter, the green roof model and the conceptual model of the mesoscale sustainable drainage system, in the case of a hundred-year torrential rainstorm, the overflow rate of pipe network inspection wells has reduced by 36% to 67.5%, the efficiency of drainage has increased by 26.3% to 61.7%, which achieves the requirements for reasonable control of airport rainwater and building a sponge airport and a sustainable drainage system.