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In synergy with hydraulic works, hydro‐meteorological forecasts and related preventive protection measures are effective tools for mitigating flood risk. Nevertheless, the performance and reliability of coupled prediction systems in real‐time operations are often influenced by errors in meteorological and hydrological models and their interactions. The paper discusses the source and magnitude of such combined errors, analyzing the functionality of a warning system to predict river floods in northern Italian catchments. The proposed flood alert tool consists of a hydrological model, driven by atmospheric forcings from various weather models and ground observations. This study aims to analyze the sources of flood forecasting errors in small urbanized river basins by disentangling the uncertainties in precipitation and discharge predictions. The results emphasize the relationship between quantitative precipitation and peak discharge forecast errors during convective and stratiform events, with a prevalent tendency toward underestimation of peak flows. The paper highlights the added value and limitations of the real‐time multi‐model approach as an effective compromise amidst the wide spread of model forecasts. This assessment is based on 4 years of operational simulations (2019–2022) on the river Seveso, where a municipal monitoring system for flood alerts (MOCAP) has also been implemented to support local civil protection procedures.

Google's operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the long short-term memory (LSTM) networks and the linear models. Flood inundation is computed with the thresholding and the manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the linear model, while the thresholding and manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area close to 470 000 km2, home to more than 350 000 000 people. More than 100 000 000 flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations and improving modeling capabilities and accuracy.

In July 2021 intense rainfall caused devastating floods in western Europe and 184 fatalities in the German federal states of North Rhine-Westphalia (NW) and Rhineland-Palatinate (RP), calling into question their flood forecasting, warning and response system (FFWRS). Data from an online survey (n=1315) reveal that 35 % of the respondents from NW and 29 % from RP did not receive any warning. Of those who were warned, 85 % did not expect very severe flooding and 46 % reported a lack of situational knowledge on protective behaviour. Regression analysis reveals that this knowledge is influenced not only by gender and flood experience but also by the content and the source of the warning message. The results are complemented by analyses of media reports and official warnings that show shortcomings in providing adequate recommendations to people at risk. Still, the share of people who did not report any emergency response is low and comparable to other flood events. However, the perceived effectiveness of the protective behaviour was low and mainly compromised by high water levels and the perceived level of surprise about the flood magnitude. Good situational knowledge and a higher number of previously experienced floods were linked to performing more effective loss-reducing action. Dissemination of warnings, clearer communication of the expected flood magnitude and recommendations on adequate responses to a severe flood, particularly with regard to flash and pluvial floods, are seen as major entry points for improving the FFWRS in Germany.

Flood warning and response systems are essential components of risk reduction strategies with the potential to reduce loss of life and impact on personal assets. However, recent flood incidents have caused significant loss of human lives due to failures in current flood warning and response mechanisms. These failures are broadly related to policies concerning, and governance aspects within, warning generation, the behaviour of communities in responding to early warnings, and weaknesses in associated tools and technologies used in communicating early warnings and responding. Capturing critical failure factors affecting flood warning and response systems can provide opportunities for making corrective measures and for developing a more advanced and futuristic system for early flood warnings. This paper reports the findings of a structured review that was conducted to identify critical failure factors in flood early warning and response systems. This study found twenty-four critical failure factors (CFFs). The interpretive structural modelling (ISM) approach conducted in this study resulted in identifying four different types of failure factors (autonomous, dependent, linkage, and independent) with varying dependence and driving powers. Analysis shows that governance, leadership, finance, standard operating procedures (SoP), and community engagement are the most dominating factors with the highest driving factor, which can overcome other dependent factors. The outcome of this review could be helpful for policymakers and practitioners in overcoming failure factors and implementing effective early warning and response systems.

Flood warning systems are longstanding success stories with respect to protecting human life, but monetary losses continue to grow. Knowledge on the effectiveness of flood early warning in reducing monetary losses is scarce, especially at the individual level. To gain more knowledge in this area, we analyze a dataset that is unique with respect to detailed information on warning reception and monetary losses at the property level and with respect to amount of data available. The dataset contains 4,468 loss cases from six flood events in Germany. These floods occurred between 2002 and 2013. The data from each event were collected by computer-aided telephone interviews in four surveys following a repeated cross-sectional design. We quantitatively reveal that flood early warning is only effective in reducing monetary losses when people know what to do when they receive the warning. We also show that particularly long-term preparedness is associated with people knowing what to do when they receive a warning. Thus, risk communication, training, and (financial) support for private preparedness are effective in mitigating flood losses in two ways: precautionary measures and more effective emergency responses.

Flood forecasting using hydrological and hydraulic models is an efficient non-structural flood management option. This paper presents a real-time flood forecasting approach for the data-scarce Jhelum basin, using MIKE 11 Nedbor-Afstromings Model (NAM), hydrodynamic (HD), and flood forecasting (FF) models. European Centre for Medium-Range Weather Forecast (ECMWF) model-forecasted precipitation and temperature data were used to forecast floods and increase the forecast lead time. The model performance was evaluated using the Coefficient of Determination (R 2 ), Nash–Sutcliffe Efficiency (NSE) and indicators of agreement between the simulated values and the observed values. These indicators show good consistency between the observed and simulated hydrographs for the calibration and validation periods, resulting in a level of reliability sufficient for flood forecasting purposes. Errors in forecasting were corrected using an automatic updating routine MIKE 11 FF with the ECMWF input. The forecast results were acceptable for lead times up to 7-days. The study concluded that the developed model was effective for flood forecasting in the data-scarce, snow-dominated Jhelum basin, and that it might be extended to other similar basins throughout the world as part of an early flood warning system.

Data-driven flow-forecasting models, such as artificial neural networks (ANNs), are increasingly featured in research for their potential use in operational riverine flood warning systems. However, the distributions of observed flow data are imbalanced, resulting in poor prediction accuracy on high flows in terms of both amplitude and timing error. Resampling and ensemble techniques have been shown to improve model performance on imbalanced datasets. However, the efficacy of these methods (individually or combined) has not been explicitly evaluated for improving high-flow forecasts. In this research, we systematically evaluate and compare three resampling methods, random undersampling (RUS), random oversampling (ROS), and the synthetic minority oversampling technique for regression (SMOTER), and four ensemble techniques, randomised weights and biases, bagging, adaptive boosting (AdaBoost), and least-squares boosting (LSBoost), on their ability to improve high stage prediction accuracy using ANNs. These methods are implemented both independently and in combined hybrid techniques, where the resampling methods are embedded within the ensemble methods. This systematic approach for embedding resampling methods is a novel contribution. This research presents the first analysis of the effects of combining these methods on high stage prediction accuracy. Data from two Canadian watersheds (the Bow River in Alberta and the Don River in Ontario), representing distinct hydrological systems, are used as the basis for the comparison of the methods. The models are evaluated on overall performance and on typical and high stage subsets. The results of this research indicate that resampling produces marginal improvements to high stage prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without resampling. Many of the techniques used produced an asymmetric trade-off between typical and high stage performance; reduction of high stage error resulted in disproportionately larger error on a typical stage. The methods proposed in this study highlight the diversity-in-learning concept and help support future studies on adapting ensemble algorithms for resampling. This research contains many of the first instances of such methods for flow forecasting and, moreover, their efficacy in addressing the imbalance problem and heteroscedasticity, which are commonly observed in high-flow and flood-forecasting models.

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.

Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events.