Articles | Volume 21, issue 7
https://doi.org/10.5194/bg-21-1801-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-1801-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and function
Dana A. Lapides
CORRESPONDING AUTHOR
USDA Southwest Watershed Research Station, Tucson, AZ, USA
W. Jesse Hahm
Department of Geography, Simon Fraser University, Burnaby, BC, Canada
Matthew Forrest
Senckenberg Biodiversity and Climate Research Centre, Senckenberg, Germany
Daniella M. Rempe
Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA
Thomas Hickler
Senckenberg Biodiversity and Climate Research Centre, Senckenberg, Germany
David N. Dralle
US Forest Service Pacific Southwest Research Station, Davis, CA, USA
Related authors
No articles found.
Mateus Dantas de Paula, Tatiana Reichert, Laynara F. Lugli, Erica McGale, Kerstin Pierick, João Paulo Darela-Filho, Liam Langan, Jürgen Homeier, Anja Rammig, and Thomas Hickler
Biogeosciences, 22, 2707–2732, https://doi.org/10.5194/bg-22-2707-2025, https://doi.org/10.5194/bg-22-2707-2025, 2025
Short summary
Short summary
This study explores how plant roots with different forms and functions rely on fungal partnerships for nutrient uptake. This relationship was integrated into a vegetation model and was tested in a tropical forest in Ecuador. The model accurately predicted root traits and showed that without fungi, biomass decreased by up to 80 %. The findings highlight the critical role of fungi in ecosystem processes and suggest that root–fungal interactions should be considered in vegetation models.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
Short summary
Short summary
An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Martin Thurner, Kailiang Yu, Stefano Manzoni, Anatoly Prokushkin, Melanie A. Thurner, Zhiqiang Wang, and Thomas Hickler
Biogeosciences, 22, 1475–1493, https://doi.org/10.5194/bg-22-1475-2025, https://doi.org/10.5194/bg-22-1475-2025, 2025
Short summary
Short summary
Nitrogen concentrations in tree tissues (leaves, branches, stems, and roots) are related to photosynthesis, growth, and respiration and thus to vegetation carbon uptake. Our novel database allows us to identify the controls of tree tissue nitrogen concentrations in boreal and temperate forests, such as tree age/size, species, and climate. Changes therein will affect tissue nitrogen concentrations and thus also vegetation carbon uptake.
Ryan Vella, Matthew Forrest, Andrea Pozzer, Alexandra P. Tsimpidi, Thomas Hickler, Jos Lelieveld, and Holger Tost
Atmos. Chem. Phys., 25, 243–262, https://doi.org/10.5194/acp-25-243-2025, https://doi.org/10.5194/acp-25-243-2025, 2025
Short summary
Short summary
This study examines how land cover changes influence biogenic volatile organic compound (BVOC) emissions and atmospheric states. Using a coupled chemistry–climate–vegetation model, we compare present-day land cover (deforested for crops and grazing) with natural vegetation and an extreme reforestation scenario. We find that vegetation changes significantly impact global BVOC emissions and organic aerosols but have a relatively small effect on total aerosols, clouds, and radiative effects.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
Short summary
Short summary
Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Blessing Kavhu, Matthew Forrest, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3595, https://doi.org/10.5194/egusphere-2024-3595, 2024
Short summary
Short summary
We developed a model to predict global wildfire patterns by examining weather, vegetation, and human activities. This tool helps forecast seasonal fire risks across diverse regions and focuses on seasonal changes, unlike existing models. Its simplicity makes it valuable for climate and fire management planning, as well as for use in global climate studies, helping communities better prepare for and adapt to rising wildfire threats.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Ryan Vella, Andrea Pozzer, Matthew Forrest, Jos Lelieveld, Thomas Hickler, and Holger Tost
Biogeosciences, 20, 4391–4412, https://doi.org/10.5194/bg-20-4391-2023, https://doi.org/10.5194/bg-20-4391-2023, 2023
Short summary
Short summary
We investigated the effect of the El Niño–Southern Oscillation (ENSO) on biogenic volatile organic compound (BVOC) emissions from plants. ENSO events can cause a significant increase in these emissions, which have a long-term impact on the Earth's atmosphere. Persistent ENSO conditions can cause long-term changes in vegetation, resulting in even higher BVOC emissions. We link ENSO-induced emission anomalies with driving atmospheric and vegetational variables.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
Short summary
Short summary
Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
Short summary
Short summary
Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
Angelica Feurdean, Roxana Grindean, Gabriela Florescu, Ioan Tanţău, Eva M. Niedermeyer, Andrei-Cosmin Diaconu, Simon M. Hutchinson, Anne Brigitte Nielsen, Tiberiu Sava, Andrei Panait, Mihaly Braun, and Thomas Hickler
Biogeosciences, 18, 1081–1103, https://doi.org/10.5194/bg-18-1081-2021, https://doi.org/10.5194/bg-18-1081-2021, 2021
Short summary
Short summary
Here we used multi-proxy analyses from Lake Oltina (Romania) and quantitatively examine the past 6000 years of the forest steppe in the lower Danube Plain, one of the oldest areas of human occupation in southeastern Europe. We found the greatest tree cover between 6000 and 2500 cal yr BP. Forest loss was under way by 2500 yr BP, falling to ~20 % tree cover linked to clearance for agriculture. The weak signs of forest recovery over the past 2500 years highlight recurring anthropogenic pressure.
Cited articles
Anderson, S. P., Blum, J., Brantley, S. L., Chadwick, O., Chorover, J., Derry, L. A., Drever, J. I., Hering, J. G., Kirchner, J. W., Kump, L. R., Richter, D., and White, A. E.: Proposed initiative would study Earth's weathering engine, Eos T. Am. Geophys. Un., 85, 265–269, 2004. a
Bond, W. J., Woodward, F. I., and Midgley, G. F.: The global distribution of ecosystems in a world without fire, New Phytol., 165, 525–538, 2005. a
Cannon, W. A.: The root habits of desert plants, vol. 131, Carnegie Institution of Washington, 1911. a
Cowling, R. M., Rundel, P. W., Lamont, B. B., Arroyo, M. K., and Arianoutsou, M.: Plant diversity in Mediterranean-climate regions, Trends Ecol. Evol., 11, 362–366, 1996. a
Cox, P. M.: Description of the “TRIFFID” dynamic global vegetation model, Met Office, 2001. a
Daly, C., Bachelet, D., Lenihan, J. M., Neilson, R. P., Parton, W., and Ojima, D.: Dynamic simulation of tree–grass interactions for global change studies, Ecol. Appl., 10, 449–469, 2000. a
Davis, S. H., Vertessy, R. A., Dunkerley, D. L., and Mein, R. G.: The influence of scale on the measurement of saturated hydraulic conductivity in forest soils, in: National Conference Publication-Institution of Engineers Australia NCP, vol. 1, pp. 103–108, Institution of Engineers, Australia, 1996. a
Dralle, D. N., Hahm, W. J., Chadwick, K. D., McCormick, E., and Rempe, D. M.: Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes, Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, 2021. a, b
Dralle, D. N., Hahm, W. J., and Rempe, D.: Inferring hillslope groundwater recharge ratios from the storage–discharge relation, Geophys. Res. Lett., 50, e2023GL104255, https://doi.org/10.1029/2023GL104255, 2023a. a
Dralle, D. N., Rossi, G., Georgakakos, P., Hahm, W. J., Rempe, D. M., Blanchard, M., Power, M., Dietrich, W., and Carlson, S.: The salmonid and the subsurface: Hillslope storage capacity determines the quality and distribution of fish habitat, Ecosphere, 14, e4436, https://doi.org/10.1002/ecs2.4436, 2023b. a
Eliades, M., Bruggeman, A., Lubczynski, M. W., Christou, A., Camera, C., and Djuma, H.: The water balance components of Mediterranean pine trees on a steep mountain slope during two hydrologically contrasting years, J. Hydrol., 562, 712–724, 2018. a
Elsenbeer, H., Newton, B. E., Dunne, T., and de Moraes, J. M.: Soil hydraulic conductivities of latosols under pasture, forest and teak in Rondonia, Brazil, Hydrol. Process., 13, 1417–1422, 1999. a
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W., Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague, C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier, J., Perket, J., Rouholahnejad‐Freund, E., Wagener, T., Zeng, X., Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B., Safeeq, M., Shen, C., van Verseveld, W., Volk, J., and Yamazaki, D.: Hillslope hydrology in global change research and earth system modeling, Water Resour. Res., 55, 1737–1772, 2019. a, b
Feng, X., Thompson, S. E., Woods, R., and Porporato, A.: Quantifying asynchronicity of precipitation and potential evapotranspiration in Mediterranean climates, Geophys. Res Lett., 46, 14692–14701, 2019. a
Friend, A., Stevens, A., Knox, R., and Cannell, M.: A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0), Ecol. Model., 95, 249–287, 1997. a
Gan, R., Zhang, Y., Shi, H., Yang, Y., Eamus, D., Cheng, L., Chiew, F. H., and Yu, Q.: Use of satellite leaf area index estimating evapotranspiration and gross assimilation for Australian ecosystems, Ecohydrology, 11, e1974, https://doi.org/10.1002/eco.1974, 2018. a
Gao, H., Fenicia, F., and Savenije, H. H. G.: HESS Opinions: Are soils overrated in hydrology?, Hydrol. Earth Syst. Sci., 27, 2607–2620, https://doi.org/10.5194/hess-27-2607-2023, 2023. a
Godsey, S. and Elsenbeer, H.: The soil hydrologic response to forest regrowth: a case study from southwestern Amazonia, Hydrol. Process., 16, 1519–1522, 2002. a
Gordon, W., Famiglietti, J., Fowler, N., Kittel, T., and Hibbard, K.: Validation of simulated runoff from six terrestrial ecosystem models: results from VEMAP, Ecol. Appl., 14, 527–545, 2004. a
Grant, G. E. and Dietrich, W. E.: The frontier beneath our feet, Water Resour. Res., 53, 2605–2609, 2017. a
Grindley, J.: Calculated soil moisture deficits in the dry summer of 1959 and forecast dates of first appreciable runoff, International Association of Scientific Hydrology, pp. 109–120, 1960. a
Grindley, J.: The estimation of soil moisture deficits, Water for Peace: Water Supply Technology, 3, 241, 1968. a
Hahm, W. J., Rempe, D. M., Dralle, D. N., Dawson, T. E., Lovill, S. M., Bryk, A. B., Bish, D. L., Schieber, J., and Dietrich, W. E.: Lithologically controlled subsurface critical zone thickness and water storage capacity determine regional plant community composition, Water Resour. Res., 55, 3028–3055, 2019. a, b, c, d, e, f, g
Hahm, W. J., Rempe, D., Dralle, D., Dawson, T., and Dietrich, W.: Oak transpiration drawn from the weathered bedrock vadose zone in the summer dry season, Water Resour. Res., 56, e2020WR027419, https://doi.org/10.1029/2020WR027419, 2020. a, b, c
Hahm, W. J., Dralle, D. N., Sanders, M., Bryk, A. B., Fauria, K. E., Huang, M.-H., Hudson-Rasmussen, B., Nelson, M. D., Pedrazas, M. A., Schmidt, L., Whiting, J., Dietrich, W. E., and Rempe, D. M.: Bedrock vadose zone storage dynamics under extreme drought: consequences for plant water availability, recharge, and runoff, Water Resour. Res., 58, e2021WR031781, https://doi.org/10.1029/2021WR031781, 2022. a, b, c, d
Hahm, W. J., Dralle, D. N., Lapides, D. A., Ehlert, R. S., and Rempe, D. M.: Geologic controls on apparent root‐zone storage capacity, Water Resour. Res., 60, e2023WR035362, https://doi.org/10.22541/essoar.168500262.25691702/v1, 2024. a
Hickler, T., Smith, B., Sykes, M. T., Davis, M. B., Sugita, S., and Walker, K.: Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA, Ecology, 85, 519–530, 2004. a
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L., Giesecke, T., Fronzek, S., Carter, T. R., Cramer, W., Kühn, I., and Sykes, M. T.: Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model, Global Ecol. Biogeogr., 21, 50–63, 2012. a, b
Horton, R. E.: The role of infiltration in the hydrologic cycle, Eos T. Am. Geophys. Un., 14, 446–460, 1933. a
Horton, R. E.: Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology, Geol. Soc. Am. Bull., 56, 275–370, 1945. a
Jayko, A., Blake, M., McLaughlin, R., Ohlin, H., Ellen, S., and Kelsey, H.: Reconnaissance geologic map of the Covelo 30-by 60-minute quadrangle, northern California, Tech. rep., US Government Printing Office, 1989. a
Jiménez-Rodríguez, C. D., Sulis, M., and Schymanski, S.: Exploring the role of bedrock representation on plant transpiration response during dry periods at four forested sites in Europe, Biogeosciences, 19, 3395–3423, https://doi.org/10.5194/bg-19-3395-2022, 2022. a
Jin, S., Dewitz, J., Li, C., Sorenson, D., Zhu, Z., Shogib, M. R. I., Danielson, P., Granneman, B., Costello, C., Case, A., and Glass, L.: National Land Cover Database 2019: A Comprehensive Strategy for Creating the 1986–2019 Forest Disturbance Product, J. Remote Sens., 3, 0021, https://doi.org/10.34133/remotesensing.0021, 2023. a, b
Joshi, J., Stocker, B. D., Hofhansl, F., Zhou, S., Dieckmann, U., and Prentice, I. C.: Towards a unified theory of plant photosynthesis and hydraulics, Nat. Plants, 8, 1304–1316, 2022. a
Keenan, T., Sabate, S., and Gracia, C.: Soil water stress and coupled photosynthesis–conductance models: Bridging the gap between conflicting reports on the relative roles of stomatal, mesophyll conductance and biochemical limitations to photosynthesis, Agr. Forest Meteorol., 150, 443–453, 2010. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, 3, https://doi.org/10.1029/2005WR004362, 2006. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the Köppen–Geiger climate classification updated, Meteorol. Z., 15, 3, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a, b, c
Langan, L., Higgins, S. I., and Scheiter, S.: Climate-biomes, pedo-biomes or pyro-biomes: which world view explains the tropical forest–savanna boundary in South America?, J. Biogeogr., 44, 2319–2330, 2017. a
Lapides, D. A., Hahm, W. J., Rempe, D. M., Dietrich, W. E., and Dralle, D. N.: Controls on stream water age in a saturation overland flow-dominated catchment, Water Resour. Res., 58, e2021WR031665, https://doi.org/10.1029/2021WR031665, 2022. a, b, c
Lapides, D. A., Hahm, W. J., Forrest, M., Rempe, D. M., Hickler, T., and Dralle, D. N.: Lapides LPJ Rock Moisture 2023, Cyverse [data set], https://data.cyverse.org/dav-anon/iplant/home/danalapides/Lapides_LPJ_Rock_Moisture_2023, last accessed: 9 April 2024. a
Lawlor, D. W. and Tezara, W.: Causes of decreased photosynthetic rate and metabolic capacity in water-deficient leaf cells: a critical evaluation of mechanisms and integration of processes, Ann. Bot.-London, 103, 561–579, 2009. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M, Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V., and Zeng, X.: CLM5 documentation, in: Technical Report, National Center for Atmospheric Research, 2019. a
Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J. P., Engelbrecht, F., Fischer, E., Fyfe, J. C., Jones, C., Maycock, A., Mutemi, J., Ndiaye, O., Panickal, S., and Zhou, T.: Future global climate: scenario-based projections and near-term information, in: Climate change 2021: The physical science basis. Contribution of working group to the sixth assessment report of the intergovernmental panel on climate change, Cambridge University Press, pp. 553–672, 2021. a
Link, P., Simonin, K., Maness, H., Oshun, J., Dawson, T., and Fung, I.: Species differences in the seasonality of evergreen tree transpiration in a Mediterranean climate: Analysis of multiyear, half-hourly sap flow observations, Water Resour. Res., 50, 1869–1894, 2014. a
Lovill, S., Hahm, W., and Dietrich, W.: Drainage from the critical zone: Lithologic controls on the persistence and spatial extent of wetted channels during the summer dry season, Water Resour. Res., 54, 5702–5726, 2018. a
Luković, J., Chiang, J. C., Blagojević, D., and Sekulić, A.: A later onset of the rainy season in California, Geophys. Res Lett., 48, e2020GL090350, https://doi.org/10.1029/2020GL090350, 2021. a
Maysonnave, J., Delpierre, N., François, C., Jourdan, M., Cornut, I., Bazot, S., Vincent, G., Morfin, A., and Berveiller, D.: Contribution of deep soil layers to the transpiration of a temperate deciduous forest: Implications for the modelling of productivity, Sci. Total Environ., 838, 155981, https://doi.org/10.1016/j.scitotenv.2022.155981, 2022. a
McDowell, N. G.: Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality, Plant Physiol., 155, 1051–1059, 2011. a
Milly, P. and Dunne, K.: Sensitivity of the global water cycle to the water-holding capacity of land, J. Climate, 7, 506–526, 1994. a
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling vegetation dynamics: the ecosystem demography model (ED), Ecol. Monogr., 71, 557–586, 2001. a
Myneni, R., Knyazikhin, Y., and Park, T.: MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061 [Data set], Tech. rep., NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.061, 2021. a
Parmesan, C., Morecroft, M. D., Trisurat, Y., Adrian, R., Anshari, G. Z., Arneth, A., Gao, Q., Gonzalez, P., Harris, R., Price, J., Stevens, N., and Talukdarr, G. H.: Terrestrial and freshwater ecosystems and their services, Cambridge University Press, 2022. a
Pelletier, J. D., Barron-Gafford, G. A., Gutiérrez-Jurado, H., Hinckley, E. S., Istanbulluoglu, E., McGuire, L. A., Niu, G.-Y., Poulos, M. J., Rasmussen, C., Richardson, P., Swetnam, T. L., and Tucker, G. E.: Which way do you lean? Using slope aspect variations to understand Critical Zone processes and feedbacks, Earth Surf. Proc. Land., 43, 1133–1154, 2018. a
Piedallu, C., Gégout, J.-C., Perez, V., and Lebourgeois, F.: Soil water balance performs better than climatic water variables in tree species distribution modelling, Global Ecol. Biogeogr., 22, 470–482, 2013. a
Pitman, A.: The evolution of, and revolution in, land surface schemes designed for climate models, Int. J. Climatol., 23, 479–510, 2003. a
Pollard, D. and Thompson, S. L.: Use of a land-surface-transfer scheme (LSX) in a global climate model: the response to doubling stomatal resistance, Global Planet. Change, 10, 129–161, 1995. a
Rempe, D. M., McCormick, E. L., Hahm, W. J., Persad, G., Cummins, C., Lapides, D. A., Chadwick, K. D., and Dralle, D. N.: Mechanisms underlying the vulnerability of seasonally dry ecosystems to drought, https://doi.org/10.31223/X5XW7D, 2023. a, b
Riebe, C. S., Hahm, W. J., and Brantley, S. L.: Controls on deep critical zone architecture: A historical review and four testable hypotheses, Earth Surf. Proc. Land., 42, 128–156, 2017. a
Ruiz, L., Varma, M. R., Kumar, M. M., Sekhar, M., Maréchal, J.-C., Descloitres, M., Riotte, J., Kumar, S., Kumar, C., and Braun, J.-J.: Water balance modelling in a tropical watershed under deciduous forest (Mule Hole, India): Regolith matric storage buffers the groundwater recharge process, J. Hydrol., 380, 460–472, 2010. a
Sakschewski, B., von Bloh, W., Drüke, M., Sörensson, A. A., Ruscica, R., Langerwisch, F., Billing, M., Bereswill, S., Hirota, M., Oliveira, R. S., Heinke, J., and Thonicke, K.: Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests, Biogeosciences, 18, 4091–4116, https://doi.org/10.5194/bg-18-4091-2021, 2021. a
Salve, R., Rempe, D. M., and Dietrich, W. E.: Rain, rock moisture dynamics, and the rapid response of perched groundwater in weathered, fractured argillite underlying a steep hillslope, Water Resour. Res., 48, 11, https://doi.org/10.1029/2012WR012583, 2012. a, b, c, d
Sato, H., Itoh, A., and Kohyama, T.: SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach, Ecol. Model., 200, 279–307, 2007. a
Seiler, C., Melton, J. R., Arora, V. K., Sitch, S., Friedlingstein, P., Anthoni, P., Goll, D., Jain, A. K., Joetzjer, E., Lienert, S., Lombardozzi, D., Luyssaert, S., Nabel, J. E. M. S., Tian, H., Vuichard, N., Walker, A. P., Yuan, W., and Zaehle, S.: Are terrestrial biosphere models fit for simulating the global land carbon sink?, J. Adv. Model. Earth Sy., 14, e2021MS002946, https://doi.org/10.1029/2021MS002946, 2022. a, b, c
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003. a, b, c
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg, J., and Zaehle, S.: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, 2014. a, b, c, d
Soil Survey Staff: Gridded National Soil Survey Geographic (gNATSGO) Database for the Conterminous United States, Tech. rep., United States Department of Agriculture, Natural Resources Conservation Service, https://nrcs.app.box.com/v/soils (last access: 9 April 2024), 2019a. a
Spence, C.: A paradigm shift in hydrology: Storage thresholds across scales influence catchment runoff generation, Geography Compass, 4, 819–833, 2010. a
Steinkamp, J. and Hickler, T.: Is drought-induced forest dieback globally increasing?, J. Ecol., 103, 31–43, 2015. a
Stocker, B. D., Wang, H., Smith, N. G., Harrison, S. P., Keenan, T. F., Sandoval, D., Davis, T., and Prentice, I. C.: P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production, Geosci. Model Dev., 13, 1545–1581, https://doi.org/10.5194/gmd-13-1545-2020, 2020. a
Sun, Y., Wang, C., Chen, H. Y., and Ruan, H.: Response of plants to water stress: a meta-analysis, Front. Plant Sci., 11, 978, https://doi.org/10.3389/fpls.2020.00978, 2020. a
Swain, D. L.: A shorter, sharper rainy season amplifies California wildfire risk, Geophys. Res Lett., 48, e2021GL092843, https://doi.org/10.1029/2021GL092843, 2021. a
Swain, D. L., Langenbrunner, B., Neelin, J. D., and Hall, A.: Increasing precipitation volatility in twenty-first-century California, Nat. Clim. Change, 8, 427–433, 2018. a
Tang, J., Pilesjö, P., Miller, P. A., Persson, A., Yang, Z., Hanna, E., and Callaghan, T. V.: Incorporating topographic indices into dynamic ecosystem modelling using LPJ-GUESS, Ecohydrology, 7, 1147–1162, 2014. a
Tang, J., Miller, P. A., Crill, P. M., Olin, S., and Pilesjö, P.: Investigating the influence of two different flow routing algorithms on soil–water–vegetation interactions using the dynamic ecosystem model LPJ-GUESS, Ecohydrology, 8, 570–583, 2015. a
Tardieu, F., Granier, C., and Muller, B.: Water deficit and growth. Co-ordinating processes without an orchestrator?, Curr. Opin. Plant Biol., 14, 283–289, 2011. a
Tezara, W., Mitchell, V., Driscoll, S., and Lawlor, D.: Water stress inhibits plant photosynthesis by decreasing coupling factor and ATP, Nature, 401, 914–917, 1999. a
Thornton, M., Shrestha, R., Wei, Y., Thornton, P., Kao, S., Wilson, B., Mayer, B., Wei, Y., Devarakonda, R., and Vose, R.: Daymet: daily surface weather data on a 1-km grid for North America, Version 4 R1, Single Pixel Extraction Tool | Daymet (ornl. gov), ORNL DAAC, Oak Ridge, Tennessee, USA, 2022. a
Tuzet, A., Perrier, A., and Leuning, R.: A coupled model of stomatal conductance, photosynthesis and transpiration, Plant Cell Environ., 26, 1097–1116, 2003. a
Vico, G. and Porporato, A.: Modelling C 3 and C 4 photosynthesis under water-stressed conditions, Plant Soil, 313, 187–203, 2008. a
Wang, S., Xu, M., Zhang, X., and Wang, Y.: Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine, Remote Sens.-Basel, 14, 2055, https://doi.org/10.3390/rs14092055, 2022. 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
Wolf, A., Blyth, E., Harding, R., Jacob, D., Keup-Thiel, E., Goettel, H., and Callaghan, T.: Sensitivity of an ecosystem model to hydrology and temperature, Climatic Change, 87, 75–89, 2008a. a
Wolf, A., Callaghan, T. V., and Larson, K.: Future changes in vegetation and ecosystem function of the Barents Region, Climatic Change, 87, 51–73, 2008b. a
Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y. Y., Miralles, D. G., and Pan, M.: Multi-decadal trends in global terrestrial evapotranspiration and its components, Sci. Rep.-UK, 6, 1–12, 2016. a
Zweifel, R., Zimmermann, L., Zeugin, F., and Newbery, D. M.: Intra-annual radial growth and water relations of trees: implications towards a growth mechanism, J. Exp. Bot., 57, 1445–1459, 2006. a
Short summary
Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models despite increasing recognition of its importance. Here, we add a weathered bedrock component to a widely used vegetation model. Using a case study of two sites in California and model runs across the United States, we show that more accurately representing subsurface water storage and hydrology increases summer plant water use so that it better matches patterns in distributed data products.
Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models...
Altmetrics
Final-revised paper
Preprint