What can we learn from the hydrogeological functioning of peri-urban lakes in the Ile-de-France region on their ecological status?

Abstract

International audienceAquatic ecosystem sustainability requires the appreciation of the ecosystem services they provide in order to develop integrated decision-making strategies. In this context, the PULSE project (Peri-Urban Lakes, Society and Environment, ANR CEPS) was dedicated (i) to assess the ecological status of suburban lakes from the Ile-de-France region, (ii) to develop management-focused indicators and (iii) to propose management guideli nes. In order to assess their environmental status, a representative set of 48 lakes was selected. Field campaigns were conducted every summer from 2011 to 2013. The dataset includes physico-chemical variables (e.g. temperature, pH, O2…), biological variables (e.g.phytoplankton abundance including toxic cyanobacteria), and contaminants (e.g. trace metals).Predictive models based on catchment-scale characteristics were built to evaluate the impact of anthropogenic pressures on some ecosystem properties. Nearly half of these lakes are only fed by groundwater, a proportion representative of the region hydrology. A better understanding of the hydrogeological functioning of these lakes could prove very useful in order to improve the accuracy of predictive models based on catchment-scale characteristics. In this paper, we will first define a typology of the Ile-de-France lakes based on (i) the geological characteristics of their catchment (ii) their relationships with the groundwater, neighbouring rivers, wetlands and sanitation network, (iii) their degree of artificialization, and (i v) the anthropogenic pressures. These indicators will be derived from GIS databases, geological and soil maps as well as management reports. The results of statistical analysis and predicti ve models used to evaluate the response of these aquatic ecosystems to anthropogenic pressures will be presented. Finally we will discuss to what extent hydrogeological characteristics contribute to increase the variance explained by these models