Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-525-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-525-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Feb 2022
Research article |
| 01 Feb 2022
| 01 Feb 2022
Does maximization of net carbon profit enable the prediction of vegetation behaviour in savanna sites along a precipitation gradient?
Catchment and Ecohydrology Group (CAT), Environmental Research and Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Jason Beringer
School of Agriculture and Environment, The University of Western Australia, Crawley, WA, 6909, Australia
Lindsay B. Hutley
Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT, 0909, Australia
Stanislaus J. Schymanski
Catchment and Ecohydrology Group (CAT), Environmental Research and Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Remko C. Nijzink and Stanislaus J. Schymanski
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Most catchments plot close to the empirical Budyko curve, which allows for estimating the long-term mean annual evaporation and runoff. We found that a model that optimizes vegetation properties in response to changes in precipitation leads it to converge to a single curve. In contrast, models that assume no changes in vegetation start to deviate from a single curve. This implies that vegetation has a stabilizing role, bringing catchments back to equilibrium after changes in climate.
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Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
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We investigated if and how roots of maize plants respond to multiple abrupt changes in soil moisture. We measured root lengths using a magnetic resonance imaging technique and calculated changes in growth rates after applying water pulses. The root growth rates increased in wetted soil layers within 48 hours and decreased in non-wetted layers, indicating fast adaptation of the root systems to moisture changes. Our findings could improve irrigation management and vegetation models.
Remko C. Nijzink and Stanislaus J. Schymanski
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Most catchments plot close to the empirical Budyko curve, which allows for estimating the long-term mean annual evaporation and runoff. We found that a model that optimizes vegetation properties in response to changes in precipitation leads it to converge to a single curve. In contrast, models that assume no changes in vegetation start to deviate from a single curve. This implies that vegetation has a stabilizing role, bringing catchments back to equilibrium after changes in climate.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
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Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 4575–4585, https://doi.org/10.5194/hess-26-4575-2022, https://doi.org/10.5194/hess-26-4575-2022, 2022
Short summary
Short summary
Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
César Dionisio Jiménez-Rodríguez, Mauro Sulis, and Stanislaus Schymanski
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Vegetation relies on soil water reservoirs during dry periods. However, when this source is depleted, the plants may access water stored deeper in the rocks. This rock moisture contribution is usually omitted in large-scale models, which affects modeled plant water use during dry periods. Our study illustrates that including this additional source of water in the Community Land Model improves the model's ability to reproduce observed plant water use at seasonally dry sites.
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Impactful open, accessible, reusable, and reproducible hydrologic research practices are being embraced by individuals and the community, but taking the plunge can seem overwhelming. We present the Open Hydrology Principles and Practical Guide to help hydrologists move toward open science, research, and education. We discuss the benefits and how hydrologists can overcome common challenges. We encourage all hydrologists to join the open science community (https://open-hydrology.github.io).
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Geosci. Model Dev., 15, 883–900, https://doi.org/10.5194/gmd-15-883-2022, https://doi.org/10.5194/gmd-15-883-2022, 2022
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The Vegetation Optimality Model (VOM) is a coupled water–vegetation model that predicts vegetation properties rather than determines them based on observations. A range of updates to previous applications of the VOM has been made for increased generality and improved comparability with conventional models. This showed that there is a large effect on the simulated water and carbon fluxes caused by the assumption of deep groundwater tables and updated soil profiles in the model.
Atbin Mahabbati, Jason Beringer, Matthias Leopold, Ian McHugh, James Cleverly, Peter Isaac, and Azizallah Izady
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We reviewed eight algorithms to estimate missing values of environmental drivers and three major fluxes in eddy covariance time series. Overall, machine-learning algorithms showed superiority over the rest. Among the top three models (feed-forward neural networks, eXtreme Gradient Boost, and random forest algorithms), the latter showed the most solid performance in different scenarios.
Cited articles
Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. a
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration - Guidelines for computing crop water requirements, FAO – Food and Agriculture Organization of the United Nations, Rome, ISBN 92-5-104219-5, 1998. a
Asrar, G., Fuchs, M., Kanemasu, E. T., and Hatfield, J. L.: Estimating
Absorbed Photosynthetic Radiation and Leaf Area Index from
Spectral Reflectance in Wheat1, Agron. J., 76, 300,
https://doi.org/10.2134/agronj1984.00021962007600020029x, 1984. a
Basler, D.: Evaluating phenological models for the prediction of leaf-out dates
in six temperate tree species across central Europe, Agr.
Forest Meteorol., 217, 10–21, https://doi.org/10.1016/j.agrformet.2015.11.007, 2016. a
Baudena, M., Dekker, S. C., van Bodegom, P. M., Cuesta, B., Higgins, S. I., Lehsten, V., Reick, C. H., Rietkerk, M., Scheiter, S., Yin, Z., Zavala, M. A., and Brovkin, V.: Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models, Biogeosciences, 12, 1833–1848, https://doi.org/10.5194/bg-12-1833-2015, 2015. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and
Wood, E. F.: Present and future Köppen-Geiger climate classification
maps at 1-km resolution, Scientific Data, 5, 180214,
https://doi.org/10.1038/sdata.2018.214,
2018. a
Beringer, J., Hutley, L. B., McHugh, I., Arndt, S. K., Campbell, D., Cleugh, H. A., Cleverly, J., Resco de Dios, V., Eamus, D., Evans, B., Ewenz, C., Grace, P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., Liddell, M. J., Macfarlane, C., Meyer, W., Moore, C., Pendall, E., Phillips, A., Phillips, R. L., Prober, S. M., Restrepo-Coupe, N., Rutledge, S., Schroder, I., Silberstein, R., Southall, P., Yee, M. S., Tapper, N. J., van Gorsel, E., Vote, C., Walker, J., and Wardlaw, T.: An introduction to the Australian and New Zealand flux tower network – OzFlux, Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, 2016. a, b
Beringer, J., McHugh, I., Hutley, L. B., Isaac, P., and Kljun, N.: Technical note: Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO), Biogeosciences, 14, 1457–1460, https://doi.org/10.5194/bg-14-1457-2017, 2017. a
Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone,
A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z.,
Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard,
C., Santanello, J. A., Stevens, L., and Vuichard, N.: The Plumbing of
Land Surface Models: Benchmarking Model Performance, J.
Hydrometeorol., 16, 1425–1442, https://doi.org/10.1175/JHM-D-14-0158.1, 2015. a, b
Bierkens, M. F. P. and van den Hurk, B. J. J. M.: Groundwater convergence as a
possible mechanism for multi-year persistence in rainfall, Geophys.
Res. Lett., 34, L02402, https://doi.org/10.1029/2006GL028396, 2007. a
Bonan, G. B., Williams, M., Fisher, R. A., and Oleson, K. W.: Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum, Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, 2014. a
Buckley, T. N., Sack, L., and Farquhar, G. D.: Optimal plant water economy,
Plant Cell Environ., 40, 881–896, https://doi.org/10.1111/pce.12823, 2017. a
Carsel, R. F. and Parrish, R. S.: Developing joint probability distributions of
soil water retention characteristics, Water Resour. Res., 24, 755–769,
https://doi.org/10.1029/WR024i005p00755, 1988. a, b, c
Cernusak, L. A., Hutley, L. B., Beringer, J., Holtum, J. A., and Turner, B. L.:
Photosynthetic physiology of eucalypts along a sub-continental rainfall
gradient in northern Australia, Agr. Forest Meteorol., 151,
1462–1470, https://doi.org/10.1016/j.agrformet.2011.01.006, 2011. a
Choudhury, B. J.: Relationships between vegetation indices, radiation
absorption, and net photosynthesis evaluated by a sensitivity analysis,
Remote Sens. Environ., 22, 209–233,
https://doi.org/10.1016/0034-4257(87)90059-9, 1987. a
Christoffersen, B. O., Gloor, M., Fauset, S., Fyllas, N. M., Galbraith, D. R., Baker, T. R., Kruijt, B., Rowland, L., Fisher, R. A., Binks, O. J., Sevanto, S., Xu, C., Jansen, S., Choat, B., Mencuccini, M., McDowell, N. G., and Meir, P.: Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro), Geosci. Model Dev., 9, 4227–4255, https://doi.org/10.5194/gmd-9-4227-2016, 2016. a
Collins, D. B. G. and Bras, R. L.: Plant rooting strategies in water-limited
ecosystems, Water Resour. Res., 43, W06407, https://doi.org/10.1029/2006WR005541 2007. a
De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma, R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal stomatal conductance scheme within the CABLE land surface model, Geosci. Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, 2015. a
Dekker, S. C., Vrugt, J. A., and Elkington, R. J.: Significant variation in
vegetation characteristics and dynamics from ecohydrological optimality of
net carbon profit, Ecohydrology, 5, 1–18, https://doi.org/10.1002/eco.177, 2010. a
Donohue, R. J., Roderick, M. L., and McVicar, T. R.: Deriving consistent
long-term vegetation information from AVHRR reflectance data using a
cover-triangle-based framework, Remote Sens. Environ., 112,
2938–2949, https://doi.org/10.1016/j.rse.2008.02.008, 2008. a, b
Duan, Q., Sorooshian, S., and Gupta, V. K.: Optimal use of the SCE-UA
global optimization method for calibrating watershed models, J.
Hydrol., 158, 265–284, https://doi.org/10.1016/0022-1694(94)90057-4, 1994. a
Duursma, R. A. and Medlyn, B. E.: MAESPA: a model to study interactions between water limitation, environmental drivers and vegetation function at tree and stand levels, with an example application to [CO2] × drought interactions, Geosci. Model Dev., 5, 919–940, https://doi.org/10.5194/gmd-5-919-2012, 2012. a
Eagleson, P. S.: Climate, soil, and vegetation: 4. The expected value of
annual evapotranspiration, Water Resour. Res., 14, 731–739,
https://doi.org/10.1029/WR014i005p00731, 1978. a
Eagleson, P. S.: Ecological optimality in water-limited natural soil-vegetation
systems: 1. Theory and hypothesis, Water Resour. Res., 18, 325–340,
https://doi.org/10.1029/WR018i002p00325, 1982. a, b
Eamus, D. and Prichard, H.: A cost-benefit analysis of leaves of four
Australian savanna species, Tree Physiol., 18, 537–545,
https://doi.org/10.1093/treephys/18.8-9.537, 1998. a
Eamus, D., O'Grady, A., and Hutley, L.: Dry season conditions determine wet
season water use in the wet-tropical savannas of northern Australia, Tree
Physiol., 20, 1219–1226, https://doi.org/10.1093/treephys/20.18.1219, 2000. a
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological
model to investigate complex interactions in cold and warm water-controlled
environments: 1. Theoretical framework and plot-scale analysis, J.
Adv. Model. Earth Sy., 4, M05002, https://doi.org/10.1029/2011MS000086, 2012. a
Franklin, O., Johansson, J., Dewar, R. C., Dieckmann, U., McMurtrie, R. E.,
Brännström, A., and Dybzinski, R.: Modeling carbon allocation in trees: a
search for principles, Tree Physiol., 32, 648–666, https://doi.org/10.1093/treephys/tpr138, 2012. a
Franklin, O., Harrison, S. P., Dewar, R., Farrior, C. E., Brännström, A.,
Dieckmann, U., Pietsch, S., Falster, D., Cramer, W., Loreau, M., Wang, H.,
Mäkelä, A., Rebel, K. T., Meron, E., Schymanski, S. J., Rovenskaya, E.,
Stocker, B. D., Zaehle, S., Manzoni, S., van Oijen, M., Wright, I. J., Ciais,
P., van Bodegom, P. M., Peñuelas, J., Hofhansl, F., Terrer, C.,
Soudzilovskaia, N. A., Midgley, G., and Prentice, I. C.: Organizing
principles for vegetation dynamics, Nat. Plants, 6, 444–453,
https://doi.org/10.1038/s41477-020-0655-x, 2020. a
Gao, H., Hrachowitz, M., Schymanski, S. J., Fenicia, F., Sriwongsitanon, N.,
and Savenije, H. H. G.: Climate controls how ecosystems size the root zone
storage capacity at catchment scale, Geophys. Res. Lett., 41,
7916–7923, https://doi.org/10.1002/2014GL061668, 2014. a
Grace, J., José, J. S., Meir, P., Miranda, H. S., and Montes, R. A.:
Productivity and carbon fluxes of tropical savannas, J. Biogeogr.,
33, 387–400, https://doi.org/10.1111/j.1365-2699.2005.01448.x, 2006. a
Guswa, A. J.: The influence of climate on root depth: A carbon cost-benefit
analysis, Water Resour. Res., 44, W02427, https://doi.org/10.1029/2007WR006384, 2008. a
Guswa, A. J.: Effect of plant uptake strategy on the water−optimal root
depth, Water Resour. Res., 46, W09601, https://doi.org/10.1029/2010WR009122, 2010. a
Hacke, U. G., Sperry, J. S., Pockman, W. T., Davis, S. D., and McCulloh, K. A.:
Trends in wood density and structure are linked to prevention of xylem
implosion by negative pressure, Oecologia, 126, 457–461,
https://doi.org/10.1007/s004420100628, 2001. a, b
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell, J. G., Isaac, P., Pickett-Heaps, C., Roxburgh, S. H., van Gorsel, E., Viscarra Rossel, R. A., and Wang, Z.: Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles, Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, 2013. a, b, c
Haverd, V., Smith, B., Raupach, M., Briggs, P., Nieradzik, L., Beringer, J., Hutley, L., Trudinger, C. M., and Cleverly, J.: Coupling carbon allocation with leaf and root phenology predicts tree–grass partitioning along a savanna rainfall gradient, Biogeosciences, 13, 761–779, https://doi.org/10.5194/bg-13-761-2016, 2016. a
House, J. I., Archer, S., Breshears, D. D., and Scholes, R. J.: Conundrums in
mixed woody–herbaceous plant systems, J. Biogeogr., 30,
1763–1777, https://doi.org/10.1046/j.1365-2699.2003.00873.x,
2003. a
Hutley, L. B., Beringer, J., Isaac, P. R., Hacker, J. M., and Cernusak, L. A.:
A sub-continental scale living laboratory: Spatial patterns of savanna
vegetation over a rainfall gradient in northern Australia, Agr.
Forest Meteorol., 151, 1417–1428, https://doi.org/10.1016/j.agrformet.2011.03.002,
2011. a, b, c, d, e, f, g, h
Hwang, T., Band, L., and Hales, T. C.: Ecosystem processes at the watershed
scale: Extending optimality theory from plot to catchment, Water Resour.
Res., 45, W11425, https://doi.org/10.1029/2009WR007775, 2009. a
Isbell, R. F.: The Australian Soil Classification, Revised Edn.,
Tech. rep., CSIRO Publishing, Collingwood, Victoria, available at: http://www.asris.csiro.au/downloads/Atlas/soilAtlas2M.zip (last access: 18 January 2022), 2002. a
Jeffrey, S. J., Carter, J. O., Moodie, K. B., and Beswick, A. R.: Using spatial
interpolation to construct a comprehensive archive of Australian climate
data, Environ. Modell. Softw., 16, 309–330,
https://doi.org/10.1016/S1364-8152(01)00008-1, 2001. a, b, c, d
Jolly, W. M., Nemani, R., and Running, S. W.: A generalized, bioclimatic index
to predict foliar phenology in response to climate, Glob. Change Biol.,
11, 619–632, https://doi.org/10.1111/j.1365-2486.2005.00930.x,
2005. a
Keeling, C. D., Piper, S. C., Bacastow, R. B., Wahlen, M., Whorf, T. P.,
Heimann, M., and Meijer, H. A.: Atmospheric CO2 and 13CO2 Exchange with
the Terrestrial Biosphere and Oceans from 1978 to 2000: Observations
and Carbon Cycle Implications, in: A History of Atmospheric CO2
and its effects on Plants, Animals, and Ecosystems,
Springer Verlag, New York, edited by: Ehleringer, J. R., Cerling, T. E., and
Dearing, M. D., 83–113, https://doi.org/10.1007/b138533, 2005. a
Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., da Costa, A.
C. L., and Gentine, P.: Implementing Plant Hydraulics in the
Community Land Model, Version 5, J. Adv. Model.
Earth Sy., 11, 485–513, https://doi.org/10.1029/2018MS001500, 2019. a
Kikuzawa, K.: A Cost-Benefit Analysis of Leaf Habit and Leaf
Longevity of Trees and Their Geographical Pattern, Am.
Nat., 138, 1250–1263, https://doi.org/10.1086/285281, 1991. a
Kleidon, A. and Heimann, M.: A method of determining rooting depth from a
terrestrial biosphere model and its impacts on the global water and carbon
cycle, Glob. Change Biol., 4, 275–286,
https://doi.org/10.1046/j.1365-2486.1998.00152.x,
1998. a, b, c
Kollet, S. J. and Maxwell, R. M.: Capturing the influence of groundwater
dynamics on land surface processes using an integrated, distributed watershed
model, Water Resour. Res., 44, W02402, https://doi.org/10.1029/2007WR006004, 2008. a
Kowalczyk, E. A., Wang, Y. P., Law, R. M., Davies, H. L., McGregor, J. L., and Abramowitz, G.: The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model, CSIRO, CSIRO Marine and Atmospheric Research paper, 013, ISBN 1 921232 39 0, 2006. a
Lehmann, C. E. R., Anderson, T. M., Sankaran, M., Higgins, S. I., Archibald,
S., Hoffmann, W. A., Hanan, N. P., Williams, R. J., Fensham, R. J., Felfili,
J., Hutley, L. B., Ratnam, J., Jose, J. S., Montes, R., Franklin, D.,
Russell-Smith, J., Ryan, C. M., Durigan, G., Hiernaux, P., Haidar, R.,
Bowman, D. M. J. S., and Bond, W. J.: Savanna Vegetation-Fire-Climate
Relationships Differ Among Continents, Science, 343, 548–552,
https://doi.org/10.1126/science.1247355, 2014. a
Lu, H.: Decomposition of vegetation cover into woody and herbaceous components
using AVHRR NDVI time series, Remote Sens. Environ., 86, 1–18,
https://doi.org/10.1016/S0034-4257(03)00054-3, 2003. a
Ma, X., Huete, A., Yu, Q., Coupe, N. R., Davies, K., Broich, M., Ratana, P.,
Beringer, J., Hutley, L. B., Cleverly, J., Boulain, N., and Eamus, D.:
Spatial patterns and temporal dynamics in savanna vegetation phenology across
the North Australian Tropical Transect, Remote Sens.
Environ., 139, 97–115, https://doi.org/10.1016/j.rse.2013.07.030, 2013. a
Maxwell, R. M., Chow, F. K., and Kollet, S. J.: The
groundwater–land-surface–atmosphere connection: Soil moisture effects
on the atmospheric boundary layer in fully-coupled simulations, Adv.
Water Resour., 30, 2447–2466, https://doi.org/10.1016/j.advwatres.2007.05.018, 2007. a
McDonnell, J. J., Sivapalan, M., Vaché, K., Dunn, S., Grant, G., Haggerty, R.,
Hinz, C., Hooper, R., Kirchner, J., Roderick, M. L., Selker, J., and Weiler,
M.: Moving beyond heterogeneity and process complexity: A new vision for
watershed hydrology, Water Resour. Res., 43, W07301,
https://doi.org/10.1029/2006WR005467, 2007. a
Mencuccini, M., Hölttä, T., Petit, G., and Magnani, F.: Sanio’s laws
revisited. Size-dependent changes in the xylem architecture of trees,
Ecol. Lett., 10, 1084–1093, https://doi.org/10.1111/j.1461-0248.2007.01104.x,
2007. a, b
Nijzink, R., Hutton, C., Pechlivanidis, I., Capell, R., Arheimer, B., Freer, J., Han, D., Wagener, T., McGuire, K., Savenije, H., and Hrachowitz, M.: The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change?, Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, 2016. a
Nijzink, R. C.: VOMcases, RenkuLab [code/data], available at: https://renkulab.io/gitlab/remko.nijzink/vomcases, last access: 25 January 2022. a
Nijzink, R. C. and Schymanski, S. J.: schymans/VOM: Code used for 2020 paper on the NATT (v0.5), Zenodo [code], https://doi.org/10.5281/zenodo.3630081, 2020. a
Nijzink, R. C. and Schymanski, S. J.: VOMcases (v0.3), Zenodo, [code/data], https://doi.org/10.5281/zenodo.5789101, 2021. a
O'Grady, A. P., Eamus, D., and Hutley, L. B.: Transpiration increases during
the dry season: patterns of tree water use in eucalypt open-forests of
northern Australia, Tree Physiol., 19, 591–597,
https://doi.org/10.1093/treephys/19.9.591, 1999. a
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian,
X., Shen, M., and Zhu, X.: Plant phenology and global climate change:
Current progresses and challenges, Glob. Change Biol., 25, 1922–1940,
https://doi.org/10.1111/gcb.14619, 2019. a, b
Pitman, A. J., Henderson-Sellers, A., Desborough, C. E., Yang, Z.-L.,
Abramopoulos, F., Boone, A., Dickinson, R. E., Gedney, N., Koster, R.,
Kowalczyk, E., Lettenmaier, D., Liang, X., Mahfouf, J.-F., Noilhan, J.,
Polcher, J., Qu, W., Robock, A., Rosenzweig, C., Schlosser, C. A., Shmakin,
A. B., Smith, J., Suarez, M., Verseghy, D., Wetzel, P., Wood, E., and Xue,
Y.: Key results and implications from phase 1(c) of the Project for
Intercomparison of Land-surface Parametrization Schemes, Clim.
Dynam., 15, 673–684, https://doi.org/10.1007/s003820050309, 1999. a
Pitman, A. J., Noblet‐Ducoudré, N. d., Cruz, F. T., Davin, E. L., Bonan,
G. B., Brovkin, V., Claussen, M., Delire, C., Ganzeveld, L., Gayler, V.,
van den Hurk, B. J. J. M., Lawrence, P. J., van der Molen, M. K., Müller, C.,
Reick, C. H., Seneviratne, S. I., Strengers, B. J., and Voldoire, A.:
Uncertainties in climate responses to past land cover change: First results
from the LUCID intercomparison study, Geophys. Res. Lett., 36, L14814,
https://doi.org/10.1029/2009GL039076, 2009. a
Radcliffe, D. E. and Rasmussen, T. C.: Soil water movement, in: Soil Physics Companion, CRC Press, Boca Raton, Fla, ISBN 9781420041651, 85–126, 2002. a
Richardson, A. D., Anderson, R. S., Arain, M. A., Barr, A. G., Bohrer, G.,
Chen, G., Chen, J. M., Ciais, P., Davis, K. J., Desai, A. R., Dietze, M. C.,
Dragoni, D., Garrity, S. R., Gough, C. M., Grant, R., Hollinger, D. Y.,
Margolis, H. A., McCaughey, H., Migliavacca, M., Monson, R. K., Munger,
J. W., Poulter, B., Raczka, B. M., Ricciuto, D. M., Sahoo, A. K., Schaefer,
K., Tian, H., Vargas, R., Verbeeck, H., Xiao, J., and Xue, Y.: Terrestrial
biosphere models need better representation of vegetation phenology: results
from the North American Carbon Program Site Synthesis, Glob.
Change Biol., 18, 566–584, https://doi.org/10.1111/j.1365-2486.2011.02562.x,
2012. a
Rodríguez-Iturbe, I. and Rinaldo, A.: Fractal River Basins: Chance and
Self-Organization, Cambridge University Press, ISBN 978-0-521-00405-3, 2001. a
Ryu, Y., Baldocchi, D. D., Kobayashi, H., van Ingen, C., Li, J., Black, T. A.,
Beringer, J., vam Gorsel, E., Knohl, A., Law, B. E., and Roupsard, O.:
Integration of MODIS land and atmosphere products with a coupled‐process
model to estimate gross primary productivity and evapotranspiration from 1 km
to global scales, Global Biogeochem. Cy., 25, GB4017,
https://doi.org/10.1029/2011GB004053, 2011. a, b
Ryu, Y., Baldocchi, D. D., Black, T. A., Detto, M., Law, B. E., Leuning, R.,
Miyata, A., Reichstein, M., Vargas, R., Ammann, C., Beringer, J., Flanagan,
L. B., Gu, L., Hutley, L. B., Kim, J., McCaughey, H., Moors, E. J., Rambal,
S., and Vesala, T.: On the temporal upscaling of evapotranspiration from
instantaneous remote sensing measurements to 8-day mean daily-sums,
Agr. Forest Meteorol., 152, 212–222,
https://doi.org/10.1016/j.agrformet.2011.09.010, 2012. a, b
Savenije, H. H. G.: The importance of interception and why we should delete the
term evapotranspiration from our vocabulary, Hydrol. Process., 18,
1507–1511, https://doi.org/10.1002/hyp.5563, 2004. a
Scheiter, S. and Higgins, S. I.: Impacts of climate change on the vegetation of
Africa: an adaptive dynamic vegetation modelling approach, Glob. Change
Biol., 15, 2224–2246, https://doi.org/10.1111/j.1365-2486.2008.01838.x,
2009. a, b
Scheiter, S., Langan, L., and Higgins, S. I.: Next-generation dynamic global
vegetation models: learning from community ecology, New Phytol., 198,
957–969, https://doi.org/10.1111/nph.12210, 2013. a
Scheiter, S., Higgins, S. I., Beringer, J., and Hutley, L. B.: Climate change
and long-term fire management impacts on Australian savannas, New
Phytol., 205, 1211–1226, https://doi.org/10.1111/nph.13130, 2015. a
Schenk, H. J., Jackson, R. B., Hall, F. G., Collatz, G. J., Meeson, B. W., Los,
S. O., Brown De Colstoun, E., and Landis, D. R.: ISLSCP II Ecosystem
Rooting Depths, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/929, 2009. a
Scholes, R. J. and Archer, S. R.: Tree-Grass Interactions in Savannas,
Annu. Rev. Ecol. Syst., 28, 517–544,
https://doi.org/10.1146/annurev.ecolsys.28.1.517, 1997. a
Schymanski, S.: VOM, GitHub [code], available at: https://github.com/schymans/VOM, last access: 18 January 2022. a
Schymanski, S. J., Roderick, M. L., Sivapalan, M., Hutley, L. B., and Beringer,
J.: A test of the optimality approach to modelling canopy properties and
CO2 uptake by natural vegetation, Plant Cell Environ., 30,
1586–1598, https://doi.org/10.1111/j.1365-3040.2007.01728.x, 2007. a, b, c
Schymanski, S. J., Sivapalan, M., Roderick, M. L., Beringer, J., and Hutley, L. B.: An optimality-based model of the coupled soil moisture and root dynamics, Hydrol. Earth Syst. Sci., 12, 913–932, https://doi.org/10.5194/hess-12-913-2008, 2008. a, b
Schymanski, S. J., Kleidon, A., and Roderick, M. L.: Ecohydrological
Optimality, in: Encyclopedia of Hydrological Sciences, edited by: Anderson, M. G. and McDonnell, J. J., John Wiley & Sons, Ltd, https://doi.org/10.1002/0470848944.hsa319, 2009a. a
Schymanski, S. J., Roderick, M. L., and Sivapalan, M.: Using an optimality
model to understand medium and long-term responses of vegetation water use to
elevated atmospheric CO2 concentrations, AoB Plants, 7, plv060,
https://doi.org/10.1093/aobpla/plv060, 2015. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol.
Biogeogr., 10, 621–637, https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x,
2001. a, b
Speich, M. J. R., Lischke, H., and Zappa, M.: Testing an optimality-based model of rooting zone water storage capacity in temperate forests, Hydrol. Earth Syst. Sci., 22, 4097–4124, https://doi.org/10.5194/hess-22-4097-2018, 2018. a
Sperry, J. S., Venturas, M. D., Anderegg, W. R. L., Mencuccini, M., Mackay,
D. S., Wang, Y., and Love, D. M.: Predicting stomatal responses to the
environment from the optimization of photosynthetic gain and hydraulic cost,
Plant Cell Environ., 40, 816–830, https://doi.org/10.1111/pce.12852, 2017. a
Tague, C. L.: RHESSys: Regional Hydro-Ecologic Simulation
System – An Object-Oriented Approach to Spatially Distributed
Modeling of Carbon, Water, and Nutrient Cycling, Earth
Interact., 8, p. 42, https://doi.org/10.1175/1087-3562(2004)8<1:RRHSSO>2.0.CO;2, 2004. a
Teckentrup, L., De Kauwe, M. G., Pitman, A. J., Goll, D. S., Haverd, V., Jain, A. K., Joetzjer, E., Kato, E., Lienert, S., Lombardozzi, D., McGuire, P. C., Melton, J. R., Nabel, J. E. M. S., Pongratz, J., Sitch, S., Walker, A. P., and Zaehle, S.: Assessing the representation of the Australian carbon cycle in global vegetation models, Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, 2021. a
Van Genuchten, M. T.: A Closed-form Equation for Predicting the
Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898,
https://doi.org/10.2136/sssaj1980.03615995004400050002x,
1980. a, b
van Wijk, M. T. and Bouten, W.: Towards understanding tree root profiles: simulating hydrologically optimal strategies for root distribution, Hydrol. Earth Syst. Sci., 5, 629–644, https://doi.org/10.5194/hess-5-629-2001, 2001. a
Viscarra Rossel, R., Chen, C., Grundy, M., Searle, R., Clifford, D., Odgers,
N., Holmes, K., Griffin, T., Liddicoat, C., and Kidd, D.: Soil and
Landscape Grid National Soil Attribute Maps – Clay (3′′
resolution) – Release 1, CSIRO Data Access Portal [data set], https://doi.org/10.4225/08/546EEE35164BF,
2014a. a, b
Viscarra Rossel, R., Chen, C., Grundy, M., Searle, R., Clifford, D., Odgers,
N., Holmes, K., Griffin, T., Liddicoat, C., and Kidd, D.: Soil and
Landscape Grid National Soil Attribute Maps – Silt (3′′
resolution) – Release 1, CSIRO Data Access Portal [data set], https://doi.org/10.4225/08/546F48D6A6D48,
2014b. a, b
Viscarra Rossel, R., Chen, C., Grundy, M., Searle, R., Clifford, D., Odgers,
N., Holmes, K., Griffin, T., Liddicoat, C., and Kidd, D.: Soil and
Landscape Grid National Soil Attribute Maps – Sand (3′′
resolution) – Release 1, CSIRO Data Access Portal [data set], https://doi.org/10.4225/08/546F29646877E,
2014c. a, b
von Caemmerer, S.: Biochemical Models of Leaf Photosynthesis, vol. 2,
Techniques in Plant Sciences, CSIRO Publishing, Collingwood, https://doi.org/10.1071/9780643103405,
2000. a
Wang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J.,
Cornwell, W. K., Evans, B. J., and Peng, C.: Towards a universal model for
carbon dioxide uptake by plants, Nat. Plants, 3, 734–741,
https://doi.org/10.1038/s41477-017-0006-8, 2017. a
Wang, P., Niu, G., Fang, Y., Wu, R., Yu, J., Yuan, G., Pozdniakov, S. P., and
Scott, R. L.: Implementing Dynamic Root Optimization in Noah‐MP
for Simulating Phreatophytic Root Water Uptake, Water Resour. Res., 54, 1560–1575, https://doi.org/10.1002/2017WR021061, 2018. a, b, c
Wang, Y. P., Kowalczyk, E., Leuning, R., Abramowitz, G., Raupach, M. R., Pak,
B., van Gorsel, E., and Luhar, A.: Diagnosing errors in a land surface model
(CABLE) in the time and frequency domains, J. Geophys. Res.-Biogeo., 116, G01034, https://doi.org/10.1029/2010JG001385, 2011. a
Wang-Erlandsson, L., Bastiaanssen, W. G. M., Gao, H., Jägermeyr, J., Senay, G. B., van Dijk, A. I. J. M., Guerschman, J. P., Keys, P. W., Gordon, L. J., and Savenije, H. H. G.: Global root zone storage capacity from satellite-based evaporation, Hydrol. Earth Syst. Sci., 20, 1459–1481, https://doi.org/10.5194/hess-20-1459-2016, 2016. a, b, c
Whitley, R., Beringer, J., Hutley, L. B., Abramowitz, G., De Kauwe, M. G., Duursma, R., Evans, B., Haverd, V., Li, L., Ryu, Y., Smith, B., Wang, Y.-P., Williams, M., and Yu, Q.: A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas, Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag, ah, ai, aj, ak, al
Williams, M., Rastetter, E. B., Fernandes, D. N., Goulden, M. L., Wofsy, S. C.,
Shaver, G. R., Melillo, J. M., Munger, J. W., Fan, S.-M., and Nadelhoffer,
K. J.: Modelling the soil-plant-atmosphere continuum in a Quercus–Acer
stand at Harvard Forest: the regulation of stomatal conductance by light,
nitrogen and soil/plant hydraulic properties, Plant Cell Environ., 19,
911–927, https://doi.org/10.1111/j.1365-3040.1996.tb00456.x, 1996a. a, b
Williams, R. J., Duff, G. A., Bowman, D. M. J. S., and Cook, G. D.: Variation
in the composition and structure of tropical savannas as a function of
rainfall and soil texture along a large-scale climatic gradient in the
Northern Territory, Australia, J. Biogeogr., 23, 747–756,
https://doi.org/10.1111/j.1365-2699.1996.tb00036.x, 1996b.
a
Yang, Y., Donohue, R. J., and McVicar, T. R.: Global estimation of effective
plant rooting depth: Implications for hydrological modeling, Water Resour. Res., 52, 8260–8276, https://doi.org/10.1002/2016WR019392, 2016. a, b
York, J. P., Person, M., Gutowski, W. J., and Winter, T. C.: Putting aquifers
into atmospheric simulation models: an example from the Mill Creek
Watershed, northeastern Kansas, Adv. Water Resour., 25,
221–238, https://doi.org/10.1016/S0309-1708(01)00021-5, 2002. a
Short summary
Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.
Most models that simulate water and carbon exchanges with the atmosphere rely on information...
