Journal of Flood Risk Management最新文献

A large-scale experimental model of instantaneous dike-break induced flow was conducted in this work. Water level variations in the river channel and floodplain, breach discharge, and the surface velocity field at the breach were measured during dike failure. The results show that: (i) The water level in the river rapidly decreased to a minimum (15%–22% of the initial water depth), then began to gradually rise, and finally approached stable. The water level in the floodplain gradually increased and ultimately tended towards stability. (ii) The breach discharge initially increased to a peak, then gradually decreased with a decreasing rate. The peak discharge was not only related to the initial river water level before dike-break, but also to the river velocity. Under the same conditions, the higher the river water level or the higher the river velocity, the greater the flood peak at the breach. And (iii) During the process of dike-break, the surface velocity of the breach flow gradually decreased. Other things being equal, a higher river water depth or a higher river velocity led to a larger surface velocity of the breach flow. These findings help better understand the hydrodynamic process and provide data support for models.

Debris-flow affected area is typically predicted using runout simulations, often estimating the hydrograph from rainfall conditions. However, rainfall is rarely considered when predicting initiation locations, which influence the occurrence number and location. This study proposes a hybrid method combining statistical source-location prediction based on rainfall conditions and runout simulations inputting the predicted source locations. First, logistic regression is used to predict the spatial probability of debris-flow initiation with rainfall as an input. Next, Monte Carlo simulation based on the initiation location generated from the rainfall-based probability yields the spatial distribution of the debris-flow hit probability. Simulation parameters are systematically determined in advance based on topographic change obtained via aerial LiDAR observations. This method was successfully employed to estimate the spatial distribution of the debris-flow hit probability at 1-m resolution for a debris-flow disaster that occurred in Hiroshima prefecture, Japan, using rainfall data obtained by radar. The simulation time indicated that hit probability can be issued prior to the event for early warning, owing to the adequate lead time of rainfall forecasts and recent developments in computational technology. The hit probability obtained in this study can be also applied to risk quantification based on rainfall conditions.

The concomitant vibration of flood discharge, which would cause structure damages to hydraulic infrastructure and thus incurs threats to nearby communities, has rarely been addressed yet cries for an effective solution in discharge scheduling of sluice gates. This work improves on the traditional practice (Model-I) that mainly aims to restrain start-up and shutdown actions of spillway gates with a new model (Model-II) that includes a flexible vibration damping rule, in which the sluice gates are grouped in priority to be sequentially committed, and in the same group, a reference gate is prioritised to enforce a uniform discharge from the active outlets and the gates are paired to ensure a symmetrical opening. The case studies in the Xiangjiaba Dam (XD) demonstrate the excellent adaptability of the model to gate opening patterns concluded with field experiments and site monitoring, and comparing the two models reveals that Model-II can enforce preferable operational rules to deliver safer discharge scheduling and potentially to reduce risks from the concomitant vibration of hydraulic facilities and the turbulent flow field around the dam during flood discharging, though leading to a much higher frequency of starting up and shutting down of sluice gates than the traditional Model-I.

Rainfall and runoff are considered the main components of the hydrological cycle, and their forecasting is of great significance in water resource management, particularly for reservoir operation. Developing an accurate model to capture the dynamic connection between rainfall and runoff remains problematic and challenging in water resource management due to the nonstationary characteristics of hydrologic processes and the effects of noise. In this study, data-driven techniques, such as the group method of data handling (GMDH), extreme learning machine (ELM), and two hybrids of artificial neural network (ANN) with Cuckoo search algorithm (ANN + Cuckoo) and genetic algorithm (ANN + GA) were used to model the rainfall–runoff relationship. For a comprehensive analysis, four scenarios were examined based on the different input combinations to test and select the best scenario and best model performance. The results indicated that the performance of ELM and GMDH in predicting runoff was more accurate than that of ANN + Cuckoo and ANN + GA. Although the GMDH predicts runoff with higher accuracy, ELM provides reliable performance in simulating both low and high values. The models' performance can be ranked based on the testing data in the following order: GMDH, ELM, ANN + GA, and ANN + CUKOO. The root mean squared error (RMSE) was recorded as 56.7 and 69.7 m³/s for the GMDH and ELM models, respectively. These low RMSE values highlight the potential of these models in effectively addressing the challenges associated with the complexity of rainfall–runoff simulations. Moreover, the results demonstrate that the machine learning models could be used as a simple, rapid, and inexpensive approach for timely and reliable runoff prediction that is expected to benefit reservoir management.

Flood risk management (FRM) involves planning proactively for flooding in high-risk areas to reduce its impacts on people and property. A key challenge for governments pursuing FRM is to pinpoint assets that are highly economically exposed and vulnerable to flood hazards in order to prioritize them in policy and planning. This paper presents a novel flood risk assessment, making use of a dataset that identifies the location, dwelling type, property characteristics, and potential economic losses of Canadian residential properties. The findings reveal that the average annual costs are $1.4B, but most of the risks are concentrated in high-risk areas. Data gaps are uncovered that justify replication through local validation studies. The results provide a novel evidence base for specific reforms in Canada's approach to FRM, with a focus on insurance that improves both implementation and effectiveness.

In small mountain catchments, the spatial and temporal resolution of rainfall can vary significantly across the catchment. However, rainfall gauging stations can be sparse in these regions, and collected data may not reflect the real rainfall distribution across the catchment. When modelling flash floods, finding a suitable approach to estimate the actual rainfall distribution is nontrivial. In this study, the effectiveness of different methods for obtaining a spatial and temporal rainfall distribution for use in numerical modelling of flash floods was investigated using a full two-dimensional depth-averaged shallow-water hydrodynamic model. It was demonstrated that the Thiessen polygon method and the inverse distance weighted interpolation method (IDW), with appropriate empirical coefficients, produce results in agreement with observed stage and discharge hydrographs. We show that the uniform distribution method cannot be used to represent realistic spatial and temporal variability of rainfall for flash flood events in small mountain catchments. By combining available data with the common IDW method, missing rainfall timeseries data in a small catchment can be estimated, even for short-duration time scales, such as a single flash flood event.

Streamflow (Q_flow) process is one of the complex stochastic processes in the hydrology cycle owing to its associated non-linearity and non-stationarity characteristics. It is an essential hydrological process to address the complex time series nonlinear phenomena. In this research, a novel approach was proposed by integrating an autoregressive conditionally heteroscedastic (ARCH) method with bootstrap model to predict future Q_flow intervals. For this purpose, two Q_flow series located at the Eastern Black Sea basin (Turkey) were subjected to the application of the proposed methodology. Among other regression and machine learning (ML) models, which are suitable for Q_flow modeling, the autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and artificial neural network (ANN) were selected for modeling validation in this study. A group of three numerical metrics and graphical presentations were used for the modeling evaluation and assessment. The proposed ARCH approach performed a superior mathematical model to address the Q_flow interval prediction. Remarkable prediction accuracy was shown against the benchmark models. Overall, the approach of coupling the bootstrap procedure with the ARCH model exhibited a robust modeling strategy for predicting Q_flow intervals suggested as a new analysis tool.

Previous research has indicated the important role of social infrastructures during and after flood events. While struggling to uphold their caring responsibilities, they are also deemed relevant for coping and rebuilding after a disaster. We revisit this line of argument for the 2021 flood event in western Germany to deepen the understanding of the societal dimension of caring, coping and rebuilding (CCR) in and after flood events. Based on 21 semi-structured interviews in three case study regions, we analyse how social infrastructures were affected during the flood, their contribution to resilience in the acute and rebuilding phases, and factors influencing their response to extreme events. Moving beyond the conventional focus on technical solutions for flood management, our study examines the significant societal aspects of responding to and recovering from flood events. Our research empirically underscores the critical role of social infrastructure during and after flood events. Recognising the assistance provided by these infrastructures, our findings offer a basis for policy recommendations. Ensuring sufficient financial and political support for social infrastructures is crucial, as is actively involving them in rebuilding initiatives. These measures are vital for facilitating the expansion of social infrastructure and enhancing its resilience potential during flood events.

Cities striving to adapt to the impacts of climate change must recognize the significant variability in flood vulnerability across different communities. By examining the interplay between physical and socio-demographic factors, this paper provides a comprehensive overview of the multidimensional aspects of flood exposure and vulnerability in Istanbul's Pendik District. The Pendik District, situated within the Istanbul Metropolitan Area, was chosen for this study as it regularly faces floods exacerbated by climate change. Utilizing a mixed-methodology approach, ranging from the analytic hierarchy process (AHP) to surveys and census data, we find that areas classified as flood-prone have residential units with lower land market values. Additionally, these high flood-prone areas within the district tend to be populated by elderly individuals, refugees, and citizens with low education levels. In sum, this study reveals that there is a sharp correlation between socio-economically disadvantaged communities and their exposure and vulnerability to urban flooding in Pendik District. As long as the current urban design and building stock fail to address the high level of flood exposure among the most disadvantaged urban communities, there is a critical need for inclusive urban planning and disaster management strategies.