Machine Learning for Hydrological and Earth Systems

Dr. Shijie Jiang

Mission

In the face of increasing environmental and climatic pressures, it is imperative to improve our understanding of the variability and causality underlying biogeochemical cycles across different spatial and temporal scales. The research of the Machine Learning for Hydrological and Earth Systems (ML4HES) group, which is also part of the ELLIS Unit Jena, is at its core motivated by the objective of exploring how environmental and climate sciences can benefit from advances in machine learning and artificial intelligence. We focus on integrating data and domain knowledge with hybrid and explainable machine learning methods to improve the fundamental understanding of the interactions of climate, water, and ecosystems.

Focus areas

Hybrid and explainable AI in Earth system science

The main methodological focus of the group is knowledge-integrated and interpretable machine learning. We emphasize the combination of advanced data-driven techniques with classical Earth system analysis to improve our modeling and understanding of the complex dynamics among interconnected Earth subsystems. We view machine learning as both a bridge and a lens, connecting extensive Earth datasets to the underlying system dynamics. We are particularly interested in exploring effective and robust methods for identifying dependencies, interactions, couplings, feedback mechanisms, and causal relationships within various biogeochemical processes.

Key publications

Terrestrial ecohydrological processes and feedbacks

One of the main research goals of our group is to understand the complex interactions between vegetation and hydroclimate at multiple scales. Using advanced data-driven techniques and mechanistic ecohydrological and ecosystem models, we aim to uncover the intricate responses and feedbacks between water, plant physiology, and climate variables. We seek to understand how shifts in hydrological patterns can regulate ecosystem responses to environmental change, as well as how changes in terrestrial ecosystems can feedback into hydroclimate systems and influence extreme events.

Key publications

Blougouras et al.; Huang et al. (coming soon)

Water and carbon cycles in watershed systems

    Another main focus of our group is to understand the processes that control water and carbon fluxes and ecosystem functioning at the watershed scale. We emphasize the role of watersheds as fundamental natural landscape units for the cycling of water, energy, carbon, and nutrients. Using advanced modeling techniques and data-driven analyses, we aim to uncover critical thresholds and interactions that influence watershed resilience to environmental change. Our overall goal is to identify, quantify, and predict hydrological and biogeochemical processes in response to natural and anthropogenic variability.

    Key publications

    News

    November 2024

    Winter is approaching, but November is full of excitement! This month we welcome two newcomers, Chao Wang and Yao Li, who will be exploring ecohydrological processes using machine learning and process-based hybrid models. Stay tuned for their exciting work!

    September 2024

    We are excited to have Jialiang Zhou join our group for his PhD journey! Jialiang will explore a new method to understand plant physiological responses. We are also delighted to welcome Shengyue Chen to our group for a one-year visit.

    August 2024

    Shijie Jiang, Lijun Wang, and Feini Huang presented their research at the AI and Data Science for Earth System Sciences workshop @ELLIS Unit Jena.

    April 2024

    Georgios Blougouras, Feini Huang, and Shijie Jiang attended and presented at EGU24 in Vienna. At the conference, Shijie also convened and chaired a successful and well-attended session on Explainable and Hybrid Machine Learning in Hydrology.

    Team

    Associated group members

    Yao Li

    Doctoral Researcher

    Jinfeng Zhao

    Doctoral Researcher

    Projects

    Knowledge Integration for Spatio-Temporal Environmental Modeling 

    The project funded by Carl Zeiss Stiftung aims for excellent research in AI and will be located at the interface between AI and environmental research. The goal is to integrate expertise into AI approaches and to draw new insights from this, e.g., for climate extremes and their effects ecosystem functions or services.

    Publications

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