Length-extension LGS microresonators for FM-AFM: microfabrication and shear effects sensitivity

Conférence internationale avec proceeding : poster 2014 IEEE International Frequency Control Symposium Taipei International Convention center. Taipei, Taiwan - 19-22 mai 2014Length extension LGS resonators have already been studied in order to make probes for frequency-modulation atomic force microscopy. Theoretical investigations are conducted on the design of the probes to improve the resolution of the microscope. Comparison of quartz and LGS crystal performances can be made using these theoretical results. The microfabrication of LGS resonators by chemical etching is proved. A monolithic length extension resonator with a tip at its end is obtained which constitutes a real advantage in regard to the existing piezoelectric probes

Weather generator: results from a pilot study

publishedVersio

Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Previous studies linking large-scale atmospheric circulation and river flow with traditional machine learning techniques have predominantly explored monthly, seasonal or annual streamflow modelling for applications in direct downscaling or hydrological climate-impact studies. This paper identifies major drivers of daily streamflow from large-scale atmospheric circulation using two reanalysis datasets for six catchments in Norway representing various Köppen-Geiger climate types and flood-generating processes. A nested loop of roughly pruned random forests is used for feature extraction, demonstrating the potential for automated retrieval of physically consistent and interpretable input variables. Random forest (RF), support vector machine (SVM) for regression and multilayer perceptron (MLP) neural networks are compared to multiple-linear regression to assess the role of model complexity in utilizing the identified major drivers to reconstruct streamflow. The machine learning models were trained on 31 years of aggregated atmospheric data with distinct moving windows for each catchment, reflecting catchment-specific forcing-response relationships between the atmosphere and the rivers. The results show that accuracy improves to some extent with model complexity. In all but the smallest, rainfall-driven catchment, the most complex model, MLP, gives a Nash-Sutcliffe Efficiency (NSE) ranging from 0.71 to 0.81 on testing data spanning five years. The poorer performance by all models in the smallest catchment is discussed in relation to catchment characteristics, sub-grid topography and local variability. The intra-model differences are also viewed in relation to the consistency between the automatically retrieved feature selections from the two reanalysis datasets. This study provides a benchmark for future development of deep learning models for direct downscaling from large-scale atmospheric variables to daily streamflow in Norway.publishedVersio

SIMULAÇÃO DE CAMPOS PROBABILÍSTICOS DE PRECIPITAÇÃO A PARTIR DE UM MÉTODO GEOESTATÍSTICO

Inúmeras regiões no mundo já foram atingidas, pelo menos uma vez, por eventos extremos de inundação os quais causaram grandes perdas socioeconômicas, ambientais entre outros. Os dados de estimação de precipitação são essenciais para realizar a previsão destes eventos e gerar alertas que possam minimizar os danos que podem ser causados. Uma das características principais destes eventos é a elevada variabilidade espacial e temporal. Devido a sua complexidade, a previsão dos mesmos possui diversas fontes de incertezas, como as incertezas provenientes dos campos de chuva observados. Estes dados, por sua vez, possuem papel importante no desempenho dos sistemas de previsão. Este estudo tem como objetivo principal desenvolver uma metodologia, baseada em um método geoestatístico, capaz de gerar cenários possíveis de chuva a partir de dados de radar meteorológico e de pluviômetros. A área de estudos localiza-se na região de Campinas, estado de São Paulo, na qual inúmeros eventos extremos já foram detectados. Os resultados obtidos apresentam que o método desenvolvido neste estudo pode ser uma solução para quantificar as incertezas que podem ser encontradas nos dados de precipitação observada

Assessing the water balance of the Upper Rhine Graben hydrosystem

International audienceThe Upper Rhine alluvial aquifer is an important transboundary water resource. However, as in many alluvial systems, the aquifer inflows and outflows are not precisely known because of the difficulty of estimating the river infiltration flux and the boundary subsurface flow. To provide a thorough representation of the aquifer system, a coupled surface-subsurface model was applied to the whole aquifer basin, and several parameter sets were tested to investigate the uncertainty due to poorly known parameters (e.g. aquifer transmissivity computed by an inverse model, river bed characteristics). Twelve simulations were run and analyzed using standard statistical criteria and also a more advanced statistical method, the Karhunen Loève transform (KLT). This analysis showed that, although the model performed reasonably well, some piezometric level underestimations persisted in the south of the basin. An accurate representation of the aquifer behaviour would require river infiltration and the functioning of irrigation canals in the Hardt area to be taken into account. It also appeared that increasing the maximum river infiltration flow deteriorated the quality of the results. River infiltration to the aquifer was estimated to represent about 80% of the aquifer inflows with a mean annual value around 115 ± 16.5 m3/s, thus with an uncertainty of 14%. This quantity is larger than estimated in previous studies but is in agreement with some results obtained during low water periods. This important conclusion highlights the vulnerability of the Upper Rhine Graben aquifer to pollution from the rivers and to climate change since it is highly probable that the rivers' regimes will be affected by reduced snow cover on the neighbouring mountain ranges

The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France

An edited version of this paper was published by AGU. Copyright (2008) American Geophysical UnionThe hydrometeorological model SIM consists in a meterological analysis system (SAFRAN), a land surface model (ISBA) and a hydrogeological model (MODCOU). It generates atmospheric forcing at an hourly time step, and it computes water and surface energy budgets, the river ow at more than 900 rivergauging stations, and the level of several aquifers. SIM was extended over all of France in order to have a homogeneous nation-wide monitoring of the water resources: it can therefore be used to forecast flood risk and to monitor drought risk over the entire nation. The hydrometeorologival model was applied over a 10-year period from 1995 to 2005. In this paper the databases used by the SIM model are presented, then the 10-year simulation is assessed by using the observations of daily stream-flow, piezometric head, and snow depth. This assessment shows that SIM is able to reproduce the spatial and temporal variabilities of the water fluxes. The efficiency is above 0.55 (reasonable results) for 66 % of the simulated rivergages, and above 0.65 (rather good results) for 36 % of them. However, the SIM system produces worse results during the driest years, which is more likely due to the fact that only few aquifers are simulated explicitly. The annual evolution of the snow depth is well reproduced, with a square correlation coeficient around 0.9 over the large altitude range in the domain. The stream ow observations were used to estimate the overall error of the simulated latent heat ux, which was estimated to be less than 4 %

RÔLE DE L'OCCUPATION DU SOL VIS À VIS DE LA MODÉLISATION DES FLUX ENERGÉTIQUES ET HYDRIQUES EN MILIEU URBAIN ET PÉRIURBAIN

National audienceLe projet Rosenhy vise à étudier l’impact de l’occupation du sol sur la modélisation météorologique et hydrologique en termes de flux énergétiques et hydriques, en milieu urbain et périurbain. Trois sites appartenant aux observatoires français OTHU et ONEVU sont au centre de ce projet. Le quartier urbain hétérogène du Pin sec (Nantes), imperméabilisé à environ 45%, a fait l’objet d’une campagne expérimentale durant le mois de juin 2012, visant à estimer les flux de chaleur sensible et latente avec une haute résolution spatiale et temporelle par rapport aux mesures réalisées en continu sur ce site depuis 5 ans. Deux bassins versant périurbains (La Chézine à Nantes et l’Yzeron à Lyon), avec un taux d’imperméabilisation moins important (environ 10%) mais grandissant depuis plusieurs décennies, sont aussi étudiés. Ces deux derniers sites bénéficient d’un suivi hydrométéorologique depuis 10 ans pour la Chézine et 15 ans pour l’Yzeron. Sur ces trois sites, différentes sources de données d’occupation du sol à différentes résolutions sont disponibles :différentes bases de données géographiques communément utilisées par la communauté scientifique et les collectivités et des données télédétectées (multispectrales et hyperspectrales). L’utilisation de ces données en entrée de différents modèles météorologiques et hydrologiques implique un travail d’analyse et de classification pour adapter les informations aux besoins des modèles. Dans ce projet, les différents modèles adaptés au milieu urbain ou périrubain sont évalués et améliorés. Ainsi, les modèles hydrologiques périrubains sont en développement pour prendre en compte les différentes pratiques de gestion des eaux pluviales existantes (noues, toitures végétalisées, ...). L’utilisation conjointe des données simulées par les différents modèles aidera à déterminer le rôle de la part des surfaces naturelles et artificielles sur les bilans énergétique et hydrique en milieu plus ou moins urbanisé. Le milieu périurbain étant en évolution, le projet s’intéressera aussi à des scénarios d’urbanisation prospectifs en regardant d’une part l’impact de la densification sur les scénarios construits pour l’Yzeron lors du projet AVuPUR (ANR-VMCS, 2008-2011) et d’autre part, en réfléchissant conjointement avec Nantes Métropole, aux possibles voies d’évolution sur le bassin de la Chézine

Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation

This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data and incorporating them into the Bayesian total error analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-runoff model GR4J is presented. It exploits a calibration period where dense rain gauge data are available to characterize the uncertainty in the catchment average rainfall using geostatistical conditional simulation. The inclusion of information about rainfall and runoff data uncertainties overcomes ill-posedness problems and enables simultaneous estimation of forcing and structural errors as part of the Bayesian inference. This yields more reliable predictions than approaches that ignore or lump different sources of uncertainty in a simplistic way (e.g., standard least squares). It is shown that independently derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff, and structural errors. In this case study, the total predictive uncertainty appears dominated by structural errors. Although further research is needed to interpret and verify this decomposition, it can provide strategic guidance for investments in environmental data collection and/or modeling improvement. More generally, this study demonstrates the power of the Bayesian paradigm to improve the reliability of environmental modeling using independent estimates of sampling and instrumental data uncertainties.Benjamin Renard, Dmitri Kavetski, Etienne Leblois, Mark Thyer, George Kuczera, Stewart W. Frank

Is Predominant Clonal Evolution a Common Evolutionary Adaptation to Parasitism in Pathogenic Parasitic Protozoa, Fungi, Bacteria, and Viruses?

We propose that predominant clonal evolution (PCE) in microbial pathogens be defined as restrained recombination on an evolutionary scale, with genetic exchange scarce enough to not break the prevalent pattern of clonal population structure. The main features of PCE are (1) strong linkage disequilibrium, (2) the widespread occurrence of stable genetic clusters blurred by occasional bouts of genetic exchange ('near-clades'), (3) the existence of a "clonality threshold", beyond which recombination is efficiently countered by PCE, and near-clades irreversibly diverge. We hypothesize that the PCE features are not mainly due to natural selection but also chiefly originate from in-built genetic properties of pathogens. We show that the PCE model obtains even in microbes that have been considered as 'highly recombining', such as Neisseria meningitidis, and that some clonality features are observed even in Plasmodium, which has been long described as panmictic. Lastly, we provide evidence that PCE features are also observed in viruses, taking into account their extremely fast genetic turnover. The PCE model provides a convenient population genetic framework for any kind of micropathogen. It makes it possible to describe convenient units of analysis (clones and near-clades) for all applied studies. Due to PCE features, these units of analysis are stable in space and time, and clearly delimited. The PCE model opens up the possibility of revisiting the problem of species definition in these organisms. We hypothesize that PCE constitutes a major evolutionary strategy for protozoa, fungi, bacteria, and viruses to adapt to parasitism