Articles | Volume 12, issue 3
https://doi.org/10.5194/esd-12-919-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-12-919-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Sep 2021
Research article |
| 13 Sep 2021
| 13 Sep 2021
Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts
ARC Centre of Excellence for Climate Extremes and Climate Change
Research Centre, University of New South Wales, Sydney 2052, Australia
Martin G. De Kauwe
ARC Centre of Excellence for Climate Extremes and Climate Change
Research Centre, University of New South Wales, Sydney 2052, Australia
School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, United Kingdom
Anna M. Ukkola
ARC Centre of Excellence for Climate Extremes and Climate Change
Research Centre, University of New South Wales, Sydney 2052, Australia
Andy J. Pitman
ARC Centre of Excellence for Climate Extremes and Climate Change
Research Centre, University of New South Wales, Sydney 2052, Australia
Weidong Guo
School of Atmospheric Sciences and Joint International Research
Laboratory of Atmospheric and Earth System Sciences, Nanjing University,
Nanjing 210023, China
Sanaa Hobeichi
ARC Centre of Excellence for Climate Extremes and Climate Change
Research Centre, University of New South Wales, Sydney 2052, Australia
Peter R. Briggs
Climate Science Centre, CSIRO Oceans and Atmosphere, Canberra 2601,
ACT, Australia
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Preprint archived
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A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
Jon Cranko Page, Martin G. De Kauwe, Gab Abramowitz, Jamie Cleverly, Nina Hinko-Najera, Mark J. Hovenden, Yao Liu, Andy J. Pitman, and Kiona Ogle
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Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
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Flux towers provide measurements of water, energy, and carbon fluxes. Flux tower data are invaluable in improving and evaluating land models but are not suited to modelling applications as published. Here we present flux tower data tailored for land modelling, encompassing 170 sites globally. Our dataset resolves several key limitations hindering the use of flux tower data in land modelling, including incomplete forcing variable, data format, and low data quality.
Sami W. Rifai, Martin G. De Kauwe, Anna M. Ukkola, Lucas A. Cernusak, Patrick Meir, Belinda E. Medlyn, and Andy J. Pitman
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Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
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Xiaolu Ling, Ying Huang, Weidong Guo, Yixin Wang, Chaorong Chen, Bo Qiu, Jun Ge, Kai Qin, Yong Xue, and Jian Peng
Hydrol. Earth Syst. Sci., 25, 4209–4229, https://doi.org/10.5194/hess-25-4209-2021, https://doi.org/10.5194/hess-25-4209-2021, 2021
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Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
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Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
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Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
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The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, and Benjamin Smith
Biogeosciences, 18, 2181–2203, https://doi.org/10.5194/bg-18-2181-2021, https://doi.org/10.5194/bg-18-2181-2021, 2021
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The El Niño–Southern Oscillation (ENSO) describes changes in the sea surface temperature patterns of the Pacific Ocean. This influences the global weather, impacting vegetation on land. There are two types of El Niño: central Pacific (CP) and eastern Pacific (EP). In this study, we explored the long-term impacts on the carbon balance on land linked to the two El Niño types. Using a dynamic vegetation model, we simulated what would happen if only either CP or EP El Niño events had occurred.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Teresa E. Gimeno, Belinda E. Medlyn, Dani Or, Jinyan Yang, and David S. Ellsworth
Hydrol. Earth Syst. Sci., 25, 447–471, https://doi.org/10.5194/hess-25-447-2021, https://doi.org/10.5194/hess-25-447-2021, 2021
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Land surface model (LSM) is a critical tool to study land responses to droughts and heatwaves, but lacking comprehensive observations limited past model evaluations. Here we use a novel dataset at a water-limited site, evaluate a typical LSM with a range of competing model hypotheses widely used in LSMs and identify marked uncertainty due to the differing process assumptions. We show the extensive observations constrain model processes and allow better simulated land responses to these extremes.
Wenkai Li, Shuzhen Hu, Pang-Chi Hsu, Weidong Guo, and Jiangfeng Wei
The Cryosphere, 14, 3565–3579, https://doi.org/10.5194/tc-14-3565-2020, https://doi.org/10.5194/tc-14-3565-2020, 2020
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Cited articles
Abramowitz, G., Leuning, R., Clark, M., and Pitman, A.: Evaluating the
performance of land surface models, J. Climate, 21, 5468–5481,
https://doi.org/10.1175/2008JCLI2378.1, 2008.
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.
Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N.,
Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D. D., Hogg, E. H.
(Ted), Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim,
J.-H., Allard, G., Running, S. W., Semerci, A., and Cobb, N.: A global
overview of drought and heat-induced tree mortality reveals emerging climate
change risks for forests, Forest Ecol. Manag., 259, 660–684,
https://doi.org/10.1016/j.foreco.2009.09.001, 2010.
Allen, C. D., Breshears, D. D., and McDowell, N. G.: On underestimation of
global vulnerability to tree mortality and forest die-off from hotter
drought in the Anthropocene, Ecosphere, 6, 129,
https://doi.org/10.1890/ES15-00203.1, 2015.
Anyah, R. O., Weaver, C. P., Miguez-Macho, G., Fan, Y., and Robock, A.:
Incorporating water table dynamics in climate modeling: 3. Simulated
groundwater influence on coupled land-atmosphere variability, J. Geophys.
Res., 113, D07103, https://doi.org/10.1029/2007JD009087, 2008.
Arora, V. K. and Boer, G. J.: A representation of variable root distribution
in dynamic vegetation models, Earth Interact., 7, 1–19,
https://doi.org/10.1175/1087-3562(2003)007<0001:AROVRD>2.0.CO;2,
2003.
Badger, M. R. and Collatz, G. J.: Studies on the kinetic mechanism of RuBP
carboxylase and oxygenase reactions, with particular reference to the effect
of temperature on kinetic parameters, Year book–Carnegie Institution of
Washington, Baltimore, Maryland, USA, 355–361 pp., 1977.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal
conductance and its contribution to the control of photosynthesis under
different environmental conditions, in Progress in Photosynthesis Research,
Springer Netherlands, Dordrecht, the Netherlands, 221–224,
https://doi.org/10.1007/978-94-017-0519-6_48, 1987.
Bartle, G. A., Murray, A. M., and Macpherson, D. K.: The distribution of root
length, and the limits to flow of soil water to roots in a dry sclerophyll
forest, Forest Sci., 26, 656–664, 1980.
Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis Jr., A. R., and Long,
S. P.: Improved temperature response functions for models of Rubisco-limited
photosynthesis, Plant. Cell Environ., 24, 253–259,
https://doi.org/10.1111/j.1365-3040.2001.00668.x, 2001.
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.
Birami, B., Gattmann, M., Heyer, A. G., Grote, R., Arneth, A., and Ruehr, N.
K.: Heat waves alter carbon allocation and increase mortality of Aleppo Pine
under dry conditions, Front. For. Glob. Chang., 1, 8,
https://doi.org/10.3389/ffgc.2018.00008, 2018.
Bonetti, S., Manoli, G., Domec, J. C., Putti, M., Marani, M., and Katul, G.
G.: The influence of water table depth and the free atmospheric state on
convective rainfall predisposition, Water Resour. Res., 51, 2283–2297,
https://doi.org/10.1002/2014WR016431, 2015.
Bureau of Meteorology: Special Climate Statement 43-extreme heat in January
2013, available at:
http://www.bom.gov.au/climate/current/statements/scs43e.pdf (last access: 29 August 2021), 2013.
Bureau of Meteorology: Special Climate Statement 61-exceptional heat in
southeast Australia in early 2017, available at:
http://www.bom.gov.au/climate/current/statements/scs61.pdf (last access: 29 August 2021), 2017.
Bureau of Meteorology: Special Climate Statement 68 – widespread heatwaves
during December 2018 and January 2019, available at:
http://www.bom.gov.au/climate/current/statements/scs68.pdf (last access: 29 August 2021), 2019.
Burgess, S. S. O., Adams, M. A., Turner, N. C., and Ong, C. K.: The
redistribution of soil water by tree root systems, Oecologia, 115,
306–311, https://doi.org/10.1007/s004420050521, 1998.
Canadell, J., Jackson, R. B., Ehleringer, J. B., Mooney, H. A., Sala, O. E.,
and Schulze, E.-D.: Maximum rooting depth of vegetation types at the global
scale, Oecologia, 108, 583–595, https://doi.org/10.1007/BF00329030, 1996.
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V.,
Aubinet, M., Buchmann, N., Bernhofer, C., Carrara, A., Chevallier, F., De
Noblet, N., Friend, A. D., Friedlingstein, P., Grünwald, T., Heinesch,
B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci,
G., Miglietta, F., Ourcival, J. M., Papale, D., Pilegaard, K., Rambal, S.,
Seufert, G., Soussana, J. F., Sanz, M. J., Schulze, E. D., Vesala, T., and
Valentini, R.: Europe-wide reduction in primary productivity caused by the
heat and drought in 2003, Nature, 437, 529–533,
https://doi.org/10.1038/nature03972, 2005.
Condon, L. E., Atchley, A. L., and Maxwell, R. M.: Evapotranspiration
depletes groundwater under warming over the contiguous United States, Nat.
Commun., 11, 873, https://doi.org/10.1038/s41467-020-14688-0, 2020.
Cosby, B. J., Hornberger, G. M., Clapp, R. B., and Ginn, T. R.: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resour. Res., 20, 682–690, https://doi.org/10.1029/WR020i006p00682, 1984.
Cowan, T., Purich, A., Perkins, S., Pezza, A., Boschat, G., and Sadler, K.:
More frequent, longer, and hotter heat waves for Australia in the
twenty-first century, J. Climate, 27, 5851–5871,
https://doi.org/10.1175/JCLI-D-14-00092.1, 2014.
Crous, K. Y., Quentin, A. G., Lin, Y.-S., Medlyn, B. E., Williams, D. G.,
Barton, C. V. M., and Ellsworth, D. S.: Photosynthesis of temperate
Eucalyptus globulus trees outside their native range has limited adjustment
to elevated CO2 and climate warming, Glob. Change Biol., 19,
3790–3807, https://doi.org/10.1111/gcb.12314, 2013.
Cunningham, S. C. and Read, J.: Do temperate rainforest trees have a greater ability to acclimate to changing temperatures than tropical rainforest trees?, New Phytol., 157, 55–64, https://doi.org/10.1046/j.1469-8137.2003.00652.x, 2003.
Dawson, T. E. and Pate, J. S.: Seasonal water uptake and movement in root
systems of Australian phraeatophytic plants of dimorphic root morphology: a
stable isotope investigation, Oecologia, 107, 13–20,
https://doi.org/10.1007/BF00582230, 1996.
De Boeck, H. J., Dreesen, F. E., Janssens, I. A., and Nijs, I.: Climatic
characteristics of heat waves and their simulation in plant experiments,
Glob. Change Biol., 16, 1992–2000, https://doi.org/10.1111/j.1365-2486.2009.02049.x,
2010.
Decker, M.: Development and evaluation of a new soil moisture and runoff
parameterization for the CABLE LSM including subgrid-scale processes, J.
Adv. Model. Earth Syst., 7, 1788–1809, https://doi.org/10.1002/2015MS000507, 2015.
Decker, M., Or, D., Pitman, A. and Ukkola, A.: New turbulent resistance
parameterization for soil evaporation based on a pore-scale model: Impact on
surface fluxes in CABLE, J. Adv. Model. Earth Sy., 9, 220–238,
https://doi.org/10.1002/2016MS000832, 2017.
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.
De Kauwe, M. G., Medlyn, B. E., Pitman, A. J., Drake, J. E., Ukkola, A., Griebel, A., Pendall, E., Prober, S., and Roderick, M.: Examining the evidence for decoupling between photosynthesis and transpiration during heat extremes, Biogeosciences, 16, 903–916, https://doi.org/10.5194/bg-16-903-2019, 2019.
De Kauwe, M. G., Medlyn, B. E., Ukkola, A. M., Mu, M., Sabot, M. E. B. B.,
Pitman, A. J., Meir, P., Cernusak, L. A., Rifai, S. W., Choat, B., Tissue,
D. T., Blackman, C. J., Li, X., Roderick, M., and Briggs, P. R.: Identifying
areas at risk of drought-induced tree mortality across South-Eastern
Australia, Glob. Change Biol., 26, 5716–5733, https://doi.org/10.1111/gcb.15215,
2020.
D'Odorico, P. and Porporato, A.: Preferential states in soil moisture and
climate dynamics, P. Natl. Acad. Sci. USA, 101, 8848–8851,
https://doi.org/10.1073/pnas.0401428101, 2004.
Döll, P., Müller Schmied, H., Schuh, C., Portmann, F. T., and Eicker,
A.: Global-scale assessment of groundwater depletion and related groundwater
abstractions: Combining hydrological modeling with information from well
observations and GRACE satellites, Water Resour. Res., 50, 5698–5720,
https://doi.org/10.1002/2014WR015595, 2014.
Drake, J. E., Tjoelker, M. G., Vårhammar, A., Medlyn, B. E., Reich, P.
B., Leigh, A., Pfautsch, S., Blackman, C. J., López, R., Aspinwall, M.
J., Crous, K. Y., Duursma, R. A., Kumarathunge, D., De Kauwe, M. G., Jiang,
M., Nicotra, A. B., Tissue, D. T., Choat, B., Atkin, O. K. and Barton, C. V.
M. M.: Trees tolerate an extreme heatwave via sustained transpirational
cooling and increased leaf thermal tolerance, Glob. Change Biol., 24,
2390–2402, https://doi.org/10.1111/gcb.14037, 2018.
Drewniak, B. A.: Simulating dynamic roots in the energy exascale earth
system land model, J. Adv. Model. Earth Sy., 11, 338–359,
https://doi.org/10.1029/2018MS001334, 2019.
Eamus, D. and Froend, R.: Groundwater-dependent ecosystems: the where, what
and why of GDEs, Aust. J. Bot., 54, 91–96,, https://doi.org/10.1071/BT06029, 2006.
Eamus, D., Zolfaghar, S., Villalobos-Vega, R., Cleverly, J., and Huete, A.: Groundwater-dependent ecosystems: recent insights from satellite and field-based studies, Hydrol. Earth Syst. Sci., 19, 4229–4256, https://doi.org/10.5194/hess-19-4229-2015, 2015.
Eberbach, P. L. and Burrows, G. E.: The transpiration response by four
topographically distributed Eucalyptus species, to rainfall occurring during
drought in south eastern Australia, Physiol. Plant., 127, 483–493,
https://doi.org/10.1111/j.1399-3054.2006.00762.x, 2006.
Fan, Y.: Groundwater in the Earth's critical zone: Relevance to large-scale
patterns and processes, Water Resour. Res., 51, 3052–3069,
https://doi.org/10.1002/2015WR017037, 2015.
Fan, Y., Li, H., and Miguez-Macho, G.: Global patterns of groundwater table
depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013.
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and
Otero-Casal, C.: Hydrologic regulation of plant rooting depth, P. Natl.
Acad. Sci. USA, 114, 10572–10577, https://doi.org/10.1073/pnas.1712381114, 2017.
Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D., and
Schär, C.: Soil moisture–atmosphere interactions during the 2003
European summer heat wave, J. Climate, 20, 5081–5099,
https://doi.org/10.1175/JCLI4288.1, 2007.
Gale, M. R. and Grigal, D. F.: Vertical root distributions of northern tree
species in relation to successional status, Can. J. Forest Res., 17,
829–834, https://doi.org/10.1139/x87-131, 1987.
Geange, S. R., Arnold, P. A., Catling, A. A., Coast, O., Cook, A. M.,
Gowland, K. M., Leigh, A., Notarnicola, R. F., Posch, B. C., Venn, S. E.,
Zhu, L., and Nicotra, A. B.: The thermal tolerance of photosynthetic tissues:
a global systematic review and agenda for future research, New Phytol.,
229, 2497–2513, https://doi.org/10.1111/nph.17052, 2021.
Gilbert, J. M., Maxwell, R. M., and Gochis, D. J.: Effects of water-table
configuration on the planetary boundary layer over the San Joaquin River
Watershed, California, J. Hydrometeorol., 18, 1471–1488,
https://doi.org/10.1175/JHM-D-16-0134.1, 2017.
GLEAM: Global Land Evaporation Amsterdam Model (GLEAM) Version 3.5, https://www.gleam.eu/, last access: 29 August 2021.
Griffith, S. J., Bale, C., and Adam, P.: Environmental correlates of coastal
heathland and allied vegetation, Aust. J. Bot., 56, 512,
https://doi.org/10.1071/BT06147, 2008.
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.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS One, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Hirsch, A. L., Evans, J. P., Di Virgilio, G., Perkins-Kirkpatrick, S. E.,
Argüeso, D., Pitman, A. J., Carouge, C. C., Kala, J., Andrys, J.,
Petrelli, P., and Rockel, B.: Amplification of Australian heatwaves via local
land-atmosphere coupling, J. Geophys. Res.-Atmos., 124, 13625–13647,
https://doi.org/10.1029/2019JD030665, 2019.
Hobeichi, S., Abramowitz, G., and Evans, J. P.: Robust historical evapotranspiration trends across climate regimes, Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, 2021a.
Hobeichi, S., Abramowitz, G., and Evans, J. P.: Derived Optimal Linear Combination Evapotranspiration – DOLCE v2.1, NCI National Research Data Collection [data set], https://doi.org/10.25914/5f1664837ef06, 2021b.
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and
Seneviratne, S. I.: Sensitivity of atmospheric CO2 growth rate to
observed changes in terrestrial water storage, Nature, 560, 628–631,
https://doi.org/10.1038/s41586-018-0424-4, 2018.
Ibáñez, I., Acharya, K., Juno, E., Karounos, C., Lee, B. R.,
McCollum, C., Schaffer-Morrison, S., and Tourville, J.: Forest resilience
under global environmental change: Do we have the information we need? A
systematic review, edited by: Zang, R., PLoS One, 14, e0222207,
https://doi.org/10.1371/journal.pone.0222207, 2019.
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E.,
and Schulze, E. D.: A global analysis of root distributions for terrestrial
biomes, Oecologia, 108, 389–411, https://doi.org/10.1007/BF00333714, 1996.
Jiang, X., Niu, G.-Y., and Yang, Z.-L.: Impacts of vegetation and groundwater
dynamics on warm season precipitation over the Central United States, J.
Geophys. Res., 114, D06109, https://doi.org/10.1029/2008JD010756, 2009.
Jones, D., Wang, W., and Fawcett, R.: High-quality spatial climate data-sets
for Australia, Aust. Meteorol. Oceanogr. J., 58, 233–248,
https://doi.org/10.22499/2.5804.003, 2009.
Jyoteeshkumar reddy, P., Sharples, J. J., Lewis, S. C., and
Perkins-Kirkpatrick, S. E.: Modulating influence of drought on the synergy
between heatwaves and dead fine fuel moisture content of bushfire fuels in
the Southeast Australian region, Weather Clim. Extrem., 31, 100300,
https://doi.org/10.1016/j.wace.2020.100300, 2021.
Kattge, J. and Knorr, W.: Temperature acclimation in a biochemical model of
photosynthesis: a reanalysis of data from 36 species, Plant. Cell Environ.,
30, 1176–1190, https://doi.org/10.1111/j.1365-3040.2007.01690.x, 2007.
Keune, J., Gasper, F., Goergen, K., Hense, A., Shrestha, P., Sulis, M., and
Kollet, S.: Studying the influence of groundwater representations on land
surface-atmosphere feedbacks during the European heat wave in 2003, J.
Geophys. Res.-Atmos., 121, 13301–13325, https://doi.org/10.1002/2016JD025426,
2016.
Kim, W., Iizumi, T., and Nishimori, M.: Global Patterns of Crop Production
Losses Associated with Droughts from 1983 to 2009, J. Appl. Meteorol.
Clim., 58, 1233–1244, https://doi.org/10.1175/JAMC-D-18-0174.1, 2019.
Kowalczyk, E. A., Wang, Y. P., and Law, R. M.: The CSIRO Atmosphere Biosphere
Land Exchange (CABLE) model for use in climate models and as an offline
model, CSIRO Mar. Atmos. Res. Pap., 13, 1–42,
https://doi.org/10.4225/08/58615c6a9a51d, 2006.
Kuginis, L., Dabovic, J., Burne, G., Raine, A., and Hemakumara, H.: Methods
for the identification of high probability groundwater dependent vegetation
ecosystems, DPI Water: Sydney, NSW, available at:
https://www.dpi.nsw.gov.au/ (last access: 29 August 2021), 2016.
Kumarathunge, D. P., Medlyn, B. E., Drake, J. E., Tjoelker, M. G.,
Aspinwall, M. J., Battaglia, M., Cano, F. J., Carter, K. R., Cavaleri, M.
A., Cernusak, L. A., Chambers, J. Q., Crous, K. Y., De Kauwe, M. G.,
Dillaway, D. N., Dreyer, E., Ellsworth, D. S., Ghannoum, O., Han, Q.,
Hikosaka, K., Jensen, A. M., Kelly, J. W. G. G., Kruger, E. L., Mercado, L.
M., Onoda, Y., Reich, P. B., Rogers, A., Slot, M., Smith, N. G., Tarvainen,
L., Tissue, D. T., Togashi, H. F., Tribuzy, E. S., Uddling, J.,
Vårhammar, A., Wallin, G., Warren, J. M., and Way, D. A.: Acclimation and
adaptation components of the temperature dependence of plant photosynthesis
at the global scale, New Phytol., 222, 768–784, https://doi.org/10.1111/nph.15668,
2019.
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T.,
Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H.,
Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D.,
McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S.,
Strekalov, D., Tamisiea, M. E., Wang, F., Watkins, M. M., Wen, H., Wiese, D.
N., and Yuan, D.: Extending the global mass change data record: GRACE
Follow-On instrument and science data performance, Geophys. Res. Lett.,
47, e2020GL088306, https://doi.org/10.1029/2020GL088306, 2020.
Leblanc, M. J., Tregoning, P., Ramillien, G., Tweed, S. O., and Fakes, A.:
Basin-scale, integrated observations of the early 21st century multiyear
drought in southeast Australia, Water Resour. Res., 45, 1–10,
https://doi.org/10.1029/2008WR007333, 2009.
Lesk, C., Rowhani, P., and Ramankutty, N.: Influence of extreme weather
disasters on global crop production, Nature, 529, 84–87,
https://doi.org/10.1038/nature16467, 2016.
Leuning, R., Kelliher, F. M., Pury, D. G. G., and Schulze, E.-D.: Leaf
nitrogen, photosynthesis, conductance and transpiration: scaling from leaves
to canopies, Plant, Cell Environ., 18, 1183–1200,
https://doi.org/10.1111/j.1365-3040.1995.tb00628.x, 1995.
LP DAAC: The Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS), available at: https://lpdaacsvc.cr.usgs.gov/appeears/, last access: 1 September 2021.
Marchionni, V., Daly, E., Manoli, G., Tapper, N. J., Walker, J. P., and
Fatichi, S.: Groundwater buffers drought effects and climate variability in
urban reserves, Water Resour. Res., 56, e2019WR026192, https://doi.org/10.1029/2019WR026192, 2020.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Martínez-de la Torre, A. and Miguez-Macho, G.: Groundwater influence on soil moisture memory and land–atmosphere fluxes in the Iberian Peninsula, Hydrol. Earth Syst. Sci., 23, 4909–4932, https://doi.org/10.5194/hess-23-4909-2019, 2019.
Martinez, J. A., Dominguez, F., and Miguez-Macho, G.: Effects of a
groundwater scheme on the simulation of soil moisture and evapotranspiration
over southern South America, J. Hydrometeorol., 17, 2941–2957,
https://doi.org/10.1175/JHM-D-16-0051.1, 2016a.
Martinez, J. A., Dominguez, F., and Miguez-Macho, G.: Impacts of a
groundwater scheme on hydroclimatological conditions over southern South
America, J. Hydrometeorol., 17, 2959–2978, https://doi.org/10.1175/JHM-D-16-0052.1,
2016b.
Massmann, A., Gentine, P., and Lin, C.: When does vapor pressure deficit
drive or reduce evapotranspiration?, J. Adv. Model. Earth Sy., 11,
3305–3320, https://doi.org/10.1029/2019MS001790, 2019.
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.
Maxwell, R. M., Lundquist, J. K., Mirocha, J. D., Smith, S. G., Woodward, C.
S., and Tompson, A. F. B.: Development of a coupled groundwater–atmosphere
model, Mon. Weather Rev., 139, 96–116, https://doi.org/10.1175/2010MWR3392.1, 2011.
McVicar, T. R., Van Niel, T. G., Li, L. T., Roderick, M. L., Rayner, D. P.,
Ricciardulli, L., and Donohue, R. J.: Wind speed climatology and trends for
Australia, 1975–2006: Capturing the stilling phenomenon and comparison with
near-surface reanalysis output, Geophys. Res. Lett., 35, 1–6,
https://doi.org/10.1029/2008GL035627, 2008.
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, C. I.,
Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.:
Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Glob. Chang. Biol., 17, 2134–2144,
https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011.
Mercado, L. M., Medlyn, B. E., Huntingford, C., Oliver, R. J., Clark, D. B.,
Sitch, S., Zelazowski, P., Kattge, J., Harper, A. B., and Cox, P. M.: Large
sensitivity in land carbon storage due to geographical and temporal
variation in the thermal response of photosynthetic capacity, New Phytol.,
218, 1462–1477, https://doi.org/10.1111/nph.15100, 2018.
Miller, G. R., Chen, X., Rubin, Y., Ma, S., and Baldocchi, D. D.: Groundwater
uptake by woody vegetation in a semiarid oak savanna, Water Resour. Res.,
46, 2009WR008902, https://doi.org/10.1029/2009WR008902, 2010.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C., and Vilà-Guerau
de Arellano, J.: Mega-heatwave temperatures due to combined soil desiccation
and atmospheric heat accumulation, Nat. Geosci., 7, 345–349,
https://doi.org/10.1038/ngeo2141, 2014.
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.:
Land-atmospheric feedbacks during droughts and heatwaves: state of the
science and current challenges, Ann. N. Y. Acad. Sci., 1436, 19–35,
https://doi.org/10.1111/nyas.13912, 2019.
Mitchell, P. J., O'Grady, A. P., Hayes, K. R., and Pinkard, E. A.: Exposure
of trees to drought-induced die-off is defined by a common climatic
threshold across different vegetation types, Ecol. Evol., 4, 1088–1101,
https://doi.org/10.1002/ece3.1008, 2014.
Mu, M.: bibivking/Groundwater_Vegetation_Heatwave_Drought: Groundwater Vegetation Heatwave Drought (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.5158498, 2021.
Mu, M., De Kauwe, M. G., Ukkola, A. M., Pitman, A. J., Gimeno, T. E., Medlyn, B. E., Or, D., Yang, J., and Ellsworth, D. S.: Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves, Hydrol. Earth Syst. Sci., 25, 447–471, https://doi.org/10.5194/hess-25-447-2021, 2021.
Mukherjee, S., Ashfaq, M. and Mishra, A. K.: Compound Drought and Heatwaves
at a Global Scale: The role of natural climate variability-associated
synoptic patterns and land-surface energy budget anomalies, J. Geophys. Res.
Atmos., 125, e2019JD031943, https://doi.org/10.1029/2019JD031943, 2020.
Nairn, J. R. and Fawcett, R. J. B.: The excess heat factor: A metric for
heatwave intensity and its use in classifying heatwave severity, Int. J.
Environ. Res. Pu., 12, 227–253, https://doi.org/10.3390/ijerph120100227,
2014.
National Climate Centre: The exceptional January-February 2009 heatwave in
south-eastern Australia, available at:
http://www.bom.gov.au/climate/current/statements/scs17c.pdf (last access: 29 August 2021), 2009.
NCI: CABLE: The Community Atmosphere Biosphere Land Exchange Model,
available at: https://trac.nci.org.au/trac/cable, last access: 4 August 2021.
Orth, R. and Destouni, G.: Drought reduces blue-water fluxes more strongly
than green-water fluxes in Europe, Nat. Commun., 9, 3602,
https://doi.org/10.1038/s41467-018-06013-7, 2018.
O’sullivan, O. S., Heskel, M. A., Reich, P. B., Tjoelker, M. G., Weerasinghe, L. K., Penillard, A., Zhu, L., Egerton, J. J. G., Bloomfield, K. J., Creek, D., Bahar, N. H. A., Griffin, K. L., Hurry, V., Meir, P., Turnbull, M. H., and Atkin, O. K.: Thermal limits of leaf metabolism across
biomes, Glob. Change Biol., 23, 209–223, https://doi.org/10.1111/gcb.13477, 2017.
Perkins-Kirkpatrick, S. E., White, C. J., Alexander, L. V, Argüeso, D.,
Boschat, G., Cowan, T., Evans, J. P., Ekström, M., Oliver, E. C. J.,
Phatak, A., and Purich, A.: Natural hazards in Australia: heatwaves, Clim.
Change, 139, 101–114, https://doi.org/10.1007/s10584-016-1650-0, 2016.
Perkins, S. E.: A review on the scientific understanding of
heatwaves – Their measurement, driving mechanisms, and changes at the global
scale, Atmos. Res., 164–165, 242–267, https://doi.org/10.1016/j.atmosres.2015.05.014,
2015.
Raupach, M. R.: Simplified expressions for vegetation roughness length and
zero-plane displacement as functions of canopy height and area index,
Bound.-Lay. Meteorol., 71, 211–216, https://doi.org/10.1007/BF00709229, 1994.
Raupach, M. R., Finkele, K., and Zhang, L.: SCAM: a soil-canopy-atmosphere
model: description and comparisons with field data, CSIRO Centre for
Environmental Mechanics, Canberra, Australia,
https://doi.org/10.4225/08/5a30195883b8f, 1997.
Raupach, M. R., Haverd, V., and Briggs, P. R.: Sensitivities of the
Australian terrestrial water and carbon balances to climate change and
variability, Agr. Forest Meteorol., 182–183, 277–291,
https://doi.org/10.1016/j.agrformet.2013.06.017, 2013.
Reich, P. B., Sendall, K. M., Rice, K., Rich, R. L., Stefanski, A., Hobbie, S. E., and Montgomery, R. A.: Geographic range predicts photosynthetic and growth response to warming in co-occurring tree species, Nat. Clim. Change, 5, 148–152, https://doi.org/10.1038/nclimate2497, 2015.
Richards, J. H. and Caldwell, M. M.: Hydraulic lift: Substantial nocturnal
water transport between soil layers by Artemisia tridentata roots,
Oecologia, 73, 486–489, https://doi.org/10.1007/BF00379405, 1987.
Ruehr, N. K., Grote, R., Mayr, S., and Arneth, A.: Beyond the extreme:
recovery of carbon and water relations in woody plants following heat and
drought stress, Tree Physiol., 39, 1285–1299,
https://doi.org/10.1093/treephys/tpz032, 2019.
Sandi, S. G., Rodriguez, J. F., Saintilan, N., Wen, L., Kuczera, G.,
Riccardi, G., and Saco, P. M.: Resilience to drought of dryland wetlands
threatened by climate change, Sci. Rep.-UK, 10, 13232,
https://doi.org/10.1038/s41598-020-70087-x, 2020.
Schenk, H. J. and Jackson, R. B.: The global biogeography of roots, Ecol.
Monogr., 72, 311–328,
doi:https://doi.org/10.1890/0012-9615(2002)072[0311:TGBOR]2.0.CO;2, 2002.
Schumacher, D. L., Keune, J., van Heerwaarden, C. C., Vilà-Guerau de
Arellano, J., Teuling, A. J., and Miralles, D. G.: Amplification of
mega-heatwaves through heat torrents fuelled by upwind drought, Nat.
Geosci., 12, 712–717, https://doi.org/10.1038/s41561-019-0431-6, 2019.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review,
Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004,
2010.
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A
scale-consistent terrestrial systems modeling platform based on COSMO, CLM,
and ParFlow, Mon. Weather Rev., 142, 3466–3483,
https://doi.org/10.1175/MWR-D-14-00029.1, 2014.
Smith, N. G. and Dukes, J. S.: Plant respiration and photosynthesis in
global-scale models: incorporating acclimation to temperature and CO2,
Glob. Change Biol., 19, 45–63, https://doi.org/10.1111/j.1365-2486.2012.02797.x,
2013.
Smith, N. G., Malyshev, S. L., Shevliakova, E., Kattge, J., and Dukes, J. S.:
Foliar temperature acclimation reduces simulated carbon sensitivity to
climate, Nat. Clim. Change, 6, 407–411, https://doi.org/10.1038/nclimate2878, 2016.
Swenson, S. C. and Lawrence, D. M.: Assessing a dry surface layer-based soil
resistance parameterization for the Community Land Model using GRACE and
FLUXNET-MTE data, J. Geophys. Res.-Atmos., 119, 10299–10312,
https://doi.org/10.1002/2014JD022314, 2014.
Teuling, A. J., Van Loon, A. F., Seneviratne, S. I., Lehner, I., Aubinet,
M., Heinesch, B., Bernhofer, C., Grünwald, T., Prasse, H., and Spank, U.:
Evapotranspiration amplifies European summer drought, Geophys. Res. Lett.,
40, 2071–2075, https://doi.org/10.1002/grl.50495, 2013.
Thorburn, P. J., Walker, G. R., and Woods, P. H.: Comparison of diffuse
discharge from shallow water tables in soils and salt flats, J. Hydrol.,
136, 253–274, https://doi.org/10.1016/0022-1694(92)90014-M, 1992.
Trudinger, C. M., Haverd, V., Briggs, P. R., and Canadell, J. G.: Interannual variability in Australia's terrestrial carbon cycle constrained by multiple observation types, Biogeosciences, 13, 6363–6383, https://doi.org/10.5194/bg-13-6363-2016, 2016.
Trugman, A. T., Medvigy, D., Mankin, J. S., and Anderegg, W. R. L. L.: Soil
moisture stress as a major driver of carbon cycle uncertainty, Geophys. Res.
Lett., 45, 6495–6503, https://doi.org/10.1029/2018GL078131, 2018.
Ukkola, A. M., De Kauwe, M. G., Pitman, A. J., Best, M. J., Abramowitz, G.,
Haverd, V., Decker, M., and Haughton, N.: Land surface models systematically
overestimate the intensity, duration and magnitude of seasonal-scale
evaporative droughts, Environ. Res. Lett., 11, 104012,
https://doi.org/10.1088/1748-9326/11/10/104012, 2016a.
Ukkola, A. M., Pitman, A. J., Decker, M., De Kauwe, M. G., Abramowitz, G., Kala, J., and Wang, Y.-P.: Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model, Hydrol. Earth Syst. Sci., 20, 2403–2419, https://doi.org/10.5194/hess-20-2403-2016, 2016b.
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. and Pitman,
A. J.: Robust future changes in meteorological drought in CMIP6 projections
despite uncertainty in precipitation, Geophys. Res. Lett., 47,
e2020GL087820, https://doi.org/10.1029/2020GL087820, 2020.
van Dijk, A. I. J. M. J. M., Beck, H. E., Crosbie, R. S., de Jeu, R. A. M.,
Liu, Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium
Drought in southeast Australia (2001–2009): Natural and human causes and
implications for water resources, ecosystems, economy, and society, Water
Resour. Res., 49, 1040–1057, https://doi.org/10.1002/wrcr.20123, 2013.
Verdon-Kidd, D. C. and Kiem, A. S.: Nature and causes of protracted droughts
in southeast Australia: Comparison between the Federation, WWII, and Big Dry
droughts, Geophys. Res. Lett., 36, L22707, https://doi.org/10.1029/2009GL041067,
2009.
Wada, Y.: Impacts of groundwater pumping on regional and global water
resources, in: Terrestrial Water Cycle and Climate Change, American
Geophysical Union, edited by: Tang, Q. and Oki, T., Hoboken, United States,
71–101, https://doi.org/10.1002/9781118971772.ch5, 2016.
Wan, Z., Hook, S., and Hulley, G.: MOD11A1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD11A1.006, 2015a
Wan, Z., Hook, S., and Hulley, G.: MYD11A1 MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MYD11A1.006, 2015b.
Wan, Z. and Li, Z.-L.: A physics-based algorithm for retrieving land-surface
emissivity and temperature from EOS/MODIS data, IEEE T. Geosci. Remote, 35, 980–996, https://doi.org/10.1109/36.602541, 1997.
Wang, K. and Dickinson, R. E.: A review of global terrestrial
evapotranspiration: Observation, modeling, climatology, and climatic
variability, Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373, 2012.
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.
Wang, Y.-P. and Leuning, R.: A two-leaf model for canopy conductance,
photosynthesis and partitioning of available energy I: Model description and
comparison with a multi-layered model, Agr. Forest Meteorol., 91,
89–111, https://doi.org/10.1016/S0168-1923(98)00061-6, 1998.
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., 116,
G01034, https://doi.org/10.1029/2010JG001385, 2011.
Warren, J. M., Hanson, P. J., Iversen, C. M., Kumar, J., Walker, A. P., and
Wullschleger, S. D.: Root structural and functional dynamics in terrestrial
biosphere models – evaluation and recommendations, New Phytol., 205,
59–78, https://doi.org/10.1111/nph.13034, 2015.
Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C., and Landerer, F. W.:
Improved methods for observing Earth's time variable mass distribution with
GRACE using spherical cap mascons, J. Geophys. Res.-Sol. Ea., 120,
2648–2671, https://doi.org/10.1002/2014JB011547, 2015.
Wiese, D. N., Landerer, F. W., and Watkins, M. M.: Quantifying and reducing
leakage errors in the JPL RL05M GRACE mascon solution, Water Resour. Res.,
52, 7490–7502, https://doi.org/10.1002/2016WR019344, 2016.
Wiese, D. N., Yuan, D.-N., Boening, C., Landerer, F. W., and Watkins, M. M.: JPL
GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height Release 06
Coastal Resolution Improvement (CRI) Filtered Version 1.0. Ver. 1.0.,
PO.DAAC, CA, USA, https://doi.org/10.5067/TEMSC-3MJC6, 2018.
Wiese, D. N., Yuan, D.-N., Boening, C., Landerer, F. W., and Watkins, M. M.: JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height RL06 CRI Filtered Version 02, PO.DAAC [data set], https://doi.org/10.5067/TEMSC-3JC62, 2019.
Yang, J., Duursma, R. A., De Kauwe, M. G., Kumarathunge, D., Jiang, M.,
Mahmud, K., Gimeno, T. E., Crous, K. Y., Ellsworth, D. S., Peters, J.,
Choat, B., Eamus, D., and Medlyn, B. E.: Incorporating non-stomatal
limitation improves the performance of leaf and canopy models at high vapour
pressure deficit, Tree Physiol., 39, 1961–1974,
https://doi.org/10.1093/treephys/tpz103, 2019.
Zencich, S. J., Froend, R. H., Turner, J. V., and Gailitis, V.: Influence of
groundwater depth on the seasonal sources of water accessed by Banksia tree
species on a shallow, sandy coastal aquifer, Oecologia, 131, 8–19,
https://doi.org/10.1007/s00442-001-0855-7, 2002.
Zeng, X. and Decker, M.: Improving the numerical solution of soil
moisture–based Richards equation for land models with a deep or shallow
water table, J. Hydrometeorol., 10, 308–319, https://doi.org/10.1175/2008JHM1011.1,
2009.
Zhang, H., Pak, B., Wang, Y. P., Zhou, X., Zhang, Y., and Zhang, L.:
Evaluating surface water cycle simulated by the Australian Community Land
Surface Model (CABLE) across Different Spatial and Temporal Domains, J.
Hydrometeorol., 14, 1119–1138, https://doi.org/10.1175/JHM-D-12-0123.1, 2013.
Zhou, S., Williams, A. P., Berg, A. M., Cook, B. I., Zhang, Y., Hagemann,
S., Lorenz, R., Seneviratne, S. I., and Gentine, P.: Land–atmosphere
feedbacks exacerbate concurrent soil drought and atmospheric aridity, P.
Natl. Acad. Sci. USA, 116, 18848–18853, https://doi.org/10.1073/pnas.1904955116, 2019.
Zipper, S. C., Keune, J., and Kollet, S. J.: Land use change impacts on
European heat and drought: remote land-atmosphere feedbacks mitigated
locally by shallow groundwater, Environ. Res. Lett., 14, 044012,
https://doi.org/10.1088/1748-9326/ab0db3, 2019.
Short summary
Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often...
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