Journal of Flood Risk Management最新文献

The clogging of bridges by loose sediments is a phenomenon that has so far been little studied in the literature but plays a decisive role in determining the spatial distribution of hazards in mountainous areas affected by these events. Lacking a systematic approach, in this paper, we propose a simple scheme describing the physics of bridge clogging derived from some field evidence, identify the requirements that a mathematical-numerical model must fulfill to be able to implement the scheme, show how the TRENT2D model matches these requirements, and how it is possible to practically implement a procedure for simulating a bridge clogging event. The application to a study area has shown reasonable results, especially regarding hazard mapping. Furthermore, although it was impossible to carry out an in-depth validation, the proposed approach appears preferable to simplified approaches, such as neglecting the presence of the bridge and its possible clogging or using fixed-bed modeling with a given increased discharge compared to the liquid estimation. Further investigations will be performed based on field and laboratory data to make the approach even more reliable.

Discharge modeling utilizing novel deep learning techniques is highly recommended due to their high efficacy in modeling nonlinear time series. In this study, a hybrid discharge model is developed, termed wavelet-based long short-term memory (WLSTM), by integrating wavelet transform and Long Short-Term Memory (LSTM). This technique focuses on improving discharge prediction by effectively denoising the input data and amplifying the most relevant temporal patterns for the model. However, since LSTM models depend on underlying data patterns, their performance can be significantly affected by the intensity of nonlinearity in hydrological time series. To address this, we introduce a novel method using ApEn (Approximate Entropy) to quantify nonlinearity intensity. Then, we applied Fuzzy Clustering to classify nonlinearity into weak, moderate, and high nonlinearity categories. The performance of both LSTM and WLSTM is evaluated using daily discharge data from 16 hydrometric stations in Hesse, Germany, for the period 2000–2017. The results notably show a remarkable reduction of 66.43% for Root Mean Squared Error (RMSE) and of 45.49% for Mean Absolute Percentage Error (MAPE) for WLSTM performance compared to LSTM. Furthermore, WLSTM increased R-squared (R²) by 2.06%. This research acknowledges that there is a direct correlation between the streamflow nonlinearity and WLSTM accuracy. With increasing nonlinearity intensity, WLSTM captures the complexity of streamflow patterns more effectively. RMSE is 0.1194, 0.0836, 0.0547 and R² is 0.9976, 0.9990, 0.9994 for weak, moderate, and high nonlinearity groups, respectively. This study highlights the importance of streamflow nonlinearity analysis in improving flood forecasting and risk management.

Rapid urbanisation has resulted in the sealing of pervious surfaces and the construction of houses in flood-vulnerable areas, aggravating urban flooding. This paper explores the impact of urban flooding on families with vulnerable members, surveying 600 residents in six case study areas in Indonesia. The research study outcomes recommend that the government adopt a community flood resilience framework to develop an inclusive urban flood resilience policy. The study findings confirmed that the elderly, children and these two groups combined experience the worst impacts of urban flooding. Social, economic and environmental factors of these vulnerable population groups can further exacerbate such impacts. Since the diversity and characteristics of vulnerable population groups vary at a location, it is recommended that the community flood resilience policies and programs should be personalised, based on human factors such as types of vulnerable population groups, and contextualised to the social, economic, natural and built infrastructure factors associated with specific vulnerable population groups. This study contributes to innovation management by proposing a novel framework that integrates local vulnerability factors into flood resilience planning. Such an approach aligns with the innovation process of transformation and diffusion, enabling the development of inclusive policies that can adapt to diverse community needs. The framework can serve as a tool for innovation management, promoting equitable innovation by explicitly addressing the challenges faced by specific vulnerable groups.

Urban flood forecasting benefits from high-resolution inundation maps, but fine-grid hydrodynamic simulations are computationally costly. We compared three CNN–based super–resolution (SR) models, ResUNet, EDSR, and RCAN, for downscaling physics–based simulations in downtown Portland, Oregon, using paired flood maps at 1 m (HR) and both 4 and 8 m (LR). Performance was assessed using image level metrics (PSNR, SSIM) and flood specific indicators: CSI for flood extent, RMSE for water depth accuracy, and a depth–based severity classification. At 4× upscaling, all SR models outperformed the LR baseline; RCAN performed best (PSNR +57%, SSIM +31%, RMSE −73%, CSI +53%), followed by EDSR (PSNR +50%, SSIM +30%, RMSE −64%, CSI +45%) and ResUNet (RMSE −55%, CSI +40%). Analysis of class–wise recall showed RCAN leading for non–flood (98.06%, +6.59 pp) and severe flood (96.48%, +16.90 pp), while EDSR led for mild flood class (97.95%, +6.49 pp). Errors were most pronounced along wet–dry boundaries and in complex urban geometries, where RCAN and EDSR reduced error magnitude more effectively than ResUNet. Models with larger numbers of parameters required longer training times. Furthermore, the computational cost further increased with more training epochs and especially at 4× upscaling relative to 8×, reflecting differences in model complexity and scaling configuration. Taken together, these findings support SR as a practical complement to physics–based modeling for real time forecasting and planning, while also providing guidance for selecting architectures under varying computational budgets.

Flooding in Ghana's White Volta basin has caused widespread displacement, fatalities, and damage to infrastructure and livelihoods in agriculturally dependent communities. Despite the presence of national agencies such as the Ghana Meteorological Agency (GMet) and Ghana Hydrological Authority (GHA), early warning capabilities remain constrained by limited real-time data, outdated infrastructure, and weak coordination. As a result, many residents continue to rely on traditional knowledge and informal coping strategies. This study qualitatively assesses the operational state of Flood Early Warning Systems (FEWS) in the White Volta basin, focusing on their effectiveness, limitations, and opportunities for improvement. Using semi-structured interviews with 18 key stakeholders spanning government agencies, technical experts, and community leaders, we analysed the institutional and technical dynamics of Ghana's FEWS through thematic analysis. Findings reveal that although the myDEWETRA-VOLTALARM platform offers 5-day flood forecasts through social media, SMS, and radio, its warnings are often mistrusted or inaccessible to rural populations. Thematic analysis identified four critical gaps: institutional fragmentation, exclusion of local knowledge, inadequate data infrastructure, and last-mile communication failures. These are complicated by the basin's unique environmental conditions, including transboundary dam releases, intense seasonal rainfall, flat terrain, and poor drainage. We conclude that the current FEWS framework remains insufficient for proactive flood risk governance. Strengthening institutional coordination, integrating community-based adaptation practices, and investing in localized data and communication infrastructure are essential to improving system legitimacy and resilience. The study contributes to broader discourses on early warning systems in resource-constrained settings.

Reservoirs play a crucial role in modifying natural flow regimes and mitigating flood peaks, yet their effectiveness depends heavily on operational strategies, particularly the initial storage level at the onset of a flood event. This study investigates, for the first time, the non-linear effects of reduced initial storage on the relationship between flood peak attenuation efficiency and flood return period for about 250 large dams in Italy. We estimate flood hydrographs via a simplified hydrological model and apply full hydraulic routing under different scenarios of initial reservoir storage, informed by historical reservoir time series and regional flood seasonality. Our findings reveal that flood peak attenuation is highly sensitive to the initial storage level, with dam performance deteriorating sharply as flood return periods increase, especially when initial storage is high. Seven distinct classes of dams are identified based on their flood attenuation capacity relative to flood severity, highlighting non-linear and threshold effects that are often overlooked in regional dam safety assessments. Notably, the commonly assumed full-reservoir condition yields overly conservative estimates: under this assumption, approximately 20% of the dams reach their maximum allowed water level for return periods of 100 years or less. This national-scale analysis provides new insights into regional differences in reservoir operation, particularly between hydropower-oriented dams in the Alps and water supply reservoirs in southern Italy. By explicitly quantifying how reduced initial storage can enhance flood mitigation, the study offers practical recommendations for optimizing reservoir operations under current and future climatic conditions.

The demand for catchment-based flood management to adapt to climate change is growing, with natural flood management (NFM) receiving increasing attention. NFM has implications for the ‘providers’ of land for measures upstream (the farmers) and the ‘beneficiaries’ of flood reduction downstream (the public). The misalignment of interests from these stakeholder groups may pose a challenge for flood risk managers during the delivery of NFM at the catchment scale. Considering this, a rapid evidence assessment (REA) of 60 peer-reviewed articles was undertaken. This REA provides an overview of catchment perspectives, compares farmer and public preferences for NFM design, and explores key determinants of scheme acceptance. The public expressed positive perceptions and willingness to pay for NFM, with preferences for measures with large water storage capacity that deliver co-benefits alongside flood management objectives. For farmers, NFM schemes that contributed to on-farm conditions, for example, soil stability, were seen as positive, but overall, their willingness to adopt measures was limited. Nevertheless, knowledge of NFM among both groups strongly determined its acceptance. This suggests that resolving misaligned values will require policymakers and practitioners to work with these stakeholders on NFM design and farmer incentives to secure the delivery of future schemes.

The temporal distribution of rainfall is a key driver of flood response. Yet, flood estimation methods are frequently based on symmetrical design profiles. Recent research using sub-hourly rainfall data from Great Britain indicates that a significant proportion of observed rainfall events are non-symmetrical. This paper investigates how different rainfall profiles affect river flow hydrographs for a set of small, flash-flooding catchments. Results show that rainfall profiles affect observed hydrograph peak flow and timing. Most importantly, back-loaded rainfall profiles lead to higher peak flows than symmetrical or front-loaded profiles. These observations are compared to current design practice, using the Revitalised Flood Hydrograph (ReFH2.3) model to simulate flows from different rainfall profiles. Simulated events reproduce the observed response of peak magnitude but differ for peak time. A comparison of modelled flows with catchment descriptors indicates that steep, low permeability, wet catchments are most sensitive to rainfall profile shape. These are also the most vulnerable catchments to flash flooding. We recommend that different rainfall profile shapes should be considered for flood risk assessments in rapid response catchments, particularly since global warming is increasing the number of intense, short-duration downpours.

Accurate flood level prediction is crucial for mitigating flood damage caused by typhoons or localized heavy rainfall. However, predicting flood levels is challenging due to changes in river environments and external factors, such as dam or weir operations. To address these challenges, this study proposes a methodology for constructing an optimal combination of input data using basic hydrological information and predicting flood levels in real time through a deep learning model. The study focuses on identifying the best input data combination tailored to each river basin's characteristics, considering both natural runoff rivers and those influenced by dam discharges. The Long Short-Term Memory (LSTM) model, known for its superior performance in time-series forecasting, was employed. The results demonstrate high accuracy in flood level prediction, particularly within a 3-h lead time.

Reliable long-term daily and extreme streamflow simulation, essential for watershed sustainable development, remains challenge in changing environments due to the complementary limitations inherent in conventional physical-driven and data-driven models. This study proposed a physics-guided machine learning (ML) approach that coupled SWAT with interpretable ML to enhance streamflow simulation accuracy for both daily and extreme streamflow whilst maintaining physical interpretability. This study systematically compared SWAT and three SWAT-ML models (SWAT-DT, SWAT-LSBoost, and SWAT-RF) to modify systematic model residuals, incorporating Shapley additive explanations (SHAP) to quantify feature contributions to streamflow simulations, and apply it to the Taoer River Basin (TRB), China. Results demonstrated that coupled models achieved daily streamflow simulation with $��\mathrm{KGE}�$ values consistently above 0.94 and $��\text{PBIAS}�$ values for extreme streamflow within 17%. In comparison with the standalone SWAT, the coupled framework further cut runtime from nearly 200 h to a few minutes. Additionally, multi-model comparisons revealed the superior performance of SWAT-LSBoost in streamflow simulations, with SHAP further highlighting the predominant role of watershed hydrological process in governing coupled model. Thus, this approach enhanced modeling precision while strengthening the reliability and transparency of outputs, offering a scientifically robust foundation for decision-making in long-term water resources planning and flood-drought disaster mitigation strategies.