Abstract
River functioning is closely influenced by land uses, along the stream itself and throughout the catchment. Land uses can be seen as both the expression of the natural environment and the result of increasing human activities. Land uses generate various pressures that affect river water quality at different scales of time and space. The assessment of multi-scale impacts of land use on river water quality is a major issue for scientists and for water managers who have to reach ambitious and rigorous legislative objectives. The ‘driving forces, pressures, state, impact, responses’ (DPSIR) conceptual framework is promoted by the European Environmental Agency for this purpose. A rigorous methodology, following a decision cycle-based approach, has been settled to help the implementation of the DPSIR on an environmental issue and its test on a field site. We develop a system of indicators based on the literature review and in accordance with water managers. The selected indicators are able to characterize the spatial and temporal evolution of water quality, land uses and their interactions. Pressure indicators are built according to the nature of the land use, the distance to rivers and the location in the watershed, at three spatial scales. We use two land-uses databases, the European Corine Land Cover database for macro-scale indicators and a high-resolution map for meso-scale and micro-scale indicators. Water quality status is characterized by a bioindicator based on population of invertebrates. The methodology is tested on the Ognon basin, which is a sub-catchment of Saône River (France). Results show that it is easy to implement the driving forces–pressures–state–impacts–responses for land use and river ecosystems framework on a medium-size catchment. But some limitations must be underlined: (1) the land-use data suffer from a lack of precision in their topological description and in the definition of the land-use nomenclature; (2) there was not enough bioindicators data to analyse, both the spatial and temporal variability of the bioindicators on the whole data set, but their spatial variability could be easily studied for a given year and (3) despite the difficulty to synthesize responses in a simple and unique indicator, the available data help to target areas for implementing actions. Lastly, this study leads us to work in close cooperation with water stakeholders, to share a common conceptual framework and to support their decision-making.