SFB 1537: ECOSENSE – Multiscale quantification of spatio-temporal dynamics of ecosys-tem processes by smart autonomous sensor networks

SFB 1537：ECOSENSE – 通过智能自主传感器网络对生态系统过程的时空动态进行多尺度量化

• 批准号: 459819582
• 金额: --
• 依托单位: Albert-Ludwigs-Universität Freiburg
• 依托单位国家: 德国
• 项目类别: Collaborative Research Centres
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/459819582?language=en
• 关键词: SFB; 1537; ECOSENSE; Multiscale; quantification

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration. However, recurrent stresses, such as heat waves, floods, and droughts, increasingly endanger even central European forests, with potentially cascading effects on their carbon sink capacity, drought resilience, and sustainability. Knowledge on the impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require an improved process understanding of carbon and water cycling across various temporal and spatial scales, from minutes to seasons, from leaves to ecosystem, covering the atmosphere, biosphere, pedosphere and hydrosphere. Many relevant processes occur at small scales and high spatial heterogeneity and their interactions and feed-back loops can be key players to amplify or dampen a system’s response to stress. Currently, we are lacking the appropriate measuring, data and modelling tools allowing for comprehensive, real time quantification of relevant processes at high spatio-temporal coverage. Moreover, climate impacts are highly unpredictable, and thus future research will require novel mobile, easy deployable, and cost-efficient approaches.Our interdisciplinary research project ECOSENSE will investigate all relevant scales in a next generation ecosystem research assessment. Our vision is to detect and forecast critical changes in ecosystem functioning based on the understanding of hierarchical process interaction. To do so ECOSENSE will develop, implement, and test a new versatile, distributed, cost-effective, 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 states and fluxes in a minimally invasive manner in naturally complex structured forests. Measured data will be transferred in real-time into a sophisticated database which can be explored for process analysis, deep learning approaches, and enhanced simulation models for now- and forecasting applications. ECOSENSE will open new horrizons for integrative ecosystem research by i) identifying hierarchies and interactions of abiotic and biotic processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors enabling the iii) 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.

全球气候变化威胁着全世界的生态系统功能。森林生态系统对碳固存特别重要。然而，经常性的压力，如热浪，洪水和干旱，甚至越来越危及中欧的森林，对它们的碳汇能力，抗旱能力和可持续性产生潜在的级联效应。在这些复杂的系统中，对驱动土壤-植物-大气相互作用的众多过程的影响的知识普遍缺乏，对未来变化的不确定性极高。因此，要预测森林对气候变化的反应，就需要更好地了解从分钟到季节，从树叶到生态系统，包括大气圈、生物圈、土壤圈和水圈的各种时空尺度的碳和水循环过程。许多相关的过程发生在小尺度和高空间异质性，它们的相互作用和反馈回路可能是放大或抑制系统对压力的反应的关键因素。目前，我们缺乏适当的测量、数据和建模工具，无法对高时空覆盖率的相关过程进行全面、真实的时间量化。此外，气候影响是高度不可预测的，因此未来的研究将需要新颖的移动的，易于部署，成本效益高的方法。我们的跨学科研究项目ECOSENSE将调查下一代生态系统研究评估的所有相关尺度。我们的愿景是根据对分层过程相互作用的理解来检测和预测生态系统功能的关键变化。为此，ECOSENSE将开发、实施和测试一种新型多功能、分布式、经济高效、自主、智能的传感器网络，该网络基于专为偏远和恶劣森林环境的特定需求量身定制的新型微传感器。他们将在自然结构复杂的森林中以最低限度的侵入方式测量生态系统状态和通量的时空动态。测量数据将实时传输到一个复杂的数据库中，该数据库可用于过程分析、深度学习方法和增强的模拟模型，以用于现在和预测应用。ECOSENSE将通过以下方式为综合生态系统研究开辟新的领域：i）识别森林碳和水交换的非生物和生物过程的层次结构和相互作用，ii）提供对复杂生态系统对环境压力的响应的深刻理解，iii）预测生态系统功能和可持续性的基于过程的改变。我们的新型ECOSENSE工具包在受控气候极端实验中进行了测试和验证，我们的ECOSENSE森林将为在广阔和偏远的生态系统中进行快速评估开辟新的视野。