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Journal articles on the topic 'Flood and Flash Flood forecasting'
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1
Chen, Y., J. Li, S. Huang, and Y. Dong. "Study of Beijiang catchment flash-flood forecasting model." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 150–55. http://dx.doi.org/10.5194/piahs-368-150-2015.
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Abstract. Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences Discussions 5, no. 1 (February 12, 2008): 345–77. http://dx.doi.org/10.5194/hessd-5-345-2008.
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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground "truth". The lack of observations in most flash flood prone basins, therefore, lead to develop a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area with leadtimes of the order of the weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with a threshold – exceedance. As case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. The short-range weather forecasts, from the Lokalmodell of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences 12, no. 4 (July 30, 2008): 1039–51. http://dx.doi.org/10.5194/hess-12-1039-2008.
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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of loss of human life and infrastructures. Over the last two decades, flash floods have caused damage costing a billion Euros in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available short-range numerical weather forecasts together with a rainfall-runoff model can be used for early indication of the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small, and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground measurements. The lack of observations in most flash flood prone basins, therefore, necessitates the development of a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area, with lead times of the order of weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. This paper describes the main aspects of using numerical weather forecasting for flash flood forecasting, together with a threshold – exceedance. As a case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. Short-range weather forecasts, from the Lokalmodell of the German national weather service, are used as input for the LISFLOOD model, a hybrid between a conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 h in advance.
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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, and Jinggang Chu. "Flash Flood Forecasting Based on Long Short-Term Memory Networks." Water 12, no. 1 (December 29, 2019): 109. http://dx.doi.org/10.3390/w12010109.
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Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1–5 h lead time and range from 84.2% to 89.5% at 6–10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.
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Chang, Tzu-Yin, Hongey Chen, Huei-Shuin Fu, Wei-Bo Chen, Yi-Chiang Yu, Wen-Ray Su, and Lee-Yaw Lin. "An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods in the Southwestern Plain Areas of Taiwan." Water 13, no. 4 (February 4, 2021): 405. http://dx.doi.org/10.3390/w13040405.
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A pluvial flash flood is rapid flooding induced by intense rainfall associated with a severe weather system, such as thunderstorms or typhoons. Additionally, topography, ground cover, and soil conditions also account for the occurrence of pluvial flash floods. Pluvial flash floods are among the most devastating natural disasters that occur in Taiwan, and these floods always /occur within a few minutes or hours of excessive rainfall. Pluvial flash floods usually threaten large plain areas with high population densities; therefore, there is a great need to implement an operational high-performance forecasting system for pluvial flash flood mitigation and evacuation decisions. This study developed a high-performance two-dimensional hydrodynamic model based on the finite-element method and unstructured grids. The operational high-performance forecasting system is composed of the Weather Research and Forecasting (WRF) model, the Storm Water Management Model (SWMM), a two-dimensional hydrodynamic model, and a map-oriented visualization tool. The forecasting system employs digital elevation data with a 1-m resolution to simulate city-scale pluvial flash floods. The extent of flooding during historical inundation events derived from the forecasting system agrees well with the surveyed data for plain areas in southwestern Taiwan. The entire process of the operational high-performance forecasting system prediction of pluvial flash floods in the subsequent 24 h is accomplished within 8–10 min, and forecasts are updated every six hours.
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Saharia, Manabendra, Pierre-Emmanuel Kirstetter, Humberto Vergara, Jonathan J. Gourley, Yang Hong, and Marine Giroud. "Mapping Flash Flood Severity in the United States." Journal of Hydrometeorology 18, no. 2 (January 25, 2017): 397–411. http://dx.doi.org/10.1175/jhm-d-16-0082.1.
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Abstract Flash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical flood fatalities database.
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Ren, Juanhui, Bo Ren, Qiuwen Zhang, and Xiuqing Zheng. "A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region." Water 11, no. 9 (September 5, 2019): 1848. http://dx.doi.org/10.3390/w11091848.
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Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.
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Nguyen Van Ha, Tran Dang Hung, Doan Tran Anh, Giang Hoang Hiep, Nguyen Thi Huyen Trang, and Doan Ha Phong. "APPLICATION OF GIS AND REMOTE SENSING FOR MAPPING FLASH FLOOD RISE IN HOA BINH PROVINCE UNDER CLIMATE CHANGE CONTEXT." Tạp chí Khoa học Biến đổi khí hậu, no. 23 (December 28, 2022): 53–68. http://dx.doi.org/10.55659/2525-2496/23.75013.
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Hoa Binh is one of the provinces strongly suffering from natural disasters, especially flash floods. High slope mountainous terrains, reduced vegetation cover and unfavorable weather conditions form favorable conditions for flash floods to occur. This article develops a map of flash flood risk zoning in Hoa Binh using remote sensing and GIS technology. First, the factors affecting the risk of flash floods are identified, and each factor is classified based on the level of influence, then proceed to overlay the component maps causing flash floods. Factors affecting flash flood risk include: Slope, soil type, land use type, forest cover density and rain. As a result, areas at risk of flash floods are identified with 3 level: High, medium and low. This information can be used as a basis for forecasting areas at high risk of flash floods in the province.
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Nadeem, Muhammad Umer, Zeeshan Waheed, Abdul Mannan Ghaffar, Muhammad Mashood Javaid, Ameer Hamza, Zain Ayub, Muhammad Asim Nawaz, et al. "Application of HEC-HMS for flood forecasting in hazara catchment Pakistan, south Asia." International Journal of Hydrology 6, no. 1 (January 17, 2022): 7–12. http://dx.doi.org/10.15406/ijh.2022.06.00296.
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Floods have become more severe and frequent as a result of climate change around the world, posing a hazard to public safety and economic development. This study investigates the use of distributed hydrological models in flash flood risk management in a small watershed in Hazara, Pakistan, with the goal of improving Pakistan's early warning lead time. First, the HEC-HMS model was built using geographic data and the river network's structure, then calibrated and verified using eight high rainfall events from 2013. demonstrating that the HEC-HMS model could simulate floods in the research area Second, given that rainfall and flood events have happened, this paper proposes an analysis approach for a flood forecasting and warning system, as well as criteria for sending urban-stream flash flood alerts based on rainfall, in order to provide sufficient lead time. The DEMs (digital elevation models) of the research regions were processed using HEC-Geo HMS, an ArcView GIS tool for catchment delineation, terrain pre-processing, and basin processing. The model was calibrated and verified using previously observed data. The proposed flood prediction and risk reduction methodology is nonstructural. The Hydrologic Modeling System (HEC-HMS), which provides a sufficient lead time forecast and computes the runoff/stage threshold conditions, is at the heart of the flood warning application. For flood risk assessment, data from the Pakistan Meteorological Department (PMD) is entered into a hydro-meteorological database and then into the HEC-HMS. A server-client application was utilised to visualise the real-time flood scenario and send out an early warning message. The outcomes of this study will be used to develop flood validation measures in the Hazara stream watershed to deal with potential flash floods.
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Martinaitis, Steven M., Jonathan J. Gourley, Zachary L. Flamig, Elizabeth M. Argyle, Robert A. Clark, Ami Arthur, Brandon R. Smith, Jessica M. Erlingis, Sarah Perfater, and Benjamin Albright. "The HMT Multi-Radar Multi-Sensor Hydro Experiment." Bulletin of the American Meteorological Society 98, no. 2 (February 1, 2017): 347–59. http://dx.doi.org/10.1175/bams-d-15-00283.1.
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Abstract There are numerous challenges with the forecasting and detection of flash floods, one of the deadliest weather phenomena in the United States. Statistical metrics of flash flood warnings over recent years depict a generally stagnant warning performance, while regional flash flood guidance utilized in warning operations was shown to have low skill scores. The Hydrometeorological Testbed—Hydrology (HMT-Hydro) experiment was created to allow operational forecasters to assess emerging products and techniques designed to improve the prediction and warning of flash flooding. Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the Multi-Radar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations. The HMT-Hydro experiment ran in collaboration with the Flash Flood and Intense Rainfall (FFaIR) experiment at the Weather Prediction Center to simulate the real-time workflow between a national center and a local forecast office, as well as to facilitate discussions on the challenges of short-term flash flood forecasting. Results from the HMT-Hydro experiment highlighted the utility of MRMS and FLASH products in identifying the spatial coverage and magnitude of flash flooding, while evaluating the perception and reliability of probabilistic forecasts in flash flood watches and warnings. NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations.
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Sharif, Hatim O., David Yates, Rita Roberts, and Cynthia Mueller. "The Use of an Automated Nowcasting System to Forecast Flash Floods in an Urban Watershed." Journal of Hydrometeorology 7, no. 1 (February 1, 2006): 190–202. http://dx.doi.org/10.1175/jhm482.1.
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Abstract Flash flooding represents a significant hazard to human safety and a threat to property. Simulation and prediction of floods in complex urban settings requires high-resolution precipitation estimates and distributed hydrologic modeling. The need for reliable flash flood forecasting has increased in recent years, especially in urban communities, because of the high costs associated with flood occurrences. Several storm nowcast systems use radar to provide quantitative precipitation forecasts that can potentially afford great benefits to flood warning and short-term forecasting in urban settings. In this paper, the potential benefits of high-resolution weather radar data, physically based distributed hydrologic modeling, and quantitative precipitation nowcasting for urban hydrology and flash flood prediction were demonstrated by forcing a physically based distributed hydrologic model with precipitation forecasts made by a convective storm nowcast system to predict flash floods in a small, highly urbanized catchment in Denver, Colorado. Two rainfall events on 5 and 8 July 2001 in the Harvard Gulch watershed are presented that correspond to times during which the storm nowcast system was operated. Results clearly indicate that high-resolution radar-rainfall estimates and advanced nowcasting can potentially lead to improvements in flood warning and forecasting in urban watersheds, even for short-lived events on small catchments. At lead times of 70 min before the occurrence of peak discharge, forecast accuracies of approximately 17% in peak discharge and 10 min in peak timing were achieved for a 10 km2 highly urbanized catchment.
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Le Bihan, Guillaume, Olivier Payrastre, Eric Gaume, David Moncoulon, and Frédéric Pons. "The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data." Hydrology and Earth System Sciences 21, no. 11 (November 28, 2017): 5911–28. http://dx.doi.org/10.5194/hess-21-5911-2017.
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Abstract. Up to now, flash flood monitoring and forecasting systems, based on rainfall radar measurements and distributed rainfall–runoff models, generally aimed at estimating flood magnitudes – typically discharges or return periods – at selected river cross sections. The approach presented here goes one step further by proposing an integrated forecasting chain for the direct assessment of flash flood possible impacts on inhabited areas (number of buildings at risk in the presented case studies). The proposed approach includes, in addition to a distributed rainfall–runoff model, an automatic hydraulic method suited for the computation of flood extent maps on a dense river network and over large territories. The resulting catalogue of flood extent maps is then combined with land use data to build a flood impact curve for each considered river reach, i.e. the number of inundated buildings versus discharge. These curves are finally used to compute estimated impacts based on forecasted discharges. The approach has been extensively tested in the regions of Alès and Draguignan, located in the south of France, where well-documented major flash floods recently occurred. The article presents two types of validation results. First, the automatically computed flood extent maps and corresponding water levels are tested against rating curves at available river gauging stations as well as against local reference or observed flood extent maps. Second, a rich and comprehensive insurance claim database is used to evaluate the relevance of the estimated impacts for some recent major floods.
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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.
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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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Láng-Ritter, Josias, Marc Berenguer, Francesco Dottori, Milan Kalas, and Daniel Sempere-Torres. "Compound flood impact forecasting: integrating fluvial and flash flood impact assessments into a unified system." Hydrology and Earth System Sciences 26, no. 3 (February 10, 2022): 689–709. http://dx.doi.org/10.5194/hess-26-689-2022.
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Abstract. Floods can arise from a variety of physical processes. Although numerous risk assessment approaches stress the importance of taking into account the possible combinations of flood types (i.e. compound floods), this awareness has so far not been reflected in the development of early warning systems: existing methods for forecasting flood hazards or the corresponding socio-economic impacts are generally designed for only one type of flooding. During compound flood events, these flood type-specific approaches are unable to identify overall hazards or impacts. Moreover, from the perspective of end-users (e.g. civil protection authorities), the monitoring of separate flood forecasts – with potentially contradictory outputs – can be confusing and time-consuming, and ultimately impede an effective emergency response. To enhance decision support, this paper proposes the integration of different flood type-specific approaches into one compound flood impact forecast. This possibility has been explored through the development of a unified system combining the simulations of two impact forecasting methods: the Rapid Risk Assessment of the European Flood Awareness System (EFAS RRA; representing fluvial floods) and the radar-based ReAFFIRM method (representing flash floods). The unified system has been tested for a recent catastrophic episode of compound flooding: the DANA event of September 2019 in south-east Spain (Depresión Aislada en Niveles Altos, meaning cut-off low). The combination of the two methods identified well the overall compound flood extents and impacts reported by various information sources. For instance, the simulated economic losses amounted to about EUR 670 million against EUR 425 million of reported insured losses. Although the compound impact estimates were less accurate at municipal level, they corresponded much better to the observed impacts than those generated by the two methods applied separately. This demonstrates the potential of such integrated approaches for improving decision support services.
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Darras, T., F. Raynaud, V. Borrell Estupina, L. Kong-A-Siou, S. Van-Exter, B. Vayssade, A. Johannet, and S. Pistre. "Neural network modeling and geochemical water analyses to understand and forecast karst and non-karst part of flash floods (case study on the <i>Lez</i> river, Southern France)." Proceedings of the International Association of Hydrological Sciences 369 (June 11, 2015): 43–48. http://dx.doi.org/10.5194/piahs-369-43-2015.
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Abstract. Flash floods forecasting in the Mediterranean area is a major economic and societal issue. Specifically, considering karst basins, heterogeneous structure and nonlinear behaviour make the flash flood forecasting very difficult. In this context, this work proposes a methodology to estimate the contribution from karst and non-karst components using toolbox including neural networks and various hydrological methods. The chosen case study is the flash flooding of the Lez river, known for his complex behaviour and huge stakes, at the gauge station of Lavallette, upstream of Montpellier (400 000 inhabitants). After application of the proposed methodology, discharge at the station of Lavallette is spited between hydrographs of karst flood and surface runoff, for the two events of 2014. Generalizing the method to future events will allow designing forecasting models specifically for karst and surface flood increasing by this way the reliability of the forecasts.
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Ntelekos, Alexandros A., Konstantine P. Georgakakos, and Witold F. Krajewski. "On the Uncertainties of Flash Flood Guidance: Toward Probabilistic Forecasting of Flash Floods." Journal of Hydrometeorology 7, no. 5 (October 1, 2006): 896–915. http://dx.doi.org/10.1175/jhm529.1.
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Abstract Quantifying uncertainty associated with flash flood warning or forecast systems is required to enable informed decision making by those responsible for operation and management of natural hazard protection systems. The current system used by the U.S. National Weather Service (NWS) to issue flash-flood warnings and watches over the Unites States is a purely deterministic system. The authors propose a simple approach to augment the Flash Flood Guidance System (FFGS) with uncertainty propagation components. The authors briefly discuss the main components of the system, propose changes to improve it, and allow accounting for several sources of uncertainty. They illustrate their discussion with examples of uncertainty quantification procedures for several small basins of the Illinois River basin in Oklahoma. As the current FFGS is tightly coupled with two technologies, that is, threshold-runoff mapping and the Sacramento Soil Moisture Accounting Hydrologic Model, the authors discuss both as sources of uncertainty. To quantify and propagate those sources of uncertainty throughout the system, they develop a simple version of the Sacramento model and use Monte Carlo simulation to study several uncertainty scenarios. The results point out the significance of the stream characteristics such as top width and the hydraulic depth on the overall uncertainty of the Flash Flood Guidance System. They also show that the overall flash flood guidance uncertainty is higher under drier initial soil moisture conditions. The results presented herein, although limited, are a necessary first step toward the development of probabilistic operational flash flood guidance forecast-response systems.
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Hao, Sijia, Qiang Ma, Xiaoyan Zhai, Guomin Lyu, Suqi Fan, Wenchuan Wang, and Changjun Liu. "A New Machine Learning Approach for parameter regionalization of Flash Flood Modelling in Henan Province, China." E3S Web of Conferences 300 (2021): 02010. http://dx.doi.org/10.1051/e3sconf/202130002010.
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China is one of the countries in the world that seriously affected by flash floods disasters. The flash flood caused by extreme rainfall occurred at mountainous small-sized watersheds in China often leads to serious economic damages and obstructs the social development. Setting up an efficient forecasting system for flash flood has been widely accepted as one of the key non-structural measures to improve the control and prevention capability of China. However, due to the data limitation, establishing forecast models in those flash flood areas is challenged by the lack of parameter references. This paper proposed a new machine learning approach based on the Random Forest (RF) algorithm for model parameter regionalization. Integrated with distributed deterministic hydrological models of 20 small-sized watersheds in Henan province, the RF algorithm has been applied for defining the watersheds’ similarity and further transferring the parameters from sample watersheds to the objective watershed. Validated through leave-one-out approach, the RF model is able to effectively improve the simulation accuracy of flash floods in Henan province. The presented approach showed high-levelled applicability to be extended in other flash flood areas in China for providing effective reference for parameter regionalization.
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Janál, Petr, and Miloš Starý. "Fuzzy Model Used for the Prediction of a State of Emergency for a River Basin in the Case of a Flash Flood - PART 2." Journal of Hydrology and Hydromechanics 60, no. 3 (September 1, 2012): 162–73. http://dx.doi.org/10.2478/v10098-012-0014-3.
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Fuzzy Model Used for the Prediction of a State of Emergency for a River Basin in the Case of a Flash Flood - PART 2This article is a continuation of a previous one named Fuzzy model use for prediction of the state of emergency of river basin in the case of flash flood (Janál&Starý, 2009), where the potential applications of fuzzy logic in the field of flash flood forecasting were described. Flash flood forecasting needs a specific approach because of the character of torrential rainfall. Storms are very difficult to forecast in space and time. The hydrological models designed for flash flood prediction have to be able to work with very uncertain input data. Moreover, the models have to be capable of evaluating the level of danger in as short a time as possible because of the highly dynamic character of the modeled process. The fuzzy model described in the previous article was modified into a form usable in operational hydrology and a simulation of its operational application was run using this model. The selected time period for the simulation was the summer of 2009, when numerous flash floods occurred in Czech Republic. The topic of this article is the preparation of the model for practical use and the results of the simulation of its operation.
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Alfieri, L., P. J. Smith, J. Thielen-del Pozo, and K. J. Beven. "A staggered approach to flash flood forecasting – case study in the Cévennes region." Advances in Geosciences 29 (February 25, 2011): 13–20. http://dx.doi.org/10.5194/adgeo-29-13-2011.
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Abstract. A staggered approach to flash flood forecasting is developed within the IMPRINTS project (FP7-ENV-2008-1-226555). Instead of a single solution system, a chain of different models and input data is being proposed that act in sequence and provide decision makers with information of increasing accuracy in localization and magnitude as the events approach. The first system in the chain is developed by adapting methodologies of the European Flood Alert System (EFAS) to forecast flash floods and has the potential to provide early indication for probability of flash floods at the European scale. The last system in the chain is an adaptation of the data based mechanistic model (DBM) to probabilistic numerical weather predictions (NWP) and observed rainfall, with the capability to forecast river levels up to 12 h ahead. The potential of both systems to provide complementary information is illustrated for a flash flood event occurred on 2 November 2008 in the Cévennes region in France. Results show that the uncertainty in meteorological forecasts largely affects the outcomes. However, at an early stage, uncertain results are still valuable to decision makers, as they raise preparedness towards prompt actions to be taken.
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Wu, Jian, Haixing Liu, Guozhen Wei, Tianyu Song, Chi Zhang, and Huicheng Zhou. "Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment." Water 11, no. 7 (June 27, 2019): 1327. http://dx.doi.org/10.3390/w11071327.
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Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow.
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Kobold, M., and M. Brilly. "The use of HBV model for flash flood forecasting." Natural Hazards and Earth System Sciences 6, no. 3 (May 24, 2006): 407–17. http://dx.doi.org/10.5194/nhess-6-407-2006.
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Abstract. The standard conceptual HBV model was originally developed with daily data and is normally operated on daily time step. But many floods in Slovenia are usually flash floods as result of intense frontal precipitation combined with orographic enhancement. Peak discharges are maintained only for hours or even minutes. To use the HBV model for flash flood forecasting, the version of HBV-96 has been applied on the catchment with complex topography with the time step of one hour. The recording raingauges giving hourly values of precipitation have been taken in calibration of the model. The uncertainty of simulated runoff is mainly the result of precipitation uncertainty associated with the mean areal precipitation and is higher for mountainous catchments. Therefore the influence of number of raingauges used to derive the areal precipitation by the method of Thiessen polygons was investigated. The quantification of hydrological uncertainty has been performed by analysis of sensitivity of the HBV model to error in precipitation input. The results show that an error of 10% in the amount of precipitation causes an error of 17% in the peak of flood wave. The polynomial equations showing the relationship between the errors in rainfall amounts and peak discharges were derived for two water stations on the Savinja catchment. Simulated discharges of half-yearly runs demonstrate the applicability of the HBV model for flash flood forecasting using the mesoscale meteorological forecasts of ALADIN/SI model as input precipitation data.
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Li, Z., D. Yang, Y. Hong, Y. Qi, and Q. Cao. "Evaluation of radar-based precipitation estimates for flash flood forecasting in the Three Gorges Region." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 89–95. http://dx.doi.org/10.5194/piahs-368-89-2015.
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Abstract. Spatial rainfall pattern plays a critical role in determining hydrological responses in mountainous areas, especially for natural disasters such as flash floods. In this study, to improve the skills of flood forecasting in the mountainous Three Gorges Region (TGR) of the Yangtze River, we developed a first version of a high-resolution (1 km) radar-based quantitative precipitation estimation (QPE) consideration of many critical procedures, such as beam blockage analysis, ground-clutter filter, rain type identification and adaptive Z–R relations. A physically-based distributed hydrological model (GBHM) was established and further applied to evaluate the performance of radar-based QPE for regional flood forecasting, relative to the gauge-driven simulations. With two sets of input data (gauge and radar) collected during summer 2010, the applicability of the current radar-based QPE to rainstorm monitoring and flash flood forecasting in the TGR is quantitatively analysed and discussed.
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Lee, Jung Hwan, Gi Moon Yuk, Hyeon Tae Moon, and Young-Il Moon. "Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream." Atmosphere 11, no. 9 (September 11, 2020): 971. http://dx.doi.org/10.3390/atmos11090971.
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The flood forecasting and warning system enable an advanced warning of flash floods and inundation depths for disseminating alarms in urban areas. Therefore, in this study, we developed an integrated flood forecasting and warning system combined inland-river that systematized technology to quantify flood risk and flood forecasting in urban areas. LSTM was used to predict the stream depth in the short-term inundation prediction. Moreover, rainfall prediction by radar data, a rainfall-runoff model combined inland-river by coupled SWMM and HEC-RAS, automatic simplification module of drainage networks, automatic calibration module of SWMM parameter by Dynamically Dimensioned Search (DDS) algorithm, and 2-dimension inundation database were used in very short-term inundation prediction to warn and convey the flood-related data and information to communities. The proposed system presented better forecasting results compared to the Seoul integrated disaster prevention system. It can provide an accurate water level for 30 min to 90 min lead times in the short-term inundation prediction module. And the very short-term inundation prediction module can provide water level across a stream for 10 min to 60 min lead times using forecasting rainfall by radar as well as inundation risk areas. In conclusion, the proposed modules were expected to be useful to support inundation forecasting and warning systems.
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Chen, Yung-Ming, Che-Hsin Liu, Hung-Ju Shih, Chih-Hsin Chang, Wei-Bo Chen, Yi-Chiang Yu, Wen-Ray Su, and Lee-Yaw Lin. "An Operational Forecasting System for Flash Floods in Mountainous Areas in Taiwan." Water 11, no. 10 (October 9, 2019): 2100. http://dx.doi.org/10.3390/w11102100.
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Flash floods are different from common floods because they occur rapidly over short time scales, and they are considered to be one of the most devastating natural hazards worldwide. Mountainous areas with high population densities are particularly threatened by flash floods because steep slopes generate high flow velocities. Therefore, there is a great need to develop an operational forecasting system (OFS) for better flash flood prediction and warning in mountainous regions. This study developed an OFS through the integration of meteorological, hydrological, and hydrodynamic models. Airborne light detection and ranging (LiDAR) data were used to generate a digital elevation model (DEM). The OFS employs high-density and high-accuracy airborne LiDAR DEM data to simulate rapid water level rises and flooding as the result of intense rainfall within relatively small watersheds. The water levels and flood extent derived from the OFS are in agreement with the measured and surveyed data. The OFS has been adopted by the National Science and Technology Center for Disaster Reduction (NCDR) for forecasting flash floods every six hours in a mountainous floodplain in Taiwan. The 1D and 2D visualization of the OFS is performed via the National Center for Atmospheric Research Command Language (NCL).
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Lee, Byong-Ju, and Sangil Kim. "Gridded Flash Flood Risk Index Coupling Statistical Approaches and TOPLATS Land Surface Model for Mountainous Areas." Water 11, no. 3 (March 11, 2019): 504. http://dx.doi.org/10.3390/w11030504.
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This study presents the development of a statistical flash flood risk index model, which is currently operating in research mode for flash flood risk forecasting in ungauged mountainous areas. The grid-based statistical flash flood risk index, with temporal and spatial resolutions of 1 h and 1 km, respectively, has been developed to simulate the flash flood risk index leading to flash flood casualties using hourly rainfall, surface flow, and soil water content in the previous 6 h. The statistical index model employs factor analysis and multi-linear regression to analyze its gridded hydrological components that are obtained from the TOPMODEL-based Land Atmosphere Transfer Scheme (TOPLATS). The performance of the developed index model has been evaluated in estimating flash flooding in ungauged mountain valleys and small streams. Numerical results show that the approach simulated 38 flash flood catastrophes in the Seoul Capital Region with 71% accuracy; therefore, this approach is potentially adequate for flash flood risk forecasting.
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Zanchetta, Andre, and Paulin Coulibaly. "Recent Advances in Real-Time Pluvial Flash Flood Forecasting." Water 12, no. 2 (February 19, 2020): 570. http://dx.doi.org/10.3390/w12020570.
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Recent years have witnessed considerable developments in multiple fields with the potential to enhance our capability of forecasting pluvial flash floods, one of the most costly environmental hazards in terms of both property damage and loss of life. This work provides a summary and description of recent advances related to insights on atmospheric conditions that precede extreme rainfall events, to the development of monitoring systems of relevant hydrometeorological parameters, and to the operational adoption of weather and hydrological models towards the prediction of flash floods. With the exponential increase of available data and computational power, most of the efforts are being directed towards the improvement of multi-source data blending and assimilation techniques, as well as assembling approaches for uncertainty estimation. For urban environments, in which the need for high-resolution simulations demands computationally expensive systems, query-based approaches have been explored for the timely retrieval of pre-simulated flood inundation forecasts. Within the concept of the Internet of Things, the extensive deployment of low-cost sensors opens opportunities from the perspective of denser monitoring capabilities. However, different environmental conditions and uneven distribution of data and resources usually leads to the adoption of site-specific solutions for flash flood forecasting in the context of early warning systems.
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Kirstetter, Geoffroy, Olivier Delestre, Pierre-Yves Lagrée, Stéphane Popinet, and Christophe Josserand. "B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement." Geoscientific Model Development 14, no. 11 (November 22, 2021): 7117–32. http://dx.doi.org/10.5194/gmd-14-7117-2021.
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Abstract. The French Riviera is very often threatened by flash floods. These hydro-meteorological events, which are fast and violent, have catastrophic consequences on life and property. The development of forecasting tools may help to limit the impacts of these extreme events. Our purpose here is to demonstrate the possibility of using b-flood (a subset of the Basilisk library http://basilisk.fr/, last access: 8 November 2021), which is a 2D tool based on the shallow-water equations and adaptive mesh refinement. The code is first validated using analytical test cases describing different flow regimes. It is then applied to the Toce river valley physical model produced by ENEL-HYDRO in the framework of the CADAM project and on a flash-flood case over the urbanized Toce area produced during the IMPACT project. Finally, b-flood is applied to the flash flood of October 2015 in Cannes in south-eastern France, which demonstrates the feasibility of using software based on the shallow-water equations and mesh refinement for flash-flood simulation in small watersheds (less than 100 km2) and on a predictive computational timescale.
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Chitwatkulsiri, Detchphol, Hitoshi Miyamoto, Kim Neil Irvine, Sitang Pilailar, and Ho Huu Loc. "Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand." Water 14, no. 10 (May 20, 2022): 1641. http://dx.doi.org/10.3390/w14101641.
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In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.
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Nguyen, Dinh Ty, and Shien-Tsung Chen. "Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods." Water 12, no. 3 (March 12, 2020): 787. http://dx.doi.org/10.3390/w12030787.
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Probabilistic flood forecasting, which provides uncertain information in the forecasting of floods, is practical and informative for implementing flood-mitigation countermeasures. This study adopted various machine learning methods, including support vector regression (SVR), a fuzzy inference model (FIM), and the k-nearest neighbors (k-NN) method, to establish a probabilistic forecasting model. The probabilistic forecasting method is a combination of a deterministic forecast produced using SVR and a probability distribution of forecast errors determined by the FIM and k-NN method. This study proposed an FIM with a modified defuzzification scheme to transform the FIM’s output into a probability distribution, and k-NN was employed to refine the probability distribution. The probabilistic forecasting model was applied to forecast flash floods with lead times of 1–3 hours in Yilan River, Taiwan. Validation results revealed the deterministic forecasting to be accurate, and the probabilistic forecasting was promising in view of a forecasted hydrograph and quantitative assessment concerning the confidence level.
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Bouilloud, Ludovic, Katia Chancibault, Béatrice Vincendon, Véronique Ducrocq, Florence Habets, Georges-Marie Saulnier, Sandrine Anquetin, Eric Martin, and Joel Noilhan. "Coupling the ISBA Land Surface Model and the TOPMODEL Hydrological Model for Mediterranean Flash-Flood Forecasting: Description, Calibration, and Validation." Journal of Hydrometeorology 11, no. 2 (April 1, 2010): 315–33. http://dx.doi.org/10.1175/2009jhm1163.1.
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Abstract Innovative coupling between the soil–vegetation–atmosphere transfer (SVAT) model Interactions between Soil, Biosphere, and Atmosphere (ISBA) and the hydrological model TOPMODEL has been specifically designed for flash-flood forecasting in the Mediterranean area. The coupled model described in this study combines the advantages of the two types of model: the accurate representation of water and energy transfer between the soil and the atmosphere within the SVAT column and an explicit representation of the lateral transfer of water over the hydrological catchment unit. Another advantage of this coupling is that the number of parameters to be calibrated is reduced by two, as only two parameters instead of four parameters concern the TOPMODEL formulation used here. The parameters to be calibrated concern only the water transfer. The model was calibrated for the simulation of flash-flood events on the three main watersheds covering the French Cévennes–Vivarais region using a subset of past flash-flood events having occurred since 2000. The complementary subset of flash-flood events was then used to carry out an objective verification of the coupled model after calibration. The evaluation on these six independent past flash-flood events shows satisfactory results. The comparison of the observed and simulated hydrographs demonstrates that no flash-flood peaks are missed. Relevant information for flash-flood forecasting can always be inferred from the simulations, even for those with quite poor results, making the system useful for real-time and operational flash-flood forecasting.
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El Afandi, Gamal, Mostafa Morsy, and Fathy El Hussieny. "Heavy Rainfall Simulation over Sinai Peninsula Using the Weather Research and Forecasting Model." International Journal of Atmospheric Sciences 2013 (January 28, 2013): 1–11. http://dx.doi.org/10.1155/2013/241050.
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Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.
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Broxton, Patrick, Peter A. Troch, Mike Schaffner, Carl Unkrich, and David Goodrich. "An All-Season Flash Flood Forecasting System for Real-Time Operations." Bulletin of the American Meteorological Society 95, no. 3 (March 1, 2014): 399–407. http://dx.doi.org/10.1175/bams-d-12-00212.1.
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Flash floods can cause extensive damage to both life and property, especially because they are difficult to predict. Flash flood prediction requires high-resolution meteorological observations and predictions, as well as calibrated hydrological models, which should effectively simulate how a catchment filters rainfall inputs into streamflow. Furthermore, because of the requirement of both hydrological and meteorological components in flash flood forecasting systems, there must be extensive data handling capabilities built in to force the hydrological model with a variety of available hydrometeorological data and predictions, as well as to test the model with hydrological observations. The authors have developed a working prototype of such a system, called KINEROS/hsB-SM, after the hydrological models that are used: the Kinematic Erosion and Runoff (KINEROS) and hillslope-storage Boussinesq Soil Moisture (hsB-SM) models. KINEROS is an event-based overland flow and channel routing model that is designed to simulate flash floods in semiarid regions where infiltration excess overland flow dominates, while hsB-SM is a continuous subsurface flow model, whose model physics are applicable in humid regions where saturation excess overland flow is most important. In addition, KINEROS/hsB-SM includes an energy balance snowmelt model, which gives it the ability to simulate flash floods that involve rain on snow. There are also extensive algorithms to incorporate high-resolution hydrometeorological data, including stage III radar data (5 min, 1° by 1 km), to assist in the calibration of the models, and to run the model in real time. The model is currently being used in an experimental fashion at the National Weather Service Binghamton, New York, Weather Forecast Office.
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Ngo, Thao Phuong Thi, Long Hung Ngo, Khanh Quang Nguyen, Tinh Thanh Bui, Phong Van Tran, Ha Viet Nhu, and Yen Hai Thi Nguyen. "Applying Random Forest approach in forecasting flash flood susceptibility area in Lao Cai region." Journal of Mining and Earth Sciences 61, no. 5 (October 31, 2020): 30–42. http://dx.doi.org/10.46326/jmes.2020.61(5).04.
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The main objectives of this research are to provide a new approach for flash flood prediction in Lao Cai, where frequent typhoons happen. This method is based on the Random Forest classification algorithm. The researcher applied GIS database in combination with construction machine learning model and verified the forecasting model, extracted the data based on field survey of the flash flood area of Lao Cai and GIS (Geographic Information System). The results have proved that the model can be a useful tool for flash flood forecasting model, providing more data for land planning and management for preventing and predicting flash flood for Lao Cai area.
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Nguyen, Duyen Thi My, and Hai Quang Ha. "Flash floods potential area mapping at Huong Khe district, Ha Tinh prov." Science and Technology Development Journal - Natural Sciences 1, T4 (December 31, 2017): 249–54. http://dx.doi.org/10.32508/stdjns.v1it4.487.
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Ha Tinh is one of the provinces most affected by natural hazards, especially flash floods. Sloping hilly terrain conditions, reduced covering density of forest and unfavorable weather conditions are potential hazards to flash floods. Flash floods potential area mapping at Huong Khe district, Ha Tinh province was carried out using Remote Sensing and GIS technologies. Factors causing flash floods was indentified and classified basing n their afecting level. Component maps of flash flood–causing factors were overlayed. Factors causing flash floods as noted by Greg Smith included: slope, soil type, forms of using land, covering density of forest. Potential areas of flash floods and the potential level of each part were indentified. The resulted maps can be used for forecasting risk regions of flash floods at the district.
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Sellami, E. M., M. Maanan, and H. Rhinane. "PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR MAPPING AND FORECASTING OF FLASH FLOOD SUSCEPTIBILITY IN TETOUAN, MOROCCO." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W3-2021 (January 11, 2022): 305–13. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w3-2021-305-2022.
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Abstract. Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index ) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area . Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.
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Biswas, Nishan Kumar, Faisal Hossain, Matthew Bonnema, A. M. Aminul Haque, Robin Kumar Biswas, Arifuzzaman Bhuyan, and Amirul Hossain. "A computationally efficient flash flood early warning system for a mountainous and transboundary river basin in Bangladesh." Journal of Hydroinformatics 22, no. 6 (October 8, 2020): 1672–92. http://dx.doi.org/10.2166/hydro.2020.202.
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Abstract A computationally efficient early warning technique is developed for forecasting flash floods during the pre-monsoon season that are associated with a complex topography and transboundary runoff in northeastern Bangladesh. Locally conditioned topographic and hydrometeorological observations are key forcings to the modeling system that simulate the hydrology and hydraulic processes. The hydrologic model is calibrated and validated using satellite-based observations to estimate the correct amount of transboundary and mountainous inflow into the flash flood-prone plains. Inflow is then forecasted using precipitation forecast from a global numerical weather prediction (NWP) system called the Global Forecasting System (GFS). The forecasted inflows serve as the upstream boundary conditions for the hydrodynamic model to forecast the water stage and inundation downstream in the floodplains. A real-time in-situ data-based error correction methodology is applied to maintain the skill of the system. The simulation grid size and time-step of the hydrodynamic model are also optimized for computational efficiency. Historical performance of the framework revealed at least 60% accuracy at 5-day lead-time in delineating flood inundation when compared against Sentinel-1 synthetic aperture radar (SAR) imagery. The study suggests that higher resolution topographic information and dynamically downscaled meteorological observations can lead to significant improvement in flash flood forecasting skills.
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Vincendon, B., V. Ducrocq, O. Nuissier, and B. Vié. "Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting." Natural Hazards and Earth System Sciences 11, no. 5 (May 23, 2011): 1529–44. http://dx.doi.org/10.5194/nhess-11-1529-2011.
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Abstract. Mediterranean intense weather events often lead to devastating flash-floods. Extending the forecasting lead times further than the watershed response times, implies the use of numerical weather prediction (NWP) to drive hydrological models. However, the nature of the precipitating events and the temporal and spatial scales of the watershed response make them difficult to forecast, even using a high-resolution convection-permitting NWP deterministic forecasting. This study proposes a new method to sample the uncertainties of high-resolution NWP precipitation forecasts in order to quantify the predictability of the streamflow forecasts. We have developed a perturbation method based on convection-permitting NWP-model error statistics. It produces short-term precipitation ensemble forecasts from single-value meteorological forecasts. These rainfall ensemble forecasts are then fed into a hydrological model dedicated to flash-flood forecasting to produce ensemble streamflow forecasts. The verification on two flash-flood events shows that this forecasting ensemble performs better than the deterministic forecast. The performance of the precipitation perturbation method has also been found to be broadly as good as that obtained using a state-of-the-art research convection-permitting NWP ensemble, while requiring less computing time.
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Javier, Julie Rose N., James A. Smith, Katherine L. Meierdiercks, Mary Lynn Baeck, and Andrew J. Miller. "Flash Flood Forecasting for Small Urban Watersheds in the Baltimore Metropolitan Region." Weather and Forecasting 22, no. 6 (December 1, 2007): 1331–44. http://dx.doi.org/10.1175/2007waf2006036.1.
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Abstract The utility of distributed hydrologic models in combination with high-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) rainfall estimates for flash flood forecasting in urban drainage basins is examined through model simulations of 10 flood events in the 14.3 km2 Dead Run watershed of Baltimore County, Maryland. The hydrologic model consists of a simple infiltration model and a geomorphological instantaneous unit hydrograph–based representation of hillslope and channel response. Analyses are based on high-resolution radar rainfall estimates from the Sterling, Virginia, WSR-88D and observations from a nested network of 6 stream gauges in the Dead Run watershed and a network of 17 rain gauge stations in Dead Run. For the three largest flood peaks in Dead Run, including the record flood on 7 July 2004, hydrologic model forecasts do not capture the pronounced attenuation of flood peaks. Hydraulic controls imposed by valley bottom constrictions associated with bridges and bridge abutments are a dominant element of the extreme flood response of small urban watersheds. Model analyses suggest that a major limitation on the accuracy of flash flood forecasting in urban watersheds is imposed by storm water management infrastructure. Model analyses also suggest that there is potential for improving model forecasts through the utilization of information on initial soil moisture storage. Errors in the rainfall field, especially those linked to bias correction, are the largest source of uncertainty in quantitative flash flood forecasting. Bias correction of radar rainfall estimates is an important element of flash flood forecasting systems.
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El Khalki, El Mahdi, Yves Tramblay, Arnau Amengual, Victor Homar, Romualdo Romero, Mohamed El Mehdi Saidi, and Meriem Alaouri. "Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco." Water 12, no. 2 (February 6, 2020): 437. http://dx.doi.org/10.3390/w12020437.
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Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood events in the Rheraya and Ourika basin located in the High-Atlas Mountains of Morocco. The model evaluation is performed by comparing for a set of flood events the observed and simulated probabilities of exceedances for different precipitation thresholds. In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency’s Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5. These results provide insights to implement robust flood forecasting approaches in the context of data scarcity that could be valuable for developing countries such as Morocco and other North African countries.
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Viteri López, Andrea Salomé, and Carlos Augusto Morales Rodriguez. "Flash Flood Forecasting in São Paulo Using a Binary Logistic Regression Model." Atmosphere 11, no. 5 (May 7, 2020): 473. http://dx.doi.org/10.3390/atmos11050473.
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This study presents a flash flood forecasting model that uses a binary logistic regression method to determine the occurrence of flash flood events in different watersheds in the city of São Paulo, Brazil. This study is based on two years (2015–2016) of rain estimates from a dual-polarization S-band Doppler weather radar (SPOL) and flood locations observed by the Climate Emergency Management Center (CGE) of São Paulo City Hall. The logistic regression model is based on daily accumulated precipitation, a maximum precipitation rate, and daily rainfall duration. The model presented a probability of detection (POD) of 46% (71%) on average for flood events (conditional), while, for events without flash flood, it reached 98% probability. Despite the low averaged POD for flash flood occurrence, the model demonstrated a good performance for watersheds located in the east of the city near the Tietê River and in the southeast with probabilities above 50%.
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Yu, Guo, Julianne J. Miller, Benjamin J. Hatchett, Markus Berli, Daniel B. Wright, Craig McDougall, and Zhihua Zhu. "The Nonstationary Flood Hydrology of an Urbanizing Arid Watershed." Journal of Hydrometeorology 24, no. 1 (January 2023): 87–104. http://dx.doi.org/10.1175/jhm-d-22-0117.1.
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Abstract The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.
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Harats, N., B. Ziv, Y. Yair, V. Kotroni, and U. Dayan. "Lightning and rain dynamic indices as predictors for flash floods events in the Mediterranean." Advances in Geosciences 23 (March 29, 2010): 57–64. http://dx.doi.org/10.5194/adgeo-23-57-2010.
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Abstract. The FLASH EU funded project aims to observe, analyze and model lightning activity in thunderstorms for use in short term forecasting of flash floods in the Mediterranean region. Two new indices, aimed to assess the potential for heavy precipitation and flash-floods, are proposed and evaluated. The first is a lightning index – the MKI, which is a modified version of the KI-index. The applied index gives more weight to the lower- and mid-level relative humidity. The second is a new rain index, the RDI, which is the integrated product of specific humidity and vertical velocity. With the aim to contribute to the aforementioned objectives, 3 flash flood events, two in Israel and one in Greece are analyzed in the present study, using the 2 proposed indices. The NCEP/NCAR reanalysis database, of 2.5°×2.5° resolution, failed to resolve the meso-scale features of the observed flash flood events. Therefore, the ECWMF database, of 0.5°×0.5° resolution, was used for calculating and displaying the two indices. Comparison between the observed rain and lightning and the respective indices for the two pieces of data was performed for the flash flood events. The results show good concordance of both indices with timing and spatial distribution in 2 of them, while in one of them is displaced by more than 50 km. The good agreement in locating the maximum between the MKI and RDI suggests that the proposed indices are good predictors for both intense lightning activity and torrential rain and consequently, for potential flash floods.
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Nikolopoulos, Efthymios I., Emmanouil N. Anagnostou, and Marco Borga. "Using High-Resolution Satellite Rainfall Products to Simulate a Major Flash Flood Event in Northern Italy." Journal of Hydrometeorology 14, no. 1 (February 1, 2013): 171–85. http://dx.doi.org/10.1175/jhm-d-12-09.1.
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Abstract Effective flash flood warning procedures are usually hampered by observational limitations of precipitation over mountainous basins where flash floods occur. Satellite rainfall estimates are available over complex terrain regions, offering a potentially viable solution to the observational coverage problem. However, satellite estimates of heavy rainfall rates are associated with significant biases and random errors that nonlinearly propagate in hydrologic modeling, imposing severe limitations on the use of these products in flood forecasting. In this study, the use of three quasi-global and near-real-time high-resolution satellite rainfall products for simulating flash floods over complex terrain basins are investigated. The study uses a major flash flood event that occurred during 29 August 2003 on a medium size mountainous basin (623 km2) in the eastern Italian Alps. Comparison of satellite rainfall with rainfall derived from gauge-calibrated weather radar estimates showed that although satellite products suffer from large biases they could represent the temporal variability of basin-averaged precipitation. Propagation of satellite rainfall through a distributed hydrologic model revealed that systematic error in rainfall was severely magnified when transformed to error in runoff under dry initial soil conditions. Simulation hydrographs became meaningful only after recalibrating the model for each satellite rainfall input separately. However, the unrealistic values of model parameters after recalibration show that this approach is erroneous and that model recalibration using satellite rainfall data should be treated with care. Overall, this study highlights the need for improvement of satellite rainfall retrieval algorithms in order to allow a more appropriate use of satellite rainfall products for flash flood applications.
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Doswell, Charles A., Harold E. Brooks, and Robert A. Maddox. "Flash Flood Forecasting: An Ingredients-Based Methodology." Weather and Forecasting 11, no. 4 (December 1996): 560–81. http://dx.doi.org/10.1175/1520-0434(1996)011<0560:fffaib>2.0.co;2.
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Hardy, Jill, Jonathan J. Gourley, Pierre-Emmanuel Kirstetter, Yang Hong, Fanyou Kong, and Zachary L. Flamig. "A method for probabilistic flash flood forecasting." Journal of Hydrology 541 (October 2016): 480–94. http://dx.doi.org/10.1016/j.jhydrol.2016.04.007.
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BLOSCHL, G., C. RESZLER, and J. KOMMA. "A spatially distributed flash flood forecasting model." Environmental Modelling & Software 23, no. 4 (April 2008): 464–78. http://dx.doi.org/10.1016/j.envsoft.2007.06.010.
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Matingo, Thomas, Webster Gumindoga, and Hodson Makurira. "Evaluation of sub daily satellite rainfall estimates through flash flood modelling in the Lower Middle Zambezi Basin." Proceedings of the International Association of Hydrological Sciences 378 (May 29, 2018): 59–65. http://dx.doi.org/10.5194/piahs-378-59-2018.
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Abstract. Flash floods are experienced almost annually in the ungauged Mbire District of the Middle Zambezi Basin. Studies related to hydrological modelling (rainfall-runoff) and flood forecasting require major inputs such as precipitation which, due to shortage of observed data, are increasingly using indirect methods for estimating precipitation. This study therefore evaluated performance of CMORPH and TRMM satellite rainfall estimates (SREs) for 30 min, 1 h, 3 h and daily intensities through hydrologic and flash flood modelling in the Lower Middle Zambezi Basin for the period 2013–2016. On a daily timestep, uncorrected CMORPH and TRMM show Probability of Detection (POD) of 61 and 59 %, respectively, when compared to rain gauge observations. The best performance using Correlation Coefficient (CC) was 70 and 60 % on daily timesteps for CMORPH and TRMM, respectively. The best RMSE for CMORPH was 0.81 % for 30 min timestep and for TRMM was 2, 11 % on 3 h timestep. For the year 2014 to 2015, the HEC-HMS (Hydrological Engineering Centre-Hydrological Modelling System) daily model calibration Nash Sutcliffe efficiency (NSE) for Musengezi sub catchment was 59 % whilst for Angwa it was 55 %. Angwa sub-catchment daily NSE results for the period 2015–2016 was 61 %. HEC-RAS flash flood modeling at 100, 50 and 25 year return periods for Angwa sub catchment, inundated 811 and 867 ha for TRMM rainfall simulated discharge at 3 h and daily timesteps, respectively. For CMORPH generated rainfall, the inundation was 818, 876, 890 and 891 ha at daily, 3 h, 1 h and 30 min timesteps. The 30 min time step for CMORPH effectively captures flash floods with the measure of agreement between simulated flood extent and ground control points of 69 %. For TRMM, the 3 h timestep effectively captures flash floods with coefficient of 67 %. The study therefore concludes that satellite products are most effective in capturing localized hydrological processes such as flash floods for sub-daily rainfall, because of improved spatial and temporal resolution.
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Herman, Gregory R., and Russ S. Schumacher. "Flash Flood Verification: Pondering Precipitation Proxies." Journal of Hydrometeorology 19, no. 11 (November 1, 2018): 1753–76. http://dx.doi.org/10.1175/jhm-d-18-0092.1.
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Abstract Quantitative precipitation estimate (QPE) exceedances of numerous different heavy precipitation thresholds—including spatially varying average recurrence interval (ARI) and flash flood guidance (FFG) thresholds—are compared among each other and against reported and warned flash floods to quantify existing deficiencies with QPEs and to identify best practices for using QPE for flash flood forecasting and analysis. QPEs from three different sources—NCEP Stage IV Precipitation Analysis (ST4), Climatology Calibrated Precipitation Analysis (CCPA), and Multi-Radar Multi-Sensor (MRMS) QPE—are evaluated across the United States from January 2015 to June 2017. In addition to evaluating different QPE sources, threshold types, and magnitudes, QPE accumulation interval lengths from hourly to daily are considered. Systematic errors with QPE sources are identified, including a radar distance dependence on extreme rainfall frequency in MRMS, spurious occurrences of locally extreme precipitation in the complex terrain of the West in ST4, and insufficient QPEs for many legitimate heavy precipitation events in CCPA. Overall, flash flood warnings and reports corresponded to each other far more than any QPE exceedances. Correspondence between all sources was at a maximum in the East and worst in the West, with ST4, CCPA, and MRMS QPE exceedances locally yielding maximal correspondence in the East, Plains, and West, respectively. Surprisingly, using a fixed 2.5 in. (24 h)−1 proxy outperformed shorter accumulation exceedances and the use of ARIs and FFGs. On regional scales, different ARI exceedances achieved superior performance to the selection of any fixed threshold; FFG exceedances were consistently too rare to achieve optimal correspondence with observed flash flooding.
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Tuyen, Do Ngoc, Tran Manh Tuan, Le Hoang Son, Tran Thi Ngan, Nguyen Long Giang, Pham Huy Thong, Vu Van Hieu, Vassilis C. Gerogiannis, Dimitrios Tzimos, and Andreas Kanavos. "A Novel Approach Combining Particle Swarm Optimization and Deep Learning for Flash Flood Detection from Satellite Images." Mathematics 9, no. 22 (November 10, 2021): 2846. http://dx.doi.org/10.3390/math9222846.
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Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture; thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach.
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Borrell Estupina, V., F. Raynaud, N. Bourgeois, L. Kong-A-Siou, L. Collet, E. Haziza, and E. Servat. "Operational tools to help stakeholders to protect and alert municipalities facing uncertainties and changes in karst flash floods." Proceedings of the International Association of Hydrological Sciences 370 (June 11, 2015): 201–8. http://dx.doi.org/10.5194/piahs-370-201-2015.
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Abstract. Flash floods are often responsible for many deaths and involve many material damages. Regarding Mediterranean karst aquifers, the complexity of connections, between surface and groundwater, as well as weather non-stationarity patterns, increase difficulties in understanding the basins behaviour and thus warning and protecting people. Furthermore, given the recent changes in land use and extreme rainfall events, knowledge of the past floods is no longer sufficient to manage flood risks. Therefore the worst realistic flood that could occur should be considered. Physical and processes-based hydrological models are considered among the best ways to forecast floods under diverse conditions. However, they rarely match with the stakeholders' needs. In fact, the forecasting services, the municipalities, and the civil security have difficulties in running and interpreting data-consuming models in real-time, above all if data are uncertain or non-existent. To face these social and technical difficulties and help stakeholders, this study develops two operational tools derived from these models. These tools aim at planning real-time decisions given little, changing, and uncertain information available, which are: (i) a hydrological graphical tool (abacus) to estimate flood peak discharge from the karst past state and the forecasted but uncertain intense rainfall; (ii) a GIS-based method (MARE) to estimate the potential flooded pathways and areas, accounting for runoff and karst contributions and considering land use changes. Then, outputs of these tools are confronted to past and recent floods and municipalities observations, and the impacts of uncertainties and changes on planning decisions are discussed. The use of these tools on the recent 2014 events demonstrated their reliability and interest for stakeholders. This study was realized on French Mediterranean basins, in close collaboration with the Flood Forecasting Services (SPC Med-Ouest, SCHAPI, municipalities).