Search Results (1,105)

Urban coastal areas along the Mediterranean are exposed to short-duration convective rainfall, producing infrastructure disruptions and flood-related impacts. This study analyzes 45 rainfall episodes in the Metropolitan Area of Barcelona between 2014 and 2022, combining radar products, rain gauge observations, and urban-scale impact datasets. Storm radar tracking enabled the identification of key spatiotemporal features and assessment of short-term forecasting performance. Convective cells were typically short-lived, lasting less than 30 min in most cases. The main goal of the research has been the comparison between VIL density (DVIL) radar field and short-duration rainfall intensity provided by rain gauges. This is the first study comparing both data types, being a pioneer in this field. We have found a linear relationship between both data types, with weaker values for larger values. More persistent cells had higher DVIL values, observing a difference in behavior with a break point at 2 g/m³. The tracking and nowcasting system were evaluated based on its ability to anticipate convective precipitation. It achieved good scores values (POD of 0.73 and FAR of 0.33), considering the difficulties of tracking this type of convective system. Finally, false alarms associated with elevated DVIL values suggested the difficulty of capturing storm severity by surface-based precipitation measurements. Full article

Background: Despite advances in monitoring and forecasting systems, natural disasters continue to cause significant human losses. During the response phase, fast decisions are required to allocate limited resources, particularly rescue helicopters, which play a key role in reaching inaccessible areas. However, helicopter allocation involves trade-offs between efficiency and operational safety under uncertain conditions. Methods: This study proposes a decision-support methodology based on Multi-Criteria Decision Analysis (MCDA) for allocating rescue helicopters during disaster response. The approach integrates Value-Focused Thinking (VFT) and Multi-Attribute Value Theory (MAVT) to structure objectives, assign weights, and evaluate alternatives using criteria related to mission safety, response time, and expected number of rescued victims. The method is illustrated through a simulated flood response scenario in a Brazilian regional context. Results: The results show that the model allows decision-makers to compare allocation scenarios and to make explicit the trade-offs between operational efficiency and safety. The application indicates that small reductions in efficiency may lead to relevant gains in operational safety, particularly under adverse weather conditions. Conclusions: The proposed approach provides a transparent and traceable structure for supporting helicopter allocation decisions during disaster response. It contributes to more consistent decision-making in critical operations, especially in contexts characterized by uncertainty and time pressure. Full article

Dry climate regions face heightened risks of flooding and infrastructure damage even with minimal rainfall. Climate change is intensifying this vulnerability by increasing the duration, frequency, and intensity of precipitation events in areas that have historically experienced arid conditions. As a result, accurate precipitation estimation in these regions is critical for effective planning, risk mitigation, and infrastructure resilience. This study evaluates the performance of five satellite- and model-based precipitation products by comparing them against in situ rain gauge observations in a dry-climate region: The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (analyzing maximum and minimum precipitation rates separately), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the Western Land Data Assimilation System (WLDAS), and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). The analysis focuses on both average daily rainfall and extreme precipitation events, with particular attention to precipitation magnitude and the accuracy of event detection, using a combination of statistical metrics—including bias ratio, mean error, and correlation coefficient—as well as contingency statistics such as probability of detection, false alarm rate, missed precipitation fraction, and false precipitation fraction. The study area is Palm Desert, a mountainous, arid, and urban region in Southern California, which exemplifies the challenges faced by dry regions under changing climate conditions. Among the products assessed, WLDAS ranked highest in measuring total precipitation and extreme rainfall amounts but performed the worst in detecting the occurrence of both average and extreme rainfall events. In contrast, IMERG and ERA5-MIN demonstrated the strongest ability to detect the timing of precipitation, though they were less accurate in estimating the magnitude of rainfall per event. Overall, this study provides valuable insights into the reliability and limitations of different precipitation estimation products in dry regions, where even small amounts of rainfall can have disproportionately large impacts on infrastructure and public safety. Full article

The accuracy and timeliness of precipitation inputs have significant impact on flood forecasting. Upstream Minjiang River Basin is characterized by complex terrain and highly variable climatic conditions, posing a significant challenge for runoff forecasting. This study proposes a combined forecasting approach integrating numerical weather prediction (NWP) models with hydrodynamic models to enhance flood process simulation. The most appropriate initial field data for the Weather Research and Forecasting Model (WRF) exist in time and space resolution. Compared with the measured series, the characteristics of precipitation forecasting are summarized from practical and scientific perspectives. InfoWorks ICM is then used to implement runoff generation calculations and flooding processes. The results indicate that the WRF model effectively simulates the spatial distribution and peak timing of precipitation in the upper Minjiang River. The model systematically underestimates both peak rainfall intensity and cumulative precipitation compared to observations. Initial field data with 0.25° spatial resolution and 3 h temporal intervals demonstrate good performance and the 10–14 h forecast period exhibits superior predictive capability in numerical simulations. Updates to elevation and land use conditions yield increased cumulative rainfall estimates, though simulated peaks remain lower than measured values. The runoff results could indicate peak flow but rely on the precipitation inputs. Full article

The SCS-CN (Soil Conservation Service Curve Number) model is important for flood forecasting as it provides a relatively simple and widely used methodology for estimating the amount of surface runoff from a rainfall event, which is a crucial input in predicting flood volumes and peaks in ungauged or data-scarce watersheds. Thus, the authors developed a hydrological model based on the SCS-CN curve methodology and GIS (Geographic Information Systems) to estimate the flood hydrograph in the upper parts of the Biała Lądecka River basin in Poland. The numerical model was calibrated based on the data available from the Polish Institute of Meteorology and Water Management (IMGW). The output of the model demonstrates the effect in the flood hydrograph at the town of Lądek-Zdrój. Additionally, hydraulic routing calculations were included to analyze the possible causes of the dam failure of the Stronie Śląskie reservoir in the year 2024. The main purpose of this study is to corroborate the influence of climate change on flood events and their consequences, as well as to assist in forecasting future catastrophic hydrological events and thus earlier adaptation and reinforce the infrastructure in our territories against future flooding. Full article

This study evaluated the applicability of the dual drainage model, Hyper Connected–Solution for Urban Flood (HC-SURF), for real-time urban flood forecasting. The model was applied to the extreme rainfall event of August 2022 in the Sillim and Daerim drainage basins in Seoul. Its accuracy and computational efficiency were quantitatively compared with those of two widely used commercial models, the Personal Computer Storm Water Management Model (PC-SWMM) and InfoWorks Integrated Catchment Modelling (ICM). Accuracy was assessed by measuring spatial agreement with observed inundation trace maps using binary indicators, including the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Computational efficiency was evaluated by comparing simulation times under identical conditions. In terms of accuracy against observations, HC-SURF achieved CSI values ranging from 0.26 to 0.45, with POD values from 0.37 to 0.81 and FAR values from 0.49 to 0.53 across the two basins. In inter-model comparisons, the model showed high hydraulic consistency, demonstrating CSI values between 0.72 and 0.88, POD between 0.82 and 0.99, and FAR between 0.08 and 0.15. In terms of computational efficiency, HC-SURF reduced calculation times by approximately 9% and 44% compared with InfoWorks ICM and PC-SWMM, respectively, for a 48 h simulation. The model also completed a 6 h rainfall simulation in approximately 8 min, meeting the lead time requirements for rapid urban flood forecasting. Overall, these findings show that HC-SURF effectively balances simulation accuracy with computational efficiency, demonstrating its suitability for real-time urban flood forecasting. Full article

This study presents a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). A rapid rise followed by a sharp fall in CTT is identified as a precursor signal of convective intensification. By quantifying the $rise-peak-fall-trough$ pattern and the $peak$-to-$trough$ amplitude ($swing$), a $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ window—representing a potential heavy-rainfall candidate period—is defined. The observed lead time between the onset of CTT decline and the subsequent radar-observed rainfall surge is calculated, while an estimated lead time is inferred from the steepness of CTT fall in the absence of a surge. Application to eight heavy rainfall events in Korea (July 2025) yielded a probability of detection (POD) of 87.5%, indicating that potential heavy rainfall could be detected approximately 1.3–8.6 h in advance. Compared with radar-based nowcasting, the CTT $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method retained predictive skill up to 3 h before numerical model guidance became effective, suggesting that satellite-based signals can bridge the forecast gap in short-term prediction. This work demonstrates a clear methodological novelty by introducing a physical interpretable, pattern-based metric. Quantitatively, the $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method improves early-warning capability by providing 1–3 h of additional lead time relative to radar nowcasting in rapidly evolving convective environments. Overall, this framework provides an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications. Full article

Accurate stream flow forecasting is essential for flood risk management and preparedness. This study compares two forecasting approaches: (a) the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Regressors (SARIMAX), a classical statistical model, and (b) Prophet, a decomposable time-series forecasting model that incorporates seasonality and exogenous predictors. Forecasts were generated for 15-day and 3-day horizons and evaluated using uncertainty bounds, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). Results indicate that SARIMAX was less effective at capturing the observed variability, producing wide uncertainty (177.7%) and high errors (MAE = 153.73; RMSE = 207.10) with a negative R² (–4.42). At shorter horizons, its performance remained limited (uncertainty = 28.04%; MAE = 61.52; RMSE = 94.88; R² = –0.14). In contrast, Prophet achieved significantly lower uncertainty (16%), high accuracy (R² = 0.95), and exceptional performance on short-term forecasts (R² = 0.99). Conventional procedures such as SARIMAX have long been relied upon by engineers for their interpretability, and remain important as part of a strategy; however, they fail to represent nonlinear dynamics and exogenous influences now captured effectively by AI-based models. These findings highlight Prophet’s superiority across horizons and its promise for enhancing operational flood forecasting through its ability to effectively capture non-linear dynamics and exogenous influences. Full article

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate. Full article

Runoff forecasting is essential for flood control, disaster mitigation, and sustainable water resources management. However, runoff processes are highly nonlinear and uncertain due to multiple interacting meteorological and underlying surface factors. Current models can be divided into process-driven and data-driven types. The former offers clear physical interpretability but involves complex calibration and simplifications, while the latter captures nonlinear relationships effectively but lacks physical consistency. To integrate their strengths, this study constructs process-based models and data-driven models, and proposes two hybrid strategies: (1) incorporating intermediate variables from physical models, such as soil moisture and runoff yield, as additional features for data-driven models, and (2) embedding physics-based constraints and synthetic data into loss functions. Using the Songxi River Basin as a case study, results show that both hybrid strategies significantly outperform standalone models. SHapley Additive exPlanations (SHAP)-based interpretability analysis further reveals the contribution mechanisms of key physical variables. This study demonstrates that coupling physical processes with data-driven learning effectively enhances runoff forecasting accuracy and offers a promising paradigm to support sustainable watershed management, climate-resilient water regulation, and flood risk reduction. Full article

Accurate simulation of rainfall–runoff processes in mountainous catchments is essential for flood forecasting and water resource management. Traditional physically based models often suffer from structural rigidity and parameter uncertainty, while deep learning models, although effective in capturing nonlinear patterns, lack physical constraints and interpretability. To address these issues, this study developed a time-varying gated hybrid model (XAJ–LSTM) that integrates the Xinanjiang (XAJ) model with a Long Short-Term Memory (LSTM) network to improve runoff prediction accuracy and physical consistency. Hourly rainfall, temperature, potential evapotranspiration, and runoff data (2015–2023) from 17 small to medium mountainous catchments in Shi Yan and En Shi, Hubei Province, were used to drive and evaluate the XAJ, LSTM, and XAJ–LSTM models. The hybrid model achieved mean NSE and KGE values of 0.971 ± 0.020 and 0.962 ± 0.024, respectively, outperforming both individual models. In about 80% of the catchments, the gating parameter λ(t) showed a negative correlation with discharge, indicating adaptive adjustment between the physical and data-driven components. The coupled model reproduced both high- and low-flow processes well, with deviations in flow duration curves generally within ±5%. These findings demonstrate that the proposed time-varying gating structure effectively balances model accuracy, stability, and interpretability. Full article

Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemble Forecasting (SAEF) method that integrates SVM, LSSVM, LSTM, and BiLSTM models to leverage their complementary strengths in capturing nonlinear and non-stationary hydrological dynamics. SAEF employs a seasonal segmentation mechanism to divide annual runoff data into four seasons (spring, summer, autumn, winter), enhancing model responsiveness to seasonal hydrological drivers. An Improved Arctic Puffin Optimization (IAPO) algorithm optimizes the model weights, improving prediction accuracy. Beyond numerical gains, the framework also reflects seasonal runoff generation processes—such as rapid rainfall–runoff in wet seasons and baseflow contributions in dry periods—providing a physically interpretable perspective on runoff dynamics. The effectiveness of SAEF was validated through case studies in the Dongjiang Hydrological Station (China), the Elbe River (Germany), and the Quinebaug River basin (USA), using four performance metrics (MAE, RMSE, NSEC, KGE). Results indicate that SAEF achieves average Nash–Sutcliffe Efficiency Coefficient (NSEC) and Kling–Gupta efficiency (KGE) coefficients of over 0.92, and 0.90, respectively, significantly outperforming individual models (SVM, LSSVM, LSTM, BiLSTM) with RMSE reductions of up to 58.54%, 55.62%, 51.99%, and 48.14%. Overall, SAEF not only strengthens predictive accuracy across diverse climates but also advances hydrological understanding by linking data-driven ensembles with seasonal process mechanisms, thereby contributing a robust and interpretable tool for runoff forecasting. Full article

Mechanical breakup of river ice cover and associated ice jam flooding is a major concern for riverine communities in cold regions. The ability to forecast breakup ice jams is essential for river ice management. Numerous studies on forecasting breakup ice jams have been conducted. This study reviews existing breakup forecasting methods, including data-driven and machine learning techniques, and discusses their shortcomings and possible improvements in selecting input parameters. Since the weather during breakup time often changes rapidly, forecasting in a Nowcasting mode to assess the risk of mechanical breakup and ice jam flooding is necessary to issue flood warnings and support emergency operations. A physically based method for rapidly forecasting ice cover breakup and ice jam flooding is developed, which also provides information to improve existing forecasting methods. Full article

Rain-on-snow (ROS) events significantly impact hydrological processes in snowy regions, yet their seasonal drivers remain poorly understood, particularly in low-elevation and low-gradient catchments. This study uses an XGBoost-SHAP explainable artificial intelligence (XAI) model to analyze meteorological and watershed controls on ROS runoff in the Laurentian Great Lakes region. We used daily discharge, precipitation, temperature, and snow depth data from 2000 to 2023, available from HYSETS, to identify ROS runoff. The XGBoost model’s performance for predicting ROS runoff was higher in winter (R² = 0.65, Nash–Sutcliffe = 0.59) than in spring (R² = 0.56, Nash–Sutcliffe = 0.49), indicating greater predictability in colder months. The results reveal that rainfall and temperature dominated ROS runoff generation, jointly explaining more than 60% of total model importance, while snow depth accounted for 8–12% depending on season. Winter runoff is predominantly governed by climatic factors—rainfall, air temperature, and their interactions—with soil permeability and slope orientation playing secondary roles. In contrast, spring runoff shows increased sensitivity to land cover characteristics, particularly agricultural and shrub cover, as vegetation-driven processes become more influential. Snow depth effects shift from predominantly negative in winter, where snow acts as storage, to positive contributions in spring at shallow to moderate depths. ROS runoff responded positively to air temperatures exceeding approximately 2.5 °C in both winter and spring. Land cover influences on ROS runoff differ by vegetation type and season. Agricultural areas consistently increase runoff in both seasons, likely due to limited infiltration, whereas shrub-dominated regions exhibit stronger runoff enhancement in spring. The seasonal shift in dominant controls underscores the importance of accounting for land–climate interactions in predicting ROS runoff under future climate scenarios. These insights are essential for improving flood forecasting, managing water resources, and developing adaptive strategies. Full article

This study aims to forecast the combined impacts of drought and flood in the future using an integrated framework. This framework integrates U-Net++, quantile mapping (QM), Copula models, and ISIMIP3b gridded large-scale discharge data (1985–2014, 2021–2050, and 2071–2100). Copula models analyze compound effects in four dimensions to determine return periods for droughts and floods. The standalone U-Net++ and its integration with multiple linear regression, multiple nonlinear regression, M5 model tree, multivariate adaptive regression splines, and QM downscaled ISIMIP3b model river flows. U-Net++QM outperformed other models, with a 58% lower RRMSE. Ensemble GCMs showed less uncertainty than other models in river flow downscaling. For the Ensemble model, the highest drought severity was −300, the maximum duration was 300 months, flood peak flow reached 12,000 m³/s, and intervals lasted up to 22 months. Moreover, the return periods of compound events for this model ranged from 50 to 3000 years. Future river flow projections, using the Ensemble model and emission scenarios (SSP126, SSP370, and SSP585), showed increased vulnerability in 2071 and 2025 versus the observed period. Introducing an integrated framework serves as a management tool for addressing extreme combined phenomena under climate change. Full article