Green Infrastructure Performance Research Articles

Shaping the general resilience of green infrastructure through integrating structures, functions, and connections

Global climate change has necessitated the implementation of green infrastructure that is resilient in a manner of sustainable development. The current understanding of green infrastructure resilience is hindered by the divergence of generic properties and performance in adapting to uncertain disturbances. This study develops an operational methodology that integrates structural and functional properties of green infrastructure, and their connections to shape the general resilience. A further empirical study is conducted in the context of Shenzhen City, where the effectiveness of resilient connections is correlated with the distribution of waterlogging. We demonstrate that green infrastructure present different levels of resilience in terms of its structural composition and functional performance. The Shenzhen city shows a high capacity to maintain soil retention stability, but a feeble capacity regarding water yield and gross primary productivity. The resilient connections of green infrastructure are highly centralized, with a few pivotal nodes performing a high degree of connectivity. It shows that a total of 52.2% of resilient lines are identified as belonging to the fourth level but linking the majority of the nodes. Enhancing the general resilience of green infrastructure could facilitate its adaptation to specific disturbances such as waterlogging. When correlated the resilient connections of green infrastructure with the distribution of waterlogging, a distance of 1.6 km from the waterlogging points is significantly identified where the residuals of the entropy index display the lowest variance. As the distance increases, the composite entropy index initially decreases and then increases. We suggest that the alignment of generic properties and specified performance of green infrastructure is essential in the pursuit of sustainable development.

A comparative analysis of international guidelines for green infrastructure performance assessment

A comparative analysis of international guidelines for green infrastructure performance assessment

Projected performance of green infrastructure strategies for flood mitigation in the Ganges-Brahmaputra-Meghna delta

Background: The Ganges-Brahmaputra-Meghna (GBM) delta – the world’s most populous river delta – faces heightened susceptibility to the rise in flooding disasters due to climate change, impacting millions annually. Current flood management strategies are unsustainable and ineffective, and resilient flood management is needed. A promising alternative is the strategic implementation of green infrastructure (GI) applications, which have proven effective in flood management in other regions. Methods: An analysis of the region’s past and future vulnerability to flooding is conducted. Then, green infrastructure performance metrics from regions with similar climatic conditions are extrapolated for the GBM. Green roofs, permeable pavements, and rain gardens were identified as the most suitable GI types for the GBM. Finally, computer simulations were employed to analyze the performance of different implementations of GI within a model city. Results: The simulations showed that 0% green rooftop coverage, 100% permeable pavement coverage, and 40% rain garden coverage were the most feasible GI layout. This configuration resulted in the most preferable balance between cost effectiveness and reduced runoff. Green rooftops were minimized due to high installation costs relative to their retention capacity, whereas permeable pavements and rain garden coverage were maximized. Conclusions: The studies show GI’s potential for flood mitigation and resilience in the GBM region. GI initiatives align with the region's flood mitigation policies and are thus feasible to implement with aid from government incentives. Furthermore, the computer program developed for this analysis could serve as a valuable tool for assessing GI implementation limits and offering guidance to policymakers.

A deep dive into green infrastructure failures using fault tree analysis

Green Infrastructure has transformed traditional urban stormwater management systems by fostering a wide range of service functions. Despite their popularity, green infrastructure's performance can deteriorate over their lifecycle, leading to operational failures. The operation of green infrastructure has predominantly relied on reactive maintenance strategies. To anticipate malfunctions and enhance the performance of green infrastructure in the long run, failure data needs to be recorded so that deterioration processes and component vulnerabilities can be recognized, modelled and included in predictive maintenance schemes. This study investigates possible failures in representative GIs and provides insights into the most important events that should be prioritized in the data collection process. A method for qualitative Fault Tree Analysis using minimal cut sets are introduced, aiming to identify potential failures with the minimum number of events. To identify events of interest fault trees were constructed for bioswales, rain gardens and green roofs, for three groups of service function failures, namely runoff quantity control, runoff quality control and additional service functions. The resulting fault trees consisted of 45 intermediate and 54 basic events. The minimal cut set analysis identified recurring basic events that could affect operation among all three green infrastructure instances. These events are ‘trash accumulation’, ‘clogging due to sediment accumulation’, and ‘overly dense vegetation’. Among all the possible cut sets, events such as ‘plants not thriving’, ‘invasive plants taking over’, and ‘deterioration caused by external influences’ could potentially disrupt most of the service functions green infrastructure provides. Furthermore, the analysis of interactions between component failures shows vegetation and filter media layer failures have the highest influence over other components. The constructed fault trees and identified basic events could be potentially employed for additional research on data collection processes and calculating the failure rates of green infrastructure and as a result, contribute to a shift toward their proactive operation and maintenance.

The impact of dynamic traffic and wind conditions on green infrastructure performance to improve local air quality

Road traffic represents the dominant source of air pollution in urban street canyons. Local wind conditions greatly impacts the dispersion of these pollutants, yet street trees complicate ventilation in such settings. This case study adopts a novel modelling framework to account for dynamic traffic and wind conditions to identify the optimal street tree configuration that prevents a deterioration in air quality. Measurement data from a shallow to moderately deep street canyon (average 0.5 H/W aspect ratio and four lanes of 1-way traffic) in Dublin, Ireland was used for model calibration. The computational fluid dynamics (CFD) models were used to examine scenarios of dynamic traffic flows within each traffic lane with respect to its impact on local PM2.5 concentrations on adjacent footpaths, segmenting air quality monitoring results based on different wind conditions for model calibration. The monitoring campaign identified higher PM2.5 concentrations on the leeward (north) footpath, with average differences of 14.1 % (2.15 μg/m3) for early evening peaks. The modelling results demonstrated how street trees negatively impacted air quality on the windward footpath in parallel wind conditions regardless of leaf area density (LAD) or tree spacing, with mixed results observed on the leeward footpath in varying traffic flows and wind speeds. Perpendicular wind direction models and high wind speed exacerbated poor air quality on the windward footpath for all tree spacing models, while improving the air quality on the leeward footpath. The findings advise against planting high-LAD trees in this type of street, with a minimum of 20 m spacing for low-LAD trees to balance reducing local air pollution and ventilation capacity in the street. This study highlights the complexities of those in key decision-marking roles and demonstrates the need to adopt a transparent framework to ensure adequate modelling evidence can inform tree planting in city streets.

Optimal utilization of low-efficiency industrial land in shrinking cities based on green infrastructure performance improvement: Take Mudanjiang as an example

Optimal utilization of low-efficiency industrial land in shrinking cities based on green infrastructure performance improvement: Take Mudanjiang as an example

Planting design influences green infrastructure performance: Plant species identity and complementarity in rain gardens.

Green infrastructure's capacity to mitigate urban environmental problems, like heat island effects and excessive stormwater runoff, is partially governed by its plant community. Traditionally, green infrastructure design has focused on engineered aspects, such as substrate and drainage, rather than on the properties of its living components. Since the functioning of these plant assemblages is controlled by ecophysiological processes that differ by species, the identity and relative abundance of the species used will influence green infrastructure performance. We used trait-based modeling to derive principles for the effective composition of green infrastructure plant assemblages, parameterizing our model using the vegetation and ecophysiological traits of the species within New York City rain gardens. Focusing on two plant traits that influence rain garden performance, leaf surface temperature and stomatal conductance, we simulated the cumulative temperature and transpiration for plant communities of differing species composition and diversity. The outcomes of the model demonstrate that plant species composition, species identity, selection effects, and interspecific complementarity increase green infrastructure performance in much the way biodiversity affects ecosystem functioning in natural systems. More diverse assemblages resulted in more consistent transpiration and surface temperatures, with the former showing a positive, saturating curve as diversity increased. While the dominant factors governing individual species leaf temperature were abiotic, transpiration was more influential at the community level, suggesting that plants within diverse communities may be cooler in aggregate than any individual species on its own. This implies green infrastructure should employ a variety of vegetation; particularly plants with different statures and physical attributes, such as low-growing ground covers, erect herbaceous perennials, and shrubs.

Comprehensive Performance of Green Infrastructure through a Life-Cycle Perspective: A Review

Climate change represents a paramount challenge for humanity in the 21st century. Green infrastructure (GI), due to its myriad environmental and societal benefits, has emerged as an essential natural life support system and a pivotal strategy to combat climate change-induced risks. Consequently, GI has garnered considerable global interest. As of now, comprehensive and systematic environmental impact assessments of GI are underway worldwide. Nonetheless, there remains a conspicuous scarcity of life-cycle approaches to delineate the evolutionary trajectory of this domain. Employing three bibliometric software tools—the R language “Bibliometrix” package (version 4.0.1), CiteSpace (version 6.2.R2 Basic), and “VOSviewer” (version 1.6.18)—this study scrutinizes the progression of the GI paradigm until 2022. An exhaustive review of 1124 documents published on the Web of Science between 1995 and 2022 facilitates an overarching evaluation of GI, encompassing environmental, economic, and social facets from a life-cycle standpoint. The analysis results reveal that (1) the majority of current studies accentuate the economic and environmental efficacy of GI throughout its life cycle, with the social performance receiving comparatively less focus, potentially due to the difficulties in formulating a social life-cycle-assessment database; (2) contemporary research predominantly concentrates on the life-cycle carbon footprint of GI, warranting further exploration into its water and carbon footprints; and (3) multi-objective optimization emerges as a promising avenue for future GI investigations. This review thus furnishes a comprehensive understanding of the performance of GI from a life-cycle perspective.

Variability of green infrastructure performance due to climatic regimes across Sweden

In the context of increasing urbanization and global warming, there is a growing interest in the implementation of green infrastructure (GI) across different climates and regions. Identifying an appropriate GI design criteria is essential to ensure that the design is tailored to satisfy local environmental requirements. This article aims to compare the hydrological performance of GI facilities in eleven Swedish cities by isolating the effect of climatic conditions using an identical GI design configuration. Long-term simulations based on 23-years of meteorological time-series were used as inputs for the Storm Water Management Model (SWMM) with Low Impact Development (LID) controls representing two types of facilities: a biofilter cell (BC) and a green roof. (GR). Large differences in potential annual and seasonal runoff retention were found between locations, driven mainly by the extent of winter/spring season, and the distribution of precipitation patterns (for BCs) and the sequence of rainy days-dry periods and evapotranspiration rates (for GRs). Winter/spring and summer demonstrated the highest/lowest differences between the seasons, results that suggest that implications for design might be aligned to the spatio-temporal distribution of precipitation patterns, and runoff regimes generated by snowmelt and rain-on-snow events, in locations where snowmelt represent high portion of runoff generation.

Ecohydrology of Green Stormwater Infrastructure in Shrinking Cities: A Two-Year Case Study of a Retrofitted Bioswale in Detroit, MI

Stormwater management is of great importance in large shrinking cities with aging and outdated infrastructure. Maintenance of vegetated areas, particularly referred to as green infrastructure, is often aimed at mitigating flooding and the urban heat island effect by stormwater storage and evaporative cooling, respectively. This approach has been applied in large cities as a cost-effective and eco-friendly solution. However, the ecohydrological processes and how the ecohydrology influences the function of green infrastructure and its potential to provide those ecosystem services are not well understood. In this study, continuous field measurements including air temperature, stomatal conductance, and phenocam images were taken in a 308 m2 bioswale retrofitted into a 4063 m2 parking lot on the Wayne State University campus in Detroit, Michigan over a two-year period. Our results suggest that plant characteristics such as water use efficiency impact the ecohydrological processes within bioswales and that retrofitted bioswales will need to be adapted over time to meet environmental demands to allow for full and sustained success. Therefore, projected shifts in precipitation regime change are expected to affect the performance of green infrastructure, and each bioswale needs to be developed and engineered to be able to adapt to changing rainfall patterns.

Evaluation of the cost-effectiveness of Green Infrastructure in climate change scenarios using TOPSIS

Urban hydrological changes caused by uncertain climate affect the implementation of water management strategies. Recently, more attention has been paid to the hydrological performance of green infrastructure (GI) in future climate scenarios. In this study, the Storm Water Management Model (SWMM) was applied to undertake integrated assessments of the runoff and water quality control effect of GI. Eight GI scenarios were proposed in Guangzhou’s Tianhe Smart City, and simulated rainfall events under different climate scenarios were assessed. Considering the factors of runoff volume control, peak flow reduction, pollutant removal, and life cycle cost, the advantages of different GI combinations were compared using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results confirmed that GI affect runoff control under changing climatic conditions, but its application to future large rainfall events is limited. According to the results of the TOPSIS assessment, decision-makers can consider different cost weights to determine the optimal scenario of GI implementation. The conclusions can be applied to GI construction planning projects to enhance the adaptability of urban water resource management systems.

Performance of green infrastructure for storm water treatment in cold climate (Canada)

Qualitative and quantitative treatment of storm water, through the implementation of best management practices, in particular green infrastructure (GI), can decrease run-off amounts and increase th...

The projected impacts of smart decline on urban runoff contamination levels

There has been mounting interest about how the repurposing of vacant land (VL) through green infrastructure (the most common smart decline strategy) can reduce stormwater runoff and improve runoff quality, especially in legacy cities characterized by excessive industrial land uses and VL amounts. This research examines the long-term impacts of smart decline on both stormwater amounts and pollutants loads through integrating land use prediction models with green infrastructure performance models. Using the City of St. Louis, Missouri, USA as the study area, we simulate 2025 land use change using the Conversion of Land Use and its Effects (CLUE-S) and Markov Chain urban land use prediction models and assess these change’s probable impacts on urban contamination levels under different smart decline scenarios using the Long-Term Hydrologic Impact Assessment (L-THIA) performance model. The four different scenarios are: (1) a baseline scenario, (2) a 10% vacant land re-greening (VLRG) scenario, (3) a 20% VLRG scenario, and (4) a 30% VLRG scenario. The results of this study illustrate that smart decline VLRG strategies can have both direct and indirect impacts on urban stormwater runoff and their inherent contamination levels. Direct impacts on urban contamination include the reduction of stormwater runoff and non-point source (NPS) pollutants. In the 30% VLRG scenario, the annual runoff volume decreases by 11%, both physical, chemical, and bacterial pollutants are reduced by an average of 19%, compared to the baseline scenario. Indirect impacts include reduction of the possibility of illegal dumping on VL through mitigation and prevention of future vacancies.

Planning London’s green spaces in an integrated water management approach to enhance future resilience in urban stormwater control

Vegetation, as a fundamental element of urban green infrastructure, plays a vital role in mitigating urban flooding. Green infrastructure performance in mitigating floods depends on plant responses to meteorological forcing. This puts urban green infrastructure in risk under a changing climate. In this study, the resilience and efficiency of London’s green infrastructure under climate change is evaluated. The coupled water and carbon dynamics were evaluated using a mechanistic ecohydrological model forced with the new generation of 2018 UK Climate Projections (UKCP18).It was found that despite overall reductions of runoff production in London under climate change in winter/autumn, current urban green infrastructure in London can lose its efficiency due to the elevated levels of plant water stress unless it operates in an integrated manner with the traditional grey infrastructure drainage system. Plant water stress induced mostly by changes in climate is expected to limit vegetation performance during the end of growing seasons. The negative effects of varying climatic factors on vegetation dynamics can only be partially alleviated by the positive effects of the elevated CO2 concentration level, and are highly uncertain due to the large uncertainty of climate projections.

Assessment of green infrastructure performance through an urban resilience lens

Green infrastructure (GI) is widely recognized for reducing risk of flooding, improving water quality, and harvesting stormwater for potential future use. GI can be an important part of a strategy used in urban planning to enhance sustainable development and urban resilience. However, existing literature lacks a comprehensive assessment framework to evaluate GI performance in terms of promoting ecosystem functions and services for social-ecological system resilience. We propose a robust indicator set consisting of quantitative and qualitative measurements for a scenario-based planning support system to assess the capacity of urban resilience. Green Infrastructure in Urban Resilience Planning Support System (GIUR-PSS) supports decision-making for GI planning through scenario comparisons with the urban resilience capacity index. To demonstrate GIUR-PSS, we developed five scenarios for the Congress Run sub-watershed (Mill Creek watershed, Ohio, USA) to test common types of GI (rain barrels, rain gardens, detention basins, porous pavement, and open space). Results show the open space scenario achieves the overall highest performance (GI Urban Resilience Index = 4.27/5). To implement the open space scenario in our urban demonstration site, suitable vacant lots could be converted to greenspace (e.g., forest, detention basins, and low-impact recreation areas). GIUR-PSS is easy to replicate, customize, and apply to cities of different sizes to assess environmental, economic, and social benefits provided by different types of GI installations.

Methodology to simulate unsaturated zone hydrology in Storm Water Management Model (SWMM) for green infrastructure design and evaluation.

Hydrologic models such as the USEPA Stormwater Management Model (SWMM) are commonly used to assess the design and performance of green infrastructure (GI). To accurately represent GI performance models used in design need to be able to address both the hydrology/hydraulics of the catchment and the GI unsaturated (vadose) zone hydrology. While hydrologic models, such as SWMM, address the need for catchment hydrology/hydraulics, they often simplify the unsaturated zone hydrology. This paper presents a methodology utilizing existing components of SWMM to represent unsaturated zone hydrology in an accessible format that does not require adjustments to the SWMM source code. The methodology simulated the unsaturated soil water movement by considering flow caused by differences of soil matric head and flow caused by gravity between soil layers with finite depth/length. The flow flux related to the soil matric head is a function of soil water diffusivity (D) and the soil moisture gradient, where D can be represented by a pump curve in SWMM. The flow flux related to gravity was controlled by unsaturated hydraulic conductivity (K) only and was also simulated by a pump. The methodology was compared to another variably saturated model, HYDRUS, with theoretical soils (with single layers of sand, loam, silt, and clay, as well as dual-layer scenarios). Field data was used to compare the methodology to HYDRUS and the SWMM LID (Low Impact Development) module. In all comparisons the presented methodology and HYDRUS delivered similar results for the vadose zone response to a storm event, while the LID module of SWMM exhibited slower water movement. The results showed that under natural conditions, the approximation of the presented methodology yielded satisfactory results to simulate flow through the unsaturated vadose zone.

The politics of multifunctional green infrastructure planning in New York City

Cities are expanding green infrastructure and presenting it as a panacea for social and environmental challenges, but the reality is more complex and inherently political. We need to advance our understanding of these politics, which I divide into the politics of green infrastructure performance and planning. I use New York City as a case study to begin unpacking these politics of planning. New York City's green infrastructure program focuses on water quality, but aims to provide other sustainability benefits. How are potential benefits prioritized, factored into planning, and what are the implications of these decisions? I examine these questions by combining spatial analyses, survey, and interview data. I survey local stakeholders about the relative importance of six benefits of green infrastructure (managing stormwater, reducing social vulnerability, increasing access to green space, improving air quality, mitigating the urban heat island effect, and increasing landscape connectivity). Second, I use spatial multi-criteria analysis to identify priority neighborhoods for green infrastructure based on those criteria. I examine tradeoffs and synergies between criteria and compare modeled priorities with existing green infrastructure locations. Interviews with key decision-makers provide a deeper understanding of planning processes. Results illustrate why spatial planning represents a critical challenge for green infrastructure planning.

Monitoring Methods on Evaluating the Water-related Green Infrastructure Performance

Green Infrastructure (GI) theoretically can provide multiple ecosystem services, but its performance is uncertain and dynamic which will vary under the different environmental conditions, time and others. In different scenarios, it will probably provide optimal ecosystem service while also probably provide extremely less ecosystem service. This paper mainly focuses on exploring how to use implementable but simple monitoring methods to evaluate the performance of water-related green infrastructure, and discussing that the performance monitor is an indispensable part of GI’s design and practice. The process of “design-practice-monitor-feedback-adjustment”, effectively enhances the local GI’s design, construction, maintenance as well as the policy development level, and helps to achieve good ecological, economic and social benefits in the subsequent process.

Integrating Green Infrastructure Into Stormwater Policy: Reliability, Watershed Management, and Environmental Psychology as Holistic Tools for Success

As cities continue to expand, the issues of flood control and urban water quality have become major modern sustainability challenges. Green infrastructure—the use of nature-based solutions to target, treat, and store stormwater at its source—has emerged as a possible solution. While green infrastructure does offer multiple benefits for urban users, its performance is also highly variable. This Article addresses a key gap in existing literature by explicitly addressing how uncertainty in environmental and anthropogenic factors affects green infrastructure performance and integration within the Clean Water Act’s municipal separate storm sewer (MS4) regulatory program.

Using rainfall measures to evaluate hydrologic performance of green infrastructure systems under climate change

ABSTRACT As climate change alters precipitation patterns, stakeholders will need to understand how performance of green stormwater infrastructure (GSI) could change in response. As an alternative to using on-site monitoring, which may not always feasible, we propose that changes in performance could be tracked using annual rainfall measures (e.g., maximum daily rainfall per year). We estimated performance of GSI in 17 U.S. cities using rainfall measures by establishing linear relationships with specific performance metrics (e.g., frequency of discharge). Prediction accuracy was evaluated in 2 cities for the period 2020 to 2060 by comparing performance predicted from rainfall trends from regional climate models (RCMs) with simulated performance in SWMM using the same RCMs as input. Findings suggest that tracking rainfall measures can provide insight into the hydrologic performance of green infrastructure by predicting the direction of change, as well as, the magnitude within 25% to 50% percent change.