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Статті в журналах з теми "Flood nowcasting"
Vivoni, Enrique R., Dara Entekhabi, Rafael L. Bras, Valeriy Y. Ivanov, Matthew P. Van Horne, Christopher Grassotti, and Ross N. Hoffman. "Extending the Predictability of Hydrometeorological Flood Events Using Radar Rainfall Nowcasting." Journal of Hydrometeorology 7, no. 4 (August 1, 2006): 660–77. http://dx.doi.org/10.1175/jhm514.1.
Повний текст джерелаSilvestro, F., and 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 (March 23, 2012): 763–76. http://dx.doi.org/10.5194/nhess-12-763-2012.
Повний текст джерелаVivoni, Enrique R., Dara Entekhabi, and 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 (June 1, 2007): 932–40. http://dx.doi.org/10.1175/jam2506.1.
Повний текст джерелаMoreno, Hernan A., Enrique R. Vivoni, and 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 (August 1, 2013): 1075–97. http://dx.doi.org/10.1175/jhm-d-12-0129.1.
Повний текст джерелаSharif, 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.
Повний текст джерелаPoméon, Thomas, Niklas Wagner, Carina Furusho, Stefan Kollet, and Ricardo Reinoso-Rondinel. "Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments." Water 12, no. 8 (July 30, 2020): 2157. http://dx.doi.org/10.3390/w12082157.
Повний текст джерелаSpyrou, Christos, George Varlas, Aikaterini Pappa, Angeliki Mentzafou, Petros Katsafados, Anastasios Papadopoulos, Marios N. Anagnostou, and John Kalogiros. "Implementation of a Nowcasting Hydrometeorological System for Studying Flash Flood Events: The Case of Mandra, Greece." Remote Sensing 12, no. 17 (August 27, 2020): 2784. http://dx.doi.org/10.3390/rs12172784.
Повний текст джерелаYoon, Seong-Sim. "Adaptive Blending Method of Radar-Based and Numerical Weather Prediction QPFs for Urban Flood Forecasting." Remote Sensing 11, no. 6 (March 16, 2019): 642. http://dx.doi.org/10.3390/rs11060642.
Повний текст джерелаLovat, Alexane, Béatrice Vincendon, and Véronique Ducrocq. "Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations." Hydrology and Earth System Sciences 26, no. 10 (May 23, 2022): 2697–714. http://dx.doi.org/10.5194/hess-26-2697-2022.
Повний текст джерелаHe, Ting, Chao Zhang, and 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.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаNardo, 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.
Повний текст джерелаEach 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.
Повний текст джерелаThe 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.
Повний текст джерелаThesis (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.
Повний текст джерелаКниги з теми "Flood nowcasting"
Xuan, Yunqing. Flood Nowcasting: From Data to Decisions. Elsevier, 2024.
Знайти повний текст джерелаЧастини книг з теми "Flood nowcasting"
Molina, Luz E. Torres. "Flood Alert System Using High-Resolution Radar Rainfall Data: Nowcasting Model Validation 1 , 2." In 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.
Повний текст джерелаMolina, Luz E. Torres. "Flood Alert System Using High-Resolution Radar Rainfall Data: Nowcasting Model Movement and Reflectivity Analysis 1 , 2." In 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.
Повний текст джерелаDouinot, Audrey, Alessandro Dalla Torre, Jérôme Martin, Jean-François Iffly, Laurent Rapin, Claude Meisch, Christine Bastian, and Laurent Pfister. "Prototype of a LPWA Network for Real-Time Hydro-Meteorological Monitoring and Flood Nowcasting." In 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.
Повний текст джерелаTermite, Loris Francesco, Emanuele Bonamente, Alberto Garinei, Daniele Bolpagni, Lorenzo Menculini, Marcello Marconi, Lorenzo Biondi, Andrea Chini, and Massimo Crespi. "A Decision Support System Based on Rainfall Nowcasting and Artificial Neural Networks to Mitigate Wastewater Treatment Plant Downstream Floods." In 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.
Повний текст джерелаVerworn, H., and S. Krämer. "Radar based nowcasting of rainfall events—analysis and assessment of a one-year continuum." In Flood Risk Management: Research and Practice, 397–402. CRC Press, 2008. http://dx.doi.org/10.1201/9780203883020.ch46.
Повний текст джерелаТези доповідей конференцій з теми "Flood nowcasting"
Janál, Petr, and Tomáš Kozel. "FUZZY LOGIC BASED FLASH FLOOD FORECAST." In 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.
Повний текст джерелаLepoittevin, Yann, and Isabelle Herlin. "Modeling high rainfall regions for flash flood nowcasting." In 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.
Повний текст джерелаKONDO, Ryoma, Yoshiaki NARUSUE, and Hiroyuki MORIKAWA. "A Pluvial Flood Nowcasting Approach using Knowledge Distillation." In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). IEEE, 2020. http://dx.doi.org/10.1109/gcce50665.2020.9291972.
Повний текст джерелаKondo, Ryoma, Bojian Du, Yoshiaki Narusue, and Hiroyuki Morikawa. "Prioritized Sampling on Knowledge Distillation for Nowcasting Pluvial Flood Prediction." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671897.
Повний текст джерелаFurquim, 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." In 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA). IEEE, 2014. http://dx.doi.org/10.1109/waina.2014.21.
Повний текст джерелаJavelle, 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." In 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.
Повний текст джерела