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TELEMAC is a component of the open-source integrated suite of solvers TELEMAC-MASCARET for use in the field of free-surface flow that solves the Reynolds Averaged Navier-Stokes equations. Generally speaking, uncertainties in the model formulation itself due to simplified physics and also in the input fields to the model such as the boundary conditions, initial conditions and hydraulic parameters translate into errors in the simulated hydraulic variables. In spite of significant advances in numerical schemes, description of geographical data (topography, bathymetry) and environmental conditions (hydrologycal and meteorological fields), the representation of the true state of a system as well as its forecasted state remains imperfect and some of these limits can be overcome combining observations with simulation via data assimilation techniques. This paper presents the implementation of a 3D-Var FGAT variational data assimilation algorithm as a proof of concept for improving TELEMAC simulations and forecast. The demonstration is made on the Berre lagoon application with TELEMAC-3D: the salinity state is sequentially corrected assimilating in situ salinity measurements.

This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-based fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

The present study describes the assimilation of discharge in situ data for operational flood forecasting. The study was carried out on the Marne River (France) catchment where lateral inflows’ uncertainty is important due to karstic areas. This source of error was partly accounted for using an Extended Kalman Filter (EKF) algorithm built on the top of a mono-dimensional hydraulic model. The lateral inflows were sequentially adjusted over a sliding 48 h time window. The correction leads to a significant improvement in the simulated water level and discharge in re-analysis and forecast modes. These results pave the way for the operational use of the data assimilation (DA) procedure for real-time forecasting at the French flood forecasting service.

This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this parametrized variance and correlation length scale with a diffusion operator makes it possible to model the converged background error covariance matrix from the EnKF without actually integrating the EnKF algorithm. This method was finally applied to a case with two different observation point with different error statistics. It was shown that the results of this emulated EnKF (EEnKF) in terms of background error variance, correlation length scale and analyzed water level is close to those of the EnKF but with a significantly reduced computational cost.

International audience

The global neutronic activity fields of a nuclear core can be reconstructed using data assimilation. Indeed, data assimilation allows to combine both measurements from instruments and information from a model, to evaluate the best possible neutronic activity within the core. We present and apply a specific procedure which evaluates the influence of measures by adding or removing instruments in a given measurement network (possibly empty). The study of various network configurations for the instruments in the nuclear core establishes that the influence of the instruments depends both on the independent instrumentation location and on the chosen network.

The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a four-day sliding window over the flood event. The background error covariances for water level and discharge were represented with anisotropic correlation functions where the correlation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improvement in the water level and discharge in both analysis and forecast modes.

We apply a data assimilation techniques, inspired from meteorological applications, to perform an optimal reconstruction of the neutronic activity field in a nuclear core. Both measurements, and information coming from a numerical model, are used. We first study the robustness of the method when the amount of measured information decreases. We then study the influence of the nature of the instruments and their spatial repartition on the efficiency of the field reconstruction.

It is common in meteorological applications of variational assimilation to specify the error covariances of the model background state implicitly via a transformation from model space where variables are highly correlated to a control space where variables can be considered to be approximately uncorrelated. An important part of this transformation is a balance operator which effectively establishes the multivariate component of the error covariances. The use of this technique in ocean data assimilation is less common. This paper describes a balance operator that can be used in a variable transformation for oceanographic applications of three- and four-dimensional variational assimilation. The proposed balance operator has been implemented in an incremental variational data assimilation system for a global ocean general-circulation model. Evidence that the balance operator can explain a significant percentage of background-error variance is presented. The multivariate analysis structures implied by the balance operator are illustrated using single-observation experiments. Copyright © 2005 Royal Meteorological Society

Two sets of ensembles of ocean initial conditions for seasonal climate prediction have been constructed following a strategy that preserves the dynamical balance of the ocean. One set has been constructed by perturbing the wind stress and sea surface temperature forcing of an ocean model. The other set is constructed by assimilating perturbed in situ temperature data using a fully multivariate three-dimensional variational system, in addition to using the perturbed forcing. Ensemble statistics are computed for the period 1990–1999. The ensemble mean temperature has a significant subsurface cold bias when no data are assimilated, indicating that some part of the DEMETER hindcast systematic error may be explained by the way the ensembles are generated. Constraining the temperature through data assimilation contributes to a reduction in this subsurface bias. The dispersion of the initial conditions is supposed to sample the uncertainty associated with the ocean initial state estimate. Therefore, a smaller spread in the assimilation case than in the forced-only case is consistent with the idea that ocean uncertainty has been reduced by constraining the ocean state through data assimilation. However, comparing ensemble spread and innovation statistics suggests that ocean uncertainty may be underestimated by the ensemble method in the assimilated case. It is also shown that wind stress perturbations mainly control the spread amplitude in the non-assimilated case, but that perturbed subsurface temperature observations have a stronger relative impact in the assimilation case.

The proposed Surface Water and Ocean Topography (SWOT) satellite mission, which is under development at NASA, the Centre National d'Etudes Spatiales (CNES), and the Canadian Space Agency, is currently planned for launch in 2020. SWOT would provide maps of water surface elevation globally, observing rivers wider than about 50–100 meters and other water bodies with dimensions (widths and lengths) greater than 250 meters.

A Global Sensitivity Analysis (GSA) is carried out on the Mascaret model of the Odet river (France, Brittany) to identify and rank the major sources of uncertainty at observing stations on the network for the simulated water level, considering the upstream and downstream boundary conditions and the area distributed friction coefficients values. Upstream, ensemble hydrologic forcings are forecasted with the rainfall-runoff distributed model MORDOR-TS, using uncertain hydrologic model parameters drawn from uniform distributions. The downstream maritime boundary condition is perturbed taking into account the temporal correlation of the errors in storm surge. The Sobol’ indices are computed at Kervir, Moulin-Vert and Justice stations given hypothesis on the statistical distribution of the aleatory variables. The study focuses on the 23 to the 26 December 2013 event. GSA highlights that the simulated water level at the three stations is mainly controlled by the immediate downstream friction coefficient when the boundary conditions are not perturbed. The flood plain friction coefficients only become important around the peak of the event. However, when the boundary conditions are also taken into account, they become predominant for the simulated water level and the value of the friction coefficients has less influence.

The accuracy of predictions is a major stake of flood forecast. This thesis deals with the development of data assimilation techniques in bidimensional numerical codes based on shallow water equations and, more specifically, the assessment of possible improvements in high water forecasting in the Gironde estuary through the development and use of these methods. The main characteristics of this work concern the unsteadiness of hydrodynamics, linked to both tidal propagation and the hydrometeorological event, and the correlation between the observations of the different available observation stations.The work carried out during the first part of the thesis focused on the proposal of a methodology for the treatment of uncertainties by global sensitivity analysis and variance decomposition for a code fed by time-dependent variables. This methodology made it possible to classify the sources of uncertainty based on a sample of the achievements of the Telemac2D numerical model for forecasting high water levels in the Gironde estuary, in order to prioritize the influence of the model's forcings and parameters.In a second step, in connection with the results of the previous study, a methodology was proposed to develop a simulation chain integrating the numerical code and an ensemblist data assimilation method, based on an Ensemble Kalman Filter, in order to reduce the uncertainties of the model related to its time-dependent forcings and parameterization. First, this methodology was applied to the Telemac2D model of the Gironde estuary with a twin experiment protocol whose objective is, on the one hand, to validate it and, on the other hand, to optimally determine the control vector, the measurement network and the parameters related to the proposed methodology. The gains for the forecast of water levels in the Gironde estuary are then evaluated using the observations available for several real events; Cette thèse s'inscrit dans le cadre du développement de techniques d'assimilation de données dans les codes de calcul bidimensionnels d'hydraulique à surface libre et, plus particulièrement, de l'évaluation des possibilités d'amélioration de la prévision des hautes eaux sur l'estuaire de la Gironde par le développement et l'utilisation de ces méthodes. Les caractéristiques principales de ces travaux concernent le caractère instationnaire de l'hydrodynamique, liée à la fois à la propagation de la marée et à l'événement hydrométéorologique considéré, et la corrélation entre les observations des différentes stations de mesures disponibles. Les travaux réalisés pendant la première partie de la thèse ont porté sur la proposition d'une méthodologie de traitement des incertitudes par analyse globale de sensibilité par décomposition de variance pour un code d'hydrodynamique alimenté par des variables fonctionnelles. Cette méthodologie a permis la classification des sources d'incertitudes à partir d'un échantillon des réalisations du modèle Telemac2D de prévision des hautes eaux de l'estuaire de la Gironde, dans le but de hiérarchiser l'influence des forçages et des paramètres du modèle. Dans un second temps, en lien avec les résultats de l'étude précédente, une méthodologie a été proposée pour développer une chaîne de simulation intégrant le code de calcul étudié et une méthode ensembliste d'assimilation de données fondée sur le Filtre de Kalman d'Ensemble, dans l'objectif de réduire les incertitudes du modèle liées à ses forçages fonctionnels et à sa paramétrisation par l'utilisation d'observations. Dans un premier temps, cette méthodologie a été appliquée au modèle Telemac2D de l'estuaire de la Gironde sous la forme d'un protocole d'expériences jumelles dont l'objectif est, d'une part, de procéder à sa validation et, d'autre part, de déterminer de manière optimale le vecteur de contrôle, le réseau de mesures et les paramètres liés à la méthodologie proposée. Les gains de la méthodologie proposée sont ensuite évalués en utilisant les observations disponibles pour plusieurs événements réels

The accuracy of predictions is a major stake of flood forecast. This thesis deals with the development of data assimilation techniques in bidimensional numerical codes based on shallow water equations and, more specifically, the assessment of possible improvements in high water forecasting in the Gironde estuary through the development and use of these methods. The main characteristics of this work concern the unsteadiness of hydrodynamics, linked to both tidal propagation and the hydrometeorological event, and the correlation between the observations of the different available observation stations.The work carried out during the first part of the thesis focused on the proposal of a methodology for the treatment of uncertainties by global sensitivity analysis and variance decomposition for a code fed by time-dependent variables. This methodology made it possible to classify the sources of uncertainty based on a sample of the achievements of the Telemac2D numerical model for forecasting high water levels in the Gironde estuary, in order to prioritize the influence of the model's forcings and parameters.In a second step, in connection with the results of the previous study, a methodology was proposed to develop a simulation chain integrating the numerical code and an ensemblist data assimilation method, based on an Ensemble Kalman Filter, in order to reduce the uncertainties of the model related to its time-dependent forcings and parameterization. First, this methodology was applied to the Telemac2D model of the Gironde estuary with a twin experiment protocol whose objective is, on the one hand, to validate it and, on the other hand, to optimally determine the control vector, the measurement network and the parameters related to the proposed methodology. The gains for the forecast of water levels in the Gironde estuary are then evaluated using the observations available for several real events, Cette thèse s'inscrit dans le cadre du développement de techniques d'assimilation de données dans les codes de calcul bidimensionnels d'hydraulique à surface libre et, plus particulièrement, de l'évaluation des possibilités d'amélioration de la prévision des hautes eaux sur l'estuaire de la Gironde par le développement et l'utilisation de ces méthodes. Les caractéristiques principales de ces travaux concernent le caractère instationnaire de l'hydrodynamique, liée à la fois à la propagation de la marée et à l'événement hydrométéorologique considéré, et la corrélation entre les observations des différentes stations de mesures disponibles. Les travaux réalisés pendant la première partie de la thèse ont porté sur la proposition d'une méthodologie de traitement des incertitudes par analyse globale de sensibilité par décomposition de variance pour un code d'hydrodynamique alimenté par des variables fonctionnelles. Cette méthodologie a permis la classification des sources d'incertitudes à partir d'un échantillon des réalisations du modèle Telemac2D de prévision des hautes eaux de l'estuaire de la Gironde, dans le but de hiérarchiser l'influence des forçages et des paramètres du modèle. Dans un second temps, en lien avec les résultats de l'étude précédente, une méthodologie a été proposée pour développer une chaîne de simulation intégrant le code de calcul étudié et une méthode ensembliste d'assimilation de données fondée sur le Filtre de Kalman d'Ensemble, dans l'objectif de réduire les incertitudes du modèle liées à ses forçages fonctionnels et à sa paramétrisation par l'utilisation d'observations. Dans un premier temps, cette méthodologie a été appliquée au modèle Telemac2D de l'estuaire de la Gironde sous la forme d'un protocole d'expériences jumelles dont l'objectif est, d'une part, de procéder à sa validation et, d'autre part, de déterminer de manière optimale le vecteur de contrôle, le réseau de mesures et les paramètres liés à la méthodologie proposée. Les gains de la méthodologie proposée sont ensuite évalués en utilisant les observations disponibles pour plusieurs événements réels