Simulation of forest ecosystems across scales | 9am PT Tues, 2025

[Grigory Bronevetsky]

Modeling Talks

Simulation of forest ecosystems across scales Werner Rammert, Technical University of Munich (TUM) Tues, March 25, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/simulation-of-forest-ecosystems-across-scales More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Forests are crucial components of the Earth system, regulating carbon cycles and supporting biodiversity, yet they face significant threats from global change. Understanding the mechanisms and dynamics within forest ecosystems, including the role of disturbances, is therefore paramount. Ecosystem models serve as essential tools for this purpose. High-resolution and process-based models are critical for capturing the complex inter-tree interactions, such as competition, and for accurately simulating the impacts of global change. In this presentation, I will first contextualize the challenges before introducing iLand, an individual-based forest landscape and disturbance model. Subsequently, I will demonstrate how a Deep Neural Network (DNN) based meta-modeling approach can effectively scale this detailed, tree-level perspective to regional and continental extents. Finally, I will present examples that illustrate current research directions and potential avenues for future investigation. Bio: Dr. Werner Rammer is a Senior Scientist at the Technical University of Munich. His expertise spans ecosystem modeling, software engineering, and machine learning, with a focus on the development and application of forest ecosystem models, including the models iLand and SVD. His research interests include climate change impacts, ecosystem services, and forest management. He has a strong background in developing and applying computational models for ecological research. https://scholar.google.com/citations?user=GroGXfYAAAAJ

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/MhDXRQiLiZc

Slides: https://drive.google.com/file/d/1fR2Zn46Rc6TBd7OYuEZ0A3KFpo581ZPx/view?usp=sharing

Summary:

• Forests are a critical ecosystem: land cover, biodiversity, water holding, carbon sequestration (same amount of carbon as atmosphere)

• Climate change is creating a new environment for trees to live and grow up

• Powerful disturbance agents

• Pest infestation (e.g. bark beetles)

• Fires

• Storms and wind 

• (in Europe disturbances increased at 1.5% per year since 1980)

• Forest modeling

• Started in 1970s

• Focus: Landscape scale modeling

• Processes

• Spatio-temporal interactions

• Management decisions

• Approach: landscape-scale modeling at the level of individual trees

• Climate - vegetation - disturbance

• Individual-based: tree-level interaction and competition

• Landscape-scale: captures large-scale disturbances

• Process-based: account for future climate change signals not observed in past data

• iLand model: https://iland-model.org

• Hierarchical multi-scale approach

• Single tree: location, height, diameter, tree crown shape/size

• From carbon perspective can divide into pools: foliage, branches/twigs, stem, roots

• Many different physical processes modeled

• Modularity: processes can be decomposed into distinct units (same crown repeated)

• Repetitiveness

• Focus: competition for light process

• Model the shade impact of different crown shapes into nearby trees (ray tracing), 

• Integrating over changing sun position

• Compute: light influence pattern over space/time

• Can build a library of precompiled light impact patterns

• Patterns from multiple trees are superimposed on each other

• Focus: demographic processes

• Growth

• Light

• Temperature

• Water/nutrients

• Predict: annual increment: GPP - respiration/turnover

• Carbon distributed into pools: leaves, branches, trunk, roots

• Regeneration

• Production of seeds

• Distribution of seeds on the landscape

• Environment filters decide where seeds establish

• Mortality

• Random tree death (parameterized by tree age)

• Stress function of carbon balance

• Competition, drought

• Stand-level processes (100m x 100m)

• Radiation interception

• Production of carbohydrates

• Environmental modifiers (temperature, water, VPD, nutrients, CO2): 3PG model

• Water cycle

• Flow of water into/through soil

• Evapotranspiration

• Disturbances and management

• Wind: wind speed at canopy edges, critical wind speeds, damage spread spatially explicit

• Bark beetles: spatially explicit dispersal, colonization and tree defense (10m resolution), wind damage can act as trigger for beetles

• iLand collaboration

• Primary focus: temperate and boreal forests (North America and Europe)

• Scaling up using deep neural networks

• Facilitating repetitiveness and abstraction

• Single tree -> impact pattern -> landscape

• Scaling Vegetation Dynamics (SVD): https://github.com/edfm-tum/SVD

• Vegetation dynamics: transition between states (probabilistic, take time)

• Composition: species abundance

• Structure: canopy height

• Functioning: density, LAI

• Compatible with remote sensing data

• Use deep learning to infer probabilities of state transitions

• Database of process model runs

• Train neural model to predict transitions given environmental drivers

• Example: fires in Greater Yellowstone ecosystem

• Used neural model to simulate different disturbances: rapid transitions across different states (logging, fire)

• Example: combine different simulation types to train DNN approximation that summarizes them all

• European forests

• Applied empirical disturbances from model trained on historical data, conditional on climate

• Fire, Wind

• Run at European-continental scale under different climate scenarios

• Fire very sensitive to climate scenario

• Wind not sensitive

• Bark beetle is in the middle

• https://www.westernfireforest.org/

• Using these models to evaluate fire future