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. 

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 the season too. A simplification would probably be to estimate which is the maximum number of leaves a tree can have. The question was raised 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. 

P.S. - Section 2.3 of G. Bonan 2019 book talks about the leaf mass per unit area and can be partially used to complement this post. 

* This part was edited by Barry Galvin.

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

Bonan, Gordon. 2019. Climate Change and Terrestrial Ecosystem Modeling. Cambridge University Press.

Chianucci F, Chiavetta U, Cutini A, 2014. The estimation of canopy attributes from digital cover photography by two different image analysis methods. iForest 7: 255-259 

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

Glenn E.P, Huete A.R., Nagler P.L, Nelson G.P., Relationship between remotely-sensed vegetation indice, canopy attribute and plat physiological processes: what vegetation indices can and cannot tell us about the landscape, Sensors, 2136-2160, 2008

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

Verrelst, J., et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review. ISPRS J. Photogram. Remote Sensing (2015) (Also here)

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

Weiss M., Baret F., Smith G.J, Jonckheere I., Coppin P, Review of methods for in situ leaf are index determination, part II: Estimation of LAI, error and sampling.

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.

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: http://geoframe.blogspot.com.
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).

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. http://doi.org/10.1016/j.jhydrol.2004.08.002

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

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!

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. 

References

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. 

Web

https://climatedataguide.ucar.edu/climate-data 

http://prudence.dmi.dk/ 

http://www.ensembles-eu.org/ 

http://www.cordex.org/