Thursday, July 4, 2024

A Ph.D. position on snow modelling and the related runoff production

 APPLY TODAY ! There is an opening until July 10 (<---Here it is the link) for a Ph.D. position on the SpaceItUP and SUPER project(Snow and glacier rUnoff Production in alpine RivER basins 1990-2050)

Due to climate change, the Alpine region is experiencing a reduction in snow quantity (snow droughts) and an increase in evapotranspiration losses (green water), with significant consequences for sustainable water resource management and ecosystem preservation. This project aims to develop new models to quantify snowmelt and evapotranspiration losses, providing practitioners with calculation tools that are different from traditional lumped parameter models but simpler than 3D process-based models. The project also intends to study the water content obtained from snow and glaciers, from the present to 2050, in the Po and Adige river basins, assessing both quantitative and temporal variations in contributions. The primary tools for the analyses will be the open-source models of the GEOframe system, integrated, modified, and improved by the new models. The modeling will be supported by the acquisition of Earth observation products derived from the MODIS and Sentinel platforms, and potentially other platforms as data becomes available. The final product of the research, concerning snow forecasts, will be developed on a regular grid of 250 meters, while flow rates will be produced for each section of interest. The analyses for the control period from 1990 to 2022 will be conducted on both daily and hourly scales, providing a valuable "reference data cube." Future projections will be produced on a monthly scale. As part of enhancing the current state of the art, which is typical for a doctoral project, reliability criteria and error estimation methods will be developed for each produced dataset. The work will be done in collaboration with dr. John Mohd Wani as co-advisor. Collaborations can include working and exchanging ideas with dr. Christian Massari (GS), Professor Manuela Girotto (GS), Prof. Stefan Gruber (GS), Giacomo Bertoldi (GS)Kelly Gleason (GS), Marco Borga (GS) and Stefano Ferraris (GS).
Old work on snow and permafrost of the group can be found here (to be updated soon).
 The deadline is approachinf very fast, therefore APPLY! To better understand the policies of the group, please give a reading here 

Thursday, June 27, 2024

How much snow is in the mountains and what is its fate? by Manuela Girotto

Water resources such as snow or groundwater can be estimated using satellite remote sensing observations and numerical models. Both models and observations have inherent uncertainties and limitations related to observation errors, model parameterization, and input uncertainties. A promising method to alleviate shortcomings in models and observations is data assimilation because it combines existing and emerging observations with model estimates, thus bridging scale and limitation gaps between observations and models. 

Using these tools, we can address the following science questions: How much water is stored as seasonal snow? How much is in the groundwater aquifers? Can we quantify hydrological changes due to human induced processes (e.g., irrigation)? This presentation will focus on the estimation of snow seasonal amounts in mountainous regions, the water towers of the world. They supply a substantial part of both natural and anthropogenic water demands and they are also highly sensitive and prone to climate change. Slides of the talk can be found by clicking on the above figure. 

The presentation was followed by an interesting discussion that you can see here below:

Sunday, June 9, 2024

On catchment analysis (modeling)

In a series of papers (Abera et al., 2016, Abera et al. 2017a, Abera et al., 2017b, Azimi et al., 2023), we have outlined a methodology for studying basins, focusing on specific locations BUT looking especially to the methodologies. They are also summarized in slides that I typically use in my hydrological modeling classes. These slides summarize the analysis requirements in seven key steps, supported by various notebooks that implement the methodologies.

Each time we begin a new catchment analysis, please ensure these methodological suggestions are considered. Overlooking them can be quite frustrating. Consistently revisit and apply the reference material to build upon previous work and past achievements. Criticize previous methods if necessary, but do not disregard them.
There are two critical steps that are often neglected. The first is data analysis—specifically, examining data coherence and comparing multiple data sources. This preliminary analysis can provide significant insights before any modeling begins, but it is rarely pursued. Instead, input data are directly used in the model, leading to issues later because something seems off.
The second neglected step is validation. There is a tendency to be satisfied with performance metrics like KGE or Nash-Sutcliffe, but these should be starting points, not final assessments. Other benchmarks, such as those proposed by Addor et al., (2018) should be used to critically evaluate the results, not just applied mechanically.
Recently, Azimi et al., 2023 introduced a more refined analysis method (called in future papers EcoProb), which allows for finer discrimination of model behavior by separating the ranges of response. This method should be considered for more precise analysis.
Additionally, since Abera (and likely earlier), we have tried to refocus our analysis on not just discharges but also on budgets. Understanding budget behavior can reveal significant insights and prevent errors, but it is often sidelined. We need to improve in this area.
Mapping is another crucial aspect. While we often rely on time series plots, spatial representation is essential to show the irreducible spatial heterogeneity. This is evident in soil moisture studies, such as the recent work by Andreis et al., and should also apply to other quantities like snow cover and depth.
I have worked with many of you to create effective graphs and maps. Using a full range of colors is beneficial, but please remember that some journals (AGU and EGU) require color-blind friendly plots. Address this requirement from the beginning to avoid last-minute modifications.

P.S. I - One distinguishing feature of GEOframe compared to other systems is its ability to explore multiple working hypotheses. Although this capability exists, it has not been utilized so far. Let's make full use of it moving forward.

P.S. II - When I read a paper from collaborators, I assume that all materials, including data, software, notebooks, and .sim files, are organized and shared as supplemental material for reproducibility. To achieve this, it is crucial to maintain order and keep the material up-to-date from the beginning. Otherwise, it becomes a nightmare.


Abera, Wuletawu, Luca Brocca, and Riccardo Rigon. 2016. “Comparative Evaluation of Different Satellite Rainfall Estimation Products and Bias Correction in the Upper Blue Nile (UBN) Basin.” Atmospheric Research 178-179 (September): 471–83.

Abera, Wuletawu, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon. 2017. “Modeling the Water Budget of the Upper Blue Nile Basin Using the JGrass-NewAge Model System and Satellite Data.” Hydrology and Earth System Sciences 21 (6): 3145–65.

Abera, Wuletawu, Giuseppe Formetta, Marco Borga, and Riccardo Rigon. 2017. “Estimating the Water Budget Components and Their Variability in a Pre-Alpine Basin with JGrass-NewAGE.” Advances in Water Resources 104 (June): 37–54.

Addor, N., G. Nearing, C. Prieto, A. J. Newman, N. Le Vine, and M. P. Clark. 2018. “A Ranking of Hydrological Signatures Based on Their Predictability in Space.” Water Resources Research 54 (11): 8792–8812.

Azimi, Shima, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon. 2023. “On Understanding Mountainous Carbonate Basins of the Mediterranean Using Parsimonious Modeling Solutions.” Hydrology and Earth System Sciences 27 (24): 4485–4503.

Monday, June 3, 2024

Roots (plants roots)

 How deep go the roots of a plant ? Certainly it depends on the age of a plant but for having a static idea, nothing is better than the collection of roots drawing that can be found at the Wageningen University repository.

Clicking on the figure, you can access it. 

Friday, May 24, 2024

How to make an iPoster with usual paper media

Most researchers aim to present their work at conferences, often considering an oral presentation as the ideal outcome. Conversely, being assigned to present a poster can sometimes be seen as a second choice. However, I believe this perception overlooks the unique opportunities posters provide. Interacting with attendees at a poster session often leads to more in-depth conversations than those following an oral presentation.

Recognizing this potential, some conferences, such as the European Geosciences Union (EGU), have innovated with new formats like the PICO (Presenting Interactive COntent) sessions. These sessions enhance the traditional poster experience by allowing researchers to present their work on touch screens, making the interaction more dynamic and engaging than paper posters. This approach has gained popularity and expanded over time.

Unfortunately, smaller conferences may not have the resources to provide touch screens for such interactive experiences. As an alternative, I've experimented with a method that partially recreates the PICO experience. The idea is to make the poster more visual and less text-heavy while incorporating QR codes that link to short videos explaining different parts of the poster. These videos can cover the abstract, references, and explanations of figures.

Implementing this iPoster strategy involves some technical steps: recording the videos, uploading supplemental material for immediate access, and creating QR codes. Recording videos is now straightforward; you can use a virtual meeting platform to record yourself presenting the poster. For hosting the videos and additional materials, I prefer using the Open Science Framework (OSF), which allows for the visualization of PDFs, videos, and Jupyter notebooks without needing to download them first. Videos can also be uploaded to platforms like YouTube or Vimeo.

Once you have your materials uploaded, you can generate QR codes using one of the many available online tools. Embed these QR codes into your poster at relevant points. This way, visitors can access the supplementary material directly from their mobile devices while viewing your poster, even if you are not present.

This approach enhances the traditional poster session, making it more interactive and informative. While there is always room for improvement, this method can significantly enrich the experience for both presenters and attendees.

Clicking on the Figure above you can see and download the iPoster Example. This was actually derived from this poster. For a more generic/general post on how to do poster, please give a look to this blog post. 

Everything is improvable !

Thursday, May 16, 2024

GEOframe-New AGE material for beginners

Dear User or Dear Explorer,

Here we aim to summarize some of the material related to GEOframe-NewAGE. The main source is certainly the GEOframe blog:

However, for a logical introduction, it may be useful to start here:

The most recent material on GEOframe-NewAGE is from the latest school, accessible from this point:

By following the links for each day, you can download the slides and watch the lesson videos. 

Another useful resource is provided through hydrological modeling tutorials:

GEOframe's infrastructure is based on the Object Modelling System v3:

Various papers and applications related to GEOframe have been written and developed; you can find them here:

For any further assistance, the GEOframe crew can be reached at

Please feel free to reach out  us if you have any questions. Next Winter School  on GEOframe-NewAGE will be in January 2025 from 7 to11 in Trento University.  Next Summer School (on Land-Atmosphere interactions will be June 2-6 2025.  This Summer will be holding one Summer School on GEOframe-NewAGE in El Cairo and one in Mumbay (both the last weeks of July).

Sunday, May 12, 2024

And finally we talk about isotope concentration

 If I understand correctly, colleagues arre measuring the concentration of an isotope in stream or some other catchment outlet.  This is quite not a measure of the age of water but the latter has to be inferred from  a hypothesis the system behavior, and possibly the knowledge of the distribution of water ages within the control volume, obtained iteratively from an initial data (derived from the isotopic data of precipitation).

Therefore, assume you have measured in the outflow a concentration of 5 of solute X in appropriate units. You mai have  the knowledge of the concentration of the same solute in the water of a given age resident in the control volume. It would be a tuple like (7,3,6,2) where the number of concentration is the number of precipitation happened before the present instant. The 5 units out of the tuple can be taken all from the oldest (7) concentration, from the youngest (3,2) where the 3 are out of the 6 of the penultimate concentration.
Therefore a concentration of 5 can be fitted with a water age of 4 in the first case or (3*2+2*1)/5=1.8 in the second case.

In fact, the concentration doesn't identify  the exact age of the water but rather a range of ages spanning from the "first in first out" age to the "last in first out" age illustrated in the simple example.
Potentially the old water is infinitely old and, therefore, the lower bound could be very large, while the upper bound for the ages is, clearly, related to the last rainfall age.
So, the division between young water and old water is reasonable. Instead of trying to guess the distribution, you're trying to guess parts of its integral.
Then SAS (StorAge Selection functions, see this previous post for understanding what they are) are summarizing the mechanics of mixing inside the control volume. If the waters mix a lot the travel time and the residence time should coincides  because of the uniform selection of isotopes (or tracers) among the various populations. SAS have a non trivial form if the mixing of waters is incomplete.
Due to the localized nature of water physics, the actual travel times are intricately linked to the specific location from which water emerges or is extracted from the soil. Within the unsaturated zone, it's logical to posit that newly introduced water is the first to flow out for two primary reasons: it occupies the empty pores, and these pores are typically where water is loosely retained. However, when young water mixes with old water in the saturated zone, the aquifer's pressure rises, causing a pressure wave that pushes out the oldest water near the streams.
Consequently, in a typical zeroth-order catchment, runoff tends to adhere to a "last in, first out" (LIFO) logic, while subsurface stormwater tends to expel old water first, following a "first in, first out" (FIFO) logic. Consequently, the recession of the flood wave is expected to contain a higher proportion of old water than young water. In this hypothetical simplified model delineating two distinct dynamics, and drawing from the aforementioned insights, it becomes feasible to estimate the global effect of the system, as proposed by Rigon and Bancheri in 2022 and applying the recent methods of TT probability determination.
Vegetation withdrawal we can think to extract water from the places where active roots are, according to root density. Plants with shallow roots should mostly sample young water, while plants with deeper roots wold sample older water but maybe also being effective in mixing water of different ages because of exudation and because of preferential water infiltration close to the roots.
The presentation (under construction) is exemplifying these concepts.