Littérature scientifique sur le sujet « Flood nowcasting »
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Articles de revues sur le sujet "Flood nowcasting"
Vivoni, Enrique R., Dara Entekhabi, Rafael L. Bras, Valeriy Y. Ivanov, Matthew P. Van Horne, Christopher Grassotti et Ross N. Hoffman. « Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting ». Journal of Hydrometeorology 7, no 4 (1 août 2006) : 660–77. http://dx.doi.org/10.1175/jhm514.1.
Texte intégralSilvestro, F., et N. Rebora. « Operational verification of a framework for the probabilistic nowcasting of river discharge in small and medium size basins ». Natural Hazards and Earth System Sciences 12, no 3 (23 mars 2012) : 763–76. http://dx.doi.org/10.5194/nhess-12-763-2012.
Texte intégralVivoni, Enrique R., Dara Entekhabi et Ross N. Hoffman. « Error Propagation of Radar Rainfall Nowcasting Fields through a Fully Distributed Flood Forecasting Model ». Journal of Applied Meteorology and Climatology 46, no 6 (1 juin 2007) : 932–40. http://dx.doi.org/10.1175/jam2506.1.
Texte intégralMoreno, Hernan A., Enrique R. Vivoni et David J. Gochis. « Limits to Flood Forecasting in the Colorado Front Range for Two Summer Convection Periods Using Radar Nowcasting and a Distributed Hydrologic Model ». Journal of Hydrometeorology 14, no 4 (1 août 2013) : 1075–97. http://dx.doi.org/10.1175/jhm-d-12-0129.1.
Texte intégralSharif, Hatim O., David Yates, Rita Roberts et Cynthia Mueller. « The Use of an Automated Nowcasting System to Forecast Flash Floods in an Urban Watershed ». Journal of Hydrometeorology 7, no 1 (1 février 2006) : 190–202. http://dx.doi.org/10.1175/jhm482.1.
Texte intégralPoméon, Thomas, Niklas Wagner, Carina Furusho, Stefan Kollet et Ricardo Reinoso-Rondinel. « Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments ». Water 12, no 8 (30 juillet 2020) : 2157. http://dx.doi.org/10.3390/w12082157.
Texte intégralSpyrou, Christos, George Varlas, Aikaterini Pappa, Angeliki Mentzafou, Petros Katsafados, Anastasios Papadopoulos, Marios N. Anagnostou et John Kalogiros. « Implementation of a Nowcasting Hydrometeorological System for Studying Flash Flood Events : The Case of Mandra, Greece ». Remote Sensing 12, no 17 (27 août 2020) : 2784. http://dx.doi.org/10.3390/rs12172784.
Texte intégralYoon, Seong-Sim. « Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting ». Remote Sensing 11, no 6 (16 mars 2019) : 642. http://dx.doi.org/10.3390/rs11060642.
Texte intégralLovat, Alexane, Béatrice Vincendon et Véronique Ducrocq. « Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations ». Hydrology and Earth System Sciences 26, no 10 (23 mai 2022) : 2697–714. http://dx.doi.org/10.5194/hess-26-2697-2022.
Texte intégralHe, Ting, Chao Zhang et Yi Zhang. « Radar based rainfall nowcasting and its characteristic prediction based on spatially correlated random field, normalized duration line and Kalman filter algorithm ». MATEC Web of Conferences 246 (2018) : 01028. http://dx.doi.org/10.1051/matecconf/201824601028.
Texte intégralThèses sur le sujet "Flood nowcasting"
NISHSHANKA, Prasanna Premachandra. « NON-PARAMETRIC STATISTICAL APPROACH TO CORRECT SATELLITE RAINFALL DATA IN NEAR-REAL-TIME FOR RAIN BASED FLOOD NOWCASTING ». Doctoral thesis, Politecnico di Torino, 2010. http://hdl.handle.net/11583/2496971.
Texte intégralNardo, Caseri Angélica. « Apport de la simulation conditionnelle géostatistique pour la prévision immédiate d'ensemble de pluies et l’alerte aux crues rapides ». Electronic Thesis or Diss., Paris, AgroParisTech, 2017. http://www.theses.fr/2017AGPT0002.
Texte intégralEach year, flash floods, generated by small fast-responding catchments hit by intense rainfall, are responsible for huge human and economic losses. To mitigate these impacts, it is necessary to use forecasting systems combining meteorological and hydrological forecasts at small temporal and spatial scales. Because of the underlying difficulties, these systems have to be able to communicate the uncertainties of their forecasts. Uncertainties associated to observed or future rainfall are often seeing as those having the most important impact, in particularly in the case of flash floods localised on small areas.The main aim of this thesis is to study the potential of a geostatistical conditional simulation method to generate an ensemble of rainfall scenarios that can be used by a flash flood warning system. We seek to generate a reliable ensemble of rain fields by making the best use of the strengths of the measurements often available for nowcasting: the spatial and temporal properties of rainfall fields provided by the radar data and the rainfall intensities measured by rain gauges. In order to achieve our objectives, we use radar and rainfall data from 17 intense rainfall events observed in the Var region (south-east France) between 2009 and 2013.The first part of this thesis was devoted to taking into account the uncertainties on the observations of rainfall. For this purpose, the SAMPO-TBM generator developed at Irstea-Lyon is adapted to provide simulations of alternative rain fields to the observed radar rain field, while respecting the rainfall values observed by the rain gauges through a conditioned simulation. The evaluation of the generated fields shows that the method implemented is able to generate a reliable ensemble of rain fields and thus to propose a quantification of the uncertainties on the observed rain fields.In the second part of this thesis, the capacity of our method to be used for the nowcasting of rainfall is evaluated. Several methods are tested for the parameterization of the rainfall generator and for the adjustment of the outputs. These methods are evaluated by considering the main attributes of forecast quality, such as accuracy, reliability, precision, discrimination and overall forecast performance. The best method is the one estimating generator parameters over the last four hours, but also using only the last hour for the parameter related to the mean of the non-zero rainfall distribution, combined by the adjustment of the outputs based on the last forecast error.Finally, in the final part of this thesis, ensemble rainfall forecasts are used as inputs of the flash flood forecasting method AIGA developed at Irstea Aix-en-Provence. The AIGA method enables return period of the ongoing event to estimate at ungauged catchments. The 3th-7th November 2011 event in the Var region is used to illustrate the potential of our method. Nowcasting maps indicating, for different lead times and for the whole hydrological network of the region, the probability to exceed a given return period are produced. They are compared to the localization of observed damages collected from field surveys, illustrating a real interest for the real time crisis management
Lovat, Alexane. « Prévision à très courte échéance des crues rapides méditerranéennes ». Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0105.
Texte intégralThe Mediterranean regions are regularly exposed to heavy precipitating events and flash floods. Hydrometeorological forecasts up to a few hours are crucial for planning the intervention of emergency services in these situations. The prediction of the hydrological consequences of Mediterranean events of intense rainfall at the nowcasting ranges (few minutes to 6h) is the topic of this Ph. D. thesis. Two areas were studied: modelling of river flows and runoff at a fine scale, and the use of rainfall nowcasting, and particularly those from Météo-France new forecasting systems, to anticipate floods. The sensitivity to a more detailed representation of land use and texture in ISBA-TOP for simulating river flows and runoff over urban and peri-urban areas was first studied. The influence of terrain descriptors and spatial resolution (1km and 300m) has been analyzed for 12 flood events, including the major flood event in 2015 over the Cannes region. A more detailed analysis of this case was conducted using streamflow estimates at fine scale obtained from the HyMeX post-event survey and exploring the potential of impact data to evaluate simulated runoff. The results reveal that the spatial resolution has the largest impact on the hydrological simulations, larger than soil texture and land cover. Then, the potential of rainfall nowcasting for forecasting Mediterranean flash floods up to 6h was studied. The rainfall forecasts from the nowcasting suite based on the numerical weather prediction system AROME (AROME–PI), and from the nowcasting system blending numerical weather prediction and extrapolation of radar estimation (PIAF) were examined. The availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. The evaluation of rainfall has adopted a hydrological point of view, by comparing observed and forecast rainfall over watersheds affected by past floods. A more classical evaluation comparing rainfall observation and forecast at the same location over Southeastern France has been also carried out. The results generally led to the same conclusions for both evaluations. The performance of PIAF is very good over the first hour of forecasting, but it deteriorates very quickly, to reach about the same or even a lower skill than AROME-PI beyond about 1h15/1h30 of forecasting. Between 2 and 3 hours of forecasting, AROME-PI performs better or at the same level as PIAF. Time-lagged ensembles based on AROME-PI and on PIAF forecasts respectively, were also studied. The sensitivity of the ensembles to their size and to the addition of a time tolerance on the forecast for each member was examined. The results indicate that the more members an ensemble has, the better it performs. The same applies to the ensembles with a time tolerance of 15 or 30 minutes. An assessment of river discharges simulated with ISBA-TOP and MARINE forced by AROME-PI and PIAF rainfall forecasting, used alone or as an ensemble, was also confducted on two exceptional past flash flood events (Aude in 2018 and Cannes in 2015). For the best scenarios based on AROME-PI, the anticipation of the flood peak intensity and of the instant of recession can reach up to 5h, and a little more for the first increase of flow. For those based on PIAF, the anticipation varies between 20 minutes and 4h, depending on the phenomenon, the watershed and the hydrological model studied
Sinclair, Scott. « Spatio-temporal rainfall estimation and nowcasting for flash flood forecasting ». Thesis, 2007. http://hdl.handle.net/10413/2247.
Texte intégralThesis (Ph.D.Eng.)-University of KwaZulu-Natal, Durban, 2007.
Scotti, Vincenzo. « Development and validation of alluvial risk identification methodologies through the integrated use of remote sensing from satellite and hydraulic modeling with particular reference to post-event analysis and nowcasting phases ». Doctoral thesis, 2020. http://hdl.handle.net/11573/1365460.
Texte intégralLivres sur le sujet "Flood nowcasting"
Xuan, Yunqing. Flood Nowcasting : From Data to Decisions. Elsevier, 2024.
Trouver le texte intégralChapitres de livres sur le sujet "Flood nowcasting"
Molina, Luz E. Torres. « Flood Alert System Using High-Resolution Radar Rainfall Data : Nowcasting Model Validation 1 , 2 ». Dans Flood Assessment, 283–306. Toronto ; New Jersey : Apple Academic Press, 2017. | Series : Innovations in agricultural & biological engineering : Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315365923-17.
Texte intégralMolina, Luz E. Torres. « Flood Alert System Using High-Resolution Radar Rainfall Data : Nowcasting Model Movement and Reflectivity Analysis 1 , 2 ». Dans Flood Assessment, 267–75. Toronto ; New Jersey : Apple Academic Press, 2017. | Series : Innovations in agricultural & biological engineering : Apple Academic Press, 2017. http://dx.doi.org/10.1201/9781315365923-15.
Texte intégralDouinot, Audrey, Alessandro Dalla Torre, Jérôme Martin, Jean-François Iffly, Laurent Rapin, Claude Meisch, Christine Bastian et Laurent Pfister. « Prototype of a LPWA Network for Real-Time Hydro-Meteorological Monitoring and Flood Nowcasting ». Dans Ad-Hoc, Mobile, and Wireless Networks, 566–74. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31831-4_40.
Texte intégralTermite, Loris Francesco, Emanuele Bonamente, Alberto Garinei, Daniele Bolpagni, Lorenzo Menculini, Marcello Marconi, Lorenzo Biondi, Andrea Chini et Massimo Crespi. « A Decision Support System Based on Rainfall Nowcasting and Artificial Neural Networks to Mitigate Wastewater Treatment Plant Downstream Floods ». Dans Communications in Computer and Information Science, 125–50. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17098-0_7.
Texte intégralVerworn, H., et S. Krämer. « Radar based nowcasting of rainfall events—analysis and assessment of a one-year continuum ». Dans Flood Risk Management : Research and Practice, 397–402. CRC Press, 2008. http://dx.doi.org/10.1201/9780203883020.ch46.
Texte intégralActes de conférences sur le sujet "Flood nowcasting"
Janál, Petr, et Tomáš Kozel. « FUZZY LOGIC BASED FLASH FLOOD FORECAST ». Dans XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.10.
Texte intégralLepoittevin, Yann, et Isabelle Herlin. « Modeling high rainfall regions for flash flood nowcasting ». Dans 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp). IEEE, 2015. http://dx.doi.org/10.1109/multi-temp.2015.7245749.
Texte intégralKONDO, Ryoma, Yoshiaki NARUSUE et Hiroyuki MORIKAWA. « A Pluvial Flood Nowcasting Approach using Knowledge Distillation ». Dans 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). IEEE, 2020. http://dx.doi.org/10.1109/gcce50665.2020.9291972.
Texte intégralKondo, Ryoma, Bojian Du, Yoshiaki Narusue et Hiroyuki Morikawa. « Prioritized Sampling on Knowledge Distillation for Nowcasting Pluvial Flood Prediction ». Dans 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671897.
Texte intégralFurquim, Gustavo, Filipe Neto, Gustavo Pessin, Jo Ueyama, Joao P. de Albuquerque, Maria Clara, Eduardo M. Mendiondo et al. « Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting ». Dans 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2014. http://dx.doi.org/10.1109/waina.2014.21.
Texte intégralJavelle, Pierre, Olivier Payrastre, Brice Boudevillain, François Bourgin, François Bouttier, Olivier Caumont, Maryse Charpentier-Noyer et al. « Flash flood impacts nowcasting within the PICS project (2018-2022) : End-users involvement and first results ». Dans FLOODrisk 2020 - 4th European Conference on Flood Risk Management. Online : Budapest University of Technology and Economics, 2021. http://dx.doi.org/10.3311/floodrisk2020.17.3.
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