Big datasets containing records from hydrological, geomorphological, water quality and biological communities on river ecosystems are not frequent, what limits advances on river ecology and management. What is more, many water agencies all over the world host large amount of data, but rarely are this field data collected for all relevant variables within the same river reaches or similar seasons. The main hypothesis behind our work is that synthetic river networks will provide a better physical basis for clarifying biophysical relationships among different hydrological, geomorphological, water quality and biological characteristics of river ecosystems. The high complexity of fluvial systems will be better represented by synthetic river networks, which have been shown as capturing successfully the hierarchical and spatial organisation of fluvial systems. Thus, by coupling modelled data from different components of river ecosystem with river networks, we expect to elucidate which are the biophysical relationships that account for the largest variability.

The implementation of Spatial Decision Support Systems has been shown as the most efficient tool for Integrated Catchment Management, and we expect to improve the methodology by coupling synthetic river networks and integrated modelling. Finally, few studies have deal with modelling multiple human pressure effects for entire river domains. Within the MARCE project we aim at creating models for predicting the effects of removing or increasing human pressures on river ecosystems, so that different scenarios could be created and evaluated for proper river management and budget planning.