Topographie

Résumé

La topographie (du grec topos, « lieu », et graphein, « dessiner ») est la science qui permet la mesure puis la représentation sur un plan ou une carte des formes et détails visibles sur le terrain, qu'ils soient naturels (notamment le relief et l'hydrographie) ou artificiels (comme les bâtiments, les routes). Son objectif est de déterminer la position et l'altitude de n'importe quel point situé dans une zone donnée, qu'elle soit de la taille d'un continent, d'un pays, d'un champ ou d'un corps de rue. La topographie s'appuie sur la géodésie, qui s'occupe de la détermination mathématique de la forme de la Terre (forme et dimensions de la Terre, coordonnées géographiques des points, altitudes, déviations de la verticale...). La topographie s'intéresse aux mêmes quantités, mais à une plus grande échelle, et elle rentre dans des détails de plus en plus fins pour établir des plans et cartes à différentes échelles. La cartographie proprement dite est l'art d'élaborer, de dessiner les cart

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https://fr.wikipedia.org/wiki/Topographie

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Publications associées (100)

Switzerland has committed itself to an ambitious energy strategy. It aims to replace the exist- ing nuclear generation capacity with predominantly indigenous renewable resources. Wind power could play a significant role in this transition, yet the wind resource in the mountainous terrain that makes up most of the country is poorly understood. There are indications that this resource could be significant, but studies undertaken so far acknowledge large uncertain- ties. This is because the complex topography of the mountains influences the flow patterns significantly, and these can become partially decoupled from the synoptic flow aloft. This thesis aims to improve the understanding of the wind resource in highly complex terrain, and thereby contribute to a well informed energy transition in Switzerland. We start out by investigating the characteristics of the Swiss wind resource based on data from two meteorological measurement networks. From the pair-wise correlation between stations, it is concluded that wind farms across the country can be combined to produce a stable power output. It is also shown that elevation plays an important role in the wind resource, with the likelihood of sustained low wind speeds decreasing as a function of elevation, while mean speeds tend to increase with elevation. Next, a state of the art Numerical Weather Prediction model is assessed in its ability to simulate wind speeds over the Alps, and is shown to improve drastically upon existing mean wind speed estimates. This same model is then used to calculate the wind turbine capacity that is required to produce significant amounts of wind power, and it is found that the required capacity can be significantly reduced by allowing for wind turbines to be built at high elevations. In the last part of this thesis, smaller areas of the Alpine domain are simulated at high resolu- tions, to investigate the effect of increased model resolution on the accuracy and height of resource assessments. While it is found that optimal model parameterization is dependent on weather and terrain, strong indications of higher wind power potential are found with high resolution models compared to a model at lower resolution. This is explained by the fact that high resolutions are required to properly resolve the complex topography, which has a significant influence on the flow patterns and therefore, on the potential energy production.

EPFL2019

Chargement

Inception of Regular Valley Spacing in Fluvial Landscapes: A Linear Stability Analysis

Shashank Kumar Anand

Incipient valley formation in mountainous landscapes is often associated with their presence at a regular spacing under diverse hydroclimatic forcings. Here we provide a formal linear stability theory for a landscape evolution model (LEM) representing the action of tectonic uplift, diffusive soil creep, and detachment-limited fluvial erosion. For configurations dominated by only one horizontal length scale, a single dimensionless quantity characterizes the overall system dynamics based on model parameters and boundary conditions. The stability analysis is conducted for smooth and symmetric hillslopes along a long mountain ridge to study the impact of the erosion law form on regular first-order valley formation. The results provide the critical condition when smooth landscapes become unstable and give rise to a characteristic length scale for incipient valleys, which is related to the scaling exponents that couple fluvial erosion to the specific drainage area and the local slope. The valley spacing at first instability is uniquely related to the ratio of the scaling exponents and expands logarithmically with an increase in this ratio. We find compelling evidence of sediment transport by diffusive creep and fluvial erosion coupled with the specific drainage area equation as a sufficient mechanism for first-order valley formation. We finally show that the predictions of the linear stability analysis conform with the results of numerical simulations for different degrees of nonlinearity in the erosion law and agree well with topographic data from a natural landscape.

2022

Chargement

From ice phase to liquid precipitation at the ground level: weather radar and machine learning

Yvan Chevalley

The relationship between the dominant ice phase hydrometeors and the intensity of precipitation at the ground level has not been extensively studied. This thesis explore and make use of the links between the respective proportions of solid phase hydrometeors and the quantitative precipitation estimation at the ground level. The solid phase hydrometeors are obtained by applying a semi-supervised classification methods based on polarimetric radar measurements on a datasets acquired by the MeteoSwiss Monte Lema radar during seven events of intense precipitation. Precipitation intensity measurements are obtained from the CombiPrecip product that combines rain-gauge and radar precipitation estimates. In the first part of this thesis, the relationship between the percentage of aggregates (AG), crystals (CR) and rimed particles (RP) solid hydrometeors and the precipitation intensity in the vertical columns above the corresponding 1x1 km ground level pixels is studied. Several regression approaches indicate a relevant non-linear relationship between solid phase hydrometeor proportions and the precipitation intensity at the ground level. In the last part, machine learning algorithms are used to estimate liquid precipitation at the ground level from the different hydrometeor proportions of CR, AG and RP. Namely, a neural network with one hidden layer and a multiple linear regression are trained and tested by mean of cross-validation with solid phase hydrometeors and other non-independent parameters. The results of the machine learning algorithms show relatively good performances in estimating precipitation at the ground level. This approach, which make use of solid phase radar measurements, could be very relevant in case of complex topography.

2018

Chargement

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EPFL2019