Hasil untuk "River protective works. Regulation. Flood control"

Evangeline Muthoni Njeru, Daniel Ochieng Olago, John Paul Odhiambo Obiero et al.

ABSTRACT Roads are ecologically sensitive infrastructure that are highly susceptible to flooding, which can impair their functionality, serviceability, and durability. Existing assessment methods are often subjective or lack the ability to incorporate domain‐specific knowledge. Therefore, approaches that integrate expert‐driven prioritization with data‐driven optimization are needed to minimize subjectivity and improve model robustness. This study evaluated the historical and future flood vulnerability of the Limuru—Mai Mahiu—Narok road in Kenya. Using rainfall, land use/land cover, NDVI, slope, curve numbers, topographic wetness index, river density, topographic factor, landforms, soil texture, sediment transport index, and GPS data, a GIS‐ensemble framework combining Multi‐Criteria Decision Evaluation (MCDE) and Principal Component Analysis (PCA) was applied. Flood vulnerability was assessed for 1991, 2002, 2011, and 2021, while future susceptibility for 2030 was predicted using Cellular Automata–Markov (CA–Markov) modeling. Across all models, the hinterlands were consistently dominated by moderate to high vulnerability levels, covering about 62%–80% of the region. The year 2002 was the most flood‐prone, with more than 47% of the hinterlands classified as highly to extremely highly vulnerable, whereas 1991 and 2021 showed relatively lower susceptibility. Road‐specific analysis indicated that 40.89–44.80 and 30.97–38.53 km of the road fell within moderate and high vulnerability classes, with up to 73 km affected by high to extremely high vulnerability in 2002. Validation of the 2021 map produced 82% overall accuracy and a Kappa coefficient of 0.73. CA–Markov validation yielded strong performance (Kno = 0.72; Klocation = 0.73; Kstandard = 0.65). The 2030 projection shows that nearly 88% of the hinterlands and about 104 km (91.4%) of the road would be exposed to moderate to extremely high flood risk. These results highlight escalating flood threats and the need for targeted mitigation and climate‐resilient infrastructure planning.

Wm. Alexander Osborne, Stuart J. McLelland, Robert E. Thomas

ABSTRACT Sustainably managing water is a global issue, with rapid land use change, climate change and ageing infrastructure increasing the risk of flooding. To help mitigate against and manage urban flood risk, Sustainable Drainage Systems (SuDS), along with Low Impact Development (LID), Sponge Cities and Water Sensitive Urban Design (WSUD), have been developed. SuDSlab is a multi‐objective testbed project based at the University of Hull, UK that employs a self‐healing mesh network of telemetry units and over 300 sensors to monitor, evaluate, engage and optimise water flowpaths ranging from quasi‐natural greenfield areas, through hybrid systems combining both green and grey infrastructure, to traditionally engineered storm drains and sewers. Data indicate that different flowpaths lead to different sub‐catchment responses depending on the time of year, influenced by water storage within soils and bedrock or changes in plant growth cycles. Findings show that SuDS can attenuate peak flows and enhance water retention, but their performance varies depending on antecedent conditions, SuDS design, and the scale at which they are deployed. The telemetry system is reproduceable and optimisable for deployment elsewhere. The aim is for similar systems to be adopted globally, supporting a comprehensive water sustainability strategy aligned with the UN's Sustainable Development Goal for Sustainable Cities.

Ziru Yang, Yong Lei, Guoru Huang et al.

ABSTRACT Accurate runoff forecasting is essential for flood prediction and disaster preparedness amid increasing hydrological extremes driven by climate change. While deep learning models offer high efficiency, most interpretability studies focus on single models. This limits understanding of how model architecture influences feature sensitivity and model applicability under different conditions, posing a challenge for developing robust urban flood forecasting systems. To address this issue, this study compares four deep learning models with a flood‐weighted loss for daily runoff forecasting: LSTM, CNN, Transformer and Informer, using SHapley Additive exPlanations (SHAP) to link predictions with local meteorological drivers at flood and non‐flood scales. Among the models, CNN is the only model that reproduces the most extreme peak, whereas LSTM captures many other high peaks. Transformer and Informer are more stable, and Informer best tracks temporal fluctuations. All models increase the importance of rainfall during floods and give more weight to temperature in non‐flood periods. Based on these patterns, we propose a framework for evaluating and selecting data‐driven models in urban flood prediction. This framework links forecasting objectives to suitable architectures and supports the development of adaptive, interpretable tools for real‐time flood forecasting and risk management in complex urban settings.

Nishyirimbere Angelique, Rui Xiaoping, Ninglei Ouyang et al.

ABSTRACT Flooding is among the most destructive natural hazards globally, and its frequency and intensity are rising. Climate change and rapid urbanization are increasing flood susceptibility, especially in cities with limited infrastructure. This study assesses current and future flood susceptibility in Kigali, Rwanda, using the Maximum Entropy (MaxEnt) model. Presence‐only flood occurrence records were combined with environmental predictors including slope, soil texture, drainage density, land use/land cover, rainfall, flow accumulation, and topographic wetness index, together with two climate change scenarios (SSP245 and SSP585). Model performance was strong (AUC = 0.84). Slope is the most influential factor (≈84.9% contribution), followed by soil texture, drainage density, and land use/land cover. Currently, about 117.16 km2 (16.19%) of Kigali is classified as high and very high flood susceptibility zones, mainly located in low‐lying areas. In the SSP585 scenario, these zones expand to 118.44 km2 (16.36%) by 2040 and remain similar (118.36 km2, 16.35%) by 2080. The projections indicate a marginal increase in high and very high flood susceptibility zones as climatic conditions intensify. These findings emphasize the need for integrated flood risk management through improved drainage infrastructure, land use regulation, and proactive urban planning to build resilience. The results provide spatially explicit risk maps to support policymakers and planners in Kigali and other rapidly growing African cities facing climate change pressures.

Edilson Freitas da Silva, José Tasso Felix Guimarães, Gabriel Negreiros Salomão et al.

Over the last several decades, extensive changes in land use and land cover (LULC) have caused substantial environmental impacts in the watersheds of southeastern Amazonia, such as the Verde River Watershed (VRW). The effects of anthropogenic activities on soil cover loss rates in the VRW were evaluated to estimate soil loss over time by applying the revised Universal Soil Loss Equation (RUSLE) in a geographic information system (GIS) environment via rainfall erosivity (R), soil erodibility (K), topography (LS), and LULC (CP) data. The VRW experiences strong seasonality, with the most significant R concentrated from January to April, which coincides with a relatively high rainfall index reaching approximately 42% of the annual total. Most of the VRW has gently undulating to flat relief (88.9%) and low LS values (0.02–0.025), but its upper course has a sandy soil texture with high K values (0.02–0.073). Pastureland has replaced forested areas during the past 40 years, dramatically changing the landscape with more significant changes in LULC rates in the upper VRW. Estimates of annual and average soil losses and areas at high and very high risk of erosion increased by more than 200% from 1984 to 2021. In conclusion, deforestation is the main factor influencing erosion patterns and volume in the VRW and has accelerated soil degradation, increasing the risks to human health and the maintenance of tropical rainforests. Additionally, simply stopping deforestation is insufficient to prevent the expansion of areas with high and very high erosion risk. It is crucial to implement reforestation projects to recover forested areas.

Louise Arnal, Alain C. Pietroniro, John W. Pomeroy et al.

Abstract Operational flood forecasting in Canada is a provincial responsibility that is carried out by several entities across the country. However, the increasing costs and impacts of floods require better and nationally coordinated flood prediction systems. A more coherent flood forecasting framework for Canada can enable implementing advanced prediction capabilities across the different entities with responsibility for flood forecasting. Recently, the Canadian meteorological and hydrological services were tasked to develop a national flow guidance system. Alongside this initiative, the Global Water Futures program has been advancing cold regions process understanding, hydrological modeling, and forecasting. A community of practice was established for industry, academia, and decision‐makers to share viewpoints on hydrological challenges. Taken together, these initiatives are paving the way towards a national flood forecasting framework. In this article, forecasting challenges are identified (with a focus on cold regions), and recommendations are made to promote the creation of this framework. These include the need for cooperation, well‐defined governance, and better knowledge mobilization. Opportunities and challenges posed by the increasing data availability globally are also highlighted. Advances in each of these areas are positioning Canada as a major contributor to the international operational flood forecasting landscape. This article highlights a route towards the deployment of capacities across large geographical domains.

Yengi Emmanuel Daro Justine, Avidesh Seenath

ABSTRACT Traditional grey solutions, such as seawalls, are increasingly recognised as being unsustainable for long‐term coastal flood risk management, due to high costs and negative environmental impacts. In response, vegetative nature‐based coastal solutions (NBCS), such as saltmarshes, are being increasingly proposed as a more sustainable alternative with wider environmental benefits. However, there is considerable uncertainty on the longevity of such solutions under sea‐level rise. We, therefore, examine the effectiveness of vegetative NBCS for mitigating coastal flood risk through scenario modelling using a verified LISFLOOD‐FP model for Absecon Island in New Jersey, USA. Specifically, we simulate various experimental vegetative NBCS scenarios, each designed to represent a saltmarsh system (young, mid‐age, and old), under alternative sea‐level conditions. Our results show that these solutions have a marginal influence on flood extent, depth, velocity, and timing under current and future projected sea‐level conditions. These findings suggest that reliance on vegetative NBCS may not be sustainable for long‐term coastal flood risk management, particularly under climate change. We discuss the wider implications of these findings and identify future research pathways towards improving and informing more robust coastal flood risk management decisions.

Jun Zhang, Yingbiao Shi, Guojian He

The critical condition of sediment incipient motion (SIM) is one of the most pivotal and fundamental topics for the mechanics of sediment transport in offshore areas. The flocculation of fine sediment exerts a complex influence on SIM, particularly in bay areas where the water salinity varies. This paper analyzes data measured in Hangzhou Bay with 50 observation points from 2005 to 2019. The objective is to ascertain the effect of salinity on the critical velocity of SIM (uc), figure out the calculation error of traditional equations for uc in saline water and improve the calculation accuracy of uc. Results indicate that the calculation error rate of traditional uc equations escalates with the increased clay content of bottom sediment and water salinity. An improved uc equation in saline water environment is constructed with the consideration of clay content and salinity based on a traditional equation. The improved equation is fitted through measured data and validated with the experimental data. The accuracy of the improved equation significantly increases compared to traditional equations in the area with clay content over 15% and salinity over 12‰.

Helene Meyer, Georg Johann

ABSTRACT The study provides insights into the impact of risk communication on adaptation behavior before and after the 2021 flood in western Germany. The aim is to ascertain how the flood influenced homeowners' adaptive behavior. The Protection Motivation Theory is employed to identify the factors that influence adaptive behavior and needs in response to flooding. To facilitate effective risk communication, we examine the FLOODLABEL, an expert‐oriented communication tool. The findings contribute to the academic debate by providing insights into the impact of risk communication on adaptation behavior. The literature discusses the discrepancy between the information deemed crucial by experts and the information citizens require to make informed decisions. This discrepancy has prompted the need to investigate how an event of this magnitude has resulted in adaptive behavior and which role risk communication plays in the implementation of measures. To this end, a quantitative survey was conducted with 773 participating homeowners. The findings revealed information gaps and ineffective risk communication strategies. This study highlights the need to expand risk communication from a one‐way, knowledge‐based format to a more interactive and two‐way process. The event caused a shift in homeowners' perceived risk; yet, this did not result in a discernible change in adaptive behavior.

Brian R. Cook

Xinran Luo, Pan Liu, Qianjin Dong et al.

Abstract Daily inflow forecasting is of vital importance in reservoir economic operation. In the context of hydrometeorological forecasting, the effectiveness of the data‐driven models has been demonstrated as bias correctors for physically‐based models or direct forecasting models. However, existing studies only highlight the performance improvements provided by the data‐driven model, lacking a comprehensive investigation on whether the data‐driven model should be used as bias correctors or direct forecasting models. This study constructs long short‐term memory (LSTM)‐based preprocessing and postprocessing techniques for a hydrological model, which are tested by linear scaling preprocessing and autoregressive (AR) postprocessing models. The integrated model is compared with the LSTM‐only model. The Shuibuya and Zuojiang reservoirs in China are selected as case studies. Results indicate that: (1) LSTM‐based bias correctors are effective in both preprocessing and postprocessing and (2) the integrated model is comparable to the LSTM‐only model when trained with four or more years of data, while it is better than the LSTM‐only model when trained with less data. These findings demonstrate that data‐driven methods can effectively correct the bias in physically‐based model output, and integrating the physical and data‐driven models is useful in improving multi‐step ahead reservoir inflow forecasting if limited data can be obtained.

Roya Sahraei, Yousef Kanani‐Sadat, Saeid Homayouni et al.

Abstract Characterizing and identification of flood‐susceptible areas can be a solution to mitigate the damages and fatality rate. This study proposes a novel hybrid MCDM framework to assess flood susceptibility in large ungauged watersheds dealing with data scarcity issues. The proposed method examines the interdependencies and causal relationships between various criteria affecting the flooding procedure using the DEcision‐MAking Trial and Evaluation Laboratory (DEMATEL). Moreover, since experts' opinions contain uncertainty, the fuzzy logic is integrated with DEMATEL to overcome this shortcoming. Then, the local weights of criteria were estimated using the Best–Worst Method (BWM) to enhance the pairwise comparisons process. Final criteria weights were obtained using Fuzzy DEMATEL and BWM results in Analytical Network Process (ANP) super‐matrix. Finally, the criteria were distributed spatially using the Complex Proportional Assessment of Alternatives (COPRAS) method based on obtained weights. The proposed method was compared with different approaches such as Fuzzy‐DEMATEL ANP, BWM, and AHP using several statistical measures. We concluded that the novel hybrid proposed method outperformed other approaches based on our results. Moreover, by overlaying classified maps with the historical flood events locations, it was concluded that 85.96% of flooded areas were classified as “high” and “very high.”

Jenna Tyler, Rebecca M. Entress, Pin Sun et al.

Abstract The United States Federal Emergency Management Agency (FEMA) provides funding to state and local governments as well as tribes and territories (SLTTs) through its Flood Mitigation Assistance (FMA) grant program to engage in flood risk management efforts. Although all communities are susceptible to flooding, flooding does not impact communities equally. This article contributes to FEMA's goal of addressing equity concerns by examining whether the FMA program is distributed equitably in counties located in eight coastal states in the United States. Using secondary data from OpenFEMA, the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry, and parcel‐level flood risk data from First Street Foundation from 2016 to 2020, results indicate that socially vulnerable counties are less likely to receive FMA funding, and counties with greater average flood risk are more likely to receive FMA funding. The findings suggest that there is an opportunity for FEMA to improve the FMA program so that funding can be more equitably distributed, such as providing grant writing and application training and support to socially vulnerable communities, educating socially vulnerable communities about the benefits of the FMA program, and extending the application deadline for socially vulnerable communities impacted by flood events.

Ismail Jesna, S. M. Bhallamudi, K. P. Sudheer

Abstract Flood management activities require development of flood maps that depict the spatial and temporal extent of floods with the help of hydrodynamic models. Two‐dimensional (2‐D) hydrodynamic models are frequently employed for flood inundation modeling and mapping with the help of high‐end computational resources and high‐resolution terrain information such as LiDAR (light detection and ranging) data. However, LiDAR data are either unavailable or not freely accessible in many parts of the world, especially in nations belonging to Global South. Hence, one‐dimensional (1‐D) models are still in practice owing to their lesser computational cost and data requirement. Nonetheless, the successful application of a 1‐D model depends mainly on the representation of the natural river and floodplain geometry, which is the primary input in the form of discrete cross‐sections. The assumed flow directions on floodplains while orienting the cross‐sections in 1‐D models induce some uncertainty in the model simulations. This study aims to evaluate the variability in the model simulations caused by the cross‐sectional orientations. Flood simulations were performed using a 1‐D hydrodynamic model for six different cross‐sectional realizations for three river reaches, having distinct morphological and topographical characteristics, and were compared with the simulations of a 2‐D hydrodynamic model and available reference inundation maps. The study suggests that the simulations of flood inundation extent and maximum flow depth variation are influenced by the cross‐sectional orientation on flood plains in river reaches characterized by broad flood plains with complex local topographical variations. In contrast, for reaches with relatively less wide and complex terrains, 1‐D models can generate robust simulations of flood inundation extent and spatial variation in maximum flood depth (R2 and NSE greater than 0.89) for high flood events.

Ryan Paulik, Conrad Zorn, Liam Wotherspoon

Abstract Flood damage arises from complex interactions between flooding processes and socio‐economic elements. Damage assessments for elements such as residential buildings rely on a modelled representation of local damage factors. Multivariable model approaches are well suited for damage prediction using detailed information on flood hazard and building characteristics. While broad explanatory variable ranges can improve model prediction performance, model transfer across geographical contexts often causes performance loss. This study aims to determine if increasing explanatory damage variables in a multivariable model improves residential building damage prediction and whether models based on local variables transfer between locations. We used empirical damage observations from six flood events to train and evaluate random forest regression model prediction performance. Spatial transfer is tested by splitting event datasets with trained models applied to original and external events. Variable analysis demonstrates model performance improvement with up to seven flood hazard and building characteristics, decreasing thereafter. Event models showed highest prediction precision for the original event, while models trained on all events transfer with comparable predictions for urban stormwater flooding. Prediction precision reduces when models transfer between locations affected by different flood types. This indicates flood damage models must replicate variability in local damage factors for reliable spatial transfer.

Teun vanWoerkom, Rens vanBeek, Hans Middelkoop et al.

Abstract Dikes often have a long history of reinforcement, with each reinforcement adding new material resulting in a heterogeneous dike. As data on the dike internal heterogeneity is sparse, it is generally overlooked in the stability assessment of dikes. We present an object‐based and process‐based model simulating dike construction history on archeological dike cross, yielding similar patterns of heterogeneity as observed in real dikes, and apply it in a dike safety assessment. Model predictions improve when being based on more accurate statistics of dike buildup, or when being conditioned to ground truth data. When incorporated in a dike stability assessment, multiple model runs can be coupled to hydrological simulations and dike slope stability calculations, resulting in a probabilistic stability assessment considering internal dike heterogeneity. While high‐resolution observations are still sparse, good model accuracies can be reached by combining regional information on dike buildup with local point observations and this model provides a parsimonious basis to include information of internal dike heterogeneity in safety assessments.

Rita Nogherotto, Adriano Fantini, Francesca Raffaele et al.

Abstract The identification of flood prone areas is essential for a range of engineering, risk reduction and research applications. Here, we describe a combined hydrological and hydraulic modelling approach for the assessment of flood‐prone areas and we present the results obtained over the Po river (Northern Italy). Runoff and river discharges are calculated through the hydrological model CHyM driven by GRIPHO, a new precipitation dataset for Italy. River flow data are used to obtain flood hydrographs for the CA2D hydraulic model, which calculates flood hazard maps at a resolution of 90 m. Flood simulations are run over a re‐shaped HydroSHEDS digital elevation model that includes information of the channel geometry. Modeled flood hydrographs are compared with observed data for a choice of gauging stations, showing a good performance of the CHyM model. We validate the flood hazard maps against observed flood events and official hazard maps. For high return periods, modelled maps can correctly identify up to 67% of the flood extent, both on the Po River and on smaller tributaries, while performances are more variable for lower return periods. Overall, the proposed approach suggests a strong potential for further applications, such as flood hazard assessment under future climate scenarios.

Rebecca Guerriero, Edmund C. Penning‐Rowsell

Abstract Innovation in flood risk management (FRM) is a driver for change. Research, however, is sparse in this area, and innovation itself appears to be left largely to chance. This paper uses a ’systems of innovation’ approach, defining ’avenues’ of innovation, to explore factors that promote or inhibit innovation. The research is based on in‐depth interviews with 10 leading figures in FRM in the United Kingdom, and describes the interactions and iterations involved. We conclude that in terms of practice the encouragement of champions should be enhanced, risk cultures require concerted attention to minimise risk aversion, learning should be facilitated, and innovation scaled up to maximise its effectiveness. We aim also to add to the literature on innovation systems, providing a case study of a complex field previously unexplored in this regard. Detailed innovation‐encouraging processes here need to be better understood and FRM policies and practices adjusted accordingly.

Jingming Hou, Xuan Li, Ganggang Bai et al.

Abstract This work presents an experiment involving detailed fluvial flood propagation process. Comparing to the existing flood experiments which collect hydrodynamical data just at gauges, flood evolution process in river channel and flood plain is measured and temporal–spatial data are provided. In the experiment, three inflow patterns are considered to reflect the different severity of the floods. The flood propagation and inundation are captured by using an array of surveillance cameras. The images are pre‐processed by applying camera calibration method to correct the barrel distortion. A deep learning technique is introduced to automatically identify inundated area. The inundation process is therefore obtained by identifying image series. In addition to the spatial data, the water level evolutions at three gauges are also monitored to supply detailed hydrodynamic information at gauges. The repeatability of the experiments and reliability of the deep learning technique are verified. The experimental data including spatial and point hydrodynamic features for flood events can be used to systematically validate numerical model and calibrate parameters.

Aparna Shankar, Devika Jagajeedas, Megha Periyappilly Radhakrishnan et al.

Abstract The floods of 2018 caused havoc in the State of Kerala, situated in the extreme south‐west of India, in terms of infrastructure and health. This research article provides the first‐ever assessment of the bacterial diversity and its antibiotic susceptibility of the inundated areas of Pampa, Periyar and Vembanad waterbodies by comparing the data collected in two different time intervals succeeding the calamitous floods that is, immediately after flood and 5 months post‐flood. An elevated total coliform count was detected in the waterbodies after the flood thereby rendering it unsafe for drinking. Variation in bacterial diversity was observed in the river and lake water samples with a distinct increase in that of the river samples immediately after flood indicated by shannon diversity index (>5.5). Resistance to ampicillin and cefotaxime was observed in a major proportion of isolates from the three biotopes thus indicating the influence of antibiotic wastes accumulated from different sources of human interventions. Furthermore, operational taxonomic units clustering to Acinetobacter, Legionella, Pseudomonas and Burkholderia genera were detected by metataxonomic analysis which portray as a potential health risk in the future. The article emphasises the importance of adopting sanitation programmes for effective management of epidemic outbreaks post floods.