Career

European forests ablaze: did historical management add fuel to the fire?

MSc level

Description:

Heat extremes, combined with drought and low moisture in dead and live fuels, can lead to extremely destructive forest fires. Moreover, droughts drive reductions in forest canopy that not only increase dead fuels (leaf and branch litterfall) but also affect the land surface’s energy balance. In doing so, droughts may either diminish or intensify heat extremes via land–atmosphere feedback, hence fire risk.

Whilst the conditions conducive to forest fire are reasonably well identified, the extent to which different tree species and species composition contribute to fire risk is less clear. This project will investigate the role played by different European forest species in driving vs. mitigating fire risk. More specifically, the contributions of historical forest conversion choices to an increase in drought intensity and fuels leading to an increase in burnt areas will be probed, using a state-of-the art land-surface model (LSM) that considers plant hydraulics and species-specific responses to atmospheric aridity and water availability (Sabot et al., 2020, 2022; De Kauwe et al., 2022).

Given steady increases in wood harvest intensity over Europe, it is plausible that historical species conversion choices (with little other consideration than a fast wood yield) have increased the risk of forest fire. Model outputs will be analysed within a new conceptual framework (Jézéquel et al., 2024) that links extreme weather impacts to human influence on both hydro-climatic drivers, through anthropogenic forcing, and exposure and vulnerability, in this case historical forest management (i.e., the choice of tree species historically planted across European forests).

For a proof of concept, this MSc project will focus on forested areas within France. Extreme fire events (causing very large burnt areas in recent years) will be identified, as well as the non-endemic tree species affected by these extreme fire events. Once interesting events are identified, the LSM will be run for the weather conditions leading the fires, to quantify the amount of drought stress non-endemic species were under. These results will be contrasted with alternative hypothetical simulations accounting for endemic species and different atmospheric [CO₂] levels.

Contact: Manon Sabot (msabot@bgc-jena.mpg.de)

Getting to the root of vegetation change: linking root growth, root biomass, and aboveground anatomical traits through time

MSc level

Description: Plants exhibit a flexible trade-off between belowground (roots) and aboveground (stem, leaf, etc) growth, which allows them to balance their resource acquisition (e.g., nutrients, water, light, carbon) in response to changing environmental conditions. Plant growth strategies greatly affect the productivity and functioning of vegetation, for example in the face of drought or nutrient stresses. Yet, our understanding of variability in root-to-leaf investments across environmental gradients and plant species is equivocal and limited, and there is considerable uncertainty concerning future adjustments in these investments as the climate continues to change.

This thesis will seek to unlock new understanding on belowground  growth patterns in two species of oaks commonly found across Europe (Quercus cerris and Q. robur). Specifically, the project will make use of timeseries of root images, captured by automated underground monitoring devices called robotic minirhizotrons, within the context of a controlled greenhouse experiment involving 104 trees and both a soil and a drought treatment. Measurements of root biomass and aboveground anatomical plant traits will complement these root images. By combining these data, the thesis will ask:

1. How well can root biomass be predicted from root images?

2. Does knowledge of above ground plant traits increase root biomass predictability?

The MSc candidate will be called to learn a variety of measurement and analytic methods, from root sampling to image analysis and statistical modelling / machine learning (e.g., Dynamic Generalized Additive Models, Random Forest), the latter which they will also have the opportunity to influence.

Working group: The thesis will contribute to an ambitious controlled greenhouse experiment, and it will be highly collaborative within the Climate and Plant Ecophysiology group of the Max Planck Institute for Biogeochemistry.
Useful skills: 

• Knowledge of a script programming language such as Python or R
• An interest in working with field/experimental data
• Basic understanding about carbon and water trade-offs in plants 

Contact: Manon Sabot (msabot@bgc-jena.mpg.de)

High-Resolution Soil Moisture Mapping Over Arctic Permafrost Regions Using an Unmanned Aerial Vehicle (UAV)-Based Thermal Imagery
 

MSc level

• Conducting a literature survey on the UAV-based thermal imaging and soil moisture estimation methodologies, including machine learning approaches
• Processing and analyzing the UAV-based thermal images to estimate the soil moisture content
• Supporting carbon flux upscaling effort

• Basic knowledge of a scripting language like R or python
• Good communication skills (oral and written) in English
• Basic understanding about statistical analysis

Frequency and intensity of extreme drought events in state-of-the-art climate model projections

BSc / MSc level

• Profound knowledge of a script programming language such as Python or R
• No fear of working with datasets that can have more than 100GB in size
• Basic understanding about the relevant processes and variables of the global carbon cycle

Identifying key plant hydraulic traits of European species using the terrestrial ecosystem model QUINCY

MSc level

Description: Terrestrial ecosystem models (TBMs) such are QUINCY are used to investigate impacts of current and future climate projections on forests, corps and vegetation in general. Plant hydraulics, i.e  more detailed physical representations of the conducting system of plants have recently been brought into focus of TBMs as they are crucial when understanding impacts of extreme events such as droughts. Such a new plant hydraulic architecture has also been introduced to QUINCY and its capability of reproducing drought induced vegetational responds have been demonstrated. However, the influence of the new physicall parameters have not yet been tested and calibrated for the most important plant/tree species in Europe or Globally. The overall objective of this thesis is to improve our undestanding of drought impacts on vegetation by improving the performance of the QUINCY model. Thereby, the student should (1) test the newly implemented plant hydraulic architecture and (2) perform a sensitivity analysis to esimate parameter ranges and estimates for the most important European tree species.

Useful skills: 

• Knowledge of a script programming language such as Python or R to perform sensititivy or related analysis
• Experiences in sensitivity analysis based methods such as latin hypercube sampling or sobol indices
• Basic understanding about the physical processes of plants, tree and ecology in general

Stochastic modeling of tundra lakes in permafrost landscapes

• Solid foundations in mathematics and stochastics
• Theoretical atmospheric physics and geophysical fluid dynamics
• High performance computing

Exploring the effect of tree mortality on the net ecosystem exchange using LPJ-GUESS

MSc level

• knowledge of a programming language like R or python 
• Some knowledge on linux and bash scripting
• Basic knowledge of the carbon cycle and ecology
• knowledge on environmental physics