Lysimiter GEO - Webinar II - An exercise step by step

 This follows the first webinar on Lysimeter Pro, a GEOframe modelling solution intended to estimate the 1D soil-vegetation-atmosphere fluxes using the GEOframe WHETGEO and GEOframe Prospero tools. No more explanations are required than those you already find in the previous webinar and in the Jupyter Notebooks inside the  OMS3 project at here. The OMS project contains all the executable, however you have to do some installations before using the GEOframe working environment. 

Please find the video of the webinar below.

Executing Lysimeter GEO from Ri Rigon on Vimeo.

The previous webinar here.  For any question please do not exitate to contact us using the GEOframe users google group: https://groups.google.com/g/geoframe-components-users

Lysimiter GEO - Webinar I

 Land-Vegetation-Atmosphere interactions are an exciting field of Hydrology. Within our system GEOframe, one branch of work is improving the physics of GEOtop and this talk shows some of the work we made to this goal. Lysimiter GEO builds a virtual lysimiter and modeling infiltration and energy transfer in soil and evaporation and transpiration. The infiltration is modeled by the component WHETGEO 1D (Water, HEat and Transport in GEOframe) that integrates the 1D Richards developed by Niccolò Tubini. The evaporation and Transpiration are modeled by the GEOframe component Prospero  developed by Michele Bottazzi  in his Ph.D. Thesis.  Lysimeter GEO, however, was completed by Concetta D'Amato who is pursuing her Ph.D. on these topics within the PRIN project WATZON.By clicking on the Figure below you can access the slides. 

If you want to run Lysimeter GEO, you have first to install the GEOframe 2021 environment.  Here below, please find the video of the talk.  The OMS project for all the run can be found on OSF here. 

The second webinar containing an exercise did step by step is in this new post. 

Krigings of positive definite fields

 Kriging is a statistical technique used to interpolate spatial sparse data and obtain a field of measures. Interpolation of data (Mitas and Mitasova, 1999) is ubiquitous i hydrology and, in fact, the GEOframe system contains a solid Kriging interpolator.

One aspect that bother hydrologists is that often they have to treat with quantities that are positive definite and Kriging does not guarantees that you get from positive measurements positive values.

In fact, in literature there exist some trials to get a version of Kriging constrained to such values. Mainly three are the relevant contributions.

My preferred one is Szidarovszky et al., 1987 which takes the problem directly by definitions and solve it. However also Deutsch, 1996 use an iterative (trial and error method to obtain the same result). A more restrictive condition which impose the conservation of the total "mass" of the quantity modeled is pursued in Walvoort and de Gruiter (2001).

Other suggest, especially for the case of precipitation of using an indicator Kriging to detect locations where rain is falling from locations where it is not (e.g. Atkinson, 1998) or cokriging techniques.

References

Adhikary, Sajal Kumar, Nitin Muttil, and Abdullah Gokhan Yilmaz. 2017. “Cokriging for Enhanced Spatial Interpolation of Rainfall in Two Australian Catchments.” Hydrological Processes 31 (12): 2143–61.

Atkinson, Peter M. 1998. “Mapping Precipitation in Switzerland with Ordinary and Indicator Kriging.” Journal of Geographic Information and Deci Sion Analysis 2 (2): 65–76.

Deutsch, Clayton V. 1996. “Correcting for Negative Weights in Ordinary Kriging.” Computers & Geosciences. https://doi.org/10.1016/0098-3004(96)00005-2.

Mitas, Lubos, and Helena Mitasova. 1999. “Spatial Interpolation.” Geographical Information Systems: Principles, Techniques, Management and Applications 1 (2). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.224.5959&rep=rep1&type=pdf.

Myers, Donald E. 1991. “Pseudo-Cross Variograms, Positive-Definiteness, and Cokriging.” Mathematical Geology. https://doi.org/10.1007/bf02068776.

Szidarovszky, F., E. Y. Baafi, and Y. C. Kim. 1987. “Kriging without Negative Weights.” Mathematical Geology. https://doi.org/10.1007/bf00896920.

Walvoort, Dennis J. J., and Jaap J. de Gruijter. 2001. “Compositional Kriging: A Spatial Interpolation Method for Compositional Data.” Mathematical Geology 33 (8): 951–66.

One Year Grant on the management of River Po catchment and related water resources.

 To whom it may concern, River Po Authority, issued a grant for a one year to apply the system GEOframe to the river Po.  The person interested is requested to:

• Implement and deploy the GEOframe system on the catchments of the District, including the main lakes and reservoirs
• calibrate and verify the models
• elaborate and the results 
• Using and applying the interface Deltares FEWS/DEWS
• Doing Analyses of the impacts of different choices of the water resources management also accounting for the climate change or modifications of land use
• Improving the models

Previous knowledge of the tools above is preferred but not required. Good will is better than erudition.

The position is in Parma, at the District Authority but interactions with our group in Trento are foreseen to happen at daily basis. The working in Trento includes, at present, Prof. Riccardo Rigon, Prof. Giuseppe Formetta, one post-doc, one Ph.D. student, one master student. Dr. Christian Massari and Dr. Silvia Barbetta of C.N.R. IRPI in Perugia collaborate to the project, too.   If colleagues reading this post wish to join our crew, at present on a free basis (we have no that incredible money), but looking forward for a challenging collaborative research, they are welcomed. We are also looking for other funding resources to get new positions.

FYI, the District will probably hire many people on the next years, and therefore, our addiction, this position as well the others could be a way to learn some skills allowing to be overcome the selection to be hired.  We, at University of Trento, are also interested to people who could eventually be enroll for doctoral studies on the topics of the call. 

You can find the official information of the call at the site of the District Authority Further information about the system GEOframe can be obtained at the GSW2021. 

Third SII Hydrology Day Webinar - Il ruolo dell'idrologo nella pratica professionale e altro

 This is the third webinar of the 2020 Hydrology days held via Web. It was certainly a successful initiative and this third appointment maintain the interest high. Please below you will find the video registration of the conference

• The opening by the SII President, Tommaso Moramarco
• The outcomes of the Florisa Melone Prize 2019 - Marco Peli and Martina Siena
• The Youngs Hydrological Society Activities
• Tavola rotonda su La certificazione dell'ingegnere idrologo
• Gaetano Attilio Nastasi CERT'Ing
• Alberto Castori CERT'Ing
• Stefano Calzolari CNI
• Carlo Cacciamani, AERPAE-ER
• Luciano Bonci - ISPRA
• Meuccio Berselli - Autorità di Distretto del Po
• Fabio Castelli, GII
• Stefano Calzolari, CNI 2
• Tommaso Moramarco
• Mauro Fiorentino, CINID
• Discussione

The decline of hydraulic conductivity with suction in plants

In the vein of understanding more on plants hydraulics I started from the (McDowell et al., 2008)} paper which is as much informative as imprecise in its two or three formulas which do not much with the concepts conveyedby the figures and the text. So definitely it is an inspiring paper but I would not start from it. Instead I would probably use as a first reading {(Venturas et al., 2017)} that is more recent and more consistent and which I would couple with Lehnebach et al., 2018), to read as acomplement. In this post, however, let's concentrate on the conductivity of stems decline with stems suction. In literature, it was found todecline with stem pressure according to a sigmoid form (I would say, an exceeding probability like s function).In literature various form were used to parametrize this behavior. Notably:

• the exponential-sigmoid (Pammenter & der Willigen., 1998)

$$ K(\psi) = K_{max} \left( 1 - \frac{1}{1+ \exp( a(\psi-b) )}\right) $$

• the Weibull (Rawlings \& Cure, 1985), Neufeld et al., 1992)

$$ K(\psi) = K_{max} e^{ - \left(\frac{\psi}{\alpha} \right)^\beta} $$

• the Gomperz (Mencuccini \& Comstock, 1997)

$$ K(\psi) = 1 - a e^{-b e^{-c \psi}} $$

• polynomial function (Pockman et al., 1995)
• the power law (Wang et al., 2017)

$$ K(\psi) = \left(\frac{1}{2}\right)^{\left(\frac{\psi}{\psi_{50}}\right)^b}$$
However, for historical reasons, this decline is modeled~in plant physiology literature using PLC curves which represent the Percent Loss Conductivity. PLC have a one to one relation with hydraulic conductivity
which is given by the formula:
$$ K(\psi) = K_{max}\left(1-\frac{PLC(\psi)}{100}\right) $$
Some researchers found useful to treat differently the parameterization as, for instance, in Ogle et al., 2009, an indication that was taken in Duursma, 2017 for implementing their R software fitplc. The latter paper is interesting also for having tried to evaluate the error of estimation and the parameters they reproduce in their Table 1 for some plant species.

References

• Nate McDowell, William T. Pockman, Craig D. Allen, David D. Breshears, Neil Cobb, Thomas Kolb, Jennifer Plaut, John Sperry, Adam West, David G. Williams, Enrico A. Yepez. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?. New Phytologist178, 719–739 Wiley, 2008. Link
• Martin D. Venturas, John S. Sperry, Uwe G. Hacke. Plant xylem hydraulics: What we understand current research, and future challenges. Journal of Integrative Plant Biology 59, 356–389 Wiley, 2017. Link
• Romain Lehnebach, Robert Beyer, Véronique Letort, Patrick Heuret. The pipe model theory half a century on: a review. Annals of Botany 121, 773–795 Oxford University Press (OUP), 2018. Link
• N. W. Pammenter, C. Van der Willigen.. A Mathematical and Statistical Analysis of the Curves Illustrating Vulnerability of Xylem to Cavitation. Tree Physiology 18 (1998).
• J. O. Rawlings, W. W. Cure. The Weibull Function as a Dose-Response Model to Describe Ozone Effects on Crop Yields 1. Crop Science 25, 807–814 Wiley, 1985. Link
• Howard S. Neufeld, David A. Grantz, Frederick C. Meinzer, Guillermo Goldstein, Gayle M. Crisosto, Carlos Crisosto. Genotypic Variability in Vulnerability of Leaf Xylem to Cavitation in Water-Stressed and Well-Irrigated Sugarcane. Plant Physiology 100, 1020–1028 American Society of Plant Biologists (ASPB), 1992.Link
• M. Mencuccini, J. Comstock. Vulnerability to cavitation in populations of two desert species,Hymenoclea salsolaandAmbrosia dumosa from different climatic regions. Journal of Experimental Botany 48, 1323–1334 Oxford University Press (OUP), 1997. Link
• William T. Pockman, John S. Sperry, James W. OLeary. Sustained and significant negative water pressure in xylem. Nature 378, 715–716 Springer Science and Business Media LLC, 1995. Link
• Han Wang, I. Colin Prentice, Trevor F. Keenan, Tyler W. Davis, Ian J. Wright, William K. Cornwell, Bradley J. Evans, Changhui Peng. Towards a universal model for carbon dioxide uptake by plants. Nature Plants 3, 734–741 Springer Science and Business Media LLC, 2017. Link
• Kiona Ogle, Jarrett J. Barber, Cynthia Willson, Brenda Thompson. Hierarchical statistical modeling of xylem vulnerability to cavitation. New Phytologist 182, 541–554 Wiley, 2009. Link
• R. & Choat Duursma. fitplc - an R package to fit hydraulic vulnerability curves. J. Plant Hydraul. (2017)

Why Excel is Evil

Why Excel is Evil is not a campaign against a commercial product. Instead, it is an analysis of the requirement for having a solid scientific product.  Thanks to Colin Caprani for this valuable contribution.