Helmholtz-Zentrum Deutsches Geoforschungszentrum

Sektion 4.7: Erdoberflächenprozessmodellierung

Forschung und Modellierung

Die Arbeit unserer Sektion ist in verschiedene Projekte gegliedert, die größtenteils eine Modellierungskomponente beinhalten. Sie decken ein breites Spektrum von Themen ab, die sich auf das quantitative Verständnis der Entwicklung der Erdoberfläche, die Parametrisierung von Prozessen und die Art und Effizienz der Verbindungen mit Klima, Tektonik und Leben konzentrieren.

Nachstehend finden Sie einige Beispiele für unsere Modellierungsarbeit. Bitte klicken Sie auf die Grafiken, um sie in größerem Format zu betrachten.                                                                                        

Modelling Grain Size Evolution within Fluvial Systems

Changes in grain size within river systems serve as an indicator of past climatic and tectonic events within the sedimentary record and play an important role in defining channel and bed morphology. Thus, it is crucial to thoroughly understand the relationship between grain size fining and landscape response over long time scales. To reproduce the fluvial stratigraphic record, Fedele and Paola (2007) developed a self-similar model of grain size fining along a river profile for riverine substrate that collapses many of the hydraulic and bed surface details relevant for simulating modern grain size transport.

We have generalized the Fedele and Paola (2007) self-similar model into three dimensions (across the basin, downstream, and over time) by removing the river length scaling and incorporating this grain size approach into the Fastscape landscape evolution model. Our three-dimensional approach to grain size fining (GSFAST) has shown that channel mobility in the alluvial basin strongly alter the deposition and grain size fining rate in a way unaccounted for using fewer dimensions. The implications of a landscape evolution model containing substrate grain size in three dimensions are in the model's application to constrain the environmental forcing conditions that would be plausible to produce observed field data. However, in our future work, we will need to first quantify what controls the avulsion rate in the model and compare this to the avulsion frequency in natural systems before applying GSFAST to interpret grain-size data. Reference: Fedele & Paola. (2007). JGR: ES 112(F2).

(c) Amanda Wild


Arctic-delta simulations

The Arctic is rapidly changing under a warming climate, which affects the polar regions even more than elsewhere on our planet. Many of these changes manifest themselves as landscape features resulting from surface processes of the Earth. We are developing deep-learning models to detect and quantify the changes related to such surface processes in the Arctic region. The outcome will enhance existing measurements and observations, providing an enriched dataset for investigating Arctic surface processes. To understand the underlying physics of this important and complex component of the Earth system, with the aid of available data, we are also developing a physics-informed deep-learning framework to enable efficient and accurate modelling of Arctic surface processes. The results should help researchers and decision makers to predict and assess the impact of climate change in this fragile region on our planet.

This movie shows an interactive visual analysis of a pair of example Arctic-delta simulations. The upper panel (with the colour bar) shows the top-down view of the deltas’ physical manifestations as one scrolls through time. In this particular instance, the bed elevation (‘η’) and unit discharge (‘qw', which is a measure of the quantity of water flowing through each pixel) are displayed in sequence. The middle panel shows the values displayed in the upper panel along the cross section marked by the yellow semi-circle. The bottom panel shows the averaged values along all similar semi-circles at a range of distances from the inlet, which is situated at the middle-bottom of the upper panel. 

(c) Dr. Ngai Ham Chan

Marine terraces and sea level

The marine terrace record is rich of information to reconstruct sea level in the past and determine how fast rocks are moving up and down under the same tectonic forces that create earthquakes. With my geologist colleagues at GFZ and elsewhere, we seek to better understand how the combination of sea level variations and rock movement result in marine terraces of different sizes and shapes. We can then use this understanding to look at natural landscapes and reconstruct the local history of rock deformation and sea level variations in the last million years.

(c) Dr. Luca Malatesta

The impact of lakes on long term evolution

On this example, we ran the exact same initial landscape with the exact same tectonic or lithologic conditions, but the left scenario explicitly takes account of lake dynamics whereas the right one simply redirects the fluxes from the lake bottom to its outlet. Note the significantly different response time and plan-view geometry after 2 millions years.

(c) Dr. Boris Gailleton

Laufende Projekte


Physics-constrained deep learning framework for quantifying surface processes across the Arctic region

The permafrost-laden landscape of the Arctic is highly susceptible to degradations in the warming climate, and harbours the potential to exacerbate climate change due to its huge store of soil organic carbon. Large-scale monitoring and fast predictive simulations of permafrost-related features and natural systems are thus urgent and important. The project aims to develop both a deep-learning model capable of detecting and quantifying permafrost-landscape changes, and a physics-informed deep-learning framework to enable rapid modelling of complex Arctic surface-processes systems.


Understanding the role of sediments in postglacial landscape evolution

Repeated glacial-interglacial cycles during the Quaternary have significantly impacted the topography of many mountain belts around the world. In recently deglaciated landscapes, the transition from glacial to fluvial/hillslope processes have induced transient adjustments in postglacial landscapes.

Velocity of escarpments and drainage divides

Rates of drainage divide migration

Bedrock river incision drives the topographic evolution of mountain ranges, orchestrating the adjustment of rivers and valleys towards a configuration with erosion rates everywhere equal to rock uplift rates.


Modelling and inverting deep marine stratigraphy

Though the deep marine environment (below the continental shelf break) contains some of the most extensive and complete deposits on Earth, there are few process-based approaches for modelling the development of deep marine stratigraphy.

How important is short-term climate variability for the efficiency of erosion?

Discharge variability and bedrock river incision on the Hawaiian Island of Kaua'i

The Hawaiian island of Kaua’i provides an ideal natural laboratory to evaluate the effects of discharge variability and thresholds on bedrock river incision because it has one of Earth’s steepest spatial gradients in mean annual rainfall...

Linking landscape evolution and life

Geomorphological processes can have a large impact on terrestrial ecosystem evolution and can therefore play an important role in macroevolutionary processes through time

Long-term topographic history of Madagascar

Since its separation from Africa at ~150 Ma and from India at ~90 Ma, Madagascar has its own geological, geomorphic and biogeographic histories, providing an exceptional opportunity for investigating the relationship between the region’s landscape evolution and its biogeography

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