Thursday, December 24, 2015

Two up-to-date contributions on Global Hydrology

I talk about global hydrology in a recent post. But, the topic is hot, and you do not have the time to relax, and a couple of authoritative papers are published on the global energy budget and the global water budget in the first slice of this century* **. Scientists never sleep.

L’Ecuyer, T. S., Beaudoing, H. K., Rodell, M., Olson, W., Lin, B., Kato, S., et al. (2015). The Observed State of the Energy Budget in the Early Twenty-First Century. Journal of Climate, 28(21), 8319–8346.

M. Rodell, H.K. Beaudoing, T.S. L’Ecuyer, W.S. Olson J.S. Famiglietti, P.R. Houser, R. Adler, M.G. Bosilovich, C.A. Clayson, D. Chambers, E. Clark, E.J. Fetzer, X. Gao, G. Gu, K. Hilburn, G.J. Huffman, D.P. Lettenmaier, W.T. Liu, F.R. Robertson, C.A. Schlosser, J. Sheffield, and E.F. Wood
 (2015). The observed state of the water cycle in the early 21st century. Journal of Climate, 28 (21), 1–80.

*An update:

Please also see this recent paper on Nature Geosciences, brought to my attention by Wuletawu Abera, on the groundwaters amount:

Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E., & Cardenas, M. B. (2015). The global volume and distribution of modern groundwater. Nature Geoscience, 1–10.

** A further update:

Zhang Y., PanM., Wood E.F., On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing, Surveys in Geophysics, DOI 10.1007/s10712-015-9354-y, 1-20, 2016

*** Yet another update

Bierkens, M. F. P. (2015), Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51,4923–4947, doi:10.1002/2015WR017173.

Wednesday, December 23, 2015

Rejection of Rejection

Dear readers, if you are tired to have your paper rejected, you can consider the send this rejection of rejection letter, whose template has been published on BMJ journal (here) authored by  Cath Chapman and Tim Slade :

Rejection of rejection letter

[insert university emblem here]
Dear Professor [insert name of editor]

[Re: MS 2015_XXXX Insert title of ground-breaking study here]

Thank you for your rejection of the above manuscript.

Unfortunately we are not able to accept it at this time. As you are probably aware we receive many rejections each year and are simply not able to accept them all. In fact, with increasing pressure on citation rates and fiercely competitive funding structures we typically accept fewer than 30% of the rejections we receive. Please don’t take this as a reflection of your work. The standard of some of the rejections we receive is very high.
In terms of the specific factors influencing our decision the failure by Assessor 1 to realise the brilliance of the study was certainly one of them. Simply stating “this study is neither novel nor interesting and does not extend knowledge in this area” is not reason enough. This, coupled with the use of Latin quotes by Assessor 2, rendered an acceptance of your rejection extremely unlikely.
We do wish you and your editorial team every success with your rejections in the future and hope they find safe harbour elsewhere. To this end, may we suggest you send one to [insert name of rival research group] for consideration. They accept rejections from some very influential journals.

Please understand that our decision regarding your rejection is final. We have uploaded the final manuscript in its original form, along with the signed copyright transfer form.

We look forward to receiving the proofs and to working with you in the future.

Yours sincerely

Dr [insert name here]

[Insert research group acronym here]

[Insert university here]

[Insert country here—that is, Australia/New Zealand/small European Country/Canada]

Do not forget to look at the replays.

Wednesday, December 16, 2015

An overview of my research and my future envisioned work. My professorship talk

I was finally asked to do this talk, that cover my research experience, for my Full Professor appointment here at the Civil, Environmental and Mechanical Department of the University of Trento.  This the abstract:
In this talk I will cover, in brief, my last 25  years of research through my main contribution in surface hydrology, river network evolution, hyperresolution and travel-time modelling of hydrological processes, hydroinformatics. Life means “my academic life” but also some recent orientation I am taking to model the non linear interactions in the water cycle. These include plants and ecosystems, which I believe will be my next research objectives, which I will pursue with the use of my model infrastructure, based on evolving  GEOtop and JGrass-NewAGE.  I will talk a little, maybe, of thermodynamics,  hydro-informatics, and optimisation principles in natural processes (I see that I do not have a post, on this, I will do it). A little on Cryosphere processes will not be absent.

Clicking on the Figure, you will be linked to the slides of my talks (with links to literature). The talk, unfortunately is in Italian: but the slides are in English. Below, please find the youtube.

Monday, December 14, 2015

Age-ranked storage equations for river Adige

This below is the presentation that Maria Laura Bancheri is giving today at the AGU fall meeting. It summarises our recent work on travel time distributions theory (no contributions so far -well look at here -, but we tried to clarify concepts and mathematics) and its ongoing application to River Adige.

All this knowledge is going to flow into the great family of JGrass-NewAGE components and being used to assess age of water and tracer movements. 
Keep looking for upgrades.

Sunday, December 13, 2015

Be prepared for Java 9

We still do not have digested Java 8 in our programming style and ( other new programming languages like Swift appear on the  stage, and others, like FORTRESS -se also Wikipedia-, die),  we have to face the new improvements of Java 9 (effective December 2016, but already at a fixed stage of development). They are a REPL called JShell and modularity.

This is a sequence of webcasts on this topic, directly from  protagonists, Mark Reinhold, and Alan Bateman
On the same topi, a 2016 issue of eMag. Finally, an interesting website here.

Thursday, December 3, 2015

Economy, Water, Climate change. Methods and scale of analysis in economy.

This talk, of the CLIMAWARE project series, was given by Martina Sartori, a young economist, part of the group of the project. The talk was the occasion to exchange information about the type of models and the scale of modelling economists use to assess the impacts of variation of water availability on the economic system.
Click on the Figure to get the presentation.
I have to say that the discussion was interesting. The scale economists use is much larger than catchment scale, mostly because their models are parametrised on the knowledge of nations' exchanges more than regional ones. Model themselves seem not to be very complex from the mathematical point of view, but they treat thousands of parameters which makes them complicates. These parameters are collected by specialised companies and/or institutions by sorting out transactions among countries and looking at the global market. Probably the theoretical foundations of these models could not be so unassailable, but, however, they embeds a lot of empirical knowledge. For us was important to talk together.

Monday, November 30, 2015

Energy Conservation

In the days in which General relativity celebrates its first one hundred years (I believe the paper was published Dec 2 1915), I want to talk about energy conservation, which is a topic relevant to general relativity and to hydrology too. 

“One of the founding principle of Thermodynamics is that energy is always conserved. It can be transformed in one form to another, but it is never created or destroyed (but see below). The first principle of Thermodynamics was given something like in its modern form by Robert Mayer (see also here) in the 1840s -shortly before the concept of energy, as we understand it, was introduced into science. Mayer, a German doctor, became intrigued by the observation that the blood  he was letting from the veins of feverous sailors in the Dutch West Indies was considerable redder than he has would expected (read the full story in the Oliver Morton’s book, the entertaining lecture for a hydrologist, I am using here)” … Mayer deduced the the sailors were using less oxygen than expected due to the warmer climate and was brought to the conclusion that respiration in animals was a specialised form of combustion (which was a common, but not universally accepted belief among the chemist’s contemporary to Mayer).  “However, Mayer decided that the total amount of heat generated in the body must be equal to the capacity of generating force latent in food. Mayer was subsequently lead to  look at the sun, and he arrive to argue that sunlight was the sustenance from plants and  these of animals. Plants were converting energy from light to a chemical form.” 
Other famous scientists were subsequently working on energy conservation law, and maybe you associate the law with the name of Joule

Therefore the early discovering of the energy conservation is extraordinarily close to what modern hydrological studies pursue, especially now that the food-climate-water nexus is at the center of entire research programs.  

A famous finding  of Albert Einstein is the equivalence of energy and mass. This looks like an exotic property, but it is actually always present in hydrological thermodynamic: what else is the “latent heat” if not an explicit statement that mass (and forms of its arrangements, the phases) is a form of energy ? 
Since water on earth and in the hydrological cycle changes phase, water energy budget must account for mass transfer.  However, because also mass, besides energy, is conserved,  energy equation  is greatly simplified. 
If mass conservation is given for granted in hydrology (but not always completely accounted for: because people focuses more -erroneously - on the fluxes), energy conservation is rarely considered. Our GEOtop is one of the few models that does it, and considers energy conservation together with mass conservation (and the budgets, not just the flows!) with an appropriate level of complexity.

Going back to the main argument, what was an experimental achievement for Mayer, became, after Emmy Noether, a Swiss mathematicians a property connected with the symmetry of the mathematical structure of Mechanics (and Electromechanics indeed) when presented in Lagrangian of Hamiltonian form. Noether’s theorems simply state that if the Lagrangian (Hamiltonian) - either classic or quantistic - of a certain system is invariant under a group of transformations, this implies a conservation law. Energy conservation is, in fact, implied by invariance in time of the Galilean physics, while, for instance, momentum conservation is implied by invariance under space translations (and angular momentum is conserved because of invariance under rotations of the reference frame).  Special relativity does not alter this situation so, in special relativity, mass, energy and momentum are conserved, as well as in Galileian/Newtonian mechanics.  The new ingredient in Special relativity is that space is not separated by time, but both are connected. So, actually the above conservation laws are all connected (and the first of the three equations in the figure says part of it). 

However, the one hundred years old second equation of the figure states that the stress tensors (a sort of generalisation of energy, that compares in the second member of the equation, please see Wikipedia) is strongly connected with the geometry of space. So, at large, energy, mass, time, space, matter are all connected, but energy is not conserved in the Universe (and BTW also some arguments about the thermodynamical death of the University are not very solid).  Please find a more detailed and technical explanation here; another explanation here.

Energy is conserved on earth, with great precision (for a Hamiltonian treatment of Earth's fluid mechanics, please refer to the Salmon book, or his paper), and studying the arrangements of matter is important in hydrology. For us it is an approximation that works, and it is very stupid not to use it. 
When accounting for it obviously we have to deal with both kinetic and potential energy, but also internal energy has a big role, especially when we arrive to talk how liquid water becomes ice, or water vapour or viceversa, which continuously happens.  The third equation in Figure reminds it, being mass hidden in enthalpies, and in thermal capacity (for general references to Thermodynamics see here). 

The game becomes thermodynamic also for Hydrology and, actually, the third equation just gives a flavour of it, because when phases are involved, also their interfaces  and their mixing must be taken into account: otherwise we do not understand how drops form, or water is retained in vadose soils, or how it freezes, or how flows in plants.

But these are stories for another day.

Thursday, November 26, 2015

Rainfall on the Blue Nile

Assume you want to forecast the discharge and the hydrological cycle of a large African river.  Meteorological stations are just a few in a wide area, and gauge stations too, indeed. So what to do,while attending that more measurements are made ?  Simply you have to use satellite data. This paper, by Abera et al., deals with the analysis of five satellite data and analyse their reliability in the Blue Nile region.
Among the five there is the innovative product by Luca Brocca SM2RAIN which infers precipitations from the soil moisture analysis.
Please find the preprint of the paper under the Figure.  The abstract of the paper reads: 

"In a region where ground-based gauge data are scarce, satellite rainfall estimation (SREs) products are a viable option for proper space-time rainfall characterization. However, their accuracy and performances vary from region to region, and must be assessed. In this study, five high resolution satellite products (TRMM, CMORPH, TAMSAT, SM2R-CCI, and CFSR) are compared and analysed using the available rain gauge data in one of the most topographically and climatologically complex regions, Upper Blue Nile basin. The basin rainfall is investigated systematically, and it is found that, at some locations, the difference in mean annual rainfall estimates between these SREs could be as much up to 2700 mm. Considering three goodness-of-fit indexes, correlation, bias and root mean square error (RMSE) between the SREs and ground-based gauge rainfall, CMORPH, TAMSAT and SM2R-CCI outperform the other two. TAMSAT has the highest (91%) detection skill for dry days, followed by the CFSR (77%). For lower rainfall values, CMORPH, TAMSAT and CFSR show a higher accuracy index than SM2R-CCI and TRMM. On the contrary, the SM2R-CCI has the highest accuracy index for medium rainfall ranges (10-20 mm). In addition to the identification of the best performing products, the study tried to determine the bias correction of the estimates. A confusion matrix is used to investigate the detection ability of satellite rainfall products for different rainfall intensities. The empirical cumulative distribution (ecd f ) mapping technique is used to correct the SREs intensities distribution. This method provides a means to improve the rainfall estimation of all SREs, and the highest improvement obtained is for CMORPH (from -70% to -4%). "

A presentation (here) on the same topics of the paper was given at the 10th Alexander von Humboldt EGU Topical Conference held in Addis Ababa,  this 18-20 November.

The page where the complimentary material for the paper can be found is here.

Tuesday, November 24, 2015

Geomorphological control on variably saturated hillslope hydrology and slope instability

This paper, which has a long history, treats the influence of geomorphology on stability. Not a new topic indeed, but usually saying that convergent topographies favour landslids was a matter of qualitative arguments.  Here it is made by using DEM analysis, a 3D Richards equation solver, and a sound model for hillslope stability. That's the difference for who can appreciate it.
Please, find the preprint, clicking on the Figure. I think the reading is enjoyable and I hope the Journal will accept it soon. 

Thursday, November 19, 2015

How many leaves has a tree ?

Sometimes ago I asked myself how many leaves a tree has (a real tree, not the homonymous informatics structure). Certainly it depends on the type of tree, the size of the leaves, and on season too. A simplification would probably be to estimate which is the maximum number of leaves a tree can have. The question was rised by the contemplation of woodland in Trentino, but also has an impact on hydrology. No leaves, no transpiration, and maybe, more leaves more transpiration, even if as a possibility ( for which hydrologists coined the infamous potential evapotranspiration concept). I started  to google around in trying to understand. 

Many internet surfers report this:

It depends on the tree's species and age, but a mature, healthy tree can have 200,000 leaves. During 60 years of life, such a tree would grow and shed 3,600 pounds of leaves, returning about 70% of their nutrients to the soil.

and cite as source the Wisconsins County Forests Association: but, on their site, I was not able to find the cited words. Anyway, anticipating the answers, this number mentioned is in the range most of leaves' counters gives, at the end (did they influences each other?).

I personally found three approaches to solve the problem. 

The first was simply to count the leaves on a tree. Probably some one really did it. But I could not find trace of it.    Some others made it indirectly Here you will find a counting exercise for kids (but that adults can enjoy).  Even a Wired's journalist got this problem to solve. Another version of the same approach is here.

These professor Morrow's students, instead, were actually interested in the weight of leaves (and I can understand they were possibly interested to estimate the gross primary production). These students of Mathematics built actually a model of plant growth. Their interesting trial, which has to do also with fractals, can be found here

The third method is based on determining the leaf area index (LAI, see Baldocchi’s Notes first), the ratio between the surface of the total area of the leaves in a canopy, divided by the projected area the canopy covers. It seems, that under many circumstances this quantity is easier to measure (it can be obtained also from satellites) that counting the the leaves (or is it a modern automatic way to do it ?)
Having the LAI and the canopy area covered by a tree (which is actually very similar to what done in the “counting methods above”) the number of leaves can be estimated, indeed even over large scale. or an entire forest. 

The problem is connected with others like; How big a tree leaf is  or how much it weights. 

See below a short bibliography on the leaf area index and on its implications. 

Leaf Area Index

Asher G.P, Scurlock J.M.O, Hicke J.E., Global synthesis of leaf are index observations: implications for ecological and remote sensing studies, Global Ecology and Biogeography, 12, 191-205, 2003

Breda N.J, Ground-based measurements of leaf area index: a review of methods, instruments and current controversies, Journal of experimental Botany, 54(392), 2403-2417, 2003

Colombo R., Bellingeri D., Pasolini D., Marino C.M., Retrieval of leaf area index in different vegetation types using high resolution data, (Also here) Remote Sensing and Environment, 86, 120-131, 2003

Grier, G.C., Running, S.W., Leaf area of mature northwesternconieferous forests: relation to site water balance, Ecology, 58: pp: 893-899, 1977

Norman, J.M., Campbell G.S., In: Canopy structure, Chapter Plant Physiological Ecology, pp 301-325, Springer-Verlag, 2000,  DOI:10.1007/978-94-010-9013-1_14

Pasolli L, Asam S., Castelli M, Bruzzone L, Wohlfahrt, Zebisch M, Notarnicola C,  Retrieval of Leaf area index in mountain grasslands in the Alps from MODIS satellite images, Remote Sensing of Environment, 159-174, 2015

Wang Q., Adieu S., Tenhunen J, Granier  A., On the relationship of NDVI with leaf area index in a deciduous  forest site, Remote Sensing, 94, 244-255, 2005

Monday, November 16, 2015

Granular Flows in Climaware

Under the name of granular flows are included snow avalanches, debris flows, and sediment generation and transport. CLIMAWARE has a task devoted to them, with application to river Adige.
Colleague Michele Larcher covers the snow avalanches part, and by clicking on the figure below, you will find his presentation of the topic.
This was actually a three parts seminar. The second part, held by Luigi Fraccarollo regarded the suspended sediment in river Adige, and the study of its origin.
The last part was mainly on debris flow and the model Trent2D, an innovative code to estimate them, by Giorgio Rosatti and co-workers (GS).

The challeng is to blend all of this in the unique framework of the project.

Thursday, November 12, 2015

The GEOframe blog

Hoping that the documentation efforts become torrential, I opened a new blog, just dedicated to the documentation of GEOtop and JGrass-NewAGE source code and executables.
The blog is linked in the Related blog banner. Otherwise, you can click here:
Geoframe is an idea first envisioned a few years ago, whose general concepts can be found in this presentation given at 2008 CUASHI biennial meeting (the only change to be done is the substitution of OpenMI with OMS).

Wednesday, November 11, 2015

Urban Hydrology

Today, I gave a seminar on perspectives in urban hydrology. The sponsor where two companies producing road paving and infrastructures for sewage systems. The audience was a group of Italian professional, and I try to convey some concepts regarding the integrated water management and how to calculate it.

Slides of the talk are obtained by clicking on the figure above. An interesting post, connected with this presentation comes from Tony Ladson and is entlitled: Does stormwater management works ?

Selected References

Berne, A., Delrieu, G., Creutin, J.-D., & Obled, C. (2004). Temporal and spatial resolution of rainfall measurements required for urban hydrology. Journal of Hydrology, 299(3-4), 166–179.

Delleur, J. W. (2003). The Evolution of Urban Hydrology: Past, Present, and Future. J.of Hydraul. Eng., 129(8), 563–573.

Livingston, E. H., & McCaron, E. (2007). STORMWATER MANAGEMENT: a guide for Floridians (pp. 1–72). U.S. Environmental Protection Agency.

Marsalek, J., Jimenez-Cisneros, B. E., Malquist, P. A., Karamouz, M., J, G., & Chocat, B. (2006). Urban water cycle processes and interactions (No. 78) (pp. 1–92). Paris.

Niemczynowicz, J. (1999). Urban hydrology and water management present and future challenges. Urban Water, 1, 1–14.

Ranzato, M., Integrated water design for a decentralized urban landscape, Doctoral School in Environmental Engineering, Trento 2011

Sunday, November 8, 2015

CLIMAWARE project (again) at the Anticipation Conference

Here in Trento, the last three days was organised a Conference on Anticipation promoted by the UNESCO chair on Anticipatory systems. I participate as auditor in one of the sessions, and the experience was, at the same time interesting and different. Interesting and different because, it was disclosing to me the view of economists and sociologists on the topic, and I had to sinchronize my brain on their language and quirks. However, there was also a session on Anticipation in Engineering in which we presented the project CLIMAWARE. Presentation was given by Lavinia Laiti, and I think it gives a consistent overview of the project.

Clicking on the above image, you will have access to the slides view. Enjoy!

Tuesday, November 3, 2015

Climate modelling of Alpine Areas

Our project CLIMAWARE started last May and will endure for the whole 2016. It tries to use climate projections to estimate impacts on water resources, and from there, to ecosystem services and society in general. The scope of the model is very wide, since it aims also to join expertise and people coming from different disciplines. In order to have a unified language inside the group we started a series of talks. The first one was given by Lavinia Laiti,  for the group of Atmospheric Physics of our Department. Here below, clicking on the image, please find her seminar's slides (in Italian). 
For sake of convenience, I report here below the bibliography that was cited. Notably, the same topics were also covered at the Alpine Convention held in September 2014 in Trento. Who is interested can find the slides here


Auer et al. (2007): HISTALP - Historical instrumental climatological surface time series of the Greater Alpine Region. Int. J. Climatol., 27, 17- 46. 

Beniston et al. (2007): Future extreme events in European climate - an exploration of regional climate model projections. Climatic Change 81, 71–95. 

Brunetti et al. (2006): Precipitation variability and changes in the greater Alpine region over the 1800-2003 period. J. Geophys. Res., 111, D11107. 

Brunetti et al. (2009): Climate variability and change in the Greater Alpine Region over the last two centuries based on multi-variable analysis. Int J Climatol, 29, 2197-2225. 

Bucchignani et al. (2013). Simulation of the climate of the XX century in the Alpine space. Nat. Hazards, 67, 981–990.

Bucchignani et al. (2015): High-resolution climate simulations with COSMO-CLM over Italy: performance evaluation and climate projections 
for the 21st century. Int. J. Climatol., DOI: 10.1002/joc.4379
Frei et al. (2003). Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps. J. Geophys. 
Res., 108(D3), 4124.

Frei et al. (2006), Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models, J. 
Geophys. Res., 111, D06105.

Frei and Schär (1998): A precipitation climatology of the Alps from high-resolution rain-gauge observations. Int. J. Climatol., 18, 873-900. Giugliacci et al. (2010). Manuale di Meteorologia, 2nd ed. Alpha Test, 763 pp.

Gobiet et al. (2014): 21st century climate change in the European Alps - A review. Sci. Tot. Env., 493, 1138-1151.

Haslinger et al. (2013). Regional climate modelling over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for the  Greater Alpine Region. Climate Dynamics 40, 511-529.

Haylock et al.(2008): A European daily high-resolution gridded dataset of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research, 113, D20.

Isotta et al. (2014): The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Climatol., 34, 1657–1675. 

Jacob et al. (2014): EURO-CORDEX: new high-resolution climate change projections for European impact research. Regional Environmental Change, 14, 563-578. 

Kotlarski et al. (2015): The elevation dependency of 21st century European climate change: an RCM ensemble perspective. Int. J. Climatol. Montesarchio et al. (2014): Performance evaluation of high-resolution regional climate simulations in the Alpine space and analysis of 
extreme events, J. Geophys. Res. Atmos.,119.

Philipona (2013): Greenhouse warming and solar brightening in and around the Alps. Int. J. Climatol., 33, 1530-1537.

Prein et al. (2011): Analysis of uncertainty in large scale climate change projections over Europe. Meteorol. Zeit., 20, 383-395.

Prein et al. (2013): Added value of convection permitting seasonal simulations. Clim. Dyn. 41, 2655–2677.

Rajczak et al. (2013). Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region, J. Geophys. Res. Atmos., 118, 3610–3626.

Schär et al. (1998): Current Alpine climate. In Cebon P. et al. (eds.), A View from the Alps: Regional Perspectives on Climate Change. MIT Press.

Suklitsch et al. (2011): Error characteristics of high resolution regional climate models over the Alpine area. Climate Dynamics, 37, 377-390. 

Torma et al. (2015), Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps, J. 
Geophys. Res. Atmos., 120, 3957–3972.

Turco et al. (2013): Assessing gridded observations for daily precipitation extremes in the Alps with a focus on northwest Italy, Nat. Hazards Earth Syst. Sci., 13, 1457-1468. 

Von Hardenberg et al. (2015):Observations and modelling of precipitation and the hydrological cycle: uncertainties and downscaling. Trento, 4 giugno 2015. 


Thursday, October 29, 2015

Studies on glaciers (snow, permafrost) in Trentino

I was invited to give a talk tonight at MUSE for the general public about the activity of our group in the cryosphere areas.
Please find the presentation (sorry in Italian) clicking on the Figure.
During the last ten years our group of hydrologists started a nice series of tasks, sometimes with higher and sometimes with lower intensity.

As a result, we have now, models, measures, expertise for doing much more. Personally I should go more on field trips! Enjoy

Wednesday, October 28, 2015

Large Scale Hydrology

Large Scale Hydrology is a fact. Its growth  became important especially in conjunction with the studies about the climate crisis and today many outstanding colleagues work in building and managing hydrological models at global or continental scale.
I participated a few months ago to a session of the Globaqua project where I was exposed to some of these researches.  The models mentioned at the meeting  by Alberto Pistocchi of JRC were LISFLOOD e SWAT. Ralph Merz used HBV and mHm. I know,  from other readings, that also VIC and  PCR-GLOBWB are of this kind of global models, an probably there exist many others (JULES can be another).

There are two aspects of these modelling efforts that can be discussed. The science behind their formulations and parameterisations, and the data sets used to drive them. 

Regarding the first aspects,  science, I think the efforts my colleagues do are necessary and, simplifications they work with have to be made. However,  science in their models is far from being assessed (but if I would myself be working on the same topics, I would probably use the same shortcuts to arrive to some result).  My concern is that in these efforts,  sometimes (I say sometimes but I mean often) hydrological models become a commodity, and their quality is given for granted. Maybe the climate change community with its models of everywhere (and everything) which fail locally had some negative influence on this attitude. As a matter of fact, that discarding  details and details of the processes and still the global results obtained are correct remains quite unproven. 

Regarding data, collecting data globally to run the models is certainly an enterprise. The goal of building these large standard datasets to be used by models and modelers is actually important by itself, and it is a pity that people do not think how to share this knowledge base. An effort for open data and open protocols for digesting them in models is a need.
 In turn there are two great data collection domains: the assessment of hydro-meteo forcing and the parametrizations of soil and vegetation properties.
While  the effort of measuring and providing meterorological data  is shared with meteorologists and climatologists, the terrestrial data sets are less available, of more uncertain application, and often too coarse grained. Or, maybe, this is just my impression. My impression actually is that the assumptions of the congruence in the use of the terrestrial data are made for sake of convenience, and according to a mute agreement not to go in deep in the analysis of their usefulness. The representativity of the data used depends mostly on how much heterogeneity  is possible to neglect: it was a topic which had its popularity in the nineties, but seems a little bit behind the scene now.  Probably the goal now, in this phase of the projects, is to have an infrastructure working, more than forecasting precision, or crystal clear science. 

The emergence of the global scale hydrology, was the title of a very beautiful paper by Peter Eagleson (a citation) (WRR, 1986), and the argument also of  Shuttleworth (1988) which I suggest as a must-to-read papers. This long history means that the topic has more than one thread. 

Notable works, instead of being river basin centred are more focused on the water cycle as a whole. For instance, it came to my attention the work of  of Trenberth  (GS) and coworkers, and of  Oki and coworkers.  Eric Wood  and his coworkers pushed the idea that the global hydrological cycle can be studied by high-resolution models driven by high-resolution remote sensing: and this is still another plot of the story.

Anyway, how much it is the uncertainty of these global water and energy budgets is revealed by the comparison of fluxes as given by OKI’s figure (the one you see in this post), which estimates practically null the outflow of groundwaters in oceans, with a recent study by Kwon et al (2014) that suggests that groundwater could be as much as 80% of the whole contribution of water to the oceans from continental masses. 

The matter, as usual, is to discerne, in this "hot" production, what is good and what is better.  Sometimes being wrong is not so important if this makes science to proceed.
Further readings are below. 

References and further links

Aeschbach-Hertig, W., & Gleeson, T. (2012). Regional strategies for the accelerating global problem of groundwater depletion. Nature Geoscience, 5(12), 853–861. doi:10.1038/ngeo1617

Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rosch, T., and  Siebert, S. (2003). Global estimates of water withdrawals and availability under current and future “business-as-usual” conditions. Hydrological Sciences Journal, 48(3), 339–348. doi:10.1623/hysj.48.3.339.45278 

Ball, P. - H2O, a biography of water, Phoenix ed., 1999

Bergström, S., 1976. Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Report RHO 7, Norrköping, 134 pp.

Burek, P., van der Knijff, J., de Roo, A., LISFLOOD. Distributed water balance and flood simulation model. Revised User Manual. JRC Technical Reports - EUR 26162 EN, 2013.

Dai, A., and K. E. Trenberth (2002), Estimates of freshwater discharge from continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3(6), 660–687, doi:10.1175/1525-7541(2002) 003<0660:EOFDFC>2.0.CO;2.

Dai, A., I. Y. Fung, and A. D. Del Genio (1997), Surface observed global land precipitation variations during 1900–88, J. Clim., 10(11), 2943–2962, doi:10.1175/1520-0442(1997)010<2943: SOGLPV>2.0.CO;2.

Dai, A., T. T. Qian, K. E. Trenberth, and J. D. Milliman (2009), Changes in continental freshwater discharge from 1948 to 2004, J. Clim., 22(10), 2773–2792, doi:10.1175/2008JCLI2592.1.

Eagleson, P, The Emergence of Global-Scale Hydrology, Water Resources Research,  1986

eWaterCycle project:

Gentine, P., Troy, T. J., Lintner, B. J., & Findell, K. L. (2012). Scaling in Surface Hydrology: Progress and Challenges. Journal of Contemporary Water Research and Education, 147, 28–40.

Kumar, R., B. Livneh, and L. Samaniego (2013), Toward computationally efficient large-scale hydrologic predictions with amultiscale regionalization scheme, Water Resour. Res., 49, 5700–5714, doi:10.1002/wrcr.20431.

Kwon, E. Y., G. Kim, F. Primeau, W. S. Moore, H.-M. Cho, T. DeVries, J. L. Sarmiento, M. A. Charette, and Y.-K. Cho (2014), Global estimate of submarine groundwater discharge based on an observationally constrained radium isotope model, Geophys. Res. Lett., 41, 8438–8444, doi:10.1002/ 2014GL061574. 

Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428. 

Neltsch, SL, Arnold, JG., Kiniri, JR, Williams, J., Soil & Water Assessment Tool Theoretical Documentation Version 2009. 

Oki, T., and S. Kanae (2006), Global hydrological cycles and world water resources, Science, 313(5790), 1068–1072, doi:10.1126/ science.1128845.

Oki, T., K. Musiake, H. Matsuyama, and K. Masuda (1995), Global atmospheric water balance and runoff from large river basins, Hydrol. Processes, 9(5–6), 655–678, doi:10.1002/ hyp.3360090513.

Samaniego, L., R. Kumar, and S. Attinger (2010), Multiscale parameter regionalization of a grid‐based hydrologicmodel at the mesoscale, Water Resour. Res., 46, W05523, doi:10.1029/2008WR007327. (see also

Seibert, J. and Vis, M. (2012). Teaching hydrological modeling with a user-friendly catchment-runoff-model software package. Hydrology and Earth System Sciences, 16, 3315–3325, 2012 (

Shiklomanov, I. A. (2000). World water resources: a new appraisal and assessment for the 21st century; 1998, 1–40.

Shuttleworth, W.I., 1988. Macrohydrology-the new challenge for process hydrology. I. Hydrol., 100: 31-56. 

Trenberth, K. E., L. Smith, T. Qian, A. Dai, and J. Fasullo (2007a), Estimates of the global water budget and its annual cycle using observational and model data, J. Hydrometeorol., 8(4), 758–769, doi:10.1175/JHM600.1.

Trenberth, K. E., J. T. Fasullo, and J. Kiehl (2009), Earth’s global energy budget, Bull. Am. Meteorol. Soc., 90(3), 311–323, doi:10.1175/2008BAMS2634.1.

Voisin, N., Wood, A. W., & Lettenmaier, D. P. (2008). Evaluation of Precipitation Products for Global Hydrological Prediction. Journal of Hydrometeorology, 9(3), 388–407. doi:10.1175/2007JHM938.1

Vörösmarty, C. J. (2000). Global Water Resources: Vulnerability from Climate Change and Population Growth. Science, 289(5477), 284–288. doi:10.1126/science.289.5477.284

Van Beek, L.P.H., and Bierkens, M.F.P., The Global Hydrological Model PCR-GLOBWB:Conceptualization, Parameterization and Verification, Utrecht University, 2009 (

Wood, E. F., et al. (2011), Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring
Earth’s terrestrial water, Water Resour. Res., 47, W05301, doi:10.1029/2010WR010090.

Xie, P., and P. A. Arkin (1996), Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions, J. Clim., 9(4), 840–858, doi:10.1175/ 1520-0442(1996)009<0840:AOGMPU>2.0.CO;2.

Friday, October 23, 2015

A solver for the 1D de Saint-Venant equation in OMS

A simplified but not so simplified way to describe the motion in channels, is to use the de Saint Venant 1d equation. Solving it is not anymore a particularly complex model and almost twenty years ago, I and collaborators implemented one of it based on Vincenzo Casulli work.  This can be seen, from Angelo Zacchia's master thesis (in Italian), which contains the main elements from page 36 to 51. A more scientific treatment of the problem is presented in Casulli and Zanolli [1998], and Aldrighetti's Ph. Thesis [2007].

This work is simpler but implemented inside the JGrasstools, by Silvia Franceschi (of Hydrologis) during her Google Summer of Code 2015 exercise,  and available as open source here.
A little of documentation is in the GSoC 2015 page of the project itself.


Aldrighetti, E., Computational hydraulic techniques for theSaint Venant Equations in arbitrarilyshaped geometry, 2007

V. Casulli and P. Zanolli. A conservative semi-implicit scheme for open channel flows. International Journal of Applied Science & Computations, 5:1–10, 1998.

Zacchia,  A., Master Thesis, Su alcuni metodi per la prevenzione e previsione del rischio idraulico, Trento University, 1997

Wednesday, October 14, 2015


Hydro_gen is the product of an old research by Alberto Bellin (and Yoram Rubin). It is a random field generator, where the variables in different points are however correlated. The theory was presented in Bellin and Rubin, 1996. So it is almost twenty years old. However, is still good, and important. Some similar modelling can be done inside R (see this contribution by Santiago Begueria).

Alberto agreed to share his old FORTRAN 77 code, and it is now on Github, at this site. Our scope is to embed it in OMS and peruse it for our scopes.


Bellin, A. Rubin, Y., Hydro_gen: A new random number generator for correlated properties, Stochastic Hydrology and Hydraulics, 10(4), 1996

Alberto Bellin and Yoram Rubin, HYDRO_GEN, A New Method for the Generation of Random Functions, Code description and User's guide, 1997

Rubin,Y and Bellin, A., Conditional Simulation of Geologic media with Evolving Scales of Heterogeneity, In: Scale dependence and Scale Invariance in Hydrology, Ed. G. Sposito, Cambridge University Press, 1997
Rubin􏰄 Y􏰂 and A􏰂 Bellin􏰄 Conditional Simulation of Geologic media with Evolving Scales of Hetero􏰅 geneity􏰄 In􏰊 Scale dependence and Scale Invariance in Hydrology􏰄 ed􏰂 G􏰂 Sp osito􏰄 Cambridge University Press􏰄 􏰁􏰌􏰌􏰑 
Rubin􏰄 Y􏰂 and A􏰂 Bellin􏰄 Conditional Simulation of Geologic media with Evolving Scales of Hetero􏰅 geneity􏰄 In􏰊 Scale dependence and Scale Invariance in Hydrology􏰄 ed􏰂 G􏰂 Sp osito􏰄 Cambridge University Press􏰄 􏰁􏰌􏰌􏰑 

Sunday, October 11, 2015

Workshop on coupled hydrological modeling in Padua, 23-24 September 2015

At the beginning there were the CATHY DAYS among friends working around the CATHY model. With time the crew has grown to include close friends of CATHY's authors, like me, Alberto Bellin, Aldo Fiori (GS). Then the group even become larger including a lot of people working with PARFLOW or Thetis-Chloris or various aspects of hydrological modelling with process based models or, like me this year, with travel times approaches. Many thanks to the organizers, Matteo Camporese (RG,GS), Mario Putti (GS) and Stefano Orlandini (RG,GS).

The two days experience was pretty good, an here you can find the contributions of those who gave the permission to publish their slides. Slides include interesting references. Clicking on the slides images brings you to the slideshare site of the workshop. I subdivided the talks in; CATHY related, PARFLOW related, GEOtop related, On Travel time approaches, and on General/Various topics.


Marcello Fiorentini (University of Modena and Reggio Emilia, Italy)
Control of coupling mass balance error in the CATHY model

Laura Gatel (INRS-ETE, Canada, and IRSTEA, France)
Reactive solute transfers in Cathy: first work on adsorption

Damiano Pasetto (EPFL, Switzerland)
Data assimilation for distributed models: an overview of applications with CATHY

Carlotta Scudeler (INRS-ETE, Canada, and University of Padova, Italy)
Hydrological modeling of coupled surface-subsurface flow and transport phenomena: the CAtchment-HYdrology Flow-Transport (CATHY_FT) model


Gabriele Baroni (UFZ, Germany)
On the role of subsurface heterogeneity at hillslope scale with Parflow

Reed Maxwell (Colorado School of Mines, USA)
Integrating models and observations to understand the hydrology and water quality impacts from beetle-impacted watersheds

Mauro Sulis (Bonn University, Germany)
Assessment of the catchment-scale energy and water balance using fully coupled simulations and observations
On GEOtop

Giacomo Bertoldi (EURAC, Italy)
Eco-hydrological modeling in a mountain laboratory: the LTSER Matsch/Mazia

On travel time theories

Gianluca Botter (University of Padova, Italy)
StorAge Selection Functions: a tool for characterizing dispersion processes and catchment-scale solute transport

Paolo Benettin (EPFL, Switzerland)
On the integrated solute response of catchments: benchmark applications using chloride and isotopic tracers

Riccardo Rigon (University of Trento, Italy)
Implementing a travel time model for the entire river Adige: the case of JGrass-NewAGE

On General topics

Alberto Bellin (University of Trento, Italy)
The value of aggregated measurements of state variables in hydrological modeling
Giorgio Cassiani (University of Padova, Italy)
Minimally invasive monitoring of soil-plant interactions: new perspectives

Simone Fatichi (ETH Zurich, Switzerland)
Soil moisture spatio-temporal variability: insights from mechanistic ecohydrological modeling

Aldo Fiori (University of Roma Tre, Italy)
Lessons learned from integrated, physically-based hydrological models
Claude Mugler (LSCE/IPSL, France)
Darcy multi-domain approach for coupling surface-subsurface flows: Application to benchmark problems

Sylvain Weill (University of Strasbourg, France)
A dimensionally-reduced approach for coupled river-subsurface flow modeling

Alfonso Senatore (University of Calabria, Italy)
OpenCAll: A New Library for ciomputational science on parallel computers based on cellular automata paradigm

Wednesday, October 7, 2015

Geomorphological modelling in 2020

This presentation, I gave in Perugia for the Italian Days of Hydrology 2015, is part of the long march towards the construction of a reasonable and modern physico-statistical theory of the water budget at catchment scale. This includes previous talks and posts on Residence/Travel time theories that were recently renewed by the work of my friends Rinaldo, Botter, Bertuzzo, Benettin (the younger scientific brother) and others.
It starts with a little of history, and it follows, at the beginning, the recent paper on GIUH theory.
 I do not pretend the presentation is very clear. Without any doubt it requires more than one reading, and the new ideas are just sketched in the last slide, not fully developed. Personally, however, I fill pretty satisfied with these seeds today, also because I feel that I could grab the core of the travel time distribution theories in a way that is understandable by most.

Tuesday, September 29, 2015

Writing a paper (in hydrologiy or related field) rule by N* L*

... therefore a paper must have:
  • Five sections
  • Five Figures
  • Eight or nine  paragraphs for section
  • Each paragraph must contain a concept
If you use words with synonyms, use the shorter one. 

   These are simple instructions. They are obviously oversimplifications, and assume that you have material to work with. They probably also gives for granted that you know all the others golden rules that you can find, for instance, here. However, if I look at some papers that I am reviewing, I say:  how I like this holy simplicity and clarity.

Friday, September 25, 2015

The model of River Adige - Step 0

This is a mostly theoretical and illustrative of some aspects of the structure of the new model of the River Adige I am building with collaborators for  CLIMAWARE and GLOBAQUA projects and for myself. In the to-do-list there is a complete treatment of fluxes according to travel times theories.

Looking at the slides, it can be seen that I recycled some material in previous posts, and, obviously there are great connections with the post related to JGrass-NewAGE, and those on the physico-statistical modelling of the hydrological cycle. The presentation was made at the 2015 Padua Conference on coupled hydrological modelling, about which I will refer in another post.

Saturday, September 19, 2015

Soil in art

I used some soilscapes by Jay Stratton Noller as cover of some of my slides, meaning that soils can be the object of some artistic research. A papaer was recently brought to my attention from the GeoLog blog .

This is published in the EGU journal Soil, and was written by C. Feller, E. R. Landa, A. Toland, and G. Wessolek. The blog and the paper have also the merit also to give an author name to the very beatiful figure above, which I use in my slides, when I tal about soils, but I did not know to whom give attribution. Now I know that it was published first in Walter Kubiena’s textbook. Landa and Feller also edited a book on soil and culture (pretty expensive indeed).


Feller, C., Landa, E. R., Toland, A., and Wessolek, G.: Case studies of soil in art, SOIL, 1, 543-559, doi:10.5194/soil-1-543-2015, 2015.

Kubiena, W. L.: Bestimmungsbuch und Systematik der Boden Europas (The soils of Europe), Ferdinand Enke Verlag, Stuttgart, 392 pp., 1953.

Landa, E. and Feller, C. (Eds.),  Soil and Culture, Springer, 2010

Friday, September 18, 2015

My wish list for the next 15 years

Last night I was asked by two colleagues what I would like to do in my next fifteen years of carrier. So this post goes quite on personal. I answered being happy. But this was obviously too generic.
Professionally-wise, I added
Obviously writing fifty papers is not a very high objective, and all of it seems, maybe, mundane. However, the real wish would be that ten out of my fifty papers, would be better that the ones I already co-authored (reasonably five of them). Having one of two of them becoming benchmark papers.

Tuesday, September 8, 2015

Rainfall and Temperature Interpolation (for hydrologic purposes)

Spatial (hydrological) models require spatial hydrological inputs. Some measurements techniques, as radars and remote sensing, usually provide this spatial information. However, it is often not quantitatively reliable if not compared to ground measurements, because remoted sensed products are themselves the outcomes of some modelling. In any case, even if, remote measurements enter every day more and more in the practice of hydrologists, ground based, in station, measurements are today's standard. They provide localised information that has to be extrapolated to space. For accomplishing this task, several techniques were developed, moving from the Thiessen (1911) method to the use of inverse distance weighting (IDW), to splines (see for instance Hutchinson, 1995) to the use of Kringing (e.g. Goovaerts, 1997). 
When data are abundant, either splines, IDW, or kriging give acceptable results in interpolating temperatures and rainfall. The choice of one or another, more than on performances issues (either as computational resources needed or in reproducing known results), is actually related to the availability of tools to perform them. However, recently Kriging gained momentum, (because of the presence of good tools for doing it like gstat and) because it was generically found to perform better than the other methods, because it allows to include the effects of other explaining variables (as, for instance, elevation) in the method, and furnishes a built-in methodology to calculate estimations errors. 
In any case, please find below, a list of papers, certainly incomplete, where the general problem was analysed, and some more specific literature on rainfall and temperature interpolation.

The future will be certainly in mixed methods, where, for instance Kriging, will be mixed with machine learning techniques (see also here). However, in this direction I saw seeds,  not yet mature, mainstream work.


Attore, F., Alfo, M., De Sanctis, M., Francesconi, F., Bruno, F., 2007: Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. Int. J. Climatol. 27, 1825-1843.

Burrough PA, McDonnell RA. 1998. Principles of Geographical Information Systems. Oxford University Press: New York; 333. 

Carrea-Hernandez, J.J., Gaskin, S.J., 2007: Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. J. Hydrol. 336, 231-249.

Caruso C. & Quarta F., 1998. Interpolation methods comparison. Comput. Math. Appl., 35(12), 109-126.

Daley, R. 1991. Atmospheric Data Analysis. Cambridge University Press, Cambridge.

Daly, C. (2006). Guidelines for assessing the suitability of spatial climate data sets. International Journal of Climatology, 26(6), 707–721.

Dubois, G., Malczewski, J. and De Cort, M. (2003). Mapping radioactivity in the environment. Spatial Interpolation Comparison 1997 (Eds.). EUR 20667 EN, EC. 268 p. 

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation (pp. 1–488). New York : Oxford University Press. 

Goovaerts P., 1998. Ordinary cokriging revisited. Mathematical Geology, 30(1), 21–42

Hengl, T., Heuvelink, G.B.M., Rossiter, D.G., 2007. About regression-kriging: from equations to case studies. Comput. Geosci. 33, 1301e1315.

Hofstra, N., M. R. Haylock, M. New, P. D. Jones, and C. Frei (2008), Comparison of six methods for the interpolation of daily European cli- mate data, J. Geophys. Res., doi:10.1029/2008JD010100, in press.

Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: A review, Environmental Modelling & Software, Volume 53, March 2014, Pages 173–189

Mitasova, H., and Mitas, L., Interpolation by regularised spline with tension, I: theory and implementation, Mathematical Geology, 25:641-655

Moore, I.D., Terrain analysis programs for the environmental sciences, Agricultural System and information Technology, 2:37-39, 1992

Myers DE (1994) Spatial Interpolation: an overview. Geoderma 62(1): 17-28

Nalder I.A. & Wein R.W., 1998. Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric. For. Meteorol., 92(4), 211- 225. 

Shepard, D. 1968. A two dimensional interpolation function for irregularly spaced data, Proceedings of the 23rd National Conference, ACM, pp. 517-523.

Rivoirard, J.. On the structural link between variables in kriging with external drift [J]. Mathematical Geology, 2002, 34: 797–808

Thornton, P., Running, S. W., & White, M. A. (1997). Generating durfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology, 190, 214–251.

Todini, E., 2001b. Influence of parameter estimation uncertainty  in Kriging. Part 1 – Theoretical Development, Hydrol. Earth. System Sci., 5, 215–223.

Todini, E., Pellegrini, F. and Mazzetti, C., 2001. Influence of  parameter estimation uncertainty in Kriging. Part 2 – Test and  case study applications, Hydrol. Earth. System Sci., 5, 225–232. 

Vizi, L., Hlasny, T., Farda, A., stepanek, P., Skalak, P., & Sitkova, S. (2011). Geostatistical modeling of high resolutionclimate change scenario data. Quartely Journal of the Hungarian Meteorological Service, 115(1-2), 1–16.

Weber, D. and Englund, E. 1992. ‘Evaluation and comparison of spatial interpolators’, Math. Geol., 24(4), 381-389.

Webster, R., Oliver, M., 2001. Geostatistics for Environmental Scientists. John Wiley
& Sons, Ltd, Chichester.


Basistha, A., Arya, D. S., and Goel, N. K.: Spatial Distribution of Rainfall in Indian Himalayas – A case study of Uttarakhand Region, Water Resour. Manag., 22, 1325–1346, 2008. 

Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, 2004.

Buytaert, W., Celleri, R., Willems, P., Bie`vre, D. B., and Wyseure, G.: Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes, J. Hydrol. 329, 413–421, 2006. 

Bussieres, N. and Hogg, W. 1989. The objective analysis of daily rainfall by distance weighting schemes on a mesoscale grid’, Atmos. Ocean, 27(3), 521-541.

Creutin, J.D., Obled, C., 1982. Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water Resources Research, 18(2), 413-431

Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158. 
Dirks K.N., Hay J.E., Stow C.D. & Harris D., 1998. High- resolution studies of rainfall on Norfolk Island. Part 2: Interpolation of rainfall data. J. Hydrol., 208(3-4), 187- 193. 

Fiorucci, P., La Barbera, P., Lanza, L.G. and Minciardi, R., 2001. A geostatistical approach to multisensor rain field reconstruction and downscaling, Hydrol. Earth. System Sci., 5, 201–213. 

Guan, H., Wilson, J.L. and Makhnin, O.. Geostatistical mapping of mountain precipitation incorporating autosearched effects of terrain and climatic characteristics. Journal of Hydrometeorology, 2005, 6: 1018–1031

Hofierka, J., Parajka, J., Mitasova, H. and Mitas, L. (2002) Multivariate interpolation of precipitation using regularized spline with tension. Transactions in GIS, 6, 135-150. doi:10.1111/1467-9671.00101 

Hutchinson MF. 1995. Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems 9: 385–403.

Hutchinson, M. F., 1998: Interpolation of rainfall data with thin plate smoothing splines: II. Analysis of topographic dependence. J. Geogr. Inf. Decis. Anal., 2, 168–185. 

Kurtzman, D., Navon, S., & Morin, E. (2009). Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators. Hydrological Processes, 23(23), 3281–3291.

Kyriakidis P.C., Kim J. & Miller N.L., 2001. Geostatistical mapping of precipitation from rain gauge data using atmospheric and terrain characteristics. J. Appl. Meteorol., 40(11), 1855-1877

Lanza L.G., Ramirez J.A. & Todini E., 2001. Stochastic rainfall interpolation and downscaling. Hydrol. Earth  Syst. Sci., 5(2), 139-143.

Ly, S., Charles, C., & Degré, A. (2011). Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium. Hydrology and Earth System Sciences, 15(7), 2259–2274., 2009) 

Ly, S, Charles, C., & Degree, A. (2013). Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale. A review.  Biotechnol. Agron. Soc. Environ., 17(2), 392–406.

Morin E, Gabella M. 2007 Radar-based quantitative precipitation estimation over Mediterranean and dry climate regimes. Journal of Geophysical Research 112: D20108. DOI:10.1029/2006JD008206.

Obled C., Wendling J. & Beven K., 1994. The sensitivity of hydrological models to spatial rainfall patterns: an evaluation using observed data. J. Hydrol., 159(1-4), 305-333.

Phillips, D.L., Dolph, J. and Marks, D., 1992. A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain. Agric. For. Meteorol., 58: 119-141.

Schiemann, R., Erdin, R., Willi, M., Frei, C., Berenguer, M., and Sempere-Torres, D.: Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland, Hydrol. Earth Syst. Sci., 15, 1515–1536, doi:10.5194/hess-15-1515-2011, 2011 

Schuurmans, J. M., Bierkens, M. F. P., Pebesma, E. J., and Uijlen- hoet, R.: Automatic prediction of high-resolution daily rainfall fields for multiple extents: The potential of operational radar, J. Hydrometeorol., 8, 1204–1224, 2007. 

Tabios,G.Q.and Salas, J.D.,1985. A comparative analysis of techniques for spatial interpolation of precipitation. Water Resour. Bull., 21: 365-380.

Tait A, Henderson R, Turner R, Zheng X. 2006. Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface. International Journal of Climatology 26: 2097–2115. 

Thiessen, A.H., 1911. Precipitation averages for large areas. Mon. Weather Rev., 39: 1082-1084.

Todini, E., 2001a. A Bayesian technique for conditioning radar precipitation estimates to rain gauge measurements, Hydrol. Earth. System Sci., 5, 187–199.

Velasco-Forero C. A., Sempere-Torres, D., Cassiraga E. F., and Gomez-Hernandez, J. J.: A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data, Adv. Water Resour., 32, 986–1002, 2009. 

Xie P, Yatagai A, Chen M, Hayasaka T, Fukushima Y, Liu C, Yang S. 2007. A gauge-based analysis of daily precipitation over east Asia. Journal of Hydrometeorology 8: 607–626. 


Blandford TR, et al. 2008. Seasonal and synoptic variations in near-surface air temperature lapse rates in a mountainous basin. Journal of Applied Meteorology and Climatology 47: 249−261. DOI: 10.1175/2007JAMC1565.1. 

Courault, D., & Monestiez, P. (1999). Spatial interpolation of air temperature according to atmospheric circulation patterns in southeast France. International Journal of Climatology, 19(4), 365–378.<365::AID-JOC369>3.0.CO;2-E

Dobrowski SZ, et al. 2009. How much influence does landscape-scale physiography have on air temperature in a mountain environment? Agricultural and Forest Meteorology 149: 1751−1758. DOI:10.10

Dodson, R., & Marks, D. (1997). Daily air temperature interpolated at high spatial resolution over a large mountainous region. Climate Research, 8, 1–20.

Fury, R. and Joly, D. 1995. ‘Interpolation spatiale a` maille fine des temperatures journalieres’, Meteorol., 8(11), 36–43.

Hudson, G., Wackernagel, H., 1994: Mapping temperature using kriging with external drift: Theory
and example from Scotland. Int. J. Climatol. 14, 77-91.

Ishida, T. and Kawashima, S. (1993) Use of cokriging to estimate surface air temperature from elevation. Theoretical and Applied Climatology, 47, 147-157. doi:10.1007/BF00867447 

Jabot, E., Lebel, T., Gautheron, A., & Obled, C. (2011). Spatial interpolation of sub-daily air temperatures for improving snow and hydrological forecasts on Alpine catchments (pp. 1–19). Presented at the th Eastern Snow Conference, Montreal. (But see Jabot on HP, 2012)

Jabot, E., Zin, I., Lebel, T., Gautheron, A., & Obled, C. (2012). Spatial interpolation of sub-daily air temperatures for snow and hydrologic applications in mesoscale Alpine catchments. Hydrological Processes, 26(17), 2618–2630. 2009.06.006. 

Lookingbill TR, and Urban DL. 2003. Spatial estimation of air temperature differences for 
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