Search Results (896)

Flood hazard studies for dam break cases are of utmost importance for understanding potential risks and minimizing the impact of such accidents. Siriu Dam, which has a clay core, is ranked as the third highest embankment dam in Romania. A fully dynamic 2D hydraulic numerical model was developed using HEC-RAS software to simulate the routing of the flood waves formed by breaching this dam. Four different failure scenarios were considered: two for overtopping and two for piping. The breach parameters were chosen based on the dam characteristics in accordance with appropriate empirical relationships. The flood hazard was quantified and analyzed in terms of depths, velocities, depth x velocity values, and flooded areas. The results provide useful information concerning flood risk mitigation, such as the dam break wave routing, peak discharges, arrival time, travel velocity, and inundation boundary. The influence of the scenario and site characteristics (topography, river morphology, and constructions) on the results was analyzed. Depths and velocities over 10 m and 15 m/s, respectively, were obtained close to the dam, while those in Buzău City (90 km away) were under 1 m and 2 m/s, respectively. The city was flooded 7–8.5 h after the breach (depending on the scenario), and over 15 to 50% of its total area was affected. Moreover, the flood hazard parameters were compared for the different scenarios, providing the practical details necessary to develop flood risk management plans and the associated response measures for the inhabited areas. This is the first numerical study to simulate the impact of a potential break accident that can occur for this dam. Full article

Flood has become a major hazard globally, and in Bhutan, with its steep terrain and erratic rainfall, it has caused significant economic damage in recent years. Given these challenges, there is a lack of accurate flood prediction and management strategies. In this study, therefore, we evaluated three hydrological models—Integrated Flood Analysis System (IFAS), Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS), and Group on Earth Observation Global Water Sustainability (GEOGloWS)—and identified the most suitable model for simulating flood events in the Wangchu River Basin in Bhutan. Furthermore, we examined the models’ performance in a large and a small basin using the Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Peak Flow Error (PFE) metrics. Overall, the GEOGloWS model demonstrated the highest accuracy in simulating flood in the large basin, achieving NSE, PBIAS, and PFE values of 0.93, 3.21%, and 4.48%, respectively. In the small basin, the IFAS model showed strong performance with an NSE value of 0.84. The GEOGloWS model provides simulated discharge but needs to be bias corrected before use. The calibrated parameters can be used in the IFAS and HEC-HMS models in future studies to simulate floods in the Wangchu River Basin and adjacent basins with similar geographical characteristics. Full article

Urban flooding, driven by extreme rainfall events and urbanization, poses substantial risks to urban safety and infrastructure. This study employed a neighborhood-scale InfoWorks ICM model to analyze the full-process impacts of urban flooding under six rainfall return periods in Haining, China. The results reveal distinct non-linear responses from the 3-year to 50-year rainfall return period: (1) the surface runoff volume increases by 64.3%, with peak timing advancing by about one minute; (2) the overflow nodes rise from 37.35% to 63.24%, with durations over 30 min increasing by 78.6%; (3) the inundation areas expand by 164.9%, with maximum depths increasing by 0.31 m, showing significant regional disparities; and (4) high-risk zones, such as Haining People’s Square and Railway Station, require targeted interventions due to severe surface overflow and inundation. This comprehensive analysis emphasizes the need for tailored and phased flood prevention measures that address each stage of urban flooding. It provides a strong framework to guide urban planning and enhance resilience against rainfall-induced urban flooding. Full article

This study employs convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU) deep learning models to simulate daily streamflow using precipitation data. Two approaches were explored: one without dimension reduction and another incorporating dimensionality reduction technique. Principal component analysis (PCA) was employed for dimensionality reduction, and partial autocorrelation function (PACF) was used to determine time lags. An augmented Dickey–Fuller (ADF) test was utilized to ascertain the stationarity of the data, ensuring optimal model performance. The data were normalized and then partitioned into features and target variables, before being split into training, validation, and test sets. The developed models were tested for their performance, robustness, and stability at three locations along the Neuse River, which is in the Neuse River Basin, North Carolina, USA, covering an area of about 14,500 km². Furthermore, the model’s performance was tested during peak flood events to assess their ability to capture the temporal resolution of streamflow. The results revealed that the CNN model could capture the variability in daily streamflow prediction, as evidenced by excellent statistical measures, including mean absolute error, root mean square error, and Nush–Sutcliffe efficiency. The study also found that incorporating dimensionality reduction significantly improved model performance. Full article

In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting. Full article

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences. Full article

Satellite altimetry technology has been widely used for the observation of oceans and inland water bodies. At present, fully focused synthetic aperture radar (FF-SAR) data, which significantly enhances along-track resolution, has good prospects for river level estimation. However, FF-SAR data with large data volumes have more complex waveforms, which brings more challenges to waveform retracking. This study developed an improved adaptive multi-scale peak detection (ImpAMPD) retracker based on Sentinel-6 FF-SAR data. Initially, sub-waveforms are identified and extracted from each waveform. Subsequently, the data are segmented according to the number of gates and the minimum gate length. Finally, retracking calculations are performed on the segmented sub-waveforms to determine river levels. In this study, the in situ data from six river sections with different features in the middle and upper reaches of the Yangtze River were used to validate the accuracy of the ImpAMPD retracker and to perform a comparison of this developed retracker with three existing retrackers (OCOG, PTR, SAMOSA+). The results indicate that the ImpAMPD retracker can fully utilize the advantage of the high posting rate of FF-SAR data to process the complex multi-peak waveforms on the river surface, accurately extract the correct water surface signals, and achieve highly precise river level estimation. The best accuracy results were obtained in four river sections, namely, Zhicheng, Shashi, Hankou, and Huantan, with STDDs of 0.18 m, 0.26 m, 0.47 m, and 0.36 m, respectively. The ImpAMPD retracker is highly automated and adaptable to rivers of varying widths, providing robust support for river level monitoring and flood management. Full article

With the increasing frequency of extreme rainfall events, enhancing urban drainage systems’ regulation capacity is crucial for mitigating urban flooding. Existing studies primarily analyze infrastructure impacts on peak flow delay but often lack a systematic exploration of time-lag mechanisms. This study introduces the time-lag parameter, using the hysteresis curve of the water level–flow rate relationship to quantify drainage system dynamics. An SWMM-based drainage model was developed for the Rongdong area of Xiong’an New District to evaluate the independent roles of green, gray, and blue infrastructures in peak flow reduction and time-lag modulation. The results indicate that green infrastructure extends the horizontal width and reduces the vertical height of the hysteresis curve, prolonging time lag and making it effective for small-to-medium rainfall. Gray infrastructure enhances drainage efficiency by compressing the hysteresis curve horizontally and increasing its vertical height, facilitating rapid drainage but offering limited peak reduction. Blue infrastructure, by lowering outlet water levels, improves drainage capacity and reduces time lag, demonstrating adaptability across various rainfall scenarios. This study systematically quantifies the role of each infrastructure type in time-lag regulation and proposes a collaborative optimization strategy for urban drainage system design. Full article

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

Climate warming has led to changes in floods in snow-packed mountain areas, but how snowmelt contributes to floods in the high-altitude Tibetan Plateau remains to be studied. To solve this problem, we propose a more reasonable method for evaluating snowmelt’s contributions to floods. We use a distributed hydrological model with the capability to track snowmelt paths in different media, such as snowpack, soil, and groundwater, to assess snowmelt’s contribution to peak discharge. The study area, the Xiying River basin, is located northeast of the Tibetan Plateau. Our results show that in the past 40 years, the average annual air temperature in the basin has increased significantly at a rate of 0.76 °C/10a. The annual precipitation (precipitation is the sum of rainfall and snowfall) decreased at a rate of 5.59 mm/10a, while the annual rainfall increased at a rate of 11.01 mm/10a. These trends were not obvious. The annual snowfall showed a significant decrease, at a rate of 14.41 mm/10a. The contribution of snowmelt to snowmelt-driven floods is 85.78%, and that of snowmelt to rainfall-driven floods is 10.70%. Under the influence of climate change, the frequency of snowmelt-driven floods decreased significantly, and flood time advanced notably, while the intensity and frequency of rainfall-driven floods slowly decreased in the basin. The causes of the change in snowmelt-driven floods are the significant increase in air temperature and the noticeable decrease in snowfall and snowmelt runoff depth. The contribution of snowmelt to rainfall-driven floods slowly weakened, resulting in a slight decrease in the intensity and frequency of rainfall-driven floods. The results also indicate that rising air temperature could decrease snowmelt-driven floods. In snow-packed mountain areas, rainfall and snowmelt together promote the formation of and change in floods. While rainfall dominates peak discharge, snowpack and snowmelt play a significant role in the formation and variability of rainfall-driven floods. The contributions of snowmelt and rainfall to floods have changed under the influence of climate change, which is the main cause of flood variability. The changed snowmelt adds to the uncertainties and could even decrease the size and frequency of floods in snow-packed high mountain areas. This study can help us understand the contributions of snowmelt to floods and assess the flood risk in the Tibetan Plateau under the influence of climate change. Full article

Flood disasters pose one of the greatest threats to humanity. Effectively addressing this challenge requires improving the accuracy of flood simulation. Taking Xunhe watershed in Shandong Province as the study area, the Random Forest model was utilized to classify historical flood events within the watershed based on rainfall conditions, such as varying rainfall durations, intensities, and total precipitations. Multiple sets of hydrological model parameters were established to conduct flood classification simulation, reducing the error caused by using a single parameter set for the entire watershed. The results indicate that the Random Forest model can be applied to flood classification simulation in Xunhe watershed. Compared to unclassified simulations, the method proposed in this study leads to an improvement in the Nash coefficient by 0.06 to 0.14, a reduction in the relative error of peak discharge by 3% to 11.24% and a reduction in the relative error of flood volume by 1.46% to 9.44%. The flood classification simulation method proposed in this study has certain applicability in reducing flood simulation errors under different rainfall scenarios and improving accuracy in the watershed, providing new insights for flood control and disaster reduction efforts. Full article

This study reports on the impact of rainfall patterns and land surface changes on flood dynamics in the Meijiang River Basin, located in the upper reaches of the Ganjiang River. We formulated a range of rainfall patterns and spatial distribution scenarios and employed the MIKE SHE model to evaluate variations in flood volume, flood peak, and the timing of flood peaks. We found that under comparable areal rainfall conditions, flood volumes fluctuated by up to 6.22% among the different rainfall patterns, whereas flood peaks exhibited differences of up to 36.23%. When the rainfall center moved from upstream to downstream, both flood volume and flood peak initially increased before decreasing, with maximum values of 4.2 billion m³ and 4900 m³/s, respectively. We selected three basin scales (i.e., 10,000, 1000, and 100 km²) for comparative analysis. In the period between 1985 and 2020, the changes in land surface conditions resulted in decreases in the flood peaks of the three basins by 7.61, 11.53, and 15.79%, respectively; a reduction in the flood volumes of the three basins by 6.58, 9.60, and 10.48%, respectively; and delayed peak times by 3, 2, and 2 h, respectively. The results of this study show the significant influence exerted by rainfall patterns, the location of the rainfall centers, and the impact of changes in land surface conditions on flood processes. In particular, when the area of the basin was reduced, the influence of the underlying surface was more obvious. These results also show that flood prediction needs to consider the complex interaction of multiple factors. Full article

Storm surge barriers are crucial for the flood protection of the Netherlands and other deltas. In the Netherlands, the reliability of flood defenses is typically assessed based on extreme water levels and wave height statistics. Yet, in the case of operated flood defenses, such as storm surge barriers, the temporal clustering of successive events may be just as important. This study investigates the evolution and associated flood risk of clusters of successive storm tide peaks at the Maeslant Storm Surge Barrier in the Netherlands. Two mechanisms are considered. Multi-peak storm surge events, as a consequence of tidal movement on top of the surge, are studied by means of stochastic storm tide events. Clusters of storm tides resulting from different, but related storms are investigated by means of time series analysis of a long sea-level record. We conclude that the tendency of extreme storm tide peaks to cluster is especially related to the seasonality in storm activity. In the current situation, the occurrence of clusters of storm tide peaks have only a minor influence of the flood risk in the area behind the Maeslant Storm Surge Barrier. We envision, however, that this influence is likely to increase with sea-level rise. The numbers are, however, uncertain due to the strong sensitivity to assumptions, model choices and the applied data set. More insight into the statistics of the time evolution of extreme sea water levels is needed to better understand and ultimately to reduce these uncertainties. Full article

This study addresses the persistent issue of urban waterlogging in Wujin District, Changzhou City, Jiangsu Province, using a comprehensive approach integrating an optimized drainage network and low-impact development (LID) measures. Utilizing the Storm Water Management Model (SWMM), calibrated with extensive hydrological and hydraulic data, the model was refined through genetic algorithm-based optimization to enhance drainage efficiency. Key results indicate a substantial reduction in the average duration of waterlogging from 7.43 h to 3.12 h and a decrease in average floodwater depth from 21.27 cm to 8.65 cm. Improvements in the drainage network layout, such as the construction of new stormwater mains, branch drains, and rainwater storage facilities, combined with LID interventions like permeable pavements and rain gardens, have led to a 56.82% increase in drainage efficiency and a 63.88% reduction in system failure rates. The implementation effectively minimized peak flood flow by 25.38%, reduced runoff, and improved groundwater recharge and rainwater utilization. The proposed solutions offer a replicable, sustainable framework for mitigating flooding in urban environments, enhancing ecological resilience, and ensuring the safety and quality of urban life in densely populated areas. Full article

The Mediterranean faces frequent heavy precipitation, deadly heatwaves, and wildfires fueled by its climate. Greece, with its complex topography, experiences severe and extreme weather events that have escalated in recent years and are projected to continue rising under future climate conditions. This paper analyzes severe weather events and trends in Greece from 2010 to 2023, leveraging data from an expanded network of weather stations spanning across Greece, as well as long-term meteorological data from the reference weather station in the center of Athens. The focus includes analysis of heat waves, intense rainfall and droughts, thunderstorms, hail, tornadoes, and fire weather conditions. The societal impact of severe weather events is also discussed. The paper aims to provide both long-term (1901–2023) and recent year analyses (2010–2023). The main results show that between 2010 and 2023, Greece experienced: nearly one heatwave per summer; heavy rainfall events, most common in winter and autumn, showing a significant increase, particularly in the eastern Aegean and western continental Greece; dry spells, which are longest in southern Greece; thunderstorm and hail events peaking in spring and summer; fire weather conditions and risk peaking in southern Greece. Finally, societal impacts from weather hazards have increased in Greece over the past 14 years, with flash floods being the most frequent and damaging events, while public preparedness and effective risk communication remain low. Full article