Statistical hydrology for evaluating peatland water table sensitivity to simple environmental variables and climate changes application to the mid-latitude/altitude Frasne peatland (Jura Mountains, France)

Peatlands are habitats for a range of fragile flora and fauna species. Their eco-physicochemical characteristics make them as outstanding global carbon and water storage systems. These ecosystems occupy 3% of the worldwide emerged land surface but represent 30% of the global organic soil carbon and 10% of the global fresh water volumes. In such systems, carbon speciation depends to a large extent on specific redox conditions which are mainly governed by the depth of the water table. Hence, understanding their hydrological variability, that conditions both their ecological and biogeochemical functions, is crucial for their management, especially when anticipating their future evolution under climate change. This study illustrates how long-term monitoring of basic hydro-meteorological parameters combined with statistical modeling can be used as a tool to evaluate i) the horizontal (type of peat), ii) vertical (acrotelm/catotelm continuum) and iii) future hydrological variability. Using cross-correlations between meteorological data (precipitation, potential evapotranspiration) and water table depth (WTD), we primarily highlight the spatial heterogeneity of hydrological reactivity across the Sphagnum-dominated Frasne peatland (French Jura Mountain). Then, a multiple linear regression model allows performing hydrological projections until 2100, according to regionalized IPCC RCP4.5 and 8.5 scenarios. Although WTD remains stable during the first half of 21th century, seasonal trends beyond 2050 show lower WTD in winter and markedly greater WTD in summer. In particular, after 2050, more frequent droughts in summer and autumn should occur, increasing WTD. These projections are completed with risk evaluations for peatland droughts until 2100 that appear to be increasing especially for transition seasons, i.e. May-June and September-October. Comparing these trends with previous evaluations of phenol concentrations in water throughout the vegetative period, considered as a proxy of plant functioning intensity, highlights that these hydrological modifications during transitional seasons could be a great ecological perturbation, especially by affecting Sphagnum metabolism.

Are surface water characteristics efficient to locate hyporheic biodiversity hotspots?

Location of river-groundwater exchange zones and biodiversity hotspot is essential for a river management plan, especially for Mediterranean karstic rivers. This location is often difficult and time-consuming when long river sectors are considered. We studied a 13 km-long sector of the Cèze River (Southern France) located in a karstic canyon. We compared five indicators of river-groundwater exchanges: longitudinal profiles of temperature, electrical conductivity and ²²²Rn concentrations in the surface water of the river, chemical characteristics of the hyporheic water and hyporheic biodiversity. Upwelling zones occurred downstream of geomorphological heterogeneities (here at the tail of gravel bars). Surface water chemistry, especially electrical conductivity and ²²²Rn concentrations, clearly traces large scale gaining sections, which were not associated to valley narrowing but with lateral springs, suggesting a crucial role of the geological structuration of the karstic plateau of Méjanne-le-Clap. Hyporheic water chemistry fits with the large-scale hydrological pattern, but with a high variability corresponding to local heterogeneities. The stygobite fauna (obligate groundwater organisms) and benthic EPTC (Ephemeroptera, Plecoptera, Trichoptera and Coleoptera) occurred preferentially in the gaining sections fed by groundwater, likely because of oligotrophic water and cooler temperature. The spatial distribution of river-groundwater exchange zone and hyporheic biodiversity may be thus predicted using changes in surface water chemistry, especially for electrical conductivity and ²²²Rn concentrations.

Specification and implementation of a digital Hopfield-type associative memory with on-chip training

The definition of the requirements for the design of a neural network associative memory, with on-chip training, in standard digital CMOS technology is addressed. Various learning rules that can be integrated in silicon and the associative memory properties of the resulting networks are investigated. The relationships between the architecture of the circuit and the learning rule are studied in order to minimize the extra circuitry required for the implementation of training. A 64-neuron associative memory with on-chip training has been manufactured, and its future extensions are outlined. Beyond the application to the specific circuit described, the general methodology for determining the accuracy requirements can be applied to other circuits and to other autoassociative memory architectures.