Research and Project Groups

Research and Project groups of the Department of Biogeochemical Integration


The Atmosphere-Biosphere Coupling, Climate and Casuality 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 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.

The goal of the Bio.AI research group is to simplify, accelerate and increasingly
automate global biogiversity monitoring through automated species identification in
citizen science projects and remote sensing. Our specifically developed AI technology for
species indentification provides large amounts of data for this, and targeted science
communication additionally contributes to raising public awareness for the protection of

The Ecosystem Function from Earth Observation group aims at rethinking
how we quanitfy and map ecosystem functional properties from space by synergistically
exploiting the increasing diversity of complementary satelliete data streams that are currently available

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.

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.

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.

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.

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.

light green = project groups                                                                                   dark green = research groups

Former Research Groups

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
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)
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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