Smart high-frequency environmental sensor networks for quantifying nonlinear hydrological process dynamics across spatial scales

ended

Regulators and industries are challenged by the difficulty to analyse and predict the impact of nonlinear environmental processes on short-term and long-term responses of ecosystems to environmental change. Until very recently, the development of most conventional monitoring, forecasting and prediction tools has been based on the assumption of stationary environmental systems. In the context of global change these tools are increasingly pushed towards and even beyond their design limits (the latter resulting in the first line from the prevailing limitations in spatial and temporal resolution of environmental observations). For this project, we propose a rationale stating that only novel, high-frequency/high-resolution environmental monitoring and predictive modelling will yield new process understanding of ecosystem functioning. Technological progress offers as many opportunities as it triggers challenges: what is needed now are new strategies to generate, manage and analyse BIG DATA at unprecedented spatial and temporal resolution. Innovation can only stand as a synonym for ‘significant positive changes’ if [a] we manage to clearly state the challenges (global change & non-stationarity) and problems (generating and managing high-frequency information) and [b] transform them into solutions, i.e. the quantification and prediction of environmental responses to global change as a prerequisite for designing and implementing adaptation and/or mitigation strategies wherever needed. The timely outcomes of this research project will hence be of great relevance for the scientific community, regulatory agencies, and the private sector.