WorldCat Identities

Biancamaria, Sylvain

Overview
Works: 8 works in 11 publications in 2 languages and 40 library holdings
Roles: Author, Thesis advisor, Opponent
Publication Timeline
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Most widely held works by Sylvain Biancamaria
L'eau à découvert by Luc Abbadie( )

2 editions published in 2017 in French and held by 29 WorldCat member libraries worldwide

Indispensable à la régulation du climat, au développement de la vie sur Terre, au maintien des écosystèmes, aux populations, au développement de l'agriculture, de l'industrie comme à la production d'énergie, l'eau est un élément vital. Il convient donc, dans un contexte de changement global, d'analyser dans toute sa diversité la place et le rôle de l'eau et de se donner ainsi les moyens de mieux la préserver. Autour de cet enjeu qui engage toute l'humanité, Agathe Euzen, Catherine Jeandel et Rémy Mosseri ont réuni près de cent cinquante contributions, visant à apporter un éclairage sur chacun des domaines et des approches que couvre cette thématique. Quelle est l'origine de l'eau? Son rapport avec l'apparition de la vie? Quel rôle a-t-elle joué dans l'histoire de la planète et dans le développement de la vie végétale, animale et humaine? Quel est son cycle? Quelles sont ses propriétés chimiques? Comment les sociétés se sont-elles emparées de cet élément précieux? Allons-nous manquer d'eau? L'eau est-elle source de conflits? Comment l'eau est-elle gérée? Comment recycle-t-on une eau polluée? Quels sont les risques pour la santé mondiale? Quels sont les grands enjeux liés à l'eau au xxie siècle? Comprendre et proposer des solutions à ces défis majeurs est l'intention de cet ouvrage
Etude du cycle hydrologique des régions boréales et apport de l'altimétrie à large fauchée by Sylvain Biancamaria( Book )

2 editions published in 2009 in French and held by 3 WorldCat member libraries worldwide

Les régions boréales seront les plus affectées par le réchauffement climatique, c'est pourquoi cette thèse s'est intéressée à l'étude du cycle hydrologique de ces régions. Une nouvelle méthodologie d'extraction du volume de neige à partir de données radiométriques sur l'ensemble des régions boréales a été validée et a permis de montrer une différence de comportement entre 1988 et 2006 sur les variations du volume de neige de l'Eurasie et celui de l'Amérique du Nord. L'étude des variations de volume des eaux de surface des régions arctiques est par contre plus difficile à estimer avec les données satellitaires actuelles. C'est pourquoi un nouveau projet de satellite, la mission SWOT (Surface Water and Ocean Topography), a été proposé qui vise à fournir des cartes de hauteurs d'eau sur l'ensemble du globe. L'apport de cette mission à l'étude des régions arctiques a été estimé en implémentant une modélisation de l'Ob inférieur, fleuve de l'Ouest sibérien, en couplant un modèle hydrologique à grande échelle et un modèle hydrodynamique d'inondations. En ajustant certains paramètres de ces modèles, il a été possible d'obtenir une modélisation réaliste du débit et des hauteurs d'eau du fleuve. L'utilisation d'un lisseur de Kalman d'ensemble local a permis de montrer que les données SWOT devraient permettre de réduire significativement (de plus de 50%) les erreurs de modélisation. L'intérêt de la mission pour l'observation du débit de l'ensemble des fleuves a aussi été estimé en se basant sur un bilan d'erreur préliminaire. L'utilisation de courbes de tarage, ainsi que la prise en compte des erreurs de mesure SWOT ont permis de montrer que ces nouvelles données devraient permettre d'estimer un débit moyen avec une erreur inférieure à 30% pour tous les fleuves ayant une profondeur de plus de 1 m. D'autre part, il a été montré que l'erreur sur l'estimation du débit mensuel due seulement à l'échantillonnage temporel de SWOT diminue avec l'aire drainée et que, pour une aire drainée supérieure à 6 900 km2, cette erreur devrait être inférieure à 20%. Enfin, une méthodologie simple a permis de calculer que la variation annuelle totale du volume de l'ensemble des lacs est de l'ordre de 9 000 km3. Les données spatiales actuelles ne peuvent pas en observer plus de 15%. Selon notre estimation, SWOT quant à lui devrait pouvoir mesurer entre 50% et 65% de cette variation de volume
Contribution de la future mission altimétrique à large fauchée SWOT pour la modélisation hydrologique à grande échelle by Charlotte Emery( Book )

2 editions published in 2017 in French and held by 2 WorldCat member libraries worldwide

Scientific objective of this PhD work is to improve water fluxes estimation on the continental surfaces, at interanual and interseasonal scale (from few years to decennial time period). More specifically, it studies contribution of remotely-sensed measurements to improve hydrology model. Notably, this work focuses on the incoming SWOT mission (Surface Water and Ocean Topography, launch scheduled for 2021) for the study of the continental water cycle at global scale, and using the land surface model ISBA-TRIP. In this PhD work, I explore the potential of satellite data to correct both input parameters of the river routing scheme TRIP and its state variables. To do so, a data assimilation platform has been set to assimilate SWOT virtual observation as well as discharge estimated from real nadir altimetry data. Beforehand, it was necessary to do a sensibility analysis of TRIP model to its parameters. The aim of such study was to highlight what are the most impacting parameters on SWOT-observed variables and therefore select the ones to correct via data assimilation. The sensibility analysis (ANOVA) has been led on TRIP main parameters. The study has been done over the Amazon basin. The results showed that the simulated water levels are sensitive to local geomorphological parmaters exclusively. On the other hand, the simulated discharges are sensitive to upstream parameters (according to the TRIP river routing network) and more particularly to the groundwater time constant. Finally, water anomalies present sensitivities similar to those of the water levels but with more pronounced temporal variations. These results also lead me to do some choices in the implementation of the assimilation scheme and have been published. Therefore, in the second part of my PhD, I focused on developing a data assimilation platform which consists in an Ensemble Kalman Filter (EnKF). It could either correct the model input parameters or directly its state. A series of twin experiments is used to test and validate the parameter estimation module of the platform. SWOT virtual-observations of water heights and anomalies along SWOT tracks are assimilated to correct the river manning coefficient, with the possibility to easily extend to other parameters. First results show that the platform is able to recover the "true" Manning distribution assimilating SWOT-like water heights and anomalies. In the state estimation mode, daily assimilation cycles are realized to correct TRIP river water storage initial state by assimilating ENVISAT-based discharge. Those observations are derived from ENVISAT water elevation measures, using rating curves from the MGB-IPH hydrological model (calibrated over the Amazon using in situ gages discharge). Using such kind of observation allows going beyond idealized twin experiments and also to test contribution of a remotely-sensed discharge product, which could prefigure the SWOT discharge product. The results show that discharge after assimilation are globally improved : the root-mean-square error between the analysis discharge ensemble mean and in situ discharges is reduced by 28 \%, compared to the root-mean-square error between the free run and in situ discharges (RMSE are respectively equal to 2.79 x 103 m3/s and 1.98 x 103 m3/s)
The SWOT Mission and Its Capabilities for Land Hydrology by Sylvain Biancamaria( )

1 edition published in 2015 in English and held by 2 WorldCat member libraries worldwide

Etude du cycle hydrologique des régions boréales et apport de l'altimétrie à large fauchée by Sylvain Biancamaria( )

1 edition published in 2010 in French and held by 1 WorldCat member library worldwide

Arctic regions will be the most affected by climate change: therefore this work aims at studying the hydrological cycle of these regions. A new methodology to extract snow volume from radiometric data has been validated for the boreal regions and exhibits a different behaviour between snow volume over Eurasia and over North America. Yet, water volume variation is more difficult to estimate from currently available satellite data. That's why the potential of the new SWOT (Surface Water and Ocean Topography) mission, which will provide global water elevation maps, has been investigated. This has been done by implementing a virtual mission. The first step has been to model a Siberian river, the lower Ob, by coupling a land surface scheme and an inundation model. A realist estimation of the river discharge and water heights has been performed by tuning some of the models parameters. Then, SWOT synthetic observations have been assimilated in the modelling using a local Ensemble Kalman Smoother, leading to a significant decrease (more than 50%) of the modelling errors. The benefit of SWOT for all surface waters has also been studied. From in-situ rating curves and SWOT instrumental error, it has been shown that SWOT will provide an estimate of instantaneous river discharge with an error below 30%, if the river depth is above 1m. The error on the monthly discharge due only to the satellite temporal sampling decreases with drainage area, and should be lower than 20% for drainage area above 6,900 km2. Finally, it has been computed that annual volume variation for all the lakes in the world is around 9,000 km3. Currently, less than 15% of this lake storage change can be monitored with nadir altimeters, whereas SWOT will be able to observe from 50% to 65% of this volume variation
La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale by Nabil El Moçayd( )

1 edition published in 2017 in French and held by 1 WorldCat member library worldwide

This work deals with the formulation of a surrogate model for the shallow water equations in fluvial hydraulics with a chaos polynomial expansion. This reduced model is used instead of the direct model to reduce the computational cost of the ensemble methods in uncertainty quantification and data assimilation. The context of the study is the flood forecasting and the management of water resources. This manuscript is composed of five parts, each divided into chapters. The first part presents a state of art of uncertainty quantification and data assimilation in the field of hydraulics as well as the objectives of this thesis. We present the framework of flood forecasting, its stakes and the tools available (numerical and observation) to predict the dynamics of rivers. In particular, we present the SWOT2 mission, which aims to measure the height of water in rivers with global coverage at high resolution. We highlight particularty their contribution and their complementarity with the in-situ measurements. The second part presents the shallow water equations, which describe the flows in the rivers. We are particularly interested in a 1D representation of the equations.We formulate a numerical discretization of these equations, as implemented in the Mascaret software. The last chapter of this part proposes some simplifications of the shallow-water equations. The third part of this manuscript presents the uncertainty quantification and reduced order methods. We present particularly the probabilistic context which makes it possible to define well-defined problem of uncertainty quantification and sensitivity analysis. It is then proposed to reduce the size of a stochastic problem when dealing with random fields in the context of geophysical models. The methods of chaos polynomial expansion are then presented ; we present in particular the different strategies for the computation of the polynomial coefficients. This section devoted to methodology concludes with a chapter devoted to Ensemble based data assimilation (specially the Ensemble Kalman filter) and the use of surrogate models in this framework. The fourth part of this manuscript is dedicated to the results. The first step is to identify the sources of uncertainty in hydraulics that should be quantified and subsequently reduced. An article, in the review state, details the method and the validation of a polynomial surrogate model for shallow water equations in steady state when the uncertainty is mainly carried by the friction coefficients and upstream inflow. The study is conducted on the river Garonne. It is shown that the statistical moments, the probability density and the spatial covariance matrice for the water height are efficiently and precisely estimated using the reduced model whose construction requires only a few tens of integrations of the direct model. The use of the surrogate model is used to reduce the computational cost of the Ensemble Kalman filter in the context of a synthetic SWOT like data assimilation exercise. The aim is to reconstruct the spatialized friction coefficients and the upstream inflow. We are interested precisely in the spatial representation of the data as seen by SWOT : global coverage of the network, spatial averaging between the observed pixels. We show in particular that at the given calculation budget (2500 simulations of the direct model) the results of the data assimilation analysis based on the use of the polynomial surrogate model are better than those obtained with the classical Ensemble Kalman filter. We are then interested in the construction of the reduced model in unsteady conditions. It is assumed initially that the uncertainty is carried with the friction coefficients. It is now necessary to judge the need for the recalculation of polynomial coefficients over time and data assimilation cycles. For this work only ponctual and in-situ data were considered. It is assumed in a second step that the uncertainty is carried by the upstr
Global surveys of reservoirs and lakes from satellites and regional application to the Syrdarya river basin( )

1 edition published in 2015 in English and held by 1 WorldCat member library worldwide

Abstract: Large reservoirs along rivers regulate downstream flows to generate hydropower but may also store water for irrigation and urban sectors. Reservoir management therefore becomes critical, particularly for transboundary basins, where coordination between riparian countries is needed. Reservoir management is even more important in semiarid regions where downstream water users may be totally reliant on upstream reservoir releases. If the water resources are shared between upstream and downstream countries, potentially opposite interests arise as is the case in the Syrdarya river in Central Asia. In this case study, remote sensing data (radar altimetry and optical imagery) are used to highlight the potential of satellite data to monitor water resources: water height, areal extent and storage variations. New results from 20 years of monitoring using satellites over the Syrdarya basin are presented. The accuracy of satellite data is 0.6 km 3 using a combination of MODIS data and satellite altimetry, and only 0.2 km 3 with Landsat images representing 2-4% of average annual reservoir volume variations in the reservoirs in the Syrdarya basin. With future missions such as Sentinel-3A (S3A), Sentinel-3B (S3B) and surface water and ocean topography (SWOT), significant improvement is expected. The SWOT mission's main payload (a radar interferometer in Ka band) will furthermore provide 2D maps of water height, reservoirs, lakes, rivers and floodplains, with a temporal resolution of 21 days. At the global scale, the SWOT mission will cover reservoirs with areal extents greater than 250 × 250 m with 20 cm accuracy
Satellite remote sensing of the variability of the continental hydrology cycle in the lower Mekong basin over the last two decades by Binh Pham-Duc( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Surface water is essential for all forms of life since it is involved in almost all processes of life on Earth. Quantifying and monitoring surface water and its variations are important because of the strong connections between surface water, other hydrological components (groundwater and soil moisture, for example), and the changing climate system. Satellite remote sensing of land surface hydrology has shown great potential in studying hydrology from space at regional and global scales. In this thesis, different techniques using several types of satellite estimates have been made to study the variation of surface water, as well as other hydrological components in the lower Mekong basin (located in Vietnam and Cambodia) over the last two decades. This thesis focuses on four aspects. First, the use of visible/infrared MODIS/Terra satellite observations to monitor surface water in the lower Mekong basin is investigated. Four different classification methods are applied, and their results of surface water maps show similar seasonality and dynamics. The most suitable classification method, that is specially designed for tropical regions, is chosen to produce regular surface water maps of the region at 500 m spatial resolution, from January 2001 to present time. Compared to reference data, the MODIS-derived surface water time series show the same amplitude, and very high temporal correlation for the 2001-2007 period (> 95%). Second, the use of SAR Sentinel-1 satellite observations for the same objective is studied. Optical satellite data are replaced by SAR satellite data to benefit the ability of their microwave wavelengths to pass through clouds. Free-cloud Landsat-8 satellite imagery are set as targets to train and optimize a Neural Network (NN). Predicted surface water maps (30 m spatial resolution) are built for the studied region from January 2015 to present time, by applying a threshold (0.85) to the output of the NN. Compared to reference free-cloud Landsat-8 surface water maps, results derived from the NN show high spatial correlation (_90%), as well as true positive detection of water pixels (_90%). Predicted SAR surface water maps are also compared to floodability maps derived from topography data, and results show high consistency between the two independent maps with 98% of SAR-derived water pixels located in areas with a high probability of inundation (>60%). Third, the surface water volume variation is calculated as the product of the surface water extent and the surface water height. The two components are validated with other hydrological products, and results show good consistencies. The surface water height are linearly interpolated over inundated areas to build monthly maps at 500 m spatial resolution, then are used to calculate changes in the surface water volume. Results show high correlations when compared to variation of the total land surface water volume derived from GRACE data (95%), and variation of the in situ discharge estimates (96%). Fourth, two monthly global multi-satellite surface water products (GIEMS & SWAMPS) are compared together over the 1993-2007 period at regional and global scales. Ancillary data are used to support the analyses when available. Similar temporal dynamics of global surface water are observed when compared GIEMS and SWAMPS, but _50% of the SWAMPS inundated surfaces are located along the coast line. Over the Amazon and Orinoco basins, GIEMS and SWAMPS have very high water surface time series correlations (95% and 99%, respectively), but SWAMPS maximum water extent is just a half of what observed from GIEMS and SAR estimates. SWAMPS fails to capture surface water dynamics over the Niger basin since its surface water seasonality is out of phase with both GIEMS- and MODIS-derived water extent estimates, as well as with in situ river discharge data
 
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Audience Level
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  General Special  
Audience level: 0.85 (from 0.81 for L'eau à d ... to 1.00 for Satellite ...)

Alternative Names
Sylvain Biancamaria wetenschapper

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