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Research groups

Our prime methodological principle is the notion that theory-driven (or hypothesis-driven) and data-driven (or exploratory) approaches have complementary strengths and should be used in synergy. Consequently, the research groups are arranged such they can bridge the gap between exploratory versus theory driven methodologies. Integrative scientific questions in the department are addressed by combining the methods of two or more of the research groups.

Soil Biogeochemistry

The Soil Biogeochemistry Group aims at understanding and quantifying the role of belowground processes for biogeochemical cycles at different spatial scales. Our main objective is explaining the persistence of organic matter in soils in order to assess its vulnerability to global environmental and land use changes.

Biosphere-Atmosphere Interactions and Experimentation

The Group on Biosphere-Atmosphere Interactions and Experimentation applies a mixed approach of long--term monitoring and experiments to analyze land-atmosphere interactions with an emphasis on the effects of nutrient limitations. The regional focus of this group is on Mediterranean Ecosystems.

Model-Data Integration

The Model-Data Integration group is strongly motivated by the challenge that representing terrestrial ecosystem fluxes in space and time poses in earth system science. We explore strategies and develop methods to extract and transfer information from data to models towards improving understanding of ecosystem function.

Global Diagnostic Modelling

The Global Diagnostic Modelling group aims to develop and analyse global data-driven estimates of carbon,water, and energy fluxes by integrating in-situ measurements, satellite remote sensing, and meteorological reanalysis using machine learning and model data fusion techniques

Flora Incognita (BMBF funded)

The aim of the “Flora Incognita” research group is to develop a method for semi-automatic plant identification via mobile devices. Challenging current standards, modern computer vision techniques such as deep neural networks will be combined to a "connected data" application, using site information (e.g. climate, geology, phenology) and plant morphological traits.

Hydrology-Biosphere-Climate Interactions

The HydroBioClim group explores the interplay of soils, vegetation and atmosphere. Through modelling and observation data analysis we contribute to (1) a better management of extremes such as droughts and heat waves, (2) improved hydro-meteorological forecasts, and (3) more reliable climate change projections.

Climate-ecosystem-disturbance interactions

The Climate-Ecosystem-Disturbance Interactions group investigates the links between climate variability and extremes, ecosystem dynamics and disturbance regimes at regional to global scales. By combining observations and modelling, we aim at better understanding the processes that drive interannual to decadal variability in the carbon cycle.

Former research groups

Empirical Inference of the Earth System

The Global Empirical Inference Group is dedicated to explore the hidden features in Earth observations to better understand global biogeochemical cycles, land-surface atmosphere interactions, and the functioning of terrestrial vegetation.

Terrestrial Biosphere Modelling

The Terrestrial Biosphere Modelling Group investigates the interactions between global biogeochemistry and the climate system. Numerical models of terrestrial biosphere dynamics are developed, focussing on the representation of ecophysiological processes, the interactions between the terrestrial carbon, nitrogen and phosphorus cycles, and feedbacks to the global climate system.

Terrestrial Ecosystem Modelling Group

The Terrestrial Ecosystem Modelling Group (2006 - 2014) led by Christian Beer investigated interactions between thermal, hydrological and carbon processes in the context of climate change by a mechanistic modeling approach with a main focus on ecosystems at high latitudes. Key goal was the improved representation of soil carbon transport, soil carbon decomposition, soil thermal dynamics, vegetation carbon density, methanogenesis and methanotropy in the land surface scheme JSBACH for predicting the past and future spatially explicit trajectories of energy, carbon and water states and fluxes.

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