PhD project offered by the IMPRS-gBGC in Jan 2026

Hybrid modeling of root zone water storage and ecosystem responses to water availability

Shijie Jiang , Alexander Brenning , Markus Reichstein

Project description

Water stored in the root zone plays a key role in regulating evapotranspiration, carbon uptake, and land-atmosphere feedbacks. However, only a fraction of subsurface water is actually accessible to vegetation at a given time, and this "ecosystem-relevant" root zone storage cannot be observed directly with existing in-situ or satellite measurements (Gao et al., 2024). This limits our ability to quantify how much water ecosystems can mobilize during dry periods, and how this water availability shapes ecosystem functioning.

This PhD project will advance hybrid, physics-aware machine learning approaches to diagnose root zone water storage from observations. Building on our recent work that integrates simplified water-balance structures within differentiable neural networks to infer active root zone storage at the catchment scale (Blougouras et al., 2025), the project will extend these ideas to broader spatial domains. A key focus will be to analyze how variability in root zone water storage and its capacity is reflected in observable surface fluxes and vegetation activity, and how different ecosystems acquire and consume water under varying climatic and environmental conditions. The results will improve observation-based understanding of ecosystem water use and of how ecosystem functioning responds to environmental variability.

The successful PhD candidate will work at the interface of ecohydrology, land-atmosphere interactions, and machine learning. The project offers opportunities to develop hybrid modeling techniques, advance knowledge integration between observations and process understanding, and help uncover hidden ecosystem responses to water availability and climatic variability.

Working group & collaboration

The successful candidate will work in the Biogeochemical Integration department at the Max Planck Institute for Biogeochemistry and will also be affiliated with Friedrich Schiller University, Jena. The working group offers long-standing expertise in ecohydrology, environmental systems modeling, and hybrid and interpretable machine learning. The PhD candidate will engage closely with the ELLIS Unit Jena as part of the European Lab for Learning and Intelligent Systems (ELLIS), benefiting from a strong international machine learning research network. 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 earth system science, hydrology, remote sensing, climate science, ecology, or a closely related field, applicants with a background in computer science or data science are also welcome if they have strong interest in environmental systems
Solid understanding of land-atmosphere interactions, ecohydrological processes, or related areas
Experience in environmental modeling, hydrological or land-surface analysis, or the interpretation of geophysical datasets, exposure to machine learning is desirable
Good programming skills (e.g., Python, R), familiarity with deep learning libraries (e.g., PyTorch, TensorFlow) is an advantage
Broad interest in terrestrial water and carbon cycling, and large-scale Earth system dynamics
Fluency in spoken and written English
Ability to communicate scientific ideas clearly within an interdisciplinary research environment

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 MPS has set itself the goal of employing more severely disabled people. Applications from severely disabled persons are expressly encouraged.

References

Gao, H., Hrachowitz, M., Wang-Erlandsson, L., Fenicia, F., Xi, Q., Xia, J., ... & Savenije, H. H. (2024). Root zone in the Earth system. Hydrology and Earth System Sciences, 28(19), 4477-4499.
Blougouras, G., Jiang, S., Brenning, A., Migliavacca, M., Slater, L. J., Zhou, J., ... & Reichstein, M. (2025). Spatiotemporal dynamics of active root zone storage revealed from hybrid machine learning. ESS Open Archive. DOI: 10.22541/essoar.176280049.93675424/v1