Journal of Flood Risk Management最新文献

With climate change, the accurate prediction of river water levels has become increasingly critical, particularly in confluence areas where multiple tributaries merge, resulting in complex hydrodynamic interactions. This study evaluates direct prediction (DP) and recursive prediction (RP) using a virtual sensor approach, with a focus on the role of forecasted rainfall. An LSTM model was trained using upstream rainfall and water level data to predict downstream levels, and its performance was assessed across various lead times (LT) using MAE, RMSE, NSE, and QER. For a short event (Event #8), DP without forecasted rainfall achieved an NSE of 0.42 at LT = 12 h, while RP dropped to −11.69. With forecasted rainfall, RP improved, maintaining an NSE of 0.75 compared to DP's 0.51. For a long multi-peak event (Event #9), RP with forecasted rainfall achieved NSE values of 0.98 at 1 h and 0.91 at 12 h, outperforming DP (0.97 at 1 h, 0.42 at 12 h). These results demonstrate that DP is more reliable when forecasted rainfall is unavailable, whereas RP becomes superior when such data are available. Overall, the study highlights the potential of virtual sensors to enhance flood forecasting and disaster preparedness in confluence zones lacking direct monitoring stations.

Dams are built for many purposes, one of the most important of which is to reduce the impact of floods. They release water downstream in a controlled manner through spillway structures. Spillways are critical structures that must be designed with low-risk criteria to withstand floods that occur once every thousand years. This paper presents a three-dimensional CFD simulation of the flow over a prototype spillway and validates the results with experimental data. The spillway belongs to the Çatalan Dam, which was constructed in 1985 for hydroelectric power generation. A 1:100 physical model of the Çatalan Dam spillway was built based on Froude similarity, and velocities in the discharge channel were measured at specific cross-sections and points. The numerical model used the Volume of Fluid (VOF) method and the k-ε Standard model. The velocity values from the numerical model were compared with experimental velocity values, showing good agreement. Additionally, pressure values were obtained from the numerical model, and cavitation index values were computed accordingly. The results indicated no cavitation risk in the prototype spillway, as the index values remained above 0.2 at all points in the cross-sections.

Involving communities in flood early warning systems (FEWS) is increasingly recognised as an essential component of flood resilience. FEWS is considered to be integrated systems of flood forecasting and warnings, impact assessment, communication and preparedness which enable stakeholders to take appropriate actions to reduce the impacts of flooding. In the United Kingdom, voluntary, community-based flood groups can play an important role in local flood resilience, adding value to the work of Flood Risk Management Agencies including the Environment Agency, Local Authorities and Water Companies. However, little literature has examined how community-based flood groups use FEWS to help their local communities. In this paper we explore the use of FEWS by communities in the broadest sense, covering the use of any flood forecast or monitoring information and how this is used by flood groups to take action in the local community. We worked with 10 flood groups in England and found they used combinations of official and community-led information: (i) official information on flood warnings, weather forecasts, river-level observations and rain gauges and (ii) community-led bespoke warning systems at local hotspots including telemetry and video. Some of the Flood Groups were considerably advanced in how they analysed and presented this information, developing accessible dashboards and/or trigger points and alerts to support actions in the community. Five of the Flood Groups felt that their use of this information had recently prevented or reduced the impacts of flooding in their local community. However, the Flood Groups faced a range of challenges including technical and funding support for FEWS and wider governance challenges which should be addressed by State support. Support is particularly important in areas of significant flood risk and where community-led FEWS could complement and be integrated with state flood warnings. For example, where official flood warnings do not cover locations in sufficient detail or for key flood sources (e.g., surface water). In addition, the Flood Groups had mainly developed in affluent areas and appropriate interventions are also required in more disadvantaged communities. The study makes a strong case for State support for voluntary flood groups.

In hydrologic modeling, forecasting peak floods is a complex and high-priority task, particularly in regions with limited in situ observations. This research investigates the potential of utilizing satellite-based precipitation products with near real-time updates (NRT-SbPP), together with deep learning, to improve peak flow estimation and flood forecasting. Using Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Final version as a reference, four NRT-SbPPs were evaluated: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic-Infrared–RainRate (PDIR), the IMERG-Early, and IMERG-Late. This study evaluated the performance of these datasets for streamflow simulation using the Hydrologic Modeling System (HEC-HMS) in a sub-basin of the Russian River watershed. Bias correction was performed using Long Short-Term Memory (LSTM) networks, with IMERG-Final serving as the reference precipitation dataset. This correction improved the quality of the input rainfall data and led to more accurate streamflow simulations. For example, the Root Mean Square Error of IMERG-Late decreased from 1.98 to 1.48 mm with LSTM, resulting in performance metrics similar to those of observed discharge (Nash-Sutcliffe Efficiency: 0.82). Most importantly, PDIR exhibited significant enhancement, with a 36% correlation coefficient increase (from 0.52 to 0.81), as well as high rates for extreme event detection. The further findings establish the potential of employing LSTM methods and using IMERG-Final as a reference to incorporate NRT-SbPPs within real-time flood forecasting and early warning frameworks. This method leverages the new technologies of satellite-based meteorological data and offers an efficient, cost-effective option to enhance flood prediction and disaster risk management, especially in data-scarce regions.

The desire to promote Natural Flood Management (NFM) has not yet been matched by implementation. In part, this reflects the lack of scientific evidence regarding the ability of NFM measures to contribute to risk reduction at the national scale. Broad scale understanding, as exemplified for Great Britain in this paper, is necessary evidence for policy development and a prerequisite for implementation at scale. This does not imply a lack of confidence in the wider benefits that NFM provide (for biodiversity, carbon sequestration, well-being and many others), but without credible quantified flood risk reduction evidence, progress has been slow. This paper integrates national-scale hydrological models (using SHETRAN and HBV-TYN) and fluvial flood risk analysis (using the Future Flood Explorer, FFE) to quantify the flood risk reduction benefits of NFM across Great Britain under conditions of future climate and socio-economic change. An optimisation of these benefits is presented considering alternative NFM policy ambitions and other demands on land (urban development, agriculture, and biodiversity). The findings suggest NFM has the potential to make a significant contribution to national flood risk reduction when implemented as part of a portfolio of measures. An optimisation through to 2100 suggests investment in NFM achieves a benefit-to-cost ratio of ~3 to 5 (based on the reduction in Expected Annual Damage (EAD) to residential properties alone). By the 2050s, this equates to an ~£80 m reduction in EAD under a scenario of low population growth and a 2°C rise in global warming by 2100. This increases to £110 m given a scenario of high population growth and a 4°C rise. Assuming current levels of adaptation continue in all other aspects of flood risk management, this represents ~9%–13% of the reduction in EAD achieved by the portfolio as a whole. By the 2080s, the contribution of NFM to risk reduction increases to ~£110 and ~£145 m under these two scenarios. These figures are based on the reduction in EAD to residential properties alone, and do not include the substantial co-benefits that would also accrue.

Objective of this paper is to study how reliability standards, expressed as probabilities of dike segment failure, can be practically updated to improve opportunities for risk-based dike design and planning. The approach to assess the economic optimal flood probability, used by the Dutch Delta Committee (1958, in this paper referred to as Van Dantzig), is adapted to reflect time-dependent effects of a.o. climate change and subsidence. Furthermore, the approach is adapted to reflect overtopping instead of overflow and it is extended to include reinforcements over time. A comparison of the results of the Adapted Van Dantzig approach with the economic optimal probabilities used as input for the recently formalised Dutch standards (2017) is performed for 73 dike segments in the Netherlands, showing good agreement. Following the Adapted Van Dantzig approach, an analytical relation is developed for economic optimal design horizons, dependent on the dike design, and characteristics of load, investment, climate effect, and economic growth. Finally, a dynamic and simple-to-use approach is developed to enable updating of the economic optimal reliability based on a proposed design and investment planning. This can serve to consider whether an existing reliability standard still fits adequately or needs updating.

Tourist-related businesses, which are key to the economies of many small island states in the Caribbean, are often vulnerable to coastal flooding. Nature-based solutions, such as coral reefs and mangroves, can help to reduce their coastal flood risk. There has been a dearth of work accurately quantifying the risk-reduction benefits for mangroves and reefs in the Caribbean and what the implications are for developing insurance products. This paper describes the modelling to estimate the expected annual damage (EAD) for buildings in the Caribbean with and without coastal nature-based solutions in place. Reefs and mangroves have the potential to reduce the EAD. However, in the case of reefs, this effectiveness is related to their health, with unhealthy ones increasing EAD in some cases. One of the limiting factors to developing traditional indemnity insurance products, which take into account coastal nature-based solutions in the Caribbean, is the accurate quantification of the reductions they have on the EAD. However, to develop indemnity insurance products which take account of reefs and mangroves would require significant updates to existing catastrophe models which are used by the insurance industry, as well as tailoring them for specific locations. Parametric insurance products offer a potential mechanism for restoring damaged reefs and mangroves; however, further research is needed to better align payout triggers with the actual damage these ecosystems sustain.

Natural Flood Management (NFM) has gained prominence as a sustainable approach to flood risk reduction, particularly in small catchments where traditional grey infrastructure is less viable. However, understanding the effectiveness of NFM is closely tied to the quantity and quality of hydrological monitoring. In small catchments, this monitoring remains inconsistent, whereas high-quality, high-frequency networks maximise the likelihood of detecting NFM effects. This is the first systematic review to analyse current approaches to streamflow and rainfall monitoring used to assess NFM performance in small catchments (defined as < 25 km²), consolidating data from 33 studies (65 catchments) into a practitioner-oriented decision matrix that links site conditions, cost and certainty to method selection. The reviewed dataset consolidates example NFM interventions and associated monitoring approaches, highlighting the benefits and limitations of each method in a single, accessible resource. The review also highlights gaps, including limited baseline data, short monitoring durations, and infrequent reporting of costs and methods. A decision matrix is presented to support practitioners in selecting streamflow monitoring methods based on site conditions and resources for small catchments. Recommendations to improve standardisation, reporting, and the adoption of low-cost, scalable techniques, including community-led and non-contact approaches (remote sensing and drone imagery) are also given.

This study develops a comprehensive framework for mapping flood susceptibility and vulnerability in the Cheshmeh-Kileh forest watershed in northern Iran by integrating remote sensing (RS), local knowledge, and machine learning (ML) algorithms. This was accomplished through the application of various MLs, such as K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Naive Bayes. In this study, flood susceptibility refers to the physical propensity of an area to experience flooding, influenced by geo-environmental factors, while flood vulnerability captures the socio-economic and institutional dimensions that determine a community's ability to cope with and recover from flood events. This research first identified critical geo-environmental factors influencing flood susceptibility and utilized remote sensing to locate areas prone to runoff generation. Flood risk zoning was then implemented using machine learning techniques in Python. To assess flood vulnerability, data were collected from local residents via questionnaires, focusing on economic, infrastructural-physical, institutional-policy, and social-cultural aspects. The flood vulnerability map was created by integrating these survey results with population density data to identify areas where high social exposure coincides with high physical susceptibility. Findings indicated that the combined remote sensing-SVR model was the most effective for sensitivity classification, identifying sub-watersheds 2 and 8 in the Sehezar River (a major basin within the study area) as the areas with the highest and lowest flooding susceptibility, respectively, with sub-watershed 10 in the Dohezar River (another major basin) being the most vulnerable. The estimated values for Mean Absolute Error (0.041), Mean Square Error (0.042), Root Mean Square Error (0.205), and Area Under the Curve (0.980) demonstrated high model accuracy. The Friedman statistical test showed that the average scores for the different dimensions of vulnerability decreased in the order of: economic (0.48), social-cultural (0.44), infrastructural-physical (0.34), and institutional-policy (0.28). Consequently, the economic dimension was prioritized for its highest score. Flood vulnerability mapping revealed that sub-watersheds 5, 11, 14, and 15, which had higher population densities, were naturally more vulnerable to floods. This finding reflects a direct relationship between population density and flood vulnerability. Overall, this study underscores the urgent need for effective planning and preventive strategies to mitigate flood risks and enhance resilience in the region.

Flooding is a disruptive and devastating natural hazard for communities all around the world. To combat the negative effects of flooding, it has become a global priority to implement and research flood early warning systems (FEWS). However, previous research did not comprehensively examine both the technological and social dimensions of FEWS nor the knowledge of their availability and status around the world. To address these gaps, this paper completes a narrative review and synthesizes best practices in FEWS warning and system design, and introduces a “FEWS Around the World” repository which catalogs 3–4 examples per region across six continents. Our analysis shows that while most FEWS include design features outlined in the literature such as timeliness, human capacity, and integration, fewer implement novel components related to impact-based warnings, participatory science, bottom-up approaches, advanced technology, and maintaining a preparedness fund, highlighting gaps and opportunities for improvement. Examples demonstrate how these characteristics manifest in diverse contexts, from community-based systems in Nepal to AI-driven systems like Google's Flood Hub. By bridging design principles with observed global practices, this paper is designed to aid researchers, practitioners, and communities in people-centered FEWS development, implementation, and operation, improving resilient flood risk management.