Dr. Y. Zeng | People Pages: Find employees & contact details

Dr. Yijian Zeng is an Associate Professor at the Department of Water Resources (WRS), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente in Enschede, the Netherlands.

He is the co-chair of ISMC(International Soil Modeling Consortium), which aims to integrate and advance soil system modeling, data gathering, and observational capabilities to address key global issues and stimulate the development of transdisciplinary and translational research activities. Additionally, he serves as a member of the GLASS Panel (Global Land/Atmosphere System Study) of the GEWEX (The Global Energy and Water Exchanges) Project, which is a part of the World Climate Research Programme (WCRP) and dedicated to understanding Earth’s water cycle and energy fluxes on and below the surface and in the atmosphere. The GLASS panel focuses on the development and evaluation of models, with a particular emphasis on the new generation of land surface models. Currently, he is the co-lead of GEWEX-SoilWat (Soil and Water) Initiative, a joint project between GEWEX and ISMC, which aims to improve the representation of soil and subsurface processes in climate models. The initiative brings together two research communities to identify benchmarking philosophy, critical datasets, challenges and unresolved issues related to this effort.

Organisations

Climate projections strongly suggest that the sweltering summer of 2022 may be a harbinger of Europe's future climate. Climate extremes, such as droughts and heatwaves, jeopardize terrestrial ecosystem carbon sequestration, impacting the EU's targets and policies to become the first climate-neutral continent by 2050. Therefore, it is pivotal to understand the climate resilience of agriculture and natural ecosystems. To address this challenge, Yijian Zeng, with colleagues of same vision, has been developing an "Open Digital Twin of Soil-Plant System," which includes three core components: i) The soil-plant model for a digital representation of the soil-plant system; ii) Physics-aware machine learning algorithms to approximate the soil-plant model; and iii) Data assimilation framework to digest Earth Observation data to update the states of soil-plant system.

Publications

2024

Dashboard of the Landscape Character of Nigeria (2024)[Dataset Types › Dataset]. Zenodo. Eneche, P. S. U., Pfeffer, K., Atun, F. & Zeng, Y.https://doi.org/10.5281/zenodo.13880381Investigating the role of groundwater in mitigating vegetation water stress (2024)[Contribution to conference › Abstract] World Groundwater Congress 2024, IAH 2024. Daoud, M. G. M., Alidoost, S. F., Schilperoort, B., Zeng, Y., van der Tol, C., Salama, M. S., Lubczynski, M. W., Yu, L. & Su, Z.Effects of vegetation on the hydro-mechanical properties of the vadose zone (2024)E3S Web of Conferences, 544. Article 16001. Anselmucci, F., Cheng, H., Fan, X., Zeng, Y. & Magnanimo, V.https://doi.org/10.1051/e3sconf/202454416001Robust drivers of urban land surface temperature dynamics across diverse landscape characters: An augmented systematic literature review (2024)Ecological indicators, 163, 1-25. Article 112056. Samson Udama Eneche, P., Atun, F., Zeng, Y. & Pfeffer, K.https://doi.org/10.1016/j.ecolind.2024.112056Understanding the effects of revegetated shrubs on fluxes of energy, water, and gross primary productivity in a desert steppe ecosystem using the STEMMUS-SCOPE model (2024)Biogeosciences, 21(4), 893-909. Tang, E., Zeng, Y., Wang, Y., Song, Z., Yu, D., Wu, H., Qiao, C., van der Tol, C., Du, L. & Su, Z.https://doi.org/10.5194/bg-21-893-2024Dashboard of Robust Landscape Drivers of LST in Diverse Landscape Characters (2024)[Other contribution › Other contribution]. Eneche, P. S. U., Pfeffer, K., Atun, F. & Zeng, Y.https://doi.org/10.17026/PT/XO0VIX

2023

Understanding the Effects of Revegetated Shrubs on Fluxes of Energy, Water and Gross Primary Productivity in a Desert Steppe Ecosystem Using STEMMUS-SCOPE Model (2023)[Dataset Types › Dataset]. Zenodo. Tang, E., Zeng, Y., Wang, Y., Song, Z., Yu, D., Wu, H., Qiao, C., van der Tol, C., Du, L. & Su, Z.https://doi.org/10.5281/zenodo.7986565Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale (2023)Geoscientific Model Development, 16(20), 5825-5845. Han, Q., Zeng, Y., Zhang, L., Cira, C. I., Prikaziuk, E., Duan, T., Wang, C., Szabó, B., Manfreda, S., Zhuang, R. & Su, B.https://doi.org/10.5194/gmd-16-5825-2023Survey questionnaire - Ahmedabad city - India (2023)[Other contribution › Other contribution]. Anand, A., Pfeffer, K., Reckien, D. & Zeng, Y.Effect of vegetation on the hydro-mechanical properties of the vadose zone (2023)In Proceedings of the 8th International Symposium on DEFORMATION CHARACTERISTICS OF GEOMATERIALS Porto, 3rd - 6th September 2023. International Society for Soil Mechanics and Geotechnical Engineering. Anselmucci, F. A. R., Cheng, H., Fan, X., Zeng, Y. & Magnanimo, V.https://www.issmge.org/uploads/publications/121/122/isdcg2023-58-1-c.pdf

Research profiles

The WRS’s mission is to “build communities of water professionals, scientists and engineers with skills to work toward a sustainable and resilient living environment using Earth Observations and Modelling of Water and Climate”. With this overarching mission, which contributes to all four profiling themes of ITC, WRS education centres on a ‘challenge-based’ format that offers potential solutions to society’s water-related problems with state-of-the-art knowledge, data, and models developed in research. This approach shifts from the concept of bringing ‘data to compute’ to bringing “compute to data” by using a cloud-computing platform like CRIB to bring questions (and needed computation) to the available data in the cloud and solve them there. Aligned with WRS’s missions and focus, Yijian Zeng has been working towards the Open Digital Twin of the Soil-Plant System, the elements of which are used in challenge-based education. Specifically, he has been actively initiating, developing, and coordinating the plan, content, and teaching for two WRS specialty courses and beyond, evolving these courses based on students’ and lecturers’ feedback.

Courses academic year 2024/2025

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2023/2024

The development of the Soil-Plant Digital Twin is linked to the EU’s green and digital transitions and contributes to ITC’s profiling themes on Resources Security, Disaster Resilience, and GEO-AI. A soil-plant digital twin refers to a highly interconnected workflow, with a data assimilation framework at its core to combine observations and the process-based model (STEMMUS-SCOPE), accompanied by an interactive and configurable platform that allows users to create and evaluate user-specific scenarios for scientific investigation and decision support. Creating an open digital twin of soil-plant system means creating a digital twin following Open Science and FAIR principles, for both data and research software.

Yijian Zeng has sustained the acquisition of multi-year scientific projects from competitive grants to stay the course to develop and create the “Open Soil-Plant Digital Twin”.

Current projects

CO-I: WUNDER - Water Use and Drought Ecohydrological Responses of Agricultural and Nature Ecosystems in the Netherlands: Towards Climate-Robust Production Systems and Water Management (2022-2028, €1,516,238)

As a result of climate change, extreme droughts are expected to occur more often in the Netherlands, potentially causing social distress and huge economic damages. The WUNDER project will develop an integrated modeling system for understanding the behaviour of soil and vegetation during prolonged drought events. The system will enable to explore scenarios and evaluate strategies for managing, planning and adapting agriculture and nature systems to extreme droughts. The project will actively engage with farmers, water managers and other decision makers and develop practical use cases for daily drought monitoring and prediction, thereby supporting climate-robust production systems and water management.

PI: EcoExtreML - Accelerating Process Understanding for Ecosystem Functioning under Extreme Climates with Physics-Aware Machine Learning (2021-2025, € 253,000 + 3 Person-Year Research Software Engineers from NLeScience Center)

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 hindered the full potential of synergizing them. This project aims to couple the vegetation photosynthesis model (SCOPE) with the soil moisture model (STEMMUS, considering dynamic root growth), and synergize it with Earth-Observation data, to understand how the water-carbon dynamics of ecosystems vary with variable environmental and climate stress. The bottleneck in applying STEMMUS-SCOPE globally is its expensive computational cost. As a first step, the coupled STEMMUS-SCOPE model will be exposed to Basic-Model-Interface. 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 cloudy condition), OpenDA will be deployed to assimilate multiscale/multi-sensor data to generate spatiotemporally continuous information on ecosystem functioning. This project will open up a variety of new opportunities for Earth-Observation, including retrieving higher-level products such as root-zone-soil-moisture and belowground-carbon-allocation, besides land-atmosphere gas exchanges.

Geoversity's interview on soil-plant digital twin: