A Past Discharges Assimilation System for Ensemble Streamflow Forecasts over France - Part 1: Description and Validation of the Assimilation System

Two Ensemble Streamflow Prediction Systems (ESPSs) have been set up at Météo-France. They are based on the French SIM distributed hydrometeorological model. A deterministic analysis run of SIM is used to initialize the two ESPSs. In order to obtain a better initial state, a past discharges assimilation system has been implemented into this analysis SIM run, using the Best Linear Unbiased Estimator (BLUE). Its role is to improve the model soil moisture by using streamflow observations in order to better simulate streamflow. The skills of the assimilation system were assessed for a 569-day period on six different configurations, including two different physics schemes of the model (the use of an exponential profile of hydraulic conductivity or not) and, for each one, three different ways of considering the model soil moisture in the BLUE state variables. Respect of the linearity hypothesis of the BLUE was verified by assessing of the impact of iterations of the BLUE. The configuration including the use of the exponential profile of hydraulic conductivity and the combination of the moisture of the two soil layers in the state variable showed a significant improvement of streamflow simulations. It led to a significantly better simulation than the reference one, and the lowest soil moisture corrections. These results were confirmed by the study of the impacts of the past discharge assimilation system on a set of 49 independent stations.JRC.H.7-Climate Risk Managemen

A Past Discharge Assimilation System for Ensemble Streamflow Forecasts over France - Part 2: Impact on the Ensemble Streamflow Forecasts

The use of ensemble streamflow forecasts is developing in the international flood forecasting services. Ensemble streamflow forecast systems can provide more accurate forecasts and useful information about the uncertainty of the forecasts, thus improving the assessment of risks. Nevertheless, these systems, like all hydrological forecasts, suffer from errors on initialization or on meteorological data, which lead to hydrological prediction errors. This article, which is the second part of a 2-part article, concerns the impacts of initial states, improved by a streamflow assimilation system, on an ensemble streamflow prediction system over France. An assimilation system was implemented to improve the streamflow analysis of the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological suite, which initializes the ensemble streamflow forecasts at Météo-France. This assimilation system, using the Best Linear Unbiased Estimator (BLUE) and modifying the initial soil moisture states, showed an improvement of the streamflow analysis with low soil moisture increments. The final states of this suite were used to initialize the ensemble streamflow forecasts of Météo-France, which are based on the SIM model and use the European Centre for Medium-range Weather Forecasts (ECMWF) 10-day Ensemble Prediction System (EPS). Two different configurations of the assimilation system were used in this study: the first with the classical SIM model and the second using improved soil physics in ISBA. The effects of the assimilation system on the ensemble streamflow forecasts were assessed for these two configurations, and a comparison was made with the original (i.e. without data assimilation and without the improved physics) ensemble streamflow forecasts. It is shown that the assimilation system improved most of the statistical scores usually computed for the validation of ensemble predictions (RMSE, Brier Skill Score and its decomposition, Ranked Probability Skill Score, False Alarm Rate, etc.), especially for the first few days of the time range. The assimilation was slightly more efficient for small basins than for large ones.JRC.H.7-Climate Risk Managemen

XFEM‐based geomechanical modelling of porous media at small scale

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An advanced approach for the generation of complex heterogeneous and cellular materials RVEs using distance fields and level sets

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XFEM-based homogenisation of the behaviour of heterogeneous materials,

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A level-set based microstructure generator for the computational homogenisation of complex heterogeneous materials

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A level-set based multi-scale computiational modelling of soil lime-treatment processes

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An advanced approach for the generation of complex cellular material Representative Volume Elements using distance fields and level sets.

A general and widely tunable method for the generation of Representative Volume Elements ( RVEs ) for cellular materials based on distance and level set functions is presented. The approach is based on random tessellations constructed from random inclusion packings. A general methodology to obtain arbitrary-shaped tessellations to produce disordered foams is presented and illustrated. These tessellations can degenerate either in classical Voronoï tessellations potentially additively weighted depending on properties of the initial inclusion packing used, or in Laguerre tessellations through a simple modification of the formulation. A versatile approach to control the particular morphology of the obtained foam is introduced. Specific local features such as concave triangular Plateau borders and non‑constant thickness heterogeneous coatings can be built from the tessellation in a straightforward way and are tuned by a small set of parameters with a clear morphological interpretation.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A unified level set based methodology for fast generation of complex microstructural multi-phase RVEs

In the frame of the multi-scale computational analysis of complex materials, the generation of Representative Volume Elements (RVE) is often a crucial step. Various microstructure generation tools may be used, depending on the material to be considered, such as Discrete Element Methods (DEM), Random Sequential Addition (RSA) based methods for particulate media requiring important computation times; or Voronoï tessellation methods for polycrystalline materials. Besides being material specific, some of these methods may become unaffordable when considering complex microstructures, large inclusions numbers or high volume fractions. The present contribution presents a unified level set based methodology for complex, periodic (or not) and random RVE generations. The presented methodology allows RVE generation for particulate granular media, polycrystalline aggregates with large size distribution and arbitrary shapes, as well as for complex three-phase or poly-phase microstructures. A level set controlled Random Sequential Addition algorithm is used for particle distribution generation, allowing increasing the RSA algorithm efficiency, generating large and dense populations of arbitrary shaped inclusions with precise control on neighboring distances. Starting from this, several methods are presented to add specific realistic features to the generated RVEs. Modifications and densifications allow the distribution pattern to fit observed real samples or to present a specific spatial organization. The addition of one (or more) phase(s) obtained from the growth of the initial inclusions allows reproducing some typical microstructural patterns such as grain bridging in clayey soils, interfacial transition zones in concrete or hydrated gel in cement paste. The versatility of the proposed RVE generation method is illustrated by means of various examples, reproducing realistic microstructural arrangements of clayey soils, irregular masonry and polycrystalline aggregates with bimodal size distributions. © 2012 Elsevier B.V.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A level set-based generalized tessellation for the generation of complex microstructural RVEs

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