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Review

Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review

by
Mo Wang
1,2,
Sijie Feng
1,
Rana Muhammad Adnan Ikram
3,*,
Tong Chen
1,
Chuanhao Sun
1,
Biyi Chen
2,
Qiuyi Rao
2,
He Jin
1,* and
Jianjun Li
1,2
1
College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
2
Architectural Design and Research Institute, Guangzhou University, Guangzhou 510091, China
3
School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(18), 13616; https://doi.org/10.3390/su151813616
Submission received: 14 July 2023 / Revised: 1 September 2023 / Accepted: 7 September 2023 / Published: 12 September 2023
(This article belongs to the Section Sustainable Water Management)
Browse Figures
Versions Notes

Abstract

:
Low-Impact Development (LID) represents a cogent strategy designed to conserve or reestablish antecedent hydrological states through an array of innovative mechanisms and methodologies. Since the dawn of the millennium, LID-centric research has demonstrated a persistent upward trajectory, mainly focusing on its capacity to mitigate climate change repercussions, particularly runoff and peak flows. However, a standardized rubric and toolkit for LID evaluation remain elusive. While numerous studies have documented the hydrological and water quality benefits of LID, the impacts of climate change on its effectiveness remain uncertain due to varying spatial and temporal climate patterns. This comprehensive review examined 1355 peer-reviewed articles in English, comprising both research articles and reviews, indexed in the Web of Science up until 2022. Findings from the bibliometric analysis revealed significant contributions and emergent trends in the field. Notably, there is an increasing emphasis on performance evaluation and efficiency of LID systems, and on understanding their impact on hydrology and water quality. However, this review identified the lack of a standardized LID evaluation framework and the uncertainty in LID effectiveness due to varying climate patterns. Furthermore, this study highlighted the urgent need for optimization of current hydrological models, advancement of LID optimization, modeling, monitoring, and performance, and stakeholder awareness about LID functionality. This review also underscored the potential future research trajectories, including the need to quantify LID’s effectiveness in urban flooding and water quality management and refining LID simulation models. Cumulatively, this review consolidates contemporaneous and prospective research breakthroughs in urban LID, serving as an indispensable compendium for academics and practitioners in the discipline.

1. Introduction

Climate change exerts a significant influence on myriad facets of our environment, from natural resource availability and infrastructure development to water accessibility. Among the multifaceted consequences of climate change are fluctuations in precipitation patterns [1], which manifest as reduced snowfall, accelerated snowmelt, and diminished summer rainfall [2,3]. Concurrently, the escalating temperatures intensify these impacts, leading to altered water quality [4], shifts in resource availability [5], changes in soil composition [6], variations in vegetation patterns [7], and disruptions in the timing of precipitation and runoff [8]. These intricacies of climate change demonstrate its profound and widespread implications, necessitating comprehensive strategies for adaptation and mitigation. Valjarević et al. [9] further elaborate on the relationship between cloudiness, water from cloud seeding, and plant distribution, illustrating the intricate ways in which atmospheric changes can influence ecological systems. The variations in climate change effects resonate globally, epitomizing the intricacies of global warming and unpredictable weather dynamics.
Urban ecosystems, characterized by high-density infrastructure and intense socio-economic activities, are particularly vulnerable to these changes. In many areas, increased rainfall frequency and extended dry periods have led to an escalation in urban flooding incidents [10,11]. Traditional storm drainage systems, designed for rapid rainwater discharge, are proving unsustainable and are economically burdensome in this new climate reality [12,13,14]. Consequently, urban planners and engineers are increasingly adopting sustainable practices for managing urban stormwater [15], integrating concepts such as integrated water resource management with climate change adaptation strategies, as expounded by Ludwig et al. [16].
Low-Impact Development (LID) stands out as a globally recognized solution. Tracing its roots back to Maryland in the United States [17], LID has been articulated and adapted under different monikers around the world such as Low Impact Urban Design and Development (LIUD) [18,19], Water Sensitive Urban Design (WSUD) [20], and Sustainable Urban Drainage Systems (SUDS) [21]. The LID approach focusses on managing rainwater runoff at its source, emulating natural hydrology to enhance infiltration and retention within urban watersheds [22]. With practices such as porous pavement, bioretention cells, and green roofs, LID has shown effectiveness in controlling stormwater runoff and has the potential to mitigate the increased surface runoff and extreme temperatures associated with climate change [22].
Previous reviews have substantiated the hydrological benefits of LID, including reduction in runoff volume and peak discharge rate, extension of lag time, nutrient and metal absorption, groundwater replenishment, and evapotranspiration-induced cooling [23,24,25,26]. Zhang and Jia [27] presented a comprehensive optimization framework for LID and highlighted the increasing shift towards large-scale optimization efforts and proposed novel strategies, such as objectives downscaling decomposition and schemes up-scaling theory, to incorporate the green-grey-blue system for multi-scale optimization. However, these standard reviews rely heavily on comprehensive methodologies involving an analysis and summary of the content of each research article [28].
The science of bibliometrics, grounded in quantitative and statistical methods, serves to expose the interconnectedness of published articles and to spotlight current trends in a specific research field through assessing the co-citation frequency of other publications [29]. Thus, supplementing traditional review methods with a bibliometric approach can enhance the comprehension of LID research development, popular research methodologies, and future research frameworks and perspectives [30]. Liu et al. [31] performed a bibliometric review on the impact of green roofs on water, temperature, and air quality, based on an analysis of 1623 articles published on the Scopus database between 1981 and 2020. Through an examination of the research and development frontier in green roof services, key research “hot topics” focusing on urban hydrology, thermal environments, and air quality were identified. Despite the extensive global studies on LID for stormwater and flood management, there is a conspicuous absence of a comprehensive review using bibliometric analysis to identify the central tendency and emerging research themes of LID [32,33,34].
This review represents a novel attempt to systematically explore the corpus of the literature on urban rainwater management via LID in the context of climate change. The distinctive attribute of this study lies in the utilization of the bibliometric tool, CiteSpace, to chart the scientific terrain, highlighting the pivotal research sectors and burgeoning trends within the LID domain. In particular, this analysis investigates (1) the scope of climate variability’s impact on LID, (2) the resilience of LID in preserving urban hydrology and water quality amidst climatic fluctuations, and (3) ideal LID deployment locales for maximizing potential benefits under anticipated climate conditions. As such, this endeavor serves to bridge an existing gap in the literature and offers directions for future research and best practices in this domain.

2. Methodology

A systematic review was conducted and bibliometric instruments were utilized to discern established and emerging research terrains within the LID domain, thereby mapping the symbiotic relationships between climate change and LID. Graphical data were instrumental in tracing the temporal evolution of this scholarly landscape, spotlighting contemporary research interests, and signposting potential trajectories for prospective investigations. These insights have, therefore, paved a comprehensive roadmap for further exploration into the influence of LID on urban rainwater management performance in a climate change context, facilitating deeper scrutiny into relatively uncharted territories. The ensuing sections delineate the search protocols and the analytical techniques employed in this bibliometric investigation.
To maintain a comprehensive scope of the target literature and to ensure the reliability of the results, the Science Citation Index Expanded (SCIE) from the Web of Science (WoS) database was utilized. The SCIE, as part of the WoS, is recognized for its extensive coverage of multidisciplinary journals, encompassing a vast array of subjects and disciplines. In contrast, the Science Citation Index (SCI) on the Master Journal List, while also part of WoS, primarily focuses on major journals with high impact in specific fields. Hence, the SCIE provides a broader spectrum of sources, ensuring a more exhaustive collection of the relevant literature. The WoS database’s widespread usage can be attributed to its extensive archival range, offering bibliographic data from 1964 to 2004, and source documents extending to the 1940s [35]. A sophisticated search modality employing Boolean operators was leveraged to comb through the literature. The search query implemented was as follows: TS = (“climate change” OR “climate variability” OR “extreme weather event” OR “global warming” OR “general circulation model” OR “representative concentration pathway” OR “socioeconomic pathway” OR “coupled model intercomparison project”) AND TS = (“green infrastructure” OR “low-impact development” OR “water sensitive urban design” OR “nature-based solutions” OR “source control”).
In order to focus on substantive contributions to the field, only two types of documents were considered in this review: research articles and review articles. The search spanned a 20-year period, from the start of 2002 to the end of 2022. This rigorous approach resulted in a corpus of 1335 publications (Figure 1).
In the bibliometric exploration, quantitative statistical methodologies were applied to the procured publications, offering objective, methodical, and replicable scrutiny of academic outputs. This dual-faceted approach encompassed a descriptive elucidation centered on publication metrics, as well as a content-centric exploration elucidating prevailing thematic undertones and research foci [36]. Utilizing the R package “bibliometrics” (version 1.2.5) in conjunction with the visual analytics capabilities of CiteSpace, the intricate dynamics, evolutionary trajectories, and emergent research nexuses within the pertinent domain were meticulously discerned [37,38].

3. Results

3.1. Bibliometrics Analysis

As of June 2022, the bibliometric analysis unveiled that between 2002 and September 2022, 256 journals published 1335 articles on LID research, boasting an average citation count of 20.44 per paper and 5.16 co-authors per document. As can be seen in the chart below (Figure 2), a scant number of studies were released in the early 2000s, with a significant surge in publication numbers not observed until 2016 (designated change point). This trend could be ascribed to China’s initiation of sponge city development from 2015 onward, sparking increased awareness in various quarters. An exponential growth was observed in the annual publication count. Preceding this point, fewer than 50 new publications surfaced annually, with a maximum of 10 articles per year until 2012. In stark contrast, over 100 articles were disseminated in 2018, indicating burgeoning research interest in this domain.
With regard to the geographical distribution, cooperative network analysis demonstrated that LID research had engaged 256 institutions spanning across 112 countries or regions (Table 1). Evaluating the quantity of papers published by each country not only yielded insight into the research terrain but also assisted researchers worldwide in pinpointing eminent affiliations and potential collaborators. The heterogeneous geographical dispersion of these affiliations emphasizes the international significance of LID research and accentuates the importance of interdisciplinary and transnational collaboration. Through amalgamating knowledge and resources from diverse countries and institutions, researchers can engender more efficacious and innovative LID practices that address the distinct challenges encountered by various regions under the influence of climate change.

3.1.1. Preeminent Sources and Contributors

Simultaneously, an analysis of subject categories within WoS documents reveals that the largest clusters of articles encompassed environmental science and ecology, green sustainable science and technology, and water resources as detailed in Table 2. These studies evidenced the mitigation potential of LID practices against the deleterious effects of urbanization and climate change. The relevance of “urban studies” to the climate change literature lies in its examination of urbanization processes, spatial planning, and land-use transformations—all of which play critical roles in shaping local climate responses and vulnerabilities. The way cities are planned and developed can significantly influence local climate factors, including urban heat islands and stormwater runoff. Therefore, “urban studies” is pivotal in understanding the synergy between urban development patterns and local climate changes. Beyond this, scholars worldwide have explored the pragmatic implementation of LID across various fields such as civil engineering and environmental engineering, and these explorations have been both practical and theoretical.
Citation analysis serves to highlight the influence and reach of particular publications and authors. Through examining the productivity of authors in this domain, key contributors who have significantly impacted urban development research in the face of climate change, offering substantial ecological, social, and economic insights, has been identified. The results of this analysis are shown in Table 3. Four of the most productive authors in the field of LID were identified: Pauleit, S. (16 articles), Liu, Y.Z. (13 articles), Vojinovic, Z. (13 articles), and Engel, B.A. (12 articles). Additionally, several other scholars have also played a critical role in advancing LID research. The most widely referenced paper, written by Kabisch [39] in 2016, suggests discussing LID as a topic for future science and policy, demonstrating its high interest and potential for use in the future. This citation analysis indicates the ongoing interest and pivotal research areas in the LID domain. The contributions of these authors, particularly those who are frequently cited, signal important topics and research directions. Future research might build upon these influential works, extending or challenging their findings and premises, thereby furthering the understanding of LID within the climate change framework.

3.1.2. Analysis of Research Topics via Keyword Co-Occurrence

A chronological overview in Figure 3 displays keyword co-occurrence in the LID field, showcasing major research focal points. Climate change and stormwater management emerged concurrently in 2002. Early stormwater management studies focused on discharge rates, expanded to climate change adaptation’s hydrological and water quality impacts. Post-2008, global discourse on climate change and green infrastructure escalated. As low-impact development, green infrastructure, and nature-based solutions gained recognition, comprehensive evaluations of LID applications increased. These strategies addressed climate challenges, mitigated effects, fostered adaptation, and regulated water quality. Furthermore, various assessment methodologies, such as AHP and TOPSIS, featured frequently, with SWMM emerging as the predominant model utilized in LID studies. An exhaustive analysis of 11 LID models found SWMM to be most suitable in terms of model properties, hydrology, and hydraulics modules [40]. Hydrological and water quality models focused on total runoff, peak, and time to maximum. New research emerged, predicting LID efficiency under climate change scenarios, using General Circulation Models (GCMs) to estimate future climates. Post-2013, optimizing LID practices gained recognition, with landscape eco-efficiency frequently cited. References underlined managing eutrophication and ecosystem services’ cost-effectiveness, reflecting ecologically conscious LID approaches.
When multiple keywords co-existed within a single paper, it often suggested an inherent interconnection, which could be assessed through their co-occurrence frequency. A prevailing notion was that the more often a word pair emerged within the same literature, the more profound the linkage between those two topics. Through computing the frequency with which two subject terms co-occurred in the same document, a co-word network that encapsulated these keyword associations could be formulated. An exploration of keyword clustering through CiteSpace (Figure 4) indicated that the clusters could predominantly be divided into three overarching categories: phenomena (like climate change, urban heat island, and climate adaptation), statistical undertakings (such as urban green spaces and wastewater), and tangible implementations (for instance, LID, nature-based solution, green infrastructure (GI)). In Table 4, we find that the top 10 keywords indicated dominant themes related to climate change, urban systems, and proactive environmental solutions. We read these articles and found that articles with keywords such as “green infrastructure” and “nature-based solutions” tended to signify innovative approaches to environmental challenges, suggesting their potential applicability in urban planning. This trend indicated a preference for natural mechanisms. Articles featuring the keywords “climate change”, “green infrastructure”, and “nature-based solutions” underscored the urgency of adopting strategies to mitigate climate-related issues. This study primarily discussed the re-evaluation of existing low-impact development (LID) techniques, the creation of LID models, and the establishment of research frameworks.

3.2. Hydrological Impacts of LID in the Context of Climate Change

Predictive models suggest that climate change-induced global temperature increases will amplify the intensity and recurrence of extreme storm events. For instance, in much of Australia, severe rainfall events are anticipated to intensify due to climatic warming [41]. Research by Zahmatkesh et al. [42] demonstrates that climate change could augment both the volume and peak discharge of urban stormwater runoff in the Bronx River watershed, New York City, USA. Frequency analysis of projected runoff also signaled a considerable escalation in the occurrence of extreme storm events. The combination of climate change and urban sprawl escalates the susceptibility of urban regions to flooding and economic damage, with climate change predicted to augment flood-induced damage by 26%. Such alterations in urban stormwater runoff attributed to climatic impacts may have significant repercussions for watershed management and the design of stormwater and flood control measures.
Historically, the design paradigms of LID have hinged upon the performance criteria derived from archival climatic data. Yet, emerging evidence from climate change impact studies underscores that contemporary LID installations must fulfill performance expectations that veer from historical climatic norms. A myriad of both direct and indirect metrics has been employed to evaluate the hydrological efficacy of LID systems [43,44,45]. Evident in Figure 5, hydrology-related keywords reflect the extensive exploration within the literature regarding the influence of LID performance on factors such as peak runoff and temperature amid storm events. Concurrently, this body of the literature conducts comparative evaluations and assessments of the integration of various LID practices. The utilization of GCMs in forecasting future climate scenarios is also discernible within the keyword assortment. This segment envelops the impact of LID on hydrology within the climate change milieu.

3.2.1. Evaluating Performance of LID in Enhancing Hydrology

A standard approach to analyzing climate impacts is to classify individual storm events according to climate traits and statistically evaluate the hydrologic yields from LID systems among the groups. This approach is based on historically recorded climate data [46]. The results generally showed that the benefits of LID systems in reducing runoff volume and peak flow as well as lengthening lag time were diminished by the group of larger storm size, higher intensity, longer duration, and wetter beginning conditions [47]. Which storm-related factors, however, have a greater impact on LID performance than others are still unknown. Hadipour et al. [15] have discovered that the majority of LID techniques are only useful for modest flood peaks. They frequently fail because the site-specific and temporally variable climatic conditions are not optimized.
An examination of various studies reveals a common theme of evaluating Low-Impact Development (LID) practices as effective solutions for climate change adaptation. Such practices include the utilization of green roofs, permeable pavements, and bioretention cells. Simulations conducted under various scenarios, such as short-duration storm analysis and climate change estimates for extended periods, consistently demonstrate the positive impact of these practices in climate change adaptation. In the study by Qin et al. [48], three LID scenarios were investigated: Scenario 1 implemented swales, Scenario 2 utilized permeable pavements, and Scenario 3 incorporated green roofs. Each of these scenarios was designed to model different strategies for managing stormwater in an urban setting. The findings revealed that all three LID scenarios—swale, permeable pavements, and green roofs—are more effective at reducing flooding during stronger and shorter storm occurrences. The location of the peak intensity, however, has a substantial impact on how well they work. For example, swales are ideally suited for early peak storm events, permeable pavements work best for middle peak storm events, and green roofs are most effective for late peak storm events.
LID strategies exhibit varying effectiveness based on storm characteristics. For example, swales prove more effective for early peak storm events, permeable pavements for middle peak storm events, and green roofs for late peak storm events. Likewise, combinations of different LID practices, such as green roofs and permeable pavements, have been found to mitigate both peak flooding and runoff significantly, with maximum runoff reduction reaching up to 56.02% for a single 10-year storm event under combined scenarios [49]. Environmental factors, such as temperature and seasonal variations, also play a crucial role in the performance of LID practices. Studies suggest key determinants of possible annual and seasonal runoff retention include the length of the winter/spring season, precipitation patterns, the sequence of wet days-dry spells, and evapotranspiration rates. Mantilla et al. [32] found the length of the winter/spring season, the distribution of precipitation patterns, the sequence of wet days-dry spells, and evapotranspiration rates (for green roofs) were determined to be the key determinants of the possible annual and seasonal runoff retention between places. Winter/spring presented the most significant contrasts, largely influenced by runoff regimes generated by snowmelt and rain-on-snow events, especially in locations where snowmelt contributes significantly to runoff generation. Summer, on the other hand, showed the least differences. Some empirical studies have evidenced better LID performance in summer than winter [50].
The performance of LIDs in terms of volume and peak flow reduction is also associated with the level of imperviousness. Studies find that runoff reduction and peak flow reduction increase linearly with reduced imperviousness [51]. Different combinations of LID strategies, each with distinctive attributes, can deliver optimal overall performance. LID practices can reduce flood volumes by 11.3–45.4% during a 10-year rainfall event and by 5.6–28.5% during a 100-year rainfall event [52]. The benefits of these mitigation measures, however, differ with integrated LID performance holding more importance than individual practices.

3.2.2. LID Efficacy in Anticipated Climate Scenarios

Urban drainage systems built to handle present and historical storm conditions have given rise to questions about their ability to continue operating effectively in the face of a changing climate. Researchers are progressively employing GCMs to gauge the robustness and overall effectiveness of LID practices under projected climatic patterns. GCMs, the cornerstone tools for approximating future climatic scenarios [53], employ mathematical equations to typify the interactions of energy and matter across the ocean, atmosphere, and land.
Mattos et al. [54] calibrated and evaluated a rainfall-runoff model in a tropical watershed located in Midwestern Brazil. An ensemble of 17 GCM outputs, dictated by Representative Concentration Pathways (RCP 4.5 and RCP 8.5), was employed to generate future climate change scenarios up to 2095. The LID efficiency was assessed based on runoff peak reduction and stormwater drainage resilience via a resilience index. Combinations of LID demonstrated a reduction in runoff peak higher than 20%, with the optimal LID combination achieving a reduction of up to 46%. The type of rainfall influenced the LID implementation strategy when measuring rainfall-runoff reduction through peak inflow reduction. Regardless of equal total daily rainfall, the variation in the subsequent day’s rainfall also influenced rainfall runoff reduction. Liu et al. [55] utilized a validation model to simulate the impact of green roofs on reducing urban catchment outflow. Results indicated a median increase in runoff volume and peak flow rate under SSP2-4.5 and SSP5-8.5 scenarios, indicating the high variability of runoff volume and peak flow changes for short-return storm events induced by climate change. Green roof implementations exhibited reasonable mitigation effects on runoff volume and peak flow amplification in urban catchments induced by climate change. Wang et al. [56] demonstrated that during relatively frequent short-duration storms (such as 1 y/1 h storms), the peak runoff under S1, S2, and S3 ranged from 37.8 L/s to 59.8 L/s, 109.1 L/s to 167.3 L/s, and 181.3 L/s to 234.1 L/s, respectively. In the absence of any LID, the peak flow in RCP 8.5 could reach as high as 1070.9 L/s, highlighting the criticality of effective LID implementation in managing peak runoff. Despite a poor flood mitigation effect during short, intense storms, LID’s performance improves with increased rainfall duration.
However, it is essential to delve into the research limitations regarding how climate change impacts LID performance, as shown by Zahmatkesh et al. [57]. A flow frequency analysis of the annual peak flow projected over 30 years (2030–2059) indicated future benefits of LIDs. With the mean precipitation of four carbon emission scenarios, results illustrated that the future runoff of a 25-year return period from existing development would coincide with that of a 50-year return period from LID practice. Still, certain locales, such as coastal cities, demand particular attention regarding sea level rise. An elevated groundwater table in the future could cause increased peak flow with the installation of infiltration-based LIDs.

3.3. Impact of LID on Water Quality under Climate Change

With the continual expansion of urban development and the relentless progression of climate change, the intricacy and severity of urban water pollution is magnifying. Of particular concern are runoffs engendered during storm events, which often bear harmful substances, including bacteria, pathogens, sediment, heavy metals, and organic pollutants. Such contaminants pose significant threats to the quality of urban water bodies [58]. In recognition of this escalating predicament, there is an imperative focus on the deployment of LID measures capable of mitigating such adverse impacts, particularly in the context of a changing climate. The keyword compilation depicted in Figure 6 pertains to the influence of LID on water quality. The most salient performance aspect discernible is the removal of pollutants. Simultaneously, model simulations are conducted for the future projection of LID to forecast its resilience.

3.3.1. Evaluating Performance of LID in Enhancing Water Quality

Recent data suggested that water quality was deteriorating as the severity and length of flood and drought events increased. These impacts differed depending on the hydrological dynamics and mass balance within a body of water (for example, a river, estuary, or lake) and its catchment area [59]. Key factors of water quality change have been identified using quantitative research where greater focus has been placed on turbidity and eutrophication. Eutrophication is the most prevalent problem in water quality worldwide [60]. Eutrophication is produced by excessive nutrient loads, especially phosphorus (P) and nitrogen (N), which have been the subject of several research on water quality measures. In freshwater ecosystems, phosphorus (P) is often identified as the limiting nutrient and principal driver of eutrophication; as a result, management practices have focused on controlling P loading [61]. In addition, it is indicated that changes in sediment yield and nutrient load due to climate change are larger than the corresponding changes in streamflow. Higher N and P loads are observed during rainy seasons, owing to increased nutrient transport via precipitation [62]. Turbidity transport models have been used in other typical water quality investigations to estimate suspended particles. Precipitation and discharge flow are important indicators of physical water quality parameters such as total suspended solids (TSS), and they are directly related to streamflow [63].
Karakouzian et al. [64] discovered that the 50% urbanization scenario resulted in more runoff and pollutant loads, compared with the 20% urbanization scenario. Under scenarios with climate variability, runoff and pollutant load peaks occurred earlier in time, due to the higher intensity rainfall events. Furthermore, LIDs decreased pollutant loads by up to 25%, indicating their effectiveness in decreasing the impact of urbanization on receiving water bodies. In an effort to quantify the effects of LID measures on water quality, several studies have employed simulation frameworks at the watershed scale. Liu et al. [65] for instance, examined the Crooked Creek watershed and modeled the implications of best management practices (BMP) and LID performance on water quantity and quality across 16 different scenarios. Notably, various degrees and amalgamations of BMPs/LID yielded runoff reductions from 0% to 26.47%, decreased total nitrogen (TN) by 0.30% to 34.20%, mitigated total phosphorus (TP) by 0.27% to 47.41%, lessened total suspended solids (TSS) by 0.33% to 53.59%, and attenuated lead (Pb) concentrations by 0.30% to 60.98%.
Additional simulation-based research such as Lam et al. [66] used the Soil and Water Assessment Tool model and underscored the efficacy of LID strategies in reducing nitrate-nitrogen and total nitrogen loads by 8.6% to 20.5%. Yet, the impact of LID implementation on the reduction in sediment loads and total phosphorus was less pronounced. Zhang et al. [67] focused on rain gardens in Kyoto, Japan, and demonstrated noteworthy pollutant reduction rates (TSS 15.50%, COD 16.17%, TN 17.34%, TP 19.07%) during short-duration storm conditions across six rainfall return periods. Other studies such as Taghizadeh et al. [68] utilized a combination of the Multi-Objective Particle Swarm Optimization algorithm and Storm Water Management Model (SWMM) software to analyze the water quality impacts of stormwater management facilities with LID strategies in Tehran, Iran. These studies collectively underscore that while LID practices significantly enhance water quality, the level and type of implementation matter, and these practices often perform better when applied collectively rather than individually.

3.3.2. Assessing the Resilience of LID in Future Scenarios

Scenario analysis showed that the LID hydraulic performance declined under all three future climate scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). Uncertainty analysis suggested that the climate change caused wide-range uncertainties on LID performance, the uncertainty of LID water quality performance was larger than that of LID hydraulic performance [69]. Given the impending climate shifts, it is crucial to evaluate the future resilience of LID strategies. Sharma et al. [70] implemented regional climate model projections as input for a 100-year-long high-resolution rainfall time series. Their findings indicated that higher flows and increased total concentrations from the catchment were likely in future climate scenarios, underscoring the need for efficient water management practices like LID. Beak et al. [71] studied the impact of LID under climate change scenarios and found that all LID measures significantly reduced both total and peak loads, despite the LID size being only 10% of the study site. This suggests that LID strategies can be effective in improving water quality even when applied on a smaller scale. Moreover, Zhang et al. [72] observed minor differences in LID’s impact on pollutant removal, flow frequency reduction, and system reliability after analyzing various climate models. Burge et al. [73] conducted a case study in Melbourne, Australia, measuring the likely impact of climate change on LID performance using adjusted historical rainfall time series. Most LID systems showed strong resilience, with only minor reductions in their pollutant load reduction performance even under worst-case climate change scenarios. In conclusion, current research supports the effectiveness of LID measures in improving water quality, but future studies need to better understand the implications of climate change on the efficacy and resilience of these strategies. Furthermore, research should also examine the optimal blend of LID strategies for maximum water quality enhancement under diverse climate scenarios.

3.4. Strategic Optimization for LID Implementation

The inherent diversity among watersheds renders a vast array of alternatives for LID control application at the urban catchment scale. This entails nuanced decisions pertaining to the dimensions, quantity, placement, and combinations of controls. In a bid to maximize runoff and peak flow reductions while maintaining minimal costs, the selection and positioning of LID controls requires meticulous scrutiny, given the primary constraint of cost in stormwater management endeavors. In this context, this segment elucidates the concept of low-impact development, interspersed with case studies demonstrating optimization in water resource management.
Tansar et al. [74] embarked on a comprehensive study to ascertain the most effective spatial placement of LID within an urban catchment and the consequent impact on Sustainable Urban Drainage Systems (SUDS) performance. The objective was to gauge the potency of LID at local and catchment scales under varying scales and rainfall intensities. Findings demonstrated a notable reduction in surface runoff, peak runoff, and flood volume when individual LID controls such as bioretention cells, green roofs, permeable pavements, and rain gardens were implemented across 25% of the impervious catchment area. Meanwhile, Eckart et al. [12] developed, calibrated, and validated an SWMM model for a sewershed in Windsor, Ontario, to evaluate the performance of LID stormwater controls under three disparate return periods. They used an optimization–simulation model to fine-tune LID implementation strategies across five unique scenarios for each of the three storm events. The primary objectives were to curtail peak flow in the storm sewers, diminish total runoff, and minimize cost. The implementation led to a reduction in peak runoff and total runoff volume by 13% and 29%, respectively, for the Windsor sewershed.
An amalgamation of various LID controls, complemented through the optimization of specific LID parameters within the physical environment of implementation, can significantly augment the efficacy of the proposed mitigation measures. This proposition was validated by Lopes et al. [75], who utilized an adaptation of the Genetic Algorithm NSGA-II in tandem with the hydrologic model SWMM to facilitate optimal LID scenario design aimed at minimizing stormwater runoff and total costs across different return periods. The results manifested that the model was proficient in identifying an array of optimal solutions with varied levels of runoff reduction and costs across all return periods considered.
However, a solely green-based system may not exhibit complete resilience to extreme climate change. Current research has predominantly focused on deriving the optimal strategy via scenario analysis methods or optimizing LID performance under the presumption of constant grey infrastructure. Leng et al. [76] applied their methodology to a case study in Suzhou, China, demonstrating that while the costs of green-grey synchronously optimized scenarios are marginally lower than green optimized scenarios, the former presented higher reductions in runoff and pollutants. Under green-grey optimized scenarios, green and grey infrastructures contributed to runoff and pollutant control in a roughly 60:40 ratio. While LID controls exhibit significant impact on runoff control under changing climate conditions, their efficacy may wane during future high-intensity rainfall events.

4. Discussion

Analyzing the co-occurrence patterns of keywords within scholarly publications offers a nuanced understanding of the prominent themes and interconnected domains in a given field. This study meticulously examines these interrelations, particularly emphasizing the intricate links between climate change, hydrology, and water quality. This manuscript intricately maps the topography of LID research, accentuating the imperativeness of climate change adaptation strategies. It is noteworthy to mention that the repercussions of climate change on stormwater management are multifaceted. Such complexities are attributed to a myriad of factors, including distinct climate alterations, inherent attributes of local watersheds, intricacies of sewer systems, and synergistic effects with prevailing management paradigms [77,78]. With regard to the future development of LID and climate change in the context of complex factors, we summarize three main limitations and directions in the discussion that follows: (1) quantifying the impacts of LID on water quality; (2) advancing LID modeling under climate change; and (3) integrating LID, climate change, and water policy.

4.1. Quantifying LID Effects on Water Quality

The efficacy of LID in mitigating urban flooding and water quality issues has been largely endorsed, yet a paucity of studies has effectively quantified these improvements, particularly concerning water quality. Standard water quality parameters often encompass nutrients, metals, pH, dissolved oxygen, and temperature. Nonetheless, there is a scarcity of data demonstrating the influence of LID measures on these parameters. The uptake of pollutants by plants, dictating the bioavailability of pollutants and thereby influencing the pH of the water column, is one such instance. Water emanating from a green roof, imbued with higher pollutant concentrations, might perturb the pH downstream, potentially instigating adverse conditions for the riverine ecosystem [79]. Despite a dominant emphasis on hydrological benefits in the literature, potential detrimental effects on water quality due to climate change are often overlooked. There exists an urgent need for quantitative data to discern the efficacy of stormwater technologies in restoring water balances and eradicating emergent and challenging-to-quantify pollutants.

4.2. Advancing LID Modeling under Changing Climates

Moving beyond the direct impact on water quality parameters, it is imperative to examine the tools used for simulating the effectiveness of LID practices, particularly in changing climatic conditions. The existing models for LID simulation have significant limitations, often neglecting certain types of LID like tree canopies, plants present in specific LIDs (e.g., biocontainment cells, rain gardens), porous pavements, biocontainment cells, and depressions. The spatial scale of climate models is a key aspect to be considered [80,81], temporal resolution, another key facet, requires substantial improvement in some models. While a substantial number of chosen models support a broad range of temporal resolutions, some models (HEC-HMS, RECARGA, and win-slam) are unsuitable for long-term modeling. While there is an undeniable inclination towards integrating spatial data visualization tools, evaluations of prevailing methods for water system hydromorphic placement revealed inconsistencies in the models [40]. As the majority of LID research leverages quantitative techniques such as cost analysis, scenario planning, and spatial analysis (inclusive of hydrological/hydraulic modeling, GIS analysis, and WRF), future efforts should pivot towards hybrid methodologies to rectify model inadequacies under the unpredictability of climate change.

4.3. Integrating LID, Climate Change, and Water Policy

The dearth of understanding regarding best practices and design guidelines for LID is a salient issue. LID construction and operation commonly adhere to standards conceived and executed in industrialized nations due to the lack of universal design standards for LID. This can engender issues with apt installation and operation given the profound influence of local climatic and geographical factors on LID development and operation. It has been established that nearly 60% of LID studies emanate from the USA, Australia, and the UK, as found by Parker et al. [82]. Urban areas in developing countries will be the most adversely affected by climate change, this is due to the increased number of people living in urban areas, high population density, settlement of people in floodplains, steep slopes and wetlands, increased impervious surfaces, land-use changes, poor drainage infrastructure, and indiscriminate disposal of waste which clog drains [83]. However, contributions from Asia and South America are sparse, despite these regions being in the crosshairs of significant climate change threats. Moreover, the majority of studies originate from cities within temperate and snowy climate categorization zones. Consequently, it is incumbent upon future research to delineate optimal LID specifications and curate suitable guidelines for LID deployment in locales with diverse climatic and geographical characteristics.

5. Conclusions

This review elucidates the criticality of employing quantitative analytics to discern the interplay between the scientific literature and terminologies pertinent to climate change. Through leveraging bibliometric platforms like CiteSpace and the “R” bibliometric package, the analysis serves as an indicative gauge for emergent research trajectories. Through an assessment of keyword co-occurrence in 1355 scholarly articles culled from Web of Science until 2022, a notable intersection of climate change considerations within LID research becomes evident. The examination underscores a burgeoning focus on LID systems, particularly on their operational evaluations and efficiency predictions. The analytic lens further extends to delineate the impact of LID interventions on urban hydrology and water quality, thereby contributing to nuanced methodologies for efficient urban stormwater source control.
LID is increasingly being recognized for its potential ecological contributions in the face of climatic shifts. These range from flood risk mitigation and non-point source pollution alleviation to biodiversity enhancement. Previous inquiries suggest that LID performance is subject to multifaceted variables such as water quality indices, vegetative attributes, and substrate heterogeneity. However, the extant literature exposes lacunae warranting future scrutiny. For instance, the necessity for comprehensive water quality measurements and the formulation of interdisciplinary approaches emerges as imperatives. Furthermore, the dearth of experimental data on the symbiosis between LID and other infrastructural modalities elucidates a need for methodological diversity in assessing LID design applications under changing climatic conditions. Therefore, this review transcends a mere scrutiny of the extant literature. It stands as an exhaustive compass pointing to both the present depth of understanding and potential directions awaiting exploration. Such profound insights aim to inform researchers and decision-makers with a robust foundation, enabling a more nuanced investigation into LID performance and its optimal refinement, ultimately pushing the boundaries of sustainable stormwater management.

Author Contributions

Conceptualization, M.W.; methodology, M.W. and S.F. software, S.F. and Q.R. validation, S.F., C.S., B.C., Q.R., R.M.A.I. and J.L.; formal analysis, T.C.; investigation, S.F.; resources, M.W., R.M.A.I. and J.L.; data curation, S.F.; writing—original draft preparation, S.F.; visualization, S.F., B.C., Q.R. and H.J.; supervision, M.W. and R.M.A.I. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of Guangdong Province, China [grant number 2023A1515030158], and the Science and Technology Program of Guangzhou, China [grant number 202201010431], and Guangzhou University (RC2023008).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

This study did not involve humans.

Data Availability Statement

This study did not report any publicly archived datasets.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

LID, low-impact development; LIUD, low-impact urban design and development; WSUD, water-sensitive urban design; SUDS, sustainable urban drainage systems; SCIE, science citation index expanded; WoS, web of science; SCI, science citation index; GI, green infrastructure; AHP, analytic hierarchy process; TOPSIS, technique for order preference by similarity to ideal solution; SWMM, stormwater management model; GCM, general circulation models; RCP, representative concentration pathways; SSP, shared socioeconomic pathways; P, phosphorus; N, nitrogen; TSS, total suspended solids; BMP, best management practices; TN, total nitrogen; TP, total phosphorus; COD, chemical oxygen demand.

References

  1. Williams, A.P.; Cook, B.I.; Smerdon, J.E. Rapid intensification of the emerging southwestern North American megadrought in 2020–2021. Nat. Clim. Chang. 2022, 12, 232–234. [Google Scholar] [CrossRef]
  2. Jouberton, A.; Shaw, T.E.; Miles, E.; McCarthy, M.; Fugger, S.; Ren, S.; Dehecq, A.; Yang, W.; Pellicciotti, F. Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau. Proc. Natl. Acad. Sci. USA 2022, 119, e2109796119. [Google Scholar] [CrossRef]
  3. Maina, F.Z.; Kumar, S.V. Diverging trends in rain-on-snow over high mountain Asia. Earth’s Future 2023, 11, e2022EF003009. [Google Scholar] [CrossRef]
  4. Tefera, G.W.; Ray, R.L. Hydrology and hydrological extremes under climate change scenarios in the Bosque watershed, North-Central Texas, USA. Environ. Sci. Pollut. Res. 2023, 1–19. [Google Scholar] [CrossRef] [PubMed]
  5. Miu, L.M. The impact of climate change on wind power production in Scotland. Energy Sustain. V Spec. Contrib. 2015, 206, 239. [Google Scholar]
  6. Garnello, A.; Marchenko, S.; Nicolsky, D.; Romanovsky, V.; Ledman, J.; Celis, G.; Schadel, C.; Luo, Y.; Schuur, E.A.G. Projecting Permafrost Thaw of Sub-Arctic Tundra with a Thermodynamic Model Calibrated to Site Measurements. J. Geophys. Res. Biogeosci. 2021, 126, e2020JG006218. [Google Scholar] [CrossRef]
  7. Qiao, Y.; Jiang, Y.; Zhang, C. Contribution of karst ecological restoration engineering to vegetation greening in southwest China during recent decade. Ecol. Indic. 2021, 121, 107081. [Google Scholar] [CrossRef]
  8. Wang, Z.; Wu, R.; Chen, Z.; Huang, G.; Yang, X. Reasons for East Siberia winter snow water equivalent increase in the recent decades. Remote Sens. 2022, 15, 134. [Google Scholar] [CrossRef]
  9. Valjarević, A.; Popovici, C.; Štilić, A.; Radojković, M. Cloudiness and water from cloud seeding in connection with plants distribution in the republic of Moldova. Appl. Water Sci. 2022, 12, 262. [Google Scholar] [CrossRef]
  10. Senes, G.; Ferrario, P.S.; Cirone, G.; Fumagalli, N.; Frattini, P.; Sacchi, G.; Valè, G. Nature-based solutions for storm water management—Creation of a green infrastructure suitability map as a tool for land-use planning at the municipal level in the province of monza-brianza (Italy). Sustainability 2021, 13, 6142. [Google Scholar] [CrossRef]
  11. Voskamp, I.M.; Van de Ven, F.H.M. Planning support system for climate adaptation: Composing effective sets of blue-green measures to reduce urban vulnerability to extreme weather events. Build. Environ. 2015, 83, 159–167. [Google Scholar] [CrossRef]
  12. Eckart, K.; McPhee, Z.; Bolisetti, T. Multiobjective optimization of low impact development stormwater controls. J. Hydrol. 2018, 562, 564–576. [Google Scholar] [CrossRef]
  13. Li, Q.; Wang, F.; Yu, Y.; Huang, Z.C.; Li, M.T.; Guan, Y.T. Comprehensive performance evaluation of LID practices for the sponge city construction: A case study in Guangxi, China. J. Environ. Manag. 2019, 231, 10–20. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, M.; Jiang, Z.; Zhang, D.; Zhang, Y.; Liu, M.; Rao, Q.; Li, J.; Tan, S.K. Optimization of integrating life cycle cost and systematic resilience for grey-green stormwater infrastructure. Sustain. Cities Soc. 2023, 90, 104379. [Google Scholar] [CrossRef]
  15. Pour, S.H.; Wahab, A.K.A.; Shahid, S.; Asaduzzaman, M.; Dewan, A. Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues, and challenges. Sustain. Cities Soc. 2020, 62, 102373. [Google Scholar] [CrossRef]
  16. Ludwig, F.; van Slobbe, E.; Cofino, W. Climate change adaptation and Integrated water resource management in the water sector. J. Hydrol. 2014, 518, 235–242. [Google Scholar] [CrossRef]
  17. Coffman, L.S.; Goo, R.; Frederick, R. Low-impact development: An innovative alternative approach to stormwater management. In WRPMD’99: Preparing for the 21st Century; Asce Library: Reston, VA, USA, 1999; pp. 1–10. [Google Scholar]
  18. Radcliffe, J. Australia’s water sensitive urban design. In Proceedings of the 2018 International Sponge City Conference, Xi’an, China, 8–10 September 2018; pp. 38–52. [Google Scholar]
  19. Eason, C.; Gray, R.; Leighton, D.; Trowsdale, S.; Vale, R. Advancing low impact stormwater management within a sustainable urban development framework. In Proceedings of the New Zealand Water & Wastes Association Stormwater Conference, Rotorua, New Zealand, 8–10 May 2006. [Google Scholar]
  20. Whelans, C.; Maunsell, H.G.; Thompson, P. Planning and Management Guidelines for Water Sensitive Urban (Residential) Design; Department of Planning and Urban Development of Western Australia: Perth, WA, Australia, 1994. [Google Scholar]
  21. Butler, D.; Parkinson, J. Towards sustainable urban drainage. Water Sci. Technol. 1997, 35, 53–63. [Google Scholar] [CrossRef]
  22. Halil, I.B.; Yılmaz, I.; Yalçın, B.; Murat, K. Water quality tendency of Akarcay River between 2006–2011. Pamukkale Univ. J. Eng. Sci. 2013, 19, 127–132. [Google Scholar]
  23. Cao, Q.; Cao, J.; Xu, R. Optimizing low impact development for stormwater runoff treatment: A case study in Yixing, China. Water 2023, 15, 989. [Google Scholar] [CrossRef]
  24. Yan, C.; Ding, J.; Wang, B.; Qin, L.; Shi, Z.; Qiu, Q.Y. An in-situ measurement and assessment of evaporative cooling effects of low impact development facilities in a subtropical city. Agric. Forest Meteorol. 2023, 332, 109363. [Google Scholar] [CrossRef]
  25. Wang, M.; Zhang, D.; Cheng, Y.; Tan, S.K. Assessing performance of porous pavements and bioretention cells for stormwater management in response to probable climatic changes. J. Environ. Manag. 2019, 243, 157–167. [Google Scholar] [CrossRef] [PubMed]
  26. Lisenbee, W.A.; Hathaway, J.M.; Winston, R.J. Modeling bioretention hydrology: Quantifying the performance of DRAINMOD-urban and the SWMM LID module. J. Hydrol. 2022, 612, 128179. [Google Scholar] [CrossRef]
  27. Zhang, X.; Jia, H. Low impact development planning through a comprehensive optimization framework: Current gaps and future perspectives. Resour. Conserv. Recycl. 2023, 190, 106861. [Google Scholar] [CrossRef]
  28. Cronin, C. Doing your literature review: Traditional and systematic techniques. Eval. Res. Educ. 2011, 24, 219–221. [Google Scholar] [CrossRef]
  29. Fetscherin, M.; Usunier, J.C. Corporate branding: An interdisciplinary literature review. Eur. J. Mark. 2012, 46, 733–753. [Google Scholar] [CrossRef]
  30. Meerow, S.; Newell, J.P. Spatial planning for multifunctional green infrastructure: Growing resilience in Detroit. Landsc. Urban Planing 2017, 159, 62–75. [Google Scholar] [CrossRef]
  31. Liu, H.; Kong, F.; Yin, H.; Middel, A.; Zheng, X.; Huang, J.; Xu, H.; Ding, W.; Wen, Z. Impacts of green roofs on water, temperature, and air quality: A bibliometric review. Build. Environ. 2021, 196, 107794. [Google Scholar] [CrossRef]
  32. Mantilla, I.; Flanagan, K.; Muthanna, T.M.; Blecken, G.T.; Viklander, M. Variability of green infrastructure performance due to climatic regimes across Sweden. J. Environ. Manag. 2023, 326, 116354. [Google Scholar] [CrossRef]
  33. Hill, B.; Liang, Q.; Bosher, L.; Chen, H.; Nicholson, A. A systematic review of natural flood management modelling: Approaches, limitations, and potential solutions. J. Flood Risk Manag. 2023, 16, 12899. [Google Scholar] [CrossRef]
  34. Martin, W.D.; Kaye, N.B. Modeling of the hydrologic performance of distributed LID stormwater under a changing climate: Municipal-scale performance improvements. J. Sustain. Water Buil. 2023, 9, 04023005. [Google Scholar] [CrossRef]
  35. Xu, D.; Yin, X.Q.; Zhou, S.; Jiang, Y.J.; Xi, X.L.; Sun, H.M.; Wang, J. A review on the remediation of microplastics using constructed wetlands: Bibliometric, co-occurrence, current trends, and future directions. Chemosphere 2022, 303, 134990. [Google Scholar] [CrossRef]
  36. Xia, Q.; Yan, S.; Li, H.; Duan, K.; Zhang, Y. A bibliometric analysis of knowledge-hiding research. Behav. Sci. 2022, 12, 122. [Google Scholar] [CrossRef]
  37. Chen, C.; Ibekwe-SanJuan, F.; Hou, J. The structure and dynamics of cocitation clusters: A multiple perspective cocitation analysis. J. Am. Soc. Inf. Sci. Technol. 2010, 61, 1386–1409. [Google Scholar] [CrossRef]
  38. Chen, C. Science mapping: A systematic review of the literature. J. Data Inf. Sci. 2017, 2, 1–40. [Google Scholar] [CrossRef]
  39. Kabisch, N.; Frantzeskaki, N.; Pauleit, S.; Naumann, S.; Davis, M.; Artmann, M.; Haase, D.; Knapp, S.; Korn, H.; Stadler, J.; et al. Nature-based solutions to climate change mitigation and adaptation in urban areas: Perspectives on indicators, knowledge gaps, barriers, and opportunities for action. Ecol. Soc. 2016, 21, 39. [Google Scholar] [CrossRef]
  40. Kaykhosravi, S.; Khan, U.; Jadidi, A. A comprehensive review of low impact development models for research, conceptual, preliminary and detailed design applications. Water 2018, 10, 1514. [Google Scholar] [CrossRef]
  41. Alexander, K.; Hettiarachchi, S.; Ou, Y.; Sharma, A. Can integrated green spaces and storage facilities absorb the increased risk of flooding due to climate change in developed urban environments? J. Hydrol. 2019, 579, 124201. [Google Scholar] [CrossRef]
  42. Zahmatkesh, Z.; Karamouz, M.; Goharian, E.; Burian, S. Analysis of the effects of climate change on urban storm water runoff using statistically downscaled precipitation data and a change factor approach. J. Hydrol. Eng. 2015, 20, 05014022. [Google Scholar] [CrossRef]
  43. Neupane, B.; Vu, T.M.; Mishra, A.K. Evaluation of land-use, climate change, and low-impact development practices on urban flooding. Hydrol. Sci. J. 2021, 66, 1729–1742. [Google Scholar] [CrossRef]
  44. Dutta, A.; Torres, A.S.; Vojinovic, Z. Evaluation of pollutant removal efficiency by small-scale nature-based solutions focusing on bio-retention cells, vegetative swale and porous pavement. Water 2021, 13, 2361. [Google Scholar] [CrossRef]
  45. Kefi, M.; Mishra, B.; Kumar, P.; Masago, Y.; Fukushi, K. Assessment of tangible direct flood damage using a spatial analysis approach under the effects of climate change: Case study in an urban watershed in Hanoi, Vietnam. ISPRS Int. J. Geo Inf. 2018, 7, 29. [Google Scholar] [CrossRef]
  46. Sohn, W.; Kim, J.H.; Li, M.H.; Brown, R. The influence of climate on the effectiveness of low impact development: A systematic review. J. Environ. Manag. 2019, 236, 365–379. [Google Scholar] [CrossRef] [PubMed]
  47. Li, Y.; Babcock, R. Green roof hydrologic performance and modeling: A review. Water Sci. Technol. A J. Int. Assoc. Water Pollut. Res. 2014, 69, 727–738. [Google Scholar] [CrossRef] [PubMed]
  48. Qin, H.; Li, Z.X.; Fu, G. The effects of low impact development on urban flooding under different rainfall characteristics. J. Environ. Manag. 2013, 129, 577–585. [Google Scholar] [CrossRef] [PubMed]
  49. Ekmekcioglu, O.; Yilmaz, M.; Ozger, M.; Tosunoglu, F. Investigation of the low impact development strategies for highly urbanized area via auto-calibrated Storm Water Management Model (SWMM). Water Sci. Technol. 2021, 84, 2194–2213. [Google Scholar] [CrossRef]
  50. Lewellyn, C.; Lyons, C.; Traver, R.; Wadzuk, B. Evaluation of seasonal and large storm runoff volume capture of an infiltration green infrastructure system. J. Hydrol. Eng. 2015, 2, 04015047. [Google Scholar] [CrossRef]
  51. Palermo, S.A.; Talarico, V.C.; Turco, M. On the LID systems effectiveness for urban stormwater management: Case study in Italy. IOP Conf. Ser. Earth Environ. Sci. 2020, 410, 012012. [Google Scholar] [CrossRef]
  52. Sun, X.; Li, R.; Shan, X.; Xu, H.; Wang, J. Assessment of climate change impacts and urban flood management schemes in central Shanghai. Int. J. Disast. Risk Reduct. 2021, 65, 102563. [Google Scholar] [CrossRef]
  53. Gharbia, S.S.; Gill, L.; Johnston, P.; Pilla, F. Multi-GCM ensembles performance for climate projection on a GIS platform. Model. Earth Syst. Env. 2016, 2, 102. [Google Scholar] [CrossRef]
  54. Mattos, T.; Oliveira, P.T.; Bruno, L.; Oliveira, N.; Vasconcelos, J.; Lucas, M. Improving urban flood resilience under climate change scenarios in a tropical watershed using low-impact development practices. J. Hydrol. Eng. 2021, 26, 05021031. [Google Scholar] [CrossRef]
  55. Liu, W.; Feng, Q.; Engel, B.A.; Zhang, X. Cost-effectiveness analysis of extensive green roofs for urban stormwater control in response to future climate change scenarios. Sci. Total Environ. 2023, 856, 159127. [Google Scholar] [CrossRef] [PubMed]
  56. Wang, Z.; Zhou, S.; Wang, M.; Zhang, D. Cost-benefit analysis of low-impact development at hectare scale for urban stormwater source control in response to anticipated climatic change. J. Environ. Manag. 2020, 264, 110483. [Google Scholar] [CrossRef]
  57. Zahmatkesh, Z.; Burian, S.; Karamouz, M.; Tavakol-Davani, H.; Goharian, E. Low-impact development practices to mitigate climate change effects on urban stormwater runoff: Case study of New York city. J. Irrig. Drain. Eng. 2014, 141, 04014043. [Google Scholar] [CrossRef]
  58. Pennino, M.J.; McDonald, R.I.; Jaffe, P.R. Watershed-scale impacts of stormwater green infrastructure on hydrology, nutrient fluxes, and combined sewer overflows in the mid-Atlantic region. Sci. Total Environ. 2016, 565, 1044–1053. [Google Scholar] [CrossRef] [PubMed]
  59. Akhtar, M.; Ahmad, N.; Booij, M.J. The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. J. Hydrol. 2008, 355, 148–163. [Google Scholar] [CrossRef]
  60. Fabian, P.S.; Kwon, H.H.; Vithanage, M.; Lee, J.H. Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review. Environ. Res. 2023, 225, 115617. [Google Scholar] [CrossRef]
  61. Schindler, D.W.; Carpenter, S.R.; Chapra, S.C.; Hecky, R.E.; Orihel, D.M. Reducing phosphorus to curb lake eutrophication is a success. Environ. Sci. Technol. 2016, 50, 8923–8929. [Google Scholar] [CrossRef]
  62. Khoi, D.N.; Phi, H.L. Impact of climate change on streamflow and water quality in the upper Dong Nai river basin, Vietnam. La Houille Blanche 2018, 104, 70–79. [Google Scholar] [CrossRef]
  63. Whitehead, P.G.; Jin, L.; Bussi, G.; Voepel, H.E.; Darby, S.E.; Vasilopoulos, G.; Manley, R.; Rodda, H.; Hutton, C.; Hackney, C.; et al. Water quality modelling of the Mekong River basin: Climate change and socioeconomics drive flow and nutrient flux changes to the Mekong Delta. Sci. Total Environ. 2019, 673, 218–229. [Google Scholar] [CrossRef]
  64. Karakouzian, M.; Taheriyoun, M.; Nazari-Sharabian, M. Surface runoff and pollutant load response to urbanization, climate variability, and low impact developments—A case study. Water Supply 2019, 19, 2410–2421. [Google Scholar]
  65. Liu, Y.; Bralts, V.F.; Engel, B.A. Evaluating the effectiveness of management practices on hydrology and water quality at watershed scale with a rainfall-runoff model. Sci. Total Environ. 2015, 511, 298–308. [Google Scholar] [CrossRef] [PubMed]
  66. Lam, Q.D.; Schmalz, B.; Fohrer, N. The impact of agricultural Best Management Practices on water quality in a North German lowland catchment. Environ. Monit. Assess. 2011, 183, 351–379. [Google Scholar] [CrossRef]
  67. Zhang, L.; Ye, Z.; Shibata, S. Assessment of rain garden effects for the management of urban storm runoff in Japan. Sustainability 2020, 12, 9982. [Google Scholar] [CrossRef]
  68. Taghizadeh, S.; Khani, S.; Rajaee, T. Hybrid SWMM and particle swarm optimization model for urban runoff water quality control by using green infrastructures (LID-BMPs). Urban For. Urban Green. 2021, 60, 127032. [Google Scholar] [CrossRef]
  69. Yang, W.; Zhang, J.; Krebs, P. Low impact development practices mitigate urban flooding and non-point pollution under climate change. J. Clean. Prod. 2022, 347, 131320. [Google Scholar] [CrossRef]
  70. Sharma, A.K.; Vezzaro, L.; Birch, H.; Arnbjerg-Nielsen, K.; Mikkelsen, P.S. Effect of climate change on stormwater runoff characteristics and treatment efficiencies of stormwater retention ponds: A case study from Denmark using TSS and Cu as indicator pollutants. Springerplus 2016, 5, 1984. [Google Scholar] [CrossRef]
  71. Baek, S.S.; Ligaraya, M.; Pyoa, J.; Parkb, J.; Kangc, J.; Pachepskyd, Y.; Chune, J.A.; Cho, K.H. A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds. J. Hydrol. 2020, 586, 124886. [Google Scholar] [CrossRef]
  72. Zhang, K.; Manuelpillai, D.; Raut, B.; Deletic, A.; Bach, P.M. Evaluating the reliability of stormwater treatment systems under various future climate conditions. J. Hydrol. 2019, 568, 57–66. [Google Scholar] [CrossRef]
  73. Burge, K.; Breen, D.; Breen, P.; Wingad, J. Water sensitive urban design in a changing climate estimating the performance of WSUD treatment measures under various climate change scenarios. WSUD 2012: Water Sensitive Urban Design; Building the Water Sensitive Community. In Proceedings of the 7th International Conference on Water Sensitive Urban Design, Melbourne, VIC, Australia, 21–23 March 2012; pp. 119–126. [Google Scholar]
  74. Tansar, H.; Duan, H.-F.; Mark, O. Catchment-scale and local-scale based evaluation of LID effectiveness on urban drainage system performance. Water Resour. Manag. 2022, 36, 507–526. [Google Scholar] [CrossRef]
  75. Lopes, M.; Silva, G. An efficient simulation-optimization approach based on genetic algorithms and hydrologic modeling to assist in identifying optimal low impact development designs. Landsc. Urban Plan. 2021, 216, 104251. [Google Scholar] [CrossRef]
  76. Leng, L.; Jia, H.; Chen, S.A.; Zhu, D.Z.; Xu, T.; Yu, S. Multi-objective optimization for green-grey infrastructures in response to external uncertainties. Sci. Total Environ. 2021, 775, 145831. [Google Scholar] [CrossRef]
  77. Ahiablame, L.M.; Engel, B.A.; Chaubey, I. Effectiveness of low impact development practices: Literature review and suggestions for future research. Water Air Soil Pollut. 2012, 223, 4253–4273. [Google Scholar] [CrossRef]
  78. Pyke, C.; Warren, M.P.; Johnson, T.; LaGro, J., Jr.; Scharfenberg, J.; Groth, P.; Groth, P.; Schroeder, W.; Main, E. Assessment of low impact development for managing stormwater with changing precipitation due to climate change. Landsc. Urban Plan. 2011, 103, 166–173. [Google Scholar] [CrossRef]
  79. Rosenberg, E.A.; Keys, P.W.; Booth, D.B.; Hartley, D.; Burkey, J.; Steinemann, A.C.; Lettenmaier, D.P. Precipitation extremes and the impacts of climate change on stormwater infrastructure in Washington State. Clim. Chang. 2010, 102, 319–349. [Google Scholar] [CrossRef]
  80. Hayes, A.T.; Jandaghian, Z.; Lacasse, M.A.; Gaur, A.; Lu, H.; Laouadi, A.; Ge, H.; Wang, L. Nature-based solutions (nbss) to mitigate urban heat island (UHI) effects in Canadian cities. Building 2022, 12, 925. [Google Scholar] [CrossRef]
  81. Wang, M.; Liu, M.; Zhang, D.; Qi, J.; Fu, W.; Zhang, Y.; Rao, Q.; Bakhshipour, A.E.; Tan, S.K. Assessing and optimizing the hydrological performance of Grey-Green infrastructure systems in response to climate change and non-stationary time series. Water Res. 2023, 232, 119720. [Google Scholar] [CrossRef]
  82. Parker, J.; de Baro, M.E.Z. Green infrastructure in the urban environment: A systematic quantitative review. Sustainability 2019, 11, 3182. [Google Scholar] [CrossRef]
  83. Membele, G.M.; Naidu, M.; Mutanga, O. Examining flood vulnerability mapping approaches in developing countries: A scoping review. Int. J. Disaster Risk Reduct. 2022, 69, 102766. [Google Scholar] [CrossRef]
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Figure 1. Research stages and description.
Figure 1. Research stages and description.
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Figure 2. Research on the effectiveness of low-impact development on climate change collected in the WOS database.
Figure 2. Research on the effectiveness of low-impact development on climate change collected in the WOS database.
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Figure 3. Timeline view of the Clusters.
Figure 3. Timeline view of the Clusters.
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Figure 4. CiteSpace keyword clustering analysis.
Figure 4. CiteSpace keyword clustering analysis.
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Figure 5. Keywords for the impact of LID in hydrology.
Figure 5. Keywords for the impact of LID in hydrology.
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Figure 6. Keywords for the impact of LID in water quality.
Figure 6. Keywords for the impact of LID in water quality.
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Table 1. Corresponding author’s country.
Table 1. Corresponding author’s country.
CountryArticlesMultiple Country PublicationsSame Country
Publications		FrequentSame Country Publications/Multiple Country Publications
China210130800.1570.615
USA199152470.1490.309
United Kingdom12761660.0951.082
Australia8142390.0610.929
Italy7545300.0560.667
Germany5839190.0430.487
Spain5835230.0430.657
Canada5334190.0400.559
Netherlands4117240.0311.412
Sweden3616200.0271.250
Table 2. The proportion of English articles in various fields in the subject categories of WOS documents.
Table 2. The proportion of English articles in various fields in the subject categories of WOS documents.
Web of Science CategoriesRecord CountRatio
Environmental Sciences94951.4%
Environmental Studies54729.6%
Water Resources34818.9%
Green Sustainable Science Technology33718.2%
Urban Studies21811.8%
Table 3. Most Local Cited Authors.
Table 3. Most Local Cited Authors.
AuthorArticlesArticles FractionalizedLocal Citations
Pauleit, S.163.05234
Liu, Y.Z.132.17114
Vojinovic, Z.131.96103
Engel, B.A.122.00123
Kumar, P.122.0273
Table 4. Ten high-frequency keywords.
Table 4. Ten high-frequency keywords.
KeywordsDegreeCentralityKeyword Frequency
Climate change330.17700
Green infrastructure230.10430
Nature-based solutions30.07243
Ecosystem service40.01222
Impact280.19205
City50.01195
Low-impact development260.28177
Ecosystem services20.01160
Model130.28126
Framework30.02112
Stormwater management30.03110
Note: Centrality captures the number of times a node (in this case, a keyword) acts as a bridge along the shortest path between two other nodes. Therefore, keywords with higher centrality are often essential ones that connect various topics or research areas in the field of Low-Impact Development in the context of climate change. Centrality is typically normalized between 0 and 1 by dividing the raw score by the maximum possible score for a network of the same size.
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MDPI and ACS Style

Wang, M.; Feng, S.; Ikram, R.M.A.; Chen, T.; Sun, C.; Chen, B.; Rao, Q.; Jin, H.; Li, J. Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review. Sustainability 2023, 15, 13616. https://doi.org/10.3390/su151813616

AMA Style

Wang M, Feng S, Ikram RMA, Chen T, Sun C, Chen B, Rao Q, Jin H, Li J. Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review. Sustainability. 2023; 15(18):13616. https://doi.org/10.3390/su151813616

Chicago/Turabian Style

Wang, Mo, Sijie Feng, Rana Muhammad Adnan Ikram, Tong Chen, Chuanhao Sun, Biyi Chen, Qiuyi Rao, He Jin, and Jianjun Li. 2023. "Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review" Sustainability 15, no. 18: 13616. https://doi.org/10.3390/su151813616

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