Regional Analysis of Annual Maximum Rainfall: A Bibliometric Analysis

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Heavy rainfall often leads to landslides, floods, and damage to infrastructure, with the most significant effects on vulnerable communities and disruptions to economic activities. These events can worsen existing inequalities and place additional pressure on local economies, making it crucial to assess and predict such severe occurrences at the regional level. This proactive strategy helps mitigate negative impacts and develop adaptive strategies to address future climate uncertainties. The review compiles data from different geographic areas, offering a comprehensive understanding of rainfall patterns across various climates. It explores the methods used to analyze maximum rainfall, with a focus on statistical models, GIS, and climate simulations that combine historical rainfall data with projections of future climate conditions. For example, Geographic Information Systems (GIS) enable spatial analysis of rainfall data, providing deeper insights into how geographical features influence precipitation patterns. Additionally, climate simulations assist in forecasting future rainfall patterns based on different greenhouse gas emission scenarios. Regional Rainfall Extreme Events Annual Maximum Figures Figure 1 Figure 2 1. Introduction In the era of increasing climatic variability, understanding the dynamics of annual maximum rainfall is essential for addressing challenges in flood mitigation, water resource management, and climate resilience (Srivastav et al., 2021 ). Heavy rainfall often leads to landslides, floods, infrastructure damage, and natural disasters that disproportionately affect vulnerable communities and disrupt socioeconomic activities (Cutter, 2021 ). Consequently, it is crucial to assess and predict such extreme events at the regional level to mitigate their adverse impacts and develop adaptive strategies to manage future climate uncertainty. To address water-related risks, localized measures and policies are developed with the assistance of regional analyses of annual maximum rainfall, which help identify spatial variability and trends in extreme rainfall events (Fofana et al., 2022 ). Regional assessments of annual maximum rainfall have been the focus of systematic literature reviews, providing a structured and comprehensive exploration of the methods, findings, and theoretical frameworks applied across different geographic contexts (Fofana et al., 2022 ). This review integrates a wide range of data, illustrating how varying meteorological conditions, geographical features, and human activities, such as urbanization and deforestation, lead to different locations experiencing varying levels of rainfall intensity. The assessment will also explore how climate change intensifies these extremes, with specific locations experiencing an increase in both the frequency and severity of maximum rainfall events (Wasko et al., 2021 ). Statistical models, geographic information systems (GIS), and climate simulations that incorporate historical rainfall data and projected climate scenarios are commonly used methodologies in the regional analysis of annual maximum rainfall (Rahman & Lateh, 2017 ). The review will assess the advantages and drawbacks of different methods, focusing on their ability to capture regional variations in rainfall patterns. Statistical techniques, such as frequency analysis and extreme value theory (EVT), are commonly used to estimate the probability and intensity of extreme rainfall events in different locations (Diriba & Debusho, 2021 ). However, the reliability of these methods heavily depends on the quality and availability of long-term rainfall data, which can vary significantly across different locations. Climate models and remote sensing technologies are sometimes used to fill data gaps in regions where information is scarce or unreliable. However, they do have limitations, especially when applied to localized contexts (Ren et al., 2022 ). Various meteorological factors, such as elevation, seasonal variations, proximity to water bodies, and air circulation patterns, can influence regional differences in maximum rainfall (Michaelides et al., 2018 ). In this context, monsoon-affected regions, such as parts of South Asia and Africa, often experience intense rainfall in concentrated bursts. In contrast, temperate areas tend to have more evenly distributed rainfall events throughout the year. Additionally, topography plays a crucial role in shaping rainfall patterns; for instance, orographic rainfall occurs in mountainous areas and can result in higher maximum rainfall levels in specific locations (Li et al., 2021 ). By examining these geographical differences, readers will gain a comprehensive understanding of the various factors that influence global maximum rainfall extremes. This systematic review aims to identify key trends, emerging patterns, and knowledge gaps in the research on annual maximum rainfall across various locations by thoroughly examining the existing literature (Shaffril et al., 2018). To address extreme rainfall events, this review aims to provide valuable insights through synthesis that can inform future research, policy frameworks, and on-the-ground interventions (Matczak & Hegger, 2021 ). The ultimate goal is to deepen our understanding of how local topography, human activity, and climate change interact to influence extreme rainfall patterns, and to apply this knowledge to build more adaptable and resilient communities in the face of increasing climate change threats. 2. Methodology The relevant articles were gathered from the Scopus database along with their bibliometric data. Bibliometric analysis was performed using the R package. Bibliometric analysis is a quantitative research technique that uses statistical and mathematical methods to assess scientific literature. It examines the relationships, impacts, and trends within publications, authors, institutions, and countries across specific research domains (Lancho-Barrantes & Cantú-Ortiz, 2019 ). The review process adheres to the guidelines established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (O’Dea et al., 2021 ). To ensure the rigor and integrity of the bibliometric review process, the PRISMA criteria were applied in accordance with global best practices. A protocol outlining the inclusion and exclusion criteria for the study, along with the recommended analytical approach, was established prior to the review to facilitate the implementation of the comprehensive analysis model. All study records were compiled using the Scopus database. 2.1 Search Procedure Data extraction was carried out using the Scopus database ( www.scopus.com ), focusing on key trends related to 'Regional and Annual Maximum Rainfall. Scopus is a comprehensive database of abstracts and citations that offers access to scholarly literature across a wide range of fields, including science, technology, medicine, social sciences, and the arts and humanities. It is one of the largest and most reputable databases worldwide, frequently used by researchers, academics, and institutions to track the impact and spread of scientific research (Hladchenko, 2024 ). Scopus facilitates access to high-quality, peer-reviewed information, ensuring that researchers and institutions remain at the forefront of scientific discovery and academic achievement. It maintains a leadership position in this field due to the features mentioned above. The study initially employed a set of keywords and search terms, both separately and in combination, using the Boolean operators 'AND' and 'OR,' along with advanced search techniques. The papers were organized based on keywords found in the titles and abstracts. Data processing was conducted using R, and the PRISMA guidelines were applied to refine the search, ensuring that relevant papers were carefully included and those outside the study's focus were excluded. 2.2 Selection Process The first phase of the study involved identifying and removing duplicate data. Next, the titles and abstracts of the papers were assessed according to predefined inclusion criteria. A comprehensive analysis of the methodology and discussion sections of the relevant research was carried out. Figure 1 illustrates the framework of this study, outlining the process for selecting papers from the Scopus database. In the first stage, a total of 94 articles were identified. During the second stage (Subject Area), five articles were excluded; in the third stage (Document Titles),16 articles were removed; in the fourth stage (Language), three articles were excluded; in the fifth stage (Keywords), six articles were removed. In the sixth stage (Open access), an eligibility assessment was conducted, resulting in the removal of 42 articles that fell outside the scope of the study. The remaining articles were thoroughly evaluated, leading to the final stage of the selection process. Ultimately, 22 articles met the inclusion criteria and were selected for analysis. Figure 1 Study Framework 3. Results and Discussion 3.1 Top Cited Documents Table 1 offers a detailed overview of the most frequently cited documents in the field of environmental and hydrological sciences. Citation counts are an important indicator of a paper's impact, reflecting how frequently other researchers cite it. This helps assess the overall academic influence and long-term relevance of these papers. "Leading this analysis is the paper by Ngongondo et al. (2011), published in Stochastic Environmental Research and Risk Assessment , which has garnered 102 citations. The high citation count indicates that the paper likely tackles a fundamental or highly pertinent topic in environmental risk assessment. Given the growing global emphasis on managing environmental risks, such as those linked to climate change, extreme weather events, or other ecological threats, it is likely that this paper offers valuable insights or methodologies with wide-ranging applications in both theoretical and practical contexts. The high citation count also suggests that the paper may have interdisciplinary appeal, being relevant not only in environmental sciences but also in fields like public policy, urban planning, and resource management. Arnone et al. (2013), published in Hydrology and Earth System Sciences , with 98 citations. This document is likely to make valuable contributions to hydrology, potentially offering new insights into water cycle dynamics, watershed management, or modeling techniques for understanding hydrological processes. The close citation counts of the papers by Arnone and Ngongondo highlight that both are key contributions in their respective research fields, addressing critical topics that resonate with a broad academic audience. Mid-Level Citations: Within the mid-tier citation range, papers such as Villarin (2016), published in the International Journal of Climatology , have gathered 53 citations. This suggests that Villarin’s work addresses climatic factors, potentially related to climate change, weather pattern forecasting, or atmospheric dynamics. Climatology has advanced rapidly in recent years, primarily due to the urgency of climate change. Villarin’s research could play a crucial role in understanding and mitigating its effects. Similarly, Herath (2016), published in the Journal of Hydrological Sciences , has garnered 43 citations, likely contributing to the field of hydrology by addressing issues such as water management, flood risk, and hydrological modeling. The citation counts for both Villarin and Herath suggest that, although their papers may not be as widely referenced as those by Ngongondo or Arnone, they still hold a significant position within their respective fields. These documents may provide specialized insights, in-depth case studies, or innovative methodologies that are valuable to experts in the field. Reed (1999) and Preti (2011), published in Hydrology and Earth System Sciences , have received 39 citations. Despite being one of the oldest papers in the dataset, Reed's work remains frequently cited, indicating its ongoing relevance in hydrological research. Reed may have introduced key concepts or methods that remain relevant, even in the face of advancements in the field. Preti’s more recent work seems to address similar topics, and its citation count reflects ongoing interest in water-related issues. Lower Cited Documents: At the lower end of the citation spectrum, papers such as Li (2017) in the Journal of Hydrology and De Luca (2018) in Water have received between 21 and 30 citations. These papers may represent emerging research that has yet to reach its full citation potential, or they may focus on specialized topics that attract a more niche academic audience. For example, Li’s research might focus on specific hydrological phenomena, while De Luca’s paper could provide insights into water quality, distribution, or policy issues. Interestingly, Faulkner (1999), published in Hydrology and Earth System Sciences , has also received 21 citations. Similar to Reed’s earlier work, Faulkner’s continued relevance after two decades may indicate a long-standing contribution to hydrological sciences, possibly in water resource management, flood prevention, or sustainable practices. Lastly, another paper by Li (2017), published in Advances in Water Resources , is the least cited document with 20 citations. It is essential to note that citation counts for recent publications, particularly those published within the last five to ten years, may be lower because they have not had sufficient time to gain widespread recognition. A lower citation count does not necessarily indicate poor quality but rather reflects the timing and extent of its dissemination within the research community. Academic Impact: From an academic perspective, citation data like this helps pinpoint the most influential papers that have shaped ongoing research and policy debates in environmental and hydrological sciences. The highly cited papers by Ngongondo (2011) and Arnone (2013) can be considered foundational works that lay the groundwork for future research or solutions in managing environmental risks and water systems. These areas are increasingly significant due to climate change and growing environmental challenges. These works likely have an impact on academia, government policies, industry practices, and global sustainability initiatives. Papers with moderate to low citation counts, such as those by Villarin (2016), Herath (2016), and Preti (2011), continue to be valuable for specialized research in fields like climatology, water resource management, and hydrological modeling. Although they may not have the same widespread impact as the most-cited papers, they remain essential for advancing specific subfields and enhancing the overall understanding of environmental and hydrological processes. To conclude, the documents included in this citation analysis represent significant contributions to the fields of hydrology, climatology, and environmental sciences. The most highly cited works likely provide foundational insights, innovative methodologies, or thorough analyses that have shaped various areas of research and practical applications. Although the documents with fewer citations may not have as broad an impact, they still offer valuable contributions, potentially focusing on specialized or emerging topics that are critical to the evolution of these fields. Table 1 Most Global Cited Documents Paper DOI Total Citations TC per Year Normalized TC NGONGONDO CS, (2011). STOCH ENVIRON RES RISK ASSESS 10.1007/s00477-011-0480-x 102 7.28571429 1.54545455 ARNONE E (2013). HYDROL EARTH SYST SCI 10.5194/Hess-17-2449-2013 98 8.16666667 1 VILLARINI G, (2016). INT J CLIMATOL 10.1002/joc.4393 52 5.77777778 1.60824742 HERATH SM, (2016). HYDROL SCI J 10.1080/02626667.2015.1083103 36 4 1.11340206 REED DW, (1999). HYDROL EARTH SYST SCI 10.5194/Hess-3-197-1999 36 1.38461538 1.18032787 PRETI F (2011). HYDROL EARTH SYST SCI 10.5194/Hess-15-3077-2011 30 2.14285714 0.45454545 LI J, 2017, J HYDROL 10.1016/j.jhydrol.2017.02.019 30 3.75 1.09090909 DE LUCA DL (2018). WATER 10.3390/w10101477 26 3.71428571 1 FAULKNER DS (1999). HYDROL EARTH SYST SCI 10.5194/Hess-3-205-1999 25 0.96153846 0.81967213 LI J, 2017, ADV WATER RESOURCES 10.1016/j.advwatres.2017.10.020 25 3.125 0.90909091 CHOI J, (2019). INT J CLIMATOL 10.1002/joc.5850 23 3.83333333 1 REQUENA AI (2021). J HYDROL REG STUD 10.1016/j.ejrh.2021.100811 18 4.5 1 BONI G. (2008). J HYDROMETEOROL 10.1175/2007JHM900.1 14 0.82352941 1 MAZZOGLIO P. (2020). WATER 10.3390/w12123308 11 2.2 1.5 HAKTANIR T, (2016). HYDROL SCI J 10.1080/02626667.2014.966722 9 1 0.27835052 OGAREKPE NM, (2020). J EARTH SYST SCI 10.1007/s12040-020-01434-9 6 1.2 0.81818182 PELOSI A. (2022). WATER 10.3390/w14071179 6 2 2 PARK HJ, 2022, SUSTAINABILITY 10.3390/su14052628 6 2 2 LIAO Y (2020). WATER 10.3390/W12041177 5 1 0.68181818 YUREKLI K, 2022, ATMOSFERA 10.20937/ATM.53024 0 0 0 KIM YT, (2024). WEATHER CLIM EXTREMES 10.1016/j.wace.2024.100688 0 0 #NUM! RAHILL-MARIER B, (2022). HYDROL EARTH SYST SCI 10.5194/Hess-26-5685-2022 0 0 0 3.2 Most relevant words The terms 'rainfall' (18 occurrences) and 'rain' (14 occurrences) appear most frequently, suggesting that the primary focus of the studies is on precipitation and rainfall. Terms such as 'extreme event,' 'annual maximum rainfalls,' and 'precipitation intensity' also appear frequently, indicating a focus on understanding rainfall extremes, their intensities, and their impacts Climate change' appears 10 times, with several mentions of related terms such as 'climate models' (4 occurrences), 'regional climate' (4), and 'climate modeling' (5). Table 2 below provides further insights by including columns showing the frequency of specific terms, along with three different year ranges (Q1, Median, Q3) representing the periods when these terms were most frequently used. The frequency column indicates the frequency with which each term appears. For example, 'rainfall' is mentioned 18 times, while 'extreme rainfall' is referenced 5 times. Q1 indicates the first significant mention or publication of the term, acting as a reference point for its initial appearance in the data. The Median year represents the middle year among all references where the term was mentioned, showing when its usage reached its peak. Finally, Q3 represents the most recent significant mention or publication involving the term, indicating its ongoing relevance over time. The term analysis offers insights into the frequency and temporal distribution of key terms related to climate and rainfall research. Precipitation assessment was cited five times, with its first significant mention in 1999, a median reference year in 2008, and later appearances up to 2013. Similarly, the term 'Italy' first appeared in this context around 2011, with mentions continuing through to 2018, highlighting the geographical focus. Australia was mentioned five times, with the first references appearing in 2016 and continuing until 2017, indicating a brief but focused interest in the region. The term 'rain' was frequently mentioned, with 14 references between 2016 and 2020, indicating a growing focus on rainfall in recent years. The growing significance of climate-related topics is also reflected in the term 'climate change,' which appeared 10 times, mainly between 2016 and 2018. This reflects a rise in research focus on climate change during these years. Similarly, 'annual maximum rainfalls' were mentioned eight times, first appearing in 2016 and continuing through 2019, emphasizing concerns about extreme rainfall events. The term 'rainfall' appears most frequently, with 18 mentions between 2016 and 2020, highlighting its central importance in the research. Discussions surrounding 'extreme events' also increased, with nine mentions between 2017 and 2022, reflecting the growing significance of extreme weather phenomena in recent discussions. The emphasis on predictive approaches is reflected in 'climate modeling,' which was mentioned five times, particularly between 2017 and 2019, aligning with broader discussions on climate change and rainfall trends. Finally, the term 'extreme rainfall' was cited five times between 2018 and 2020, indicating a sustained focus on extreme precipitation events as an important area of research. The table indicates that topics such as rainfall, climate change, and extreme events have garnered significant attention in recent years, particularly from 2016 to 2020. Australia and Italy emerge as important geographic regions of focus. The earlier quartile dates (Q1) indicate when specific topics first appeared, while the median and Q3 columns reflect sustained or increasing interest over time. This suggests a growing research focus on precipitation, extreme weather events, and climate modeling over the past decade, with particular attention to studying the impacts of climate change on rainfall and precipitation patterns, as well as modeling future scenarios. Regions and countries such as 'Australia' (5 occurrences), 'Italy' (5), 'Western Australia' (2), and 'Turkey' (2) are mentioned, suggesting that case studies or data from these areas are frequently incorporated in the analysis. The frequent references to statistical methods like 'regression analysis' (3), 'time series analysis' (3), 'cluster analysis' (3), and 'probability distributions' (3) highlight the focus on quantitative approaches. Techniques such as 'Monte Carlo methods' (3), 'frequency analysis' (5), and 'estimation method' (5) demonstrate the use of probabilistic modeling in rainfall and flood risk assessments. Several terms related to water and flood management, such as 'flood control,' 'flood frequency,' 'floods,' and 'flood mitigation,' emphasize the importance of studying how rainfall patterns influence flooding, particularly in the context of climate change, Regional frequency analysis' (4) and 'regional climate models' (2) focus on understanding the variations in rainfall and climate at a regional level, potentially addressing how different regions are impacted differently by extreme weather events. Table 2 Most Relevant Words Term Frequency Year (Q1) Year (Median) Year (Q3) precipitation assessment 5 1999 2008 2013 Italy 5 2011 2013 2018 Australia 5 2016 2016 2017 rain 14 2016 2017 2020 climate change 10 2016 2017 2018 annual maximum rainfalls 8 2016 2017 2019 rainfall 18 2016 2018 2020 extreme event 9 2017 2018 2022 climate modeling 5 2017 2018 2019 extreme rainfall 5 2018 2020 2020 3.3 Word Cloud Analysis Word clouds, also known as text clouds or tag clouds, are visual representations of the frequency of words within a text passage. With the size of each word corresponding to its frequency in the text, they graphically represent the most commonly used words. In textual data, word clouds are valuable tools for quickly identifying key themes and concepts, as the more frequently a word appears, the larger and bolder it is displayed (Zucco et al., 2020 ). This image is a word cloud, visually representing the frequency of key terms by their size. The larger the word, the more frequently it appears in the dataset or discussion. The analysis shows that 'Rainfall' and 'Rain' are the most prominent terms in the dataset, reflecting their high frequency of occurrence. Terms like 'Climate change,' 'Extreme event,' and 'Annual maximum rainfalls' also appear frequently, indicating that these concepts are key themes in discussions about climate and precipitation. Other Significant Terms Moderately sized terms like 'Estimation method,' 'Climate modeling,' 'Australia,' 'Italy,' and 'Precipitation assessment' indicate frequent mentions, though not to the same degree as the most prominent terms. Furthermore, terms such as "Extreme rainfall," "Frequency analysis," "Regional climate," and "Precipitation intensity" are notable for their recurring use in discussions related to weather patterns, climate models, and rainfall dynamics. Metrics Interpretation The table below presents several research studies, including their respective Digital Object Identifier (DOI), overall citations, mean citations per year (TC/yr), and standardized overall citations (Normalized TC), based on a pre-specified baseline. Top-Cited Papers Among highly cited papers, NGONGONDO CS (2011) was exemplary, having been cited 102 times, with an average of 7.29 citations annually and a normalized score for citations of 1.55. Similarly, a highly cited paper, ARNONE E (2013), has been cited 98 times and has an average of 8.17 citations annually, but a normalized score of 1, which makes it a point of comparison. Recent Papers Regarding recent journals, Requena AI (2021) has garnered a total of 18 citations, a relatively new addition, with a remarkable average of 4.5 citations per year and a normalized score of 1. On the other hand, KIM YT (2024), being a relatively recent addition, has not yet garnered any citations, which is to be expected since it was recently issued. Notable Patterns The data also show significant patterns for citation trends. Omitting older publications like REED DW (1999) and FAULKNER DS (1999) that have significantly low average citations per year (about 1), which may be attributed to their age, but still have a normalized citation score higher than 1. On the contrary, recent works such as MAZZOGLIO P (2020) and PELOSI A (2022) have high scores of normalized citations of 1.5 and 2, which are signs that they are receiving higher attention than older journals. No Citations Yet On the contrary, recent studies such as MAZZOGLIO P (2020) and PELOSI A (2022) have high normalized citation score values of 1.5 and 2, respectively, which shows that compared to older publications, they are receiving increasing popularity. This lack is not uncommon in extremely recent work, as it may take time for its impact to become apparent throughout the academic community. On a general level, the table illustrates the impact of citations on various research papers over time, including both highly cited older studies and new publications that are just starting to gain momentum in citations. Normalized TC values provide a perspective on the relative penetration in the field, illustrating how recent works may have more or less citation traction than older ones. The term' cloud' highlights climate events and analysis, specifically rainfall and extreme climate phenomena. Analysis, regional, Monte Carlo, and general circulation models indicate a clear direction in modeling and analysis content. Conclusion A bibliometric review examined the literature on annual maximum rainfall in various locations, integrating a vast volume of data on meteorological parameters, geography, and human activities that influence rainfall intensity. The assessment aimed to identify key trends and areas of ignorance in recognizing extreme rainfall occurrences, highlighting the mounting threats of climate change. The analysis provides information that may inform prospective research, policy, and practical actions to make communities more resilient and better adapt to mounting climate threats. Declarations Ethics/Informed Consent Informed permission, anonymity, confidentiality, and adherence to institutional ethical norms for data collection and management were all assured Consent for publication All authors have approved the final manuscript and authorship order. This work complies with institutional policies, and no barriers to publication exist. Availability of data and materials Not Applicable Competing interests The authors declare that they have no conflicts of interest. Funding Not Applicable Authors' contributions M.N. wrote the manuscript. Z.A. proofread the manuscript critically and participated in the literature review. Each author made an equal contribution to the manuscript's drafting and revision, and they all gave their approval for publishing Muhammad Nura and Zahratul Amani Zakaria collaboratively conducted a formal analysis and investigation of the data, contributing equally to developing the original draft and creating visualizations for the study. Methodology and Writing Review & Editing by Muhammad Nura developed the research methodology and, alongside Zahratul Amani Zakaria, reviewed and edited the manuscript to enhance clarity and coherence. Zahratul Amani Zakaria supervised the research process and secured the necessary funding to support the project . Acknowledgements The author would like to express his sincere gratitude to Kano State Polytechnic, Kano, Nigeria, for the invaluable support and scholarly guidance he received while undertaking a PhD at Unizsa. We sincerely thank Qaribu Yahaya Nasidi for his invaluable support and guidance throughout this review. His insights and expertise significantly contributed to the depth and quality of our work. We sincerely appreciate his encouragement and dedication, which were instrumental in the completion of this paper. Declarations of Competing Interests The authors declare that they have no conflicts of interest. References Cutter, S. L. (2021). The Changing Nature of Hazard and Disaster Risk in the Anthropocene. Annals of the American Association of Geographers , 111 (3), 819–827. Diriba, T. A., & Debusho, L. K. (2021). Statistical modeling of extreme rainfall indices using multivariate extreme value distributions. Environmental Modeling & Assessment , 26 (4), 543–563. 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Sentiment Analysis for Mining Texts and Social Network Data: Methods and Tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 10 (1), e1333. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7819753","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542878719,"identity":"88422a9b-6d0b-4dde-a96d-8f045c298751","order_by":0,"name":"Muhammad 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1","display":"","copyAsset":false,"role":"figure","size":15829,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7819753/v1/814a4c69f2936dde75d19e9a.png"},{"id":95718810,"identity":"5e748c9f-8963-4033-a297-8982bb470cd5","added_by":"auto","created_at":"2025-11-12 09:10:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107467,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1: \u003c/strong\u003eWord Cloud\u003c/p\u003e","description":"","filename":"01.png","url":"https://assets-eu.researchsquare.com/files/rs-7819753/v1/21cb992defecf04717b13e19.png"},{"id":109762409,"identity":"bfcd8c43-e4d3-44c2-8611-91e30401bf16","added_by":"auto","created_at":"2026-05-22 07:31:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":354470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7819753/v1/57eb74ac-2016-437a-8abe-370fa1a3e5d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRegional Analysis of Annual Maximum Rainfall: A Bibliometric Analysis\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the era of increasing climatic variability, understanding the dynamics of annual maximum rainfall is essential for addressing challenges in flood mitigation, water resource management, and climate resilience (Srivastav et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Heavy rainfall often leads to landslides, floods, infrastructure damage, and natural disasters that disproportionately affect vulnerable communities and disrupt socioeconomic activities (Cutter, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, it is crucial to assess and predict such extreme events at the regional level to mitigate their adverse impacts and develop adaptive strategies to manage future climate uncertainty. To address water-related risks, localized measures and policies are developed with the assistance of regional analyses of annual maximum rainfall, which help identify spatial variability and trends in extreme rainfall events (Fofana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegional assessments of annual maximum rainfall have been the focus of systematic literature reviews, providing a structured and comprehensive exploration of the methods, findings, and theoretical frameworks applied across different geographic contexts (Fofana et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This review integrates a wide range of data, illustrating how varying meteorological conditions, geographical features, and human activities, such as urbanization and deforestation, lead to different locations experiencing varying levels of rainfall intensity. The assessment will also explore how climate change intensifies these extremes, with specific locations experiencing an increase in both the frequency and severity of maximum rainfall events (Wasko et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStatistical models, geographic information systems (GIS), and climate simulations that incorporate historical rainfall data and projected climate scenarios are commonly used methodologies in the regional analysis of annual maximum rainfall (Rahman \u0026amp; Lateh, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The review will assess the advantages and drawbacks of different methods, focusing on their ability to capture regional variations in rainfall patterns.\u003c/p\u003e\u003cp\u003eStatistical techniques, such as frequency analysis and extreme value theory (EVT), are commonly used to estimate the probability and intensity of extreme rainfall events in different locations (Diriba \u0026amp; Debusho, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the reliability of these methods heavily depends on the quality and availability of long-term rainfall data, which can vary significantly across different locations. Climate models and remote sensing technologies are sometimes used to fill data gaps in regions where information is scarce or unreliable. However, they do have limitations, especially when applied to localized contexts (Ren et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eVarious meteorological factors, such as elevation, seasonal variations, proximity to water bodies, and air circulation patterns, can influence regional differences in maximum rainfall (Michaelides et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this context, monsoon-affected regions, such as parts of South Asia and Africa, often experience intense rainfall in concentrated bursts. In contrast, temperate areas tend to have more evenly distributed rainfall events throughout the year. Additionally, topography plays a crucial role in shaping rainfall patterns; for instance, orographic rainfall occurs in mountainous areas and can result in higher maximum rainfall levels in specific locations (Li et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By examining these geographical differences, readers will gain a comprehensive understanding of the various factors that influence global maximum rainfall extremes.\u003c/p\u003e\u003cp\u003eThis systematic review aims to identify key trends, emerging patterns, and knowledge gaps in the research on annual maximum rainfall across various locations by thoroughly examining the existing literature (Shaffril et al., 2018). To address extreme rainfall events, this review aims to provide valuable insights through synthesis that can inform future research, policy frameworks, and on-the-ground interventions (Matczak \u0026amp; Hegger, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The ultimate goal is to deepen our understanding of how local topography, human activity, and climate change interact to influence extreme rainfall patterns, and to apply this knowledge to build more adaptable and resilient communities in the face of increasing climate change threats.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eThe relevant articles were gathered from the Scopus database along with their bibliometric data. Bibliometric analysis was performed using the R package. Bibliometric analysis is a quantitative research technique that uses statistical and mathematical methods to assess scientific literature. It examines the relationships, impacts, and trends within publications, authors, institutions, and countries across specific research domains (Lancho-Barrantes \u0026amp; Cant\u0026uacute;-Ortiz, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The review process adheres to the guidelines established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (O\u0026rsquo;Dea et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To ensure the rigor and integrity of the bibliometric review process, the PRISMA criteria were applied in accordance with global best practices. A protocol outlining the inclusion and exclusion criteria for the study, along with the recommended analytical approach, was established prior to the review to facilitate the implementation of the comprehensive analysis model. All study records were compiled using the Scopus database.\u003c/p\u003e\u003ch2\u003e2.1 Search Procedure\u003c/h2\u003e\u003cp\u003eData extraction was carried out using the Scopus database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.scopus.com\u003c/span\u003e\u003cspan address=\"http://www.scopus.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), focusing on key trends related to 'Regional and Annual Maximum Rainfall. Scopus is a comprehensive database of abstracts and citations that offers access to scholarly literature across a wide range of fields, including science, technology, medicine, social sciences, and the arts and humanities. It is one of the largest and most reputable databases worldwide, frequently used by researchers, academics, and institutions to track the impact and spread of scientific research (Hladchenko, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Scopus facilitates access to high-quality, peer-reviewed information, ensuring that researchers and institutions remain at the forefront of scientific discovery and academic achievement. It maintains a leadership position in this field due to the features mentioned above. The study initially employed a set of keywords and search terms, both separately and in combination, using the Boolean operators 'AND' and 'OR,' along with advanced search techniques. The papers were organized based on keywords found in the titles and abstracts. Data processing was conducted using R, and the PRISMA guidelines were applied to refine the search, ensuring that relevant papers were carefully included and those outside the study's focus were excluded.\u003c/p\u003e\u003ch2\u003e2.2 Selection Process\u003c/h2\u003e\u003cp\u003eThe first phase of the study involved identifying and removing duplicate data. Next, the titles and abstracts of the papers were assessed according to predefined inclusion criteria. A comprehensive analysis of the methodology and discussion sections of the relevant research was carried out. Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the framework of this study, outlining the process for selecting papers from the Scopus database. In the first stage, a total of 94 articles were identified. During the second stage (Subject Area), five articles were excluded; in the third stage (Document Titles),16 articles were removed; in the fourth stage (Language), three articles were excluded; in the fifth stage (Keywords), six articles were removed. In the sixth stage (Open access), an eligibility assessment was conducted, resulting in the removal of 42 articles that fell outside the scope of the study. The remaining articles were thoroughly evaluated, leading to the final stage of the selection process. Ultimately, 22 articles met the inclusion criteria and were selected for analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/strong\u003e\u003cp\u003eStudy Framework\u003c/p\u003e\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Top Cited Documents\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e offers a detailed overview of the most frequently cited documents in the field of environmental and hydrological sciences. Citation counts are an important indicator of a paper's impact, reflecting how frequently other researchers cite it. This helps assess the overall academic influence and long-term relevance of these papers. \"Leading this analysis is the paper by Ngongondo et al. (2011), published in \u003cem\u003eStochastic Environmental Research and Risk Assessment\u003c/em\u003e, which has garnered 102 citations. The high citation count indicates that the paper likely tackles a fundamental or highly pertinent topic in environmental risk assessment. Given the growing global emphasis on managing environmental risks, such as those linked to climate change, extreme weather events, or other ecological threats, it is likely that this paper offers valuable insights or methodologies with wide-ranging applications in both theoretical and practical contexts. The high citation count also suggests that the paper may have interdisciplinary appeal, being relevant not only in environmental sciences but also in fields like public policy, urban planning, and resource management.\u003c/p\u003e\u003cp\u003eArnone et al. (2013), published in \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, with 98 citations.\u003c/p\u003e\u003cp\u003eThis document is likely to make valuable contributions to hydrology, potentially offering new insights into water cycle dynamics, watershed management, or modeling techniques for understanding hydrological processes. The close citation counts of the papers by Arnone and Ngongondo highlight that both are key contributions in their respective research fields, addressing critical topics that resonate with a broad academic audience.\u003c/p\u003e\u003cp\u003eMid-Level Citations:\u003c/p\u003e\u003cp\u003eWithin the mid-tier citation range, papers such as Villarin (2016), published in the \u003cem\u003eInternational Journal of Climatology\u003c/em\u003e, have gathered 53 citations. This suggests that Villarin\u0026rsquo;s work addresses climatic factors, potentially related to climate change, weather pattern forecasting, or atmospheric dynamics. Climatology has advanced rapidly in recent years, primarily due to the urgency of climate change. Villarin\u0026rsquo;s research could play a crucial role in understanding and mitigating its effects.\u003c/p\u003e\u003cp\u003eSimilarly, Herath (2016), published in the \u003cem\u003eJournal of Hydrological Sciences\u003c/em\u003e, has garnered 43 citations, likely contributing to the field of hydrology by addressing issues such as water management, flood risk, and hydrological modeling. The citation counts for both Villarin and Herath suggest that, although their papers may not be as widely referenced as those by Ngongondo or Arnone, they still hold a significant position within their respective fields. These documents may provide specialized insights, in-depth case studies, or innovative methodologies that are valuable to experts in the field.\u003c/p\u003e\u003cp\u003eReed (1999) and Preti (2011), published in \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, have received 39 citations. Despite being one of the oldest papers in the dataset, Reed's work remains frequently cited, indicating its ongoing relevance in hydrological research. Reed may have introduced key concepts or methods that remain relevant, even in the face of advancements in the field. Preti\u0026rsquo;s more recent work seems to address similar topics, and its citation count reflects ongoing interest in water-related issues.\u003c/p\u003e\u003cp\u003eLower Cited Documents:\u003c/p\u003e\u003cp\u003eAt the lower end of the citation spectrum, papers such as Li (2017) in the \u003cem\u003eJournal of Hydrology\u003c/em\u003e and De Luca (2018) in \u003cem\u003eWater\u003c/em\u003e have received between 21 and 30 citations. These papers may represent emerging research that has yet to reach its full citation potential, or they may focus on specialized topics that attract a more niche academic audience. For example, Li\u0026rsquo;s research might focus on specific hydrological phenomena, while De Luca\u0026rsquo;s paper could provide insights into water quality, distribution, or policy issues.\u003c/p\u003e\u003cp\u003eInterestingly, Faulkner (1999), published in \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, has also received 21 citations. Similar to Reed\u0026rsquo;s earlier work, Faulkner\u0026rsquo;s continued relevance after two decades may indicate a long-standing contribution to hydrological sciences, possibly in water resource management, flood prevention, or sustainable practices.\u003c/p\u003e\u003cp\u003eLastly, another paper by Li (2017), published in \u003cem\u003eAdvances in Water Resources\u003c/em\u003e, is the least cited document with 20 citations. It is essential to note that citation counts for recent publications, particularly those published within the last five to ten years, may be lower because they have not had sufficient time to gain widespread recognition. A lower citation count does not necessarily indicate poor quality but rather reflects the timing and extent of its dissemination within the research community.\u003c/p\u003e\u003cp\u003eAcademic Impact:\u003c/p\u003e\u003cp\u003eFrom an academic perspective, citation data like this helps pinpoint the most influential papers that have shaped ongoing research and policy debates in environmental and hydrological sciences. The highly cited papers by Ngongondo (2011) and Arnone (2013) can be considered foundational works that lay the groundwork for future research or solutions in managing environmental risks and water systems. These areas are increasingly significant due to climate change and growing environmental challenges. These works likely have an impact on academia, government policies, industry practices, and global sustainability initiatives.\u003c/p\u003e\u003cp\u003ePapers with moderate to low citation counts, such as those by Villarin (2016), Herath (2016), and Preti (2011), continue to be valuable for specialized research in fields like climatology, water resource management, and hydrological modeling. Although they may not have the same widespread impact as the most-cited papers, they remain essential for advancing specific subfields and enhancing the overall understanding of environmental and hydrological processes.\u003c/p\u003e\u003cp\u003eTo conclude, the documents included in this citation analysis represent significant contributions to the fields of hydrology, climatology, and environmental sciences. The most highly cited works likely provide foundational insights, innovative methodologies, or thorough analyses that have shaped various areas of research and practical applications. Although the documents with fewer citations may not have as broad an impact, they still offer valuable contributions, potentially focusing on specialized or emerging topics that are critical to the evolution of these fields.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMost Global Cited Documents\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaper\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDOI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Citations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTC per Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormalized TC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNGONGONDO CS, (2011). STOCH ENVIRON RES RISK ASSESS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00477-011-0480-x\u003c/span\u003e\u003cspan address=\"10.1007/s00477-011-0480-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.28571429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.54545455\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARNONE E (2013). HYDROL EARTH SYST SCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/Hess-17-2449-2013\u003c/span\u003e\u003cspan address=\"10.5194/Hess-17-2449-2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.16666667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVILLARINI G, (2016). INT J CLIMATOL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/joc.4393\u003c/span\u003e\u003cspan address=\"10.1002/joc.4393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.77777778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.60824742\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHERATH SM, (2016). HYDROL SCI J\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/02626667.2015.1083103\u003c/span\u003e\u003cspan address=\"10.1080/02626667.2015.1083103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.11340206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREED DW, (1999). HYDROL EARTH SYST SCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/Hess-3-197-1999\u003c/span\u003e\u003cspan address=\"10.5194/Hess-3-197-1999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38461538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18032787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRETI F (2011). 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HYDROL EARTH SYST SCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/Hess-3-205-1999\u003c/span\u003e\u003cspan address=\"10.5194/Hess-3-205-1999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96153846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81967213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLI J, 2017, ADV WATER RESOURCES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.advwatres.2017.10.020\u003c/span\u003e\u003cspan address=\"10.1016/j.advwatres.2017.10.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90909091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHOI J, (2019). INT J CLIMATOL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/joc.5850\u003c/span\u003e\u003cspan address=\"10.1002/joc.5850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.83333333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eREQUENA AI (2021). J HYDROL REG STUD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejrh.2021.100811\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrh.2021.100811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBONI G. (2008). 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J EARTH SYST SCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12040-020-01434-9\u003c/span\u003e\u003cspan address=\"10.1007/s12040-020-01434-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81818182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePELOSI A. (2022). WATER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/w14071179\u003c/span\u003e\u003cspan address=\"10.3390/w14071179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePARK HJ, 2022, SUSTAINABILITY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su14052628\u003c/span\u003e\u003cspan address=\"10.3390/su14052628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLIAO Y (2020). WATER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/W12041177\u003c/span\u003e\u003cspan address=\"10.3390/W12041177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68181818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYUREKLI K, 2022, ATMOSFERA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20937/ATM.53024\u003c/span\u003e\u003cspan address=\"10.20937/ATM.53024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKIM YT, (2024). WEATHER CLIM EXTREMES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wace.2024.100688\u003c/span\u003e\u003cspan address=\"10.1016/j.wace.2024.100688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e#NUM!\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAHILL-MARIER B, (2022). HYDROL EARTH SYST SCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/Hess-26-5685-2022\u003c/span\u003e\u003cspan address=\"10.5194/Hess-26-5685-2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Most relevant words\u003c/h2\u003e\u003cp\u003eThe terms 'rainfall' (18 occurrences) and 'rain' (14 occurrences) appear most frequently, suggesting that the primary focus of the studies is on precipitation and rainfall. Terms such as 'extreme event,' 'annual maximum rainfalls,' and 'precipitation intensity' also appear frequently, indicating a focus on understanding rainfall extremes, their intensities, and their impacts Climate change' appears 10 times, with several mentions of related terms such as 'climate models' (4 occurrences), 'regional climate' (4), and 'climate modeling' (5).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below provides further insights by including columns showing the frequency of specific terms, along with three different year ranges (Q1, Median, Q3) representing the periods when these terms were most frequently used. The frequency column indicates the frequency with which each term appears. For example, 'rainfall' is mentioned 18 times, while 'extreme rainfall' is referenced 5 times.\u003c/p\u003e\u003cp\u003eQ1 indicates the first significant mention or publication of the term, acting as a reference point for its initial appearance in the data. The Median year represents the middle year among all references where the term was mentioned, showing when its usage reached its peak. Finally, Q3 represents the most recent significant mention or publication involving the term, indicating its ongoing relevance over time.\u003c/p\u003e\u003cp\u003eThe term analysis offers insights into the frequency and temporal distribution of key terms related to climate and rainfall research. Precipitation assessment was cited five times, with its first significant mention in 1999, a median reference year in 2008, and later appearances up to 2013. Similarly, the term 'Italy' first appeared in this context around 2011, with mentions continuing through to 2018, highlighting the geographical focus. Australia was mentioned five times, with the first references appearing in 2016 and continuing until 2017, indicating a brief but focused interest in the region. The term 'rain' was frequently mentioned, with 14 references between 2016 and 2020, indicating a growing focus on rainfall in recent years. The growing significance of climate-related topics is also reflected in the term 'climate change,' which appeared 10 times, mainly between 2016 and 2018. This reflects a rise in research focus on climate change during these years. Similarly, 'annual maximum rainfalls' were mentioned eight times, first appearing in 2016 and continuing through 2019, emphasizing concerns about extreme rainfall events.\u003c/p\u003e\u003cp\u003eThe term 'rainfall' appears most frequently, with 18 mentions between 2016 and 2020, highlighting its central importance in the research. Discussions surrounding 'extreme events' also increased, with nine mentions between 2017 and 2022, reflecting the growing significance of extreme weather phenomena in recent discussions. The emphasis on predictive approaches is reflected in 'climate modeling,' which was mentioned five times, particularly between 2017 and 2019, aligning with broader discussions on climate change and rainfall trends. Finally, the term 'extreme rainfall' was cited five times between 2018 and 2020, indicating a sustained focus on extreme precipitation events as an important area of research.\u003c/p\u003e\u003cp\u003eThe table indicates that topics such as rainfall, climate change, and extreme events have garnered significant attention in recent years, particularly from 2016 to 2020. Australia and Italy emerge as important geographic regions of focus. The earlier quartile dates (Q1) indicate when specific topics first appeared, while the median and Q3 columns reflect sustained or increasing interest over time. This suggests a growing research focus on precipitation, extreme weather events, and climate modeling over the past decade, with particular attention to studying the impacts of climate change on rainfall and precipitation patterns, as well as modeling future scenarios. Regions and countries such as 'Australia' (5 occurrences), 'Italy' (5), 'Western Australia' (2), and 'Turkey' (2) are mentioned, suggesting that case studies or data from these areas are frequently incorporated in the analysis.\u003c/p\u003e\u003cp\u003eThe frequent references to statistical methods like 'regression analysis' (3), 'time series analysis' (3), 'cluster analysis' (3), and 'probability distributions' (3) highlight the focus on quantitative approaches. Techniques such as 'Monte Carlo methods' (3), 'frequency analysis' (5), and 'estimation method' (5) demonstrate the use of probabilistic modeling in rainfall and flood risk assessments. Several terms related to water and flood management, such as 'flood control,' 'flood frequency,' 'floods,' and 'flood mitigation,' emphasize the importance of studying how rainfall patterns influence flooding, particularly in the context of climate change, Regional frequency analysis' (4) and 'regional climate models' (2) focus on understanding the variations in rainfall and climate at a regional level, potentially addressing how different regions are impacted differently by extreme weather events.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMost Relevant Words\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear (Q1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYear (Median)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYear (Q3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprecipitation assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItaly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eclimate change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eannual maximum rainfalls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eextreme event\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eclimate modeling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eextreme rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Word Cloud Analysis\u003c/h2\u003e\u003cp\u003eWord clouds, also known as text clouds or tag clouds, are visual representations of the frequency of words within a text passage. With the size of each word corresponding to its frequency in the text, they graphically represent the most commonly used words. In textual data, word clouds are valuable tools for quickly identifying key themes and concepts, as the more frequently a word appears, the larger and bolder it is displayed (Zucco et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This image is a word cloud, visually representing the frequency of key terms by their size. The larger the word, the more frequently it appears in the dataset or discussion. The analysis shows that 'Rainfall' and 'Rain' are the most prominent terms in the dataset, reflecting their high frequency of occurrence. Terms like 'Climate change,' 'Extreme event,' and 'Annual maximum rainfalls' also appear frequently, indicating that these concepts are key themes in discussions about climate and precipitation.\u003c/p\u003e\u003cp\u003eOther Significant Terms\u003c/p\u003e\u003cp\u003eModerately sized terms like 'Estimation method,' 'Climate modeling,' 'Australia,' 'Italy,' and 'Precipitation assessment' indicate frequent mentions, though not to the same degree as the most prominent terms. Furthermore, terms such as \"Extreme rainfall,\" \"Frequency analysis,\" \"Regional climate,\" and \"Precipitation intensity\" are notable for their recurring use in discussions related to weather patterns, climate models, and rainfall dynamics.\u003c/p\u003e\u003cp\u003eMetrics Interpretation\u003c/p\u003e\u003cp\u003eThe table below presents several research studies, including their respective Digital Object Identifier (DOI), overall citations, mean citations per year (TC/yr), and standardized overall citations (Normalized TC), based on a pre-specified baseline.\u003c/p\u003e\u003cp\u003eTop-Cited Papers\u003c/p\u003e\u003cp\u003eAmong highly cited papers, NGONGONDO CS (2011) was exemplary, having been cited 102 times, with an average of 7.29 citations annually and a normalized score for citations of 1.55. Similarly, a highly cited paper, ARNONE E (2013), has been cited 98 times and has an average of 8.17 citations annually, but a normalized score of 1, which makes it a point of comparison. Recent Papers\u003c/p\u003e\u003cp\u003eRegarding recent journals, Requena AI (2021) has garnered a total of 18 citations, a relatively new addition, with a remarkable average of 4.5 citations per year and a normalized score of 1. On the other hand, KIM YT (2024), being a relatively recent addition, has not yet garnered any citations, which is to be expected since it was recently issued.\u003c/p\u003e\u003cp\u003eNotable Patterns\u003c/p\u003e\u003cp\u003eThe data also show significant patterns for citation trends. Omitting older publications like REED DW (1999) and FAULKNER DS (1999) that have significantly low average citations per year (about 1), which may be attributed to their age, but still have a normalized citation score higher than 1. On the contrary, recent works such as MAZZOGLIO P (2020) and PELOSI A (2022) have high scores of normalized citations of 1.5 and 2, which are signs that they are receiving higher attention than older journals.\u003c/p\u003e\u003cp\u003eNo Citations Yet\u003c/p\u003e\u003cp\u003eOn the contrary, recent studies such as MAZZOGLIO P (2020) and PELOSI A (2022) have high normalized citation score values of 1.5 and 2, respectively, which shows that compared to older publications, they are receiving increasing popularity. This lack is not uncommon in extremely recent work, as it may take time for its impact to become apparent throughout the academic community. On a general level, the table illustrates the impact of citations on various research papers over time, including both highly cited older studies and new publications that are just starting to gain momentum in citations. Normalized TC values provide a perspective on the relative penetration in the field, illustrating how recent works may have more or less citation traction than older ones. The term' cloud' highlights climate events and analysis, specifically rainfall and extreme climate phenomena. Analysis, regional, Monte Carlo, and general circulation models indicate a clear direction in modeling and analysis content.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA bibliometric review examined the literature on annual maximum rainfall in various locations, integrating a vast volume of data on meteorological parameters, geography, and human activities that influence rainfall intensity. The assessment aimed to identify key trends and areas of ignorance in recognizing extreme rainfall occurrences, highlighting the mounting threats of climate change. The analysis provides information that may inform prospective research, policy, and practical actions to make communities more resilient and better adapt to mounting climate threats.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eEthics/Informed Consent\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Informed permission, anonymity, confidentiality, and adherence to institutional ethical norms for \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; data collection and management were all assured\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eConsent for publication\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors have approved the final manuscript and authorship order. This work complies with institutional policies, and no barriers to publication exist.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAvailability of data and materials\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCompeting interests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFunding\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAuthors\u0026apos; contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eM.N. wrote the manuscript. Z.A. proofread the manuscript critically and participated in the literature review. Each author made an equal contribution to the manuscript\u0026apos;s drafting and revision, and they all gave their approval for publishing\u003c/p\u003e\n\u003cp\u003eMuhammad Nura and Zahratul Amani Zakaria collaboratively conducted a formal analysis and investigation of the data, contributing equally to developing the original draft and creating visualizations for the study.\u003c/p\u003e\n\u003cp\u003eMethodology and Writing Review \u0026amp; Editing by Muhammad Nura developed the research methodology and, alongside Zahratul Amani Zakaria, reviewed and edited the manuscript to enhance clarity and coherence.\u003c/p\u003e\n\u003cp\u003eZahratul Amani Zakaria supervised the research process and secured the necessary funding to support the project\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe author would like to express his sincere gratitude to Kano State Polytechnic, Kano, Nigeria, for the invaluable support and scholarly guidance he received while undertaking a PhD at Unizsa.\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Qaribu Yahaya Nasidi for his invaluable support and guidance throughout this review. His insights and expertise significantly contributed to the depth and quality of our work. We sincerely appreciate his encouragement and dedication, which were instrumental in the completion of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of Competing Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCutter, S. L. (2021). The Changing Nature of Hazard and Disaster Risk in the Anthropocene. \u003cem\u003eAnnals of the American Association of Geographers\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(3), 819\u0026ndash;827.\u003c/li\u003e\n \u003cli\u003eDiriba, T. A., \u0026amp; Debusho, L. K. (2021). 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Evidence of shorter, more extreme rainfalls and increased flood variability under climate change. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e603\u003c/em\u003e, 126994.\u003c/li\u003e\n \u003cli\u003eZucco, C., Calabrese, B., Agapito, G., Guzzi, P. H., \u0026amp; Cannataro, M. (2020). Sentiment Analysis for Mining Texts and Social Network Data: Methods and Tools. \u003cem\u003eWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), e1333.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Regional, Rainfall, Extreme Events, Annual, Maximum","lastPublishedDoi":"10.21203/rs.3.rs-7819753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7819753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs climate variability increases, understanding the patterns of annual maximum rainfall becomes vital for addressing challenges in flood prevention, water resource management, and enhancing climate resilience. Heavy rainfall often leads to landslides, floods, and damage to infrastructure, with the most significant effects on vulnerable communities and disruptions to economic activities. These events can worsen existing inequalities and place additional pressure on local economies, making it crucial to assess and predict such severe occurrences at the regional level. This proactive strategy helps mitigate negative impacts and develop adaptive strategies to address future climate uncertainties. The review compiles data from different geographic areas, offering a comprehensive understanding of rainfall patterns across various climates. It explores the methods used to analyze maximum rainfall, with a focus on statistical models, GIS, and climate simulations that combine historical rainfall data with projections of future climate conditions. For example, Geographic Information Systems (GIS) enable spatial analysis of rainfall data, providing deeper insights into how geographical features influence precipitation patterns. Additionally, climate simulations assist in forecasting future rainfall patterns based on different greenhouse gas emission scenarios.\u003c/p\u003e","manuscriptTitle":"Regional Analysis of Annual Maximum Rainfall: A Bibliometric Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 09:08:53","doi":"10.21203/rs.3.rs-7819753/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":"1f7eedeb-6d0f-4c09-8db5-7c6059d26f8f","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-20T10:15:13+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T10:25:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 09:08:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7819753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7819753","identity":"rs-7819753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}