Where do we stand

 Aristotle had it all wrong.

Dalton, Horton, Sherman and Leopold played the starting gong.

Eagleson, Rodriguez-Iturbe went for a grand theory, in which they believed.

Ignacio (Vujica teaches) dated with randomness.

It is (dis-)organized complexity, Jim Dooge said.

Richards, Richardson, Harlan and Freeze insisted on using PDEs.

Horton said the runoff is infiltration excess,

Dunne said that it is saturation excess,

Hewlett and Hibbert said that overland flow is not necessary.

Tracer research screwed it all up.

Darcy and Buckingham — it is all a matter of gradients, they thought.

Beven and Germann set up a mountain of doubts.

And many, I forgot, I do not know.

(Klemeš complains.)

Now we do not really know what we know,

except that we know more than before,

better data we have,

satellites see it all (but what you see, you do not believe).

Modelers give numbers without caring,

machine learning thinks it can do all without understanding —

and because we did not have it when we thought we did,

they probably sing the right song.

Musical coda

Probability and Statistics basics: a very short simple overview of concepts for my students

These lectures cover both the class of Hydrology and Hydraulic Constructions that share the necessity to talk a little of statistics. In four steps I talk about simple concepts about statistics and probability. Very basic stuff to remind to my students what they should already know. Probably in the second series of slides I performed better.

Samples, Population, empirical distributions

Same topic as above but different class

Introduction to visual statistics, location and scale parameters.

Same topic as above but different class

Probability axioms and some derived concepts  visualised

Same topic as above, different class

Acting with Real numbers

Almost the same as above bur with a couple of slides more

Hydrology 2017

This year I decided to introduce strong news in my Hydrology course.  Not only a change of topics, but also a change of perspective. I increased widely the hours in the lab (up to 60%) of the class, and I arranged the lectures in a way that they could be followed by a three hour laboratory. Almost no lecture will be without numerical experiments. Another innovation is the use of Python instead of R.
I made this because of the large endorsement Python had among hydrologist and because:

•  its object oriented structure is much more firm than the R one. 
•  Besides, Python seems to be easy to learn by engineering students. 
• Some of my colleagues seem to agree to converge toward the use of Python in their classes

R remains the first choice to do statistics. However, we have limited time. The class is 60 hours, and the material to convey a lot.

Here it is the foreseen schedule of the class:

Corso di Idrologia 2017

Legend: T - Theoretical lecture  - L - Laboratory class (this can include theoretical parts, but mostly students will exercise with tools)

1. T - Introduction to the class. 
• Water on Earth (optional). 
• The hydrological cycle (YouTube)
• The hydrosphere parts (optional). 
• Modern hydrological information (YouTube). 
• The water budget (YouTube). 
• The energy budget (YouTube) 
• Fluxes, Reservoirs, Residence times (optional) 
• The Budyko scheme. 
• Further Readings
2. T - A terrain analysis  primer. 
• Elevation, Slopes, Curvatures. (YouTube)
• River network delineation. 
• Contributing areas. 
• Geomorphic laws (optional)
• Further Readings
3. L - Introduction to QGIS. Introduction to the JGrasstools in OMS.
4. T - A little of Statistics and Probability. 
• Descriptive Statistics
• Location indicators  (YouTube)
• Form and Shapes of data
• Tests of hypothesis
• Stationarity And Ergodicity
• Further readings on Statistics (see also here)
• Probability's Axioms (optional)
• Univariate distributions
• Further readings on Probability
5. L -  Delineation of catchments' characteristics with JGrasstools and QGIS.
6. T - Precipitations. Mechanisms  of formation of precipitation. Ground based statistics. Extreme precipitations. 
• See the points 6 to 11 in this post.
• Further readings (Point 1-5 and 17 in the Precipitations' post)
7. L - Intro to Python - Loading/reading files. Time series and their visualisation. (See Notebook 0 an 1 here.)
8. T - Extreme precipitation statistics (parameters' estimation)
• See points 12-15 in the Precipitations post
• Further readings (Point 1-5 and 17 in the Precipitations' post)
9. L - Estimation of extreme distributions parameters. (See Notebook 2 to 5 here.)
10. T -  Radiation (YouTube 2017). 
• The Sun (YouTube 2017)
• Stefan-Boltzmann law and radiation spectrum (YouTube 2017)
• Sun  to Earth (YouTube 2017)
• Coping with latitude and longitude (YouTube 2017)
• Atmospheric Absorptions (YouTube 2017)
• Clouds (YouTube 2017)
• Coping with terrain (YouTube 2017)
• Long wave radiation (YouTube 2017)
• Table of symbols
• Further readings:
• Corripio, J. G. (2002). Modelling the energy balance of high altitude glacierised basins in the Central Andes. Ph.D Dissertation, 1–175.
• Corripio, J. G. (2003). Vectorial algebra algorithms for calculating terrain parameters from DEMs and solar radiation modelling in mountainous terrain. Int. J. Geographical Information Science, 17(1), 1–23. 
• Formetta, G., Rigon, R., Chávez, J. L., & David O. (2013). Modeling shortwave solar radiation using the JGrass-NewAge system. Geoscientific Model Development, 6(4), 915–928. http://doi.org/10.5194/gmd-6-915-2013 
• Formetta, G., Bancheri, M., David, O., & Rigon, R. (2016). Performance of site-specific parameterizations of longwave radiation. Hydrology and Earth System Sciences, 20(11), 4641–4654. http://doi.org/10.5194/hess-20-4641-2016
11. L - Estimation of shortwave and longwave radiation in a catchment (data, executables, sim files are available through Zenodo. Who is interested in the source code and further information, plese refers to GEOframe or the Github GEOframe components site). 
• A brief rehearsal of the matter given by Michele Bottazzi (M.B.) (YouTube)
• Estimation of solar radiation with JGrass-NewAGE components (YouTube) by M.B. Part I
• Estimation of solar radiation with JGrass-NewAGE components by M.B. (YouTube) Part II
12. T - Spatial interpolation of environmental data
• Some concepts about the spatial representation of environmental quantities (YouTube 2017)
• Simple Kriging (YouTube 2017) (This is more or less covered in Raspa work from page 75)
• More on variance and covariance (YouTube 2017)
• Further readings
• Dispense Geostatistica G. Raspa. 
• A. Bellin slides
13. L - Practical spatial interpolation of rainfall and temperature.  
• Data, sim files, etc 
• Video lectures by Marialaura Bancheri (Parte I, II e III)
14. T - Water in soils. - Darcy-Buckhingham law- Soil water retention curves and hydraulic conductivity. 
• Soils (YouTube 2017)
• Texture and Structure of soils (YouTube 2017)
• Aquifers (optional)
• Definitions (YouTube 2017)
• Darcy-Buckingham law (YouTube 2017)
• Soil Water RetentionCurves (YouTube 2017)
• Hydraulic Conductivity
• Conductivity in unsaturated soils
• Saturated conductivity
• Further readings
• Soil depth
• Soil Water Retention Curves
• The old post on soils
• Preferential flow in hillslopes
• Ning Lu's lectures on soil water
15. L - Numerical experiments on soil water retention curves and hydraulic conductivity.
• Soil Water Retention curves (data)
• Saturated Hydraulic Conductivity (data)
16. T -  Richards equation and its extensions.
• Mass conservation - Richards equation (YouTube2017)
• Pedotransfer Functions (Used during the lab 15) - (YouTube2017)
• Simplifications of Richards 1D in a hillslope (YouTube2017)
• Phenomenology of infiltration (according to Richards equation) in a hillslope (YouTube2017)
• Macropores (Optional)
• Water Tables equations (Optional)
• Water in soils measures
• Notation Summary
• Further Readings (or view)
• Older presentation, slides and Audios
• Video talk given at a Summer School
• A lecture on this topic at Todini's Symposium (a little advanced)
• Preferential flows
• Bimodal Water retention Curves
• An R package for getting soil hydrology
• About the Petri Net representation of Richards 1d
17. L - Experiments with a Richards 1D simulator
• Readme First
• Data
• Notebooks: Input; Outputs
• Explanation of the sim file (YouTube by Niccolò Tubini: Part Ihttps://www.youtube.com/watch?v=e3vGgHQXvTM and II)
• Executable, Source Code and Data
• Another Richards 1D solver
18. T - Elements of theory of evaporation from water and soils - Dalton. Penman-Monteith. Priestley-Taylor
• The Thermodynamical origin of evaporation (YouTube2017)
• Vapor transport by turbulence (YouTube2017)
• Evaporation from free water surfaces (YouTube2017)
• Evaporation from soils (YouTube2017)
• Penman-Monteith (YouTube2017)
• Further Readings
• About Drying Porous media
19. T - Estimation of evaporation and Transpiration at hillslope scale
• Transpiration (YouTube2017)
• Estimation of ET over large areas (YouTube2017)
• Evaporation and Transpiration from the energy budget (YouTube2017)
20. L -  Estimation of evaporation and transpiration at catchment scale
21. T - Water movements in a hillslope and runoff generation
• See the posts on runoff generation
• Hymod as an example of the "reservoirs" models (YouTube2017)
22. T - On the impact of climate change on the hydrological cycle (YouTube2017)

Verifications and tests 2017

Questions of the 2017 intermediate test.
Grades of the intermediate test.

Italian Hydrology 2016

I am summarising here what I saw that rised my interest in the Italian biennial meeting of Hydraulics, Hydrology and Hydraulic constructions. Obviously I followed just the hydrology section and missed the rest. Therefore I could have not seen some very fundamental in one of the other subdisciplines. Do not blame me !

As a general observation, I have to say that few are still really producing models. Many are using products from others notably: tRibs, WRF-Hydro, SWAT, among the foreign. Cathy, Topkapi-X and GEOtop among genuine Italian products were presented at the Conference. Many young people also work on remote and proximate sensing research, where they exploit the capabilities of the new tools (especially UAVs but also on traditional remote sensing). Some work on statistical and probability models.  Some on eco-hydrology. Many we know they work on groundwater, but almost no abstract was presented on the topic that dominated last century literature. Remote sensing is certainly a great topic but today I will not talk about.
In doing my choices I keep an eye on the three step conceptualisation of learning processes represented in figure. There is stuff that is completely mainstream and has the maximum of attention in these years, other on which interest is growing, and other that foresees the future of the discipline.

The Italian hydrological community, especially if we include the numerous those who live abroad, is pretty alive (at present the WRR, HESS, ADWR, PNAS have Italian Editors). However, did I see material changing the paradigms ? Let's see in my comments below.

So, my favourite (if you click on figures, you are redirected to the slides):

Statistics

Marco Marani (GS) and coworkers rethink the estreme value concepts, observinfg that Pearson's distributions are obtained as a limit of an infinite number of events. He proposed intermediate distributions, when the number of observations is limited. He, they, called these distributions "metastistical". This is, I think, good and pretty much necessary too. Authors assume that extremes are sampled from iid variables, while others, Jim Smith for instance, think that extreme events are sampled out from a separate population. Is there any method to infer it from data ?

Elena Volpi and coworkers discussed the idea of return period. Her statement is that statistical independence is not a requirement for obtaining the classical equation of return period (post coming soon).
This is inscribed in the old story of stochastic processes used as representation of phenomena that appear highly variable. The field needs some refreshment after the discovery of climate change. Volpi's et al. Has the merit to bring in some novelty. I was intrigued by the separation and relation between return period and waiting time.

In the same subfield I registered attention to copulas, as a means to move from a univariate dominated applications to multivariate.

Strangely not application in machine learning or pattern recognition which could be os some interest when coupled with complex time and spatially varying signals.

Eco-hydrology

Gabriele Manoli (GS) uses a simplified ABL theory to study the effects of vegetation on precipitations, and, in particular he sorted out the effects of vegetation ages. Some about the theory of ABL came from an evolution of thet good old model by John Albertson (GS) but the novelty here is that eco-hydrology enters in the merit of phenology-plants evolution. Conditions in which conclusions are drawn are pretty uniform (probably Durham forest can be considered a nice approximation of it). the problem of heterogeneity calls for a treatment made with a process-based model.

Nadia Ursino and Chiara Callegaro explore the formation of some vegetational patterns made possible by water availability in water-limited environments. 

Measures

Tuscia’s guys (Salvatore Grimaldi et al., GS) make a lot f interesting things, but here, I have chose their 100 square meters pluviometer. Details on on it, the paper, slides and poster are below.

Process-based modelling

We’ve got some good work by Giacomo Bertoldi (GS), but he is too close to me for me being neutral. I mention two works. One by Alfonso Senatore (GS) and coworkers. He uses WRF coupled with WRF-Hydro. I am not sure of the contents of the latter. The good and the new could be that there is one model that treats with very detail the interactions with the atmosphere (thanks to WRF) or should. If it is a real thing, I am envy, because it is one thing I believe we have to add to GEOtop.

Monica Piras showed various comparison between process based models. She and co-workers used the model “blindly” without giving direct judgement about the performances of each one. Anyway, it is apparent that models behave differently, and someone should be wrong. Interesting part of her presentaion was the mention to downscaling techniques, necessary to couple climate projections and hydrological models. Below, please find her presentation.

I did not mention travel time theories. We already talk about it extensively. Nothing especially new was presented that it is not already in my previous posts, just some incremental advancement.

Talking about Drones or UAVs (Unmanned Aerial Vehicles)

This post collects the slides of the Summer School on the use of UAVs for hydrology monitor, organised by Salvatore Manfreda  (GS) of University of Basilicata. UAVs are getting an increasing attention by hydrologist, and a little literature is growing on the subject.

Here they are the presentations, available on slideshare.

• The introduction to the course

The lectures by:

•  Kelly Caylor (GS): Ecohydrological applications of UAVs
•  Lyndon Estes (GS): What UAV(s) Do That Cheap Satellites (and Fields Scientists) Can't ?
•  Xurxo Gago (GS): Plant water assessment through aerial tomography
• Sabino Bufo (RG): HV-UAV multispectral compared to hyperspectral data collection as applied to vegetation indexes calculation
• Salvatore Manfreda: Surface Hydrology and flood planes
• AIDroni's Luciana de Fino:About the Drones Market

And, last but not least, the exercises of the students

• Novelli, Beerloco, Loiudice, Qeraxhiu
• Albano, De Luca, Luiz Perez, Samela Tauro
• Debas, Faridani, Mayes

Flavia Tauro presentation at the Ph.D. days was on drones too. It was on estimating discharges withUAVs. (Coming soon)

GEOtop 2.0 at AGU 2013

I was invited to talk at a Fall AGU Meeting section about High Resolution Hydrological modelling. This is the topic of the H21M session of the meeting.  The following, below (clicking on) the figure is my interpretation of the topic.

I describe GEOtop 2.0, present a few case studies, and took the occasion to do some synthesis of this work. All the merits go to my co-authors, that in the last years strongly believed and pushed GEOtop beyond what it was. The presentation does not cover the cryospheric part of the models, which will be the focus of the second presentation.

This other posts covers my second presentation at AGU, talking about how the cryosphere is modeled in GEOtop 2.0.

GEOtop bibliography (so far)

Bertoldi, G., Rigon, R., & Over, T. M. (2006). Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets. Journal of Hydrometeorology,, 7, 389–403.

Bertoldi, G., Notarnicola, C., Leitinger, G., Endrizzi, S., Della Chiesa, S., Zebisch, M., & Tappeiner, U. (2010). Topographical and ecohydrological controls on land surface temperature in an Alpine catchment. Ecohydrology, 3(doi:10.1002/eco.129), 189–204.

Bertoldi G.; Della Chiesa, S; Notarnicola, C.; Pasolli, L.; Niedrist, G; Tappeiner, U. (2013), Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT 2 images and hydrological modeling, submitted to Journal of Hydrology


Bertoldi, G., Della, S., Notarnicola, C., Pasolli, L., Niedrist, G., & Tappeiner, U. (2014). Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling. Journal of Hydrology, 516, 245–257. https://doi.org/10.1016/j.jhydrol.2014.02.018
Dall’Amico, M.; Endrizzi, S., Gruber, S; and Rigon, R. (2011), An energy-conserving model of freezing variably-saturated soil, The Cryosphere.

Della Chiesa, S.; Bertoldi, G.; Niedrist G., Obojes, N.; Albertson, J. D.; Wohlfahrt,G.; Hörtnagl L., Tappeiner U.,  (2014),  Modelling changes in grassland hydrological cycling along an elevational gradient in the Alps, Ecohydrol. 7, 1453–1473 (2014), DOI: 10.1002/eco.1471

Eccel, E., Cordano, E., & Zottele, F. (2015). A project for climatologic mapping of soil water content in Trentino. Italian Journal of Agrometeorology, 1(500 m), 5–20.
Endrizzi S. and Marsh P. Observations and modeling of turbulent fluxes during melt at the shrub-tundra transition zone 1: point scale variations, (2010) Hydrology Research

Endrizzi S., Gruber S., Investigating the effects of lateral water flow on spatial patterns of ground temperature, depth of thaw and ice content, Peer reviewed proceedings of the 10th International Conference on Permafrost, 25–29 June 2012, Salekhard, Russia, 91–96, 2012

Endrizzi S., Gruber S., Dall’Amico M., Rigon R., GEOtop 2.0. (2014), Simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, 7(6), 2831–2857. https://doi.org/10.5194/gmd-7-2831-2014

Fiddes, J., Endrizzi, S., & Gruber, S. (2015). Large-area land surface simulations in heterogeneous terrain driven by global data sets : application to mountain permafrost. The Cryosphere, 9, 411–426. https://doi.org/10.5194/tc-9-411-2015

Fiddes, J., & Gruber, S. (2012). TopoSUB: a tool for efficient large area numerical modelling in complex topography at sub-grid scales. Geoscientific Model Development, 5(5), 1245–1257. https://doi.org/10.5194/gmd-5-1245-2012

Formetta, G., Rigon R., David, O., Green, T. R., Capparelli, G. (2016), Integration of a spatial hydrological model (GEOtop) into the Object Modeling System (OMS), Water 8(1), 12

Gubler S., Endrizzi S., Gruber S., Purves R. S., Sensitivity and uncertainty of modeled ground temperatures and related variables in mountain environments, Geosci. Model Dev., 6, 1319–1336, 2013.

Gebremichael, M., Rigon, R., Bertoldi, G., & Over, T. M. (2009). On the scaling characteristics of observed and simulated spatial soil moisture fields, Nonlin. Processes Geophys., 16, 141–150.

Hingerl, L., Kunstmann, H., Wagner, S., Mauder, M., Bliefernicht, J., & Rigon, R. (2016). Spatio-temporal variability of water and energy fluxes - a case study for a mesoscale catchment in pre-alpine environment. Hydrological Processes. https://doi.org/10.1002/hyp.10893

Kunstmann, H.,  Hingerl, L., Mauder, M.,  Wagner, S., and Rigon, R., A combined water and energy flux observation and modelling study at the TERENO-preAlpine observatory, Climate and Land-surface Changes in Hydrology, Proceedings of H01, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden, July 2013 (IAHS Publ. 359, 2013)

Lewis, C., Albertson, J., Zi, T., Xu, X., & Kiely, G. (2013). How does afforestation affect the hydrology of a blanket peatland? A modelling study. Hydrological Processes, 27(25), 3577–3588. https://doi.org/10.1002/hyp.9486

Rigon, R., Bertoldi, G., & Over, T. M. (2006). GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets. Journal of Hydrometeorology, 7, 371–388.

Simoni, S., Zanotti, F., Bertoldi, G., & Rigon, R. (2007). Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS. Hydrological Processes, doi: 10.10.

Zanotti, F., Endrizzi, S., Bertoldi, G., & Rigon, R. (2004). The GEOtop snow module. Hydrol. Proc., 18, 3667–3679. DOI:10.1002/hyp.5794.

Zi, T., Kumar, M., Kiely, G., Lewis, C., & Albertson, J. (2016). Simulating the spatio-temporal dynamics of soil erosion, deposition , and yield using a coupled sediment dynamics and 3D distributed hydrologic model. Environmental Modelling and Software, 83, 310–325. https://doi.org/10.1016/j.envsoft.2016.06.004

Ph.D Thesis


Giacomo Bertoldi (2004) The water and energy balance at basin scale: a distributed modeling approach. Download PDF

Stefano Endrizzi (2009), Snow cover modelling at a local and distributed scale over complex terrain

Silvia Simoni (2009), A Comprehensive Approach to Landslide Triggering.

Matteo Dall'Amico (2011), Coupled Water and Heat Transfer in Permafrost Modeling.

Ageel Ibrahim Bushara (2011), Hydrological simulations at basin scale using distributed model and remote sensing with a focus of soil moisture.

GEOtop Manual

GEOtop Manual (a little out-of-date with respect to GEOtop 2.0 ... but not so much).