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ISMC News Feb 28 2024

Announcements + Featured Paper + Featured Soil Modeller

Announcements

4th ISMC Conference

The abstract submission for the 4th ISMC Conference “Advances in Modeling Soil Systems, Earth System Science, and Beyond” from May 7-10, 2024 at Tianjin, China has been extended to March 1. 2012 (Friday)

Conference information, abstract submission and registration can be found here,
 
Special section in Vadose Zone Journal dedicated to the 8th Galileo Conference seeks for submission


For  a special section of Vadose Zone Journal dedicated to the 8th Galileo Conference the editors seek for submissions. If you have any paper fitting into the topic of the conference feel free to contact the  lead editors Nunzio Romano, Paolo Nasta, Harry Vereecken, or Karsten H. Jensen. The deadline for receiving submission is on March 20, 2024. Information can be found on the following VZJ webpage: https://acsess.onlinelibrary.wiley.com/journal/15391663/specialsectioncall

 
Featured Paper
Do you want your paper featured?
Please share your recent paper if you want to be featured in the ISMC newsletter. With your contributions, we will select one paper to be featured in every newsletter. Submission can be done here.


Soil carbon sequestration potential bounded by population growth, land availability, food production, and climate change

Improving soil management to enhance soil carbon sequestration (SCS)—a cost-efficient carbon dioxide (CO2) removal approach—can result in co-benefits or trade-offs. Here we address this issue by setting up a modeling framework for Switzerland that combines soil carbon (C) storage, food production and agricultural greenhouse gas (GHG) emissions. The link to food production is crucial because crop types and livestock numbers influence soil organic C (SOC) stocks, through soil C inputs from plants and manure. We estimated SCS rates for the years 2020–2050 for three scenarios, each with two variants for biochar: cover cropping (0.30 t CO2 equivalents [CO2-eq] ha−1 yr−1), biochar addition (0.36–1.8 t CO2-eq ha−1 yr−1) and agroforestry-biochar addition (2.2–2.3 t CO2-eq ha−1 yr−1). Different limiting factors (land and biomass availability, population growth) affected SCS rates and indicated that they cannot be sustained until 2100 under all scenarios (cover cropping: 0.10 t CO2-eq ha−1 yr−1 [2051–2100]; biochar addition: 0.35–1.8 t CO2-eq ha−1 yr−1; agroforestry-biochar addition: 1.0–1.7 t CO2-eq ha−1 yr−1). This information together with the associated GHG emissions is critical for planning net zero strategies and highlights the importance of integrated assessments that capture links between SCS and the food system. 

Full text can be found here: https://doi.org/10.1080/17583004.2023.2244456

Featured Soil Modeller Gabriëlle De Lannoy


Land surface modeling and satellite data assimilation
Gabriëlle De Lannoy is a professor at the KU Leuven, Belgium, Department of Earth and Environmental Sciences, faculty of Bioscience Engineering. She studied Bioscience Engineering and obtained degrees in Industrial Automation (Civil Engineering) and Academic Teaching. Her PhD research (2006) focused on soil moisture data assimilation and was performed at the Ghent University and NASA Goddard Space Flight Center (GSFC, US). For her postdoctoral research, prof. De Lannoy worked on snow data assimilation (Center for Ocean, Land and Atmosphere, US). Thereafter, she became a senior research scientist at NASA GFSC’s Global Modeling and Assimilation Office (GMAO), where she worked on the operational surface and root-zone soil moisture data assimilation product for the Soil Moisture Active Passive (SMAP) mission, i.e. the SMAP L4_SM. Currently, her research team at KU Leuven works on the development of a range of various modeling and satellite data assimilation systems, and various practical applications thereof.
 
- Please tell us briefly about yourself and your research interest
We do science because nature is intelligible, logical, and beautiful. I have always been fascinated by nature. It seems only normal to try to understand nature and foresee how we as humans can best steward nature and protect our fellow humans. I am very passionate about data assimilation in Earth system models, or parts thereof, because I believe that combining models and observations leads us to the pinnacle of our current understanding of Earth system components (soil-water-plant-atmosphere interactions, dynamic vegetation, groundwater in peatlands, snow, irrigation, or other processes). It is thus no surprise that our team works on (i) land surface, crop, and peatland model development, on (ii) microwave-based soil moisture, vegetation and snow retrievals, on (iii) the development of multisensor and multivariate data assimilation systems, and on (iv) applications, such as landslide and fire hazards, at a range of different spatial scales, i.e. from the field to the global scale.
 
-  How did you first become interested in soil modelling and learn about ISMC?

Soil and water processes sparked my interest during my studies in Bioscience Engineering. Without much premeditation from my part, but inspired by senior researchers in our field (I have many people to thank!), my PhD research happened to turn to land surface modelling and data assimilation, where a full understanding of soil modelling was of utmost importance. When I started to use satellite-based microwave observations in data assimilation systems, understanding of soil-water processes became (perhaps ironically) even more important. I believe that it is essential to physically understand elementary processes before using either models or (any, but in particular satellite) observations, and before combining both. Not sure when I first learned about ISMC, but Yijian Zeng most recently rekindled my attention for ISMC.
 
-Can you share with us your current research focus? And, please tell us briefly how your research could contribute to ISMC Science Panel’s activities
Specifically, our current team roughly consists of `peatlanders’, `aquacroppers’, `snow people’, and people who generally apply land surface data assimilation and machine learning for soil, vegetation, irrigation and weather modeling. Peatland research focuses on improving the representation of soil-water processes of boreal and tropical peatlands within land surface models (Bechtold et al., 2019, Apers et al., 2022): many existing large-scale models do not simulate peatland hydrology which is problematic to estimate the associated carbon fluxes. Our advancements are subsequently used to improve fire predictions over peatlands (Mortelmans et al, 2024). Just imagine the amount of carbon released by a peatland fire!
Research on crop, soil and irrigation modelling mainly started when we released the FAO model AquaCrop as open source code (https://www.fao.org/aquacrop/en/, de Roos et al., 2021, Busschaert et al., 2022) and implemented it into NASA’s Land Information System to facilitate microwave data assimilation for soil moisture and biomass estimation. Furthermore, we study the simulation of irrigation based on simulated soil water thresholds (Modanesi et al., 2022), but we can also trigger simulated irrigation based on observed outliers in satellite observations. Irrigation is one of the big unknowns in our water cycle estimation, it is high time to better monitor it.
Our work with microwave-based satellite missions revealed that C-band radar is sensitive to snow, and not only to soil moisture and vegetation (Lievens et al., 2022, Brangers et al., 2023). Therefore, we develop high resolution snow depth retrieval products, do tower-based experiments, test machine learning, and assimilate satellite-based snow observations into land surface models. Did you know that there is no single satellite mission dedicated to estimating snow water yet?
Finally, we are pushing the frontier of land surface data assimilation towards coupled land-atmosphere modelling systems, and other applications such as landslide estimation (Felsberg et al., 2023). Depending on the coupling mechanisms between selected land and atmosphere variables, land surface data assimilation will have more or less impact on weather predictions. We currently assimilate satellite observations related to soil moisture and vegetation (Heyvaert et al., 2023, Scherrer et al, 2023) with an eye for their impact on boundary layer fluxes. If we want to use satellite observations more effectively to inform unobserved Earth system compartments (De Lannoy et al., 2022), we need to get the coupling mechanisms right.
We have earlier contributed to model intercomparison efforts by ISMC and are happy to further contribute with AquaCrop and our peatland model developments.
 
-Please tell us how can ISMC help you advance in your career?
ISMC can help to advance the career of our Msc and PhD students, and postdoctoral students, by offering training and attracting them to workshops with interesting programs. It is great to see how ISMC already shares vacancies, groups information about models, etc. Now it is just a matter to point young researchers to your information sources and engage them to contribute.
 
- What resources or skills would you recommend that early career members of ISMC should acquire? And how can ISMC help and support early career members in this regard?
Integrity and perseverance, analytical and critical thinking, and a sense of wonder are probably some of the key ingredients of any successful and resilient early career scientist. Such skills will never be replaced by artificial intelligence. Ideally, soil or land surface modellers should further develop good programming and writing skills, physical process understanding, mathematics and statistics insights. ISMC can help by channelling ideas about good practices and technical information, and by bringing early career scientists and senior experts together.

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