Machine Learning for Hydrological and Earth Systems
Dr. Shijie Jiang
Mission
In the face of increasing environmental and climatic pressures, understanding the drivers and responses of coupled Earth system processes is essential. The Machine Learning for Hydrological and Earth Systems (ML4HES) group, also part of the ELLIS Unit Jena, combines data-driven methods with physical and ecological knowledge to study water, energy, and carbon dynamics across hydrological, ecological, and climate systems. We aim to identify key drivers, interpret system interactions and feedbacks, and improve the representation of coupled dynamics to support process understanding under environmental change.
Our approach
To support system-level analysis of coupled processes, we integrate remote sensing and in-situ observations across spatial and temporal scales to extract meaningful signals. Leveraging recent advances in machine learning, we use knowledge-integrated, data-driven models, particularly hybrid and explainable approaches, to connect observations with underlying processes, identify drivers, interpret responses, and inform process-based modeling.
Focus areas
Land-atmosphere interactions
We investigate how vegetation redistributes water and energy across landscapes and into the atmosphere. Our work examines the biophysical processes that mediate land–atmosphere coupling, including both local and non-local effects, to enhance our understanding of hydroclimatic variability across various scales.
Soil-plant-atmosphere continuum
We study how vegetation interacts with soil and atmospheric conditions to regulate water movement through the continuum from root to canopy. Our research aims to characterize water fluxes and states and to understand plant water-use strategies under environmental stress.
Watershed hydro-biogeochemistry
We study hydrological extremes and riverine carbon dynamics in response to climate and landscape variability, including how extreme events emerge and how water flow regimes and land surface processes shape carbon fluxes and biogeochemical conditions. Our work analyzes patterns across regions and scales to understand potential mechanisms.
News
July 2025
[Events] Shijie Jiang attended the ISMC–GEWEX SoilWat Meeting at the University of Reading. He gave a talk and chaired the session The Soil–Plant–Hydraulic–Energetic Continuum.
June 2025
[People] Lu Tian joined the group as a postdoctoral researcher. [Events] Shijie Jiang gave an invited talk titled Learning Earth Systems with AI: From Observations to Prediction at the Nucleus Jena – Vergangene Veranstaltungen series.
May 2025
[Events] Georgios Blougouras, Jialiang Zhou, Lijun Wang, Shengyue Chen, Chao Wang, and Shijie Jiang presented their work at EGU25 in Vienna. Shijie also convened and chaired the session Explainable and hybrid machine learning in hydrology and Earth system sciences.
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
2025
Shi, Z.; Zhao, W.; Xiong, Y.; Lian, X.; Fang, J.; Jiang, S.; Yan, C.; Winkler, A.; Qiu, G. Y.: A theoretical framework for critical canopy temperature and its potential to improve remote-sensing-based estimation of evapotranspiration. SSRN Conference Paper Series (2025)
Agathangelidis, I.; Blougouras, G.; Cartalis, C.; Polydoros, A.; Tzanis, C. G.; Philippopoulos, K.: Global climatology of the daytime surface cooling of urban parks using satellite observations. Geophysical Research Letters 52 (2), e2024GL112887 (2025)
Wang, C.; Jiang, S.; Zheng, Y.; Han, F.; Kumar, R.; Rakovec, O.; Li, S.: Distributed hydrological modeling with physics‐encoded deep learning: A general framework and its application in the Amazon. Water Resources Research 60 (4), e2023WR036170 (2024)
