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 Bozzoli^2,4, Valentina Premier⁴, and Riccardo Rigon^2,3

 

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 (Video)

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 (Video)

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. (Video)

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.

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.

Two possibile positions, if the right people show up

We are once again looking for exceptional individuals to join our team for PhD (3 years) or postdoc (2 years) positions.

Position 1: GEOframe-NewAGE System Application and Methodologies to build a Po Digital Twin

This role focuses on the implementation and application of the GEOframe-NewAGE system to the Po River Basin. You will assist the team in finalizing the calibration and analysis of the hydrology of the Po catchment for the period 1990-1991, with exciting outcomes expected in climatology, drought studies, and hydroinformatics applications. This project is not purely applicative as it may initially seem. It aims to encompass numerous methodological aspects and offers a wealth of research opportunities, along with the data needed to pursue them.

Position 2: Earth Observations and GEOSPACE System Development

This position focuses on integrating Earth Observations into the GEOSPACE system, with further development and applications in the Po River Basin and the Val di Non (Noce River catchment). The primary objective is to advance the integration of high-resolution remote sensing with hydrological modeling, while also improving our land-surface modeling capabilities. The role involves exploring and testing various transpiration models, incorporating increasing levels of physical realism and plant physiology to enhance model reliability.

We encourage interested candidates to explore my blog, where you’ll find detailed information about our working methods, the tools we use, and the philosophy we follow. An insightful candidate will recognize how well we might work together by reviewing the contents they find.

If you’re interested, please reach out to us at abouthydrology@gmail.com.

Let's start with Permafrost and Freezing soil ! A selection of readings for beginners compiled by John Mohd Wani

It looks like we have a new students willing to work on Permafrost and Freezing Soil. So I asked to John Mohd Wani to gather a few introductory readings. Please find below the list, for others who would start to learn about this fascinating topic.

1. Recent advances in permafrost modelling
2. Permafrost distribution in the European Alps: calculation and evaluation of an index map and summary statistics
3. A statistical approach to modelling permafrost distribution in the European Alps or similar mountain ranges
4. GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses
5. A robust and energy-conserving model of freezing variably-saturated soil
6. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects
7. Statistical modelling of mountain permafrost distribution: local calibration and incorporation of remotely sensed data
8. Sensitivities and uncertainties of modeled ground temperatures in mountain environments
9. Mountain permafrost: development and challenges of a young research field
10. Permafrost and climate in Europe: Monitoring and modelling thermal, geomorphological and geotechnical responses
11. Transient thermal effects in Alpine permafrost
12. A method for solving heat transfer with phase change in ice or soil that allows for large time steps while guaranteeing energy conservation
13. Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment
14. Theoretical and numerical tools for studying the Critical Zone from plot to catchments
15. Theoretical progress freezing-thawing processes study
16. A sensitivity study of factors influencing warm/thin permafrost in the Swiss Alps
17. Application of Satellite Remote Sensing Techniques to Frozen Ground Studies
18. Derivation and analysis of a high-resolution estimate of global permafrost zonation
19. How Much of the Earth's Surface is Underlain by Permafrost?
20. Influence of snow cover on ground surface temperature in the zone of sporadic permafrost, Tatra Mountains, Poland and Slovakia
21. Influence of the seasonal snow cover on the ground thermal regime: An overview
22. Mapping and modelling the occurrence and distribution of mountain permafrost
23. Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale
24. Numerical simulations of the influence of the seasonal snow cover on the occurrence of permafrost at high latitudes
25. Permafrost distribution modelling in the semi-arid Chilean Andes

26. The changing thermal state of permafrost
27. Permafrost is warming at a global scale
28. The new database of the Global Terrestrial Network for Permafrost (GTN-P)

29. Thermal state of permafrost and active layer in Central Asia during the international polar year

30. Twenty years of European mountain permafrost dynamics—the PACE legacy

Additionally, you can find well-documented information on the long-term state and changes of mountain permafrost in the Swiss Alps through the Swiss Permafrost Monitoring Network (PERMOS). Also, they have documented and compiled best practice recommendations for the long-term measurement of permafrost temperatures.

Finally the permafrost Glossary (old) that will give you the definitions of the permafrost related terms.

Don't forget to keep an eye on the International Permafrost Association (IPA) website for events and other stuff related to permafrost. Also subscribe to the Permafrost Young Researcher's Network (PYRN), that promotes the future generation of permafrost researchers under the patronage of IPA.