Journal of Flood Risk Management最新文献

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(GMet) and Ghana Hydrological Authority (GHA), early warning capabilities remain constrained by limited real-time data, outdated infrastructure, and weak coordination.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.

Across the world, communities face annual and increasingly extreme flood events, yet there is a widespread lack of proactive preparedness. This failure to anticipate and mitigate flood risks deepens the damages experienced, stalling development, undermining environmental sustainability, and driving many communities deeper into poverty. Anticipatory action has emerged as a proactive strategy in river flood risk management, aiming to reduce vulnerabilities and enhance community resilience before disasters strike. This study assesses the implementation of anticipatory action strategies in Nigeria by building on qualitative data to assess community vulnerabilities and capacities. Findings indicate that over 70% of the total number of respondents in the selected nine communities in Nigeria lacked access to timely early warnings, and more than half viewed floods as unavoidable, reducing their engagement in long-term resilience planning. Communities demonstrated a stronger preference for short-term relief over proactive preparedness for disasters. Findings reveal a convergence of structural and behavioral vulnerabilities within the population. This highlights the study's contribution by connecting behavioral insights with anticipatory frameworks in high-risk communities. The study shows that there is a clear need for community-driven approaches that combine anticipatory action with economic support, sustained engagement, and other adaptive measures. By closing both behavioral and structural gaps, more effective anticipatory action policies can be institutionalized.

Mountainous river basins, typically located in river source areas, are characterized by steep terrain and dynamic landforms. These regions experience diverse climates due to topographic uplift, making them susceptible to frequent flash floods. The rapid onset and brief response time of flash floods pose significant challenges for achieving accurate and timely forecasting within limited warning periods. Deep learning models have emerged as powerful tools for high-precision streamflow forecasting. This study develops an LSTM-based multi-sliding window flood forecasting model for various lead times and applies it to the Qinling Mountains watershed, with an emphasis on analyzing the model's interpretability. Results from the Maduwang Basin demonstrate the model's excellent performance in flood prediction for 1- and 3-h lead times. While incorporating historical data can enhance model performance for long lead times, excessive historical inputs may be detrimental. Historical runoff significantly influences model performance. However, its contribution neither consistently increases with temporal proximity to the prediction time nor remains uniformly positive. The contribution of input features varies across different flood stages and can be explained by existing hydrological knowledge. This research demonstrates the potential of deep learning for flood forecasting in mountainous basins while providing insights into the interpretation of deep learning models. This provides scientific support for flood warning systems and emergency management.

Synthetic rating curves (SRCs) are often used to translate streamflow forecasts into flood inundation maps. Previous studies have investigated the development and errors in SRCs at local, regional, and continental scales. In this analysis, we used the latest global methodology and datasets to develop SRCs for use in flood inundation map forecasting. Using the Yellowstone River Basin and the 2022 floods that affected the region, we analyzed the error in the SRCs assessment of stage and water surface elevation (WSE). We then investigated the error in flood inundation maps produced using the SRCs. Comparing SRCs to locally derived rating curves from 29 U.S. Geological Survey (USGS) stream gages, median error in SRC stage ranged from 0.45 to 0.65 m and SRC error was greatest at higher magnitude streamflows. This error increased to a median of 1.98–2.30 m when converting the stage to a WSE. After using the SRC WSE estimates to create an estimated flood inundation map, the WSE error at observed high-water marks (1.99 m) was nearly proportional to average WSE error at the stream gage locations along the same river reach. Our results provide the first regional assessment of globally derived SRCs that are used in flood inundation mapping.

Urban Flood Risk Management (FRM) is a critical aspect of developing resilient environments for future generations to inhabit. It is now interconnected with the requirement to be more environmentally conscious through blue-green infrastructure and the delivery of wider co-benefits. The complexity of balancing urban growth with environmental drivers and increasing resilience is a key challenge for strategic urban decision-making. Through computational modelling developments, new approaches to assess the spatial contribution of area to flood hazard are improving our understanding of the catchment response and our ability to develop multifunctional, multi-beneficial projects. Yet at present, these approaches remain largely theoretical or are a ‘best intention’. This study uses an adapted ‘Unit Flood Response’ approach to generate Flood Source Area (FSA) maps for an urban catchment in the UK. A user-focused engagement approach is applied using FSA outputs to generate key insight into its applicability from a practitioner perspective. The FSA modelling identified several hazard sources, from widespread contributions upstream to discrete contributions downstream. Stakeholders concluded that the FSA can support FRM at the pre-planning stage by providing a clearer strategic vision across the catchment to support traditional ‘receptor-led’ decision-making. Improved identification and negotiation of project partners and the potential to support/identify wider scale options that integrate with existing and planned infrastructure in other sectors, for example, housing and transport, were additional benefits of this approach. While the computational aspects of FSA analyses could be improved for model robustness (e.g., calibration, validation), they must do so with a full understanding of the practicalities of applying these techniques on the ground, demonstrating the importance of co-development of research with practitioners and decision-makers.