PhD project offered by the IMPRS-gBGC in Jan 2025

Improving soil-plant-atmosphere modeling under drought conditions via coupled processes and data integration

Shijie Jiang, Yijian Zeng, Sung-Ching Lee, Gregory Duveiller, Alexander Brenning, Markus Reichstein

Project description

Climate change has intensified droughts worldwide, putting ecosystems under increasing water stress and threatening their resilience. Understanding how ecosystems respond to water limitation is essential for predicting future impacts on carbon and water cycles. In particular, soil-plant-atmosphere interactions - such as root water uptake, plant hydraulic function, and canopy energy exchange - are key to understanding how ecosystems cope and adapt to drought. However, existing models often struggle to accurately capture these interactions under varying environmental conditions due to the complexity of coupling processes across soil, root, hydraulic, and canopy systems. This project seeks to address these gaps by applying and further developing a Soil-Plant-Atmosphere Continuum model (e.g., the STEMMUS-SCOPE model) that incorporates plant hydraulic dynamics (e.g., PSInet data). In addition, data-driven parameterization using machine learning will be considered to improve specific process representations to create a flexible and accurate hybrid model for simulating water-carbon dynamics under drought. Particularly, interpretable machine learning techniques will be used to deepen our understanding of key mechanisms and to complement existing mechanistic models.

The overarching goal of this PhD project is to improve our understanding and predictive ability of soil-plant-atmosphere interactions under drought conditions through the development of a coupled, data-integrated model. Using state-of-the-art data-driven approaches, the research aims to reveal hidden relationships and provide actionable insights into the complex non-linear dynamics linking soil moisture, plant hydraulics, and canopy fluxes, particularly in response to water stress and changing environmental conditions. Ultimately, these efforts aim to provide early warning indicators of ecosystem stress and insights for better ecosystem management under a changing climate. The prospective PhD student will be encouraged to explore their own innovative approaches within this integrated modeling framework, emphasizing both mechanistic understanding and data-driven improvements.

Working group

The successful candidate will work in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry and will also be affiliated with the Friedrich Schiller University, Jena. The working group offers long-standing expertise and experience in the various fields relevant to this project. There will be a close collaboration with the Department of Water Resources of the University of Twente, the Netherlands. For further information, please contact Shijie Jiang.

Requirements

Applications to the IMPRS-gBGC are open to well-motivated and highly-qualified students from all countries. Prerequisites for this PhD project are:

Master's degree in ecohydrology, environmental science, earth system science, biogeosciences, climate science or related fields.
Strong background in eco(-/)hydrological modeling. Experience in data integration and machine learning (e.g., neural networks) for environmental applications is preferred but not required.
Proficiency in a modern programming language (e.g., Python, R, Julia).
Broad interest in soil-plant-atmosphere processes and water-carbon dynamics.
Fluency in spoken and written English.
Willingness to work in an interdisciplinary environment with geoscientists and computer scientists.

The Max Planck Society (MPS) strives for gender equality and diversity. The MPS aims to increase the proportion of women in areas where they are underrepresented. Women are therefore explicitly encouraged to apply. We welcome applications from all fields. The Max Planck Society has set itself the goal of employing more severely disabled people. Applications from severely disabled persons are expressly encouraged.

References

Wang, Y., et al. (2021). Integrated modeling of canopy photosynthesis, fluorescence, and the transfer of energy, mass, and momentum in the soil–plant–atmosphere continuum (STEMMUS–SCOPE v1. 0.0). Geoscientific Model Development, 14(3), 1379-1407.
Reichstein, M., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.
Green, J. K. (2024). The intricacies of vegetation responses to changing moisture conditions. New Phytologist.