BIOGEOCHEMICAL INTEGRATION | PROF. REICHSTEIN

Research and Project Groups

Research and Project groups of the Department of Biogeochemical Integration

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The Atmosphere-Biosphere Coupling, Climate and Causality research group focuses
on identifying feedbacks and casual links in the exchange of carbon, water and
energy fluxes between the terrestrial biosphere and atmosphere.

The Eco-Meteorology group utilizes different micrometeorological methods and modelling approaches
to investigate land-atmosphere interactions for dryland (e.g., Mediterranean savanna) and wetland ecosystems (e.g. Blue Carbon).

The aim of the Bio.AI research group is to simplify, accelerate and increasingly automate global biodiversity
monitoring through automated species recognition in citizen science projects and remote sensing.Our specially
developed AI technology for species identification provides large amounts of data and targeted science communication
also helps to raise public awareness for the protection of biodiversity.

Bio.AI

Jana Wäldchen

The Ecosystem Function from Earth Observation group aims to rethink how we
can quantify and map the functional properties of ecosystems from space.

The Global Diagnostic Modeling group develops and analyzes 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.

The Model-Data Integration group is strongly motivated by the challenges of
representing terrestrial ecosystem fluxes in space and time in Earth system science.
We explore strategies and develop methods to extract information from data and translate
it into models to improve the understanding of ecosystem function.

The Soil Biogeochemistry group aims to understand and quantify the role of subsurface processes
in biogeochemical cycles at different spatial scales. Our main goal is to explain the persistence
of organic matter in soils,m their vulnerability to global environmental and land use changes.

The research group investigates the integration of data
and domain knowledge with hybrid and explainable machine learning
methods to improve the understanding of the interaction of climate
water and ecosystems.

The Cross-Scale Terrestrial Ecophysiology (XTE) group is focused on taking
understanding gained from on the ground measurements into the context of broad
scale carbon, water and energy cycles, via knowledge guided, data driven methodologies.

The Modelling Interactions in Soil Systems (MISS) project group concentrates on understanding the
dynamics and feedbacks in soil organic carbon (SOC) formation and decomposition. The group emphasizes the role of
mineral-associated organic carbon (MOC) and particulate organic carbon (POC) under changing environmental conditions.

light green = project groups                                                                                   dark green = research groups

Former Research Groups

Climate-ecosystem-disturbance interactions group

Climate-ecosystem-disturbance interactions group

The Climate-Ecosystem-Disturbance Interactions group focuses on the links between climate variability and change, disturbance regimes and ecosystem structure and functioning, at regional to global scales. More specifically, the group’s research aims to:
Thematic overview of the group (i) quantify ecosystem vulnerability and resilience to climate extremes and changes in disturbance patterns, including the role of management; (ii) understand the effects of compound disturbances (climatic and/or biotic) on ecosystem dynamics and biogeochemical cycling; (iii) gain insights on the drivers of inter-annual to decadal variability in the carbon cycle with a focus on ocean-atmosphere-land teleconnections.
  more
Hydrology-Biosphere-Climate Interactions

Hydrology-Biosphere-Climate Interactions

Group leader: René Orth

The HydroBioClim group explored the interplay of soils, vegetation and atmosphere. Through modelling and observation data analysis the group contributed 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. more

Hybrid and explainable deep learning (HDL)

Project group leader: Basik Kraft

The research group investigated approaches to use deep learning for process understanding via explainable machine learning (XAI) and hybrid modeling, the combination of physically-based modeling and machine learning. Scientific insights can either be achieved via built-in mechanisms (e.g., hybrid modeling) or via post-hoc explanations. Both approaches are motivated by the ever-growing amounts of Earth observation data, the limited capability of traditional, physically-based models to reproduce observed patterns, and the capability of modern deep learning approaches to approximate the behavior of complex Earth system processes. more
Terrestrial Biosphere Modelling (TBM)

Terrestrial Biosphere Modelling (TBM)

Group Leader: Sönke Zaehle
The Terrestrial Biosphere Modelling Group (TBM) aims at improving the understanding of the interactions of the biogeochemical cycles of carbon, nitrogen and phosphorus at temporal and spatial scales for relevant for the Earth System. To accomplish this goal, the group develops and employs numerical models of terrestrial biosphere processes, and uses observational constraints obtained from biosphere monitoring or ecosystem manipulation to challenge model formulations. An improved representation of key (eco-physiological) processes, in particular those affecting nutrient availability and its role in ecosystem dynamics, is a key component of the group's research. The group investigates the consequences of the coupling of the terrestrial biogeochemical cycles for biogeochemical and biogeophysical interactions with the climate system. more

Empirical Inference of the Earth System

Group Leader: former: Miguel Mahecha. Current: Markus Reichstein
The Global Empirical Inference Group reveled insights from observations by means of data-driven research is a key element in Earth system sciences. Long-term observations of multiple Earth system properties encode our knowledge on how land-surface processes respond to climatic variability and interact with biodiversity. They developed methods to extract the valuable information in these data in order to confront them with models, and gain new insights. We aim at a more profound understanding of changing land ecosystems and their responses to and interactions with climate anomalies..

Terrestrial Ecosystem Modelling Group

Group Leader: Christian Beer
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.
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