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Review

Thematic Fragmentation and Convergence in Urban Flood Simulation Research: A 45-Year Bibliometric Mapping

by
Ahmad Gamal
1,2,*,
Mohammad Raditia Pradana
2,3,
Bambang Hari Wibisono
4,
Prananda Navitas
5 and
Jagannath Aryal
6
1
Department of Architecture, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
2
Scientific Modeling, Application, Research, and Training for City-centered Innovation and Technology (SMART CITY) Universitas Indonesia, Depok 16424, Indonesia
3
Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
4
Department of Architecture and Planning, Universitas Gadjah Mada, Yogyakarta 55284, Indonesia
5
Department of Urban and Regional Planning, Faculty of Civil, Planning, and Geo-engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
6
Department of Infrastructure Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Urban Sci. 2025, 9(7), 280; https://doi.org/10.3390/urbansci9070280
Submission received: 20 May 2025 / Revised: 1 July 2025 / Accepted: 12 July 2025 / Published: 17 July 2025
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Versions Notes

Abstract

Urban flooding presents a growing challenge amid rapid urbanization, climate variability, and fragmented governance. Although simulation and risk assessment tools have advanced considerably, their integration into urban planning remains limited. This study utilized a comprehensive bibliometric analysis of 1293 articles from the Scopus database, selected through a PRISMA-guided workflow, to examine the temporal, structural, and conceptual evolution of simulation, flood risk, and planning in urban flood research from 1980 to 2025. The findings reveal a thematic progression from engineering-centric approaches to broader discourses on resilience, adaptation, and systemic risk. However, disciplinary fragmentation persists, with technical modeling, infrastructure planning, and governance still weakly connected. Despite a shared vocabulary around climate risk and resilience, practical integration into decision-making frameworks remains underdeveloped. The study highlights the need for more cohesive research-practice linkages and calls for frameworks that better align simulation outputs with urban planning imperatives.

1. Introduction

Urban flooding is a major challenge of the 21st century, driven by rapid urbanization, inadequate planning, and climate-induced hydrological extremes [1,2,3]. Expanding impervious surfaces and underperforming drainage systems increase flood exposure, especially in informal or poorly serviced areas [4,5,6]. Between 1985 and 2015, settlements in high-risk flood zones grew almost 120%—outpacing safer areas and exposing a sharp development–risk mismatch [7]. This pattern is especially pronounced in parts of Asia and sub-Saharan Africa, where urban growth often outpaces institutional capacity for risk-informed planning [2,3,8,9,10]. The World Bank projects that annual flood losses in major cities could rise from USD 6 billion in 2005 to over USD 52 billion by 2050 in the absence of improved intervention frameworks [2]. Despite growing technical advancements in flood modeling, the alignment between simulation outputs and decision-making in urban governance remains limited [1,11,12]. Urban flooding is no longer solely a hydrological issue. It also involves social [9], infrastructural [13], and governance dimensions [14]—calling for nature-based solutions, spatial planning, and inclusive policy design [3,8,15]. Recent global frameworks recognize the complexity of urban flooding. These frameworks emphasize integrated strategies that link hazard understanding, institutional coordination, and long-term spatial planning to strengthen urban flood resilience.
Simulation and modeling have become essential for representing urban flood dynamics, enabling decision-makers to assess inundation extent, depth, and duration based on rainfall, topography, and land-use data [15,16,17,18,19,20]. Advances such as 2D hydrodynamic models, surface–subsurface integration, and GIS-based mapping enhance hotspot detection and intervention evaluation [21,22]. Despite this, many planning processes remain disconnected from modeling due to institutional fragmentation and complexity barriers. Emerging data sources, like social media, support rapid impact assessments and real-time mapping [23,24,25], yet their integration into formal systems remains limited. While decision-support tools exist [26,27], their operational uptake is still evolving.
Despite advances in modeling and risk quantification, urban flood risk assessments often remain disconnected from real-world implementation strategies [5,12]. Simulation models provide detailed spatial and temporal insights into inundation dynamics. However, their outcomes are often underutilized in planning due to complexity and limited accessibility for non-experts [2,3,13,28]. A key challenge lies in translating technical diagnosis into actionable policies. Despite widespread use, simulation tools and hazard maps have limited impact on urban development due to fragmented institutions and weak actor coordination [14,20,28,29,30,31,32]. Urban planning, especially when embedded within risk-informed frameworks, holds the potential to proactively reduce exposure and vulnerability. Zhou [4] linked rising flood volumes in China to delayed drainage upgrades, revealing institutional inertia. Miguez and Veról [33] proposed a resilience index to address Brazil’s fragmented flood control, highlighting weak planning integration. Other studies have similarly cited fragmented governance, outdated regulations, and poor cross-sector coordination (e.g., [1,2,28]). Even when flood risk is mapped with precision, cities continue to expand into flood-prone areas, increasing potential losses [20,34,35,36]. Although widely used for prediction, simulation outputs need to be better integrated into planning, investment, and regulatory frameworks [13,37,38]. To understand how simulation, planning, and risk reduction intersect conceptually, this paper maps the thematic evolution of urban flood research, evaluates its relevance to resilience and planning objectives, and identifies the positioning of key thematic clusters within the broader scholarly landscape.
This study seeks to clarify how key concepts—simulation, planning, risk, and resilience—are situated and interlinked within the body of urban flood research. To achieve this, it sets out the following objectives:
  • To map the thematic evolution of urban flood research, especially in relation to climate adaptation, risk, and resilience;
  • To identify dominant thematic clusters and assess the extent of disciplinary integration or fragmentation;
  • To analyze the conceptual positioning of simulation, risk, and infrastructure planning, and evaluate their role in connecting research with planning practice.
These objectives guide the following research questions:
  • RQ1: How has the thematic landscape of urban flood research evolved over time, particularly in relation to climate, risk, and resilience?;
  • RQ2: What are the dominant thematic structures in urban flood literature, and how do they reflect disciplinary integration or fragmentation?;
  • RQ3: How are simulation, risk, and infrastructure planning positioned within the field’s conceptual structure, and what does this suggest about theory–practice integration?

2. Materials and Methods

2.1. Data Sources

This study used bibliometric analysis to examine the evolution and conceptual integration of urban flood research, focusing on simulation, risk, and infrastructure planning. Data were retrieved from the Scopus database due to its wide disciplinary coverage across environmental sciences, engineering, social sciences, and urban studies [39,40]. Scopus also ensures standardized metadata across indexed publications, making it suitable for structured analysis using bibliometric tools.
The database was accessed on 13 May 2025, and the search included literature published between 1980 and 2025, encompassing over four decades of scientific development. This timespan allowed the study to trace both early-stage modeling contributions and more recent shifts toward interdisciplinary themes such as resilience, adaptation, and urban planning or management due to urban flood. All documents were imported into biblioshiny, the Bibliometrix R-package (v4.2) interface, for metadata screening, keyword harmonization, and visualization [41].

2.2. Query Strategy

The search query strategy in this study was carefully designed to ensure the semantic precision and conceptual completeness of the dataset. The overarching goal was to capture publications that intersected the three foundational pillars of this review:
  • Urban flooding as the hazard context;
  • Simulation and modeling as methodological approaches;
  • Planning, infrastructure, or risk as governance and action-oriented domains.
To refine the conceptual triad into a structured Boolean query, we used ChatGPT-4o to identify synonyms and semantically related terms for “urban flood,” “simulation,” and “planning.” This included assessing whether terms such as “forecasting” and “simulation” or “urban flooding” and “urban flood” could be grouped without losing contextual accuracy across disciplinary sources. The final set of keywords was reviewed for thematic consistency and used to construct a query within the TITLE-ABS-KEY (title, abstract, and keyword) search field of the Scopus database. The full string was:
  • TITLE-ABS-KEY (“urban flood*” OR “urban flooding”) AND
  • TITLE-ABS-KEY (“simulation” OR “forecasting” OR “modeling”) AND
  • TITLE-ABS-KEY (“planning” OR “resilience” OR “infrastructure” OR “risk”)
This query was executed on 13 May 2025, and followed a multi-step filtering process to arrive at the final set of documents used in the analysis (Table 1). The finalized query string used to search the dataset was as follows:
(TITLE-ABS-KEY (“urban flood*” OR “urban flooding”) AND TITLE-ABS-KEY (“simulation” OR “forecasting” OR “modeling”) AND TITLE-ABS-KEY (“planning” OR “resilience” OR “infrastructure” OR “risk”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”)) AND (LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “SOCI”) OR LIMIT-TO (SUBJAREA, “EART”) OR LIMIT-TO (SUBJAREA, “COMP”) OR LIMIT-TO (SUBJAREA, “AGRI”) OR LIMIT-TO (SUBJAREA, “DECI”) OR LIMIT-TO (SUBJAREA, “MULT”) OR LIMIT-TO (SUBJAREA, “ECON”))
Table 1. Multi-step filtering process.
Table 1. Multi-step filtering process.
StepFilter DescriptionRemaining Documents
1Initial Boolean query1525
2Filter: English language only1320
3Filter: Article or conference paper (document type)1320
4Filter: Subject areas relevant to environmental science, engineering, social science, earth science, computer science, agriculture, decision science, multidisciplinary, and economics1307
5Filter: biblioshiny metadata completeness (title, abstract, keywords, and references)1293
The complete selection process is visualized in the PRISMA flow diagram (see Figure 1) [42,43]. A total of 205 records were excluded due to subject or document mismatch, and 8 more for incomplete metadata (e.g., missing abstracts or keywords). No duplicate entries or automation filters were used in the screening phase, and no documents were excluded post-screening since the filtering logic was fully implemented at the metadata level.

2.3. Analysis Process

This study conducted a multi-stage bibliometric analysis using the Bibliometrix R-package (v4.2) via the biblioshiny interface, structured to correspond directly with the study’s research questions (RQ1–RQ3) [41]. The overall workflow consisted of four analytical components:
Firstly, descriptive statistical profiling. To contextualize the field, we first extracted bibliographic indicators such as annual publication counts, average citations per document, co-authorship metrics, geographic affiliations, and source distributions. It served as a diagnostic framework for subsequent conceptual analyses.
Secondly, thematic evolution and trend analysis (RQ1). To address RQ1 on the temporal shifts in urban flood research themes—especially related to climate, risk, and resilience—we applied:
  • Thematic evolution mapping (Sankey plots) to trace longitudinal changes in dominant keywords across three temporal segments.
  • Trend topic analysis to visualize the chronological emergence and decline of high-frequency terms.
  • Keyword co-occurrence networks to observe thematic clustering and transitions in terminology.
These techniques jointly captured the semantic trajectory of the field and revealed the transition from traditional hazard-based paradigms to resilience- and planning-oriented approaches.
Thirdly, for structural thematic mapping (RQ2), which examined the dominant thematic clusters and their degrees of integration or fragmentation, we performed:
  • Co-word network analysis to identify major keyword clusters and their semantic linkages.
  • Thematic quadrant mapping (centrality vs. density plots) to classify topics as motor, niche, basic, or emerging themes based on their structural properties.
This structural mapping provided insight into whether the field exhibits cohesive integration across domains or remains partitioned into specialized domains.
Fourthly, for conceptual positioning and cross-domain linkages (RQ3) concerning how simulation, risk, and planning are positioned conceptually, we applied:
  • Multiple Correspondence Analysis (MCA) to generate a factorial map, positioning core concepts spatially based on their co-occurrence profiles.
These analyses revealed how simulation methods align—or fail to align—with planning and resilience concepts, highlighting gaps and overlaps in the conceptual structure of urban flood research.

3. Results

3.1. Overview

The bibliometric analysis drew on a dataset of 1293 documents published between 1980 and 2025, spanning 407 unique sources (Figure 2). This dataset reflects over four decades of scholarly development in urban flood research. Publication activity remained modest until the early 2000s (Figure 3). A notable increase began after 2010, with rapid and sustained growth starting around 2015. By 2024, annual output exceeded 200 articles, indicating rising academic interest and advancement of the urban flood research. The field’s annual growth rate reached 11.55%, driven by accelerating urbanization [44,45,46], intensifying climate risks [47,48,49,50,51], and the global push for resilience-based infrastructure planning [47,50,52].
The dataset comprises contributions from 3738 authors, highlighting the field’s diversity and collaborative nature. With an average of 4.54 co-authors per document and only 43 individually authored papers, urban flood research demonstrates strong interdisciplinary engagement (e.g., hydrology, planning, engineering, and climate science). International collaborations represent 30.47% of the output, indicating robust cross-border knowledge exchange on this globally shared issue. The average document age of 4.84 years and citation rate of 22.39 per paper suggest that the literature remains current and influential, reflecting both the maturity of foundational methods and the continued development of emerging approaches.
In terms of dissemination (see Figure 4), the Journal of Hydrology ranks as the most prolific source (e.g., [16,21,53,54,55,56]), followed by Water (Switzerland) (e.g., [57,58,59]) and Sustainability (Switzerland) [5,60,61,62,63]. Other frequent sources—Natural Hazards (e.g., [64]), Science of the Total Environment (e.g., [4,29,49,65,66]), and the Journal of Flood Risk Management (e.g., [23,28,67])—reflect the field’s blend of applied and theoretical work. This diversity underscores its multidisciplinary nature, linking hydrology, environmental management, infrastructure, and climate.
The global distribution of urban flood research reveals substantial disparities (see Figure 5). China leads with 1123 publications (e.g., [19,29,30,68,69,70]), more than triple the output of the United States (350) (e.g., [49,71,72]) and the United Kingdom (304) (e.g., [73]). This dominance reflects China’s rapid urban expansion and significant investment in flood-related research. Other active contributors include India (138) (e.g., [20]), Italy (127) (e.g., [44,74]), Brazil (114) (e.g., [75,76]), and Germany (108) (e.g., [77,78]), demonstrating engagement from both Global North and South. However, the concentration of output in a few countries highlights regional clustering and the underrepresentation of high-risk areas such as parts of Africa (e.g., [51]) and Southeast Asia (e.g., [8]). Numbers of keywords underscores the field’s expansive scope, with 3323 author-defined keywords that are expected to represent a wide range of research directions and methodological approaches across the global research community.
The growing scale and complexity of urban flooding have positioned it as a critical topic within global academic discourse. Research on simulation and flood management is expanding rapidly over time. Interest is no longer confined to high-risk regions; instead, it reflects a global academic agenda. The rise in interdisciplinary and international collaborations signals a collective effort to understand and manage urban flooding through diverse perspectives, laying the groundwork for deeper structural and thematic analysis in the sections that follow. The following sections examine its temporal development (Section 3.2), structural organization (Section 3.3), and the conceptual roles of simulation, risk, and planning (Section 3.4).

3.2. Thematic Evolution of Urban Flood Research

Over the past four decades, urban flood research has shifted from narrowly focused engineering studies to more integrated discourses on climate adaptation, spatial risk, and resilience. Early literature (1980–2006) centered on “hazard management” (e.g., [61,79,80,81]), “sewers” (e.g., [82]), and “urban planning” (e.g., [63,65,83]), emphasizing physical infrastructure and technical risk framing (Figure 6). During this phase, urban flooding was largely seen as a localized engineering problem—addressed through structural controls like drainage systems—rather than a multi-scalar socio-spatial challenge.
Between 2007 and 2016, the field diversified with cross-cutting terms such as “urban flooding” (e.g., [61]), “risk assessment” (e.g., [84]), and “storm-water management” (e.g., [55]). This shift aligned with growing concern over climate-driven hydrological extremes. Flood risk began to be understood not only through physical factors (e.g., rain, runoff, drainage), but also through exposure, vulnerability, and decision-making systems. The emergence of terms like “analytic hierarchy process,” “optimization,” and “decision support systems” reflects a methodological turn toward prioritization tools and scenario-based planning under uncertainty.
This evolution matured further in the 2017–2025 phase, where terms like “floods” and “flooding” emerged as integrative anchors. These umbrella concepts now encompass sub-themes such as urban flooding, climate change, resilience, and risk governance [14,85], reflecting a shift toward holistic, system-level perspectives that connect physical processes with socio-political responses.
The trend topic analysis (see Figure 7) reveals a thematic progression in urban flood research—from early hydrological engineering terms such as “discharge,” “dams,” and “river control” to more integrated, adaptive, and data-driven approaches. Since the 2010s, increased usage of keywords like “climate change,” “urban flooding,” and “risk management” reflects growing concern with socio-environmental dimensions. Recently, the emergence of “machine learning,” “cloud modeling,” and “digital elevation model” points to a computational turn toward predictive analytics (e.g., [58,86,87]), spatial simulation (e.g., [88,89]), and scenario-based assessments (e.g., [90,91,92]). Simultaneously, keywords such as “adaptive management,” “low impact development,” and “land use planning” suggest deeper engagement with decentralized governance and nature-based solutions. Together, these trends indicate a shift toward resilience-centered urban flood research, where simulation is increasingly embedded in broader planning and policy frameworks.
The thematic trajectory of urban flood research reveals a fundamental epistemological shift—from deterministic, hazard-centric models toward integrative frameworks that recognize flooding as a socio-environmental and governance challenge. Rather than focusing solely on physical processes, recent scholarship has interrogated the spatiality of risk, the politics of vulnerability, and the institutional dimensions of adaptation. This evolution reflects a growing awareness that urban floods are not merely natural events to be engineered against, but manifestations of systemic interactions between climate, urbanization, infrastructure, and governance. The increasing use of decision-support tools, scenario modeling, and resilience discourse signals a methodological and conceptual convergence, positioning simulation not as an isolated technical tool but as embedded within broader socio-political processes of planning, prioritization, and policy negotiation.

3.3. Structural Patterns and Disciplinary Intersections

The structural configuration of urban flood research displays a multi-nodal thematic network, with clusters centered on hydrological modeling, risk assessment, and climate-exposure governance. The keyword co-occurrence map (Figure 8) identifies three main poles: (1) technical modeling and hydrodynamics (green cluster); (2) risk assessment, vulnerability, and management (blue cluster); and (3) climate, flood control, and spatial exposure (red cluster). Despite their interconnectedness, the partial separation between clusters indicates moderate thematic fragmentation, reflecting distinct disciplinary languages and methodological approaches. This pattern underscores the field’s evolution from narrowly technical problem framing to broader, yet still compartmentalized, research perspectives.
The word frequency ranking (Figure 9) strengthens this interpretation. Frequently occurring terms, such as “floods” (726), “risk assessment” (615), “flooding” (605), and “flood control” (569), reflect a dual emphasis on hazard characterization and response frameworks. However, terms like “urban planning” (202) and “hydrodynamics” (166) appear less frequently despite their theoretical significance—suggesting a gap between modeling capability and policy translation. The word cloud (Figure 9) visually reinforces this hybrid structure, with high-weight technical terms (e.g., “runoff,” “hydrological modeling”) juxtaposed with integrative concepts such as “climate change,” “resilience,” and “urban flooding.” This visual co-occurrence implies that while integration is emerging, conceptual boundaries still exist between simulation, planning, and implementation agendas.
The thematic map (Figure 10) classifies key concepts based on centrality and development. Motor themes—such as “floods,” “urban area,” “climate change,” “risk assessment,” and “urban flooding”—form the conceptual core of the field. In contrast, “hydrodynamics,” “numerical model,” and “digital elevation model” appear in the under-integrated quadrant, suggesting that simulation tools are increasingly embedded within broader, decision-oriented frameworks [93,94,95,96,97]. Meanwhile, niche terms like “simulation,” “models,” and “controlled study” represent specialized methods that remain peripheral, underscoring the need for deeper methodological integration into system-level urban flood research.
The structural mapping shows that urban flood research is expanding, but core domains remain unevenly connected. High-centrality themes like floods and climate change reflect growing coherence, yet simulation-related terms remain peripheral. This indicates a disconnect between technical modeling and its practical uptake in planning and governance. While integration is often discussed, it lacks operational clarity. Simulation focuses on precision, whereas planning demands flexibility and context. The absence of bridging frameworks limits cross-domain translation. Thus, despite emerging overlaps, the field remains fragmented—highlighting the need for models that align technical tools with institutional and spatial decision-making processes.

3.4. Conceptual Positioning of Simulation, Risk, and Planning

To examine how methodological development aligns with governance practices, a factorial analysis (Figure 11) visualize the conceptual positioning of terms like “simulation,” “risk assessment,” and “planning” in urban flood research. The factorial map shows a dominant red cluster centered on “floods,” “flooding,” “disaster prevention,” and “urban flood risks.” This cluster reflects the convergence of operational modeling with policy-oriented agendas, highlighting the centrality of risk assessment in both technical and governance-related domains [98,99,100,101,102].
Although conceptually adjacent, “simulation” and “computer simulation” remain peripheral within the dominant cluster, indicating their limited integration into planning or governance frameworks. Their alignment remains primarily technical—focused on hydrological modeling and predictive accuracy—rather than institutional application. This separation from terms such as “urban planning” and “disaster management” reflects a critical gap, whereby simulation tools are not systematically embedded within spatial or regulatory decision-making processes. As a result, the knowledge they produce remains underutilized in guiding urban development or informing adaptive policy interventions.
Similarly, infrastructure-related terms such as “storm sewers”, “urban drainage systems”, and “runoff” occupy distinct clusters, reinforcing their continued framing within engineering and hazard control paradigms. These terms are weakly connected to socio-spatial planning concepts, suggesting that hazard management remains focused on physical mitigation rather than integrated risk governance. This thematic disjunction illustrates a structural fragmentation: simulation enhances technical foresight, and hazard management advances control measures, yet both operate largely outside the spatial planning domain. Addressing this requires methodological realignment and frameworks that connect technical modeling with land-use planning, regulatory mechanisms, and governance capacities.

4. Discussion

Urban flood research has evolved from infrastructure-focused hazard control to a broader framing of flood risk as a spatial [103], political [104], and institutional issue [105], aligning with trends in environmental governance that prioritize socio-environmental resilience over hydraulic efficiency [106,107]. Despite this shift, the field remains fragmented: technical modeling—centered on simulations and data-driven forecasting—dominates, yet remains poorly integrated with planning and governance frameworks. As noted by Smith et al. [23] and Wang et al. [108], even innovative tools like social media analytics face institutional barriers, highlighting a gap between methodological advancement and policy implementation.
The persistent disconnect between hydrodynamic modeling and urban planning reflects a misalignment between diagnosing flood risks and implementing effective solutions. As Dong et al. [109] have pointed out, even sophisticated, high-resolution simulations rarely inform land-use regulation or infrastructure investment, leading to a situation where technical models advance while planning remains bound to outdated, rigid frameworks. Similarly, while resilience has become central in flood discourse—frequently framed through system exposure and adaptive capacity via terms like climate change, urban area, and risk assessment—its governance dimensions remain underexamined. The marginal presence of concepts like institutional integration or policy instruments suggests that the structural conditions necessary to operationalize resilience are not yet fully integrated, a gap also highlighted by Alshammari et al. [110], Wu et al. [111], and Li et al. [5].
Perhaps the most striking insight is that simulation, while omnipresent, still hovers at the edge of policy relevance. This is not due to a lack of technical rigor, but likely due to a lack of institutional interoperability. Our conceptual structure maps suggest that simulation is positioned as a methodological input, but not yet as a shared planning language. To move forward, future research may benefit from embracing methodological hybridization—combining high-resolution modeling techniques (e.g., UAV-LiDAR, 2D hydraulics) with scenario-based governance tools or participatory risk assessments [112]. A promising avenue lies in bridging these clusters through collaborative frameworks that explicitly couple risk modeling with planning decisions, much like the socio-hydrological systems proposed by Vigilione et al. [113]. In so doing, the urban flood research community can better respond to the dual imperatives of technical precision and societal relevance.
Taken together, the results point toward a maturing but fragmented field—one that increasingly speaks the language of integration, but still operates within segmented research traditions. The opportunity going forward is not only to bridge gaps between domains, but to redefine how knowledge itself is mobilized in the service of urban resilience. This demands not just interdisciplinarity in publication, but interdependence in problem-framing, tool development, and planning action.

5. Limitations and Future Research Opportunities

This study is limited by its reliance on the Scopus database and the use of ChatGPT-4o for synonym normalization, which may constrain the thematic scope and introduce semantic bias in keyword clustering. While the analysis aimed to objectively map the metadata of a large body of literature using a structured bibliometric approach, it is important to recognize that some articles may have conducted deeply integrative or comprehensive research that remains underrepresented. This could be due to limitations in how keywords were formulated, how themes were labeled, or how articles were cited—factors that can affect the visibility of complex or interdisciplinary work in bibliometric mapping. Such trade-offs are inherent in metadata-driven reviews, where the structure of representation may not always reflect the substantive depth of certain studies.
Nonetheless, the findings of this study open several directions for future research. Empirical studies should investigate the institutional conditions under which flood simulation tools are incorporated—or excluded—from urban policy and planning systems. There is a pressing need to explore mechanisms for embedding high-resolution models into adaptive zoning practices, participatory planning, and cross-sectoral decision-making. Future research should also focus on designing collaborative, socio-hydrological frameworks that bridge methodological innovation with governance processes, enabling simulation tools to evolve from technical outputs into shared languages of planning and negotiation.

6. Conclusions

Urban flood research has advanced from engineering-driven studies to complex system modeling supported by high-resolution simulations and data analytics. Over time, technical methods have become more sophisticated, yet the integration of these tools into planning and governance frameworks remains limited. The clustering patterns reveal that while the field has expanded thematically, technical and governance domains still operate in parallel. There is growing methodological depth, but decision-making processes often remain disconnected from simulation outputs. This reflects a lack of co-development between technical tools and institutional mechanisms. Bridging this requires embedding simulations into spatial and regulatory workflows, ensuring models are not only accurate but also actionable—shaped by, and responsive to, governance needs.

Author Contributions

Conceptualization, A.G. and M.R.P.; methodology, M.R.P.; software, M.R.P.; validation, A.G. and M.R.P.; formal analysis, M.R.P.; investigation, A.G. and M.R.P.; resources, A.G.; data curation, M.R.P.; writing—original draft preparation, A.G. and M.R.P.; writing—review and editing, A.G.; visualization, M.R.P.; supervision, A.G.; project administration, A.G., B.H.W., P.N. and J.A.; funding acquisition, A.G., B.H.W., P.N. and J.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by PRIME Engineering Seed Fund for the project titled “Collaborative Approaches in Mapping & Mitigating Exposures and Vulnerable Groups for a Resilient Capital City with Generative AI”, grant number NKB-989/UN2.RST/HKP.05.00/2024.

Data Availability Statement

This research did not generate new data. All data used in this study were obtained from publicly accessible sources, specifically the Scopus database.

Acknowledgments

The authors gratefully acknowledge the financial support provided by the Universitas Indonesia through the PRIME Engineering Seed Fund, Batch I 2024. This funding has supported the research activities and publication process of this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
RQResearch questions
PRISMAPreferred Reporting Items for Systematic Reviews and Meta-Analyses
MCAMultiple Correspondence Analysis

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Urbansci 09 00280 g001
Figure 1. PRISMA flow diagram (generated by PRISMA 2020).
Figure 1. PRISMA flow diagram (generated by PRISMA 2020).
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Figure 2. Main information of literature metadata from Scopus database (generated by biblioshiny).
Figure 2. Main information of literature metadata from Scopus database (generated by biblioshiny).
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Figure 3. Annual scientific production ranging from 1980–2025 (generated by biblioshiny).
Figure 3. Annual scientific production ranging from 1980–2025 (generated by biblioshiny).
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Figure 4. Top 10 relevant sources (generated by biblioshiny).
Figure 4. Top 10 relevant sources (generated by biblioshiny).
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Figure 5. Top 10 countries scientific production (generated by biblioshiny).
Figure 5. Top 10 countries scientific production (generated by biblioshiny).
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Figure 6. Thematic evolution between 1980–2006, 2007–2016, and 2017–2025 (generated by biblioshiny).
Figure 6. Thematic evolution between 1980–2006, 2007–2016, and 2017–2025 (generated by biblioshiny).
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Figure 7. Trend topics ranging between 1980–2925 (generated by biblioshiny).
Figure 7. Trend topics ranging between 1980–2925 (generated by biblioshiny).
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Figure 8. Co-occurrence network (generated by biblioshiny).
Figure 8. Co-occurrence network (generated by biblioshiny).
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Figure 9. Top 20 most relevant words (generated by biblioshiny).
Figure 9. Top 20 most relevant words (generated by biblioshiny).
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Figure 10. Thematic mapping (generated by biblioshiny).
Figure 10. Thematic mapping (generated by biblioshiny).
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Figure 11. Result of factorial analysis (generated by biblioshiny).
Figure 11. Result of factorial analysis (generated by biblioshiny).
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MDPI and ACS Style

Gamal, A.; Pradana, M.R.; Wibisono, B.H.; Navitas, P.; Aryal, J. Thematic Fragmentation and Convergence in Urban Flood Simulation Research: A 45-Year Bibliometric Mapping. Urban Sci. 2025, 9, 280. https://doi.org/10.3390/urbansci9070280

AMA Style

Gamal A, Pradana MR, Wibisono BH, Navitas P, Aryal J. Thematic Fragmentation and Convergence in Urban Flood Simulation Research: A 45-Year Bibliometric Mapping. Urban Science. 2025; 9(7):280. https://doi.org/10.3390/urbansci9070280

Chicago/Turabian Style

Gamal, Ahmad, Mohammad Raditia Pradana, Bambang Hari Wibisono, Prananda Navitas, and Jagannath Aryal. 2025. "Thematic Fragmentation and Convergence in Urban Flood Simulation Research: A 45-Year Bibliometric Mapping" Urban Science 9, no. 7: 280. https://doi.org/10.3390/urbansci9070280

APA Style

Gamal, A., Pradana, M. R., Wibisono, B. H., Navitas, P., & Aryal, J. (2025). Thematic Fragmentation and Convergence in Urban Flood Simulation Research: A 45-Year Bibliometric Mapping. Urban Science, 9(7), 280. https://doi.org/10.3390/urbansci9070280

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