Water table position in GEOtop simulations

One thing I learn from Aldo Fiori at CATHY's meeting in Padua on Monday, Jan. 17, was that having a water table at the base of a hillslope is changing the way that the hillslope responds to the rainfall input. This is particularly important for getting the right residence time of water, that through this water table is recirculated and resides more time.

The assumption seems to me reasonable. This has also clear implications on the setting of the initial conditions. In fact, it seems reasonable to put a water table at the same level where there are surface water, at least for most of the year, interpolating the water levels of the blue lines.

This obviously does not mean that the spin-out runs can be avoided (see previous posts), which should be performed to gain a dynamical equilibrium level of the water tables.

Certainly this implies a radical change with respect to the habit to assume an impermeable bedrock a few meters below the terrain surface, and imposes the necessity of a more deep discussion of the hydraulic properties of the bedrock itself.

Notable References to Aldo's work

Fiori, A., M. Romanelli, D.J. Cavalli, D.Russo, Numerical experiments of streamflow generation in steep catchments, JOURNAL OF HYDROLOGY, 339, 183-192, 2007.

Fiori, A., D. Russo, Numerical Analyses of Subsurface Flow in a Steep Hillslope under Rainfall: The Role of the Spatial Heterogeneity of the Formation Hydraulic Properties, WATER RESOURCES RESEARCH, 43, W07445, doi:10.1029/2006WR005365, 2007

Russo, D., A. Fiori. Equivalent Vadose Zone Steady-State Flow: An Assessment of its Capability to Predict Transport in a Realistic Combined Vadose Zone - Groundwater Flow System. WATER RESOURCES RESEARCH, 44, W09436, doi:10.1029/ 2007WR006170, 2008.

Fiori, A., D. Russo, Travel Time Distribution in a Hillslope: Insight from Numerical Simulations. WATER RESOURCES RESEARCH, 44, W12426, doi:10.1029/2008WR007135, 2008.

Russo, D., and A. Fiori, Stochastic analysis of transport in a combined heterogeneous vadose zone–groundwater flow system. WATER RESOURCES RESEARCH, 45, W03426, doi:10.1029/2008WR007157, 2009.

Fiori, A., D. Russo, M. Di Lazzaro. Stochastic analysis of transport in hillslopes: Travel time distribution and source zone dispersion. WATER RESOURCES RESEARCH, 45, W08435, 2009.

The geomorphic structure of Peak Flows: Rigon, D'Odorico and Bertoldi

This is a long-awaited paper, since the theory it tries to explain has been implemnted from a few year in JGrass , and successfully applied to several case study.

This paper hase been submitted to HESSD for open discussion, and can be found here:

Peak Flow paper

Any comment and suggestion is welcomed

References of the paper

Barling, R. D., Moore, I. D., and Grayson, R. B.: A quasi-dynamic wetness index for characterise the spatial distribution of zones of surface saturation and soil-water content, Water Resour. Res., 30, 1029--1044, 1994.

Bell, F. C.: Generalized rainfall duration-frequency relationships, J.Hydraul. Eng., 95(HY1), 311--327, 1969.

Beven, K. and Kirkby, M. J.: A physically-based variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, 43--69, 1979.


Beven, K. J.: Rainfall-Runoff Modelling: the Primer, Wiley, Chichester,360~pp., 2001.

Beven, K. J. and Wood, E. F.: Flow routing and the hydrological response of channel networks, in: Channel Network Hydrology, edited by: Beven, K. J. and Kirkby, M. J., Wiley, Chichester, Chap.~5., 1993.


Botter, G. and Rinaldo, A.: Scale effect on geomorphologic and kinematic dispersion, Water Resour. Res., 39(10), 1286, doi:10.1029/2003WR002154,
2003.

Brutsaert, W.: Hydrology: an introduction, Cambridge university Press, Cambridge, UK, 2005.

Burlando, P. and Rosso, R.: Scaling and multiscaling Depth-Duration-Frequency curves of storm precipitation, J. Hydrol., 187/1-2, 45--64, 1996.

Chow, V.T., D.R. Maidment, L.W. Mays, {\it Applied Hydrology}, McGraw-Hill,
1988.

Da Ross, D. and Borga, M.: Use of digital elevation model data for the derivation of the geomorphological instantaneous unit hydrograph, Hydrol. Process., 11, 13--33, 1997.

Doodge, J. C. I.: The rational method for estimating flood peaks, Engineering, 184, 311--313, 374--377, 1957.

Doodge, J. C. I.: Linear theory of hydrologic systems, EGU reprints series, Katlenburg-Lindau, Germany, 1, 2003.

D'Odorico, P. and Rigon, R.: Hillslope and channel contributions to the hydrologic response, Water Resour. Res., 39(5), doi:10.1029/2002WR001708, 2003.

D'Odorico, P., Fagherazzi, S., and Rigon, R.: Potential for landsliding: dependence on hyetograph characteristics, J. Geophys. Res.-Earth, 110, F01007, 2005.

Franchini, M. and OConnell, P. E.: An analysis of the dynamic component of the geomorphologic instantaneous unit hydrograph, J. Hydrol., 175(1--4), 407--428, 1996.

Gupta, V. J. and Mesa, O. J.: Runoff generation and the hydrologic response via
channel network geomorphology -- recent progress and open problems, J. Hydrol., 102, 3--28, 1988.

Gupta, V. J., Waymire, E., and Wang, C. T.: A representation of an IUH from
geomorphology, Water Resour. Res., 16, 885--892, 1980.

Henderson, F. M.: Some Properties of the Unit Hydrograph, J. Geophys. Res., 68(16), 4785--4794, 1963.

Kirkby, M.: A runoff simulation model based on hillslope topography, in: Scale problem in Hydrology, edited by: Gupta, V. K., Rodriguez-Iturbe, I.,and Wood, E., D.~Reidel, Dordrecht, 1986.


Mesa, O. J. and Mifflin, E. R.: On the relative role of hillslope and network
geometry in hydrologic response, in: Scale problem in Hydrology, edited by:
Gupta, V. K., Rodriguez-Iturbe, I., and Wood, E., D.~Reidel, Dordrecht, 1986.

Mulvaney, T. J.: On the use of self-registering rain and flood gauges inmaking observation of the relations of rainfall and flood discharges in a given catchment, Proceedings of the Institution of Civil Engineers of Ireland, 4, 19--31, 1851.

Myninink, W. J. and Cordery, I.: Critical duration of rainfall for Flood
estimation, Water Resour. Res., 12(6), 1209--1214, 1976.

Naden, P. S.: Spatial variability in flood estimation for large catchments: the exploitation of channel network structure, Hydrolog. Sci. J., 37, 53--71, 1992.

Naden, P., Broadhurst, P., Tauveron, N., and Walker, A.: River routing at the continental scale: use of globally-available data and an a priori method of parameter estimation, Hydrol. Earth Syst. Sci., 3, 109--123, doi:10.5194/hess-3-109-1999, 1999.

Nash, J. E.: The form of the instantaneous unit hydrograph, International Association of Science and Hydrology, 45(3), 114--121, 1957.

Rigon, R., Cozzini, A., and Tiso, C.: The Horton machine: a suite of tools for
the analysis of DEMs, Dipartimento Di Ingegneria Civile ed Ambientale e
CUDAM, Universit\`a di Trento, 2006.

Rinaldo, A. and Rodriguez-Iturbe, I.: Geomorphological theory of the hydrological response, Hydrol. Process., 10(6), 803--829, 1996.

Rinaldo, A., Marani, A., and Rigon, R.: Geomorphological dispersion, Water Resour. Res., 27(4), 513--525, 1991.

Rinaldo, A., Vogel, G. K., Rigon, R., and Rodriguez-Iturbe,I.: Can one gauge the shape of a basin?, Water Resour. Res., 31(4), 1119--1127, 1995.

Robinson, J. S. and Sivapalan, M.: An investigation into the physical causes of scaling and heterogeneity of regional flood frequency, Water Resour.Res., 33(5), 1045--1059, 1997.

Rodriguez-Iturbe, I. and Valdes, J. B.: The geomorphic structure of hydrologic
response, Water Resour. Res., 18(4), 877--886, 1979.

Ross, C. N.: The calculation of flood discharge by the use of time contour plan isochrones, Transaction of the Institute of Engineers, Australia,2, 85--95, 1921.

Saco, P. M. and Kumar, P.: Kinematic dispersion in stream networks, Part~1: Coupling hydraulic and network geometry, Water Resour. Res., 38(11), 1244,
doi:10.1029/2001WR000695, 2002a.

Saco, P. M. and Kumar, P.: Kinematic dispersion in stream networks, Part~2:
Scale issues and self-similar network organization, Water Resour. Res.,
38(11), 1245, doi:10.1029/2001WR000694, 2002b.

Sherman, L. K.: Streamflow from rainfall by the unit-graph method, Engineering News-Record, 108, 501--505, 1932.

Shreve, R.L.: Stream lengths and basin areas in topologically random channel
networks, J. Geol., 77, 397--414, 1969.

Sivapalan, M., Beven, K. J., and Wood, E. F.: On the hydrologic similarity, 2.~A scaled model of storm runoff production, Water Resour. Res., 23(12),2266--2278, 1987.

Snell, J. D. and Sivapalan, M.: On geomorphological dispersion in natural catchments and the geomorphological unit hydrograph, Water Resour. Res., (30(7), 2311--2324, doi:10.1029/94WR00537, 1994.

Wood, E. F., Sivapalan, M., and Beven, K.: Similarity in small catchments storm
response, Rev. Geophys., 28, 1--18, 1990.

US~Department of Commerce Weather Bureau, Hershfield, D. M.: Rainfall Frequency Atlas of the United States, Technical Paper~40, Government Printing Office, Washington, DC, 62~pp., 1961.

Woods, R. A. and Sivapalan, M.: A connection between topographically driven
runoff generation and channel network structure, Water Resour. Res.,
33(12), 2939--2950, 1997.

Woods, R. A. and Sivapalan, M.: A synthesis of space-time variability in storm response: rainfall, runoff generation and routing, Water Resour. Res., 35(8), 2469--2485, 1999.


Yang, D., Herath, S., and Musiake, K.: A hillslope-based hydrological model using catchment area and width functions, Hydrolog. Sci. J., 1, 49--65, 2002.

GEOtop

GEOtop, guidelines for the distributed hydrology modelers

Our recent work emphasizes some aspect of using a distributed model which should be always kept in mind when planning simulations with GEOtop.

Space and Time Resolutions: How are the space and time resolutions selected?
In terms of spatial discretization, the model uses grid-based DEMs with varying depths for individual grid boxes and a temporal resolution of any calculations or forcings (e.g. Richards equation, energy balance, channel flow time steps, etc.). What are the consideration which leaded to space-time resolutions and computational burden, also in terms of the numerical solution strategies adopted ?

Addressing subgrid variability. Once spatial and temporal variability are chosen, the modeler should ask if spatial variability within the grid is addressed in a consistent way. Otherwise, she/he can produce inconsistent results.

Computational Burden:
As a general rule, distributed models are computationally expensive. The authors should address the computational limitations, if any, of the model, in particular for watershed applications which involve comparison to remotely sensed imagery. What tradeoffs are required between computational expense and the space-time resolution of the model?

Non calibrated parameters: A sound literature review should be chosen for the parameters of the models which are kept fixed. Many of them, especially those which regard the soil properties, including its depth, are, however, derived from a mixing of field measurements,their local estimation, and their spatial extension (for this see also the paragraph below). These procedures can vary from case study to case study, depending also on the data available. Some suggestions and procedures are however in the paper by Simoni et al., 2006 and, more extensively, in the report of the work for Sauris (Armanini et al., 2006) and Simoni (2007). It can be seen, that many procedure are still open issues and this makes more important the discussion. Parameter including the atmospheric boundary layer (ABL) quantities, have a introductory discussion in Endrizzi (2007) but clearly, the discussion remains open.

Calibration Strategy: A calibration strategy should be documented. The calibration strategy should explicitly indicate over what time periods it is performed (e.g. event, seasonal, annual), what data are used for calibration (e.g. discharge, soil moisture), what parameters are related to which data sets or portions of the data (e.g. hydrograph peak or recession). In many of hydrological studies a split sample test is used to validate the calibrated model w.r.t. events not used in the calibration period. Keep in mind that calibration to a single flood event in any hydrological model is an extremely weak test of model performance.

Model initialization. Modelers need to adequately describe initial conditions and be aware of their effects on the simulations, which is particularly critical for a distributed model with vertical profiles of soil moisture and temperature and a water table position. For example, the model should be run for a sufficiently long period of time prior to ensure both static and dynamical equilibration in the surface and subsurface states prior to the analysis period.
This can be performed by running a drainage experiment starting from totally saturated conditions in each basin (with no ET or rainfall) and allowing the basin to drain for sufficiently long periods. This will lead to a ‘static’ equilibrium condition which is particularly adapted to each terrain, soil depth, channel network combination. A ‘dynamical’ equilibration can be achieved by forcing the model with the meteorological conditionsrepeatedly (periodic forcing) lasting sufficiently long and then analyzing the results from a period after various periodic cycles have passed. This allows each combination of prognostic quantities to dynamically adapt to the forcing.
Both techniques minimize initialization errors. To be specific: two processes have a very slow adaptation to "mean conditions", the water content and temperature in the ground, say below 1 m. This implies that incorrect initial conditions at the bottom layer can influence the time evolution of the system for years. Thus the dynamical equilibrium to obtain can be very far from a set of arbitrary initial condition and the modelers always needs to make an "educated guess" to be successful in his/her simulations. A rule of thumb for the initial soil moisture distribution is to take an equilibrium condition, which implies hydrostatic distribution of pressure in both the vadose and saturated zones (e.g. Cordano and Rigon, 2006). The latter condition, in turn, implies to guess the initial position of the water table, which could be below the bottom boundary of the control volume. Obviously some measures of this quantity would help, however local (in a point) measurements imply to search a method for their spatial extensions (see below). Either if the water table is above of below the bottom layer, a flux or gradient condition must be given at the bottom. One reasonable assumption to build this condition is to use the state of the system itself, especially to give the hydraulic conductivity. Clearly this boundary condition is dynamic with the varying soil moisture contents at the bottom. Please, note that assuming constant water content throughout the ground layers, would imply a constant water input into the considered volume.
A rule of thumb for the bottom temperature would be to assign at some depth (1.2 m, for instance) the mean annual air temperature (which is spatially varying). These last hints are of general validity, however for particular sets of simulations, other choices can be made. What it is important is to discuss consciously the choices made.

Spatial series of soil properties

These include soil depth, soil permeability, and van Genucthen parameters (if the van Genuchten parametrization has been chosen). Soil depth can be assessed locally by measurements and, assuming an equilibrium soil profile, using the Heimsath theory as in Bertoldi et al. (2006). Many aspects of the issue are still to be addressed. Also the other quantities estimation are very relevant topics. A. Bellin gave some major contributions on the aspects related tosaturated soil conductivity. Other clues could arrive from the literature by David Russo (mainly on Water Resorces Res.).

Spatial time series of meteorological data

A prerequisite for any model is to provide it with the best input data as is reasonablypossible. If these data are of poor quality, then the entire hydrologic simulation is flawed from the outset.

Particularly in mountainous areas, where complex interactions among topography, vegetation canopies, and climate exist, it is essential that spatial interpolation methods that account for these effects be applied to estimate the spatial fields of meteorological input. A good and up-to-date reference for method of interpolation is Garen and Marks (2005) (and references therein). In that paper, the authors show show a combination of limited measurements, models, and carefully applied spatial interpolation methods can be used to develop the spatial field time series of the forcing data required for the simulation of the development and melting of the seasonal snowcover. The arguments of the papers apply with almost no change to the general case of simulating the hydrological cycle. The meteorological input includes spatial field time series of precipitation, air temperature, dew point temperature, wind speed, and solar and thermal radiation. These variables have particular characteristics and levels of data availability that make it necessary to use a variety of procedures to develop spatial fields of each. It is also essential to consider the effects of the forest canopy on the solar and thermal radiation.


Mass is conserved. Water in input needs not to be lost and equate storage and outflow, in all of their forms.
Energy must be conserved (if the energy budget is evaluated): this is much more difficult to assess, meaning that energy in input must equate the energy storage, plus energy outflow and dissipation (usually thermal effect of frictions are neglected).

Validation strategies I - Makes some null hypothesis

Regarding the prognostic mean and variance of the quantities. The modeler should make the statement that they equals the measured quantities and validate the statement statistically (are the mean value of observed and distributed data the same? The variances are the same ?) . A possible validation of the distribution of the quantities can follow (the "bulk" distribution are the same ?). For instance in our work on Little Washita we found that soil moisture distributions derived from remote sensing are significantly different from the modeled ones. Yet the model reproduce with high fidelity  the ground truth.  In fact remote sensed data are not measurements but models themselves.

Validation strategies: II Assessing the physical realism.

Must be explicated in the planning phase of the simulations. The modeler should provide an indication of the dynamics at the pixel scale, illustrating the rainfall, infiltration fronts, soil moisture variations, water table dynamics, lateral transfers, etc. Although the validation data may not be present, this type of analysis allows readers to assess the ‘physical realism’ present in the model. Without it, the model capabilities are not well identified and cannot be related to observations (e.g. discharge, soil moisture patterns).

Validation strategies: III Assessing the spatial patterns.
Methods for comparing spatial patterns should be defined. As a minimum requirement, global distributions of parameters and main moments should be compared (computed-measured). The spatial analysis of many authors tend to be qualitative. The reader is essentially asked to visually compare maps. This is not enough.

A scheme for presenting an analysis of a basin

I believe a good an simple scheme for presenting a hydrological analysis od a catchment could be:

- Description of the basins: geography and morphometric characteristics relevant to the study
- Description and reference to the data available and used for validation with reference to who gave the data (it is assumed that the method of validation are presented elsewhere)
- Description of the initial and boundary conditions (when applicable) and the rational behind there choice
- Rational behind the choice of the parameters to calibrate
- Calibration procedure and period of calibration
- Simulations (the simulation setup must be well specified)
- Analysis of the results, possibly looking at quantitative benchmarks of models' performances.

A longer discussion mostly related to distributed modeling in one of the next posts.

Lawren S. Harris, Mount Lefroy

From a few years, I have an eye for paintings representing the hydrological cycle or emotions that call to me something connected with the arguments or the tools, or something else I am doing. The origin of this mood came from a paper by Bill Dietrich and collaborators, about the realism of modeling nature, the paper entitled
"Geomorphic Transport Laws for Predicting Landscape Form and Dynamic" was published in "Prediction in Geomorphology, Geophysical Monograph, 13, 2003, and can be found here. Whatever one thinks about the paper, she/he could not stay indifferent behind the pieces of art, like the one here. Obviously what realism is, is a matter of opinions. However, with Galilei, physical science chose to perform experiments to assess it. Which is not matter of opinions, but of set-ups, and repeatability. Unfortunately Earth Science cannot, at the very end, perform very controlled experiments, in which single factors, and boundary conditions can be controlled. We have events, in place of experiments. This makes our work more challenging.

Presentation at CATHY's meeting

Here there is the presentation I gave yeaterday at the Cathy's meeeting:

Presentation at Padua Cathy meeting

Actually this is a synthesis of two previous presentations given at Wien EGU meeting last year

Wien's reflections on Hydrological modelling

and at Boulder in 2008.

How to do, and why to do, modern modelling

They cover the topic of modelling, and particularly, the modelling of the hydrological cycle. With respect to Boulder's presentation, currently we switched from OpenMi to OMS3. Please find the reasons in the presentations.

An interview (in Italian)

About my Job for high school students. An interview I gave in Italian for the project Scienza Attiva.
As you can notice that day my voice was almost gone ;-(

From the work "the thousand rivers” (i mille fiumi) by Arrigo Boetti and Anna-marie Sauzeau-Boetti

classification by order of magnitude is the most common method for classifying information relative to a certain category, in the case of rivers, size can be understood to the power of one, two, or three, that is, it can be expressed in km, km2, or km3 (length, catchment area, or discharge), the length criterion is the most arbitrary and naive but still the most widespread, and yet it is impossible to measure the length of a river for the thousand and more perplexities that its fluid nature brings up (because of its meanders and its passage through lakes, because of its ramifications around islands or its movements in the delta areas, because of man’s intervention along its course, because of the elusive boundaries between fresh water and salt water...) many rivers have never been measured because their banks and waters are inaccessible, even the water spirits sympathize at times with the flora and the fauna in order to keep men away, as a consequence some rivers flow without name, unnamed because of their untouched nature, or unnamable because of human aversion (some months ago a pilot flying low over the brazilian forest discovered a “new” tributary of the amazon river). other rivers cannot be measured, instead, because they have a name, a casual name given to them by men (a single name along its entire course when the river, navigable, becomes means of human communication; different names when the river, formidable, visits isolated human groups); now the entity of a river can be established either with reference to its name (trail of the human adventure), or with reference to its hydrographic integrity (the adventure of the water from the remotest source point to the sea, independently of the names assigned to the various stretches), the problem is that the two adventures rarely coincide, usually the adventure of the explorer is against the current, starting from the sea; the adventure of the water, on the other hand, finishes there, the explorer going upstream must play heads or tails at every fork, because upstream of every confluence everything rarefies: the water, sometimes the air, but always one’s certainty, while the river that descends towards the sea gradually condenses its waters and the certainty of its inevitable path, who can say whether it is better to follow man or the water? the water, say the modern geographers, objective and humble, and so the begin to recompose the identity of the rivers, an example: the mississippi of new orleans is not the extension of the mississippi that rises from lake itasca in minnesota, as they teach at school, but of a stream that rises in western montana with the name jefferson red rock and then becomes the mississippi-missouri in st louis, the number of kilometres upstream is greater on the missouri side, but in fact this “scientific” method is applied only to the large and prestigious rivers, those likely to compete for records of length, the methodological rethinking is not wasted on minor rivers (less than 800km) which continue to be called, and measured, only according to their given name, even if, where there are two source course (with two other given names), the longer of the two could be rightly included in the main course, the current classification reflects this double standard, this follows the laws of water and the laws of men, because that is how the relevant information is given, in short, it reflects the biased game of information rather than the fluid life of water, this classification was began in 1970 and ended in 1973, some data were transcribed from famous publications, numerous data were elaborated from material supplied non-european geographic institution, governments, universities, private research centres, and individual accademics from all over the world, this convergence of documentation constitutes the the substance and the meaning of the work, the innumerable asterisks contained in these thousand record cards pose innumerable doubts and contrast with the rigid classification method, the partialness of the existing information, the linguistic problems associated with their identity, and the irremediably elusive nature of water all mean that this classification, like all those that proceeded it or that will follow, will always be provisional and illusionary

Anne-marie Sauzeau-Boetti

(TN the text is published without capital letters)
(translation by Joseph Tomasi)

A comment on the opera can be found, for instance, here, at Moma site.

To my students

I chose this job (well I was also chosen) because I am curious and I like research.  To my collaborators I ask enthusiasm: knowledge will follow enthusiasm and cause serendipity.
Ignacio Rodriguez-Iturbe, one of my three masters,  told me that creativity is better than knowledge: in fact true knowledge originates from creativity. However, I have to warn my students than creativity does not come from natural skills alone, but also from consistent, and intelligent work. In a world of gifted people, is the hard and consistent that makes the difference.

In my scientific activity I realized that one can work alone, but for many objectives it is much better to work cooperatively. Working alone is the exception more than the rule in modern Academia. Bringing to some maturity GEOtop, JGrass and JGrass-NewAGE would not have been possible if I had to do it all alone.

In order cooperation really works, I also realized that what I was developing needed to be openly shared, and I choose to go open  for my products (models, slides, research, papers).
As Andrea Antonello told: "the fact is that to be 'open source' does not mean to give away software for free, it is to believe that with some shared rules, many can work together and produce things that, the singles could never do". This also favour Reproducible and Replicable Research, which goes at the core of any scientific work.

Actually this was not really understood by many of my Ph.D students in the past. But now, as time pass by, they are slowly convincing themselves that cooperation can be better and much more productive than competition.

Behave. Nature challenges us already. Understanding nature is our competition.

So:

1 - Like research
2 - Work for creating enthusiasm
3 - Pursue creativity more than erudition
4 - Work hard
5 - Work cooperatively
6 - Go open without hesitation
7 - Struggle for the understanding and not with people

My scientific history in brief

I took a while to understand what I would have liked to do.
When I graduate in Physics on supersimmetries, I knew just one thing: I wanted to change subject. Reasons were many. Among them I felt myself the criticism that is nowadays present in a minority of Physicists (see Lee Smolin's  comment)  who claim that possibly Strings are not the way to obtain the Grand Unified Theory.

After the military service, I had the occasion to gain a grant in Venice, to work with Sandro Marani, a man of unsurpassed visionariness, where I quietly I approached hydrology, by studying river networks  and the way messages (the flood) propagate inside them.
Eventually we produced a paper on the fractal structure of river networks, and, I believe, an interesting contribution on understanding the shape of the hydrograph as formed by the (fractal) geomorphology of the network.

After a few years of wandering I had the occasion to pursue a doctoral degree in hydrodynamics. I worked under the supervision of Andrea Rinaldo, and in strict contact with Ignacio Rodriguez-Iturbe. The main goal was to show the dynamics behind fractals (at least in the case of river networks, and basin's landscapes). Why there were such similarities in the structure of the networks, despite the different geology, and climate conditions ? What was the rational of Horton's laws ? We ended with the Optimal channel networks theory, with some invasion in the terrain of the Self Organizing Criticality. All of this appeared in the book by Ignacio and Andrea: Fractal river networks: chance and self-organization.

Further work, mainly by Andrea Rinaldo, Amos Maritan e Jaynath Banavar has also shown that the topological structures of river networks has parallel in living beings, and possibly a trade-off by minimal energy dissipation and maximization of entropy is governing the network structures.

Collaboration of the first years, include Texas A&M, MIT, and Princeton University.
In late nineties, already assistant professor in Trento (BTW a tenured position), and having spent a couple of years in College Station, Texas, at TAMU, I did not succeeded in getting the Italian equivalent of associate professorship I finally decided to stick with Trento, and directed my work toward which are my main research activities now.

I started to write my notes on hydrology, that I will try to complete in a book sometimes (not yet assembled at the end of 2014), I started the GEOtop project, and, taking the opportunity offered by the Cofinlab 2001 that funded CUDAM, I began the adventure of JGrass.

What pushed me was:

- learning about the "rest of hydrology" that was closely unknown to me;
- learning by doing;
- believing that the best knowledge about hydrology could be captured in numerical models, and through models communicated to people, who could learn by doing and virtual experiments .
- arriving to have tools where the interactions between processes could be studied in their non-linearity;
- having models ready to be coupled with distributed data like those produced by remote sensing.

Well it took a while to arrive to a decent level in all of this.

Eventually, after some financial support by the Basin Authority of River Adige, I also started the JGrass-NewAGE project. In recent years I started a fruitful collaboration with Olaf David of CSU and ARS in Fort Collins, which I visited in summer 2014. I also collaborate on permafrost and snow studies with Stephan Gruber, of Carleton University.


My complete CV can be found here; my past research here; my future foreseen steps here.