ISMC News 25 Jan
Awards 2021
Early Career ISMC Presentation Awards 2021
Early Career ISMC Presentation Awards 2021 will be awarded to honour outstanding presentations at the ISMC Conference 2021.
ISMC Publication Award 2020
ISMC Publication Award 2020 will be awarded to honour outstanding peer-reviewed publication of the Year #2020. Nominators: ISMC members. Nominees can be all, except ISMC Executive Board. Justification for Nomination and DOI (with title and abstract) need to be included.
Submission: https://forms.gle/6ibdfdV7aEXidQnx9
Announcements
(MS25) Subsurface Water Flow and Contaminant Transport Processes – Special Session in Honor of Harry Vereecken
- Rien van Genuchten - Federal University of Rio de Janeiro, Brazil
- Andrew Binley - Lancaster University, UK
- Dani Or - Swiss Federal Institute of Technology, Zurich, Switzerland
- Jan Hopmans - University of California, Davis, USA
- Jesus Carrera - University of Barcelona, Spain
This session is in honor of Harry Vereecken, the 2020 recipient of Interpore’s Honorary Membership Award. Harry’s work has covered a broad range of research in the soil, hydrogeologic, and environmental science and engineering disciplines. We invite contributions that are closely connected to Harry’s research endeavors over the past 35 years or more. This includes experimental and theoretical studies of subsurface fluid flow and contaminant transport from the pore scale to the catchment scale, hydrogeophysics, terrestrial ecosystems and critical zone observatories, vadose zone hydrology, pedotransfer functions, soil-plant atmosphere interactions, coupled hydrologic and biogeochemical processes, subsurface heterogeneity, data assimilation techniques, and related studies, as well as tributes to his tireless service to the profession.
https://events.interpore.org/event/25/
Special Issue "Global Gridded Soil Information Based on Machine Learning"
Brigitta Szabó (Tóth), Eyal Ben-Dor, Yijian Zeng, Salvatore Manfreda, Madlene Nussbaum
Data-intensive computing solutions to process and analyze the exploding amount of environmental information are continuously updated. Machine learning algorithms are among the most frequently used tools for data preprocessing and describing the complex relationship between soil properties and environmental covariates with the ability to assess the uncertainty of the predictions. One of the greatest challenges in deriving global gridded soil information is to make the most of the predictive power of machine learning algorithms with the continuously increasing amount of environmental information. This Special Issue is dedicated to machine learning-based methods in:
- proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);
- computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;
- preprocessing Earth observation data to feed into global soil mapping;
- data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;
- optimizing temporal resolution to globally track the changes of soil properties,
- uncertainty assessment of the derived gridded soil information;
- specifying algorithms to local soil specificities in, e.g., proximal soil mapping;
- the engagement of remote sensing data with digital soil mapping;
- downscaling of large-scale soil feature;
- other related topics.
- Review contributions on the abovementioned topics are welcomed as well.
Featured Soil Modeler
Sagar Gautam is a postdoctoral researcher at Sandia National Laboratory, Livermore, California. He is an active member of the ISMC community and currently serving on the executive board as an early career representative. He is an environmental engineer motivated to work on the environmental impacts of land use and climate change on soil, water and air. He develops and implements the models (process model and machine learning models) to quantify the impact of human induced change on different environmental fluxes.
Please read more here.
- Please tell us briefly about yourself and your research interest.
The overall goal of my research is to use the environmental flux observation datasets and different genera of models to predict the fate of environmental fluxes (flow, water quality, soil carbon etc) under changing climate and land use, benchmark and improve process representation in models to reduce uncertainty in prediction. Currently, I am working to predict the optimum distribution (extent and spatial location) of multiple bioenergy crops to optimize biomass production with lower environmental impacts to meet the renewable biofuel production goal for continental US.
- How did you first become interested in soil modelling and learn about ISMC?
My interest in soil modeling started during my undergraduate studies. I realized the value of modeling tools to understand the bound of the system while attending different hydrology and soil modeling classes. This interest motivated me to pursue my master and Phd in environmental modeling. I learned about the ISMC in 2019 from my co-workers and attended the ISMC meeting in 2019 AGU. Since then I have been actively involved with this community to exchange ideas and to network with like minded scientists around the world.
- 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.
Currently, I am involved in bioenergy landscape impact assessment [https://doi.org/10.1111/gcbb.12736] , estimation of soil carbon [https://doi.org/10.3389/fdata.2020.528441] and benchmarking of Earth System carbon model. I am involved in the data model integration research, and my research activities help to better predict soil carbon and reduce uncertainty in model prediction by benchmarking with observation and improvement of process representation. This kind of work is of interest for land surface modeler and soil scientist to understand the future change of soil carbon and representation of soil carbon in the process model.
- Please tell us how can ISMC help you advance in your career?
ISMC offers me an opportunity to network with scientists across the world. I am involved in a different working group, where we are collectively working on multiple soil modeling tasks. ISMC provides a great opportunity to learn about the soil modeling, research needs and future directions. In addition, it helped me to grow my network and connection with soil modeling scientists across the world.
- 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?
I think the most important skills for early career members is to develop themself as independent researchers by finding their own niche within the research field he/she is working on. Important resources can be finding appropriate groups to communicate/network with like minded individuals. ISMC can help support early career members by organizing different programmes and panels with the involvement of early career researchers. Another way will be to encourage early career members to actively participate in relevant events and discussions organized by ISMC and find a way to collaborate among themselves to improve the soil modeling technique.