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Urban Flood Frequency Analysis and Risk Assessment

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (31 May 2025) | Viewed by 10722

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Special Issue Editors

Dr. Khaled Haddad

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Guest Editor

School of Engineering, Design and Built Environment, Penrith Campus, Western Sydney University, Building XB, Kingswood, NSW 2751, Australia
Interests: hydrology; floods; statistical methods; environmental modeling; education
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ataur Rahman

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Guest Editor

Core Member, Renewable Energy and Water Research Group (Sustainability and Resilience Theme), School of Engineering, Design and Built Environment, Western Sydney University, Penrith, Australia
Interests: water and environmental engineering; hydrology; climate change impacts; floods; water-sensitive urban design; rainwater harvesting; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on flood estimation in catchments using flood frequency analysis and different modelling methods.

Flood frequency analysis (FFA) is often adopted to estimate design floods, which are needed for many water resource management tasks, e.g., to size hydraulic structures and to carry out flood risk and ecology assessments, as well as flood insurance studies. Therefore, FFA remains an active area of interest and research. The most direct method of design flood estimation is at-site FFA analysis, which relies on a relatively long period of recorded streamflow data at a given site. Hence, the selection of an appropriate probability distribution-associated parameter estimation procedure, accounting for climate change and uncertainty is of prime importance in at-site FFA. With recent advancements in statistical and computational modelling and computing facilities, FFA estimates can be assessed more reliably and efficiently.

We invite original research articles that contribute to the continuing efforts to understand hydrological data and the complex hydrological processes that they exhibit to study furthermore reliable FFA estimates. This Special Issue also welcomes manuscripts on uncertainty analysis in FFA and the application of flood modelling to support decision making.

Potential topics for this Special Issue include, but are not limited to, the following:

Flood frequency analysis: advances in methods, regional case studies, variability, and trend analysis.
Annual maximum and peaks-over-threshold flood frequency analysis.
Impacts of climate change on flood frequency analysis: stationary vs. non-stationary flood frequency analysis.
Uncertainty in flood frequency analysis.
Impact of rating curve errors on flood frequency analysis.
Impact of distributional assumptions, parameter estimates, record lengths, and outliers on flood frequency analysis.
Bayesian methods and Monte Carlo simulation in flood frequency analysis.
Goodness-of-fit methods for flood frequency analysis.
Bivariate flood frequency analysis using copulas.
Historical and paleohydrologic information in flood frequency analysis.
Entropy-based flood frequency analysis.
Urbanization effects on flood frequency analysis.
Insights and lessons learnt from streamflow data preparation for flood frequency analysis.
Regional flood frequency analysis including and not limited to linear and nonlinear modelling approaches.

Dr. Khaled Haddad
Prof. Dr. Ataur Rahman
Guest Editors

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Keywords

flood frequency analysis
Bayesian
regional flood frequency analysis
distributions
uncertainty
modelling
annual maximum data
peaks-over-threshold data

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Published Papers (13 papers)

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Research

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32 pages, 2679 KiB

Open AccessArticle

An In-Depth Statistical Analysis of the Pearson Type III Distribution Behavior in Modeling Extreme and Rare Events

by Cristian-Gabriel Anghel and Dan Ianculescu

Water 2025, 17(10), 1539; https://doi.org/10.3390/w17101539 - 20 May 2025

Viewed by 411

Abstract

Statistical distributions play a crucial role in water resources management and civil engineering, particularly for analyzing data variability and predicting rare events with extremely long return periods (e.g., T = 1000 years, T = 10,000 years). Among these, the Pearson III (PE3) distribution is widely used in hydrology and flood frequency analysis (FFA). This study aims to provide a comprehensive guide to the practical application of the PE3 distribution in FFA. It explores five parameter estimation methods, presenting both exact and newly developed approximate relationships for calculating distribution parameters and frequency factors. The analysis relies on data from four rivers with varying morphometric characteristics and record lengths. The results highlight that the Pearson III distribution, when used with the L-moments method, offers the most reliable quantile estimates, characterized by the smallest biases compared to other methods (e.g., 31% for the Nicolina River and, respectively, 5% for the Siret and Ialomita Rivers) and the highest confidence in predicting rare events. Based on these findings, the L-moments approach is recommended for flood frequency analysis to improve the accuracy of extreme flow forecasts. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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21 pages, 6159 KiB

Open AccessArticle

Coastal Flooding Hazards in Northern Portugal: A Practical Large-Scale Evaluation of Total Water Levels and Swash Regimes

by Jose Eduardo Carneiro-Barros, Ajab Gul Majidi, Theocharis Plomaritis, Tiago Fazeres-Ferradosa, Paulo Rosa-Santos and Francisco Taveira-Pinto

Water 2025, 17(10), 1478; https://doi.org/10.3390/w17101478 - 14 May 2025

Viewed by 442

Abstract

The northern Portuguese coast has been increasingly subjected to wave-induced coastal flooding, highlighting a critical need for comprehensive overwash assessment in the region. This study systematically evaluates the total water levels (TWLs) and swash regimes over a 120 km stretch of the northern coast of Portugal. Traditional approaches to overwash assessment often rely on detailed models and location-specific data, which can be resource-intensive. The presented methodology addresses these limitations by offering a pragmatic balance between accuracy and practicality, suitable for extended coastal areas with reduced human and computational resources. A coastal digital terrain model was used to extract essential geomorphological features, including the dune toe, dune crest, and/or crown of defense structures, as well as the sub-aerial beach profile. These features help establish a critical threshold for flooding, alongside assessments of beach slope and other relevant parameters. Additionally, a wave climate derived from a SWAN regional model was integrated, providing a comprehensive time-series hindcast of sea-states from 1979 to 2023. The wave contribution to TWL was considered by using the wave runup, which was calculated using different empirical formulas based on SWAN’s outputs. Astronomical tides and meteorological surge—the latter reconstructed using a long short-term memory (LSTM) neural network—were also integrated to form the TWL. This integration of geomorphological and oceanographic data allows for a straightforward evaluation of swash regimes and consequently overwash potential. The accuracy of various empirical predictors for wave runup, a primary hydrodynamic factor in overwash processes, was assessed. Several reports from hazardous events along this stretch were used as validation for this method. This study further delineates levels of flooding hazard—ranging from swash and collision to overwash at multiple representative profiles along the coast. This regional-scale assessment contributes to a deeper understanding of coastal flooding dynamics and supports the development of targeted, effective coastal management strategies for the northern Portuguese coast. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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19 pages, 18748 KiB

Open AccessArticle

Regional Flood Risk Assessment and Prediction Based on Environmental Attributes and Pipe Operational Characteristics

by Jinping Zhang, Yirong Yang, Lixin Zhang, Xi Zhang and Yao Wang

Water 2025, 17(10), 1477; https://doi.org/10.3390/w17101477 - 14 May 2025

Viewed by 319

Abstract

Urban flood risk assessments play a crucial role in urban resilience and disaster management. This paper proposes a comprehensive method for urban flood risk assessment and prediction that is based on environmental attributes and the operational characteristics of pipe networks. Using the central urban area of Zhengzhou as a case study, an integrated urban flood risk evaluation index system was developed, and the entropy weight method was applied to quantify risk indicators. A loosely coupled RF-XGBoost model was constructed to predict the flood risk of different rainfall scenarios. The results indicate that (1) the overall flood risk in the study area exhibits an increasing trend from the northeast to the southwest, with medium- to high-risk zones being predominant; (2) the spatial distribution pattern of the comprehensive flood risk closely aligns with that of the environmental risk but shows slight variations under the influence of pipe network operational risks; (3) the RF-XGBoost model demonstrates superior predictive accuracy under multi-factor coupling scenarios. When rainfall characteristics, environmental attributes, and pipe network operational risks are comprehensively considered, the Nash–Sutcliffe Efficiency (NSE) of the predictions improves from 0.85 (when using only rainfall characteristics) to 0.94. This study provides valuable insights and technical support for mitigating urban flood risks. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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21 pages, 20296 KiB

Open AccessArticle

Urban Flood Prediction Model Based on Transformer-LSTM-Sparrow Search Algorithm

by Zixuan Fan, Jinping Zhang, Yanpo Chen and Hongshi Xu

Water 2025, 17(9), 1404; https://doi.org/10.3390/w17091404 - 7 May 2025

Viewed by 534

Abstract

Global climate change and accelerated urbanization have intensified extreme rainfall events, exacerbating urban flood risks. Although data-driven models have shown potential in urban flood prediction, the ability of single models to capture complex nonlinear relationships and their sensitivity to hyperparameters still limit prediction accuracy. To address these challenges, this study proposes an urban flood prediction model by integrating Transformer, Long Short-Term Memory (LSTM), and Sparrow Search Algorithm (SSA), combining Transformer’s global feature extraction with LSTM’s temporal modeling. The SSA was adopted to optimize hyperparameters for the Transformer-LSTM model. Dropout and early stopping techniques were adopted to mitigate overfitting. Applied to Zhengzhou city of Henan province, China, the model achieves a Nash-Sutcliffe Efficiency (NSE) of 0.971, indicating that the proposed model has high prediction performance for urban flooding. The experimental results demonstrate that the Transformer-LSTM-SSA model outperforms the standalone Transformer, LSTM, and Transformer-LSTM models by 12.9%, 10.1%, and 2.9% in NSE accuracy, respectively, while reducing MAE by 62.12%, 56.9%, and 34.21%, respectively, and MAPE by 21.69%, 22.2%, and 10.89%, respectively. Furthermore, the proposed model exhibits enhanced stability and superior generalization capability. The Transformer-LSTM-SSA model exhibits superior performance among the comparative methods, thereby demonstrating the model’s viability for providing a reliable solution for real-time flood prediction and early warning. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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36 pages, 14933 KiB

Open AccessArticle

Spatiotemporal Classification of Short-Duration Heavy Rainfall in Beijing Using K-Shape Clustering

by Zefeng Qiu, Binbin Wu, Qi Chu, Xianpeng Xie, Ruhao Sun and Shuhui Jia

Water 2025, 17(7), 968; https://doi.org/10.3390/w17070968 - 26 Mar 2025

Viewed by 293

Abstract

Understanding the spatiotemporal dynamics of short-duration heavy rainfall (SDHR) is critical for urban flood management. This study applies the K-shape clustering algorithm to classify 105 SDHR events in Beijing (2009–2021) using hourly rainfall data. Compared to K-means and DTW, K-shape prioritizes temporal shape alignment, crucial for capturing phase-shifted rainfall patterns. Three clusters emerged: (1) localized moderate-intensity events (13.3% of events) peaking at noon (11:00–14:00 LST) in western/southeastern regions, with weak burstiness (44.3% stations peak within 0–1 h) and moderate spatial variability (Cv = 1.08); (2) highly variable, intense urban rainfall (47.6% of events) characterized by rapid burstiness (72.5% stations peak within 0–1 h) and extreme spatial heterogeneity (Cv = 1.21), concentrated in central urban areas with peak intensities >130 mm/h; (3) prolonged heavy rainfall (39.1% of events) lasting >6 h, featuring significant accumulation (mean > 50 mm/day) in northeastern plains. The framework identifies high-risk zones (e.g., Cluster 2’s urban flash floods) and informs adaptive drainage design (e.g., prolonged resilience for Cluster 3). This study highlights the necessity of combining statistical metrics with domain expertise for robust SDHR classification and provides insights for urban flood management, emphasizing targeted strategies for different rainfall patterns. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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19 pages, 9421 KiB

Open AccessArticle

Risk Analysis of Urban Drainage System Siltation Based on Complex Networks

by Jinping Zhang, Yao Wang, Lixin Zhang, Xi Zhang and Yirong Yang

Water 2025, 17(7), 951; https://doi.org/10.3390/w17070951 - 25 Mar 2025

Viewed by 401

Abstract

The performance of urban drainage systems can be significantly compromised by siltation in pipeline networks. This study focuses on the drainage network of central Zhengzhou, analyzing operational risks under current siltation conditions. Using complex network theory, the study examines the structural characteristics and propagation mechanisms of the siltation propagation chain, quantifying node risks through indicators such as pipeline risk factors and degree centrality. Edge vulnerability is incorporated to evaluate the risk values of siltation propagation paths. The study’s findings indicate the following: (1) Despite a relatively low overall siltation risk, regular pipeline inspection and maintenance is necessary. (2) A total of 22 critical nodes, primarily located in main pipelines or confluence manholes, exhibit high risk and require priority attention. (3) Siltation propagation shows significant chain characteristics, with main pipeline and junction node failures potentially leading to systemic crises. In the central Zhengzhou stormwater network presented in this paper, high-risk factors are concentrated in a southern downstream outlet caused by an edge identified as critical that propagates siltation risks to the downstream nodes, forming a long path with elevated risk levels. This study provides crucial insights into the risk management and prevention of sedimentation and blockages in urban drainage networks, not only offering important technical references and a solid scientific basis for pipeline maintenance and network upgrades—thereby contributing to drainage system planning and the enhancement of urban flood protection capabilities—but also serving as a valuable technical reference for improving the overall resilience and operational efficiency of drainage systems. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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20 pages, 2900 KiB

Open AccessArticle

Relationship Between Urbanization–Induced Land Use Changes and Flood Risk: Case Study in Chiang Mai, Thailand

by Zhaolong Gu, Sartsin Phakdimek, Kozo Nagami and Daisuke Komori

Water 2025, 17(3), 327; https://doi.org/10.3390/w17030327 - 24 Jan 2025

Cited by 1 | Viewed by 1442

Abstract

Urban flooding has long been a critical issue, particularly in rapidly urbanizing regions of developing countries, where land use changes—especially the conversion of rice paddies into urban areas—have significantly increased flood risks. This study investigated the impact of urbanization on flood risk taking Chiang Mai, Thailand, as a case study. Based on historical flood data, the study identified and analyzed frequent flood–prone areas in Chiang Mai during the period from 1990 to 2018. By integrating the Rainfall–Runoff–Inundation (RRI) model simulation results and the remote sensing data, the research quantified dynamics in flood risk, exposure, and vulnerability across these frequent flood–prone areas. The findings demonstrated that the conversion of high–exposure paddy fields into urban areas markedly elevated area flood risks, primarily due to the reduction in water retention capacity and the inheritance of the high–exposure characteristics of paddy fields. This study highlighted the importance of integrating sustainable urban planning and land management strategies in rapidly urbanizing regions. Furthermore, this study examined the feasibility of adopting flood characteristics quantification in frequent flood–prone areas as a systematic approach to analyze the dynamic interplay between flood risks and urbanization in developing countries. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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23 pages, 4056 KiB

Open AccessArticle

Generalised Additive Model-Based Regional Flood Frequency Analysis: Parameter Regression Technique Using Generalised Extreme Value Distribution

by Laura Rima, Khaled Haddad and Ataur Rahman

Water 2025, 17(2), 206; https://doi.org/10.3390/w17020206 - 14 Jan 2025

Cited by 1 | Viewed by 852

Abstract

This study examines the effectiveness of Generalised Additive Models (GAMs) and log-log linear models for estimating the parameters of the generalised extreme value (GEV) distribution, which are then used to estimate flood quantiles in ungauged catchments. This is known as the parameter regression technique (PRT). Using data from 88 gauged catchments in New South Wales, Australia, flood quantiles were estimated for various annual exceedance probabilities (AEPs) of 50%, 20%, 10%, 5%, 2%, and 1%, corresponding to return periods of 2, 5, 10, 20, 50, and 100 years, denoted by Q₂, Q₅, Q₁₀, Q₂₀, Q₅₀, and Q₁₀₀, respectively. These flood quantiles were then used as dependent variables, while several catchment characteristics served as independent variables in the regression. GAMs were employed to capture non-linearities in flood generation processes. This study evaluates different GAMs and log-log linear models, identifying the best ones based on significant predictors and various statistical metrics using a leave-one-out (LOO) validation approach. The results indicate that GAMs provide more accurate and reliable predictions of flood quantiles compared to the log-log linear models, demonstrating better performance in capturing observed values across different quantiles. The absolute median relative error percentage (REr%) ranges from 33% to 39% for the GAMs and from 36% to 45% for the log-log models. GAMs demonstrate better performance compared to the log-log linear models for quantiles Q₂, Q₅, Q₁₀, Q₂₀, and Q₅₀; however, their performances appear to be similar for Q₁₀₀. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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14 pages, 2303 KiB

Open AccessArticle

Decomposing Future Exposure from Increasing Flood Risk and Forecast Population Changes Across Shared Socioeconomic Pathways (SSPs) in the United States

by Jeremy R. Porter, Evelyn G. Shu, Matthew Hauer, Zachary M. Hirsch and Jasmina Buresch

Water 2024, 16(22), 3289; https://doi.org/10.3390/w16223289 - 16 Nov 2024

Cited by 1 | Viewed by 1174

Abstract

Extreme weather events, like flooding, are expected to become more severe due to climate change and increasingly impact populations across the US. Adding to this challenge, populations have concurrently settled in risky areas that were previously thought to have low, or no, exposure. Objective: This research seeks to understand the unique contribution of population growth and climate change as independent components of future risk levels in the US. To do so, future population level forecasts are coupled with future flood projections along all five Shared Socioeconomic Pathways (SSPs) at the block group level across the US. The results indicate that, across the five SSPs, the most increase in exposure will occur in SSP5 (+470,719), and the least will occur in SSP 3 (+57,189). By decomposing the contributions from flood and population growth, we find that the population growth-induced effect contributed to an increase in the population exposure for all of the SSPs except for SSP3. This research, and these results, provide a foundation for understanding future risks of flood exposure in an isolated framework and lay the groundwork for the development and integration of planning, adaptation, and mitigation efforts that may be used to address the growing risk of flooding in the context of the population forecasts provided here. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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13 pages, 8412 KiB

Open AccessArticle

Grain Size in an Alpine Lake from the Chinese Loess Plateau: Implications for Paleofloods and East Asian Summer Monsoon Variability

by Chao Zhang, Keke Yu, Aizhen Li, Tianao Li and Suyue Xin

Water 2024, 16(21), 3129; https://doi.org/10.3390/w16213129 - 1 Nov 2024

Cited by 1 | Viewed by 875

Abstract

Reliable paleoflood proxies can help reconstruct past flood variation patterns. Here, we investigated the grain-size data of a 63 cm core retrieved from Lake Chaonaqiu, western Chinese Loess Plateau, in order to build a long time-series of flood occurrence from sedimentology that extends the period of instrumental data. Our results indicate that three parameters (mean, standard deviation and grain-size ratio of 16–63/2–16 μm) are sensitive to hydrodynamic changes in Lake Chaonaqiu, which are further linked to high-energy inflow associated with high-intensity rainfall or flood events. These three parameters’ variations were well correlated with the precipitation records reconstructed from tree-rings and historical documents in neighboring regions and overlapped with 109 historical flood events from historical documents in counties around the lake for the past 300 years. Therefore, we propose that the grain size in the sediments of Lake Chaonaqiu is a reliable paleoflood proxy. The sensitivity of flood signals to grain size may be related to the precipitation and vegetation cover in the catchment of the lake, which are further linked to the strength of the East Asian summer monsoon. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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22 pages, 9161 KiB

Open AccessArticle

Modeling Three-Dimensional Exfiltration Rates from Permeable Street Stormwater Inlets as One-Dimensional Water Flux in Urban Hydrological Models

by Ryuga Iinuma, Shigeki Harada and Nana Yamauchi

Water 2024, 16(21), 3076; https://doi.org/10.3390/w16213076 - 27 Oct 2024

Viewed by 1027

Abstract

Climate change has increased the intensity and frequency of weather systems, increasing the risk of inundation in urban areas. To mitigate these risks, not only rivers but also entire catchments need to be managed, and the use of infiltration and retention units needs to be expanded. The ability to evaluate the effects of promoting infiltration and retention in catchments using distributed hydrological models, clarify the three-dimensional behavior of exfiltration from catchments into natural base soils, and parameterize this flow as a one-dimensional hypothetical water flux is essential. Using VGFlow2D (Forum8) and field observations, numerical analyses were conducted to parametrize the flux and assess the features of q/Ks values, representing the volume of three-dimensional water exfiltration from stormwater inlet bases into natural soils relative to the saturated hydraulic conductivity (Ks) of the soils. The findings were integrated into the hydrological model Infoworks ICM (Innovyze) by adding a single parameter, the “exfiltration loss rate”, to each inlet without increasing computational demands. The obtained q/Ks values were compared to previously reported values, and variations were evaluated using infiltration theory. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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20 pages, 22795 KiB

Open AccessArticle

Runoff Control Performance of Three Typical Low-Impact Development Facilities: A Case Study of a Community in Beijing

by Jiayi Xiao, Zhiwei Zhou, Zhiyu Yang, Zhili Li, Xiaolong Li, Jinjun Zhou and Hao Wang

Water 2024, 16(17), 2373; https://doi.org/10.3390/w16172373 - 23 Aug 2024

Viewed by 1260

Abstract

The development of sponge cities advocates for sustainable urban rainwater management, effectively alleviating urban flood disasters, reducing non-point-source pollution, and promoting the recycling of rainwater resources. Low-Impact Development (LID) serves as a key strategy in this context, providing essential support for urban rainwater control and pollution reduction. To investigate the runoff control effects of LID measures and to reveal the relationship between facility runoff control performance and installation scale, this study focuses on a sponge community in Beijing. A SWMM model was constructed to analyze the rainwater flood control and pollutant load reduction effects of different LID facilities, including bio-retention cells, green roofs, and permeable pavements. Using evaluation indicators such as surface runoff, node overflow, and pollutant control rates, this study examined how facility performance varies with installation scale under different rainfall conditions. The combination scheme of LID equipment optimal configuration is designed by using multiple criteria decision analysis (MCDA) and cost–benefit theory. The results indicate significant differences in performance among the various LID facilities across different rainfall scenarios. Specifically, the optimal installation proportion for runoff and overflow control of permeable pavements were found to be between 30% and 70%. Green roofs demonstrate superior performance in handling extreme rainfall events, while bio-retention cells exhibit significant effectiveness in controlling Total Suspended Solids (TSSs). Through comprehensive performance evaluation, this study identified the optimal combination scale under a 3-year rainfall recurrence interval as 30% permeable pavements, 20% green roof, and 60% bio-retention cells. This combination effectively leverages the strengths of each facility, ensuring system stability and efficiency while also demonstrating optimal management efficiency in cost–benefit analyses. The findings of this research provide valuable insights for future urban water management and infrastructure development. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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Review

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17 pages, 3997 KiB

Open AccessReview

A Systematic Review of Programs and Mechanisms for Industry Engagement in Flood Water Management: Global Challenges and Perspectives

by Yerlan Issakov, Karlygash Shynbergenova, Murat Qasenuly, Tamara Gajić and Aizhan Skakova

Water 2025, 17(8), 1155; https://doi.org/10.3390/w17081155 - 13 Apr 2025

Viewed by 650

Abstract

Floods represent one of the most significant global risks, threatening human lives, infrastructure, and economic development. Although various strategies for flood water management have been developed, their effectiveness and applicability vary depending on geopolitical, economic, and climatic factors. This systematic review aims to analyze and critically assess existing mechanisms and programs focused on industry engagement in flood risk reduction and flood water management. Through a comprehensive literature review, key strategies have been identified, including nature-based solutions such as blue-green infrastructure, technological innovations in flood prediction, and regulatory frameworks designed to strengthen cooperation between the public and private sectors. Special attention is given to the limitations of previous research, including methodological shortcomings, the lack of empirical evidence on the long-term effects of strategies, and challenges in implementing existing policies. The findings highlight the need for an integrated approach that combines technical, regulatory, and socio-economic solutions for more effective flood risk reduction. This study contributes to academic and practical discussions by providing a comprehensive analysis of current strategies and offering guidelines for future research. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)

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