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Search Results (1,760)

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29 pages, 2766 KiB  
Article
(H-DIR)2: A Scalable Entropy-Based Framework for Anomaly Detection and Cybersecurity in Cloud IoT Data Centers
by Davide Tosi and Roberto Pazzi
Sensors 2025, 25(15), 4841; https://doi.org/10.3390/s25154841 - 6 Aug 2025
Abstract
Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)2, a hybrid entropy-based framework designed to detect and mitigate [...] Read more.
Modern cloud-based Internet of Things (IoT) infrastructures face increasingly sophisticated and diverse cyber threats that challenge traditional detection systems in terms of scalability, adaptability, and explainability. In this paper, we present (H-DIR)2, a hybrid entropy-based framework designed to detect and mitigate anomalies in large-scale heterogeneous networks. The framework combines Shannon entropy analysis with Associated Random Neural Networks (ARNNs) and integrates semantic reasoning through RDF/SPARQL, all embedded within a distributed Apache Spark 3.5.0 pipeline. We validate (H-DIR)2 across three critical attack scenarios—SYN Flood (TCP), DAO-DIO (RPL), and NTP amplification (UDP)—using real-world datasets. The system achieves a mean detection latency of 247 ms and an AUC of 0.978 for SYN floods. For DAO-DIO manipulations, it increases the packet delivery ratio from 81.2% to 96.4% (p < 0.01), and for NTP amplification, it reduces the peak load by 88%. The framework achieves vertical scalability across millions of endpoints and horizontal scalability on datasets exceeding 10 TB. All code, datasets, and Docker images are provided to ensure full reproducibility. By coupling adaptive neural inference with semantic explainability, (H-DIR)2 offers a transparent and scalable solution for cloud–IoT cybersecurity, establishing a robust baseline for future developments in edge-aware and zero-day threat detection. Full article
(This article belongs to the Special Issue Privacy and Cybersecurity in IoT-Based Applications)
9 pages, 1406 KiB  
Proceeding Paper
Disaster-Based Mobile Learning System Using Technology Acceptance Model
by John A. Bacus
Eng. Proc. 2025, 103(1), 5; https://doi.org/10.3390/engproc2025103005 - 6 Aug 2025
Abstract
Recently, the usage of mobile phone-based games has increased due to the growing accessibility and convenience they provide. Using a descriptive-quantitative design, a disaster-based mobile application was developed in this study to enhance disaster literacy among the private senior high schools in science, [...] Read more.
Recently, the usage of mobile phone-based games has increased due to the growing accessibility and convenience they provide. Using a descriptive-quantitative design, a disaster-based mobile application was developed in this study to enhance disaster literacy among the private senior high schools in science, technology, engineering, and mathematics (STEM) education in Davao City, the Philippines. The developed application was provided together with survey questionnaires to 364 students randomly selected from different schools in Davao City usingF a simple random sampling method. The technology acceptance (TAM) model was used to explain how users accepted the new technology. The mobile application was designed with features in four disaster scenarios—fire, flood, volcano, and earthquake. The results revealed a high acceptance, with an average score of the perceived usefulness (PE) of 4.52, perceived ease of use (PEOU) of 4.44, and a behavioral intention (BI) of 4.12. The students accepted the application to enhance disaster risk reduction and management. Aligned with SDG 4 and SDG 11, the application can be used to equip users with the knowledge to respond to disasters and ensure community resilience. Full article
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22 pages, 3483 KiB  
Review
The Paradigm Shift in Scientific Interest on Flood Risk: From Hydraulic Analysis to Integrated Land Use Planning Approaches
by Ángela Franco and Salvador García-Ayllón
Water 2025, 17(15), 2276; https://doi.org/10.3390/w17152276 - 31 Jul 2025
Viewed by 303
Abstract
Floods are natural hazards that have the greatest socioeconomic impact worldwide, given that 23% of the global population live in urban areas at risk of flooding. In this field of research, the analysis of flood risk has traditionally been studied based mainly on [...] Read more.
Floods are natural hazards that have the greatest socioeconomic impact worldwide, given that 23% of the global population live in urban areas at risk of flooding. In this field of research, the analysis of flood risk has traditionally been studied based mainly on approaches specific to civil engineering such as hydraulics and hydrology. However, these patterns of approaching the problem in research seem to be changing in recent years. During the last few years, a growing trend has been observed towards the use of methodology-based approaches oriented towards urban planning and land use management. In this context, this study analyzes the evolution of these research patterns in the field by developing a bibliometric meta-analysis of 2694 scientific publications on this topic published in recent decades. Evaluating keyword co-occurrence using VOSviewer software version 1.6.20, we analyzed how phenomena such as climate change have modified the way of addressing the study of this problem, giving growing weight to the use of integrated approaches improving territorial planning or implementing adaptive strategies, as opposed to the more traditional vision of previous decades, which only focused on the construction of hydraulic infrastructures for flood control. Full article
(This article belongs to the Special Issue Spatial Analysis of Flooding Phenomena: Challenges and Case Studies)
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14 pages, 1983 KiB  
Article
Numerical Approach for Predicting Levee Overtopping in River Curves Through Dimensionless Parameters
by Chanjin Jeong, Dong Hyun Kim and Seung Oh Lee
Appl. Sci. 2025, 15(15), 8422; https://doi.org/10.3390/app15158422 - 29 Jul 2025
Viewed by 164
Abstract
Recent climate changes have led to an increase in flood intensity, often resulting in frequent levee overtopping, which causes significant human and property damage. High vulnerability to such breaches is expected in general, especially at river curves. This study aims to predict the [...] Read more.
Recent climate changes have led to an increase in flood intensity, often resulting in frequent levee overtopping, which causes significant human and property damage. High vulnerability to such breaches is expected in general, especially at river curves. This study aims to predict the occurrence of levee overtopping at these critical points and to suggest a curve, the levee overtopping risk curve, to assess overtopping probabilities. For this purpose, several dimensionless parameters, such as superelevation relative to levee height (y/H) and the channel’s Froude number, were examined. Based on dimensional analysis, a relationship was developed, and the levee overtopping curve was finally proposed. The accuracy of this curve was validated through numerical analysis using a selected levee case, which clearly distinguished between safe and risky conditions for levee overtopping. The curve is designed for immediate integration into the hydraulic design processes, providing engineers with a reliable method for optimizing levee design to mitigate overtopping risks. It also serves as a critical decision-making tool in flood risk management, particularly for urban planning and infrastructure development in areas prone to flooding. Full article
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21 pages, 4181 KiB  
Article
Addressing Volatility and Nonlinearity in Discharge Modeling: ARIMA-iGARCH for Short-Term Hydrological Time Series Simulation
by Mahshid Khazaeiathar and Britta Schmalz
Hydrology 2025, 12(8), 197; https://doi.org/10.3390/hydrology12080197 - 27 Jul 2025
Viewed by 440
Abstract
Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes [...] Read more.
Selecting an appropriate model for discharge simulation remains a fundamental challenge in modeling. While artificial neural networks (ANNs) have been widely accepted due to detecting streamflow patterns, they require large datasets for efficient training. However, when short-term datasets are available, training ANNs becomes problematic. Autoregressive integrated moving average (ARIMA) models offer a promising alternative; however, severe volatility, nonlinearity, and trends in hydrological time series can still lead to significant errors. To address these challenges, this study introduces a new adaptive hybrid model, ARIMA-iGARCH, designed to account volatility, variance inconsistency, and nonlinear behavior in short-term hydrological datasets. We apply the model to four hourly discharge time series from the Schwarzbach River at the Nauheim gauge in Hesse, Germany, under the assumption of normally distributed residuals. The results demonstrate that the specialized parameter estimation method achieves lower complexity and higher accuracy. For the four events analyzed, R2 values reached 0.99, 0.96, 0.99, and 0.98; RMSE values were 0.031, 0.091, 0.023, and 0.052. By delivering accurate short-term discharge predictions, the ARIMA-iGARCH model provides a basis for enhancing water resource planning and flood risk management. Overall, the model significantly improves modeling long memory, nonlinear, nonstationary shifts in short-term hydrological datasets by effectively capturing fluctuations in variance. Full article
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24 pages, 6552 KiB  
Article
Assessing Flooding from Changes in Extreme Rainfall: Using the Design Rainfall Approach in Hydrologic Modeling
by Anna M. Jalowska, Daniel E. Line, Tanya L. Spero, J. Jack Kurki-Fox, Barbara A. Doll, Jared H. Bowden and Geneva M. E. Gray
Water 2025, 17(15), 2228; https://doi.org/10.3390/w17152228 - 26 Jul 2025
Viewed by 390
Abstract
Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study [...] Read more.
Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km2) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. Full article
(This article belongs to the Section Water and Climate Change)
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24 pages, 10881 KiB  
Article
Dynamics of Water Quality in the Mirim–Patos–Mangueira Coastal Lagoon System with Sentinel-3 OLCI Data
by Paula Andrea Contreras Rojas, Felipe de Lucia Lobo, Wesley J. Moses, Gilberto Loguercio Collares and Lino Sander de Carvalho
Geomatics 2025, 5(3), 36; https://doi.org/10.3390/geomatics5030036 - 25 Jul 2025
Viewed by 342
Abstract
The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the [...] Read more.
The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article
(This article belongs to the Special Issue Advances in Ocean Mapping and Hydrospatial Applications)
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28 pages, 9894 KiB  
Article
At-Site Versus Regional Frequency Analysis of Sub-Hourly Rainfall for Urban Hydrology Applications During Recent Extreme Events
by Sunghun Kim, Kyungmin Sung, Ju-Young Shin and Jun-Haeng Heo
Water 2025, 17(15), 2213; https://doi.org/10.3390/w17152213 - 24 Jul 2025
Viewed by 240
Abstract
Accurate rainfall quantile estimation is critical for urban flood management, particularly given the escalating climate change impacts. This study comprehensively compared at-site frequency analysis and regional frequency analysis for sub-hourly rainfall quantile estimation, using data from 27 sites across Seoul. The analysis focused [...] Read more.
Accurate rainfall quantile estimation is critical for urban flood management, particularly given the escalating climate change impacts. This study comprehensively compared at-site frequency analysis and regional frequency analysis for sub-hourly rainfall quantile estimation, using data from 27 sites across Seoul. The analysis focused on Seoul’s disaster prevention framework (30-year and 100-year return periods). Employing L-moment statistics and Monte Carlo simulations, the rainfall quantiles were estimated, the methodological performance was evaluated, and Seoul’s current disaster prevention standards were assessed. The analysis revealed significant spatio-temporal variability in Seoul’s precipitation, causing considerable uncertainty in individual site estimates. A performance evaluation, including the relative root mean square error and confidence interval, consistently showed regional frequency analysis superiority over at-site frequency analysis. While at-site frequency analysis demonstrated better performance only for short return periods (e.g., 2 years), regional frequency analysis exhibited a substantially lower relative root mean square error and significantly narrower confidence intervals for larger return periods (e.g., 10, 30, 100 years). This methodology reduced the average 95% confidence interval width by a factor of approximately 2.7 (26.98 mm versus 73.99 mm). This enhanced reliability stems from the information-pooling capabilities of regional frequency analysis, mitigating uncertainties due to limited record lengths and localized variabilities. Critically, regionally derived 100-year rainfall estimates consistently exceeded Seoul’s 100 mm disaster prevention threshold across most areas, suggesting that the current infrastructure may be substantially under-designed. The use of minute-scale data underscored its necessity for urban hydrological modeling, highlighting the inadequacy of conventional daily rainfall analyses. Full article
(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment)
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17 pages, 2548 KiB  
Article
Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach
by Ming Fan, Dan Lu and Sudershan Gangrade
Geosciences 2025, 15(8), 279; https://doi.org/10.3390/geosciences15080279 - 24 Jul 2025
Viewed by 266
Abstract
Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, [...] Read more.
Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article
(This article belongs to the Special Issue AI and Machine Learning in Hydrogeology)
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27 pages, 48299 KiB  
Article
An Extensive Italian Database of River Embankment Breaches and Damages
by Michela Marchi, Ilaria Bertolini, Laura Tonni, Luca Morreale, Andrea Colombo, Tommaso Simonelli and Guido Gottardi
Water 2025, 17(15), 2202; https://doi.org/10.3390/w17152202 - 23 Jul 2025
Viewed by 235
Abstract
River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, [...] Read more.
River embankments are critical flood defense structures, stretching for thousands of kilometers across alluvial plains. They often originated as natural levees resulting from overbank flows and were later enlarged using locally available soils yet rarely designed according to modern engineering standards. Substantially under-characterized, their performance to extreme events provides an invaluable opportunity to highlight their vulnerability and then to improve monitoring, management, and reinforcement strategies. In May 2023, two extreme meteorological events hit the Emilia-Romagna region in rapid succession, causing numerous breaches along river embankments and therefore widespread flooding of cities and territories. These were followed by two additional intense events in September and October 2024, marking an unprecedented frequency of extreme precipitation episodes in the history of the region. This study presents the methodology adopted to create a regional database of 66 major breaches and damages that occurred during May 2023 extensive floods. The database integrates multi-source information, including field surveys; remote sensing data; and eyewitness documentation collected before, during, and after the events. Preliminary interpretation enabled the identification of the most likely failure mechanisms—primarily external erosion, internal erosion, and slope instability—often acting in combination. The database, unprecedented in Italy and with few parallels worldwide, also supported a statistical analysis of breach widths in relation to failure mechanisms, crucial for improving flood hazard models, which often rely on generalized assumptions about breach development. By offering insights into the real-scale behavior of a regional river defense system, the dataset provides an important tool to support river embankments risk assessment and future resilience strategies. Full article
(This article belongs to the Special Issue Recent Advances in Flood Risk Assessment and Management)
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14 pages, 4489 KiB  
Article
Modifying Design Standards: The 2023 Extreme Flood’s Impact on Design Discharges in Slovenia
by Mojca Šraj and Nejc Bezak
Water 2025, 17(15), 2198; https://doi.org/10.3390/w17152198 - 23 Jul 2025
Viewed by 446
Abstract
An extreme flood event occurred in Slovenia in August 2023. This study evaluated the influence of this extreme flood on the design discharges in Slovenia. This evaluation was based on flood frequency analysis for the data from 33 gauging stations. Analyses were conducted [...] Read more.
An extreme flood event occurred in Slovenia in August 2023. This study evaluated the influence of this extreme flood on the design discharges in Slovenia. This evaluation was based on flood frequency analysis for the data from 33 gauging stations. Analyses were conducted with and without the 2023 peak discharge, i.e., for the periods 1961–2022 and 1961–2023, using eight different theoretical distribution functions. In addition, specific discharge values for the 2023 flood event were analyzed and compared with regional envelope curves for Europe. The findings of the study indicate that the impact of a single flood event on the design discharge values can be substantial. Moreover, an analysis of the specific discharges resulting from the 2023 flood event in Slovenia reveals that the values for all gauging stations considered are below the regional envelopes. Concurrently, the analysis indicates that a flood event larger than the 2023 event may occur in the future. Full article
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23 pages, 2274 KiB  
Review
Nature-Based Solutions for Water Management in Europe: What Works, What Does Not, and What’s Next?
by Eleonora Santos
Water 2025, 17(15), 2193; https://doi.org/10.3390/w17152193 - 23 Jul 2025
Viewed by 474
Abstract
Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European [...] Read more.
Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article
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14 pages, 4599 KiB  
Article
Predictive Flood Uncertainty Associated with the Overtopping Rates of Vertical Seawall on Coral Reef Topography
by Hongqian Zhang, Bin Lu, Yumei Geng and Ye Liu
Water 2025, 17(15), 2186; https://doi.org/10.3390/w17152186 - 22 Jul 2025
Viewed by 211
Abstract
Accurate prediction of wave overtopping rates is essential for flood risk assessment along coral reef coastlines. This study quantifies the uncertainty sources affecting overtopping rates for vertical seawalls on reef flats, using ensemble simulations with a validated non-hydrostatic SWASH model. By generating extensive [...] Read more.
Accurate prediction of wave overtopping rates is essential for flood risk assessment along coral reef coastlines. This study quantifies the uncertainty sources affecting overtopping rates for vertical seawalls on reef flats, using ensemble simulations with a validated non-hydrostatic SWASH model. By generating extensive random wave sequences, we identify spectral resolution, wave spectral width, and wave groupiness as the dominant controls on the uncertainty. Statistical metrics, including the Coefficient of Variation (CV) and Range Uncertainty Level (RUL), demonstrate that overtopping rates exhibit substantial variability under randomized wave conditions, with CV exceeding 40% for low spectral resolutions (50–100 bins), while achieving statistical convergence (CV around 20%) requires at least 700 frequency bins, far surpassing conventional standards. The RUL, which describes the ratio of extreme to minimal overtopping rates, also decreases markedly as the number of frequency bins increases from 50 to 700. It is found that the overtopping rate follows a normal distribution with 700 frequency bins in wave generation. Simulations further demonstrate that overtopping rates increase by a factor of 2–4 as the JONSWAP spectrum peak enhancement factor (γ) increases from 1 to 7. The wave groupiness factor (GF) emerges as a predictor of overtopping variability, enabling a more efficient experimental design through reduction in groupiness-guided replication. These findings establish practical thresholds for experimental design and highlight the critical role of spectral parameters in hazard assessment. Full article
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21 pages, 12252 KiB  
Article
Changes in Intra-Annual River Runoff in the Ile and Zhetysu Alatau Mountains Under Climate Change Conditions
by Rustam G. Abdrakhimov, Victor P. Blagovechshenskiy, Sandugash U. Ranova, Aigul N. Akzharkynova, Sezar Gülbaz, Ulzhan R. Aldabergen and Aidana N. Kamalbekova
Water 2025, 17(14), 2165; https://doi.org/10.3390/w17142165 - 21 Jul 2025
Viewed by 328
Abstract
This paper presents the results of studies on intra-annual runoff changes in the Ile River basin based on data from gauging stations up to 2021. Changes in climatic characteristics that determine runoff formation in the mountainous and foothill areas of the river catchment [...] Read more.
This paper presents the results of studies on intra-annual runoff changes in the Ile River basin based on data from gauging stations up to 2021. Changes in climatic characteristics that determine runoff formation in the mountainous and foothill areas of the river catchment have led to alterations in the water regime of the watercourses. The analysis of the temporal and spatial patterns of river flow formation in the basin, as well as its distribution by seasons and months, is essential for solving applied water management problems and assessing the risks of hazardous hydrological phenomena, such as high floods and low water levels. The statistical analysis of annual and monthly river runoff fluctuations enabled the identification of relatively homogeneous estimation periods during stationary observations under varying climatic conditions. The obtained characteristics of annual and intra-annual river runoff in the Ile River basin for the modern period provide insights into changes in average monthly water discharge and, more broadly, runoff volume during different phases of the water regime. In the future, these characteristics are expected to guide the design of hydraulic structures and the rational use of surface runoff in this intensively developing region of Kazakhstan. Full article
(This article belongs to the Section Water and Climate Change)
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28 pages, 2140 KiB  
Article
Application of the GEV Distribution in Flood Frequency Analysis in Romania: An In-Depth Analysis
by Cristian Gabriel Anghel and Dan Ianculescu
Climate 2025, 13(7), 152; https://doi.org/10.3390/cli13070152 - 18 Jul 2025
Viewed by 752
Abstract
This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may [...] Read more.
This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article
(This article belongs to the Special Issue Hydroclimatic Extremes: Modeling, Forecasting, and Assessment)
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