9 research outputs found
Prévisions des crues en temps réel sur le bassin de la Marne : assimilation in situ pour la correction du modÚle hydraulique mono-dimensionnel Mascaret
La prĂ©vision des crues et des inondations reste aujourdâhui un dĂ©fi pour anticiper et assurer la sĂ©curitĂ© des biens et des personnes. En France, le SCHAPI, qui dĂ©pend du MEDDE, assure ce rĂŽle. Les niveaux et les dĂ©bits dâun cours dâeau dĂ©pendent Ă©troitement des interactions Ă diffĂ©rentes Ă©chelles entre les prĂ©cipitations, les caractĂ©ristiques gĂ©omĂ©triques du cours dâeau et les propriĂ©tĂ©s topographiques, gĂ©ologiques et pĂ©dologiques du bassin versant. Les modĂšles hydrauliques, utilisĂ©s dans le cadre de la prĂ©vision des crues, sont entachĂ©s dâincertitudes quâil est nĂ©cessaire de quantifier et de corriger afin de mieux anticiper lâĂ©volution hydrodynamique du cours dâeau en temps rĂ©el. Lâobjectif de ces travaux de thĂšse est dâamĂ©liorer les prĂ©visions de hauteurs dâeau et de dĂ©bits, sur le bassin de la Marne, issues des modĂšles hydrauliques utilisĂ©s dans le cadre opĂ©rationnel de la prĂ©vision des crues Ă partir de mĂ©thodes dâassimilation de donnĂ©es. Ces prĂ©visions reposent sur une modĂ©lisation mono-dimensionnelle (1D) de lâhydrodynamique du cours dâeau Ă partir du code hydraulique 1D Mascaret basĂ© sur la rĂ©solution des Ă©quations de Saint-Venant, enrichie par une mĂ©thode dâassimilation de donnĂ©es in situ utilisant un Filtre de Kalman Ătendu (EKF). Ce mĂ©moire de thĂšse sâarticule en cinq chapitres, trois dĂ©diĂ©s Ă la recherche et les deux derniers Ă lâapplication opĂ©rationnelle. Le chapitre 1 prĂ©sente les donnĂ©es et les outils utilisĂ©s pour caractĂ©riser le risque inondation dans le cadre de la prĂ©vision des crues, ainsi que les modĂšles hydrauliques Marne Amont Global (MAG) et Marne Moyenne (MM), sujets dâapplication des mĂ©thodes dâassimilation de donnĂ©es dĂ©veloppĂ©es dans cette Ă©tude. Le chapitre 2 est dĂ©diĂ© Ă la mĂ©thodologie : il traite des diffĂ©rentes sources dâincertitudes liĂ©es Ă la modĂ©lisation hydraulique et prĂ©sente les approches dâassimilation de donnĂ©es de type EKF appliquĂ©es dans cette Ă©tude Ă travers la maquette DAMP pour les rĂ©duire. Dans le chapitre 3, cette approche est appliquĂ©e aux modĂšles MAG et MM en mode rĂ©analyse pour un ensemble de crues ayant touchĂ© le bassin de la Marne par le passĂ©. Deux publications ont Ă©tĂ© insĂ©rĂ©es dans ce chapitre "Ă©tude". Dans le chapitre 4, les corrections appliquĂ©es dans le chapitre 3, sont validĂ©es Ă partir du rejeu de la crue de 1983 en condition opĂ©rationnelle avec le modĂšle MM. La quantification des incertitudes de prĂ©vision et la rĂ©alisation de cartes de zones inondĂ©es potentielles y sont aussi abordĂ©es. Lâapplication de ces mĂ©thodes dâassimilation de donnĂ©es pour les modĂšles MAG et MM en opĂ©rationnel au SCHAPI au niveau national et au SPC SAMA au niveau local est prĂ©sentĂ©e dans le chapitre 5. Cette thĂšse sâinscrit dans un contexte collaboratif oĂč chacun apporte son expertise : la modĂ©lisation hydraulique pour le LNHE, les mĂ©thodes numĂ©riques pour le CERFACS et la prĂ©vision opĂ©rationnelle pour le SCHAPI. Lâensemble de ces travaux de thĂšse a permis de dĂ©montrer les bĂ©nĂ©fices et la complĂ©mentaritĂ© de lâestimation des paramĂštres et de lâĂ©tat hydraulique par assimilation de donnĂ©es sur les hauteurs dâeau et les dĂ©bits prĂ©vus par un modĂšle hydraulique 1D, ce qui constitue un enjeu dâimportance pour lâanticipation du risque hydrologique. Ces mĂ©thodes ont Ă©tĂ© intĂ©grĂ©es dans la chaĂźne opĂ©rationnelle de prĂ©vision du SCHAPI et du SPC SAMA
Estimation of lateral inflows using data assimilation in the context of real-time flood forecasting for the Marne catchment in France
Water Qualit
Operational flood forecasting on the Marne catchment : data assimilation for hydraulic model Mascaret correction
La prĂ©vision des crues et des inondations reste aujourdâhui un dĂ©fi pour anticiper et assurer la sĂ©curitĂ© des biens et des personnes. En France, le SCHAPI, qui dĂ©pend du MEDDE, assure ce rĂŽle. Les niveaux et les dĂ©bits dâun cours dâeau dĂ©pendent Ă©troitement des interactions Ă diffĂ©rentes Ă©chelles entre les prĂ©cipitations, les caractĂ©ristiques gĂ©omĂ©triques du cours dâeau et les propriĂ©tĂ©s topographiques, gĂ©ologiques et pĂ©dologiques du bassin versant. Les modĂšles hydrauliques, utilisĂ©s dans le cadre de la prĂ©vision des crues, sont entachĂ©s dâincertitudes quâil est nĂ©cessaire de quantifier et de corriger afin de mieux anticiper lâĂ©volution hydrodynamique du cours dâeau en temps rĂ©el. Lâobjectif de ces travaux de thĂšse est dâamĂ©liorer les prĂ©visions de hauteurs dâeau et de dĂ©bits, sur le bassin de la Marne, issues des modĂšles hydrauliques utilisĂ©s dans le cadre opĂ©rationnel de la prĂ©vision des crues Ă partir de mĂ©thodes dâassimilation de donnĂ©es. Ces prĂ©visions reposent sur une modĂ©lisation mono-dimensionnelle (1D) de lâhydrodynamique du cours dâeau Ă partir du code hydraulique 1D Mascaret basĂ© sur la rĂ©solution des Ă©quations de Saint-Venant, enrichie par une mĂ©thode dâassimilation de donnĂ©es in situ utilisant un Filtre de Kalman Ătendu (EKF). Ce mĂ©moire de thĂšse sâarticule en cinq chapitres, trois dĂ©diĂ©s Ă la recherche et les deux derniers Ă lâapplication opĂ©rationnelle. Le chapitre 1 prĂ©sente les donnĂ©es et les outils utilisĂ©s pour caractĂ©riser le risque inondation dans le cadre de la prĂ©vision des crues, ainsi que les modĂšles hydrauliques Marne Amont Global (MAG) et Marne Moyenne (MM), sujets dâapplication des mĂ©thodes dâassimilation de donnĂ©es dĂ©veloppĂ©es dans cette Ă©tude. Le chapitre 2 est dĂ©diĂ© Ă la mĂ©thodologie : il traite des diffĂ©rentes sources dâincertitudes liĂ©es Ă la modĂ©lisation hydraulique et prĂ©sente les approches dâassimilation de donnĂ©es de type EKF appliquĂ©es dans cette Ă©tude Ă travers la maquette DAMP pour les rĂ©duire. Dans le chapitre 3, cette approche est appliquĂ©e aux modĂšles MAG et MM en mode rĂ©analyse pour un ensemble de crues ayant touchĂ© le bassin de la Marne par le passĂ©. Deux publications ont Ă©tĂ© insĂ©rĂ©es dans ce chapitre "Ă©tude". Dans le chapitre 4, les corrections appliquĂ©es dans le chapitre 3, sont validĂ©es Ă partir du rejeu de la crue de 1983 en condition opĂ©rationnelle avec le modĂšle MM. La quantification des incertitudes de prĂ©vision et la rĂ©alisation de cartes de zones inondĂ©es potentielles y sont aussi abordĂ©es. Lâapplication de ces mĂ©thodes dâassimilation de donnĂ©es pour les modĂšles MAG et MM en opĂ©rationnel au SCHAPI au niveau national et au SPC SAMA au niveau local est prĂ©sentĂ©e dans le chapitre 5. Cette thĂšse sâinscrit dans un contexte collaboratif oĂč chacun apporte son expertise : la modĂ©lisation hydraulique pour le LNHE, les mĂ©thodes numĂ©riques pour le CERFACS et la prĂ©vision opĂ©rationnelle pour le SCHAPI. Lâensemble de ces travaux de thĂšse a permis de dĂ©montrer les bĂ©nĂ©fices et la complĂ©mentaritĂ© de lâestimation des paramĂštres et de lâĂ©tat hydraulique par assimilation de donnĂ©es sur les hauteurs dâeau et les dĂ©bits prĂ©vus par un modĂšle hydraulique 1D, ce qui constitue un enjeu dâimportance pour lâanticipation du risque hydrologique. Ces mĂ©thodes ont Ă©tĂ© intĂ©grĂ©es dans la chaĂźne opĂ©rationnelle de prĂ©vision du SCHAPI et du SPC SAMA.Flood forecasting remains a challenge to anticipate and insure security of people. In France, the SCHAPI, wich depends on the MEDDE, takes this function. Water levels and discharges are highly dependent on interactions at different scales between rainfall, geometric characteristics of rivers and topographic, geological and soil properties of the watershed. Hydraulic models, used in the context of flood forecasting, are tainted by uncertainties which necessist to be quantified and corrected in order to better anticipate flow evolution in real time. The work carried out for this PhD thesis aims to improve water level and discharge forecasts on the Marne watershed, from hydraulic models used in the operational framework of flood forecasting using data assimilation methods. These forecasts come from a mono-dimensional (1D) hydraulic model Mascaret based on the resolution of Saint-Venant equations, improved by data assimilation methods using an Extended Kalman Filter (EKF). This thesis consists of five chapters, three dedicated to research and the two last to the operational application. The first presents data, tools and methods used to characterize the flood risk in the context of flood forecasting, as well as the Marne Amont Global (MAG) and Marne Moyenne (MM) models, subjects of application of data assimilation methods developed in this study. The second chapter covers hydraulic model uncertainties and data assimilation methodology (Kalman filter) applied in this thesis through DAMP in order to reduce them. In the third chapter, this approach is applied to the MAG and MM models for different flood events. In the fourth chapter, the April 1983 flood event allows to validate the corrections applied in the previous chapter for the MM model in an operational context. The uncertainties evaluations and the mapping of potential flooded zones are also reported. The real-time application of these data assimilation methods for MAG and MM models by SCHAPI and SPC SAMA is presented in the fifth chapter. This thesis takes place in a collaborative work where each member brings his own expertise : the hydraulic modeling for LNHE, the numeric methods for the CERFACS and operational forecasting for the SCHAPI. This thesis shows the benefits and complementarity of the evaluation of parameters and hydraulic state using data assimilation on water levels and discharges forcasted by a 1D hydraulic model, which is an important issue for the anticipation of hydrologic risk. These methods have already been integrated to the operational chain of flood forecasting of the SCHAPI and the SPC SAMA
Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting
Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D), SCHAPI and CE-REMA resulted in the implementation of a Data Assimilation (DA) method on top of MASCARET in the framework of real-time forecasting. This prototype was based on a simplified Kalman filter where the description of the background error covariances is prescribed based on off-line climatology constant over time. This approach showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations. An ensemble-based DA algorithm has recently been implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. It was demonstrated that the flow dependent description of the background error covariances with the EnKF algorithm leads to a more realistic correction of the hydraulic state with significant impact of the hydraulic network characteristic
Multimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness
International audienceIntroduction: Functional brain-imaging techniques have revealed that clinical examination of disorders of consciousness (DoC) can underestimate the conscious level of patients. FDG-PET metabolic index of the best preserved hemisphere (MIBH) has been reported as a promising measure of consciousness but has never been externally validated and compared with other brain-imaging diagnostic procedures such as quantitative EEG.Methods: FDG-PET, quantitative EEG and cognitive evoked potential using an auditory oddball paradigm were performed in minimally conscious state (MCS) and vegetative state (VS) patient. We compared out-sample diagnostic and prognostic performances of PET-MIBH and EEG-based classification of conscious state to the current behavioral gold-standard, the Coma Recovery Scale - revised (CRS-R).Results: Between January 2016 and October 2019, 52 patients were included: 21 VS and 31 MCS. PET-MIBH had an AUC of 0.821 [0.694-0.930], sensitivity of 79% [62-91] and specificity of 78% [56-93], not significantly different from EEG (p = 0.628). Their combination accurately identified almost all MCS patients with a sensitivity of 94% [79-99%] and specificity of 67% [43-85]. Multimodal assessment also identified VS patients with neural correlate of consciousness (4/7 (57%) vs. 1/14 (7%), p = 0.025) and patients with 6-month recovery of command-following (9/24 (38%) vs. 0/16 (0%), p = 0.006), outperforming each technique taken in isolation.Conclusion: FDG-PET MIBH is an accurate and robust procedure across sites to diagnose MCS. Its combination with EEG-based classification of conscious state not only optimizes diagnostic performances but also allows to detect covert cognition and to predict 6-month command-following recovery demonstrating the added value of multimodal assessment of DoC
Ensemble-based algorithm for error reduction in hydraulics in the context of flood forecasting
Over the last few years, a collaborative work between CERFACS, LNHE (EDF R&D), SCHAPI and CE-REMA resulted in the implementation of a Data Assimilation (DA) method on top of MASCARET in the framework of real-time forecasting. This prototype was based on a simplified Kalman filter where the description of the background error covariances is prescribed based on off-line climatology constant over time. This approach showed promising results on the Adour and Marne catchments as it improves the forecast skills of the hydraulic model using water level and discharge in-situ observations. An ensemble-based DA algorithm has recently been implemented to improve the modelling of the background error covariance matrix used to distribute the correction to the water level and discharge states when observations are assimilated from observation points to the entire state. It was demonstrated that the flow dependent description of the background error covariances with the EnKF algorithm leads to a more realistic correction of the hydraulic state with significant impact of the hydraulic network characteristic
Multimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness.
peer reviewedINTRODUCTION: Functional brain-imaging techniques have revealed that clinical examination of disorders of consciousness (DoC) can underestimate the conscious level of patients. FDG-PET metabolic index of the best preserved hemisphere (MIBH) has been reported as a promising measure of consciousness but has never been externally validated and compared with other brain-imaging diagnostic procedures such as quantitative EEG.
METHODS: FDG-PET, quantitative EEG and cognitive evoked potential using an auditory oddball paradigm were performed in minimally conscious state (MCS) and vegetative state (VS) patient. We compared out-sample diagnostic and prognostic performances of PET-MIBH and EEG-based classification of conscious state to the current behavioral gold-standard, the Coma Recovery Scale - revised (CRS-R).
RESULTS: Between January 2016 and October 2019, 52 patients were included: 21 VS and 31 MCS. PET-MIBH had an AUC of 0.821 [0.694-0.930], sensitivity of 79% [62-91] and specificity of 78% [56-93], not significantly different from EEG (p = 0.628). Their combination accurately identified almost all MCS patients with a sensitivity of 94% [79-99%] and specificity of 67% [43-85]. Multimodal assessment also identified VS patients with neural correlate of consciousness (4/7 (57%) vs. 1/14 (7%), p = 0.025) and patients with 6-month recovery of command-following (9/24 (38%) vs. 0/16 (0%), p = 0.006), outperforming each technique taken in isolation.
CONCLUSION: FDG-PET MIBH is an accurate and robust procedure across sites to diagnose MCS. Its combination with EEG-based classification of conscious state not only optimizes diagnostic performances but also allows to detect covert cognition and to predict 6-month command-following recovery demonstrating the added value of multimodal assessment of DoC