Search Results (42)

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

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 ($\gamma$) 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

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model. Full article

With the impacts of climate change, floods have become increasingly frequent in recent years. Estimating flood hazard thresholds and peak floodwater levels based on flood frequency analysis is crucial for anticipating and preparing for potential flooding events. This study aims to estimate flood hazard thresholds, flood occurrence probabilities, and the return periods of peak floodwater levels in the Nokoue lake watershed in Benin. To achieve this, the standardized water level index, also known as the Flood hazard Index, was calculated to estimate flood hazard thresholds. The three best probability distribution models, Gumbel, Generalized Extreme Value (GEV), and Generalized Pareto (GPA), were selected to project future floodwater levels using annual maximum daily water level data for extreme floods from 1997 to 2022, obtained from a water gauge site at Nokoue lake. Three goodness-of-fit tests were applied to identify the best-fitting probability distribution model: a Taylor diagram (three-dimensional analysis), a cumulative probability density diagram based on the root-mean-square error (RMSE), and an L-moment diagram (two-dimensional analysis). The Flood hazard Index values ranged from −1.10 to +3.40, with 77.78% showing positive indices and 22.22% showing negative indices. The flood hazard thresholds were classified in ascending order of index values: limited hazards, moderate hazards, significant hazards, and critical hazards. The analysis results indicate that the flood hazard thresholds are defined as follows: below 3.94 m for limited hazards, from 3.94 m up to 4.04 m for moderate hazards, from 4.04 m to 4.14 m for significant hazards, and above 4.14 m for critical hazards. The distribution model analysis showed that the Gumbel distribution best fits the Nokoue lake watershed, with an RMSE of 0.0724, compared to 0.0754 and 0.0761 for the GEV and GPA models, respectively. The annual maximum daily water levels for various non-exhaustive return periods, 2, 3, 5, 10, 25, 50, and 100 years, were estimated and compared. The return period for the highest recorded annual maximum daily water levels (4.4 m/day) in the Nokoue lake watershed were calculated to be 12, 15, and 15 years using the Gumbel, GEV, and GPA models, respectively. Quantile analysis revealed that the Gumbel distribution produced overestimated results compared to the GEV and GPA models for return periods exceeding 10 years. Exceptional and very exceptional hydrological events have return periods of 100 and 150 years, corresponding to peak flow levels of 4.95 m and 5.05 m respectively. Finally, the results of this study will be invaluable for flood hazard managers in monitoring flood alerts and for water resource engineers in determining dimensions for designing flood control structures such as spillways, dams, and bridges, thereby improving the management of recurrent flooding events. Full article

Under the combined influence of climate change, accelerated urbanization, and inadequate urban flood defense standards, urban pluvial flooding has become an increasingly severe issue. This not only poses significant challenges to social stability and economic development but also makes accurate flood risk assessment crucial for improving urban flood control and drainage capabilities. This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. By constructing a hydrological–hydrodynamic coupled model using the SWMM and LISFLOOD-FP, this study evaluates the drainage capacity of the pipe network and surface inundation characteristics under both historical and design rainfall scenarios. An agent-based model (ABM) is developed to analyze the dynamic risks and vulnerabilities of population and building agents under different rainfall scenarios, capturing macroscopic emergent patterns from individual behavior rules and analyzing them in both time and space dimensions. Additionally, using multi-source remote sensing data, dynamic population vulnerability, and flood hazard processes, a quantitative dynamic flood risk analysis is conducted based on cloud models. The results demonstrated the following: (1) PSO performed best in calibrating the SWMM in the study area, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.93 to 0.69. (2) Drainage system capacity was low, with over 90% of the network exceeding capacity in scenarios with return periods of 1 to 100 years. (3) The vulnerability of people and buildings increased with higher flood intensity and duration. Most affected individuals were located on roads. In Event 6, 11.41% of buildings were at risk after 1440 min; in the 20-year flood event, 26.69% of buildings were at risk after 180 min. (4) Key features influencing vulnerability included the DEM, PND, NDVI, and slope. High-risk areas in the study area expanded from 36.54% at 30 min to 38.05% at 180 min. Full article

Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate. Full article

Under the dual pressures of climate change and urbanization, cities in China are experiencing increasingly severe flooding. Using the Yushan Lake area in Ma’anshan City, Anhui Province, as a case study, we employed the InfoWorks Integrated Catchment Management (ICM) hydraulic model to analyze the drainage and flood prevention system of the region and assess the current infrastructure for drainage and flood control. There are 117 pipelines with a return period lower than one year for stormwater and combined sewer systems, accounting for 12.3% of the total number of pipelines. The number of pipelines meeting the one-year but not the three-year return period standard is 700, representing 70.2%. Only 17.5% of the pipelines are capable of handling events exceeding the one-year standard. In simulating a 24 h, 30-year return period rainfall event, the results indicate that floodwater accumulation in the study area is predominantly between 0.15 m and 0.3 m. Most risk areas are classified as low risk, covering an area of 36.398 hectares, followed by medium and high-risk areas, which cover 8.226 hectares and 3.087 hectares, respectively. The Ma’anshan Yushan Lake area has, overall, certain flood control capabilities but faces flood risks during storms with return periods exceeding three years. This research offers valuable insights for improving urban flood management in Ma’anshan City through the development of a stormwater management model for the Yushan Lake area. Full article

In recent years, reservoir flood control and dam safety have faced severe challenges due to changing environmental conditions and intense human activities. There has been a significant increase in the proportion of dam breaks caused by floods exceeding reservoir design levels. Dam breaks have periodically occurred due to flood overtopping, threatening people’s lives and properties. This highlights the importance of describing the challenges encountered in reservoir flood risk prevention and control under extreme climatic conditions and proposing strategies to safeguard reservoirs against floods and to protect downstream communities. This study conducts a statistical analysis of dam breaks resulting from floods exceeding reservoir design levels, revealing new risk indicators in these settings. The study examines recent representative engineering cases involving flood risks and reviews research findings pertaining to reservoir flood risks under extreme climatic conditions. By comparing flood prevention standards at typical reservoirs and investigating the problems and challenges associated with current standards, the study presents the challenges and strategies associated with managing flood risks in reservoirs under extreme climatic conditions. The findings show that the driving forces and their effects shaping flood risk characteristics in specific regions are influenced by atmospheric circulation and vegetative changes in underlying surfaces or land use. There is a clear increasing probability of dam breaks or accidents caused by floods exceeding design levels. Most dam breaks or accidents occur in small and medium-sized reservoirs, due to low flood control standards and poor management. Therefore, this paper recommends measures for improving the flood prevention capacity of these specific types of reservoirs. This paper proposes key measures to cope with floods exceeding reservoir design levels, to supplement the existing standard system. This includes implementing an improved flood standard based on dam risk level and the rapid reduction in the reservoir water level. To prevent breaks associated with overtopping, earth–rock dams should be designed to consider extreme rainfall events. More clarity is needed in the execution principles of flood prevention standards, and the effectiveness of flood calculations should be studied, adjusted, and validated. The research results provide better descriptions of flood risks in reservoirs under extreme climatic conditions, and the proposed strategies have both theoretical and practical implications for building resilience against flood risks and protecting people’s lives and properties. Full article

To study the characteristics and sources of nitrogen in the Baiyangdian Lake, this research conducted water quality monitoring during three hydrological periods (normal period, flood period, and dry period), and 165 pieces of routine water quality monitoring data were collected from the three national control sections for Baiyangdian Lake and its inflow rivers. By integrating water chemical analysis with multivariate statistical techniques, the study comprehensively investigated the spatiotemporal variation patterns of nitrogen in Baiyangdian Lake and identified the sources of nitrogen pollution. The results showed that the concentration of total nitrogen (TN) was highest during the dry period, reaching an average of 0.924 mg/L, and 31.3% of the sites exceeded the national Grade III surface water quality standard, reflecting a potential risk of nitrogen pollution. Based on the ion ratio method and principal component analysis (PCA), the main sources of nitrogen pollution in Baiyangdian Lake were identified as manure and domestic sewage, with agricultural fertilizers also having a certain impact on water nitrogen pollution. In addition, the study also compared the nitrogen concentration in Baiyangdian Lake with several important lakes in China. The results showed that the concentrations of TN and ammonium nitrogen (NH₄⁺-N) in Baiyangdian Lake are lower than those in lakes in areas with similar human activity intensity, indicating that the water quality of Baiyangdian is relatively good. This study can provide a scientific basis for water quality management and pollution prevention for Baiyangdian Lake. Full article

The deposition of Yellow River sediment in the middle and lower reaches is a significant factor in the siltation of reservoirs and the occurrence of serious flooding along the river. The efficient and valuable utilization of Yellow River sediment has already become a key research topic in this field. In this study, we have employed Yellow River sediment as the primary material, in conjunction with commercially available slag, fly ash, and quicklime as the binder, to develop a novel type of artificial flood-prevention stone. Following a 28-day standard curing procedure, the highest compressive strength of the prepared artificial stone was recorded at 4.29 MPa, with a value exceeding 0.7 MPa under wet conditions. The results demonstrated that the prepared artificial stone met the specifications for artificial flood-prevention stones. The curing mechanism, as evidenced by analyses from SEM and XRD testing, indicated that the alkali excitation process in the binder, which produced C-A-S-H gel, was the key factor in enhancing the compressive strength of the specimens. Notably, an evaluation of the amount of CO₂ emissions and the cost of the artificial stone concluded that the preparation process was both environmentally friendly and cost-effective. Full article

In recent years, urban waterlogging disasters have become increasingly prominent. Physically based urban waterlogging simulation models require considerable computational time. Therefore, rapid and accurate simulation and prediction of urban pluvial floods are important for disaster prevention and mitigation. For this purpose, we explored an urban waterlogging prediction method based on a long short-term memory neural network model that integrates an attention mechanism and a 1D convolutional neural network (1DCNN–LSTM–Attention), using the diversion of the Jinshui River in Zhengzhou, China, as a case study. In this method, the 1DCNN is responsible for extracting features from monitoring data, the LSTM is capable of learning from time-series data more effectively, and the Attention mechanism highlights the impact of features on input effectiveness. The results indicated the following: (1) The urban waterlogging rapid prediction model exhibited good accuracy. The Pearson correlation coefficient exceeded 0.95. It was 50–100 times faster than the InfoWorks ICM model. (2) Diversion pipelines can meet the design flood standard of a 200-year return period, aligning with the expected engineering objectives. (3) River channel diversion significantly reduced the extent of inundation. Under the 30-year return period rainfall scenario, the maximum inundation area decreased by 1.46 km², approximately equivalent to 205 international standard soccer fields. Full article

Previous studies have investigated the characteristics and influencing factors of plume bulge in the Pearl River Estuary (PRE) using observations and numerical simulations. However, the understanding of how river discharge affects plume bulge is not consistent, and the response mechanism of plume bulge to changes in river discharge has not been revealed in detail. In this study, a three-dimensional hydrodynamic Finite-Volume Coastal Ocean Model (FVCOM) is constructed, and five experiments were set to characterize the horizontal and vertical distribution of the plume bulge outside the PRE under different river discharge conditions during spring tide. The physical mechanisms of plume bulge generation and its response mechanisms to river discharge were discussed through standardized analysis and momentum diagnostic analysis. The results indicate that the plume bulge is sensitive to changes in river discharge. When the river discharge is relatively low (e.g., less than 11,720 m³/s observed in the dry season), the bulge cannot be formed. Conversely, when the river discharge is relatively high (e.g., exceeding 23,440 m³/s observed in flood season), the bulge is more significant. The plume bulge is formed by the anticyclonic flow resulting from the action of the Coriolis force on the strongly mixed river plume. The bulge remains stable under the combined effects of barotropic force, baroclinic gradient force, and Coriolis force. The reduction of river discharge weakens the mixing of freshwater and seawater, resulting in the reduction of both the volume and momentum of the river plume, and the balance between advective diffusion and Coriolis forces are altered, resulting in the plume, which is originally flushed out from the Lantau Channel, not being able to maintain the anticyclonic structure and instead floating out along the coast of the western side of the PRE, with the disappearance of the plume bulge. Due to the significant influence of plume bulges on the physical and biogeochemical interactions between estuaries and terrestrial environments, studying the physical mechanisms behind the formation of plume bulges is crucial. Full article

The Linhuaigang flood control project (LFCP), situated on the Huaihe River, China, uses the river channels upstream of the LFCP, together with the hinterland areas outside the channels, to retain and store fluvial floodwaters that exceed the downstream channel’s discharge capacity. The hinterland areas are split into seven flood storage areas, three floodplain areas, eight polder areas, and three flood-control-protected areas, and they are connected to the river in various ways. A coupled hydrodynamic model was established to simulate the hydrodynamic and water volume exchange between the river channels and the hinterland areas. The flood storage area, under the control of a flood diversion sluice, was simulated with a 2D hydrodynamic model, and the inflow process initiated by the flood diversion sluice was simulated as a control structure. The polder area was generalized as a reservoir that would be filled in several hours once put into use because of its small size. The uncontrolled inflow process between the flood-control-protected areas and the channel was simulated by means of a dam break model, which could simulate levee breaching. The flooding within the flood-control-protected area, which represents a vast space, was simulated with a 2D hydrodynamic model. The floodplain area was laterally connected to the river channel along the river levee. The difference between the simulated and the measured flood peak water stage did not exceed 0.2 m in 2003 and 2007, indicating that the accuracy of the model was relatively high. In the scenario of a design flood with a return period of 100 years, the flood storage areas and the LFCP were used in the following order: Mengwa, Qiujiahu, Nanrunduan, Shouxihu, Jiangtanghu, Chengxihu, Chengdonghu, and the LFCP. When the Huaihe River encounters a flood with a return period of 1000 years that exceeds the design standard, the highest water stage upstream of the LFCP and Zhengyangguan shall not exceed 29.30 m and 27.96 m after the use of all the flood storage areas, floodplain areas, and flood-control-protected areas. The results of this research can provide technical support for the flood risk management of the LFCP. Full article

Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal resolutions because of intrinsic model limitations and remote sensing data. In this regard, images collected by web cameras can be used to provide estimates of water levels during flooding or the presence/absence of water within a scene. Here, we report the results of an assessment of an algorithm which uses web camera images to estimate water levels and detect the presence of water during flooding events. The core of the algorithm is based on a combination of deep convolutional neural networks (D-CNNs) and image segmentation. We assessed the outputs of the algorithm in two ways: first, we compared estimates of time series of water levels obtained from the algorithm with those measured by collocated tide gauges and second, we performed a qualitative assessment of the algorithm to detect the presence of flooding from images obtained from the web under different illumination and weather conditions and with low spatial or spectral resolutions. The comparison between measured and camera-estimated water levels pointed to a coefficient of determination R² of 0.84–0.87, a maximum absolute bias of 2.44–3.04 cm and a slope ranging between 1.089 and 1.103 in the two cases here considered. Our analysis of the histogram of the differences between gauge-measured and camera-estimated water levels indicated mean differences of −1.18 cm and 5.35 cm for the two gauges, respectively, with standard deviations ranging between 4.94 and 12.03 cm. Our analysis of the performances of the algorithm to detect water from images obtained from the web and containing scenes of areas before and after a flooding event shows that the accuracy of the algorithm exceeded ~90%, with the Intersection over Union (IoU) and the boundary F1 score (both used to assess the output of segmentation analysis) exceeding ~80% (IoU) and 70% (BF1). Full article