ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks

Abstract

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration, thereby buffering climate change and providing socio-economic services. However, recurrent stresses, such as heat waves, droughts and floods can affect forests with potential cascading effects on their carbon sink capacity, drought resilience and sustainability. Knowledge about the stress impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex forest systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require a dramatically improved process understanding of carbon and water cycling across various temporal (minutes to seasons) and spatial (leaf to ecosystem) scales covering atmosphere, biosphere, pedosphere and hydrosphere components. Many relevant processes controlling carbon and water exchange occur at small scales (e.g. rhizosphere, single leaf) with a high spatial and temporal variability, which is poorly constrained. However, interactions and feedback loops can be key players that amplify or dampen a system’s response to stress. Moreover, spatial and temporal scaling rules for these non-linear processes in structurally and functionally diverse ecosystems are unknown. Legacy effects, for example, altered response after previous stress and retarded recovery of forests after climate extremes, are not captured in state-of-the-art models. Currently, we are lacking the appropriate and interconnected measurement, data assimilation and modelling tools allowing for a comprehensive, real-time quantification of key processes at high spatio-temporal coverage in heterogeneous environments. Moreover, since climate impacts are highly unpredictable with respect to timing and location, future research will require novel mobile, easily deployable and cost-efficient approaches. ECOSENSE, therefore, assembles expertise from environmental and engineering sciences, both being excellently paired at the University of Freiburg. Our interdisciplinary research project will investigate all relevant scales in a next-generation ecosystem research assessment (ECOSENSE). Our vision is to detect and forecast critical changes in ecosystem functioning, based on the understanding of hierarchical process interaction. In the first phase, ECOSENSE will explore these process interactions by investigating pools and fluxes of water and carbon, i.e. CO2 exchange, isotope discrimination and volatile organic compounds (VOC), as well as stress indicators by remotely and in situ sensed chlorophyll fluorescence. To address these research tasks, ECOSENSE will develop, implement and test a distributed, autonomous, intelligent sensor network, based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem pools and fluxes in a naturally complex structured forest system with minimal physiological impact. Measured data will be transferred in real-time into a sophisticated database, which will be explored for process analysis, conducted by Artificial Intelligence and close to real-time process-based ecosystem models for now- and forecasting applications. Thereby, ECOSENSE will: i) break new ground for integrative ecosystem research by identifying hierarchies and interactions of abiotic and physiological processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors and iii) enable the prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE toolkit, tested and validated in controlled climate extreme experiments and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems. Thereby, ECOSENSE will allow for a unique avenue of data acquisition and, consequently, for unprecedented scale-crossing ecosystem understanding and modelling.