Thursday, December 12, 2024

Positions in snow modelling, Po River basin hydrology, soil-plant-atmosphere interactions and GEOframe system development @UniTrento

 Dear All,

I am seeking motivated  master graduated interested in working in areas related to Snow modelling, Po River basin hydrology, Soil-plant-atmosphere interactions and GEOframe system development. Below are some exciting thesis opportunities, each with potential for continuation into a Ph.D. program. Post doc positions could be considered as well for appropriate persons.


1. Snow Modelling (SUNSET PRIN Project)

This topic focuses on snow dynamics modeling using GEOframe-NewAGE and GEOtop, within the SUNSET PRIN project (Details). Opportunities include fieldwork, guided by Prof. Stefano Ferraris (University of Turin), with Dr. John Mohd Wani as co-supervisor.

2. Po River Basin Projects (ADBPo Collaboration)

The Po River Basin thesis topics align with the long-term collaboration with the Basin Authority of River Po (Details). These projects could lead to Ph.D. opportunities and professional roles.

Topics include:

- Modeling Romagna Catchments for Drought and Flood Prevention
  Focused on hourly-scale modeling for water management.
- Co-Supervisor: Ing. Gaia Roati (Po Basin Authority)
- Includes periods at the Po Basin Authority in Parma.
- Earth Observation for Po River Basin Calibration
  Systematic use of satellite data to validate and improve GEOframe-NewAGE models.
- Part of: SpaceItUp! PNRR (Italian Space Agency) and an upcoming ESA project
- Collaboration: Ing. Hossein Salehi, Fondazione Edmund Mach, and Prof. Manuela Girotto (UC Berkeley).

3. Land-Surface Interactions with the use of the GEOSPACE system and its development (EPFL collaboration, ESA Projects, EU Projects)
- Modeling Transpiration and Soil-Atmosphere Interactions
  Utilize GEOframe-NewAGE and GEOSPACE for basin-scale modeling.
- Co-Supervisor: Dr. Concetta D’Amato (EPFL, Sion Campus)
- Includes a potential study period in Sion.
- Depending on the specific topic other collaboration should be envisioned

Topics include:
- Estimating the effects of evaporation and transpiration at Po scale, integrating GEOSPACE with Earth Observation.
- Understanding the effects of soil evolution under the action of biota and under global warming.
- New parameterizations of the atmosphere - plant interactions

4. Informatics-Oriented GEOframe Development (SIM Project)
- Integrating Large Language Models with GEOframe
  Explore the potential of Generative AI for improving user interaction and programming within GEOframe.
- Transforming GEOframe into a DARTH
  Enhance the GEOframe infrastructure and Object Modelling System (OMS) codebase.
- Co-Supervisors: Prof. Giuseppe Formetta and Dr. Olaf David (Colorado State University)
- Includes a potential study period in Fort Collins, Colorado.


Additional Information

All theses involve using and extending GEOframe tools, requiring proficiency in Python and Java. Coding skills are especially critical for informatics-oriented topics.
These projects provide an excellent foundation for doctoral studies and professional development in hydrology, environmental modeling, and computational science.

Please feel free to share this information and contact me for further details.

Best regards, 
Riccardo Rigon
riccardo<dot> rigon<@>unitn<dot>it

The anticipated salary for pre-doctoral and Ph.D. students is €1,350 per month (net), plus an additional €3,000 annually for supplementary activities. For postdoctoral researchers, the net annual salary ranges from €24,000 to €30,000, depending on individual qualifications and experience. Additional income opportunities may also be available. The cost of living in the region is more affordable compared to many other European countries.


IMPORTANT !!!!

P.S. - In your response, please specify which of the above proposals you are interested in pursuing, along with a brief explanation of your motivation. Send your CV with your age and gender included. If you do not have prior experience with the GEOframe system,, we kindly request that you first enroll in our  GEOframe School (we are happy to waive your subscription fees). Please complete the enrollment and mention in your communication that you have done so.
The school has already covered the installation process and some theoretical aspects, but all materials are available online for self-paced learning. The next session will take place in January, and individuals currently in Italy are encouraged to attend in person.

Saturday, December 7, 2024

AGU Fall Meeting 2024 poster - Digital eaRth Twins Hydrology Systems

The "Digital Earth" (DE) metaphor is highly beneficial for both end users and hydrological modelers. In this contribution, we analyze different categories of models with the aim of incorporating them into Digital eARth Twin Hydrology systems (DARTHs). We emphasize that DARTHs are not merely models; they are a comprehensive infrastructure that hosts specific types of models and provides essential services for connecting to input data. We advocate for a modeling-by-component strategy to meet the requirements of the DE. We envision four technological steps to advance from the current state of modeling.
  1. Decomposition of Models: Models are broken down into interacting modules, with agnostic parts handling inputs and outputs separated from model-specific parts containing algorithms.
  2. Software Layers Addition: Appropriate software layers are added to enable transparent model execution in the cloud, independent of hardware and operating systems, without human intervention.
  3. Cloud Execution:
  4.  Interchangeability of Models: Models can be selected as interchangeable without providing deceptive answers. This includes the use of hypothesis testing, error estimation, literate programming, and guidelines for clean, informative code.
We argue for the urgency of making DARTHs open source in alignment with the open-science movement. DARTHs should promote a new participatory approach to hydrological science, allowing researchers to cooperatively contribute to characterizing and controlling model outcomes in various territories.
Finally, we discuss three enabling technologies within the context of DARTHs: Earth observations (EOs), high-performance computing (HPC), and machine learning (ML). We explore how these technologies can be integrated into the overall system to enhance scientific research and generate knowledge.
By clicking here please find the pdf of the poster.  Please find here the slides used in videos.  

Modelling by Components and Modular Systems

The modeling-by-components (MBC) approach has been conceptually present for over 40 years (Holling, 1978). However, it is only in the past two decades that it has gained significant traction within the environmental modeling community (Argent, 2004). Often referred to as integrated environmental modeling (IEM), MBC arose from the need to analyze heterogeneous processes collectively, integrating knowledge across diverse disciplines (Moore and Hughes, 2017).


In the more specialized hydrological and meteorological domains, examples of MBC applications are relatively limited but include prominent frameworks such as TIME (Rahman et al., 2003), OpenMI (Gregersen et al., 2007), CSDMS (Peckham et al., 2013), ESMF (Collins et al., 2005), and OMS (David et al., 2013), RAVEN (Craig et al., 2020). A more comprehensive list can be found in Chen et al. (2020).

Figure from UniFHy v0.1. another modular framework to investigate
While MBC concepts and their associated technological implications are highly appealing, their practical implementation can be challenging. One key challenge is their “invasiveness”—in some cases, MBC frameworks require programmers to adapt their habits and adopt new programming styles (Lloyd et al., 2011). Among the aforementioned examples, the OMS framework explicitly tackled this issue and provided encouraging solutions (Lloyd et al., 2011).

MBC inherently supports a service-oriented architecture (SOA), a software design approach particularly suited for integrating heterogeneous data sources. SOA frameworks are designed to work across different machines and scales, accommodating a variety of hardware architectures. Importantly, SOA abstracts the computational details, allowing users to focus on modeling rather than the intricacies of the underlying engines. Infrastructures that do not implement such type of architecture, probably abuse the word "framework" and should be named differently. 


Key Features of the MBC Approach:


1. Encapsulation and Testing:

The framework employs encapsulation, making code easier to inspect. Each component can operate independently within the system’s infrastructure and can be tested in isolation.

2. Ownership and Collaboration:

MBC allows for clear intellectual property ownership. Components are often developed by a small group of contributors, enabling diverse collaboration without dispersing efforts across thousands of lines of code. Adding new components is straightforward and does not require recompiling the entire system.

3. Component Substitution and Hypothesis Testing:

The modularity of MBC simplifies the replacement of components, making it a valuable tool for hypothesis testing (Beven, 2019).

4. Built-in Services:

Features like implicit parallelism and tools for model parameter calibration are provided seamlessly, reducing the burden on developers.

5. Flexibility Across Disciplines:

A well-designed MBC system allows for nearly unlimited composition of modeling solutions. Components can address tasks across disciplines, breaking down silos and fostering interdisciplinary collaboration.


In summary, the MBC approach offers a robust and scalable framework for tackling complex environmental modeling challenges. Its emphasis on modularity, flexibility, and interoperability makes it a powerful tool for advancing scientific understanding across domains.


References on Modelling by Components

An initial and certainly non-exhaustive list of reference for who wants to know more about modelling by components


  • Argent, R. M.: An overview of model integration for environmental applications – components, frameworks and semantics, Environ. Modell. Softw., 19, 219–234, 2004. 
  • Beven, K.: Towards a methodology for testing models as hypotheses in the inexact sciences, Proc. Math. Phys. Eng. Sci., 475, 20180862, https://doi.org/10.1098/rspa.2018.0862, 2019.
  • Chen, M., Voinov, A., Ames, D. P., Kettner, A. J., Goodall, J. L., Jakeman, A. J., Barton, M. C., Harpham, Q., Cuddy, S. M., DeLuca, C., Yue, S., Wang, J., Zhang, F., Wen, Y., and Lü, G.: Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications, Earth Sci. Rev., 207, 103223, https://doi.org/10.1016/j.earscirev.2020.103223, 2020.
  • Collins, N., Theurich, G., DeLuca, C., Suarez, M., Trayanov, A., Balaji, V., Li, P., Yang, W., Hill, C., and da Silva, A.: Design and Implementation of Components in the Earth System Modeling Framework, Int. J. High Perform. Comput. Appl., 19, 341–350, 2005. 
  • Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, R. W., Jost, G., Lee, K., Mai, J., Serrer, M., Sgro, N., Shafii, M., Snowdon, A. P., and Tolson, B. A.: Flexible watershed simulation with the Raven hydrological modelling framework, Environ. Modell. Softw., 129, 104728, https://doi.org/10.1016/j.envsoft.2020.104728, 2020.
  • David, O., Ascough, II, J. C., Lloyd, W., Green, T. R., Rojas, K. W., Leavesley, G. H., and Ahuja, L. R.: A software engineering perspective on environmental modeling framework design: The Object Modeling System, Environ. Modell. Softw., 39, 201–213, 2013. 
  • David, O., Lloyd, W., Rojas, K., Arabi, M., Geter, F., Ascough, J., Green, T., Leavesley, G., and Carlson, J.: Modeling-as-a-Service (MaaS) using the Cloud Services Innovation Platform (CSIP), in: International Congress on Environmental Modelling and Software, scholarsarchive.byu.edu, 13, https://digitalcommons.tacoma.uw.edu/tech_pub/13 (last access: 23 September 2022), 2014. 
  • Gregersen, J. B., Gijsbers, P. J. A., and Westen, S. J. P.: OpenMI: Open modelling interface, J. Hydroinform., 9, 175–191, 2007. 
  • Holling, C. S.: Adaptive Environmental Assessment and Management. John Wiley & Sons. http://pure.iiasa.ac.at/id/eprint/823/ (last access: 27 September 2022), ISBN 0471996327, 402 pp., 1978. 
  • Lloyd, W., David, O., Ascough, J. C., Rojas, K. W., Carlson, J. R., Leavesley, G. H., Krause, P., Green, T. R., and Ahuja, L. R.: Environmental modeling framework invasiveness: Analysis and implications, Environ. Modell. Softw., 26, 1240–1250, 2011.
  • Moore, R. V. and Hughes, A. G.: Integrated environmental modelling: achieving the vision, Geological Society, London, Special Publications, 408, 17–34, 2017. 
  • Peckham, S. D., Hutton, E. W. H., and Norris, B.: A component-based approach to integrated modeling in the geosciences: The design of CSDMS, Comput. Geosci., 53, 3–12, 2013.
  • Rahman, J. M., Seaton, S. P., Perraud, J. M., Hotham, H., Verrelli, D. I., and Coleman, J. R.: It's TIME for a new environmental modelling framework, in: MODSIM 2003 International Congress on Modelling and Simulation, vol. 4, 1727–1732, Modelling and Simulation Society of Australia and New Zealand Inc. Townsville, http://www.research.div1.com.au/RESOURCES/research/publications/conferences/20030714ff_MODSIM2003/RahmanSeatonPerraudHothamVerrelliColeman2003_1727.n.pdf (last access: 27 September 2022), 2003. 

Advanced Topics in Snow Hydrology: Measurements, Modeling, & Remote Sensing

In Partnership with the Fulbright Scholars Fellowship, University of Trento Italy, Portland State University USA, and EURAC Italy

 

18 – 21 February, 2025 – In-person at University of Trento, Italy, and remotely via Zoom.

 

Instruction Format: Collaborative teaching with structured lectures (morning) and hands-on exercises or fieldwork (afternoon). Lectures will be recorded and posted for future access.

Instructors: Kelly E. Gleason1, John Mohd Wani2, Giacomo Bertoldi4, Michele Bozzoli2,4, Valentina Premier4, and Riccardo Rigon2,3

 

The School will be held in person at the Department of Civil, Environmental, and Mechanical Engineering in Trento

  • All sessions will be recorded and made available online after the course.
  • No registration fee is required.
  • Participants are responsible for their own travel and accommodation expenses.

✉️ To register, please contact: johnmohd.wani [at] unitn.it

1. Department of Environmental Science and Management, Portland State University, Portland, Oregon, USA

2. Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy

3. C3A - Center Agriculture Food Environment, University of Trento, San Michele all‘Adige, Trento, Italy

4. Institute for Alpine Environment, EURAC Research Bolzano, Italy


Day 1: Introduction to Snow Hydrology and Snow Processes

 

Morning (9:00-12:30) 

Lecture Session: Intro to Snow Hydrology

Instructor: Kelly Gleason (9:00-10:00)

  • General snow hydrology: Overview of snow hydrology. 
  • Snow energy and mass balance: Energy and mass dynamics in snowpacks.

Break: 10:00-10:15

Lecture Session: Spatial and Temporal Distribution of Snow, and how is it changing?

Instructor: Michele Bozzoli (10:30-11:30)

  • Snowfall trends in the Alps: Historical and current trends; climate change implications.

Break: 11:30-11:45

Instructor: Kelly Gleason (11:30-12:00)

  • Snow-climate-forest interactions: Influence of forests and climate on snow hydrology.

Instructor: Riccardo Rigon (12:00-12:30)

  • Permafrost and permafrost/snow relations: Overview of permafrost and its interactions with snow processes and modeling.

Afternoon (14:00-17:00) 

  • Exercise 1: Snow mass balance calculations (Instructor: Kelly Gleason)
    • Students calculate snow energy and mass balance using sample datasets. 
  • Exercise 2: Snowfall trend analysis (Instructor: Michele Bozzoli)
    • Students conduct analysis of real-world snowfall data using statistical tools.

Day 2: Snow Modeling and Data Integration into Snow Modeling

 

Morning (9:00-12:30)

Lecture Session: Intro to Snow Modeling

Instructor: Kelly Gleason (9:00-10:00)

  • General snow modeling: Principles, methodologies, and applications of snow modeling.
  • Parameterization of snow processes and integration of empirical data into snow models: Role of observational data in improving model accuracy.

Break 10:00-10:15

Lecture Session: Intro to Snow Modeling with GEOTop 

Instructor: John Mohd Wani (10:30-12:30)

  • Snow Modeling with the GEOtop Model: Introduction and case study overview.

Lunch 12:30-14:00

Afternoon Lecture

Instructor: Giacomo Bertoldi (14:00-15:00)

o   GEOtop Modeling: EURAC research case study in snow modeling

Afternoon 15:00-17:00 (Exercises)

  • Exercise 3: Snow model output analysis (Instructor: John Mohd Wani)
    • Students use the GEOtop and GEOFrame model output data to evaluate model performance under different conditions relative to snow station data.


Day 3: Remote Sensing and Advanced Applications

 

Morning 9:00-12:30

Lecture Session: Remote Sensing of Snow

Instructor: Kelly Gleason (9:00-10:00)

·      Introduction of Remote Sensing of Snow across Scales: Discussing the principles of detecting snow from field based, drone, airborne, and satellite-based methods. 

Break 10:00-10:15

Lecture Session: Remote Sensing of Snow and Snow Modeling Case Study

Instructor: Valentina Premier (10:15-11:15)

·      Remote Sensing of Snow: Introduction and research in remote sensing of snow

Break 11:15-11:30

Instructor: Michele Bozzoli (11:30-12:30)

  • Integration of Remote Sensing and Snow Modeling in the GEOFrame Model: Applications through the lens of PhD dissertation work.

Lunch 12:30-14:00

Afternoon Lecture

Instructor: Kelly Gleason (14:00-14:30)

o   Forest/Snow Case Study: Snow and forest interactions and uncertainty in remote sensing observations

Afternoon 14:30-17:00 (Exercises)

·      Exercise 4: Remote sensing data uncertainty across scales (Instructor: Kelly Gleason)

o   Students use remote sensing observations to estimate snow properties over space and time, and evaluate uncertainty of remote sensing across scales.


Day 4: Fieldwork and Integration of Snow Hydrology Concepts

 

All day field trip (9:00-17:00) to measure snow properties including snow depth, SWE, density, and grain size.

Morning (9:00-12:30)

Instructors: Giacomo Bertoldi and Kelly Gleason

  • Field Trip Introduction: Overview of field methods and objectives.
  • Field Data Collection:
    • Snowpack measurement techniques (density, SWE, snow depth, grain size).
    • Observations of forest-snow and permafrost-snow interactions.

Lunch (12:30-14:00)

Afternoon (14:00-17:00)

Field Data Analysis and Wrap-Up

  • Exercise 5: Field snow data analysis (Instructors: Kelly Gleason)
    • Students analyze collected snow hydrology data and apply principles learned throughout the course.
  • Wrap-Up Discussion:
    • Synthesis of course concepts, student reflections, and Q&A.


Graduate Students will conduct a final project conducting mini snow hydrology research project using existing data and analyzing it to answer a simple research question or learning to apply these concepts into the GEOtop model. This mini research project will be documented in a 4-page short form manuscript for submission to instructors for evaluation in the style of the Geophysical Research Letters journal format.

______________________________________________________________________________

Materials and Resources

  • Daily readings will be provided to prepare for the next day’s coursework.
  • Lecture slides and notes (provided by instructors).
  • Datasets for exercises and fieldwork preparation.
  • Access to GEOtop and GEOframe modeling tools.


Assessment

  • Participation in lectures, exercises, and fieldwork (40%).
  • Daily exercises will primarily be completed in class during the afternoons, but any unfinished work will be finished by students as homework (30%) 
  • Graduate student final project conducting mini snow hydrology research project using existing data and analyzing it to answer a simple research question or learning to apply these concepts into the GEOtop model (30%).

This structure ensures a balance of theoretical learning and practical application, allowing students to immediately apply knowledge from lectures to real-world and simulated contexts.