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1
Moy de Vitry, Matthew, Simon Dicht, and João P. Leitão. "floodX: urban flash flood experiments monitored with conventional and alternative sensors." Earth System Science Data 9, no. 2 (September 4, 2017): 657–66. http://dx.doi.org/10.5194/essd-9-657-2017.
Повний текст джерелаАнотація:
Abstract. The data sets described in this paper provide a basis for developing and testing new methods for monitoring and modelling urban pluvial flash floods. Pluvial flash floods are a growing hazard to property and inhabitants' well-being in urban areas. However, the lack of appropriate data collection methods is often cited as an impediment for reliable flood modelling, thereby hindering the improvement of flood risk mapping and early warning systems. The potential of surveillance infrastructure and social media is starting to draw attention for this purpose. In the floodX project, 22 controlled urban flash floods were generated in a flood response training facility and monitored with state-of-the-art sensors as well as standard surveillance cameras. With these data, it is possible to explore the use of video data and computer vision for urban flood monitoring and modelling. The floodX project stands out as the largest documented flood experiment of its kind, providing both conventional measurements and video data in parallel and at high temporal resolution. The data set used in this paper is available at https://doi.org/10.5281/zenodo.830513.
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Kumar, K., H. Ledoux, and J. Stoter. "DYNAMIC 3D VISUALIZATION OF FLOODS: CASE OF THE NETHERLANDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W10 (September 12, 2018): 83–87. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w10-83-2018.
Повний текст джерелаАнотація:
<p><strong>Abstract.</strong> In this paper, we review state of the art 3D visualization technologies for floods and we focus on the Netherlands since it has a long history of dealing with floods and developing information technology solutions to prevent and predict them. We discuss the most recent advances in using 3D city models for modelling floods and discuss future directions. We argue that 3D city models provide a more realistic interpretation and assessment of floods e.g. information about the height of the water level and the number of floors that will be flooded. We present our framework to dynamically visualize floods in 3D using the Cesium 3D webglobe. An open platform using 3D city models for interactive visualization of different flood simulations can serve as a hub to involve all relevant parties such as water experts, policy developers, decision makers, and general public. We created a 3D terrain model with buildings of a study area in the Netherlands in CityJSON format. We implemented a software prototypes for converting 3D city models from CityJSON to Cesium specific glTF format for rendering over Cesium. We propose using CZML (Cesium Language) to represent time dynamic properties, water levels in our case. The developed framework which uses only open data and open-source software can be supportive in real applications such as planning for a city or municipal corporation, or for decision making.</p>
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Craciunescu, Vasile, Gheorghe Stancalie, Anisoara Irimescu, Simona Catana, Denis Mihailescu, Argentina Nertan, George Morcov, and Stefan Constantinescu. "MODIS-based multi-parametric platform for mapping of flood affected areas. Case study: 2006 Danube extreme flood in Romania." Journal of Hydrology and Hydromechanics 64, no. 4 (December 1, 2016): 329–36. http://dx.doi.org/10.1515/johh-2016-0040.
Повний текст джерелаАнотація:
Abstract Flooding remains the most widely distributed natural hazard in Europe, leading to significant economic and social impact. Earth observation data is presently capable of making fundamental contributions towards reducing the detrimental effects of extreme floods. Technological advance makes development of online services able to process high volumes of satellite data without the need of dedicated desktop software licenses possible. The main objective of the case study is to present and evaluate a methodology for mapping of flooded areas based on MODIS satellite images derived indices and using state-of-the-art geospatial web services. The methodology and the developed platform were tested with data for the historical flood event that affected the Danube floodplain in 2006 in Romania. The results proved that, despite the relative coarse resolution, MODIS data is very useful for mapping the development flooded area in large plain floods. Moreover it was shown, that the possibility to adapt and combine the existing global algorithms for flood detection to fit the local conditions is extremely important to obtain accurate results.
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Lindenschmidt, Karl-Erich, Knut Alfredsen, Dirk Carstensen, Adam Choryński, David Gustafsson, Michał Halicki, Bernd Hentschel, et al. "Assessing and Mitigating Ice-Jam Flood Hazards and Risks: A European Perspective." Water 15, no. 1 (December 26, 2022): 76. http://dx.doi.org/10.3390/w15010076.
Повний текст джерелаАнотація:
The assessment and mapping of riverine flood hazards and risks is recognized by many countries as an important tool for characterizing floods and developing flood management plans. Often, however, these management plans give attention primarily to open-water floods, with ice-jam floods being mostly an afterthought once these plans have been drafted. In some Nordic regions, ice-jam floods can be more severe than open-water floods, with floodwater levels of ice-jam floods often exceeding levels of open-water floods for the same return periods. Hence, it is imperative that flooding due to river ice processes be considered in flood management plans. This also pertains to European member states who are required to submit renewed flood management plans every six years to the European governance authorities. On 19 and 20 October 2022, a workshop entitled “Assessing and mitigating ice-jam flood hazard and risk” was hosted in Poznań, Poland to explore the necessity of incorporating ice-jam flood hazard and risk assessments in the European Union’s Flood Directive. The presentations given at the workshop provided a good overview of flood risk assessments in Europe and how they may change due to the climate in the future. Perspectives from Norway, Sweden, Finland, Germany, and Poland were presented. Mitigation measures, particularly the artificial breakage of river ice covers and ice-jam flood forecasting, were shared. Advances in ice processes were also presented at the workshop, including state-of-the-art developments in tracking ice-floe velocities using particle tracking velocimetry, characterizing hanging dam ice, designing new ice-control structures, detecting, and monitoring river ice covers using composite imagery from both radar and optical satellite sensors, and calculating ice-jam flood hazards using a stochastic modelling approach.
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Riedel, Lukas, Thomas Röösli, Thomas Vogt, and David N. Bresch. "Fluvial flood inundation and socio-economic impact model based on open data." Geoscientific Model Development 17, no. 13 (July 10, 2024): 5291–308. http://dx.doi.org/10.5194/gmd-17-5291-2024.
Повний текст джерелаАнотація:
Abstract. Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting.
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Llasat, María Carmen. "Floods evolution in the Mediterranean region in a context of climate and environmental change." Cuadernos de Investigación Geográfica 47, no. 1 (May 17, 2021): 13–32. http://dx.doi.org/10.18172/cig.4897.
Повний текст джерелаАнотація:
Floods are the most important risk in the Mediterranean region, both due to their frequency and impact. Studies of historical floods show flood-rich periods that could be associated with climate causes, but there is also a certain growing trend as a result of changes in land use and increased vulnerability. If climate scenarios point to an increase in the torrentiality of precipitation, with longer dry periods and more intense rainfall, there is still a high level of uncertainty in their impact in floods. This paper addresses this issue, also considering the complex role of changes to hazards, vulnerability, exposure and capacity. It presents a synthesis of the state of the art, with particular incidence in the first results of MedECC and the most recent bibliography on floods trend. Conclusions show that floods in this region are mainly consequence of flash-flood events. A common positive trend of flash floods in the past probably due to land use changes and the occupation of flood-prone areas has been found (high confidence). The increase of convective precipitation could also justify this positive trend in the most recent period, in some regions (low confidence). Vulnerabilities to water related hazards are expected to be influenced by the future socio-economic conditions at the regional scale (medium confidence). Although expected changes in flood risks are not univocal, nor evenly distributed, flood impacts will increase in the entire Mediterranean region, mainly as a consequence of global changes in the catchments (land use, vulnerability, exposure), joined in the Northern part of the basin to the increase of heavy rainfalls (medium confidence).
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Benito, Gerardo, Olegario Castillo, Juan A. Ballesteros-Cánovas, Maria Machado, and Mariano Barriendos. "Enhanced flood hazard assessment beyond decadal climate cycles based on centennial historical data (Duero basin, Spain)." Hydrology and Earth System Sciences 25, no. 12 (December 2, 2021): 6107–32. http://dx.doi.org/10.5194/hess-25-6107-2021.
Повний текст джерелаАнотація:
Abstract. Current climate modelling frameworks present significant uncertainties when it comes to quantifying flood quantiles in the context of climate change, calling for new information and strategies in hazard assessments. Here, state-of-the-art methods on hydraulic and statistical modelling are applied to historical and contemporaneous flood records to evaluate flood hazards beyond natural climate cycles. A comprehensive flood record of the Duero River in Zamora (Spain) was compiled from documentary sources, early water-level readings and continuous gauge records spanning the last 500 years. Documentary evidence of flood events includes minute books (municipal and ecclesiastic), narrative descriptions, epigraphic marks, newspapers and technical reports. We identified 69 flood events over the period 1250 to 1871, of which 15 were classified as catastrophic floods, 16 as extraordinary floods and 38 as ordinary floods. Subsequently, a two-dimensional hydraulic model was implemented to relate flood stages (flood marks and inundated areas) to discharges. The historical flood records show the largest floods over the last 500 years occurred in 1860 (3450 m3 s−1), 1597 (3200 m3 s−1) and 1739 (2700 m3 s−1). Moreover, at least 24 floods exceeded the perception threshold of 1900 m3 s−1 during the period (1500–1871). Annual maximum flood records were completed with gauged water-level readings (pre-instrumental dataset, PRE: 1872–1919) and systematic gauge records (systematic dataset, SYS: 1920–2018). The flood frequency analyses were based on (1) the expected moments algorithm (EMA) and (2) the maximum likelihood estimator (MLE) method, using five datasets with different temporal frameworks (historic dataset, HISTO: 1511–2018; PRE–SYS: 1872–2018; full systematic record, ALLSYS: 1920–2018; SYS1: 1920–1969; and SYS2: 1970–2018). The most consistent results were obtained using the HISTO dataset, even for high quantiles (0.001 % annual exceedance probability, AEP). PRE–SYS was robust for the 1 % AEP flood with increasing uncertainty in the 0.2 % AEP or 500-year flood, and ALLSYS results were uncertain in the 1 % and 0.2 % AEP floods. Since the 1970s, the frequency of extraordinary floods (>1900 m3 s−1) declined, although floods on the range of the historical perception threshold occurred in 2001 (2075 m3 s−1) and 2013 (1654 m3 s−1). Even if the future remains uncertain, this bottom-up approach addresses flood hazards under climate variability, providing real and certain flood discharges. Our results can provide a guide on low-regret adaptation decisions and improve public perception of extreme flooding.
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Yan, Yuna, Na Zhang, and Han Zhang. "Applications of Advanced Technologies in the Development of Urban Flood Models." Water 15, no. 4 (February 5, 2023): 622. http://dx.doi.org/10.3390/w15040622.
Повний текст джерелаАнотація:
Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system.
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Chamatidis, Ilias, Denis Istrati, and Nikos D. Lagaros. "Vision Transformer for Flood Detection Using Satellite Images from Sentinel-1 and Sentinel-2." Water 16, no. 12 (June 12, 2024): 1670. http://dx.doi.org/10.3390/w16121670.
Повний текст джерелаАнотація:
Floods are devastating phenomena that occur almost all around the world and are responsible for significant losses, in terms of both human lives and economic damages. When floods occur, one of the challenges that emergency response agencies face is the identification of the flooded area so that access points and safe routes can be determined quickly. This study presents a flood detection methodology that combines transfer learning with vision transformers and satellite images from open datasets. Transformers are powerful models that have been successfully applied in Natural Language Processing (NLP). A variation of this model is the vision transformer (ViT), which can be applied to image classification tasks. The methodology is applied and evaluated for two types of satellite images: Synthetic Aperture Radar (SAR) images from Sentinel-1 and Multispectral Instrument (MSI) images from Sentinel-2. By using a pre-trained vision transformer and transfer learning, the model is fine-tuned on these two datasets to train the models to determine whether the images contain floods. It is found that the proposed methodology achieves an accuracy of 84.84% on the Sentinel-1 dataset and 83.14% on the Sentinel-2 dataset, revealing its insensitivity to the image type and applicability to a wide range of available visual data for flood detection. Moreover, this study shows that the proposed approach outperforms state-of-the-art CNN models by up to 15% on the SAR images and 9% on the MSI images. Overall, it is shown that the combination of transfer learning, vision transformers, and satellite images is a promising tool for flood risk management experts and emergency response agencies.
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Zeng, Zhongping, Yujia Li, Jinyu Lan, and Abdur Rahim Hamidi. "Utilizing User-Generated Content and GIS for Flood Susceptibility Modeling in Mountainous Areas: A Case Study of Jian City in China." Sustainability 13, no. 12 (June 19, 2021): 6929. http://dx.doi.org/10.3390/su13126929.
Повний текст джерелаАнотація:
Floods are threats seriously affecting people’s lives and property globally. Risk analysis such as flood susceptibility assessment is one of the critical approaches to mitigate flood impacts. However, the inadequate field survey and lack of data might hinder the mapping of flood susceptibility. The emergence of user-generated content (UGC) in the era of big data provides new opportunities for flood risk management. This research proposed a flood susceptibility assessment model using UGC as a potential data source and conducted empirical research in Ji’an County in China to make up for the lack of ground survey data in mountainous-hilly areas. This article used python crawlers to obtain the geographic location of the floods in Ji’an City from 2016 to 2019 from social media, and the state-of-the-art MaxEnt algorithm was adopted to obtain the flood occurrence map. The map was verified by the flood data crawled from reliable official media, which achieved an average AUC of 0.857% and an overall accuracy of 93.1%. Several novel indicators were used to evaluate the importance of conditioning factors from different perspectives. Land use, slope, and distance from the river were found to contribute most to the occurrence of floods. Our findings have shown that the proposed historical UG C-based model is practical and has good flood-risk-mapping performance. The importance of the conditioning factors to the occurrence of floods can also be ranked. The reports from stakeholders are a great supplement to the insufficient field survey data and tend to be valuable resources for flood disaster preparation and mitigation in the future. Finally, the limitations and future development directions of UGC as a data source for flood risk assessment are discussed.
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Hall, J., B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, et al. "Understanding flood regime changes in Europe: a state of the art assessment." Hydrology and Earth System Sciences Discussions 10, no. 12 (December 18, 2013): 15525–624. http://dx.doi.org/10.5194/hessd-10-15525-2013.
Повний текст джерелаАнотація:
Abstract. There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has been obtained through two approaches. The first approach is the detection of change based on observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for non-linear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities are discussed again such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on flood-rich and flood-poor periods rather than on flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.
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Stucchi, Leonardo, Daniele Fabrizio Bignami, Daniele Bocchiola, Davide Del Curto, Andrea Garzulino, and Renzo Rosso. "Assessment of Climate-Driven Flood Risk and Adaptation Supporting the Conservation Management Plan of a Heritage Site. The National Art Schools of Cuba." Climate 9, no. 2 (January 23, 2021): 23. http://dx.doi.org/10.3390/cli9020023.
Повний текст джерелаАнотація:
This work illustrates the contribution of flood risk assessment and adaptation to set up a conservation management plan for a masterpiece of 20th-century architecture. Case study is the iconic complex, internationally known as the National Art Schools of Cuba. It consists of five buildings built in the early 1960s within a park of Habana next to the Caribbean Sea. The path of the river (Rio Quibù) crossing the estate was modified to fit the landscape design. The complex has then been exposed to the risk of flooding. The School of Ballet, located in a narrow meander of the river, slightly upstream of a bridge and partially obstructing the flow, is particularly subject to frequent flash floods from the Rio Quibù, and it needs urgent restoration. Keeping ISA Modern is a project aimed at preserving the Schools complex. Based upon in situ surveys on the Rio Quibù and local area measurements during 2019, numerical modelling, and previous work by the Cuban National Institute of Hydraulic Resources, we pursued a flood risk analysis for the area, and a preliminary analysis of available risk reduction strategies. Using HEC-RAS 2D software for hydraulic modelling, we evaluated the flooded area and the hydraulic conditions (flow depth, velocity) for floods with given return periods. Our results show that SB is a building most subject to flooding, with high levels of risk. Defense strategies as designed by Cuban authorities may include a (new) wall around the School of Ballet and widening of the river channel, with high impact and cost, although not definitive. Temporary, light, permanent, and low cost/impact flood proofing structures may be used with similar effectiveness. We demonstrate that relatively little expensive hydraulic investigation may aid flood modelling and risk assessment in support of conservation projects for historically valuable sites. This may support brainstorming and the selection of (low to high cost) adaptation and risk reduction measures in the coastal areas of Cuba in response to ever increasing extreme storms and sea level rise controlling flood dynamics under transient climate change.
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Hall, J., B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, et al. "Understanding flood regime changes in Europe: a state-of-the-art assessment." Hydrology and Earth System Sciences 18, no. 7 (July 30, 2014): 2735–72. http://dx.doi.org/10.5194/hess-18-2735-2014.
Повний текст джерелаАнотація:
Abstract. There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has traditionally been obtained through two alternative research approaches. The first approach is the data-based detection of changes in observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for nonlinear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities associated with flood change scenarios are discussed such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on long duration records and flood-rich and flood-poor periods rather than on short duration flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.
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Turcotte, Benoit, Brian Morse, and Gabriel Pelchat. "Impact of Climate Change on the Frequency of Dynamic Breakup Events and on the Risk of Ice-Jam Floods in Quebec, Canada." Water 12, no. 10 (October 16, 2020): 2891. http://dx.doi.org/10.3390/w12102891.
Повний текст джерелаАнотація:
In cold regions, every year, river-ice jams generate sudden, surprising, intense flooding that challenges the capacity of public security services. This type of flood is commonly unpredictable and often appears chaotic because its occurrence depends on multiple, interacting weather, hydrological, ice and morphological parameters. This paper presents the findings of a research project assessing how climate change impacts dynamic river-ice breakup and associated floods along seven rivers of the province of Quebec, Canada. A combination of empirical river-ice breakup models, state-of-the-art hydrological simulations and standardized climate projections was used to estimate the historical (1972–2000) and future (2042–2070) frequencies of dynamic breakup events. Ice jam flood damage reimbursement data were used to predict changes to financial risk associated with dynamic breakup events. Results show that, overall, ice-jam floods will generate more damage in the future, which justifies watershed-based flood adaptation plans that take into account cold regions hydrological processes. The success of the methodology also sets the table for a comparable project that would include more rivers from different regions of Northeastern America.
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Molinari, D., F. Ballio, and S. Menoni. "Modelling the benefits of flood emergency management measures in reducing damages: a case study on Sondrio, Italy." Natural Hazards and Earth System Sciences 13, no. 8 (August 1, 2013): 1913–27. http://dx.doi.org/10.5194/nhess-13-1913-2013.
Повний текст джерелаАнотація:
Abstract. The European "Floods Directive" 2007/60/EU has produced an important shift from a traditional approach to flood risk management centred only on hazard analysis and forecast to a newer one which encompasses other aspects relevant to decision-making and which reflect recent research advances in both hydraulic engineering and social studies on disaster risk. This paper accordingly proposes a way of modelling the benefits of flood emergency management interventions calculating the possible damages by taking into account exposure, vulnerability, and expected damage reduction. The results of this model can be used to inform decisions and choices for the implementation of flood emergency management measures. A central role is played by expected damages, which are the direct and indirect consequence of the occurrence of floods in exposed and vulnerable urban systems. How damages should be defined and measured is a key question that this paper tries to address. The Floods Directive suggests that mitigation measures taken to reduce flood impact need to be evaluated also by means of a cost–benefit analysis. The paper presents a methodology for assessing the effectiveness of early warning for flash floods, considering its potential impact in reducing direct physical damage, and it assesses the general benefit in regard to other types of damages and losses compared with the emergency management costs. The methodology is applied to the case study area of the city of Sondrio in the northern Alpine region of Italy. A critical discussion follows the application. Its purpose is to highlight the strengths and weaknesses of available models for quantifying direct physical damage and of the general model proposed, given the current state of the art in damage and loss assessment.
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Kumar, Vijendra, Hazi Md Azamathulla, Kul Vaibhav Sharma, Darshan J. Mehta, and Kiran Tota Maharaj. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management." Sustainability 15, no. 13 (July 4, 2023): 10543. http://dx.doi.org/10.3390/su151310543.
Повний текст джерелаАнотація:
Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.
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Noor, Fahima, Sanaulla Haq, Mohammed Rakib, Tarik Ahmed, Zeeshan Jamal, Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan, and Rashedur M. Rahman. "Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network." Water 14, no. 4 (February 17, 2022): 612. http://dx.doi.org/10.3390/w14040612.
Повний текст джерелаАнотація:
Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.
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Mosavi, Amir, Pinar Ozturk, and Kwok-wing Chau. "Flood Prediction Using Machine Learning Models: Literature Review." Water 10, no. 11 (October 27, 2018): 1536. http://dx.doi.org/10.3390/w10111536.
Повний текст джерелаАнотація:
Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.
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Sofia, Giulia, Qing Yang, Xinyi Shen, Mahjabeen Fatema Mitu, Platon Patlakas, Ioannis Chaniotis, Andreas Kallos, et al. "A Nationwide Flood Forecasting System for Saudi Arabia: Insights from the Jeddah 2022 Event." Water 16, no. 14 (July 9, 2024): 1939. http://dx.doi.org/10.3390/w16141939.
Повний текст джерелаАнотація:
Saudi Arabia is threatened by recurrent flash floods caused by extreme precipitation events. To mitigate the risks associated with these natural disasters, we implemented an advanced nationwide flash flood forecast system, boosting disaster preparedness and response. A noteworthy feature of this system is its national-scale operational approach, providing comprehensive coverage across the entire country. Using cutting-edge technology, the setup incorporates a state-of-the-art, three-component system that couples an atmospheric model with hydrological and hydrodynamic models to enable the prediction of precipitation patterns and their potential impacts on local communities. This paper showcases the system’s effectiveness during an extreme precipitation event that struck Jeddah on 24 November 2022. The event, recorded as the heaviest rainfall in the region’s history, led to widespread flash floods, highlighting the critical need for accurate and timely forecasting. The flash flood forecast system proved to be an effective tool, enabling authorities to issue warnings well before the flooding, allowing residents to take precautionary measures, and allowing emergency responders to mobilize resources effectively.
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Amirebrahimi, S., A. Rajabifard, S. Sabri, and P. Mendis. "SPATIAL INFORMATION IN SUPPORT OF 3D FLOOD DAMAGE ASSESSMENT OF BUILDINGS AT MICRO LEVEL: A REVIEW." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W1 (October 5, 2016): 73–81. http://dx.doi.org/10.5194/isprs-annals-iv-2-w1-73-2016.
Повний текст джерелаАнотація:
Floods, as the most common and costliest natural disaster around the globe, have adverse impacts on buildings which are considered as major contributors to the overall economic damage. With emphasis on risk management methods for reducing the risks to structures and people, estimating damage from potential flood events becomes an important task for identifying and implementing the optimal flood risk-reduction solutions. While traditional Flood Damage Assessment (FDA) methods focus on simple representation of buildings for large-scale damage assessment purposes, recent emphasis on buildings’ flood resilience resulted in development of a sophisticated method that allows for a detailed and effective damage evaluation at the scale of building and its components. In pursuit of finding the suitable spatial information model to satisfy the needs of implementing such frameworks, this article explores the technical developments for an effective representation of buildings, floods and other required information within the built environment. The search begins with the Geospatial domain and investigates the state-of-the-art and relevant developments from data point of view in this area. It is further extended to other relevant disciplines in the Architecture, Engineering and Construction domain (AEC/FM) and finally, even some overlapping areas between these domains are considered and explored.
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Anafi, Nurin Fadhlina Mohd, Norzailawati Mohd Noor, and Hasti Widyasamratri. "A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge." Jurnal Planologi 20, no. 2 (October 31, 2023): 150. http://dx.doi.org/10.30659/jpsa.v20i2.30765.
Повний текст джерелаАнотація:
Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards it
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He, Xiaogang, Ming Pan, Zhongwang Wei, Eric F. Wood, and Justin Sheffield. "A Global Drought and Flood Catalogue from 1950 to 2016." Bulletin of the American Meteorological Society 101, no. 5 (May 1, 2020): E508—E535. http://dx.doi.org/10.1175/bams-d-18-0269.1.
Повний текст джерелаАнотація:
Abstract Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.
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Sanders, Will, Dongfeng Li, Wenzhao Li, and Zheng N. Fang. "Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages." Water 14, no. 5 (February 26, 2022): 747. http://dx.doi.org/10.3390/w14050747.
Повний текст джерелаАнотація:
Heavy rainfall leads to severe flooding problems with catastrophic socio-economic impacts worldwide. Hydrologic forecasting models have been applied to provide alerts of extreme flood events and reduce damage, yet they are still subject to many uncertainties due to the complexity of hydrologic processes and errors in forecasted timing and intensity of the floods. This study demonstrates the efficacy of using eXtreme Gradient Boosting (XGBoost) as a state-of-the-art machine learning (ML) model to forecast gauge stage levels at a 5-min interval with various look-out time windows. A flood alert system (FAS) built upon the XGBoost models is evaluated by two historical flooding events for a flood-prone watershed in Houston, Texas. The predicted stage values from the FAS are compared with observed values with demonstrating good performance by statistical metrics (RMSE and KGE). This study further compares the performance from two scenarios with different input data settings of the FAS: (1) using the data from the gauges within the study area only and (2) including the data from additional gauges outside of the study area. The results suggest that models that use the gauge information within the study area only (Scenario 1) are sufficient and advantageous in terms of their accuracy in predicting the arrival times of the floods. One of the benefits of the FAS outlined in this study is that the XGBoost-based FAS can run in a continuous mode to automatically detect floods without requiring an external starting trigger to switch on as usually required by the conventional event-based FAS systems. This paper illustrates a data-driven FAS framework as a prototype that stakeholders can utilize solely based on their gauging information for local flood warning and mitigation practices.
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Chitwatkulsiri, Detchphol, and Hitoshi Miyamoto. "Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects." Water 15, no. 1 (January 1, 2023): 178. http://dx.doi.org/10.3390/w15010178.
Повний текст джерелаАнотація:
Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand, have recently been experiencing unprecedentedly intense flash floods due to climate change. The rapid flood inundation has caused extremely severe damage to urban residents and social infrastructures. In addition, urban Southeast Asia usually has inadequate capacities in drainage systems, complicated land use patterns, and a large vulnerable population in limited urban areas. To reduce the urban flood risk and enhance the resilience of vulnerable urban communities, it has been of essential importance to develop real-time urban flood forecasting systems for flood disaster prevention authorities and the urban public. This paper reviewed the state-of-the-art models of real-time forecasting systems for urban flash floods. The real-time system basically consists of the following subsystems, i.e., rainfall forecasting, drainage system modelling, and inundation area mapping. This paper summarized the recent radar data utilization methods for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. This paper also dealt with available technologies for modelling, e.g., digital surface models (DSMs) for the finer urban terrain of drainage systems. The review indicated that an obstacle to using process-based hydraulic models was the limited computational resources and shorter lead time for real-time forecasting in many urban areas in tropical Southeast Asia. The review further discussed the prospects of data-driven AI models for real-time forecasting systems.
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Shi, Haiyun, Erhu Du, Suning Liu, and Kwok-Wing Chau. "Advances in Flood Early Warning: Ensemble Forecast, Information Dissemination and Decision-Support Systems." Hydrology 7, no. 3 (August 13, 2020): 56. http://dx.doi.org/10.3390/hydrology7030056.
Повний текст джерелаАнотація:
Floods are usually highly destructive, which may cause enormous losses to lives and property. It is, therefore, important and necessary to develop effective flood early warning systems and disseminate the information to the public through various information sources, to prevent or at least mitigate the flood damages. For flood early warning, novel methods can be developed by taking advantage of the state-of-the-art techniques (e.g., ensemble forecast, numerical weather prediction, and service-oriented architecture) and data sources (e.g., social media), and such developments can offer new insights for modeling flood disasters, including facilitating more accurate forecasts, more efficient communication, and more timely evacuation. The present Special Issue aims to collect the latest methodological developments and applications in the field of flood early warning. More specifically, we collected a number of contributions dealing with: (1) an urban flash flood alert tool for megacities; (2) a copula-based bivariate flood risk assessment; and (3) an analytic hierarchy process approach to flash flood impact assessment.
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Hoes, O. A. C., R. W. Hut, N. C. van de Giesen, and M. Boomgaard. "Reconstruction of the 1945 Wieringermeer Flood." Natural Hazards and Earth System Sciences Discussions 1, no. 2 (March 11, 2013): 417–41. http://dx.doi.org/10.5194/nhessd-1-417-2013.
Повний текст джерелаАнотація:
Abstract. The present state-of-the-art in flood risk assessment focuses on breach models, flood propagation models, and economic modelling of flood damage. However, models need to be validated with real data to avoid erroneous conclusions. Such reference data can either be historic data, or can be obtained from controlled experiments. The inundation of the Wieringermeer polder in the Netherlands in April 1945 is one of the few examples for which sufficient historical information is available. The objective of this article is to compare the flood simulation with flood data from 1945. The context, the breach growth process and the flood propagation are explained. Key findings for current flood risk management addresses the importance of the drainage canal network during the inundation of a polder, and the uncertainty that follows from not knowing the breach growth parameters. This case study shows that historical floods provide valuable data for the validation of models and reveal lessons that are applicable in current day flood risk management.
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Zuhairi, Ainaa Hanis, Fitri Yakub, Sheikh Ahmad Zaki, and Mohamed Sukri Mat Ali. "Review of flood prediction hybrid machine learning models using datasets." IOP Conference Series: Earth and Environmental Science 1091, no. 1 (November 1, 2022): 012040. http://dx.doi.org/10.1088/1755-1315/1091/1/012040.
Повний текст джерелаАнотація:
Floods are among the most destructive natural disasters, and they are extremely difficult to model. Over the last two decades, machine learning (ML) methods have made significant contributions to the advancement of prediction systems that provide better performance and cost-effective solutions by mimicking the complex mathematical expressions of physical flood processes. Because of the numerous benefits and potential of ML, its popularity has skyrocketed. Researchers hope to discover more accurate and efficient prediction models by introducing novel ML methods and hybridising existing ones. The main focus of this paper is to show the state of the art of hybridising ML models in flood prediction. The most effective strategies for improving ML methods are hybridization, data decomposition, algorithm ensemble, and model optimization.
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Rehman, Abdul, Nadeem Akhtar, and Omar H. Alhazmi. "Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems." Scientific Programming 2021 (April 15, 2021): 1–17. http://dx.doi.org/10.1155/2021/6685978.
Повний текст джерелаАнотація:
Floods after monsoon rains are frequent disasters that affect millions of lives in Pakistan. Human lives are lost, agriculture economies are destroyed, and livestock animals, houses, fruit farms, and crops are lost which are the major livelihoods of thousands of people in Punjab. Each year there are heavy rains in the monsoon season and, due to global warming, there is the rapid melting of snow in northern glaciers; these factors subsequently cause floods. There is also loss of life due to the spread of waterborne diseases and snake bites. Flood monitoring provides early detection of a flood and the calculation of its intensity, which results in reduced human life losses and economic losses. Most casualties are caused by the lack of timely real-time, authentic information about the high-risk areas, and flood intensity, speed, and direction. Therefore, the proposed approach is centered on formal modeling and verification of safety and liveness properties of flood monitoring perceivers. Each flood perceiver has several sensors. It requires the collection of information starting from the flood perceiver, observer, and environmental forecast. This information is processed to determine the flood intensity level. We have developed a CP-Nets’ formal model and model-checked it. We have verified the safety and liveness properties of correctness by exhaustive verification of the system using model-based proof obligations (Event-B method using Rodin). Our objective in this research is to propose a correct, reliable, and efficient flood warning, monitoring, and rescue (WMR) SoS based on formal methods. We have used formal modeling and model-checking based on state-of-the-art hierarchical CP-Nets supported by exhaustive formal proof obligations of Event-B.
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Caloiero, Tommaso. "Hydrological Hazard: Analysis and Prevention." Geosciences 8, no. 11 (October 26, 2018): 389. http://dx.doi.org/10.3390/geosciences8110389.
Повний текст джерелаАнотація:
As a result of the considerable impacts of hydrological hazard on water resources, on natural environments and human activities, as well as on human health and safety, climate variability and climate change have become key issues for the research community. In fact, a warmer climate, with its heightened climate variability, will increase the risk of hydrological extreme phenomena, such as droughts and floods. The Special Issue “Hydrological Hazard: Analysis and Prevention” presents a collection of scientific contributions that provides a sample of the state-of-the-art and forefront research in this field. In particular, innovative modelling methods for flood hazards, regional flood and drought analysis, and the use of satellite and climate data for drought analysis were the main topics and practice targets that the papers published in this Special Issue aimed to address.
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Atashi, Vida, Hamed Taheri Gorji, Seyed Mojtaba Shahabi, Ramtin Kardan, and Yeo Howe Lim. "Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North." Water 14, no. 12 (June 20, 2022): 1971. http://dx.doi.org/10.3390/w14121971.
Повний текст джерелаАнотація:
The Red River of the North is vulnerable to floods, which have caused significant damage and economic loss to inhabitants. A better capability in flood-event prediction is essential to decision-makers for planning flood-loss-reduction strategies. Over the last decades, classical statistical methods and Machine Learning (ML) algorithms have greatly contributed to the growth of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. To make improvements to flood prediction for the Red River of the North, this paper presents effective approaches that make use of a classical statistical method, a classical ML algorithm, and a state-of-the-art Deep Learning method. Respectively, the methods are seasonal autoregressive integrated moving average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). We used hourly level records from three U.S. Geological Survey (USGS), at Pembina, Drayton, and Grand Forks stations with twelve years of data (2007–2019), to evaluate the water level at six hours, twelve hours, one day, three days, and one week in advance. Pembina, at the downstream location, has a water level gauge but not a flow-gauging station, unlike the others. The floodwater-level-prediction results show that the LSTM method outperforms the SARIMA and RF methods. For the one-week-ahead prediction, the RMSE values for Pembina, Drayton, and Grand Forks are 0.190, 0.151, and 0.107, respectively. These results demonstrate the high precision of the Deep Learning algorithm as a reliable choice for flood-water-level prediction.
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Jackson, Jehoiada, Sophyani Banaamwini Yussif, Rutherford Agbeshi Patamia, Kwabena Sarpong, and Zhiguang Qin. "Flood or Non-Flooded: A Comparative Study of State-of-the-Art Models for Flood Image Classification Using the FloodNet Dataset with Uncertainty Offset Analysis." Water 15, no. 5 (February 24, 2023): 875. http://dx.doi.org/10.3390/w15050875.
Повний текст джерелаАнотація:
Natural disasters, such as floods, can cause significant damage to both the environment and human life. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. In this paper, we aimed to evaluate the performance of state-of-the-art (SOTA) computer vision models for flood image classification, by utilizing a semi-supervised learning approach on a dataset named FloodNet. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. Furthermore, we also introduced a technique of varying the uncertainty offset λ in the models to analyze its impact on the performance. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. The results of this study provide a quantitative comparison of the performance of different CNN architectures for flood image classification, as well as the impact of different uncertainty offset λ. These findings can aid in the development of more accurate and efficient disaster response and recovery systems, which could help in minimizing the impact of natural disasters.
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WANGSA, ANAK AGUNG RATU RITAKA, IDA BAGUS SURYATMAJA, and A. A. MERI PUJA ANDINI. "ANALISIS HIDROLOGI RANCANGAN MENGGUNAKAN METODE RASIONAL PADA SALURAN DRAINASE DI KELURAHAN SUMERTA KELOD KOTA DENPASAR." GANEC SWARA 17, no. 2 (June 1, 2023): 607. http://dx.doi.org/10.35327/gara.v17i2.463.
Повний текст джерелаАнотація:
Tukad Kelandis is a tributary of the Tukad Ayung Watershed which is located downstream. In 2021 floods occurred in Tukad Kelandis resulting in one of the places affected by flooding, namely the Art Center area. So it is necessary to do research on the analysis of peak flood discharge on the Tukad Kelandis river using rational methods. Maximum runoff occurs when the rainfall is during the same time as the catchment area concentration time in the rational method. Based on the research results, the amount of planned flood discharge in the Tukad Kelandis watershed was obtained using the rational method for return periods of 2, 5, 10, 20, and 25 years, namely: Q2 Year = 273,890 m3/s, Q5 Year = 337,372 m3/s, Q10 Year = 373,194 m3/ s, Q20 Year = 399,610 m3/ s, Q25 Year = 413,406 m3/ s.
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Zulkarnain, Siti Hafsah, Maki Tsujimura, Muhamad Ali Yuzir, Muhammad Najib Razali, and Zakri Tarmidi. "A review of climate change (floods) and economic attributes response to residential property value in Malaysia." Journal of Water and Climate Change 11, no. 4 (December 23, 2019): 1084–94. http://dx.doi.org/10.2166/wcc.2019.044.
Повний текст джерелаАнотація:
Abstract The art and science of determining residential property value has evolved due to the changing external factors, such as the economy, environmental (climate change) and social aspects. This research aims to identify the impact of climate change (floods) to determine residential economic attributes that could affect the value for residential property in flood risk areas. The case study covers all residential housing schemes in Langat River Basin area, which has been considered as the highest flood risk area in the State of Selangor, Malaysia. The methodology of this research is based on the conceptual analysis from previous studies from local and international scenarios. The systematic analysis of previous literature of real estate valuation theory consists of economic attributes such as structural, locational and environmental attributes involved in residential property valuation in relation to flooding. The findings reveal that the economic attributes' response to flood hazards for residential properties can be divided into three conditions, and they are: positive, negative or no effect on the climate change factor.
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Akhtar, Zainab, Umair Qazi, Aya El-Sakka, Rizwan Sadiq, Ferda Ofli, and Muhammad Imran. "Flood Insights: Integrating Remote and Social Sensing Data for Flood Exposure, Damage, and Urgent Needs Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 22716–24. http://dx.doi.org/10.1609/aaai.v38i21.30305.
Повний текст джерелаАнотація:
The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end-to-end system that ingests data from multiple non-traditional data sources such as remote sensing, social sensing, and geospatial data. We employ state-of-the-art natural language processing and computer vision models to identify flood exposure, ground-level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real-world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through geographic regression analysis using official ground-truth data, showcasing its strong performance and explanatory power. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard-hit districts and enhancing disaster response.
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Dahm, R. J., F. C. Sperna Weiland, U. K. Singh, M. Lal, M. Marchand, S. K. Singh, and M. P. Singh. "Assessment of future rainfall for the Brahmani-Baitarani river basin – practical implications of limited data availability." Journal of Water and Climate Change 10, no. 4 (April 16, 2018): 782–98. http://dx.doi.org/10.2166/wcc.2018.004.
Повний текст джерелаАнотація:
Abstract Severe floods are common in the Brahmani-Baitarani river basin in India. Insights into the implications of climate change on rainfall extremes and resulting floods are of major importance to improve flood risk analysis and water system design. A wide range of statistical and dynamical downscaling and bias-correction methods for the generation of local climate projections exists. Yet, the applicability of these methods highly depends on availability of meteorological data. In developing countries, data availability is often limited, either because data do not exist or because of restrictions on use. We here present a climate change analysis for the Brahmani-Baitarani river basin focusing on changes in rainfall using data from three GCMs from the Fifth Coupled Model Intercomparison Project (CMIP5) that were selected based on their performance. We apply and compare two widely used and easy to implement bias-correction methods. These were selected because reliable open historical meteorological datasets required for advanced methods were not available. The results indicate likely increases in monsoon rainfall especially in the mountainous regions and likely increases in the number of heavy rain days. We conclude with a discussion on the gap between state-of-the-art downscaling techniques and the actual options in regional climate change assessments.
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Eilander, Dirk, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward. "A globally applicable framework for compound flood hazard modeling." Natural Hazards and Earth System Sciences 23, no. 2 (February 27, 2023): 823–46. http://dx.doi.org/10.5194/nhess-23-823-2023.
Повний текст джерелаАнотація:
Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.
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Saravi, Sara, Roy Kalawsky, Demetrios Joannou, Monica Rivas Casado, Guangtao Fu, and Fanlin Meng. "Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events." Water 11, no. 5 (May 9, 2019): 973. http://dx.doi.org/10.3390/w11050973.
Повний текст джерелаАнотація:
The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.
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Chakrabortty, Rabin, Subodh Chandra Pal, Dipankar Ruidas, Paramita Roy, Asish Saha, and Indrajit Chowdhuri. "Living with Floods Using State-of-the-Art and Geospatial Techniques: Flood Mitigation Alternatives, Management Measures, and Policy Recommendations." Water 15, no. 3 (January 31, 2023): 558. http://dx.doi.org/10.3390/w15030558.
Повний текст джерелаАнотація:
Flood, a distinctive natural calamity, has occurred more frequently in the last few decades all over the world, which is often an unexpected and inevitable natural hazard, but the losses and damages can be managed and controlled by adopting effective measures. In recent times, flood hazard susceptibility mapping has become a prime concern in minimizing the worst impact of this global threat; but the nonlinear relationship between several flood causative factors and the dynamicity of risk levels makes it complicated and confronted with substantial challenges to reliable assessment. Therefore, we have considered SVM, RF, and ANN—three distinctive ML algorithms in the GIS platform—to delineate the flood hazard risk zones of the subtropical Kangsabati river basin, West Bengal, India; which experienced frequent flood events because of intense rainfall throughout the monsoon season. In our study, all adopted ML algorithms are more efficient in solving all the non-linear problems in flood hazard risk assessment; multi-collinearity analysis and Pearson’s correlation coefficient techniques have been used to identify the collinearity issues among all fifteen adopted flood causative factors. In this research, the predicted results are evaluated through six prominent and reliable statistical (“AUC-ROC, specificity, sensitivity, PPV, NPV, F-score”) and one graphical (Taylor diagram) technique and shows that ANN is the most reliable modeling approach followed by RF and SVM models. The values of AUC in the ANN model for the training and validation datasets are 0.901 and 0.891, respectively. The derived result states that about 7.54% and 10.41% of areas accordingly lie under the high and extremely high flood danger risk zones. Thus, this study can help the decision-makers in constructing the proper strategy at the regional and national levels to mitigate the flood hazard in a particular region. This type of information may be helpful to the various authorities to implement this outcome in various spheres of decision making. Apart from this, future researchers are also able to conduct their research byconsidering this methodology in flood susceptibility assessment.
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Umgiesser, Georg, Marco Bajo, Christian Ferrarin, Andrea Cucco, Piero Lionello, Davide Zanchettin, Alvise Papa, et al. "The prediction of floods in Venice: methods, models and uncertainty (review article)." Natural Hazards and Earth System Sciences 21, no. 8 (September 1, 2021): 2679–704. http://dx.doi.org/10.5194/nhess-21-2679-2021.
Повний текст джерелаАнотація:
Abstract. This paper reviews the state of the art in storm surge forecasting and its particular application in the northern Adriatic Sea. The city of Venice already depends on operational storm surge forecasting systems to warn the population and economy of imminent flood threats, as well as help to protect the extensive cultural heritage. This will be more important in the future, with the new mobile barriers called MOSE (MOdulo Sperimentale Elettromeccanico, Experimental Electromechanical Module) that will be completed by 2021. The barriers will depend on accurate storm surge forecasting to control their operation. In this paper, the physics behind the flooding of Venice is discussed, and the state of the art of storm surge forecasting in Europe is reviewed. The challenges for the surge forecasting systems are analyzed, especially in view of uncertainty. This includes consideration of selected historic extreme events that were particularly difficult to forecast. Four potential improvements are identified: (1) improve meteorological forecasts, (2) develop ensemble forecasting, (3) assimilation of water level measurements and (4) develop a multimodel approach.
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Chang, Li-Chiu, Fi-John Chang, Shun-Nien Yang, I.-Feng Kao, Ying-Yu Ku, Chun-Ling Kuo, and Ir Amin. "Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems." Water 11, no. 1 (December 21, 2018): 9. http://dx.doi.org/10.3390/w11010009.
Повний текст джерелаАнотація:
Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving online forecast capability and flood risk management. The holistic framework of the IHIP includes five layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters. The IHIP provides real-time flood-related data, such as rainfall and multi-step-ahead regional flood inundation maps. The interface of Google Maps fused into the IHIP significantly removes the obstacles for users to access this system, helps communities in making better-informed decisions about the occurrence of floods, and alerts communities in advance. The IHIP has been implemented in the Tainan City of Taiwan as the study case. The modular design and adaptive structure of the IHIP could be applied with similar efforts to other cities of interest for assisting the authorities in flood risk management.
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Wilhelm, Bruno, Benjamin Amann, Juan Pablo Corella, William Rapuc, Charline Giguet-Covex, Bruno Merz, and Eivind Støren. "Reconstructing Paleoflood Occurrence and Magnitude from Lake Sediments." Quaternary 5, no. 1 (February 1, 2022): 9. http://dx.doi.org/10.3390/quat5010009.
Повний текст джерелаАнотація:
Lake sediments are a valuable archive to document past flood occurrence and magnitude, and their evolution over centuries to millennia. This information has the potential to greatly improve current flood design and risk assessment approaches, which are hampered by the shortness and scarcity of gauge records. For this reason, paleoflood hydrology from lake sediments received fast-growing attention over the last decade. This allowed an extensive development of experience and methodologies and, thereby, the reconstruction of paleoflood series with increasingly higher accuracy. In this review, we provide up-to-date knowledge on flood sedimentary processes and systems, as well as on state-of-the-art methods for reconstructing and interpreting paleoflood records. We also discuss possible perspectives in the field of paleoflood hydrology from lake sediments by highlighting the remaining challenges. This review intends to guide the research interest in documenting past floods from lake sediments. In particular, we offer here guidance supported by the literature in how: to choose the most appropriate lake in a given region, to find the best suited sedimentary environments to take the cores, to identify flood deposits in the sedimentary sequence, to distinguish them from other instantaneous deposits, and finally, to rigorously interpret the flood chronicle thus produced.
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Unnikrishnan, Poornima, Kumaraswamy Ponnambalam, Nirupama Agrawal, and Fakhri Karray. "Joint Flood Risks in the Grand River Watershed." Sustainability 15, no. 12 (June 7, 2023): 9203. http://dx.doi.org/10.3390/su15129203.
Повний текст джерелаАнотація:
According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent compared to the previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flow values in the rivers in a basin coincide. Therefore, estimating the joint flood risks in a region is vital, especially when frequent occurrences of extreme events are experienced. This study focuses on estimating the joint flood risks due to river flow extremes in the Grand River watershed in Canada. For this purpose, the study uses copula analysis to investigate the joint occurrence of extreme river flow events in the Speed and Grand Rivers in the Grand River Watershed in Ontario, Canada. By estimating the joint return period for extreme flows in both rivers, we demonstrate the interdependence of the two river flows and how this interdependence influences the behavior of river flow extreme patterns. Our findings suggest that the interdependence between the two river flows leads to changes in the river flow extreme pattern. Determining the interdependence of floods at multiple locations using state-of-the-art tools will benefit various stakeholders, such as the insurance industry, the disaster management sector, and most importantly, the public.
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Hanif, Muhammad, Muhammad Waqas, Amgad Muneer, Ayed Alwadain, Muhammad Atif Tahir, and Muhammad Rafi. "DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events." Sustainability 15, no. 7 (March 31, 2023): 6049. http://dx.doi.org/10.3390/su15076049.
Повний текст джерелаАнотація:
Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively.
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Haer, Toon, W. J. Wouter Botzen, Vincent van Roomen, Harry Connor, Jorge Zavala-Hidalgo, Dirk M. Eilander, and Philip J. Ward. "Coastal and river flood risk analyses for guiding economically optimal flood adaptation policies: a country-scale study for Mexico." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2121 (April 30, 2018): 20170329. http://dx.doi.org/10.1098/rsta.2017.0329.
Повний текст джерелаАнотація:
Many countries around the world face increasing impacts from flooding due to socio-economic development in flood-prone areas, which may be enhanced in intensity and frequency as a result of climate change. With increasing flood risk, it is becoming more important to be able to assess the costs and benefits of adaptation strategies. To guide the design of such strategies, policy makers need tools to prioritize where adaptation is needed and how much adaptation funds are required. In this country-scale study, we show how flood risk analyses can be used in cost–benefit analyses to prioritize investments in flood adaptation strategies in Mexico under future climate scenarios. Moreover, given the often limited availability of detailed local data for such analyses, we show how state-of-the-art global data and flood risk assessment models can be applied for a detailed assessment of optimal flood-protection strategies. Our results show that especially states along the Gulf of Mexico have considerable economic benefits from investments in adaptation that limit risks from both river and coastal floods, and that increased flood-protection standards are economically beneficial for many Mexican states. We discuss the sensitivity of our results to modelling uncertainties, the transferability of our modelling approach and policy implications. This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.
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Dimitriou, Elias, Andreas Efstratiadis, Ioanna Zotou, Anastasios Papadopoulos, Theano Iliopoulou, Georgia-Konstantina Sakki, Katerina Mazi, et al. "Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks." Water 16, no. 7 (March 28, 2024): 980. http://dx.doi.org/10.3390/w16070980.
Повний текст джерелаАнотація:
Storm Daniel initiated on 3 September 2023, over the Northeastern Aegean Sea, causing extreme rainfall levels for the following four days, reaching an average of about 360 mm over the Peneus basin, in Thessaly, Central Greece. This event led to extensive floods, with 17 human lives lost and devastating environmental and economic impacts. The automatic water-monitoring network of the HIMIOFoTS National Research Infrastructure captured the evolution of the phenomenon and the relevant hydrometeorological (rainfall, water stage, and discharge) measurements were used to analyse the event’s characteristics. The results indicate that the average rainfall’s return period was up to 150 years, the peak flow close to the river mouth reached approximately 1950 m3/s, and the outflow volume of water to the sea was 1670 hm3. The analysis of the observed hydrographs across Peneus also provided useful lessons from the flood-engineering perspective regarding key modelling assumptions and the role of upstream retentions. Therefore, extending and supporting the operation of the HIMIOFoTS infrastructure is crucial to assist responsible authorities and local communities in reducing potential damages and increasing the socioeconomic resilience to natural disasters, as well as to improve the existing knowledge with respect to extreme flood-simulation approaches.
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Alexander, David. "Newspaper Reporting of the May 1993 Florence Bomb." International Journal of Mass Emergencies & Disasters 13, no. 1 (March 1995): 45–65. http://dx.doi.org/10.1177/028072709501300104.
Повний текст джерелаАнотація:
On 27 May 1993 a powerful bomb exploded in the center of Florence, Italy, killing five people and doing severe damage to art and architectural treasures, including the Uffizi Gallery and Accademia dei Georgofili. It was the first disaster since the floods of 1966 simultaneously to cause victims and damage the city's cultural heritage. In this study local and international newspaper coverage of the bomb outrage is analyzed and compared with reporting on the 1966 floods. Once again, questions of artistic damage and the safety of tourists occupied the foreign papers while human interest stories dominated the Florentine ones indeed, the English and American newspapers treated the damaged art treasures were almost as if they were human casualties. But since 1966 (and the collapse of the Eastern Bloc) Western news reporting has become depoliticized and dominated by new contexts, such as the pre-eminence of commercialism and, in the case of Italy, the struggle against the mafia. It is concluded that the nature and extent of newspaper coverage of the bomb outrage was determined, not by objective or moral assessments of newsworthiness, but by a mixture of ad hoc considerations and snap assessments of what the readership wanted to learn about.
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Mishra, Satish Chandra. "Challenge of Sustainability: Turning Science into Art." Sustainability Science and Resources 3 (January 1, 2023): 55–84. http://dx.doi.org/10.55168/ssr2809-6029.2022.3004.
Повний текст джерелаАнотація:
This paper addresses the critical question of how to turn climate concern into climate action as we move forward towards the 1.5 degree Celsius global warming target adopted by COP 26 in 2021 and reaffirmed by COP 27 in 2022. It argues that it has taken scientists more than a Century to accept, first: that climate change is real and second: that it is anthropogenic. Scientists still continue to debate the precise effects of greenhouse gases on weather, fires, floods and food security. Climate optimists continue to rely on the search for new miracle technologies, such as fusion energy or carbon capture. This is all very good. But this is the easy part. What is more critical is to motivate people towards collective action in pursuit of a zero-emission target. This requires harnessing the art of fostering humanist, economically just, collective action rooted in local commitment and transparency. The real challenge of sustainability today is to turn science into art. We do not have over a century and half, as the scientists did to practice this art. Repeatedly, pointing to climate change apocalypse will not be enough.
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Nearing, Grey, Deborah Cohen, Vusumuzi Dube, Martin Gauch, Oren Gilon, Shaun Harrigan, Avinatan Hassidim, et al. "Global prediction of extreme floods in ungauged watersheds." Nature 627, no. 8004 (March 20, 2024): 559–63. http://dx.doi.org/10.1038/s41586-024-07145-1.
Повний текст джерелаАнотація:
AbstractFloods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
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Rahmati, Omid, Saleh Yousefi, Zahra Kalantari, Evelyn Uuemaa, Teimur Teimurian, Saskia Keesstra, Tien Pham, and Dieu Tien Bui. "Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran." Remote Sensing 11, no. 16 (August 20, 2019): 1943. http://dx.doi.org/10.3390/rs11161943.
Повний текст джерелаАнотація:
Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.
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Ronco, P., V. Gallina, S. Torresan, A. Zabeo, E. Semenzin, A. Critto, and A. Marcomini. "The KULTURisk Regional Risk Assessment methodology for water-related natural hazards – Part 1: Physical–environmental assessment." Hydrology and Earth System Sciences 18, no. 12 (December 23, 2014): 5399–414. http://dx.doi.org/10.5194/hess-18-5399-2014.
Повний текст джерелаАнотація:
Abstract. In recent years, the frequency of catastrophes induced by natural hazards has increased, and flood events in particular have been recognized as one of the most threatening water-related disasters. Severe floods have occurred in Europe over the last decade, causing loss of life, displacement of people and heavy economic losses. Flood disasters are growing in frequency as a consequence of many factors, both climatic and non-climatic. Indeed, the current increase of water-related disasters can be mainly attributed to the increase of exposure (elements potentially at risk in flood-prone area) and vulnerability (i.e. economic, social, geographic, cultural and physical/environmental characteristics of the exposure). Besides these factors, the undeniable effect of climate change is projected to strongly modify the usual pattern of the hydrological cycle by intensifying the frequency and severity of flood events at the local, regional and global scale. Within this context, the need for developing effective and pro-active strategies, tools and actions which allow one to assess and (possibly) to reduce the flood risks that threatens different relevant receptors becomes urgent. Several methodologies to assess the risk posed by water-related natural hazards have been proposed so far, but very few of them can be adopted to implement the last European Flood Directive (FD). This paper is intended to introduce and present a state-of-the-art Regional Risk Assessment (RRA) methodology to appraise the risk posed by floods from a physical–environmental perspective. The methodology, developed within the recently completed FP7-KULTURisk Project (Knowledge-based approach to develop a cULTUre of Risk prevention – KR) is flexible and can be adapted to different case studies (i.e. plain rivers, mountain torrents, urban and coastal areas) and spatial scales (i.e. from catchment to the urban scale). The FD compliant KR-RRA methodology is based on the concept of risk being function of hazard, exposure and vulnerability. It integrates the outputs of various hydrodynamic models with site-specific bio-geophysical and socio-economic indicators (e.g. slope, land cover, population density, economic activities etc.) to develop tailored risk indexes and GIS-based maps for each of the selected receptors (i.e. people, buildings, infrastructure, agriculture, natural and semi-natural systems, cultural heritage) in the considered region. It further compares the baseline scenario with alternative scenarios, where different structural and/or non-structural mitigation measures are planned and eventually implemented. As demonstrated in the companion paper (Part 2, Ronco et al., 2014), risk maps, along with related statistics, allow one to identify and classify, on a relative scale, areas at risk which are more likely to be affected by floods and support the development of strategic adaptation and prevention measures to minimizing flood impacts. In addition, the outcomes of the RRA can be eventually used for a further socio-economic assessment, considering the tangible and intangible costs as well as the human dimension of vulnerability.