| Summary: | International audience The management of water resources and hydrological hazards requires a good understanding of the variability of hydrological regimes. Most management methods are based on the assumption that the hydrological regime does not evolve with time. This hypothesis may appear questionable in a climate change context. Moreover, climate naturally follows specific modes of variability, quantified by some climate indices (e.g. North Atlantic Oscillation NAO, Atlantic Multi-decadal Oscillation AMO, etc.). These modes of variability are due to large-scale climatic processes affecting large areas, and whose temporal scales range from a few years to a few decades. In this context, understanding the relationship between climate variability/change on the one hand, and hydrological variability on the other hand is a major research challenge. This relationship is difficult to observe due to the large natural variability of hydrological regimes, especially in the extreme domain. Consequently, the power of local methods for detecting trends or teleconnections is generally quite limited. A possibility to improve this detection power is to use regional detection methods. Such methods are based on the assumption that climate variability (be it climate change or low-frequency variability) should affect hydrological regimes on a large spatial scale. Consequently, its impact should be consistent within homogeneous hydro-climatic regions. This assumption can then be used to derive statistical methods to detect trends or teleconnections at a regional scale. The objective of this presentation will be twofold: (i) present and discuss regional detection methods; (ii) illustrate their application based on extensive hydrological datasets. For the first objective, parametric and semi-parametric models will be described. Emphasis will be put on the issue of accounting for the spatial dependence existing between sites. The second objective will be achieved by using two extensive datasets, gathered during recent research ...
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