Yijian Zeng

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

Key Qualifications:

• Scientist and Educator embracing ‘Challenged-based’ and ‘Open Science’ principles;
• Expertise in monitoring and predicting Soil-Water-Plant-Energy interactions, and linking physically-based processes above and below ground to the Earth Observation Observables in the visible, infrared, thermal and microwave domains.

He is elected to be 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.

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.

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.

Towards a Digital Twin of Soil-Plant System

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 (see Figure1): 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.

Figure 1 Three Core Components for a Digital Twin of Soil-Plant System

i) Digital Representation of the Soil-Plant System

STEMMUS: Physically-based Modelling of Soil and Subsurface Processes

A two-phase heat and mass transport model (STEMMUS – Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) was developed to understand the coupling mechanism between liquid water, water vapor, dry air and heat transport in the soil, using field observations and numerical simulations. It shows that the STEMMUS outperforms the traditional theory in estimating surface evaporation over arid and semi-arid regions [Zeng et al. 2011a WRR; Zeng et al. 2011b JGR].

Figure 2  Diagram of STEMMUS Physics (http://blogs.itc.nl/stemmus/)

STEMMUS-SCOPE: Understanding Water, Energy and Carbon Fluxes from Leaf to Ecosystem Scales

The coupled STEMMUS-SCOPE model integrates the SIF remote sensing with the plant-hydraulics-based SPAC model to advance our mechanism understanding of the complex soil-water-plant-energy interaction. The SIF remote sensing can acquire explicit information about photosynthetic light responses and steady-state behaviors in vegetation to evaluate photosynthesis and water-stress effects, across a range of biological, spatial and temporal scales. The plant-hydraulics-based SPAC model links mechanistically tissue-level stress to ecosystem-level water and carbon fluxes, via a resistor-based manner with the tissue-level hydraulic traits (of roots, stems and leaves) and stomatal optimality theory (i.e., photosynthetic gain vs. hydraulic risk).

Figure 3 The coupling scheme of STEMMUS-SCOPE (Wang et al. 2020 GMD)

STEMMUS-FT: Liquid-Vapor-Air Flow in the Frozen Soil

Accurate representing freeze-thaw (FT) process is of great importance in cold region hydrology and climate studies. With the STEMMUS-FT model (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil), we are able to depict the simultaneous movement of soil moisture and heat flow in frozen soil. The analysis of water/vapor fluxes indicated that both the liquid water and vapor fluxes move upward to the freezing front and highlighted the crucial role of vapor flow during soil FT cycles as it connects the water/vapor transfer beneath the freezing front and above the evaporation front.

Figure 4 Simulated vertical profiles of the thermal (q_LT, q_VT) and isothermal (q_Lh, q_Vh) liquid water and vapor fluxes, as well as soil moisture liquid content θ_L , ice content θ_i , and temperature T. (Yu et al. 2018 JGR)

STEMMUS-TeC: Ecohydrological Responses to Freeze-Thaw Cycles

The vadose zone is a zone sensitive to environmental changes and exerts a crucial control in ecosystem functioning and even more so in cold regions considering the rapid change in seasonally frozen ground under climate warming. The STEMMUS(-FT)-TeC model was used to investigate the role of vadose zone physics in the ecohydrological response of an Alpine meadow to freeze–thaw cycles.

Figure 5 STEMMUS-FT model was coupled to T&C model (Yu et al. 2021 TC)

STEMMUS-UEB: Integrated Modeling of Snowpack and Soil Water and Energy Transfer

A snowpack has a profound effect on the hydrology and surface energy conditions of an area through its effects on surface albedo and roughness and its insulating properties. STEMMUS-UEB (Utah energy Balance Snowpack Model, UEB) was developed to couple the snowpack and underlying soil layer to investigate the coupled liquid–vapor–air flow mechanisms considering the snowpack effect.

Figure 6 Evaporation using the Models with and without the Snow Module (Yu et al. 2021 GMD).

CLAP: Linking Land (Sub)Surface Processes to Earth Observation Observables

Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) aims to simulate the emission and backscattering signals of land surfaces at different frequencies, which have distinctive responses to soil and vegetation physical states. The use of multi-frequency combined active and passive microwave signals provides complementary information to better understand and interpret the observed signals in relation to surface states and the underlying physical processes. CLAP can improve our ability to retrieve surface parameters and states such as soil moisture, freeze-thaw dynamics and vegetation biomass and vegetation water content (VWC) for ecosystem monitoring.

Figure 7 Flowchart illustrating the procedure for the forward backscatter coefficient and brightness temperature simulations by CLAP (the coupled ATS-AIEM-TVG model) (Zhao et al. 2023 HESS).

ii) Physics-Aware Machine Learning

The physically-based process model like STEMMUS-SCOPE can serve perfectly as the virtual laboratory to study responses of ecosystem functioning to various climate stressors (e.g., rising CO2, temperature, and increasingly frequent extreme drought events). However, a major bottleneck using such advanced model, in routine processing at global scale, is its very expensive computational cost (with a large number and variety of input variables and a long processing time). A physics-aware machine learning approach can be adopted for accelerating STEMMUS-SCOPE’s running. The core idea is to approximate the original model by a surrogate machine learning model (i.e., emulator). Based on a limited number of STEMMUS-SCOPE runs, the input-output pairs (corresponding to training samples) are used to establish the emulator, which is then used to infer the model output given a yet-unseen input configuration. Currently, the Random Forest model was adopted for this purpose with the guide of physical principles (see Figure 8).

This physics-aware machine learning algorithm has been applied to produce global soil moisture products (Han et al. 2023 Sci. Data; Zhang et al. 2021 Remote Sens.), and will be applied to develop emulators for radiative transfer models (e.g., for CLAP and STEMMUS-SCOPE).