Friday, February 16, 2018

Conoscere, comunicare, gestire il rischio idrogeologico in Ambiente montano

On Wednesday 7  and Thursday 15 February 2018 we held at our Department (DICAM) a workshop entitled: Know, communicate and manage hydro-geological risks in mountain environments. This workshop was one of the events of the Life FRANCA project. Please find below, in Italian, the YouTube of the talks. 

Introduction to the workshop by Luigi Fraccarollo
Introduction to Life FRANCA by Rocco Scolozzi
A review on hydrological hazards for non specialists by Riccardo Rigon
What is hazard ? by Giorgio Rosatti
The experience of a major in managing a post hazard by Ugo Grisenti
Information from the institutional channel by Giampaolo Pedrotti
What journalists care about and the constraint of newspapers journalism by Andrea Selva
The hard position of the judge by Carlo Ancona

Monday, January 29, 2018

Grids - Notes for an implementation

This post talks about the same subject already analyzed in a previous post but from a slightly different point of view, hoping to add clarity to the concepts. We assume to already have the grid delineated, as for instance the one in Figure. Some other program or someone else provided to us. All the information is written in a file, maybe in a redundant form, but it is there and we just have to read it.
Assume we are talking about a three-dimensional grid. Nodes, edges, faces, and volume are identified by a number (key, label) which are specified in the grid’s file.

Therefore the problem is to read this file and implement the right (Java) structures/objects to contain it, keeping in mind that our goal, besides to upload the data in memory is to estimate the time marching of a variable $A$ (and, maybe some other variable) in a given volume. Its time variation depends on fluxes of the same quantity (mass, to simplify) that are localised at the face that constitute the boundary of the volume.

Getting the things done

The simplest thing to do is then, to associate a vector whose entries are the values of $A$ for any of the volumes in the grid. Let say, that forgetting any problem which could be connected with execution speed, caching [1], boxing-unboxing of variables, we use a Hashmap to represent these values.
We will use also a Hashmap to contain the fluxes in each face. This hasmap contains $F$ elements: as many as the number of faces. The file from which we started contains all of this information and therefore we do not have any problem to build and fill these “vectors”.
Let’s give a look to what our system to solve can look like. The problem we have to solve changes but, schematically it could be:
For any volume (we omit the index, $i$ of the volume for simplicity):
$$ A^t = A^{t-1} + \sum_l a_l^{t} *i_l^{t}*f_l^t/d_l $$
$t$ is time (discretized in steps) and $t-i1$ is the previous step;
$l$ is the index of faces belonging to the volume
$d_l$ is the distance between the centroids of the two volumes that share the same face;
$i_l$ is a sign, +1 or -1, which depends on the volume and the face we are considering (volume index omitted);
$a_l$ is the area of the face $l$ or some function of of it.

For generality, the r.h.s. member of the equation is evaluated at time $t$, i.e. the equation is assumed to be implicit, but at a certain moment of the resolution algorithm, the function will be expressed as depending of some previous time (even if from the point of view of internal iterations). For a more detailed case than this simplified scheme, please see, for instance [2].
The Hashmap of $A$ contains the information about the number of volumes, i.e., $V$.
(I) an indication of the faces belonging to each volumeIl vettore (hash map) and
(II) the information about which volumes are adjacent
To obtain this, we have to store information about the topology of our grid. In the previous posts, we tried to investigate and answer to the question: which is the most convenient to store these informations ? (Right, more from a conceptual point of view than from a practical one).
From our previous analysis, we know that that for encoding the number of faces for any volume, we have to introduce a second (2) container that has has many position as the number of volumes, and for any volume a variable number of slots, each for any face of that volume (if the grids is composed by volumes of the same shape, the latter number of slots is constant for the internal elements of the grids, and variable just for the boundary volumes).
In this preliminary analysis, a Hasmap seems appropriate to contain this information, letting, for the moment, unspecified what types or objects contains this topology Hashmap, but eventually, they will contain a key or a number which identifies in a unique way a given face.
In this way the information about any face is present in two slots, belonging to the volumes that share the same face.
We have then the various quantities to store in each face:

  • $a_l$ (3) 
  • $f_l$ (4) 
  • $d_l$ (5) 

Anyone of the above quantites require a container with as many elements as the faces. We could, then, use three Hasmaps, whose indexes (keys) coincide with the numbers (keys) that in the topology Hasmap (2) realate faces to volumes.
To elaborate our equation we need then five containers, of which the topology one has a structure to be specified later. Well, actually all the hashmap internals has to be specified.
The elements of $a$ and $d$ are geometrical quantities that can - and has- to be specified outside the temporal cycle, if the grid structure is not modified during the computation. However, to be estimated they require further topological information that we still do not have (but can be in the grid file).
To estimate faces’ area, we need to know the nodes of the grid [3] which can be a sixth (6) container, and the way they are arranged in the faces, which is a seventh (7) container. Since the choices we did, we still choose to use Hashmaps to contain them. The Hashmap of nodes just contains the number (or the key) of nodes (and is, maybe, in most problems, pleonastic). The Hasmap of faces need to contain the arrangement of nodes, ordered in one of the two direction (left-hand -rule or right-hand-rule, clockwise and counterclockwise depends on the side you observe the face, so what is clockwise from a volume is counterclockwise for the other).
The (7) container has to have as many elements as the faces and each element contains the ordered nodes (a link, a reference, to). To estimate the area of the faces we need actually the geometry of the nodes, meaning their coordinates in some coordinate system. Usually, in most of the approaches, nodes are directly identified by their coordinates, which therefore are inserted directly (in the appropriate way) in container (7) instead that the link/reference to nodes' number (key, label).
However, I think that probably keeping the geometry separated from topology could be useful, because topology has its own scope, for instance in guiding the iteration in the summation that appears in our template equation.
Therefore we need a further container (the eight, 8) for the geometry, containing the coordinates of points. This container has $N$ elements, as many are the nodes.
The container of distances, d, to be filled needs to know between which volumes distances have to be calculated. This information, about volumes adjacency, needs another, further container (the nineth, 9) with length as the faces, i.e. with F elements. Every element, in turn, must contains the index of the elements between which is estimated.
This information that goes into the container 9, should already be in the file from which we are reading all the information. However, we should recover it by scanning all the volumes and finding which have a face in common. The latter, is a calculation that can be made off-line and we can, in any case consider it an acquired.
At this stage, we do not have much information about $f_l$. Certainly it will need to know which are adjacent volumes and requires the knowledge in container (9). Because $f_l$ is time varying it implies that information in (9) has to be maintained all along the simulation.
Every other information will require a further container. To sum up, we have a container of:

  1. quantity A;
  2. topology of Volumes;
  3. the area of faces;
  4. fluxes;
  5. distances between volumes’ centroids;
  6. nodes number (label, key)
  7. nodes that belong to a face
  8. coordinates of nodes
  9. topology of faces (referring to the volumes they separate)

Towards generalizations that look to information hiding and encapsulation

We can observe that we have three types of containers: the ones which contain topological information (2,6,7,9), those which contain physical quantities (1,4), those which contains geometric quantities (3,5,8).
If, instead than a 3D problems, we would have a 2D or 1D one, the number of container change, but not their types.
To go further deep, the first problem to deal with could be to understand how, in the topology container, for instance of volumes (2) how to make room for the slots indicating their faces, since they are of variable dimension. In traditional programming, usually they would have adopted a “brute force” approach: each slot would have been set to have the dimension of the larger number of elements to be contained. The empty element replaced by a conventional number to be check. Essentially all of it would have resulted in a matrix whose rows (columns) would correspond to the the number of elements (volumes, faces) and whose columns (rows) to the variable number of elements they contain (in the case of volumes, faces; in the case of faces, edges, and so on).
In a OO language, like Java, the sub-containers of variable dimensions can be appropriate object, for instance called generically “cell” containing an array of int[ ]. Therefore the global container of a topology could be a hashmap of cells.
In principle we could use the container defined above without any wrapper, directly defining them in term of standard objects in the library of Java 9.
However, we would like, maybe, to use other types eith resepcts to those we defined. For instance, in some cases, for speed reasons, we could substitute ArrayList to Hasmap or, someone of us, working on the complexity of caching could come out with some more exotic objects.
To respond to these cases, we would like then to introduce some abstraction which, without penalizing (too much) performances. Sure, we can define wrapper classes, for instance:
  • for topologies (essentially used to drive iterations)
  • for geometries
  • for physical quantities (used to contains data, immutable for parameters, and time-varying for variables)
These three classes would allow to fit all the cases for any dimension (1D, 2D, 3D): just the number of topology element would be varying.
However, this strategy could not be open enough to extensions which do not require breaking the code (be closed to modifications).
Using instead of classes, interfaces or abstract classes could be the right solution.
Classes, or BTW, interfaces could have also the added value to contain enough field to specify the characteristics of the entities, (es. if they work in 2D or 3D, their “name”, their units, all those type of information requested by the Ugrid convention). All these types of information are, obviously, also useful to make the programs or the libraries we are going to implement more readable and easier to be inspected by an external user.
While the topology class is self-explanatory, the geometry class (interface) has a connection to its topology. Therefore the geometry class should contain a reference to its topology to make explicit its dependence. A quantity object, for the same reason, should contain a reference to both its topology and its geometry.
The simplicity to use classes directly could be tantalizing, however, the investment for generality made by interposing interfaces or abstract classes is an investment for future.
Berti [3] advise, in fact, to separate the algorithm from the data structure, allowing therefore to write a specific algorithm once forever, and changing the data it uses, as we like. This would be a ideal condition maybe impossible to gain, but working to maintain in any case the possible changes in limited parts of the codebase is an add value to keep as reference. That is why “encapsulation” is one of paradigms of OO programming.

Some final notes

1 - In using cw-complexes to manage topology there could be overhead for speed. For instance, for accessing the values in a face of a volume, vi have to

access the volume,
access the address of the face
redirect to the appropriate quantity container to access the value
It could be useful then to eliminate one phase and once accessing the volume, having directly associate to it not the address of of the faces but the values contained in it.
If we have more than one value for face to access, related to different quantities and parameters, than maybe this added computational overhead could be considered negligible with respect to the simplicity of management of many quantities. In any case, an alternative to test.

2- At any time step, it is not only requested the quantity at time $t$, $A^{t}$, but also at the previous time, $t-1$, $A^{t-1}$. The two data structures share the same topology (which could represent a memory save). During time marching an obvious attention that the programmer needs to have is not to allocate a new grid to any time step. We can limit ourself to use only two grids across the simulation.

As an example, let us assume that time $t-1$ is going to be contained in vector $A^1$ and time $t$ in $a^2$. Then the above requirement could be obtained by switching the two matrixes as schematized as follows:
  • Create A1and A2,
  • Set A1 to initial conditions
  • For any t
  • A2=f(A1)
  • cwComplex.switch(A1,A2)
The switch method exchanges the names, but does now write anything in memory of $A^1$ and $A^2$. It could be schematised as follows
  • cwComplex.switch(A1,A2)
  • B = A1;
  • A1=A2;
  • A2=B;
It is clear that, in this way, all the vectors are always filled by values, while, for some operation, cleaning them could be worth.

3 - At the core of the method os solution of the equation under scrutiny, there could usually be a Newton method, e.g. [3], Appendix A, equation A8. Any efficiency improvement for the solver is then reduce to improve the speed of this core, that, eventually can be parallelised.


[1] - Lund, E. T. (2014). Implementing High-Performance Delaunay Triangolation in Java. Master Thesis (A. Maus, Ed.).

[2] - Cordano, E., and R. Rigon (2013), A mass-conservative method for the integration of the two-dimensional groundwater (Boussinesq) equation, Water Resour. Res., 49, doi:10.1002/wrcr.20072.

[3] - O'Rourke, J., Computational geometry in C, Cambridge University Press, 2007

[4] - Berti, G. (2000, May 25). Generic Software Components for Scientific Computing. Ph.D. Thesis

Wednesday, January 24, 2018

My Questions for the 23 Hydrological Questions initiative

In November 2017 IAHS launched the new initiative to generate the 23 unsolved problems in Hydrology that would revolutionise research in the 21st century with the following YouTube video:

I probably have to formulate them differently. However at present my points are

1- What future for process based modelling beyond persistent dilettantism ? How can we converge towards new types of open models infrastructures for hydrology where the crowd can contribute, big institutions do not dominate, and reinventing the wheel will not be necessary anymore ?

2 - How to solve the energy budget, the carbon budget and the sediment budget together to constrain hydrologic models results ?

3 - Which new mathematics to choose for the hydrology of this century ? Does new hydrology (Earth System Science) needs new mathematics ?

4 - Will machine learning have a real role in hydrological modelling ?

5 - How can we really cope hydrological modeling with remote sensing measures ?

6 - How plants and grass work and interact with soil and atmosphere to produce evaporation ? Can we converge to unifying concepts that overcome present fragmented understanding ?

7 - How can we detect and measure spatial hydrological patterns ?

8- Does hydrology needs non-equilibrium thermodynamics or even a new type of thermodynamics ?

9 - How can we do hydrology science more open and replicable ?

10 - How dominant hydrological processes emerge and disappear across the scales. What tools are needed to follow the entanglement of processes ? Will we be finally able to cope with  feedbacks among processes?

Tuesday, January 23, 2018

My Hydrology Class 2018

Work in progress !

Foreseen schedule

  • 2018-02-27 - T - (a) Syllabus; (b) Explaining how to use the Open Science Framework for this class.- (c) Introduction to hydrology - (30 minutes each) - Homework: Installation of Python and Jupiter 

  • 2018-03-02 - L - A little (very little) of GIS (for those who do not know anything about GIS). Where environmental data comes from. 
  • 2018-03-06 - T - Revision of Statistics and Probability knowledge base 
  • 2018-03-09 - L - Introduction to Python with Jupyter 
  • 2018-03-13 - T - Ground based Precipitations and their statistics Separation snow-rainfall - measure of precipitation 
  • 2018-03-16 - L - Intro to Python - Simple statistics - Empirical distribution functions - Statistical distributions - Extreme precipitations datasets 
  • 2018-03-20 - T - Extreme precipitations. Return period. Distribution of extreme precipitations. 
  • 2018-03-23 -L - Estimation of Extreme Precipitation with Python 
  • 2018-03-27 - T - OMS console - Installation and description. GEOframe modules. 

  • 2018-04-06 - L - Extreme Precipitations estimation Personal work under the assistance of tutors 
  • 2018-04-10 - T - Energy Budget. Radiation. Long wave, short wave. Theory and measure. 
  • 2018-04-13 - L - Estimation of radiation in a single location and over an area with GEOframe tools. 
  • 2018-04-17 - T - Spatial interpolation of environmental data 
  • 2018-04-20 - L- Estimation of areal precipitation and temperature with GEOFRAME-SIK 
  • Intermediate Exam 
  • 2018-05-06 - L - Problem solving lab class 
  • 2018-05-08 - T -Water in soil. Darcy-Buckingham. Hydraulic conductivity. Soil water retention curves. 
  • 2018-05-11 - L- Numerical experiments on hydraulic conductivity, soil water retention curves. Grids. - Cancelled for "Festa degli Alpini" 
  • 2018-05-15 - T -Richards equation and its extensions 
  • 2018-05-18 - L - Simulations of 1d infiltration with GEOframe-Richards-1d 
  • 2018-05-22 -T- Elements of theory of evaporation from soils 
  • 2018-05-25 -L-Simulation of evaporation from soild with GEOframe-PT, Geoframe-PM and other GEOframe tools 
  • 2018-05-29 -T- Transpiration- Theory 
  • 2018-06-1 - L- Estimating transpiration at catchment scale with GEOframe tools 
  • 2018-06-5 -T- Runoff generation 
  • 2018-06-08-L- Estimating runoff generation with GEoframe tools. 
  • 2018-06-12 - Conclusive seminar on a topic to be defined 
  • 2018-06-15-L - Problem solving with Tutor

Tuesday, January 9, 2018

Project: La gestione del sedimento nella realizzazione di servizi ecosistemici e nel controllo dei processi alluvionali.

The propoposal "La gestione del sedimento nella realizzazione di servizi ecosistemici e nel controllo dei processi alluvionali" was submitted yesterday for the call of MATTM.
The call is at this link (and it is for Geologists ?!). Actually the topics require some geology and a loto of hydrology and hydraulics. This is how the world goes.
The proposal can be found in this OSF site, called: "Gestione del Sedimento".  It is in Italian, but I will provide the translation of the following:

Abstract: The management of sediments for providing  ecosystem services and control alluvional processes. 

The project is about the management of sediments in mountains catchments with the quantitative determination of erosion and mass transport. The research is made looking at the applicatio of 2000/60 and 2007/60 EU directives.
In the project's first phase:
Hydrological analysis utilises a multi-model strategy based on GEOtop and GEOFRAME-NewAGE and other open-source models.
It is estimated the sediment availability and its connectivity to the river network, by using field surveys, data made available from previous research and models.
Transport of sediments will be will be obtained with obtained with biphasic models where water and sediment are treated separately.
Objective of the above phases is to localise the sources and the sediment residence time, to detect its interaction with anthropic works and infrastructures and determine how they (the sediments) can interact with the climatic forcings.

Objective of the application phase are:
  • the production of flooding hazard and risk maps;
  • the forecasting on the proximate and long period of the morphologic chages or river beds, under climate change simulated through “weather generators”.
  • The estimation of the impact of hydraulic works, also back in the years. 
In the present project we will use a connectivity index to estimate the connection between hillslope (source sediment areas) and some target catchments’ elements (the river network, specific streams, the outlet). Sediment source areas are, partially already available from existing databases (CNR IRPI, Provincia Autonoma di Trento, Regione Sicilia), from field surveys and from remote sensing. These data are partially already available from previous projects (ASI MORFEO, CLIMAWARE, AQUATERRA, GLOBAQUA) and by the local Institutions (Geological Service of Trento Province and Regione Sicilia).

Terrein analysis will be coupled with models of landslide triggering, able to account for climate and soil use variability (in space and time) as described as variation of:

  • intensity and frequency of precipitation,
  • precipitation from snow to rain,
  • phenology of vegetation cover

Two areas will be studied, one in the Alps and another in Apennines. The first is the Avisio torrent, and in in particolar the subcatchment closed at the Stramentizzo dam (Molina di Fiemme, TN), analysed with detailed especially in some specific parts.

The Apennine basin is the Giampilieri torrent in Messina Province.

References (that appears in the State-of-Art):

Badoux, A., Andres, N., and Turowski, J.,M., Damage costs due to bedload transport processes in Switzerland, Nat. Hazards Earth Syst. Sci., 14, 279-294, 2014.

Bertoldi et al., 2006 Bertoldi, G., Rigon, R., & Over, T. (2006). Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets. Journal of Hydrometeorology, 7(3), 389–403.

Berzi, D., Fraccarollo, L., Turbulence Locality and Granularlike Fluid Shear Viscosity in Collisional Suspensions (2015), Physical Review Letters, 115 (19), art. no. 194501. Comiti F., and

Farabegoli, E; Morandi, M.C.; Onorevoli G.; and Tonidandel, D.; Shallow landsliding susceptibility in a grass mantled alpine catchment (Duron valley, Dolomites, Italy), in preparation, 2018

Mao, L., Recent advances in the dynamics of steep channels, in Gravel-bed Rivers: Processes, Tools, Environments, John Wiley&Sons, Chichester, UK, 351-377, 2012.

Bracken, C., B. Rajagopalan, and E. Zagona (2014), A hidden Markov model combined with climate indices for multidecadal streamflow simulation, Water Resour. Res., 50, 7836–7846, doi:10.1002/2014WR015567.

Montgomery D.R., and Buffington J.M., Channel-reach morphology in mountain drainage basins. Geol. Soc. Am. Bull, v. 109, no. 5, pp. 596–611, 1997.

Renard, 1997 Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool and D.C. Yoder. 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agr. Handbook No. 703. Washington, D.C.: USDA, Government Printing Office.

Rigon et al., 2006, Rigon, R., Bertoldi, G., Over, T. M., & Over, T. (2006). GEOtop: a distribute hydrological model with coupled water and energy budgets. Journal of Hydrometeorology, 7, 371–388.

Rosatti, G., Zorzi, N., Zugliani, D., Piffer, S. and Rizzi, A., Web Service ecosystem for high-quality, cost-effective debris-flow hazard assessment, 33-47, Env. Modelling & Software,  2018.

Smith, T.R., e F.P. Bretherton. «Stability and the conservation of mass in drainagebasin evolution.» Water Resource Research 8 (1972): 1506-1529. 

Sofia, G., Di Stefano, C, Ferro, V., Tarolli, P. (2017). Morphological similarity of channels: from hillslopes to alpine landscapes. Land Degradation & Development, 28, 1717–1728, doi:10.1002/esp.4081. 

Tarolli, P. (2016). Humans and the Earth’s surface, Earth Surface Processes and Landforms, 41, 2301–2304, doi:10.1002/esp.4059. 

Tucker et al., 2001 Tucker, G. E., Lancaster, S. T., Gasparini, N. M., & Bras, R. L. (2006). The Channel-Hillslope Integrated Landscape Development Model (CHILD), 1–32.

Wainwright, J., A. J. Parsons, J. R. Cooper, P. Gao, J. A. Gillies, L. Mao, J. D. Orford, and P. G. Knight (2015), The concept of transport capacity in geomorphology, Rev. Geophys., 53, 1155–1202, doi:10.1002/2014RG000474.

Saturday, January 6, 2018

Miles Traer - It is time for superheores to be environmentally concerned ;-)

I could not do it, to go to New Orleans Fall Meeting this year (but we had a couple of presentations). 
Among others, ut came to my attention a funny session entitled: PA13C Science and Sci-Fi: Using Real Science to Explore Fictional Worlds Posters, with which is nice to begin the series od 2018 posts.

The argument is made to attract attention on Climate Change and Earth Sciences, and have some fun in doing it (see here the Washington Post report)

A couple of poster of the session are available: the first one by the Convener, Traer himself analizes the energy requirements of some superheroes and you can see  the poster in the figure above. His arguments remind me the history of the banned superheroes in The Incredibles

The science below is kind of weak because you have to do some violation of physics (at least the known one) since the beginning when you accept that they can exist (but see the celebrate Kakalios book which takes another route to is), and actually many concerns can be raised on calculation (Geoscientists are nerds too). 

A second poster of some interest is the Engelman and Chure’s one concerned about T-Rex and Godzilla.

Miles Traer’s blog is nice to visit too, either for the comics and the rest.  

Sunday, December 31, 2017

Baldocchi's Classics

I took the  freedom to reproduce Dennis Baldocchi's classic. The original post is on his website to which I dedicated another post. A must for who is interested in Soil-Vegetation-Atmosphere interactions.  I just added the link to the publications (on the title when pdf is open).
As a post of mine, it can be seen as a companion of the two recent posts on plant-atmosphere interactions where further references are presented (I and II).

Agriculture and Climate

1. Lobell, D.B., Schlenker, W. and Costa-Roberts, J., 2011. Climate trends and global crop production since 1980. Science, 333(6042): 616-20.

2. Lobell, D.B. and Gourdji, S.M., 2012. The influence of climate change on global crop productivity. Plant Physiology, 160(4): 1686-97.

3. Foley, J.A. et al., 2011. Solutions for a cultivated planet. Nature, 478(7369): 337-42.

4. Rosenzweig, C. and Parry, M.L., 1994. Potential impact of climate-change on world food-supply. Nature, 367(6459): 133-138.


1. Bolin, B. and H. Rodhe. 1973. Note on Concepts of Age Distribution and Transit-Time in Natural Reservoirs. Tellus 25:58-62.

Boundary Layer Micrometeorology
1. Kaimal, J. C., Y. Izumi, J. C. Wyngaard, and R. Cote. 1972. Spectral Characteristics of Surface-Layer Turbulence. Quarterly Journal of the Royal Meteorological Society 98:563-&.

2. Hogstrom, U. 1988. Non-Dimensional Wind and Temperature Profiles in the Atmospheric Surface-Layer - a Re-Evaluation. Boundary-Layer Meteorology 42:55-78.

3. Kaimal, J. C. and J. C. Wyngaard. 1990. The Kansas and Minnesota Experiments. Boundary-Layer Meteorology 50:31-47.

4. Wyngaard, J.C., 1992. Atmospheric-Turbulence. Annual Review of Fluid Mechanics, 24: 205-233.

5. Hogstrom, U. 1996. Review of some basic characteristics of the atmospheric surface layer. Boundary-Layer Meteorology 78:215-246.

6. Foken, T., 2006. 50 Years of the Monin–Obukhov Similarity Theory. Boundary-Layer Meteorology, 119(3): 431-447.

7. Wyngaard, J. C. 1990. Scalar Fluxes in the Planetary Boundary-Layer - Theory, Modeling, and Measurement. Boundary-Layer Meteorology 50:49-75.

Canopy Conductance

1. Finnigan, J. J. and M. R. Raupach. 1987. Transfer processes in plant canopies in relation to stomatal characteristics. Pages 385-429 in E. Zeiger, editor. Stomatal Function. Stanford University Press, Palo Alto, CA.

2. Raupach, M.R., 1995. Vegetation-atmosphere interaction and surface conductance at leaf, canopy and regional scales. Agricultural and Forest Meteorology, 73(3-4): 151-179.

3. Kelliher, F.M., Leuning, R., Raupach, M.R. and Schulze, E.-D., 1995. Maximum conductances for evaporation from global vegetation types. Agricultural and Forest Meteorology, 73(1-2): 1-16.

Canopy micrometeorology and turbulence

1. Denmead, O. T. and E. F. Bradley. 1987. On Scalar Transport in Plant Canopies. Irrigation Science 8:131-149.

2. Finnigan, J., 2000. Turbulence in Plant Canopies. Annu. Rev. Fluid Mech., 32(1): 519-571.

3. Raupach, M.R. and Thom, A.S., 1981. Turbulence in and above Plant Canopies. Annual Review of Fluid Mechanics, 13: 97-129.

4. Raupach, M. R., J. J. Finnigan, and Y. Brunet. 1996. Coherent eddies and turbulence in vegetation canopies: The mixing-layer analogy. Boundary-Layer Meteorology 78:351-382.

CO2 Fluxes, Pioneering Studies

1. Monteith, J. L. and G. Szeicz. 1960. et. Quarterly Journal of the Royal Meteorological Society 86:205-214.

J2. Desjardins, R. 1974. Technique to Measure Co2 Exchange under Field Conditions. International Journal of Biometeorology 18:76-83.

3. Anderson, D. E., S. B. Verma, and N. J. Rosenberg. 1984. Eddy-correlation measurements of CO2, latent-heat, and sensible heat fluxes over a crop surface. Boundary-Layer Meteorology 29:263-272.

CO2 Fluxes, syntheses

1. Baldocchi, D.D., 2008. TURNER REVIEW No. 15. 'Breathing' of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Australian Journal of Botany 56, 1-26.

2. Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, M.A., Baldocchi, D., Bonan, G.B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K.W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F.I., Papale, D., 2010. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. Science 329, 834-838.

3. Luyssaert, S., Inglima, I., Jung, M., Richardson, A.D., Reichsteins, M., Papale, D., Piao, S.L., Schulzes, E.D., Wingate, L., Matteucci, G., Aragao, L., Aubinet, M., Beers, C., Bernhoffer, C., Black, K.G., Bonal, D., Bonnefond, J.M., Chambers, J., Ciais, P., Cook, B., Davis, K.J., Dolman, A.J., Gielen, B., Goulden, M., Grace, J., Granier, A., Grelle, A., Griffis, T., Grunwald, T., Guidolotti, G., Hanson, P.J., Harding, R., Hollinger, D.Y., Hutyra, L.R., Kolar, P., Kruijt, B., Kutsch, W., Lagergren, F., Laurila, T., Law, B.E., Le Maire, G., Lindroth, A., Loustau, D., Malhi, Y., Mateus, J., Migliavacca, M., Misson, L., Montagnani, L., Moncrieff, J., Moors, E., Munger, J.W., Nikinmaa, E., Ollinger, S.V., Pita, G., Rebmann, C., Roupsard, O., Saigusa, N., Sanz, M.J., Seufert, G., Sierra, C., Smith, M.L., Tang, J., Valentini, R., Vesala, T., Janssens, I.A., 2007. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Global Change Biology 13, 2509-2537.

Dry Deposition

1. Wesely, M. L. and B. B. Hicks. 2000. A review of the current status of knowledge on dry deposition. Atmospheric Environment 34:2261-2282.

2. Wesely, M. L. 1989. Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmospheric Environment 23:1293-1304.

3.Wesely, M. L. and B. B. Hicks. A review of current status of knowledge on dry deposition, 2000 Atmospheric Environment 34:2261-2282.

4. Fowler, D., K. Pilegaard, M. A. Sutton, P. Ambus, M. Raivonen, J. Duyzer, D. Simpson, H. Fagerli, S. Fuzzi, J. K. Schjoerring, C. Granier, A. Neftel, I. S. A. Isaksen, P. Laj, M. Maione, P. S. Monks, J. Burkhardt, U. Daemmgen, J. Neirynck, E. Personne, R. Wichink-Kruit, K. Butterbach-Bahl, C. Flechard, J. P. Tuovinen, M. Coyle, G. Gerosa, B. Loubet, N. Altimir, L. Gruenhage, C. Ammann, S. Cieslik, E. Paoletti, T. N. Mikkelsen, H. Ro-Poulsen, P. Cellier, J. N. Cape, L. Horváth, F. Loreto, Ü. Niinemets, P. I. Palmer, J. Rinne, P. Misztal, E. Nemitz, D. Nilsson, S. Pryor, M. W. Gallagher, T. Vesala, U. Skiba, N. Brüggemann, S. Zechmeister-Boltenstern, J. Williams, C. O'Dowd, M. C. Facchini, G. de Leeuw, A. Flossman, N. Chaumerliac, and J. W. Erisman. 2009. Atmospheric composition change: Ecosystems–Atmosphere interactions. Atmospheric Environment 43:5193-5267.

Ecosystem Atmosphere Interactions

1. Watson, A. and J. Lovelock. 1983. Biological homeostasis of the global environment: the parable of Daisyworld. Tellus 35b:286-289.

2. Odum, E. P. 1969. Strategy of Ecosystem Development. Science 164:262-270.

Ecosystem Structure and Function

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2. Reich, P. B., M. B. Walters, and D. S. Ellsworth. 1997. From tropics to tundra: Global convergence in plant functioning. PNAS 94:13730-13734.

3. Wright, I. J., P. B. Reich, M. Westoby, D. D. Ackerly, Z. Baruch, F. Bongers, J. Cavender-Bares, F. A. Chapin, J. H. C. Cornelissen, M. Diemer, J. Flexas, E. Garnier, P. K. Groom, J. Gulias, K. Hikosaka, B. B. Lamont, T. Lee, W. Lee, C. Lusk, J. J. Midgley, M.-L. Nava, Ü. Niinemets, J. Oleksyn, N. Osada, H. Poorter, P. Poot, L. Prior, V. I. Pyankov, C. Roumet, S. C. Thomas, M. G. Tjoelker, E. J. Veneklaas, and R. Villar. 2004. The worldwide leaf economics spectrum. Nature 428:821-827.

Eddy Covariance Flux measurements

1. Moore, C. J. 1986. Frequency response corrections for eddy covariance systems. Boundary Layer Meteorology 37:17-35.

2. McMillen, R. T. 1988. An Eddy-Correlation Technique with Extended Applicability to Non-Simple Terrain. Boundary-Layer Meteorology 43:231-245.

3. Baldocchi, D. D., B. B. Hicks, and T. P. Meyers. 1988. Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology. 69:1331-1340.

4. Foken, T. and B. Wichura. 1996. Tools for quality assessment of surface-based flux measurements. Agricultural and Forest Meteorology 78:83-105.

5. Aubinet, M. et al., 2000. Estimates of the annual net carbon and water exchange of European forests: the EUROFLUX methodology. Advances in Ecological Research, 30: 113-175.

6. Baldocchi, D.D., 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems:past, present and future. Global Change Biol, 9: 479-492.

7. Lee, X.H., Massman, W.J., 2011. A Perspective on Thirty Years of the Webb, Pearman and Leuning Density Corrections. Boundary-Layer Meteorology 139, 37-59.

Energetics of crop production

1. Monteith, J. L. 1977. Climate and Efficiency of Crop Production in Britain. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 281:277-294.

2. Loomis, R. S. 1971. Agricultural Productivity. Annual Review of Plant Physiology 22:431-&.

3. Lemon, E., D. W. Stewart, and Shawcroft, R.W.. 1971. Sun work in a cornfield. Science 174:371

Energy Balance Closure

1. Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., 2002. Energy balance closure at FLUXNET sites. Agricultural and Forest Meteorology 113, 223-243.

2. Foken, T. 2008. The energy balance closure problem: An overview. Ecological Applications 18:1351-1367.

3. Leuning, R., van Gorsel, E., Massman, W.J., Isaac, P.R., 2012. Reflections on the surface energy imbalance problem. Agricultural and Forest Meteorology 156, 65-74.


1. Monteith, J. L. 1965. Evaporation and Environment. Pages 205-234 Symposium Society of Experimental Biology XIX.

2. Monteith, J. L. 1981. Evaporation and Surface-Temperature. Quarterly Journal of the Royal Meteorological Society 107:1-27.

3. Jarvis, P.G. and McNaughton, K.G., 1986. Stomatal Control of Transpiration - Scaling up from Leaf to Region. Advances in Ecological Research, 15: 1-49.

4. Raupach, M.R., 2001. Combination theory and equilibrium evaporation. Quarterly Journal of the Royal Meteorological Society, 127(574): 1149-1181.

5. Shuttleworth, W.J., 2007. Putting the 'vap' into evaporation. Hydrology and Earth System Sciences 11, 210-244.

6. Katul, G. G., R. Oren, S. Manzoni, C. Higgins, and M. B. Parlange. 2012. Evapotranspiration: A process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system.
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Flux Footprint

1. Schmid, H. P. 2002. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective. Agricultural and Forest Meteorology 113:159-183.

2. Vesala, T., U. Rannik, M. Leclerc, T. Foken, and K. Sabelfeld. 2004. Flux and concentration footprints. Agricultural and Forest Meteorology 127:111-116.

3. Hsieh, C. I. and G. Katul. 2009. The Lagrangian stochastic model for estimating footprint and water vapor fluxes over inhomogeneous surfaces. International Journal of Biometeorology 53:87-100.

Flux Processing, Partitioning and Gap filling

1. Falge, E., D. Baldocchi, R. Olson, P. Anthoni, M. Aubinet, C. Bernhofer, G. Burba, R. Ceulemans, R. Clement, and H. Dolman. 2001. Gap filling strategies for long term energy flux data sets. Agricultural and Forest Meteorology 107:71-77.

2. Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., Valentini, R., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11, 1424-1429.

3. Moffat, A.M., Papale, D., Reichstein, M., Hollinger, D.Y., Richardson, A.D., Barr, A.G., Beckstein, C., Braswell, B.H., Churkina, G., Desai, A.R., Falge, E., Gove, J.H., Heimann, M., Hui, D., Jarvis, A.J., Kattge, J., Noormets, A., Stauch, V.J., 2007. Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes. Agricultural and Forest Meteorology 147, 209-232.

Gross Primary Production from Remote Sensing, Regional and Global Upscaling

1. Running, S. W., D. D. Baldocchi, D. Turner, S. T. Gower, P. Bakwin, and K. Hibbard (1999), A global terrestrial monitoring network, scaling tower fluxes with ecosystem modeling and EOS satellite data, Remote Sensing of the Environment., 70, 108-127.

2. Anav, A., P. Friedlingstein, C. Beer, P. Ciais, A. Harper, C. Jones, G. Murray-Tortarolo, D. Papale, N. C. Parazoo, P. Peylin, S. Piao, S. Sitch, N. Viovy, A. Wiltshire, and M. Zhao. 2015. Spatiotemporal patterns of terrestrial gross primary production: A review. Reviews of Geophysics: doi 10.1002/2015RG000483.

3. Xiao, X., C. Jin, and J. Dong. 2014. Gross Primary Production of Terrestrial Vegetation. Pages 127-148 in J. M. Hanes, editor. Biophysical Applications of Satellite Remote Sensing. Springer Berlin Heidelberg.

4. Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, M.A., Baldocchi, D., Bonan, G.B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K.W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F.I., Papale, D., 2010. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. Science 329, 834-838.

Hyperspectral remote sensing and surface Fluxes

1. Gamon, J. A., et al. (2011), SpecNet revisited: bridging flux and remote sensing communities, Canadian Journal of Remote Sensing, 36, S376-S390.

2. Ustin, S. L., D. A. Roberts, J. A. Gamon, G. P. Asner, and R. O. Green. 2004. Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54:523-534.

3. Porcar-Castell, A., E. Tyystjarvi, J. Atherton, C. van der Tol, J. Flexas, E. E. Pfundel, J. Moreno, C. Frankenberg, and J. A. Berry. 2014. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany 65:4065-4095.


1. Wyngaard, J. C. 1981. Cup, Propeller, Vane, and Sonic Anemometers in Turbulence Research. Annual Review of Fluid Mechanics 13:399-423.

2. Werle, P., F. Slemr, K. Maurer, R. Kormann, R. Mücke, and B. Jänker. 2002. Near- and mid-infrared laser-optical sensors for gas analysis. Optics and Lasers in Engineering 37:101-114.

3. Long, S. P., P. K. Farage, and R. L. Garcia. 1996. Measurement of leaf and canopy photosynthetic CO2 exchange in the field. Journal of Experimental Botany 47:1629-1642.

Land-Atmosphere-Climate Interactions

1. Dickinson, R. E. 1983. Land surface processes and climate-surface albedos and energy balance. Advances in Geophysics 25:305-353.

2. Sellers, P.J. et al., 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 275(5299): 502-509.

3. Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Levis, X. B. Zeng, Y. J. Dai, R. E. Dickinson, and Z. L. Yang. 2002. The land surface climatology of the community land model coupled to the NCAR community climate model. Journal of Climate 15:3123-3149.

4. Bonan, G. B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444-1449.

5. Jackson, R. B., J. T. Randerson, J. G. Canadell, R. G. Anderson, R. Avissar, D. D. Baldocchi, G. B. Bonan, K. Caldeira, N. S. Diffenbaugh, C. B. Field, B. A. Hungate, E. G. Jobb, Protecting climate with forests, Environmental Research Letters, 3, 4, 2008

6. Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. S. Chapin, M. T. Coe, G. C. Daily, H. K. Gibbs, J. H. Helkowski, T. Holloway, E. A. Howard, C. J. Kucharik, C. Monfreda, J. A. Patz, I. C. Prentice, N. Ramankutty, and P. K. Snyder. 2005. Global consequences of land use. Science 309:570-574.

Leaf Area Index and Canopy Structure

1. Wilson, J. W. 1965. Stand Structure and Light Penetration. I. Analysis by Point Quadrats. Journal of Applied Ecology 2:383-390.

2. Lang, A. R. G. 1987. Simplified estimate of leaf area index from transmittance of the sun's beam. Agricultural and Forest Meteorology 41:179-186.

3. Chen, J.M., 1996. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology, 80(2-4): 135-163.

4. Lefsky, M. A., W. B. Cohen, G. Parker, and D. J. Harding. 2002. Lidar remote sensing for ecosystem studies. Bioscience 52:19-30.

5. Jonckheere, I. et al., 2004. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1-2): 19-35.

6. Ryu, Y., Sonnentag, O., Nilson, T., Vargas, R., Kobayashi, H., Wenk, R., Baldocchi, D.D., 2010. How to quantify tree leaf area index in an open savanna ecosystem: A multi-instrument and multi-model approach. Agricultural and Forest Meteorology 150, 63-76.

Leaf Boundary Layers

1. Leuning, R. 1983. Transport of Gases into Leaves. Plant Cell and Environment 6:181-194

2. Schuepp, P., 1993. Tansley Review No. 59. Leaf Boundary Layers. New Phytologist 125, 477-507.

Leaf Energy Balance

1. Paw U, K. T. and W. Gao. 1988. Applications of solutions to non-linear energy budget equations. Agricultural and Forest Meteorology 43:121-145.

2. Leuning, R. 1989. Leaf Energy Balances - Developments and Applications. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 324:191-206.

Leaf photosynthesis/transpiration/stomatal conductance models
1. Jarvis, P. G. 1976. Interpretation of Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in Field. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 273:593-610.

2. Farquhar, G. D., S. V. Caemmerer, and J. A. Berry. 1980. A Biochemical-Model of Photosynthetic Co2 Assimilation in Leaves of C-3 Species. Planta 149:78-90.

3. Farquhar, G.D. and Sharkey, T.D., 1982. Stomatal Conductance and Photosynthesis. Annual Review of Plant Physiology and Plant Molecular Biology, 33: 317-345.

4. Collatz, G.J., Ball, J.T., Grivet, C. and Berry, J.A., 1991. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology, 54(2-4): 107-136.

5. Leuning, R., 1995. A Critical-Appraisal of a Combined Stomatal-Photosynthesis Model for C-3 Plants. Plant Cell and Environment, 18(4): 339-355.

Leaf-Canopy Modeling, Carbon, Water and Heat Fluxes and Microclimate
1. DeWit, C. T. 1965. Photosynthesis of leaf canopies. Centre for Agricultural Publications and Documentation.

2. Duncan, W. G., R. S. Loomis, W. A. Williams, and R. Hanau. 1967. A Model for Simulating Photosynthesis in Plant Communities. Hilgardia 38:181-&.

3. Sinclair, T. R., C. E. Murphy, and K. R. Knoerr. 1976. Development and Evaluation of Simplified Models for Simulating Canopy Photosynthesis and Transpiration. Journal of Applied Ecology 13:813-829.

4. Goudriaan, J. 1977. Crop micrometeorology: a simulation study.

5. Norman, J.M., 1979. Modeling the complete crop canopy. In: B.J. Barfield and J.F. Gerber (Editor), Modification of the aerial environment of plants. , American Society of Agricultural Engineering, St. Joseph, MI, pp. 249

6. Raupach, M.R. and Finnigan, J.J., 1988. Single-Layer Models of Evaporation from Plant Canopies Are Incorrect but Useful, Whereas Multilayer Models Are Correct but Useless - Discuss. Australian Journal of Plant Physiology, 15(6): 705-716.

7. Baldocchi, D. D. and P. C. Harley. 1995. Scaling carbon dioxide and water vapor exchange from leaf to canopy in a deciduous forest: model testing and application. Plant, Cell and Environment 8:1157-1173.

8. dePury, D. G. G. and G. D. Farquhar. 1997. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell and Environment 20:537-557.

9. Amthor, J. S. 1994. Scaling Co2-Photosynthesis Relationships from the Leaf to the Canopy. Photosynthesis research 39:321-350.


1. Cicerone, R.J. and Oremland, R.S., 1988. Biogeochemical aspects of atmospheric methane. Global Biogeochem. Cycles, 2: 299-327.

2. Conrad, R., 1989. Control of methane production in terrestrial ecosystems. In: M.O. Andreae and D.S. Schimel (Editors), Exchange of Trace Gases between Terrestrial Ecosystems and the Atmosphere. Wiley, Chichester, UK, pp. 39-58.

3. Conrad, R., 1996. Soil microorganisms as controllers of atmospheric trace gases (H-2, CO, CH4, OCS, N2O, and NO). Microbiological Reviews, 60(4): 609-+.

4. Whalen, S.C., 2005. Biogeochemistry of Methane Exchange between Natural Wetlands and the Atmosphere. Environmental Engineering Science, 22(1): 73-94.

5. Bridgham, S. D., H. Cadillo-Quiroz, J. K. Keller, and Q. L. Zhuang. 2013. Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Global Change Biology 19:1325-1346.


1. Richardson, A. D., T. F. Keenan, M. Migliavacca, Y. Ryu, O. Sonnentag, and M. Toomey. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology 169:156-173.

2. Kramer, K., I. Leinonen, and D. Loustau. 2000. The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean ecosystems, an overview. International Journal of Biometeorology 44:67-75.

3. Menzel, A., T. H. Sparks, N. Estrella, E. Koch, A. Aasa, R. Ahas, K. Alm-KÜBler, P. Bissolli, O. G. BraslavskÁ, A. Briede, F. M. Chmielewski, Z. Crepinsek, Y. Curnel, Å. Dahl, C. Defila, A. Donnelly, Y. Filella, K. Jatczak, F. MÅGe, A. Mestre, Ø. Nordli, J. PeÑUelas, P. Pirinen, V. RemiŠOvÁ, H. Scheifinger, M. Striz, A. Susnik, A. J. H. Van Vliet, F.-E. Wielgolaski, S. Zach, and A. N. A. Zust. 2006. European phenological response to climate change matches the warming pattern. Global Change Biology 12:1969-1976.

Planetary Boundary Layer and Surface Flux Feedbacks
1. McNaughton, K.G. and Spriggs, T.W., 1986. A Mixed-Layer Model for Regional Evaporation. Boundary-Layer Meteorology, 34(3): 243-262.

2. Raupach, M.R., 1998. Influences of local feedbacks on land-air exchanges of energy and carbon. Global Change Biology, 4(5): 477-494.

3. Juang, J.-Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick. 2007. Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophysical Research Letters 34.

4. van Heerwaarden, C. C., J. Vilà-Guerau de Arellano, A. F. Moene, and A. A. M. Holtslag. 2009. Interactions between dry-air entrainment, surface evaporation and convective boundary-layer development. Quarterly Journal of the Royal Meteorological Society 135:1277-1291.

5. Juang, J. Y., G. G. Katul, A. Porporato, P. C. Stoy, M. S. Siqueira, M. Detto, H. S. Kim, and R. Oren. 2007. Eco-hydrological controls on summertime convective rainfall triggers. Global Change Biology 13:887-896.

6. Juang, J. Y., G. G. Katul, A. Porporato, P. C. Stoy, M. S. Siqueira, M. Detto, H. S. Kim, and R. Oren. 2007. Eco-hydrological controls on summertime convective rainfall triggers. Global Change Biology 13:887-896.

Radiative Transfer in vegetation (Phytoactinometry)

1. Lemeur, R. and Blad, B.L., 1974. A critical review of light models for estimating the shortwave radiation regime of plant canopies. Agricultural Meteorology, 14(1-2): 255-286.

2. Ross, J., 1976. Radiative Transfer in Plant Communities. In: J.L. Monteith (Editor), Vegetation and the Atmosphere, vol 1. Academic Press, London.

3. Ross, J. 1980. The Radiation Regime and Architecture of Plant Stands. Dr. W Junk, The Hague.

4. Myneni, R.B., Ross, J. and Asrar, G., 1989. A review on the theory of photon transport in leaf canopies. Agricultural and Forest Meteorology, 45(1-2): 1-153.

5. Ustin, S. L., S. Jacquemoud, and Y. Govaerts. 2001. Simulation of photon transport in a three-dimensional leaf: implications for photosynthesis. Plant Cell Environ 24:1095-1103.

6. Jacquemoud, S., W. Verhoef, F. Baret, C. Bacour, P. J. Zarco-Tejada, G. P. Asner, C. Francois, and S. L. Ustin. 2009. PROSPECT plus SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment 113:S56-S66.

Scientific Method

1. Tuomivaara, T., P. Hari, H. Rita, and R. Hakkinen. 1994. The guide-dog approach: a methodology for ecology. Department of Forest Ecology publications.

Soil Respiration

1. Raich, J., Schlesinger, W., 1992. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus 44B, 81 - 90.

2. Trumbore, S., 2009. Radiocarbon and Soil Carbon Dynamics. Annu. Rev. Earth Planet. Sci. Annual Reviews, Palo Alto, pp. 47-66.

3. Kuzyakov, Y., Gavrichkova, O., 2010. REVIEW: Time lag between photosynthesis and carbon dioxide efflux from soil: a review of mechanisms and controls. Global Change Biology 16, 3386-3406.

Soil Respiration, Flux-Gradient and Chamber measurements

1. Livingston, G.P. and Hutchinson, G.L., 1995. Enclosure-based measurement of trace gas exchange: Applications and sources of error. In: R.C. Harriss (Editor), Biogenic trace gases: Measuring emissions from soil and water. Blackwell Scientific, London, pp. 14-51.

2. Hutchinson, G.L. and Rochette, P., 2003. Non-Flow-Through Steady-State Chambers for Measuring Soil Respiration: Numerical Evaluation of Their Performance. Soil Sci Soc Am J, 67(1): 166-180.

3. Maier, M., and H. Schack-Kirchner (2014), Using the gradient method to determine soil gas flux: A review, Agricultural and Forest Meteorology, 192

Soil-Plant-Atmosphere Continuum

1. Shawcroft, R. W., E. R. Lemon, L. H. Allen, D. W. Stewart, and S. E. Jensen. 1974. SOIL-PLANT-ATMOSPHERE MODEL AND SOME OF ITS PREDICTIONS. Agricultural Meteorology 14:287-307.

2. Jarvis, P. G., W. R. N. Edwards, and H. Talbot. 1981. Models of Plant and Crop Water Use. Pages 151-193 in D. A. Rose and D. A. Charles-Edwards, editors. Mathematics and Plant Physiology. Academic Press, London.

3. Tuzet, A., A. Perrier, and R. Leuning. 2003. A coupled model of stomatal conductance, photosynthesis and transpiration. Plant Cell and Environment 26:1097-1116.

4. Katul, G., R. Leuning, and R. Oren. 2003. Relationship between plant hydraulic and biochemical properties derived from a steady-state coupled water and carbon transport model. Plant Cell Environ 26:339-350.

Soils, moisture, heat, CO2

1. Clapp, R.B. and Hornberger, G.M., 1978. Empirical Equations for Some Soil Hydraulic-Properties. Water Resources Research, 14(4): 601-604.

2. van Genuchten, M.T. and Sudicky, E.A., 1999. Recent advances in Vadose zone flow and transport modeling. In: M. Parlange and J.W. Hopmans (Editors), Vadose Zone Hydrology. Oxford Press, New York, pp. 155-193.

3. Simunek, J. and Suarez, D.L., 1993. Modeling of carbon-dioxide transport and production in soil. 1. Model development. Water Resources Research, 29: 487-497.

Stable isotopes

1. Bowling, D.R., Pataki, D.E., Randerson, J.T., 2008. Carbon isotopes in terrestrial ecosystem pools and CO2 fluxes. New Phytologist 178, 24-40.

2. Dawson, T.E., Mambelli, S., Plamboeck, A.H., Templer, P.H., P.Tu, K., 2002. Stable isotope in plant ecology. Annual Review Ecology Systematics 33, 507-559.

3. Griffis, T. J. (2013), Tracing the flow of carbon dioxide and water vapor between the biosphere and atmosphere: A review of optical isotope techniques and their application, Agricultural and Forest Meteorology, 174

Stomatal Optimization Models

1. Cowan, I. and G. Farquhar. 1977. Stomatal function in relation to leaf metabolism and environment. Symposium of the Society of Experimental Biology 31:471-505.

2. Hari, P., A. Makela, E. Korpilahti, and M. Holmberg. 1986. Optimal control of gas exchange. Tree Physiology 2:169-175.

3. Makela, A., F. Berninger, and P. Hari. 1996. Optimal Control of Gas Exchange during Drought: Theoretical Analysis. Annals of Botany 77:461-468.

4. Katul, G., S. Manzoni, S. Palmroth, and R. Oren. 2010. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration. Annals of Botany 105:431-442

Trace Gas Exchange, VOCs

1. Fuentes, J.D. et al., 2000. Biogenic hydrocarbons in the atmospheric boundary layer: A review. Bulletin of the American Meteorological Society, 81(7): 1537-1575.

2. Monson, R.K. and Holland, E.A., 2001. Biospheric trace gas fluxes and their control over tropospheric chemistry. Annual Review of Ecology and Systematics, 32: 547-+.

3. Sharkey, T.D. and Yeh, S., 2001. Isoprene emission from plants. Annual Review of Plant Physiology and Plant Molecular Biology, 52(1): 407-436.

4. Megonigal, J.P., Hines, M.E. and Visscher, P.T., 2003. Anaerobic Metabolism: Linkages to Trace Gases and Aerobic Processes. In: H.D. Holland and K.K. Turekian (Editors), Treatise on Geochemistry. Pergamon, Oxford, pp. 317-424.

5. Laothawornkitkul, J., J. E. Taylor, N. D. Paul, and C. N. Hewitt. 2009. Biogenic volatile organic compounds in the Earth system. The New phytologist 183:27-51.