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.

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. 

The Advanced Topics in Snow Hydrology School

Work in progress for now

Course Title: Advanced Topics in Snow Hydrology

Course Duration: 4 days (February 18-21)
Instruction Format: Collaborative teaching with structured lectures (morning) and hands-on exercises or fieldwork (afternoon).

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Day 1: Introduction to Snow Hydrology and Snow Processes

Morning (Lecture Session)

Lead Instructor: Kelly

• General Snow Hydrology: Overview of snow hydrology (20 min).

• Snow Energy and Mass Balance: Energy and mass dynamics in snowpacks (30 min).

Lead Instructor: Giacomo

• Snowfall Trends in the Alps: Historical and current trends; climate change implications (40 min).

Lead Instructor: Kelly

• Snow-Climate-Forest Interactions: Influence of forests and climate on snow hydrology (30 min).

Mini-Lecture Discussion/Q&A: Interactive discussion (30 min).

Afternoon (Exercises)

• Exercise 1: Snow Mass Balance Calculations (Instructor: Kelly)

• Students calculate snow energy and mass balance using sample datasets.

• Exercise 2: Snowfall Trend Analysis (Instructor: Giacomo)

• Analysis of real-world snowfall data using statistical tools.

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Day 2: Snow Modeling and Data Integration

Morning (Lecture Session)

Lead Instructor: Kelly

• General Snow Modeling: Principles, methodologies, and applications of snow modeling (30 min).

• Integration of Empirical Data into Snow Models: Role of observational data in improving model accuracy (30 min).

Lead Instructor: John

• Snow Modeling with the GEOtop Model: Introduction and case study overview (60 min).

Lead Instructor: Riccardo

• Permafrost and Permafrost/Snow Relations: Overview of permafrost and its interactions with snow (30 min).

Mini-Lecture Discussion/Q&A: Interactive discussion (30 min).

Afternoon (Exercises)

• Exercise 3: Snow Model Setup and Calibration (Instructor: Kelly)

• Hands-on setup of a snow model using provided datasets.

• Exercise 4: GEOtop Model Introduction (Instructor: John)

• Students use the GEOtop model to simulate snowpack-permafrost interactions.

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Day 3: Remote Sensing and Advanced Applications

Morning (Lecture Session)

Lead Instructor: Michele

• Integration of Remote Sensing and Snow Modeling in the GEOframe Model: Introduction and applications (60 min).

Lead Instructor: Riccardo

• Permafrost/Snow Case Study: Detailed presentation of a case study highlighting snow and permafrost modeling (45 min).

Lead Instructor: Kelly

• Forest/Snow Case Study: Detailed presentation of a case study highlighting snow and forest interactions in modeling (45 min).

Mini-Lecture Discussion/Q&A: Interactive discussion (30 min).

Afternoon (Exercises)

• Exercise 5: Remote Sensing Data Integration (Instructor: Michele)

• Using satellite data to improve snow model predictions in the GEOframe model.

• Exercise 6: Case Study Application (Instructor: Riccardo)

• Students apply knowledge of snow-permafrost interactions in a modeling scenario.

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Day 4: Fieldwork and Integration of Concepts

Morning (Field Trip Preparation & Fieldwork)

Lead Instructor: Giacomo

• Field Trip Introduction: Overview of field methods and objectives (30 min).

• Field Data Collection:

• Snowpack measurement techniques (density, SWE, snow depth).

• Observations of forest-snow and permafrost-snow interactions.

Afternoon (Field Data Analysis and Wrap-Up)

• Exercise 7: Field Data Integration into Models (Instructors: All)

• Students analyze collected data and evaluate how to incorporate into GeoTop

• Wrap-Up Discussion:

• Synthesis of course concepts, student reflections, and Q&A (30 min).

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Materials and Resources

• Lecture slides and notes (provided by instructors).

• Datasets for exercises and fieldwork preparation.

• Access to GEOtop and GEOframe modeling tools.

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Assessment

• Pre-course quiz on foundational concepts (10%).

• Participation in exercises and fieldwork (40%).

• Final project integrating lecture topics, exercises, and field data (50%).

Notes of a performance on Water and Time that I gave sometime ago

 They told me that I should simply speak, without the aid of those tools and images I usually surround myself with. So, I’ll give it a try, speaking off the cuff and relying, forgive me, on some notes and a glass of water. Tracing back to the origin of water, we are drawn to the springs from which it flows. In reality, this bubbling forth is the result of an accumulation that pervades a portion of a mountain, a summary of underground stories and, in its most intense phases, even exposed to the sunlight.

This prehistory gives way to a constant and undeniable element of progress, from the source downwards. Now it is a stream, now a brook tumbling over rocks, now a river. With only minor diversions, there is but one direction—an accumulation. Time advances, entropy grows, aerial energy is both stored and dissipated. It’s not just water that flows; there’s sediment too, a piece of the mountain being carried toward the sea. Gutta cavat lapidem. Tectonic forces built it, and unyielding patience has worn it down.

Time accumulates, and there is a story that can only be remembered.

The above is the incipit of a performance-talk I gave a few years ago at MUSE. It is in Italian and you can find the notes here. 

How Generative AI Can Improve Hydrological Modeling and Support Scientists' Work

Generative artificial intelligence, with its ability to process and generate human-like text based on vast datasets, offers significant potential to enhance the interpretation of hydrological models, predict water cycle dynamics, and understand complex environmental interactions. By leveraging large language models (LLMs), researchers can optimize data processing, automate the extraction of relevant information from the extensive scientific literature, receive informed support in model programming, and generate descriptions of phenomena in natural language—extracting insights from vast datasets that go beyond the feasibility of manual analysis. Moreover, LLMs facilitate interdisciplinary collaboration by translating complex hydrological concepts into accessible language for diverse audiences. This seminar provides a brief introduction for selected high school students to generative model concepts, explores current applications, and examines the benefits and challenges of integrating LLMs in hydrology and related fields, highlighting their potential to advance scientific understanding and practical water resource management.

La presentazione in Italiano può essere trovata qui.

What is C3A (In Italian) ? My presentation to the students of Viticulture and Oenology of the Center of Agricolture, Food and Environment of the University of Trento

This is an unusual topic for the blog. Because it happens that I am the director of the Centro Agricoltura, Alimenti, Ambiente of the University of Trento, in my academical duties there is also to present to students what our Center is about.  You can find the presentation in Italian clicking on the Figure below.

The research program (in English) of the Center can be found also in another post, here.

The Center is not unknown to ChatGPT that, asked to answer says:

The **Center for Agriculture, Food, and Environment** (C3A) at the University of Trento is an interdisciplinary research and educational center created through a collaboration between the University of Trento and the Edmund Mach Foundation (FEM) in Trentino. C3A is dedicated to studying and developing innovative solutions in agriculture, food, and environmental sectors, with a strong focus on environmental sustainability and natural resource management.

The center's activities cover various areas, including:

1.  **Sustainable Agriculture**: Research on agricultural practices that optimize resource use and minimize environmental impact.
2. **Climate Change**: Study of the effects of climate change on agriculture and the environment, with the development of adaptation and mitigation strategies.
3. **Agri-food Technologies**: Development of innovative technologies for agricultural management, food production, and food safety.
4.  **Biodiversity and Ecosystem Management**: Research on biodiversity conservation and the impact of agricultural activities on natural ecosystems.

In addition to research, C3A offers **educational programs**, including a master’s degree program in Viticulture and Enology. These courses aim to train experts who can respond to new challenges in food production and environmental sustainability.

The Center for Agriculture, Food, and Environment (C3A) at the University of Trento also offers a Master’s Degree in Agrifood Innovation Management. This program is dedicated to innovation in the agri-food sector, with a focus on sustainable technologies, food supply chain management, and data science applications to optimize agricultural and food production.

MARRMOT (and others) models represented using the Extended Petri Nets

 This is a place holder - Document still missing

The draft presented in this post represents all the MARRMoTs hydrological models using the Extended Petri Nets graphs. This show in practice that the statement that all the integral distributed hydrological models  (a.k.a. lumped distributed models, the hydrological dynamical systems, semi-distributed hydrological models) can be represented by means of the EPN is actually true. 

The MARRMoT (Modular Assessment of Rainfall-Runoff Models Toolkit) is a collection of hydrological models designed to simulate rainfall-runoff processes across different scales and conditions. Developed in MATLAB, MARRMoT provides a modular framework that allows researchers to compare, assess, and implement various conceptual hydrological models consistently

Key Features and Purpose:

• Model Variety: MARRMoT includes 47 different rainfall-runoff models, ranging from simple lumped models to more complex, semi-distributed structures.
• Comparative Framework: Its standardized environment enables researchers to test models on the same data sets and conditions, making it easier to assess model performance, suitability, and sensitivity.
• Customization: Users can adapt model configurations or modify parameters within the toolkit to better fit specific research objectives or study basins.
• - Open-source and Accessible: MARRMoT is open-source, encouraging community contributions, modifications, and application across various hydrological research areas.

This toolkit is particularly useful for researchers looking to improve or validate hydrological models and for those who want a structured way to compare the effects of model structure on hydrological simulation outcomes.

Extended Petri Nets (EPNs) are an enhanced version of traditional Petri Nets, a mathematical modeling language originally designed to describe distributed systems with concurrent, asynchronous processes. EPNs incorporate additional features to increase modeling flexibility and allow for more complex system representations. In scientific modeling, they are used for representing and analyzing systems in which discrete and continuous interactions are critical, like biological networks, chemical reactions, ecological models, or engineering processes.

In hydrology, an EPN might represent a soil-plant-atmosphere model, where state variables (such as soil moisture or leaf temperature) are stored in places (nodes) and fluxes (like water or energy flow) are represented as transitions. The EPN structure could allow the model to account for dependencies like temperature effects on transpiration, thus helping visualize and simulate complex feedbacks and interactions in the water cycle.

References

Bancheri, Marialaura, Francesco Serafin, and Riccardo Rigon. 2019. “The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN).” Water Resources Research 55 (11): 8895–8921. https://doi.org/10.1029/2019WR025099.

Marialaura, Bancheri, Francesco Serafin, and Riccardo Rigon. 2019. “Supporting Material for: The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN).” Water Resources Research.

Knoben, W. J. M., J. Freer, K. J. A. Fowler, M. C. Peel, and R. A. Woods. 2019. “Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) v1.0: An Open Source, Extendable Framework Providing Implementations of 46 Conceptual Hydrologic Models as a Ontinuous Space-State Formulations.” GMDD, February, 1–26.

Knoben, W. J. M., J. Freer, K. J. A. Fowler, M. C. Peel, and R. A. Woods. 2019, “Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: An Open-Source, Extendable Framework Providing Implementations of 46 Conceptual Hydrologic Models as Continuous State-Space Formulations.” 2019. Copernicus GmbH. https://doi.org/10.5194/gmd-12-2463-2019-supplement.

Rigon, Riccardo, and Marialaura Bancheri. 2021. “On the Relations between the Hydrological Dynamical Systems of Water Budget, Travel Time, Response Time and Tracer Concentrations.” Hydrological Processes 35 (1). https://doi.org/10.1002/hyp.14007.

Trotter, L., W. Knoben, K. Fowler, M. Saft, and M. Peel. 2022. “Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: An Object-Oriented Implementation of 47 Established Hydrological Models for Improved Speed and Readability.” Geoscientific Model Development, August. https://doi.org/10.5194/gmd-15-6359-2022.