PURPOSFULLY MONITORING CHANGES AND RELIABLY PROJECTING THEM
Themenbereichs Icon

Thematic Area

Smart Models / Monitoring

Vision

Smart models and monitoring allow complex environmental systems to be analysed and reliable predictions to be made about how environmental systems will react to anthropogenic disturbance.

To this end we have to succeed in reducing models to the absolute minimum required level of complexity and in optimally parameterizing them. This is a tremendous challenge in light of the complexity of environmental systems, the countless human-nature interactions, the extent, range or limitations of data, the different temporal and spatial scales and the human factor.


Challenges

Environmental systems are extremely dynamic, shifting from chaotic and unstructured complex states to diverse but structured complex states and vice-versa. Their characteristics and driving forces are not only heterogeneous, but also move on different temporal and spatial scales. If one then considers the multiplicity of interacting change processes on different scales that can lead to nonlinear and delayed reactions in environmental systems, it becomes clear just how difficult it is to explore and observe environmental systems in a measurable way or to describe and analyse them using models. Even if we succeed in developing predictive regional models for water -, energy- and material flows, biodiversity or ecosystem functions, then these results are largely afflicted with factors of uncertainty and it is imperative that such uncertainties are quantified and communicated. Furthermore, we are becoming more and more conscious of just how important it is to include the unpredictable human factor in models.

Uncertainties in predictions need to be quantified and communicated.


Questions

What is the right amount of simplicity or reduced complexity for environmental system models that will still provide us with reliable predictions? How does one reduce complexity? How can environmental systems such as the geological underground, soils, entire forest systems or river catchments be explored and observed over longer periods of time? How can gaps in the data be filled? Is the answer only through monitoring? Or can existing data also be extrapolated? Is there any quality control for large data sets? How (in)accurate are forecasts? Can the human factor really be portrayed in models?
 

So far the scientific community has taken two different paths. Scientists in this field either tend to develop multi-processing, complex models with an ultra-high resolution both temporally and spatially that are extremely complex and simulate a clearly too accurate forecast. Alternatively, they use over-simplified conceptual models, which have been adapted to specific regions, ecosystems or species communities and cannot therefore easily be transferred to other environmental systems or regions.

Scientists in the thematic field "smart models and monitoring" on the other hand have adopted a completely new approach. They have developed a hydrological modelling system based on the knowledge that large-scale phenomena such as the regional flow of a catchment area does not necessarily depend on all of the small-scale characteristics of this catchment area.

Investigation-, monitoring- and measuring campaigns must be driven from the model approach and the question we wish to answer.

Hence, it follows that a model can be made much simpler without losing its predictive power. This characteristic is referred to as the self-averaging property. The model has an optimal degree of complexity, is practical and can be transferred to other regions. These kinds of models are called "smart models". If the UFZ intends to keep following the smart model path, then data sets that are already available from various sources need to be prepared in such a way and undergo quality control to ensure that they are suitable to answer the respective question.

The same applies, if data are missing: investigation -, monitoring- and measuring campaigns must be driven by the model approach and the question that we wish to answer (goal orientation). In order to be able to uniformly and mathematically describe biotic and abiotic environmental systems, gaps need to be closed in the formulation of theories and scaling methods that work in theoretical hydrology must be fine-tuned for more complex environmental systems.

The UFZ develops smart models for three major areas: for terrestrial hydrology, for terrestrial and aquatic ecology and for geo-systems. Regional catchment models are being developed for hydrology, which help to conduct monitoring and measuring campaigns in a more goal-oriented manner or to optimise the management of water resources with better projections. Thereby scientists want to make the leap from complexity-reduced hydrological models to complexity-reduced ecosystem and matter flux models on regional scales.

In ecology the goal is to develop a common theoretical fundament for describing environmental systems by sufficiently incorporating biotic and abiotic factors,, processes and feedbacks. With this fundament ‒ the core of a new generation of more regional, more integrated, "smarter" environmental system models, it should be possible to mathematically describe and project ecosystem processes on the landscape level and at the same time reliably project them for the future.

In the field of geothermics THMC-modelling is implemented using strongly interlinked processes (thermal, hydraulic, mechanical and chemical) in order to analyse multi-physical processes in complex natural and technical energy systems.

 

At the heart of the environmental monitoring and valuable data suppliers is the hydrological and ecological observatory TERENO (Terrestrial Environmental Observatories). This Helmholtz observation platform is to be extended with a modular architecture in the future from the observatory MOSES (Modular Observation Solutions for Earth Systems). It is to be rapidly and flexibly implemented on a European-wide scale to record extreme events or in regions known for their trends in increasingly longer dry periods. The research infrastructure ACROSS (Advanced Remote Sensing) provides the necessary remote-sensing data on changes to the Earth’s water and biomass regimes. The visualization center VISLab has established itself as part of the UFZs infrastructure that is specialized in environmental data, enabling processes in technical energy systems, soil particles, aquifers or entire river catchments to be visualised in 3D.

In order to meet the higher arithmetic performance requirements of the models, a common computer concept is being developed for earth system modelling with partners such as the German Climate Computing Center (DKRZ) and the Research Center Juelich.  

As a cross sectional field, the UFZ’s thematic field "Smart models and Monitoring" is linked to all thematic areas, in particular "Water Resources and the Environment" and "Ecosystems of the Future" and within the Helmholtz Community itself in particular with the centers of the research field "Earth and Environment", and under it the German Aerospace Center (DLR) in the field of remote sensing and the Climate Service Center Germany (GERICS) at the Helmholtz Center Geesthacht for climate modelling.

Important strategic partnerships also exist with specialist authorities such as the the German weather service or the National Center for Atmospheric Research NCAR. Whether it is remote sensing, big datasets, data quality and data availability or the evaluation of modelling and monitoring concepts, it is only through the exchange of knowledge and expertise on the scientific level and from feedback with users that will allow us to come up with future scenarios as well as early-warning and planning systems as reliable decision-making tools.