For a few decades, climate models are used to provide future scenarios of precipitation with increasingly higher spatial resolution. However, this resolution is not yet sufficient to describe efficiently what happens at local scale. Dynamical and statistical methods of downscaling have been developed and allow us to make the link between two levels of resolution and enable us to get values at a local scale based on large-scale information from global or regional climate models. Nevertheless, both the extreme behavior and the spatial structures are not well described by these downscaling methods. We propose a two-step methodology, called spatial hybrid downscaling (SHD), to solve this problem. The first step consists in applying a univariate (i.e., one-dimensional) statistical downscaling to link the high-and low-resolution variables at some given locations. Once this 1d-link is performed, a conditional simulation algorithm of max-stable processes adapted to the extremal t process enables us to get conditional distributions of extreme precipitation at any point of the region. An application is performed on precipitation data in the south of France where extreme (Cevenol) events have major impacts (e.g., floods). Different versions of the SHD approach are tested. Most of them show particularly good results regarding univariate and multivariate criteria and overcome classical downscaling techniques tested in comparison. Furthermore, these conclusions are robust to the choice of the 1d-link functions tested and to the choice of the conditioning points to drive the conditional local-scale simulations performed by the SHD approach.

a b s t r a c tThe last decade has seen max-stable processes emerge as a powerful tool for the statistical modeling of spatial extremes and there are increasing works using them in a climate framework. One recent utilization of max-stable processes in this context is for conditional simulations that provide empirical distribution of a spatial field conditioned by observed values in some locations. In this work conditional simulations are investigated for the extremal t process taking benefits of its spectral construction.The methodology of conditional simulations proposed by Dombry et al. (2013) for Brown-Resnick and Schlather models is adapted for the extremal t process with some original improvements which enlarge the possible number of conditional points. A simulation study enables to highlight the role of the different parameters of the model and to emphasize the importance of the steps of the algorithm.An application is performed on precipitation data in the south of France where extreme precipitation events (Cevenol) may generate major floods. This shows that the model and the algorithm perform well provided the stationary assumptions are fulfilled.

No abstract

Licensed Shared Access (LSA) is a complementary solution allowing Mobile Network Operators (MNOs) to use another incumbent's frequency spectrum after obtaining a proper license from the regulator.Using auctions to allocate those LSA-type licenses is a natural approach toward an efficient use of spectrum, by controlling the incentives for MNOs to declare their true valuation for the spectrum and allocating it to those who value it the most. A specificity of LSA licenses lies in the interactions among buyers, due to possibly overlapping coverage areas, this allows for allocating the same spectrum to several MNOs.In this paper, we review the existing mechanisms taking into account such radio interference constraints, propose new ones, and compare their performance. We show how to increase the revenue, while maintaining truthful-telling, of all-or-nothing auction mechanisms by introducing a reserve price per bidder. We also investigate extensions of those mechanisms, namely when the management of interference among base stations is more subtle than partitioning base stations into groups of non-interfering base stations. For each mechanism, we show how to optimize a trade-off between expected fairness, expected revenue and expected efficiency by carefully working with groups and reserve prices. Simulations suggests that the extension of those mechanisms may lead to increase an indicator combining allocation fairness, social welfare and seller's revenue by more than 20%.