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Contribution to ZALF research

For a sustainable use of landscapes, a profound understanding of the multitude of processes that interact in the landscape is a central prerequisite. These landscape processes are studied at ZALF in a variety of networked research activities, which generate large amounts of empirical data with varying degrees of uncertainty. These require extensive documentation as well as powerful methods of statistical analysis. The research platform "Data Analysis & Simulation" ensures the collection of meta-data, long-term storage of well-documented data and the creation of user-friendly interfaces for research. In addition, powerful methods for the analysis of high-dimensional, heterogeneous data sets of different temporal and spatial coverage are further developed and applied, with special focus on typical characteristics of such data sets, such as non-linearity, instationarity, spatial correlations and memory effects.

Hypotheses derived from the data analysis will be tested by means of models. Modelling is an established tool in many disciplines and the number and complexity of available models is constantly increasing. Last but not least, the complexity of landscape processes has led to a multitude of model approaches which have to be combined in newly developed model platforms to support integrated landscape research. But also beyond model development challenges arise which require a more in-depth scientific consideration: Methods of model calibration and validation, data assimilation techniques, data-driven modelling approaches as well as the behaviour of models and model ensembles at the limits of their application. In particular, models that act at the landscape level and integrate processes of different instances in order to describe interactions at the interface of nature, economy and society have not yet been fully researched. The research platform "Data Analysis & Simulation" closes these gaps and provides a framework for the integration of different disciplines and modelling approaches for the understanding of the agricultural landscape function.


Working groups


Dimensionality Assessment and Reduction

Image of the WG Data Dimensionality 

Despite the large number of potential observables, landscapes are regarded as highly constrained systems with rather low effective degrees of freedom. The working group will quantify the number of effective degrees of freedom of large environmental datasets (e.g. via correlation dimension) and subsequently determine the dominating processes using methods of dimensionality reduction (e.g. principal component analysis, isometric feature mapping, etc.). To this end, the working group will use methods that explicitly consider the challenges of high-dimensional, heterogeneous datasets with different temporal and spatial coverage and typical characteristics like non-linearity, non-stationarity, spatial correlation, and memory effects. To date, these methods are rarely applied in most disciplines of environmental or related socio-economic science.

Contact: Prof. Dr. Gunnar Lischeid


Landscape Modelling

Image of the WG Integrated Landscape Modelling

This newly established working group develops the framework for the coupling of fundamentally different modelling approaches into an integrate landscape simulation. Initially, the working group will concentrate on a concept for the integration of process-based simulation models for cropland, grassland and forest ecosystems into a single modelling framework, with a special focus on transition zones between these ecosystems and the observed abiotic processes in these transition zones. In addition, remote sensing data is to be used to parameterise, initialise and drive one-dimensional process-based and other types of two- to three-dimensional models at the landscape scale. This includes models for socioeconomic processes.

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Contact: Prof. Dr. Claas Nendel


Ecosystem Modelling

Image of the WG Ecosystem Modelling 

The working group deals with point- and grid-based simulations with respect to topics like food security, climate protection and resource management. A focus lies on the numerous process interactions between soil and plants in managed agricultural systems and the effects their representation in models has on simulations. The working group builds on existing process-based agroecosystem, forest ecosystem and hydrological models, which form the core of future model development. Model improvement will focus on the reduction of uncertainties associated with model structure and parameters as well as on the expansion of the represented processes. To this end, the working group cooperates with different national and international networks and projects, such as e.g. MACSUR and AgMIP.

Contact: Prof. Dr. Claas Nendel


Simulation Methods and Data-driven Models

Image of the WG Simulation Methods 

This methodologically oriented working group deals with the analysis of the data-model-data loop in agricultural landscape research. The overarching task is to test the suitability of established methods from neighbouring disciplines and sectors for the simulation of interrelations and patterns in landscapes and the deduction of new insights from their application. To this end, the working group uses methods such as machine learning, deep learning, cluster computing, fuzzy models as well as Bayesian modelling.

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Contact: Dr. Ralf Wieland


Research Data (Service)

Image of the WG Research Data (Service)  

The working group delivers high-quality services for all ZALF researchers in all aspects of research data management and at all stages of the research process. Our key objective is the liberation of research potentials through an interoperable research data network with orchestrated workflows within and out of ZALF. This ensures the emerging of our ZALF “knowledge nexus” as a unique asset for every ZALF researcher. Important work results are ZALF Data Policy, ZALF Core Data Set, ZALF Framework, ZALF Repository and ZALF Data Management Plans. Our ZALF impact is catalyzed by our network activities and the brokering of new AI projects.

Contact: Prof. Dr. Gunnar Lischeid


Geodata (Service)

Image of the WG Geodata (Service)  

It is the task of this service group to provide a common, high-quality, consistent and well-documented geodatabase for agricultural landscape research as part of the ZALF geodata infrastructure (GDI), accounting for different aggregation levels and in consideration of the highly variable previous knowledge of the data users regarding the handling of geodata. This includes both ZALF data as well as data from external sources.

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Contact: Dr. Xenia Specka


Model & Simulation Infrastructure (Service)

Image of the WG Model & Simulation Infrastructure (Service) 

This working group is engaged with the development of a sustainable base for the modelling and simulation activities of the ZALF working groups and, in particular, the Research Platform “Models & Simulation”. The initial focus of the WG is on the consolidation of models already in use at ZALF and the development of a common IT infrastructure which executes these models and, in collaboration with the Research Platform “Data”, provides input data and stores simulation results, respectively. Building upon this work, the WG will implement the technological foundation to integrate models of different kinds. Additionally, the WG will establish mechanisms for quality management at different levels (model development, simulation, data provisioning/analysis).

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Contact: Michael Berg-Mohnicke


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