Systematic evidence-mapping of climate change adaptation challenges in urban water systems

Systematic evidence-mapping of climate change adaptation challenges in urban water systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Systematic evidence-mapping of climate change adaptation challenges in urban water systems Shefali Nayak, Zayra Castillo, Rana Ekmekcioglu, Franziska S Hanf, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9569973/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite many studies on climate change adaptation, implementation challenges are understudied. After screening over 39,000 articles, we used large language models on 892 articles to map global adaptation challenges in urban water systems. Publications have grown exponentially since 2011, but research is biased toward the Global North. Floods dominate literature, leaving droughts underrepresented despite their socio-economic impact. Our analysis of measure-challenge associations reveals: i) institutional and social challenges are pervasive in all adaptation types, ii) institutional challenges have moderate associations across measures where high frequencies often hinder CCA measures, iii) structural and technological CCA measures receive more research attention than behavioral, social, and anticipatory approaches. Our synthesis identifies research gaps and policy priorities: focus on socio-behavioral and anticipatory adaptation, research in understudied regions, and urgent institutional reform as an adaptation prerequisite. Social science/Environmental studies Scientific community and society/Geography Social science/Geography Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Intergovernmental Panel on Climate Change (IPCC) has indicated that the global population is anticipated to face a reduction in renewable water resources, exacerbating the risks of hydrological drought and water insecurity 1 . Climate-related disasters are becoming more frequent and intense, endangering lives, livelihoods, and economic assets 2 . Nearly one-third of urban populations face water scarcity, with projections indicating that this could reach half of urban residents by 2050 3 . Unplanned urbanization contributes to climate change and urban heat islands, while climate change increases urban vulnerabilities through extreme weather, rising seas, and altered precipitation patterns 4,5 . Recent research indicates that transformational adaptation and international cooperation are vital for urban water security amid climate change 6 . Although there is a burgeoning body of scientific knowledge on climate change 7 and adaptation research 8 , the volume of information on adaptation in the form of reviews 9,10 remains fragmented. Adaptation in urban areas is frequently characterized as advancing too slowly 11 . The challenges highlighted are at various governance levels, from local to national, and span different sectors, interacting in a dynamic way that makes adaptation a complex and path-dependent task 7,12–14 . Additionally, research on the adaptation of urban water systems has either been case-study specific 6 , focused on nature-based solutions 15 , water quality 16 , flood and drought impacts 5 , and wastewater infrastructure 17 , or has highlighted only the impact of climate change and adaptation strategies on urban water systems 18 . This continues to provide only partial answers to the various forms of climate change adaptation (CCA) of urban water systems. Previous CCA research has explicitly highlighted the need for more and better synthesis methods that cover overlooked sectors 13 . Therefore, a consolidated knowledge synthesis of CCA is necessary, especially with a focus on urban water systems. Knowledge synthesis is a process where knowledge is systematically collected, analyzed, and integrated to draw comprehensive conclusions on specific topics and questions 19 . They are underrepresented in climate change research field 20,21 . Systematic evidence-mapping is an endorsed methodology for knowledge synthesis to extract novel and relevant insights from an extensive corpus of literature 22 . Traditionally, reviews require human coders to synthesize data from peer-reviewed articles, rendering the process laborious and restricted to a limited number of documents 23 . Lately, natural language processing (NLP) is being used to manage vast volumes of literature in the CCA field to overcome previous constraints 13,24,25 . However, there exists a large number of scientific articles that are thematically distributed broadly, which hinders the precise data extraction of scientific evidence from long-format texts in peer-reviewed journals. Due to this, as of now, many NLP-based meta-analyses and data syntheses have focused on extracting data only from the abstract 4,6 . This narrows the scope of the research by oversimplifying and disregarding details in the full-text article. With current advancements in large language models (LLMs), NLP enables the automated extraction of information from full-text articles. This creates new potential for information analysis 25–27 . Our study leverages advanced NLP automation and aims to fill the persistent knowledge gap on the relationship between urban development constraints, CCA measures, and CCA challenges in the context of urban water systems. We identified 892 relevant studies that formed the basis for a global mapping of adaptation measures and challenges, providing an overview of the current state of research. We used an improved evidence synthesis method as a robust, science-informed global stocktake of CCA challenges. This work may inform the 7th IPCC assessment report, particularly in identifying strategies to address urban water-related risks and contribute to supporting progress towards the Global Goal on Adaptation. Results Patterns in climate change adaptation research for urban water systems As shown in Fig. 1(a), there has been strong exponential growth in the number of publications on CCA research on urban water systems since 2011. Note that data extraction was concluded in July 2024, which accounts for the shorter length of the 2024 bar. Given that this point in time is approximately mid-year, it can be inferred that the bar length would be approximately double if the data for the entire year were considered. Certain years stand out in particular; 2011 was the most expensive year ($380 billion globally) for losses due to natural hazards. Many countries in the developed world (Thailand, New Zealand, Japan, Australia, and the USA) have been hard hit by various types of water-related natural hazards 28 . In 2012, the IPCC published its Special Report on Managing Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX), which analyzed the link between climate change and extreme events and set a new standard for climate risk research and adaptation strategies. The report became a foundational guideline for global adaptation and disaster risk planning 2 . Several international research programs and collaborative studies, such as the "governance of adaptation to climate change," outlined in scientific papers, have been initiated or extended. These efforts emphasize real-world trials, stakeholder participation, and hotspot collaboration 29 . The next spike in publications occurred after 2015, which can be attributed to the Paris Agreement, especially as the agreement intensified global scientific efforts to better understand climate impacts, adaptation, and mitigation strategies 30 . There was a decline in publication outcomes in 2021, which we link to the global COVID-19 crisis. Fig. 1(b) shows the number of identified case study cities mentioned in peer-reviewed publications categorized by their population size. The distribution broadly reflects the global proportion of cities within each population bracket, with slightly greater representation of megacities exceeding 10 million inhabitants based on World Urbanization Prospect 2025 31,32 . The slight emphasis on megacities is valuable as it highlights the influence of population dynamics in large and densely populated urban environments. Despite their slightly higher relative representation in our dataset, the absolute number of studies specifically addressing water management challenges in megacities under conditions of rapid population growth remain limited 33–35 . This thematic gap constrains effective planning and implementation in cities exceeding certain population thresholds, even though research on urban expansion and flood risk consistently shows that large-scale urbanization amplifies such challenges 33–35 . The highest number of publications originated from Asia, followed by Europe, North America, Africa, Oceania, and South America (Fig. 1c). Although the Emergency Events Database (EM-DAT) similarly identifies Asia as the most disaster-affected region, Tin et al. 68 reported a different ordering of incident counts for 1995–2022, namely, the Americas, followed by Africa, Europe, and Oceania. This divergence between the disaster incidence ranking and publication output suggests a geographic imbalance in the literature, with Global North cities disproportionately represented. The number of research studies is strongly dominated by floods, and this pattern is noticeable across all continents except Oceania. Floods are among the most extensively studied natural hazards because of their high frequency and significant human and economic impact 36–38 , where urban areas are comparatively underrepresented in studies addressing global flood risk. Global reviews also confirm that the literature on flood risk, modelling, and response is more developed than that on drought 39 , which is due to the contrasting nature of their occurrences. Floods have a rapid onset and impact, whereas droughts have a prolonged onset and impact 40 . Droughts are one of the most expensive natural hazards for society 41 ; however, there is lower public awareness and visibility compared to dramatic flood events, Olazabal et al. 42 highlights the mismatch between physical drought risks and institutional/ public perception of drought. This has led to a disparity in the volume of drought and flood research globally 36 . Research focusing exclusively on droughts as isolated hazards, particularly within an urban environment 43 , is less common than studies on floods and sequential hazards. Here, we confirm that this disparity also applies to studies on the challenges of CCA in urban water systems. An analysis of the geographical and regional patterns of the reviewed publications shows a heterogeneous spatial distribution of urban water research (Fig. 1d). Most case studies are published for China and the USA, followed by Australia, India, Brazil, and South Africa. There is a lack of peer-reviewed publications from Eastern Europe, Central Asia, parts of North and Northwest Africa, and countries such as Paraguay, Venezuela, Bolivia, Guatemala, Suriname, Trinidad and Tobago, and Barbados. Within Europe, there are strong disparities in the distribution of the identified publications. The Netherlands and the United Kingdom exhibit the highest volume of scientific research on climate change adaptation and urban water management, with Germany and Italy closely following. France is comparatively underrepresented in Western Europe. Several factors may have contributed to this pattern. Research capacity in climate adaptation may be concentrated in Paris, with limited coverage of other urban contexts. In addition, the institutional positioning of urban planning and development within administrative structures has been reported to be comparatively weak 44 . Methodological factors may also play a role, as the search string may not capture locally specific terminology and relevant studies may be published in French-language outlets. There are studies indicating structural challenges in integrating adaptation into urban water policy, as recognized by the OECD and World Bank in Central Asia and Eastern European countries, while lacking robust local academic literature or systematic case studies in international reviews 45 . Our results indicate that Africa has a disproportionately low publication rate, except for South Africa. The study by Doswald et al. 46 maps an evidence gap that highlights that the geographic coverage of urban water management systems is clustered within a selected group of countries. Many North and Central African nations are either underrepresented or are almost absent in the published research on climate change adaptation with respect to urban water. Overall, evidence-mapping the case studies of the 892 selected articles reveals that most publications are from China, the United States, and India. While the number of cities in a country may drive the research needs and opportunities, our analysis shows that some countries are studied more or less than their urban population suggests (Fig. SX1). In Australia 47 , the United Kingdom 48 , and the Netherlands 48 , the number of publications is high despite the relatively small number of large cities, indicating a disproportionately strong research focus on the urban scale. Conversely, Japan, Vietnam, Nigeria, France, Russia, and the Philippines, despite having numerous cities, are underrepresented in publications, indicating a disparity between the urban population and research output. Countries such as Brazil, Indonesia, Mexico, and South Africa occupy an intermediate position, with a moderate number of large cities and publications. Also, Italy, Spain, Germany, and Poland, display a balanced representation in the European domain. Urban development constraints linked to measures and challenges in climate change adaptation Based on the strong growth in CCA research on urban water systems after 2011 (Fig. 1a), we narrowed our analysis from 2011 to 2024 to examine the links between urban development constraints and CCA measures and challenges. In Fig. 2(a), our analysis shows that environmental, institutional, and infrastructural urban development constraints dominate research across all continents, whereas social and economic issues receive comparatively less attention. In Asia, intensifying environmental, infrastructural, and institutional pressures are driven by rapid urbanization, population growth, and climate change. Europe shows a similar pattern, although social challenges are relatively more prominent in Asia. In contrast, in Africa and South America, a proportionally greater emphasis is placed on social urban development constraints, whereas in North America and Oceania, the focus is primarily on institutional, infrastructural, and environmental aspects. Structural/physical types of CCA measures are consistently one of the most published categories across all continents, closely followed by technological measures, indicating growing research interest, investment in innovation, and prioritization of infrastructural solutions for CCA (Fig. 2b). These are followed by ecosystem-based CCA measures, with a slightly higher publication rate since 2020. Institutional/policy CCA measures are recognized as critical, yet the growth in scientific research is less steep compared to technological and structural CCA measures. Behavioral/social CCA measures are often published on par with institutional/policy CCA measures, highlighting the acknowledged need for social research in climate science and societal shift 49 in CCA practice. Anticipatory CCA measures remain the lowest in publication numbers, suggesting that explicitly proactive adaptation strategies remain underexplored relative to other CCA classifications. Studies show that this is due to the high cost of early adaptation and budgetary constraints, to which countries adapt reactively 50 . Reactive measures with the lowest publication rates indicate a scholarly shift away from reactive solutions. However, the ground reality tells a different story: The IPCC report indicates that local governments that have begun to implement adaptation strategies often take a reactive or event-driven approach, primarily relying on technical solutions 51 . This underscores the disconnection between local governmental entities and the broader scientific community. Publications on cities in Africa and South America exhibited a sharp decline in 2021 (Fig. SX2), attributable to the global COVID-19 pandemic, whereas research output in Europe, Asia, North America, and Oceania remained largely unaffected. This disparity underscores persistent differences in global scientific research infrastructures. An analysis of the classification of CCA challenges across the reviewed scientific literature (Fig. 2c) highlights that institutional and structural/physical CCA challenges are consistently the highest category of challenges mentioned in the scientific literature. We find this pattern to be stable across all continents (Fig. SX3) as well as from 2011 to 2024. This is followed by social CCA challenges and then technical CCA challenges, which are the third and fourth most frequently mentioned challenges across all continents. Since 2016, there has been a slight rise in ecosystem-related CCA challenges, which can be linked to the increased implementation of ecosystem-based adaptation strategies, such as nature-based solutions, after the Paris Agreement. Although economic CCA challenges are comparatively less frequently mentioned, they are reported sufficiently to warrant the creation of a distinct classification. The classification with the fewest publications is anticipatory CCA challenges, which are directly linked to social or institutional issues related to the acceptance of a specific CCA measure. Evidence synthesis of the association strength between climate change adaptation measures and challenges Fig. 3 shows the association strength of CCA measures and CCA challenges, indicating thematic linkages between their categories. However, association strength does not imply causality. In the case of measures and challenges from the same categorical domain, strong association strengths align with their thematic linkage, providing empirical confirmation of the robustness of the methodology. We find the strongest positive associations between behavioral/social CCA measures and social CCA challenges, followed by ecosystem-based CCA measures and ecosystem-based CCA challenges. Ecosystem-based CCA challenges are likely due to ecosystem complexities, which are the inherent, intricate, and unpredictable nature of ecosystem-based CCA measures, sometimes further worsened by climate change 52 . Finally, the technological CCA measures and technical CCA challenges are strongly associated. It is imperative to prioritize the resolution of technical issues, including the operation and maintenance of technology 73 to effectively implement future-proofing measures. By resolving these human, technological, and systemic barriers, the implementation of solutions becomes considerably feasible. Moreover, institutional CCA challenges exhibit low to moderate association strengths (close to 1.0) with all other adaptation measure types, and their high marginal frequency of over 8,900 mentions indicate that they are the most frequently reported. This suggests that institutional CCA challenges, such as leadership dependence, silo organization, and spatial policy mismatches, are pervasive across all forms of adaptation measures, illustrating the importance of institutions for adaptation 53 . Further, we find a notable positive association between institutional/policy CCA measures and social CCA challenges. This is in line with recent assessments of social disparities 54,55 , highlighting that equitable adaptation requires transformative changes in planning, governance, and resource management, guided by social justice principles to address the fundamental drivers of urban water-related risk. Furthermore, we analyzed the association strength between the categories of urban development constraints and the categories of CCA measures (Fig. SX4), as well as categories of urban development constraints with the categories of CCA challenges (Fig. SX5). For all possible combinations, we find no significant dependence between the presence of specific urban development constraints and the existence of particular CCA measures or challenges. However, this finding is solely based on the mention of urban development constraints, but not their magnitude. While the magnitude of social, environmental, institutional, infrastructural, and economic urban development constraints varies substantially across the globe 3,56,57 , our assessment cannot provide insight into whether the severity of constraints affects CCA measures or CCA challenges. Discussion Addressing the relationship between CCA measures and challenges is a critical component of climate change research. It identifies the most prevalent obstacles encountered by scientists, policymakers and practitioners in the implementation process. Recognizing these challenges in advance is crucial for developing revised plans that avoid similar or recurring impediments. We identified four key findings by exploring their significance in research and policy-making concerning CCA measures and challenges in urban water systems. First, our findings reveal a notable global bias in research and publication. Even when most countries are well-represented in the scientific literature, the proportion relative to the number of cities with populations over 100,000 is imbalanced. The results also show a heterogeneous spatial distribution of case studies investigated in peer-reviewed articles. Our results indicate that Africa has a disproportionately low publication rate, except for South Africa. Neglecting to study climate adaptation and urban development constraints in Africa increases the vulnerabilities of cities that are rapidly urbanizing and facing multiple climate-related threats. Mwenje et al. 58 and Servières et al. 59 highlight that this lack of attention worsens existing issues such as insufficient infrastructure and governance deficiencies, resulting in significant socioeconomic and environmental impacts. Second, our results highlight the disparity between flood and drought research. The data indicate that research studies are predominantly focused on floods, a trend observable across all continents, with the exception of Oceania. Australia successfully managed its millennium drought (1996-2009) with record-low rainfall; however, the drought response was not adaquate 60 as decision-making was crisis driven and fragmented rather than long-term adaptive governance. Urban drought can lead to water shortages, with reduced urban water system reliability. Previous research on urban water systems shows drought responses are tied to maintaining supply reliability, but drought still strains demand and can expose weak points in urban water management 62 . Further research on urban drought is crucial in identifying vulnerable systems and communities, so cities can plan water-saving, adaptation, and emergency response measures before shortages become crises. Cape Town gained attention in the period of 2016-2018 while affected by severe drought, highlighting the importance of infrastructure diversification and better groundwater management 63 . Despite projections of more frequent and severe occurrences, particularly in cities, research gaps concerning floods, droughts, and sequential events persist owing to ongoing issues with awareness, funding, and visibility of these crises 42 . The current adaptation approach is fragmented, often focusing on either flood or drought 64 . This approach of targeting only one extreme can fail under rapid transition 65 . Sustainable urban water futures require a radical shift in society’s urban climate adaptation imaginaries toward transformative adaptation that addresses the root causes of water-related risks and vulnerabilities. This approach must also be holistic, considering synergies between flood and drought measures to effectively manage the complex urban water risk–adaptation nexus 66 . Therefore, we call for further research that examines floods and droughts as functionally interconnected phenomena, with a focus on integrated adaptation strategies. Third, although ecosystem-based CCA measures are increasingly researched and impacted 67,68 , we find a concentration in adaptation literature primarily focusing on infrastructural and technological adaptation measures, alongside integrated approaches where an ecosystem-based strategy is incorporated. We find a comparatively low research focus on socio-behavioral adaptation measures. Socio-behavioral adaptation measures encourage individuals and communities to modify or adopt behaviors in response to climate change. These measures recognize human behavior is complex, often needing information, incentives, and structural support to shift practices and enhance adaptive capacity. Aoki et al. 69 highlight that technical or infrastructure-based climate solutions often underperform if they ignore human behavioral responses or community norms. This underscores the necessity for further scientific investigation in policy-oriented and socio-behavioral adaptation research fields. The results also indicate a slight trend towards anticipatory CCA measures mentioned in the scientific literature, as well as a scholarly shift away from reactive measures based on the data collected in our study. However, the reality is that most governments take an event-driven and tangible solution 51 . This highlights the opportunity for more transdisciplinary collaboration to bridge the science-policy-practice gap. Fourth, the clustering of the strongest associations is identified around social CCA challenges, with the link to CCA measures of socio-behavioral adaptations being the most pronounced. Future research should focus on areas of socio-behavioral CCA, for example, to promote water conservation education in disadvantaged neighborhoods, thereby contributing to the empowerment of resident 70 . Furthermore, the strong link to institutional CCA measures indicates that policy goals and local social conditions cannot be considered independently from one another; it is important to address limited community engagement and participation and the social vulnerabilities 71 of communities at risk. Ignoring these CCA challenges renders any type of CCA measure inefficient. Ecosystem-based CCA measures are strongly associated with structural/physical measures due to their inherent combination (e.g., bio-swales, infiltration trenches, green roofs). They show a moderate-low association with social, technical, and institutional challenges. Ried et al. 52 highlight their role in enhancing social resilience by maintaining services like water retention and erosion prevention, often outperforming traditional methods. These measures are multi-purpose and more efficient than conventional solutions, using natural processes. Institutional challenges are minor or addressable through better coordination. Policy integration is feasible, with successes in frameworks like EU strategies 72 emphasizing ecosystem approaches. Although institutional CCA challenges exhibit significant moderate-low associations, they are reported at a high frequency across all categories of CCA measures, highlighting that fragmented governance 73 and institutional inertia 74 as a critical bottleneck globally affect CCA progress. While the identified associations suggest potential links, we recommend further studies with complementary approaches, including system dynamics modeling 75–77 , network analysis 78 and agent-based modeling 79 , to further support the causation and complexity of these interactions and emergent behavior across scales. Moser et al. 80 suggests adopting a structured diagnostic tool for identifying barriers in planned adaptation processes, which identifies three phases: understanding (problem detection, information gathering, and redefinition), planning (option development, assessment, and selection), and management (implementation, monitoring, and evaluation. Although evidence mapping supported by LLMs offers a valuable approach to synthesizing global scientific progress, it is subject to certain limitations. Our abstract screening filtered only English-language articles. We infer that the lack of scientific studies in parts of Europe, Central Asia, and South America is due to them being published in a language other than English. Further, our data collection excludes grey literature, books, and reports that may cover localized CCA challenges, especially from the Global South, which were not part of our analysis. Furthermore, we constrained our article selection to require the abstract to mention barriers, challenges, or barriers. There might be articles on CCA where challenges are mentioned in the full text but not in the abstract. Our methodology is capable of assessing the mention of urban development constraints, CCA measures, and CCA challenges, but it does not capture the magnitude of these categories. In a recent study 81 , the importance of measuring urban climate adaptation through indicators and metrics is emphasized as crucial for successful CCA management. Nevertheless, they emphasize that there is a lack of comprehensive understanding regarding the measurement of CCA, particularly concerning the methods of measurement. Notably, in approximately 48% of cases, the indicators and metrics did not specify a unit of measurement across the adaptation cycle, scale, and sectors 81 . The evidence mapping in this study builds on peer-reviewed scientific articles reporting adaptation case studies, while articles are rarely published on missed opportunities or non-implemented adaptation measures ( publication bias ). Hence, our assessment strongly underrepresents barriers such as undetected problems, unavailable information, and insufficient planning. However, this bias emphasizes the importance of exploring and understanding the scientifically documented challenges and barriers in this study. In the context of large-scale review text mining, LLMs can accelerate repetitive tasks. However, they do not yet possess the reliability required to supplant thorough human validation, especially in scenarios where accuracy, transparency, and reproducibility are of paramount importance 82 . Petzold et al. 83 highlighted in their research that 65% of the researchers from the Global Adaptation Mapping Initiative consider NLP at least somewhat useful; to ensure reliability it is important to combine NLP with other analytical methods. We employed NLP with human involvement to aid in synthesizing the evidence for CCA in urban water systems. This facilitated a comprehensive review of a substantial number of papers, which would have otherwise necessitated significantly more time and human resources. This novel method illustrates how AI can be combined with thorough and transparent review processes for evidence synthesis in adaptation research, thereby improving policy-relevant evaluations to address disparities between the Global North and Global South. Methods We conceptualized a novel hybrid workflow for qualitative text analysis, integrating human- and LLM-guided elements within five iterative phases. The general direction consists of human scoping, LLM-guided narrowing, and human refinement of the results. We used a well-established method for a systematic quantitative literature review developed by Pickering and Byrn 84 , along with a similar approach by Berrang-Ford 85 . We then adapted the existing method to assess journal articles on CCA measures and challenges within urban water systems worldwide. Systematic quantitative reviews adhere to the Preferred Reporting Items for Systematic Reviews (PRISMA) guideline 86 , where other studies 87–89 have used systematic reviews to advance CCA research in the themes of adaptation planning in large cities, adaptation policy research, and the effectiveness of adaptation. Our study uses the existing PRISMA guidelines but adapts them to include LLM-guided coding. Fig. 4 presents an overview of the review methodology merging PRISMA guidelines with LLM support, where the five phases are described in detail in the following sections. Identification We generated a search string using an interactive query formulation search approach 90 to test and evaluate the keywords and data that were gathered. Thereafter, we ran the final search terms through the databases (Web of Science and Scopus). Subsequently, the resulting datasets, comprising peer-reviewed journal articles, conference papers, books, and book chapters published between 1990 and 2024, were extracted on July 26, 2024. The total number of imported articles was 39332. Human screening We then uploaded the references into Rayyan.ai 91 , an online tool that helps organize and review research papers for systematic reviews and related evidence syntheses. It allows researchers to obtain search results from different databases, find and remove duplicates, and review titles and summaries using customizable inclusion and exclusion criteria in a single interface. We used it to remove duplicates from the combined articles of Web of Science and Scopus, where the remaining number of articles amounted to 22602. To select the barriers and challenges criteria for a more thorough assessment, we screened the articles based on their titles, keywords, and abstracts based on our fundamental thematic criteria, such as adaptation, urban setting, and inclusion of a case study. We excluded any references to adaptation that concentrated on particular species or ecosystem 9 and instead adopted the IPCC's definition of climate-resilient water-related adaptation 92 . At this stage, we applied our initial exclusion criteria, which eliminated any article that was “not urban,” “not a city (towns and villages),” “related to agriculture,” “not directly water-related,” “related to organisms and biology,” or categorized as a “review.” No inclusion criteria were applied at this stage. These steps reduced the number of articles to 1843. Human-coding and review We iteratively designed a coding matrix based on a research outline guiding this knowledge synthesis. Therefore, two authors independently coded a random sub-selection of 150 test papers and compared their results to ensure clarity of the questions within the coding matrix. The resulting matrix consisted of 12 questions to be answered by reviewers coding the articles (see Supplement). While nine questions are binary or optional, three questions require elaborate answers linked to the categories classified for them. We tokenized the categories for each classification to guide the reviewers. Based on the existing urban development literature 93,94 , we classified urban development constraints into five broad categories (Table 1): environmental, institutional, social, infrastructural, and economic. Table 1. Categories of urban development constraints with examples Category Selection of examples Overlapping category Environmental encroachment of rivers Institutional floodplains by physical developments Infrastructural Institutional path dependence unplanned land-use Environmental Social fear of the unknown Institutional limited community engagement, empowerment, and participation Infrastructural use of non-permeable paving in urban areas Environmental outdated gray infrastructure Institutional and Environmental Economic insufficient resources (capital and human) Institutional urban flooding Social and Environmental Furthermore, we classified CCA measures into seven distinct categories (Table 2): structural/physical, ecosystem-based, institutional/policy, behavioral/social, technological, anticipatory, and reactive, based on the existing literature 95–99 . Table 2. Categories of CCA measures with examples Category Selection of examples Overlapping category Structural/ Physical permeable pavements Technological rainwater harvesting systems Anticipatory and Technological Ecosystem-based bio-swales; artificial ponds; Structural/ Physical and Technological green-roofs; rain gardens Structural/ Physical and Technological Institutional/ Policy adopting sponge city regulations and permits public, private partnership and financing Behavioral/ Social Behavioral/ Social implementing reduced irrigation regime in water scarce region Institutional water conservation education Institutional Technological underground retention tanks Structural/ Physical leak detection and repair program Institutional Anticipatory relocation of residents from flood-prone areas Structural/ Physical early warning systems Institutional Reactive water trucking Institutional pluvial flood insurance Institutional and Behavioral/ Social Additionally, we identified the common interacting challenges associated with these adaptation measures, classifying them into seven categories (Table 3): structural/physical, ecosystem-based, institutional, social, economic, technical, and anticipatory, as informed by current scientific discours 53 . We studied the different types of challenges in adapting to climate change and how they are governed. Then, we decided to add a new category of barrier to the list as ‘economic’, which is not independently listed as a measure but has the capacity to derail a measure by coming up as a challenge 100 . Table 3. Categories of CCA challenges with examples Category Selection of examples Overlapping category Structural/ Physical large land area requirement Institutional old water piping system Technical Ecosystem-based selection of the proper type of vegetation higher and steeper flood walls have degraded habitats and obstructed public riverside amenities Social Institutional siloed implementation short-term policies Social litter and inadequate community cooperation Institutional contested community leadership Economic high operational cost Institutional high retrofitting cost Social Technical lack of sufficient technical references, design standards and guidelines Institutional experiments are necessary to test hydraulic parameters Anticipatory political contestation over land and housing subsidies Institutional uncertainty in future extreme rainfall intensities at sub-daily scales Technical We then developed a human review protocol along with additional information relevant to ensuring consistency within the human reviewers (see Supplement). This protocol outlines the research approach, details the literature search strategy, explains how the papers should be reviewed, and indicates how the final findings will be presented 101 . Although no review methodology can fully capture all relevant literature, this process is a well-established method for obtaining a comprehensive understanding of the literature in a transparent, consistent, and reproducible manner 84 . The authors of this study formed an interdisciplinary team of reviewers and conducted a human full-text analysis of 369 articles out of 1843 (equaling 20 %) to support prompt engineering for the LLM (Fig. 4). Based on their answers within the coding matrix, we iteratively developed prompts (see Code Supplement) for the LLM to guide the review process 102 . LLM-screening and review In this analysis, we used the LLM-automated reflective generative pre-trained transformer (GPT) OpenAI o3 103 . The o3 model achieves an accuracy on par with human performance in full-text reviews with well-designed prompts. For review tasks with clear instructions and robust prompts, the performance of LLMs can rival human performance 104 . Integrating LLMs into the full-text screening process can reduce the time spent manually assessing documents while maintaining or even improving accuracy 105 . We then used the LLM to code all of the imported 1843 articles. By leveraging the LLM’s coding capabilities for a large number of articles with accuracy, we implemented the inclusion criteria (Fig. 4). We included articles that were peer-reviewed, focused on case study(s), as the evidence map is designed to build on concrete, locally observed challenges at the urban scale rather than on studies addressing broader regional, national, or continental levels. In the next step, we added further exclusion criteria, excluding all books, conference papers, and opinion articles, followed by articles with no explicit adaptation focus, with large case study areas instead of city cases, and studies without urban development constraints or natural hazards. In order to evaluate the results of the LLM, we conducted human-led statistical validation. Therefore, we randomly selected ten articles from the review matrix (see Supplement). Seven human reviewers independently reviewed the same ten predetermined articles. Answers to the eight questions that were one-word or yes/no were evaluated by virtue of the majority and assigned binary scores (0 or 1). In the case of the four descriptive questions, we compared the answers of the LLM against the union of all human answers (not the consensus) to calculate the F1 score 106 , precision, recall, and hallucination for each question within each paper. We calculated an F1 score for each paper by averaging the scores for the 12 questions and then calculated the average of the ten papers. The LLM achieved an F1 score of 82%, indicating a strong performance (see Supplement) with a hallucination rate of 18% (Table 4). We note that the reported hallucination rate does not necessarily only consist of LLM hallucinations but may also contain cases where none of the seven human reviewers found the respective answer. Table 4. Validation metrics of the LLM performance based on seven human reviewers and ten articles F1 Score Precision Recall Hallucination ~82% ~83% ~61% ~18% Ultimately, we included 892 articles representing a diverse body of literature addressing themes such as climate adaptation, flood and drought management, nature-based solutions, urban water governance, and heat management through water. Data analysis We conducted a descriptive analysis to explore the publication time, location of the case study(s), population of the studied city(s), and type of water-related hazards based on LLM output. For thematic analysis, we tokenized various urban development constraints, CCA measures, and CCA challenges into categories (see Table 1-3). The tokenization process helps classify themes and extract topics. In order to relate the results from the thematic analysis, we conducted a relational analysis between the categories of urban development constraints with CCA measures and challenges, as well as CCA measures with CCA challenges. We calculated the association strength for each combination, following Steijn 107 . Here, the association strength between the categories is a probabilistic normalization of their co-occurrence counts, expressed as the ratio of their joint probability to the product of their marginal probabilities. This measure quantifies the non-random relatedness between terms relative to their individual frequencies, enabling a robust assessment of their joint occurrences 107 . A strong positive association strength (>1.0) between two categories indicates that they co-occur significantly more frequently than expected under random chance, given their individual occurrence rates. This suggests a meaningful thematic linkage or conceptual overlap between the categories in the analyzed articles, beyond mere frequency biases. Conversely, a value below 1.0 reflects co-occurrence less frequent than random, while exactly 1.0 denotes independence. Further, we test the independence of the co-occurrences of categories based on a χ²-test at a 95% significance level. Declarations Data availability All data underlying the figures in this manuscript are openly available at https://doi.org/10.5281/zenodo.19866111 Code availability The code underlying the results is available at https://doi.org/10.5281/zenodo.19866118 Acknowledgements The authors gratefully acknowledge Jan Christoph Bernack for his support with Software, which contributed to data processing and analysis. The authors also thank Ines Nofz for her contributions to the human review stage and Iva Stefani for her assistance in the identification and screening stage of the raw data. This study is a contribution to project ‘C1: Sustainable Adaptation Scenarios for Urban Areas – Water from Four Sides’ of the Cluster of Excellence ‘CLICCS - Climate, Climatic Change, and Society’ (EXC 2037, Project Number 390683824) at HafenCity University and the Department of Earth System Sciences, Faculty of Mathematics, Informatics and Natural Sciences, Earth and Society Research Hub (ESRAH), University of Hamburg, Hamburg, Germany. It is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy. Contributions S.N.: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Visualization, Writing—Original Draft. B.P.: Conceptualization, Methodology, Formal Analysis, Validation, Writing—Review & Editing, Supervision. R.E.: Formal Analysis, Review & Editing. F.S.H., N.K., M.S.: Validation, Writing—Review & Editing. Z.C., L.M., F. S., and L.M.: Validation, Review, & Editing. 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Each publication may contribute multiple cities mentions across population categories.\u003cstrong\u003e (c)\u003c/strong\u003eCategorization per continent, where each publication may contribute multiple cities mentions across different continents. \u003cstrong\u003e(d)\u003c/strong\u003eSpatial representation of the case studies in the selected literature, where each publication may contribute multiple case studies across different countries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/393575a8b179e251840d7615.png"},{"id":108806464,"identity":"dcb6c1db-e356-4395-a15d-440cd4743d1b","added_by":"auto","created_at":"2026-05-08 15:28:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Fraction of urban development constraint categories per continent (each urban development constraint mentioned in a publication is assigned to a category and counted) \u003cstrong\u003e(b) \u003c/strong\u003eNumber of mentioned CCA measure categories per year (each CCA measure is assigned to a category and counted. Multiple measures per publication are allowed.) \u003cstrong\u003e(c) \u003c/strong\u003eNumber of mentioned CCA challenge categories per year (each CCA challenge is assigned to a category and counted. Multiple measures per publication are allowed.)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/8826836f7e1a5c1747afa6a9.png"},{"id":108668172,"identity":"ac47675f-b553-4c52-96e8-d5b5a56e9421","added_by":"auto","created_at":"2026-05-07 07:03:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204490,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the association strength between CCA measures and CCA challenges. Asterisks denote non-independent co-occurrences based on the χ²-test at a 95% significance level.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/7009d949c3f8039a53cdc4d1.png"},{"id":108668175,"identity":"99fe004e-4749-46ba-91bb-808a9f4514e1","added_by":"auto","created_at":"2026-05-07 07:03:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224673,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the methodological review workflow with the inclusion of human (purple) and LLM (orange) support. The workflow adheres to the PRISMA (blue) guideline\u003csup\u003e86\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/96000961054181ef1282d18f.png"},{"id":109209270,"identity":"ba56ee60-5d7c-49e2-ae2d-2609ddc7bce5","added_by":"auto","created_at":"2026-05-13 15:28:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1043876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/c56e7758-1825-425e-8f0e-629e67df730f.pdf"},{"id":108805597,"identity":"2df3601c-31a6-4c5b-b2ed-03ed63e1706f","added_by":"auto","created_at":"2026-05-08 15:26:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1343326,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/35668fb484e40978f23b5de2.pdf"},{"id":108805598,"identity":"b47e109f-a336-4841-a8b8-1cc88fc4d11e","added_by":"auto","created_at":"2026-05-08 15:26:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3458508,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFiguresNayak.docx","url":"https://assets-eu.researchsquare.com/files/rs-9569973/v1/0677a7e3ff15919865e89387.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Systematic evidence-mapping of climate change adaptation challenges in urban water systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Intergovernmental Panel on Climate Change (IPCC) has indicated that the global population is anticipated to face a reduction in renewable water resources, exacerbating the risks of hydrological drought and water insecurity\u003csup\u003e1\u003c/sup\u003e. Climate-related disasters are becoming more frequent and intense, endangering lives, livelihoods, and economic assets\u003csup\u003e2\u003c/sup\u003e. Nearly one-third of urban populations face water scarcity, with projections indicating that this could reach half of urban residents by 2050\u003csup\u003e3\u003c/sup\u003e. Unplanned urbanization contributes to climate change and urban heat islands, while climate change increases urban vulnerabilities through extreme weather, rising seas, and altered precipitation patterns\u003csup\u003e4,5\u003c/sup\u003e. Recent research indicates that transformational adaptation and international cooperation are vital for urban water security amid climate change\u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough there is a burgeoning body of scientific knowledge on climate change\u003csup\u003e7\u003c/sup\u003e and adaptation research\u003csup\u003e8\u003c/sup\u003e, the volume of information on adaptation in the form of reviews\u003csup\u003e9,10\u0026nbsp;\u003c/sup\u003eremains fragmented.\u0026nbsp;Adaptation in urban areas is frequently characterized as advancing too slowly\u003csup\u003e11\u003c/sup\u003e. The challenges highlighted are at various governance levels, from local to national, and span different sectors, interacting in a dynamic way that makes adaptation a complex and path-dependent task\u003csup\u003e7,12\u0026ndash;14\u003c/sup\u003e. Additionally, research on the adaptation of urban water systems has either been case-study specific\u003csup\u003e6\u003c/sup\u003e, focused on nature-based solutions\u003csup\u003e15\u003c/sup\u003e, water quality\u003csup\u003e16\u003c/sup\u003e, flood and drought impacts\u003csup\u003e5\u003c/sup\u003e, and wastewater infrastructure\u003csup\u003e17\u003c/sup\u003e, or has highlighted only the impact of climate change and adaptation strategies on urban water systems\u003csup\u003e18\u003c/sup\u003e. This continues to provide only partial answers to the various forms of climate change adaptation (CCA) of urban water systems. Previous CCA research has explicitly highlighted the need for more and better synthesis methods that cover overlooked sectors\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;Therefore, a consolidated knowledge synthesis of CCA is necessary, especially with a focus on urban water systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKnowledge synthesis is a process where knowledge is systematically collected, analyzed, and integrated to draw comprehensive conclusions on specific topics and questions\u003csup\u003e19\u003c/sup\u003e. They are underrepresented in climate change research field\u003csup\u003e20,21\u003c/sup\u003e. Systematic evidence-mapping is an endorsed methodology for knowledge synthesis to extract novel and relevant insights from an extensive corpus of literature\u003csup\u003e22\u003c/sup\u003e. Traditionally, reviews require human coders to synthesize data from peer-reviewed articles, rendering the process laborious and restricted to a limited number\u0026nbsp;of documents\u003csup\u003e23\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLately, natural language processing (NLP) is being used to manage vast volumes of literature in the CCA field to overcome previous constraints\u003csup\u003e13,24,25\u003c/sup\u003e. However, there exists a large number of scientific articles that are thematically distributed broadly, which hinders the precise data extraction of scientific evidence from long-format texts in peer-reviewed journals. Due to this, as of now, many NLP-based meta-analyses and data syntheses have focused on extracting data only from the abstract\u003csup\u003e4,6\u003c/sup\u003e. This narrows the scope of the research by oversimplifying and disregarding details in the full-text article. With current advancements in large language models (LLMs), NLP enables the automated extraction of information from full-text articles. This creates new potential for information analysis\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study leverages advanced NLP automation and aims to fill the persistent knowledge gap on the relationship between urban development constraints, CCA measures, and CCA challenges in the context of urban water systems. We identified 892 relevant studies that formed the basis for a global mapping of adaptation measures and challenges, providing an overview of the current state of research. We used an improved evidence synthesis method as a robust, science-informed global stocktake of CCA challenges. This work may inform the 7th IPCC assessment report, particularly in identifying strategies to address urban water-related risks and contribute to supporting progress towards the Global Goal on Adaptation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatterns in climate change adaptation research for urban water systems\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 1(a),\u0026nbsp;there has been strong exponential growth in the number of publications on CCA research on urban water systems since 2011. Note that data extraction was concluded in July 2024, which accounts for the shorter length of the 2024 bar. Given that this point in time is approximately mid-year, it can be inferred that the bar length would be approximately double if the data for the entire year were considered. Certain years stand out in particular; 2011 was the most expensive year ($380 billion globally) for losses due to natural hazards. Many countries in the developed world (Thailand, New Zealand, Japan, Australia, and the USA) have been hard hit by various types of water-related natural hazards\u003csup\u003e28\u003c/sup\u003e. In 2012, the IPCC published its Special Report on Managing Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX), which analyzed the link between climate change and extreme events and set a new standard for climate risk research and adaptation strategies. The report became a foundational guideline for global adaptation and disaster risk planning\u003csup\u003e2\u003c/sup\u003e. Several international research programs and collaborative studies, such as the \u0026quot;governance of adaptation to climate change,\u0026quot; outlined in scientific papers, have been initiated or extended. These efforts emphasize real-world trials, stakeholder participation, and hotspot collaboration\u003csup\u003e29\u003c/sup\u003e. The next spike in publications occurred after 2015, which can be attributed to the Paris Agreement, especially as the agreement intensified global scientific efforts to better understand climate impacts, adaptation, and mitigation strategies\u003csup\u003e30\u003c/sup\u003e. There was a decline in publication outcomes in 2021, which we link to the global COVID-19 crisis.\u003c/p\u003e\n\u003cp\u003eFig. 1(b) shows the number of identified case study cities mentioned in peer-reviewed publications categorized by their population size. The distribution broadly reflects the global proportion of cities within each population bracket, with slightly greater representation of megacities exceeding 10 million inhabitants based on World Urbanization Prospect 2025\u003csup\u003e31,32\u003c/sup\u003e. The slight emphasis on megacities is valuable as it highlights the influence of population dynamics in large and densely populated urban environments. Despite their slightly higher relative representation in our dataset, the absolute number of studies specifically addressing water management challenges in megacities under conditions of rapid population growth remain\u003csup\u003e\u0026nbsp;\u003c/sup\u003elimited\u003csup\u003e33\u0026ndash;35\u003c/sup\u003e. This thematic gap constrains effective planning and implementation in cities exceeding certain population thresholds, even though research on urban expansion and flood risk consistently shows that large-scale urbanization amplifies such challenges\u003csup\u003e33\u0026ndash;35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe highest number of publications originated from Asia, followed by Europe, North America, Africa, Oceania, and South America (Fig. 1c). Although the Emergency Events Database (EM-DAT) similarly identifies Asia as the most disaster-affected region, Tin et al.\u003ca href=\"https://www.zotero.org/google-docs/?broken=aJ5LWz\"\u003e\u003csup\u003e68\u003c/sup\u003e\u003c/a\u003e reported a different ordering of incident counts for 1995\u0026ndash;2022, namely, the Americas, followed by Africa, Europe, and Oceania. This divergence between the disaster incidence ranking and publication output suggests a geographic imbalance in the literature, with Global North cities disproportionately represented. The number of research studies is strongly dominated by floods, and this pattern is noticeable across all continents except Oceania. Floods are among the most extensively studied natural hazards because of their high frequency and significant human and economic\u003csup\u003e\u0026nbsp;\u003c/sup\u003eimpact\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e, where urban areas are comparatively underrepresented in studies addressing global flood risk. Global reviews also confirm that the literature on flood risk, modelling, and response is more developed than that on drought\u003csup\u003e39\u003c/sup\u003e, which is due to the contrasting nature of their occurrences. Floods have a rapid onset and impact, whereas droughts have a prolonged onset and impact\u003csup\u003e40\u003c/sup\u003e.\u0026nbsp;Droughts are one of the most expensive natural hazards for\u003csup\u003e\u0026nbsp;\u003c/sup\u003esociety\u003csup\u003e41\u003c/sup\u003e; however, there is lower public awareness and visibility compared to dramatic flood events, Olazabal et al.\u003csup\u003e42\u003c/sup\u003e highlights the mismatch between physical drought risks and institutional/ public perception of drought. This has led to a disparity in the volume of drought and flood research globally\u003csup\u003e36\u003c/sup\u003e. Research focusing exclusively on droughts as isolated hazards, particularly within an urban environment\u003csup\u003e43\u003c/sup\u003e, is less common than studies on floods and sequential hazards. Here, we confirm that this disparity also applies to studies on the challenges of CCA in urban water systems.\u003c/p\u003e\n\u003cp\u003eAn analysis of the geographical and regional patterns of the reviewed publications shows a heterogeneous spatial distribution of urban water research (Fig. 1d). Most case studies are published for China and the USA, followed by Australia, India, Brazil, and South Africa. There is a lack of peer-reviewed publications from Eastern Europe, Central Asia, parts of North and Northwest Africa, and countries such as Paraguay, Venezuela, Bolivia, Guatemala, Suriname, Trinidad and Tobago, and Barbados. Within Europe, there are strong disparities in the distribution of the identified publications. The Netherlands and the United Kingdom exhibit the highest volume of scientific research on climate change adaptation and urban water management, with Germany and Italy closely following. France is comparatively underrepresented in Western Europe. Several factors may have contributed to this pattern. Research capacity in climate adaptation may be concentrated in Paris, with limited coverage of other urban contexts. In addition, the institutional positioning of urban planning and development within administrative structures has been reported to be comparatively weak\u003csup\u003e44\u003c/sup\u003e. Methodological factors may also play a role, as the search string may not capture locally specific terminology and relevant studies may be published in French-language outlets.\u003c/p\u003e\n\u003cp\u003eThere are studies indicating structural challenges in integrating adaptation into urban water policy, as recognized by the OECD and World Bank in Central Asia and Eastern European countries, while lacking robust local academic literature or systematic case studies in international\u003csup\u003e\u0026nbsp;\u003c/sup\u003ereviews\u003csup\u003e45\u003c/sup\u003e. Our results indicate that Africa has a disproportionately low publication rate, except for South Africa.\u0026nbsp;The study by Doswald et al.\u003csup\u003e46\u003c/sup\u003e maps an evidence gap that highlights that the geographic coverage of urban water management systems is clustered\u003csup\u003e\u0026nbsp;\u003c/sup\u003ewithin a selected group of countries. Many North and Central African nations are either underrepresented or are almost absent in the published research on climate change adaptation with respect to urban water.\u003c/p\u003e\n\u003cp\u003eOverall, evidence-mapping the case studies of the 892 selected articles reveals that most publications are from China, the United States, and India. While the number of cities in a country may drive the research needs and opportunities, our analysis shows that some countries are studied more or less than their urban population suggests (Fig. SX1). In Australia\u003csup\u003e47\u003c/sup\u003e, the United Kingdom\u003csup\u003e48\u003c/sup\u003e, and the Netherlands\u003csup\u003e48\u003c/sup\u003e, the number of publications is high despite the relatively small number of large cities, indicating a disproportionately strong research focus on the urban scale. Conversely, Japan, Vietnam, Nigeria, France, Russia, and the Philippines, despite having numerous cities, are underrepresented in publications, indicating a disparity between the urban population and research output. Countries such as Brazil, Indonesia, Mexico, and South Africa occupy an intermediate position, with a moderate number of large cities and publications. Also, Italy, Spain, Germany, and Poland, display a balanced representation in the European domain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrban development constraints linked to measures and challenges in climate change adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the strong growth in CCA research on urban water systems after 2011 (Fig. 1a), we narrowed our analysis from 2011 to 2024 to examine the links between urban development constraints and CCA measures and challenges.\u0026nbsp;In Fig. 2(a), our analysis shows that environmental, institutional, and infrastructural urban development constraints dominate research across all continents, whereas social and economic issues receive comparatively less attention. In Asia, intensifying environmental, infrastructural, and institutional pressures are driven by rapid urbanization, population growth, and climate change. Europe shows a similar pattern, although social challenges are relatively more prominent in Asia. In contrast, in Africa and South America, a proportionally greater emphasis is placed on social urban development constraints, whereas in North America and Oceania, the focus is primarily on institutional, infrastructural, and environmental aspects.\u003c/p\u003e\n\u003cp\u003eStructural/physical types of CCA measures are consistently one of the most published categories across all continents, closely followed by technological measures, indicating growing research interest, investment in innovation, and prioritization of infrastructural solutions for CCA (Fig. 2b). These are followed by ecosystem-based CCA measures, with a slightly higher publication rate since 2020. Institutional/policy CCA measures are recognized as critical, yet the growth in scientific research is less steep compared to technological and structural CCA measures. Behavioral/social CCA measures are often published on par with institutional/policy CCA measures, highlighting the acknowledged need for social research in climate science and societal\u003csup\u003e\u0026nbsp;\u003c/sup\u003eshift\u003csup\u003e49\u003c/sup\u003e in CCA practice. Anticipatory CCA measures remain the lowest in publication numbers, suggesting that explicitly proactive adaptation strategies remain underexplored relative to other CCA classifications. Studies show that this is due to the high cost of early adaptation and budgetary constraints, to which countries adapt reactively\u003csup\u003e50\u003c/sup\u003e. Reactive measures with the lowest publication rates indicate a scholarly shift away from reactive solutions. However, the ground reality tells a different story: The IPCC report indicates that local governments that have begun to implement adaptation strategies often take a reactive or event-driven approach, primarily relying on technical solutions\u003csup\u003e51\u003c/sup\u003e. This underscores the disconnection between local governmental entities and the broader scientific community. Publications on cities in Africa and South America exhibited a sharp decline in 2021 (Fig. SX2), attributable to the global COVID-19 pandemic, whereas research output in Europe, Asia, North America, and Oceania remained largely unaffected. This disparity underscores persistent differences in global scientific research infrastructures.\u003c/p\u003e\n\u003cp\u003eAn analysis of the classification of CCA challenges across the reviewed scientific literature (Fig. 2c) highlights that\u0026nbsp;institutional and structural/physical CCA challenges are consistently the highest category of challenges mentioned in the scientific literature. We find this pattern to be stable across all continents (Fig. SX3) as well as from 2011 to 2024. This is followed by social CCA challenges and then technical CCA challenges, which are the third and fourth most frequently mentioned challenges across all continents. Since 2016, there has been a slight rise in ecosystem-related CCA challenges, which can be linked to the increased implementation of ecosystem-based adaptation strategies, such as nature-based solutions, after the Paris Agreement. Although economic CCA challenges are comparatively less frequently mentioned, they are reported sufficiently to warrant the creation of a distinct classification. The classification with the fewest publications is anticipatory CCA challenges, which are directly linked to social or institutional issues related to the acceptance of a specific CCA measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvidence synthesis of the association strength between climate change adaptation measures and challenges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 3 shows the association strength of CCA measures and CCA challenges, indicating thematic linkages between their categories. However, association strength does not imply causality. In the case of measures and challenges from the same categorical domain, strong association strengths align with their thematic linkage, providing empirical confirmation of the robustness of the methodology. We find the strongest positive associations between behavioral/social CCA measures and social CCA challenges, followed by\u0026nbsp;ecosystem-based CCA measures and ecosystem-based CCA challenges. Ecosystem-based CCA challenges are likely due to ecosystem complexities, which are the inherent, intricate, and unpredictable nature of ecosystem-based CCA measures, sometimes further worsened by climate change\u003csup\u003e52\u003c/sup\u003e. Finally, the technological CCA measures and technical CCA challenges are strongly associated. It is imperative to prioritize the resolution of technical issues, including the operation and maintenance of technology\u003ca href=\"https://www.zotero.org/google-docs/?broken=kRv1fw\"\u003e\u003csup\u003e73\u003c/sup\u003e\u003c/a\u003e to effectively implement future-proofing measures. By resolving these human, technological, and systemic barriers, the implementation of solutions becomes considerably feasible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, institutional CCA challenges exhibit low to moderate association strengths (close to 1.0) with all other adaptation measure types, and their high marginal frequency of over 8,900 mentions indicate that they are the most frequently reported. This suggests that institutional CCA challenges, such as leadership dependence, silo organization, and spatial policy mismatches, are pervasive across all forms of adaptation measures, illustrating the importance of institutions for adaptation\u003csup\u003e53\u003c/sup\u003e. Further, we find a notable positive association between institutional/policy CCA measures and social CCA challenges. This is in line with recent assessments of social disparities\u003csup\u003e54,55\u003c/sup\u003e, highlighting that equitable adaptation requires transformative changes in planning, governance, and resource management, guided by social justice principles to address the fundamental drivers of urban water-related risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we analyzed the association strength between the categories of urban development constraints and the categories of CCA measures\u0026nbsp;(Fig. SX4), as well as categories of urban development constraints with the categories of CCA challenges (Fig. SX5). For all possible combinations, we find no significant dependence between the presence of specific urban development constraints and the existence of particular CCA measures or challenges. However, this finding is solely based on the mention of urban development constraints, but not their magnitude. While the magnitude of social, environmental, institutional, infrastructural, and economic urban development constraints varies substantially across the globe\u003csup\u003e3,56,57\u003c/sup\u003e, our assessment cannot provide insight into whether the severity of constraints affects CCA measures or CCA challenges.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAddressing the relationship between CCA measures and challenges is a critical component of climate change research. It identifies the most prevalent obstacles encountered by scientists, policymakers and practitioners in the implementation process. Recognizing these challenges in advance is crucial for developing revised plans that avoid similar or recurring impediments. We identified four key findings by exploring their significance in research and policy-making concerning CCA measures and challenges in urban water systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, our findings reveal a notable global bias in research and publication. Even when most countries are well-represented in the scientific literature, the proportion relative to the number of cities with populations over 100,000 is imbalanced. The results also show a heterogeneous spatial distribution of case studies investigated in peer-reviewed articles. Our results indicate that Africa has a disproportionately low publication rate, except for South Africa. Neglecting to study climate adaptation and urban development constraints in Africa increases the vulnerabilities of cities that are rapidly urbanizing and facing multiple climate-related threats. Mwenje et al.\u003csup\u003e58\u003c/sup\u003e and Servi\u0026egrave;res et al.\u003csup\u003e59\u003c/sup\u003e highlight that this lack of attention worsens existing issues such as insufficient infrastructure and governance deficiencies, resulting in significant socioeconomic and environmental impacts.\u003c/p\u003e\n\u003cp\u003eSecond, our results highlight the disparity between flood and drought research. The data indicate that research studies are predominantly focused on floods, a trend observable across all continents, with the exception of Oceania. Australia successfully managed its millennium drought (1996-2009) with record-low rainfall; however, the drought response was not adaquate\u003csup\u003e60\u003c/sup\u003e as decision-making was crisis driven and fragmented rather than long-term adaptive governance. Urban drought can lead to water shortages, with reduced urban water system reliability. Previous research on urban water systems shows drought responses are tied to maintaining supply reliability, but drought still strains demand and can expose weak points in urban water management\u003csup\u003e62\u003c/sup\u003e. Further research on urban drought is crucial in identifying vulnerable systems and communities, so cities can plan water-saving, adaptation, and emergency response measures before shortages become crises. Cape Town\u003csup\u003e\u0026nbsp;\u003c/sup\u003egained attention in the period of 2016-2018 while affected by severe drought, highlighting the importance of infrastructure diversification and better groundwater management\u003csup\u003e63\u003c/sup\u003e. Despite projections of more frequent and severe occurrences, particularly in cities, research gaps concerning floods, droughts, and sequential events persist owing to ongoing issues with awareness, funding, and visibility of these crises\u003csup\u003e42\u003c/sup\u003e. The current adaptation approach is fragmented, often focusing on either flood or drought\u003csup\u003e64\u003c/sup\u003e. This approach of targeting only one extreme can fail under rapid transition\u003csup\u003e65\u003c/sup\u003e. Sustainable urban water futures require a radical shift in society\u0026rsquo;s urban climate adaptation imaginaries toward transformative adaptation that addresses the root causes of water-related risks and vulnerabilities. This approach must also be holistic, considering synergies between flood and drought measures to effectively manage the complex urban water risk\u0026ndash;adaptation nexus\u003csup\u003e66\u003c/sup\u003e. Therefore, we call for further research that examines floods and droughts as functionally interconnected phenomena, with a focus on integrated adaptation strategies.\u003c/p\u003e\n\u003cp\u003eThird, although ecosystem-based CCA measures are increasingly researched and impacted\u003csup\u003e67,68\u003c/sup\u003e, we find a concentration in adaptation literature primarily focusing on infrastructural and technological adaptation measures, alongside integrated approaches where an ecosystem-based strategy is incorporated. We find a comparatively low research focus on socio-behavioral adaptation measures. Socio-behavioral adaptation measures encourage individuals and communities to modify or adopt behaviors in response to climate change. These measures recognize human behavior is complex, often needing information, incentives, and structural support to shift practices and enhance adaptive capacity. Aoki et al.\u003csup\u003e69\u003c/sup\u003e highlight that technical or infrastructure-based climate solutions often underperform if they ignore human behavioral responses or community norms. This underscores the necessity for further scientific investigation in policy-oriented and socio-behavioral adaptation research fields. The results also indicate a slight trend towards anticipatory CCA measures mentioned in the scientific literature, as well as a scholarly shift away from reactive measures based on the data collected in our study. However, the reality is that most governments take an event-driven and tangible solution\u003csup\u003e51\u003c/sup\u003e. This highlights the opportunity for more transdisciplinary collaboration to bridge the science-policy-practice gap.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, the clustering of the strongest associations is identified around social CCA challenges,\u0026nbsp;with\u0026nbsp;the link to CCA measures of socio-behavioral adaptations being the most pronounced. Future research should focus on areas of socio-behavioral CCA, for example, to promote water conservation education in disadvantaged neighborhoods, thereby contributing to the empowerment of resident\u003csup\u003e70\u003c/sup\u003e. Furthermore, the strong link to institutional CCA measures indicates that policy goals and local social conditions cannot be considered independently from one another; it is important to address limited community engagement and participation and the social vulnerabilities\u003csup\u003e71\u003c/sup\u003e of communities at risk. Ignoring these CCA challenges renders any type of CCA measure inefficient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEcosystem-based CCA measures are strongly associated with structural/physical measures due to their inherent combination (e.g., bio-swales, infiltration trenches, green roofs). They show a moderate-low association with social, technical, and institutional challenges. Ried et al.\u003csup\u003e52\u003c/sup\u003e highlight their role in enhancing social resilience by maintaining services like water retention and erosion prevention, often outperforming traditional methods. These measures are multi-purpose and more efficient than conventional solutions, using natural processes. Institutional challenges are minor or addressable through better coordination. Policy integration is feasible, with successes in frameworks like EU strategies\u003csup\u003e72\u003c/sup\u003e emphasizing ecosystem approaches. Although institutional CCA challenges exhibit significant moderate-low associations, they are reported at a high frequency across all categories of CCA measures, highlighting that fragmented governance\u003csup\u003e73\u003c/sup\u003e and institutional inertia\u003csup\u003e74\u003c/sup\u003e as a critical bottleneck globally affect CCA progress. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the identified associations suggest potential links, we recommend further studies with complementary approaches, including system dynamics modeling\u003csup\u003e75\u0026ndash;77\u003c/sup\u003e, network analysis\u003csup\u003e78\u003c/sup\u003e and agent-based modeling\u003csup\u003e79\u003c/sup\u003e, to further support the causation and complexity of these interactions and emergent behavior across scales. Moser et al.\u003csup\u003e80\u003c/sup\u003e suggests adopting a structured diagnostic tool for identifying barriers in planned adaptation processes, which identifies three phases: understanding (problem detection, information gathering, and redefinition), planning (option development, assessment, and selection), and management (implementation, monitoring, and evaluation.\u003c/p\u003e\n\u003cp\u003eAlthough evidence mapping supported by LLMs offers a valuable approach to synthesizing global scientific progress, it is subject to certain limitations. Our abstract screening filtered only English-language articles. We infer that the lack of scientific studies in parts of Europe, Central Asia, and South America is due to them being published in a language other than English. Further, our data collection excludes grey literature, books, and reports that may cover localized CCA challenges, especially from the Global South, which were not part of our analysis.\u0026nbsp;Furthermore, we constrained our article selection to require the abstract to mention barriers, challenges, or barriers. There might be articles on CCA where challenges are mentioned in the full text but not in the abstract. Our methodology is capable of assessing the mention of urban development constraints, CCA measures, and CCA challenges, but it does not capture the magnitude of these categories. In a recent study\u003csup\u003e81\u003c/sup\u003e, the importance of measuring urban climate adaptation through indicators and metrics is emphasized as crucial for successful CCA management. Nevertheless, they emphasize that there is a lack of comprehensive understanding regarding the measurement of CCA, particularly concerning the methods of measurement. Notably, in approximately 48% of cases, the indicators and metrics did not specify a unit of measurement across the adaptation cycle, scale, and sectors\u003csup\u003e81\u003c/sup\u003e. The evidence mapping in this study builds on peer-reviewed scientific articles reporting adaptation case studies, while articles are rarely published on missed opportunities or non-implemented adaptation measures (\u003cem\u003epublication bias\u003c/em\u003e). Hence, our assessment strongly underrepresents barriers such as undetected problems, unavailable information, and insufficient planning. However, this bias emphasizes the importance of exploring and understanding the scientifically documented challenges and barriers in this study.\u003c/p\u003e\n\u003cp\u003eIn the context of large-scale review text mining, LLMs can accelerate repetitive tasks. However, they do not yet possess the reliability required to supplant thorough human validation, especially in scenarios where accuracy, transparency, and reproducibility are of paramount importance\u003csup\u003e82\u003c/sup\u003e.\u0026nbsp;Petzold et al.\u003csup\u003e83\u0026nbsp;\u003c/sup\u003ehighlighted in their research that 65% of the researchers from the Global Adaptation Mapping Initiative consider NLP at least somewhat useful; to ensure reliability it is important to combine NLP with other analytical methods. We employed NLP with human involvement to aid in synthesizing the evidence for CCA in urban water systems. This facilitated a comprehensive review of a substantial number of papers, which would have otherwise necessitated significantly more time and human resources. This novel method illustrates how AI can be combined with thorough and transparent review processes for evidence synthesis in adaptation research, thereby improving policy-relevant evaluations to address disparities between the Global North and Global South.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conceptualized a novel hybrid workflow for qualitative text analysis, integrating human- and LLM-guided elements within five iterative phases. The general direction consists of human scoping, LLM-guided narrowing, and human refinement of the results. We used a well-established method for a systematic quantitative literature review developed by Pickering and Byrn\u003csup\u003e84\u003c/sup\u003e, along with a similar approach by Berrang-Ford\u003csup\u003e85\u003c/sup\u003e. We then adapted the existing method to assess journal articles on CCA measures and challenges within urban water systems worldwide. Systematic quantitative reviews adhere to the Preferred Reporting Items for Systematic Reviews (PRISMA) guideline\u003csup\u003e86\u003c/sup\u003e, where other studies\u003csup\u003e87\u0026ndash;89\u003c/sup\u003e have used systematic reviews to advance CCA research in the themes of adaptation planning in large cities, adaptation policy research, and the effectiveness of adaptation. Our study uses the existing PRISMA guidelines but adapts them to include LLM-guided coding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 4 presents an overview of the review methodology merging PRISMA guidelines with LLM support, where the five phases are described in detail in the following sections.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe generated a search string using an interactive query formulation search approach\u003csup\u003e90\u003c/sup\u003e to test and evaluate the keywords and data that were gathered. Thereafter, we ran the final search terms through the databases (Web of Science and Scopus). Subsequently, the resulting datasets, comprising peer-reviewed journal articles, conference papers, books, and book chapters published between 1990 and 2024, were extracted on July 26, 2024. The total number of imported articles was 39332.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe then uploaded the references into Rayyan.ai\u003csup\u003e91\u003c/sup\u003e, an online tool that helps organize and review research papers for systematic reviews and related evidence syntheses. It allows researchers to obtain search results from different databases, find and remove duplicates, and review titles and summaries using customizable inclusion and exclusion criteria in a single interface. We used it to remove duplicates from the combined articles of Web of Science and Scopus, where the remaining number of articles amounted to 22602. To select the barriers and challenges criteria for a more thorough assessment, we screened the articles based on their titles, keywords, and abstracts based on our fundamental thematic criteria, such as adaptation, urban setting, and inclusion of a case study. We excluded any references to adaptation that concentrated on particular species or ecosystem\u003csup\u003e9\u003c/sup\u003e and instead adopted the IPCC\u0026apos;s definition of climate-resilient water-related adaptation\u003csup\u003e92\u003c/sup\u003e. At this stage, we applied our initial exclusion criteria, which eliminated any article that was \u0026ldquo;not urban,\u0026rdquo; \u0026ldquo;not a city (towns and villages),\u0026rdquo; \u0026ldquo;related to agriculture,\u0026rdquo; \u0026ldquo;not directly water-related,\u0026rdquo; \u0026ldquo;related to organisms and biology,\u0026rdquo; or categorized as a \u0026ldquo;review.\u0026rdquo; No inclusion criteria were applied at this stage. These steps reduced the number of articles to 1843.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman-coding and review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe iteratively designed a coding matrix based on a research outline guiding this knowledge synthesis. Therefore, two authors independently coded a random sub-selection of 150 test papers and compared their results to ensure clarity of the questions within the coding matrix. The resulting matrix consisted of 12 questions to be answered by reviewers coding the articles (see Supplement). While nine questions are binary or optional, three questions require elaborate answers linked to the categories classified for them. We tokenized the categories for each classification to guide the reviewers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on the existing urban development literature\u003csup\u003e93,94\u003c/sup\u003e, we classified urban development constraints into five broad\u0026nbsp;categories (Table 1): environmental, institutional, social, infrastructural, and economic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Categories of urban development constraints with examples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003eSelection of examples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eOverlapping category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironmental\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003eencroachment of rivers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eInstitutional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003efloodplains by physical developments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eInfrastructural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003epath dependence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003eunplanned land-use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003efear of the unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003elimited community engagement, empowerment, and participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfrastructural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003euse of non-permeable paving in urban areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eEnvironmental\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003eoutdated gray infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eInstitutional and Environmental\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003einsufficient resources (capital and human)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.2272%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41.1277%;\"\u003e\n \u003cp\u003eurban flooding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.6451%;\"\u003e\n \u003cp\u003eSocial and Environmental\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFurthermore, we classified CCA measures into seven distinct\u0026nbsp;categories (Table 2): structural/physical, ecosystem-based, institutional/policy, behavioral/social, technological, anticipatory, and reactive, based on the existing literature\u003csup\u003e95\u0026ndash;99\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Categories of CCA measures with examples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003eSelection of examples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eOverlapping category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructural/ Physical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003epermeable pavements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eTechnological\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003erainwater harvesting systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eAnticipatory and Technological\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcosystem-based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003ebio-swales; artificial ponds;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eStructural/ Physical and Technological\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003egreen-roofs; rain gardens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eStructural/ Physical and Technological\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional/ Policy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003eadopting sponge city regulations and permits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003epublic, private partnership and financing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eBehavioral/ Social\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehavioral/ Social\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003eimplementing reduced irrigation regime in water scarce region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003ewater conservation education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnological\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003eunderground retention tanks\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eStructural/ Physical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003eleak detection and repair program\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnticipatory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003erelocation of residents from flood-prone areas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eStructural/ Physical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003eearly warning systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReactive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003ewater trucking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1924%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.6551%;\"\u003e\n \u003cp\u003epluvial flood insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional and Behavioral/ Social\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, we identified the common interacting challenges associated with these adaptation measures, classifying them into seven\u0026nbsp;categories (Table 3): structural/physical, ecosystem-based, institutional, social, economic, technical, and anticipatory, as informed by current scientific discours\u003csup\u003e53\u003c/sup\u003e. We studied the different types of challenges in adapting to climate change and how they are governed. Then, we decided to add a new\u0026nbsp;category of barrier to the list as \u0026lsquo;economic\u0026rsquo;, which is not independently listed as a measure but has the capacity to derail a measure by coming up as a challenge\u003csup\u003e100\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Categories of CCA challenges with examples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003eSelection of examples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eOverlapping category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructural/ Physical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003elarge land area requirement\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003eold water piping system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcosystem-based\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003eselection of the proper type of vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003ehigher and steeper flood walls have degraded habitats and obstructed public riverside amenities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitutional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003esiloed implementation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003eshort-term policies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003elitter and inadequate community cooperation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003econtested community leadership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003ehigh operational cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003ehigh retrofitting cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003elack of sufficient technical references, design standards and guidelines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003eexperiments are necessary to test hydraulic parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnticipatory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003epolitical contestation over land and housing subsidies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eInstitutional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.524%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3234%;\"\u003e\n \u003cp\u003euncertainty in future extreme rainfall intensities at sub-daily scales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.1526%;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe then developed a human review protocol along with additional information relevant to ensuring consistency within the human reviewers (see Supplement). This protocol outlines the research approach, details the literature search strategy, explains how the papers should be reviewed, and indicates how the final findings will be presented\u003csup\u003e101\u003c/sup\u003e. Although no review methodology can fully capture all relevant literature, this process is a well-established method for obtaining a comprehensive understanding of the literature in a transparent, consistent, and reproducible manner\u003csup\u003e84\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe authors of this study formed an interdisciplinary team of reviewers and conducted a human full-text analysis of 369 articles out of 1843 (equaling 20 %) to support prompt engineering for the LLM (Fig. 4). Based on their answers within the coding matrix, we iteratively developed prompts (see Code Supplement) for the LLM to guide the review process\u003csup\u003e102\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLLM-screening and review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this analysis, we used the LLM-automated reflective generative pre-trained transformer (GPT) \u003cem\u003eOpenAI o3\u003csup\u003e103\u003c/sup\u003e\u003c/em\u003e. The \u003cem\u003eo3\u003c/em\u003e model achieves an accuracy on par with human performance in full-text reviews with well-designed prompts. For review tasks with clear instructions and robust prompts, the performance of LLMs can rival human performance\u003csup\u003e104\u003c/sup\u003e. Integrating LLMs into the full-text screening process can reduce the time spent manually assessing documents while maintaining or even improving accuracy\u003csup\u003e105\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then used the LLM to code all of the imported 1843 articles.\u0026nbsp;By leveraging the LLM\u0026rsquo;s coding capabilities for a large number of articles with accuracy, we implemented the inclusion criteria (Fig. 4). We included articles that were peer-reviewed, focused on case study(s), as the evidence map is designed to build on concrete, locally observed challenges at the urban scale rather than on studies addressing broader regional, national, or continental levels. In the next step, we added further exclusion criteria, excluding all books, conference papers, and opinion articles, followed by articles with no explicit adaptation focus, with large case study areas instead of city cases, and studies without urban development constraints or natural hazards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to evaluate the results of the LLM, we conducted human-led statistical validation. Therefore, we randomly selected ten articles from the review matrix (see Supplement). Seven human reviewers independently reviewed the same ten predetermined articles. Answers to the eight questions that were one-word or yes/no were evaluated by virtue of the majority and assigned binary scores (0 or 1). In the case of the four descriptive questions, we compared the answers of the LLM against the union of all human answers (not the consensus) to calculate the F1 score\u003csup\u003e106\u003c/sup\u003e, precision, recall, and hallucination for each question within each paper. We calculated an F1 score for each paper by averaging the scores for the 12 questions and then calculated the average of the ten papers. The LLM achieved an F1 score of 82%, indicating a strong performance (see Supplement) with a hallucination rate of 18% (Table 4). We note that the reported hallucination rate does not necessarily only consist of LLM hallucinations but may also contain cases where none of the seven human reviewers found the respective answer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Validation metrics of the LLM performance based on seven human reviewers and ten articles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"475\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4737%;\"\u003e\n \u003cp\u003eF1 Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8947%;\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1053%;\"\u003e\n \u003cp\u003eRecall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5263%;\"\u003e\n \u003cp\u003eHallucination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4737%;\"\u003e\n \u003cp\u003e~82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.8947%;\"\u003e\n \u003cp\u003e~83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1053%;\"\u003e\n \u003cp\u003e~61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5263%;\"\u003e\n \u003cp\u003e~18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUltimately, we included 892 articles representing a diverse body of literature addressing themes such as climate adaptation, flood and drought management, nature-based solutions, urban water governance, and heat management through water. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a descriptive analysis to explore the publication time, location of the case study(s), population of the studied city(s), and type of water-related hazards based on LLM output.\u003c/p\u003e\n\u003cp\u003eFor thematic analysis, we tokenized various urban development constraints, CCA measures, and CCA challenges into categories (see Table 1-3). The tokenization process helps classify themes and extract topics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to relate the results from the thematic analysis, we conducted a relational analysis between the categories of urban development constraints with CCA measures and challenges, as well as CCA measures with CCA challenges. We calculated the association strength for each combination, following Steijn\u003csup\u003e107\u003c/sup\u003e. Here, the association strength between the categories is a probabilistic normalization of their co-occurrence counts, expressed as the ratio of their joint probability to the product of their marginal probabilities. This measure quantifies the non-random relatedness between terms relative to their individual frequencies, enabling a robust assessment of their joint occurrences\u003csup\u003e107\u003c/sup\u003e. A strong positive association strength (\u0026gt;1.0) between two categories indicates that they co-occur significantly more frequently than expected under random chance, given their individual occurrence rates. This suggests a meaningful thematic linkage or conceptual overlap between the categories in the analyzed articles, beyond mere frequency biases. Conversely, a value below 1.0 reflects co-occurrence less frequent than random, while exactly 1.0 denotes independence. Further, we test the independence of the co-occurrences of categories based on a \u0026chi;\u0026sup2;-test at a 95% significance level.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data underlying the figures in this manuscript are openly available at https://doi.org/10.5281/zenodo.19866111\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code underlying the results is available at https://doi.org/10.5281/zenodo.19866118\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge Jan Christoph Bernack for his support with Software, which contributed to data processing and analysis. The authors also thank Ines Nofz for her contributions to the human review stage and Iva Stefani for her assistance in the identification and screening stage of the raw data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is a contribution to project \u0026lsquo;C1: Sustainable Adaptation Scenarios for Urban Areas \u0026ndash; Water from Four Sides\u0026rsquo; of the Cluster of Excellence \u0026lsquo;CLICCS - Climate, Climatic Change, and Society\u0026rsquo; (EXC 2037, Project Number 390683824) at HafenCity University and the Department of Earth System Sciences, Faculty of Mathematics, Informatics and Natural Sciences, Earth and Society Research Hub (ESRAH), University of Hamburg, Hamburg, Germany. It is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany\u0026rsquo;s Excellence Strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.N.: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Data Curation, Visualization, Writing\u0026mdash;Original Draft. B.P.: Conceptualization, Methodology, Formal Analysis, Validation, Writing\u0026mdash;Review \u0026amp; Editing, Supervision. R.E.: Formal Analysis, Review \u0026amp; Editing. F.S.H., N.K., M.S.: Validation, Writing\u0026mdash;Review \u0026amp; Editing. Z.C., L.M., F. S., and L.M.: Validation, Review, \u0026amp; Editing. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChapter 4: Water. https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-4/. \u003c/li\u003e\n\u003cli\u003eIPCC launches \u0026lsquo;Managing Risks of Extreme Events and Disasters to Advance Climate Change Adaptation\u0026rsquo; Report | Pacific Environment. https://www.sprep.org/news/ipcc-launches-managing-risks-extreme-events-and-disasters-advance-climate-change-adaptation. \u003c/li\u003e\n\u003cli\u003eHe, C. \u003cem\u003eet al.\u003c/em\u003e Future global urban water scarcity and potential solutions. \u003cem\u003eNat. 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Improvement on the association strength: Implementing a probabilistic measure based on combinations without repetition. \u003cem\u003eQuant. Sci. Stud. \u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, 778\u0026ndash;794 (2021). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9569973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9569973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Despite many studies on climate change adaptation, implementation challenges are understudied. After screening over 39,000 articles, we used large language models on 892 articles to map global adaptation challenges in urban water systems. Publications have grown exponentially since 2011, but research is biased toward the Global North. Floods dominate literature, leaving droughts underrepresented despite their socio-economic impact. Our analysis of measure-challenge associations reveals: i) institutional and social challenges are pervasive in all adaptation types, ii) institutional challenges have moderate associations across measures where high frequencies often hinder CCA measures, iii) structural and technological CCA measures receive more research attention than behavioral, social, and anticipatory approaches. Our synthesis identifies research gaps and policy priorities: focus on socio-behavioral and anticipatory adaptation, research in understudied regions, and urgent institutional reform as an adaptation prerequisite.","manuscriptTitle":"Systematic evidence-mapping of climate change adaptation challenges in urban water systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 07:02:57","doi":"10.21203/rs.3.rs-9569973/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59c3915a-1eab-42fe-9579-2945d6218de9","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"checksComplete","content":"","date":"2026-05-05T16:27:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Urban Sustainability","date":"2026-04-29T21:22:12+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67578566,"name":"Social science/Environmental studies"},{"id":67578567,"name":"Scientific community and society/Geography"},{"id":67578568,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-05-07T07:02:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 07:02:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9569973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9569973","identity":"rs-9569973","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}