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.

Biogeochemistry

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.
https://link.springer.com/article/10.1007%2FBF00119875?LI=true

3. Kaimal, J. C. and J. C. Wyngaard. 1990. The Kansas and Minnesota Experiments. Boundary-Layer Meteorology 50:31-47.
https://link.springer.com/article/10.1007/BF00120517

4. Wyngaard, J.C., 1992. Atmospheric-Turbulence. Annual Review of Fluid Mechanics, 24: 205-233.
http://www.annualreviews.org/doi/abs/10.1146/annurev.fl.24.010192.001225

5. Hogstrom, U. 1996. Review of some basic characteristics of the atmospheric surface layer. Boundary-Layer Meteorology 78:215-246.
https://link.springer.com/article/10.1007%2FBF00120937?LI=true

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.
https://link.springer.com/article/10.1007/BF00120518

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.
https://link.springer.com/article/10.1007/BF00259477

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.
http://www.annualreviews.org/doi/abs/10.1146/annurev.fl.13.010181.000525?journalCode=fluid

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.
https://link.springer.com/article/10.1007/BF00120941


CO2 Fluxes, Pioneering Studies


1. Monteith, J. L. and G. Szeicz. 1960. et. Quarterly Journal of the Royal Meteorological Society 86:205-214.
http://onlinelibrary.wiley.com/doi/10.1002/qj.49708636810/abstract


J2. Desjardins, R. 1974. Technique to Measure Co2 Exchange under Field Conditions. International Journal of Biometeorology 18:76-83.
https://link.springer.com/article/10.1007/BF01450667


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.
http://www.sciencedirect.com/science/article/pii/S1352231099004677

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.
http://www.sciencedirect.com/science/article/pii/S1352231099004677

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.
http://www.sciencedirect.com/science/article/pii/S1352231009006633


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

1. Van Bodegom, P. M., J. C. Douma, J. P. M. Witte, J. C. Ordoñez, R. P. Bartholomeus, and R. Aerts. 2012. Going beyond limitations of plant functional types when predicting global ecosystem-atmosphere fluxes: exploring the merits of traits-based approaches. Global Ecology and Biogeography 21:625-636.
http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2011.00717.x/abstract

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.
https://link.springer.com/article/10.1007%2FBF00128405?LI=true

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.
http://www.sciencedirect.com/science/article/pii/0168192395022481

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.
http://www.sciencedirect.com/science/article/pii/S0065250408600185

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-&.
http://www.annualreviews.org/doi/pdf/10.1146/annurev.pp.22.060171.002243

3. Lemon, E., D. W. Stewart, and Shawcroft, R.W.. 1971. Sun work in a cornfield. Science 174:371
http://science.sciencemag.org/content/174/4007/371


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.

Evaporation

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.
https://www.sciencedirect.com/science/article/pii/S0065250408601191

4. Raupach, M.R., 2001. Combination theory and equilibrium evaporation. Quarterly Journal of the Royal Meteorological Society, 127(574): 1149-1181.
http://onlinelibrary.wiley.com/doi/10.1002/qj.49712757402/full

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.
Reviews of Geophysics 50.

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.
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2005.001002.x/abstract

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.
https://link.springer.com/chapter/10.1007/978-3-642-25047-7_5

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.
http://www.tandfonline.com/doi/abs/10.5589/m10-067?journalCode=ujrs20

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.

Instruments

1. Wyngaard, J. C. 1981. Cup, Propeller, Vane, and Sonic Anemometers in Turbulence Research. Annual Review of Fluid Mechanics 13:399-423.
http://www.annualreviews.org/doi/abs/10.1146/annurev.fl.13.010181.002151?journalCode=fluid

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.
http://www.sciencedirect.com/science/article/pii/S0065268708601764

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.
https://www.jstor.org/stable/2401487?seq=1#page_scan_tab_contents

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.
http://www.sciencedirect.com/science/article/pii/0168192387900785

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
http://onlinelibrary.wiley.com/doi/10.1111/1365-3040.ep11587617/abstract

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.
http://www.sciencedirect.com/science/article/pii/0168192388900871

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.
http://www.annualreviews.org/doi/abs/10.1146/annurev.pp.33.060182.001533?journalCode=arplant.1

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.
http://www.sciencedirect.com/science/article/pii/0168192391900028

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.
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-3040.1995.tb00370.x/abstract


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.
http://www.jstor.org/stable/2402257?seq=1#page_scan_tab_contents

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
https://www.abebooks.com/9780916150150/Modification-Aerial-Environment-Plants-ASAE-0916150151/plp

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.
http://www.publish.csiro.au/fp/PP9880705

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.
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-3040.1995.tb00625.x/abstract

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.
https://link.springer.com/article/10.1007/BF00014590

Methane

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.
https://www.amazon.com/Exchange-Terrestrial-Ecosystems-Atmosphere-Workshop/dp/0471925519

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.
http://online.liebertpub.com/doi/pdf/10.1089/ees.2005.22.73

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.

Phenology

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.
https://link.springer.com/article/10.1007%2FBF00122381?LI=true

2. Raupach, M.R., 1998. Influences of local feedbacks on land-air exchanges of energy and carbon. Global Change Biology, 4(5): 477-494.
http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2486.1998.t01-1-00155.x/abstract

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.
http://www.sciencedirect.com/science/article/pii/0002157174900247

2. Ross, J., 1976. Radiative Transfer in Plant Communities. In: J.L. Monteith (Editor), Vegetation and the Atmosphere, vol 1. Academic Press, London.
https://www.cambridge.org/core/journals/experimental-agriculture/article/vegetation-and-the-atmosphere-ed-monteith-j-l-london-academic-press-1975-vol-1-principles-pp-298-10-vol-2-case-studies-pp-459-15/04F40452ABFD73983036C698417851F1
(https://jrls2017.to.ee/download/m59a7aaf73b44f)

3. Ross, J. 1980. The Radiation Regime and Architecture of Plant Stands. Dr. W Junk, The Hague. http://link.springer.com/book/10.1007%2F978-94-009-8647-3

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.
http://www.sciencedirect.com/science/article/pii/0168192389900026

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.
http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2010.02179.x/abstract

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.
https://www.amazon.com/Vadose-Zone-Hydrology-Cutting-Disciplines/dp/0195109902

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
http://www.sciencedirect.com/science/article/pii/S0168192313000373


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.
https://academic.oup.com/treephys/article-abstract/2/1-2-3/169/1623649

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.

Stealing from Dennis Baldocchi ;-)

Dennis Baldocchi (GS) is one of the major contributors to studies of plant-atmosphere interactions.  Recently he also published a book on this topic, entitled Terrestrial Biosphere-Atmosphere Fluxes with Russell Monson (nice videos at his site).  I think the book is "must have" for hydrologists, even if after my first quick reading I realised that the approaches to some (more hydrological) topics perpetuate the "standard model" and interpretations of turbulence (but, it is a textbook!): full of up-todate-information  indeed^1.

 However, he also have the merit to put a lot of material in his group website, including slides and lectures. You can find them here:

• Plant-Ecosystem-Atmosphere Interactions (this apparently contains the material of the book)
• Advanced topics in biometeorology and micrometeorology 
• Ecosystem Ecology
• The Biosphere

Also available are some conferences talks which include videos. For instance:

• Estimating Evaporation everywhere

• Measuring and Interpreting Fluxes of Trace Gases Across Local and Global Networks:

• Scaling Information on 'Biosphere Breathing' from Chloroplast to the Globe. Microsoft eScience Workshop, Oct 2010, Berkeley, C
• Defend Science Presentation on Climate Change, April, 2006 Berkeley

^1 - Do not try to lear thermodynamics from here (look here instead). At least from chapter 2. Nothing especially wrong indeed, but very far from the clearness of my favorite books.

STRADIVARI Project II: Atmospheric Boundary Layer (ABL) Dynamics

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The equations modeling the ABL are extensively covered under various aspects (Stull, 1988; Yin and Porporato, 2022, Honnert at al., 2020; Canché-Cab et al., 2024) and implemented in various software. However, the interaction between soil moisture, land surface fluxes, and convection initiation, leading to rainfall, remains challenging (Dirmeyer et al., 2006; Koster et al., 2004; Santanello et al., 2007). This stems from the complexity of the Soil-Plant-Atmosphere Continuum interactions across multiple spatial and temporal scales. The ABL, influenced by mechanical and thermal turbulence, links surface processes with synoptic phenomena, while plant physiology regulates sensible and latent heat fluxes. These fluxes affect the energy needed for convection and ABL growth, as well as the transfer of water vapor from the root zone to the atmosphere and determine the Lifting Condensation Level (LCL) (Siqueira et al., 2009; Cuxart et al., 2020), and its intersection with the ABL, critical for rainfall initiation. The height of this crossing, visible as cloud base, highlights the soil-plant system's control over hydrological self-regulation, which implies that drier soils may increase sensible heat flux, enhancing convection and raising ABL depths, thus elevating the likelihood of ABL-LCL crossing and rainfall, an example of negative feedback. Conversely, reduced latent heat flux can lower ABL water vapor concentration, raising the LCL above the ABL, leading to sustained dry conditions, exemplifying positive feedback.

Stradivari's Hellier

Beyond the conceptual framework, a critical implementation challenge emerges from the fundamental mismatch between hydrological and atmospheric process scales and their characteristic timescales. Figure below illustrates the multi-scale coupling mechanisms central to STRADIVARI, adapted from Miralles et al. (2025). 

Vegetation controls surface energy, water, and carbon fluxes at local scales through processes governed by soil moisture dynamics and groundwater table fluctuations. These surface controls propagate to the atmosphere via turbulent fluxes, driving convective and mechanical instability that alters the diurnal evolution of the atmospheric boundary layer (ABL). The ABL growth dynamics regulate moisture and heat entrainment processes, determining the lifting condensation level (LCL) and subsequent convective cloud formation—the critical link between local surface processes and regional precipitation patterns. While contemporary atmospheric models can resolve these multi-scale interactions, their representation of surface phenomena remains heavily parameterized, obscuring the mechanistic coupling that STRADIVARI seeks to capture. 

Critical Gap: Current Land Surface Models rely predominantly on bulk aerodynamic formulations and Monin-Obukhov Similarity Theory parameterizations that treat the ABL as a prescribed boundary condition rather than solving governing transport equations (Santanello et al., 2018). PLUMBER-2 analysis shows systematic LSM failures in water-limited regions where soil-plant coupling becomes critical, while TRENDY simulations reveal persistent discrepancies in vegetation-atmosphere CO2 exchange (Friedlingstein et al., 2023). These failures stem from models using parameters as "garbage collectors" (sensu Beven, 2006) rather than physically meaningful quantities corresponding to independently measurable soil, plant, and atmospheric properties. The most widespread conceptual framework in treating these issues reduces the SPAC complexity to an electrical circuit analogy (Monson & Baldocchi, 2015; Bonan, 2019), conflating aerodynamic transport, physiological regulation, and soil physics into parameterized "resistances" that obscure actual mechanisms at play. While full resolution of precipitation recycling mechanisms remains beyond current capabilities, the hydrological modeling community lacks tools to explore even minimal surface-atmosphere processes complexity.

STRADIVARI innovation: Rather than claiming to resolve precipitation recycling, STRADIVARI deconstructs the resistance framework by resolving atmospheric turbulence through governing equations rather than parameterizations. This allows temperature, wind velocity, and humidity profiles to emerge naturally from ABL physics, creating boundary conditions at leaf and soil surfaces that couple directly with plant hydraulic solutions while isolating plant conductance as a purely physiological phenomenon rather than a catch-all for system-level behaviors we fail to resolve mechanistically. The approach bridges the gap between hydrological and micrometeorological communities, providing tools for collaborative investigation of coupled processes at scales where both communities can contribute observational constraints and process understanding. STRADIVARI addresses this challenge by starting the implementation of hierarchical ABL modeling framework with four levels of increasing complexity: The framework implements a hierarchical ABL approach: (1) Wood (2000) statistical-dynamical corrections for basic terrain effects, (2) spectral methods for turbulent scalar transport following Katul et al. (2011) and Poggi et al. (2004), (3) multi-scale atmospheric boundary layer modeling incorporating canopy-atmosphere interactions (Finnigan et al., 2009; Brunet & Irvine, 2000), and (4) machine learning-enhanced parameterizations for complex terrain effects (Cheng et al., 2021; Rasp et al., 2018). Validation against Alpine meteorological stations determines minimum complexity required for meaningful surface-atmosphere coupling.

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References - Atmospheric Boundary Layer

• Anderson, M. C., et al. 2003. "A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales." Remote Sensing of Environment 90(4): 521-531.
• Best, M. J., et al. 2015. "The Plumbing of Land Surface Models: Benchmarking Model Performance." Journal of Hydrometeorology 16(3): 1425-42.
• Beven, Keith. 2006. "A Manifesto for the Equifinality Thesis." Journal of Hydrology 320(1-2): 18-36.
• Bonan, Gordon. 2019. Climate Change and Terrestrial Ecosystem Modeling. Cambridge University Press.
• Brunet, Y., and M. R. Irvine. 2000. "The Control of Coherent Eddies in Vegetation Canopies." Boundary-Layer Meteorology 94(1): 139-63.
• Canché-Cab, Linda, et al. 2024. "The Atmospheric Boundary Layer: A Review of Current Challenges and a New Generation of Machine Learning Techniques." Artificial Intelligence Review 57(12).
• Cheng, Y. 2021. "Machine Learning Methods Turbulence Modeling Atmospheric Boundary Layer Flows." Physics Fluids 33.
• Cuxart, Joan, et al. 2020. "Current Challenges in Evapotranspiration Determination, GEWEX News."
• Dirmeyer, Paul A., et al. 2006. "GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface." Bulletin of the American Meteorological Society 87(10): 1381-98.
• Finnigan, John J., Roger H. Shaw, and Edward G. Patton. 2009. "Turbulence Structure above a Vegetation Canopy." Journal of Fluid Mechanics 637: 387-424.
• Foken, Thomas. 2006. "50 Years of the Monin–Obukhov Similarity Theory." Boundary-Layer Meteorology 119(3): 431-47.
• Friedlingstein, Pierre, et al. 2023. "Global Carbon Budget 2023."
• Honnert, Rachel, et al. 2020. "The Atmospheric Boundary Layer and the 'Gray Zone' of Turbulence: A Critical Review." Journal of Geophysical Research Atmospheres 125(13).
• Jiménez, Pedro A., et al. 2012. "A Revised Scheme for the WRF Surface Layer Formulation." Monthly Weather Review 140(3): 898-918.
• Katul, Gabriel G., et al. 2011. "A mixing-layer theory for flow resistance in shallow streams." Water Resources Research 47(11).
• Koster, Randal D., et al. 2004. "Regions of Strong Coupling between Soil Moisture and Precipitation." Science 305(5687): 1138-40.
• Lawrence, David M., et al. 2019. "The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty." Journal of Advances in Modeling Earth Systems 11(12): 4245-87.
• Miralles, Diego G., et al. 2025. "Vegetation-Climate Feedbacks across Scales." Annals of the New York Academy of Sciences 1544(1): 27-41.
• Monson, Russell, and Dennis Baldocchi. 2015. Terrestrial Biosphere-Atmosphere Fluxes. Cambridge University Press.
• Poggi, D., G. G. Katul, and J. D. Albertson. 2004. "A Note on the Contribution of Dispersive Fluxes to Momentum Transfer within Canopies." Boundary-Layer Meteorology 111(3): 615-21.
• Rasp, Stephan, Michael S. Pritchard, and Pierre Gentine. 2018. "Deep Learning to Represent Subgrid Processes in Climate Models." Proceedings of the National Academy of Sciences 115(39): 9684-89.
• Santanello, Joseph A., et al. 2018. "Land–Atmosphere Interactions: The LoCo Perspective." Bulletin of the American Meteorological Society 99(6): 1253-72.
• Siqueira, Mario, Gabriel Katul, and Amilcare Porporato. 2009. "Soil Moisture Feedbacks on Convection Triggers." Journal of Hydrometeorology 10(1): 96-112.
• Stull, R. B. 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers.
• Wood, Eric F., et al. 2011. "Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water." Water Resources Research 47(5).
• Yin, Jin, and Amilcare Porporato. 2022. Ecohydrology: Dynamics of Life and Water in the Critical Zone. Cambridge University Press.

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Thinking on Optimal Theories in Hydrology

In Nature we have to deal with forms that we somewhat recognize and distinguish from the rest (Thom, R., 1975). These forms (shapes), as we know since D'Arcy Thomson (1917, see also Ball, 2013), have functionalities that are shaped by some dynamics that we struggle since then (and maybe before) to find and understand. Because forms and functional forms are so ubiquitous we are brought to think that there is some design to produce them (Zanella,  G. Sopra una conchiglia fossile nel mio studio, About a Fossil shell in my room) but this is from the science point of view an error of perspective (Monod, J., 1970) and an undecidable question. K. Lorenz, on the other side warns often that evolution does not produce always functional forms (or behaviours) but let grow also unnecessary “neutral” stuff which is not useful nor a handicap. The discussion can grow very general and philosophical, and pursuing it could be the topic of another post.

We are interested here to grasp the grow of forms and patterns by means of methods that are proper of mathematics, physics and chemistry (on the illuminating example of E. Schroedinger, What is life ?, 1944). It is to be remarked here that to appeal to hard sciences does not mean a priori a reductionist approach, in which the systems are pruned apart and loose their quality. This is especially true for living systems but also for complex Earth science cycles as the hydrological one, and we aim to keep the systems and their dynamics together, emphasizing the interactions that makes forms to emerge at various scales. 

The general but qualitative understanding of the physics and mathematics related to these problems, is that, deprived of its teleology to be investigated elsewhere and eventually, behind forms and patterns there is some “optimality principle” or, stated in other words, that Newtonian mechanics, physics and chemistry of complex systems evolve solutions that require spatio-temporal structures and patterns. The creation of “forms” is extended not only to the immediately appearance that we perceive with our (highly biased by evolution, though) senses, but are minimal or maximal solutions when observed from the point of view of a certain variable. Unfortunately we are not really able to move, based on the present knowledge, from the basic principles to the appropriate laws of structures interactions, just through derivation of statistical law or integration over degrees of freedom.

The theories of optimality assume among the driving forces moving the dynamics of a system in Nature there are some extensive quantities like, for instance, entropy that are pushed to increase. For instance entropy, because,  entropy of a closed thermodynamic system is shown to grow. In fact this is a principle of Equilibrium Thermodynamics. However,  in Non Equilibrium Thermodynamics  it can be derived, at least  for some simple systems, i.e. is not anymore a principle,  (e.g. see the Thermodynamics derivation of evaporation in Monsoon and Baldocchi, 2014).

Moreover, in open systems, entropy of a subsystem can decrease, so in such context it cannot be used to understand the asymptotic state of the system. Fortunately,  the rate of production of entropy could. As a matter of fact, in non-equilibrium, non-autonomous, open systems, asymptotic states could not be so relevant, and instead what matters are “intermediate asymptotics” as pointed out for fluid dynamics by Barenblatt, 1996 and, before Baranblatt, by Ilya Prigogine work on dissipative structures, i.e. structures that are identified because, they represents steady (or at least somewhat persisting) states of a (thermo)dynamic system out of equilibrium.

It can happen, for instance, that intermediate asymptotics are obtained by minimizing energy dissipation, i.e. the quantity of energy that is transformed into heat (or non-usable energy). This is the case of river networks (Rodriguez-Iturbe et al., 1992) and implies that the maximum entropy of the comprehensive system is obtained as slowly as possible, meaning that the available energy is used at its best for producing work.

This behavior seems logically correct for living systems, and seems justified by evolution and selection: the most efficient survives and reproduces (Virgo, 2001) but is intriguing the fact that river networks are not living systems and anyway they obey such a type law.  Therefore it seems implied that some general dynamic law is the origin of all (e.g. Prigogine, 1945). The mathematical problem is all but trivial, and such minimization (or maximization) problems has received recently the attention of the Field prize (e.g. see the work of Alessio Figalli), but, forgive me if I dare to say that is simpler than the physical problem to understand why certain equations that bring to optimisation problems are valid. Anyway, optimisation problems are mathematically obtained by expressing the large scale dynamics of the (river) networks as a functional to be optimised (actually expressed in Rodriguez-Iturbe et al work in discrete form). In optimal channel network (by Rodriguez-Iturbe et al, 1992), this functional is, obtained by using the basic Newton law (in appropriate form) and some additional hypothesis, derived from observations or educated guesses. One would like to obtain it without heuristics but this seems out of our present possibilities notwithstanding the large literature on optimal transportation networks (e.g Barabasi, Network science, 2018). It should be noted, however, that the case of networks is produced by some twofold optimality: a tradeoff between maintaining an optimal transport and optimally maintaining the the structure that conveys optimally the transported.

An epitome of structure analysis is also the Navier-Stokes equation and turbulence. Here we have the formation of structures that dissipate kinetic energy, the vortexes, actually of all dimension from the scale of observation to the dissipation scale.
We recognize these patterns as preferential flow of energy, or, in the case of turbulence as a form of quasi-random disorganization whose structure is particularly evident in the 4/3 Kolmogorov law. In this case, the equation is directly the Newton law (plus Newton’s hypothesis on stresses), so, despite the complexity of the outcomes, the physics is very directly reduced to mathematics which, BTW, in this case is unable to completely solve the problem.

A third piece of the elephant is the water flow in vegetation. It happens to maintain the temperature of the plant system in a range acceptable for plants to comfortably survive subject to weather and climate forcings and at the same time, not secondarily or maybe primarily, fix $CO_2$ to build their structure and carbohydrates trough photosynthesis. So plants need to optimise their fit to varying weathers for maximizing their production. A plant can be decomposed in its main structural parts: root, steams, leaves and their functional counterparts, xylem and phloem (e.g. Stroock et al., 2014), each part can be disassembled back to the singles cells and chloroplasts. But after the reductionist operation, plants overall functioning remain partially elusive and resistant to quantitative treatment if we do not treat plant’s part as a system (with a lot of osmoregulatory subsystems, e.g. Perri et al., 2019) and ecosystems where plants of various species interacts in competition, cooperation and coopetition for light, water, nitrogen, or phosphorus. The structure of plant and ecosystems and their interactions, evidently does not violate basic physical laws, their functioning respect mass and energy conservation, and momentum (of water, for instance) is peculiarly dissipated to obtain the scope of water supply to very high height and very negative pressures (up to -30 MPa). Optimization here involves various aspects, including the scaling of xylem dimensions (e.g. Olson et al., 2014), to obtain optimal sapping performances. Besides, recent work by Hildebrandt et al. (2016) shows evidence of optimal use of water when the energy budget is properly accounted for.

Soils are not a passive medium, first because themselves contain a lot of aggregated microbic biota (so far mainly neglected in hydrological analyses) and secondly because it is the environment where soil-roots interact. Also in soil, even according to more traditional views, there are optimisation processes when, during evaporation, the rate of water uptake is maintained constant up to critical soil water content (stage I evaporation) after which, evaporation strongly decreases (stage II evaporation). This is provided by a series of feedbacks among small and large soil pores, viscous forces and cohesion processes still not well understood (but kind of well described in Lehman et al., 2008).

Hydrology in the critical zone (CZ, the elephant) is therefore overwhelming difficult because it is the compendium of optimisation processes regulated by networks, vegetation, NS equations and water flowing in soil. According to what we focus on, we can isolate various non trivial dynamics. However the challenge is to model their comprehensive interplay for which still we do not have appropriate mathematics, observations and tools.

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References

• Ball, Philip (7 February 2013). "In retrospect: On Growth and Form". Nature. 494 (7435): 32–33. doi:10.1038/494032a.
• Barabási, Albert-László (2018). Network science. Cambridge University Press. ISBN 978-1107076266.
• Barenblatt, G.I. (1996), Scaling, self-similarity, and intermediate asymptotics, Cambridge University Press, 1996
• D’Arcy W. Thomson (2017), On growth and forms, Cambridge university Press
• Hildebrandt, A., Kleidon, A., & Bechmann, M. (2016). A thermodynamic formulation of root water uptake. Hydrology and Earth System Sciences, 20(8), 3441–3454. http://doi.org/10.5194/hess-20-3441-2016
• Lehmann, P., Assouline, S., & Or, D. (2008). Characteristic lengths affecting evaporative drying of porous media. Physical Review E, 77(5), 354–16. http://doi.org/10.1103/PhysRevE.77.056309
• Chance and Necessity: An Essay on the Natural Philosophy of Modern Biology by Jacques Monod, New York, Alfred A. Knopf, 1971, ISBN 0-394-46615-2
• Olson M.E., AnfodilloT., Rosell J.A., Petit G., Crivellaro A., Isnard S., León-Gómez C., Aalvarado CardenasL.O., Castorena M. (2014). Universal hydraulics of the flowering plants: Vessel diameter scales with stem length across angiosperm lineages, habits and climates. Ecology Letters 17 (8), 988–997.
• Perri, S., Katul, G. G., & Molini, A. (2019). Xylem‐ phloem hydraulic coupling explains multiple osmoregulatory responses to salt‐stress. New Phytologist, nph.16072–51. http://doi.org/10.1111/nph.16072
• Prigogine, Ilya (1945). "Modération et transformations irréversibles des systèmes ouverts". Bulletin de la Classe des Sciences, Académie Royale de Belgique. 31: 600–606
• Rodriguez-Iturbe I. , Rinaldo R., R. Rigon, Bras R.L., Marani A. and Ijjasz- Vasquez E.J. (1992), Energy dissipation, runoff production, and the 3-dimensional structure of river basin, Water Resources Research, (28)4, 1095-1103.
• Schroedinger, E. (1944), What is Life ?, Cambridge University Press
• Stroock, A. D., Pagay, V. V., Zwieniecki, M. A., & Michele Holbrook, N. (2014). The Physicochemical Hydrodynamics of Vascular Plants. Annu. Rev. Fluid Mech., 46(1), 615–642. http://doi.org/10.1146/annurev-fluid-010313-141411
• Thom, R. (1975), Structural stability and morphogenesis, An Outline of a General Theory of Models, Addison-Wesley
• Virgo, N. (2011, March 23). Thermodynamics and structure of Living Systems, Ph.D. dissertation, University of Sussex

Material for the GEOframe Winter School - Evaporation and Transpiration

Evapotranspiration accounts for most of fifty percent of the terrestrial hydrological cycle. We illustrate here some ways to estimate it with the tools offered by the GEOframe system. 

• Momentum and water vapor transport in atmosphere (YouTube video)
• Penman-Monteith according to Shymanski and Or (PMSO- YouTube video)
• PMSO with surfaces temperatures feedbacks (YouTube Video)
• Evaporation from soil and Traspiration mechanisms (YouTube Video)
• From leaves to canopies (YouTube Video)
• Documentation of GEOframe-NewAge component
• Jupyter notebook with exercises
• Live YouTube video on the sim files and Jupyter notebooks

Out of schedule (for Chhay)

• Budyko curves derivation

Exercises

• You can follow this Jupyter notebook

References

Primarily for historic papers browse to the list by Dennis Baldocchi

• Haghighi, E., Shahraeeni, E., Lehmann, P., & Or, D. (2013). Evaporation rates across a convective air boundary layer are dominated by diffusion. Water Resources Research, 49(3), 1602–1610. http://doi.org/10.1002/wrcr.20166
• Lehmann, P., & Or, D. (2013). Effect of wetness patchiness on evaporation dynamics from drying porous surfaces. Water Resources Research. http://doi.org/10.1002/2013WR013737
• Or, D., Lehmann, P., & Shahraeeni, E. (2013). Advances in soil evaporation Physics - A review. Vadose Zone Journal.
• Schymanski, S. J., & Or, D. (2017). Leaf-scale experiments reveal an important omission in the Penman–Monteith equation. Hydrology and Earth System Sciences, 21(2), 685–706. http://doi.org/10.5194/hess-21-685-2017
• Schymanski, S. J., Or, D., & Zwieniecki, M. (2013). Stomatal Control and Leaf Thermal and Hydraulic Capacitances under Rapid Environmental Fluctuations. PLoS ONE, 8(1), e54231–16. http://doi.org/10.1371/journal.pone.0054231
Stroock, A. D., Pagay, V. V., Zwieniecki, M. A., & Michele Holbrook, N. (2014). The Physicochemical Hydrodynamics of Vascular Plants. Annual Rev. Fluid Mech., 46(1), 615–642. http://doi.org/10.1146/annurev-fluid-010313-141411

See also the discussions here:

• The vapour budget
• Soil-Plants fluxes

And the mini-series: 

• The man who planted trees.

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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.