On the assimilation of SWOT type data into 2D shallow-water models

In river hydraulics, assimilation of water level measurements at gauging stations is well controlled, while assimilation of images is still delicate. In the present talk, we address the richness of satellite mapped information to constrain a 2D shallow-water model, but also related difficulties. 2D shallow models may be necessary for small scale modelling in particular for low-water and flood plain flows. Since in both cases, the dynamics of the wet dry front is essential, one has to elaborate robust and accurate solvers. In this contribution we introduce robust second order, stable finite volume scheme [CoMaMoViDaLa]. Comparisons of real like tests cases with more classical solvers highlight the importance of an accurate flood plain modelling. A preliminary inverse study is presented in a flood plain flow case, [LaMo] [HoLaMoPu]. As a first step, a 0th order data processing model improves observation operator and produces more reliable water level derived from rough measurements [PuRa]. Then, both model and flow behaviours can be better understood thanks to variational sensitivities based on a gradient computation and adjoint equations. It can reveal several difficulties that a model designer has to tackle. Next, a 4D-Var data assimilation algorithm used with spatialized data leads to improved model calibration and potentially leads to identify river discharges. All the algorithms are implemented into DassFlow software (Fortran, MPI, adjoint) [Da]. All these results and experiments (accurate wet-dry front dynamics, sensitivities analysis, identification of discharges and calibration of model) are currently performed in view to use data from the future SWOT mission

Increasing Data Resilience of Mobile Devices with a Collaborative Backup Service

Whoever has had his cell phone stolen knows how frustrating it is to be unable to get his contact list back. To avoid data loss when losing or destroying a mobile device like a PDA or a cell phone, data is usually backed-up to a fixed station. However, in the time between the last backup and the failure, important data can have been produced and then lost. To handle this issue, we propose a transparent collaborative backup system. Indeed, by saving data on other mobile devices between two connections to a global infrastructure, we can resist to such scenarios. In this paper, after a general description of such a system, we present a way to replicate data on mobile devices to attain a prerequired resilience for the backup

DassFow-Shallow, Variational Data Assimilation for Shallow-Water Models: Numerical Schemes, User and Developer Guides

DassFlow is a computational software for free-surface flows includingvariational data assimilation (4D-VAR), sensitivity analysis, calibration features (adjoint method). The code version "shallow" solves shallow-water like models (Saint-Venant's type).The other version (ALE, not detailed in the present document) includes free-surface Stokes like models (low Reynolds, power-law rheology, ALE surface dynamics). All source files are written in Fortran 2003 / MPI. For more details and references, please consult DassFlow website.In the present manuscript, we describe: the equations, the compilation/execution instructions, the input / output files (user guide), the finite volume schemes, few validation test cases included in the archive, and the code structure (developer guide)

Using OWL Ontologies for Selective Waste Sorting and Recycling

International audienceWe are seeing the emergence of the Internet of Things, where digitally enabled objects can interact in smart environments. RFID can play an important role in linking common objects to the digital world. In this paper, we focus on e cient processing of collective waste items. These are considered to be smart by tagging them with RFID which bears the description of its properties. We have demonstrated a model using OWL ontology to sort these smart waste items for better recycling of materials. Our motive for using ontologies is for representing and reasoning of the domain knowledge to be autonomous, reducing the need for frequent references and updates for knowledge de nitions from external sources in real time

Robust finite volume schemes for 2D shallow water models. Application to flood plain dynamics

This study proposes original combinations of higher order Godunov type finite volume schemes and time discretization schemes for the 2d shallow water equations, leading to fully second-order accuracy with well-balanced property. Also accuracy, positiveness and stability properties in presence of dynamic wet/dry fronts is demonstrated. The test cases are the classical ones plus extra new ones representing the geophysical flow features and difficulties

Inverse computational algorithms for flood plain dynamic modelling

Flood plain dynamic modelling remains a challenge because of the complex multi-scale data, data uncertainties and the uncertain heterogeneous flow measurements. Mathematical models based on the 2d shallow water equations are generally suitable but wetting-drying processes can be driven by small scale data features. The present study aims at deriving an accurate and robust direct solver for dynamic wet-dry fronts and a variational inverse method leading to sensitivity analyses and data assimilation processes. The numerical schemes and algorithms are assessed on academic benchmarks representing well some flood dynamic features and a real test case (Lèze river, southwestern of France). Original sensitivity maps with respect to the (friction , topography) fields are performed and discussed. Furthermore, the identification of inflow discharges (time series) or roughness coefficients defined by land covers (spatially distributed parameters) demonstrate the relevance of the approach and the algorithm efficiency. Inverse computational methods may contribute to breakthrough in flood plain modelling

Dependability of Aggregated Objects, a pervasive integrity checking architecture

International audienceRFID-enabled security solutions are becoming ubiquitous; for example in access control and tracking applications. Well known solutions typically use one tag per physical object architecture to track or control, and a central database of these objects. This architecture often requires a communication infrastructure between RFID readers and the database information system. Aggregated objects is a different approach presented in this paper, where a group of physical objects use a set of RFID tags to implement a self-contained security solution. This distributed approach offers original advantages, in particular autonomous operation without an infrastructure support, and enhanced security

Using Context to Combine Virtual and Physical Navigation

In this paper we present how context can be used to support an user that is navigating both virtually and physically an information system. Navigation is seen as the act of going from one object to another. Navigation can be virtual like the Web navigation or physical like with a digital museum guide. We start by giving a formal definition of context that relies on the notion of proximity. An important peculiarity of this definition is that it permits us to build context according to several dimensions at a time, like the thematic dimension, the physical dimension or the temporal dimension. This definition permits us to build two applications that ease the navigation by constantly proposing relevant destinations to the navigating user. The first application is an assistant to help an user that navigates the web. The second application is a digital guide that permits the user to navigate through an information system composed of digital photos