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Accelerating Process Understanding for Ecosystem Functioning under Extreme Climates with Physics-Aware Machine Learning

Droughts and heatwaves impact ecosystem water, energy and carbon fluxes, and jeopardize terrestrial ecosystem carbon sequestration. Remote sensing of fluorescence and plant-hydraulics- based vegetation models are state-of-the-art approaches to monitor and predict drought responses of ecosystem functioning. However, the disciplinary disconnect between the two approaches has hazed the full potential of synergizing them. This project will couple the vegetation photosynthesis model (SCOPE) with the soil moisture model (STEMMUS, considering dynamic root growth), synergized with Earth-Observation data, to understand how water-carbon dynamics of ecosystem vary with variable environmental and climate stress.

The bottleneck in applying STEMMUS-SCOPE globally is its expensive computational cost. As the first step, both STEMMUS and SCOPE will be exposed to Basic-Model-Interface, which will be subsequently coupled using Python-Modeling-Toolkit, serving as the first level acceleration. Second, a physics-aware machine learning emulator, based on a limited number of STEMMUS-SCOPE runs, will be developed. Furthermore, to address the ‘data-gap’ issue of satellite reflectance products (i.e., revisit-time (5–27days) and cloudycondition), OpenDA will be deployed to assimilate multiscale/multi-sensor data to generate spatiotemporally continuous information on ecosystem functioning. This project will open a variety of new opportunities for Earth-Observation for retrieving higher-level products like root-zone-soil-moisture and belowground-carbon-allocation, besides land-atmosphere gas exchanges.

Participating organisations

Netherlands eScience Center
University of Twente
Environment & Sustainability


Yijian Zeng
Lead Applicant
University of Twente
Zhongbo Su
Raul Zurita Milla
Bart Schilperoort
Fakhereh (Sarah) Alidoost
Fakhereh (Sarah) Alidoost
eScience Research Engineer
Netherlands eScience Center
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center
Yang Liu
Yang Liu