Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3165-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3165-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
06 Jun 2023
Model description paper |
| 06 Jun 2023
| 06 Jun 2023
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Miquel De Cáceres
CORRESPONDING AUTHOR
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Roberto Molowny-Horas
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Antoine Cabon
Wilkes Center for Climate Science and Policy, University of Utah, Salt Lake City, UT 84112, USA
School of Biological, University of Utah, Salt Lake City, UT 84112, USA
Jordi Martínez-Vilalta
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Universitat Autònoma de Barcelona, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Maurizio Mencuccini
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
ICREA, E08010 Barcelona, Spain
Raúl García-Valdés
Department of Biology and Geology, Physics and Inorganic Chemistry, Rey Juan Carlos University, c/ Tulipán s/n, 28933 Móstoles, Spain
Daniel Nadal-Sala
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, University of Barcelona (UB),
Av. Diagonal 645, E08028 Barcelona, Spain
Santiago Sabaté
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, University of Barcelona (UB),
Av. Diagonal 645, E08028 Barcelona, Spain
Nicolas Martin-StPaul
Écologie des Forêts Méditerranéennes (URFM), INRAE, Avignon, France
Xavier Morin
CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
Francesco D'Adamo
CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Enric Batllori
Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Facultat de Biologia, University of Barcelona (UB),
Av. Diagonal 645, E08028 Barcelona, Spain
Aitor Améztegui
Department of Agricultural and Forest Engineering, Universitat de Lleida, E25198 Lleida, Spain
Joint Research Unit CTFC-Agrotecnio-CERCA, Ctra. Sant Llorenç km.2, E25280 Solsona, Spain
Related authors
Jesús Sánchez-Dávila, Miquel De Cáceres, Jordi Vayreda, and Javier Retana
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-255, https://doi.org/10.5194/hess-2023-255, 2023
Revised manuscript under review for HESS
Short summary
Short summary
Forest blue water is determined by climate, functional traits and stand structure variables. LAI is the main driver in the trade-off between the blue and green water. Blue water is concentrated in the autumn-winter season and deciduous trees can increase the relative blue water. The leave phenology and seasonal distribution are determinants in the relative blue water.
Jesús Sánchez-Dávila, Miquel De Cáceres, Jordi Vayreda, and Javier Retana
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-255, https://doi.org/10.5194/hess-2023-255, 2023
Revised manuscript under review for HESS
Short summary
Short summary
Forest blue water is determined by climate, functional traits and stand structure variables. LAI is the main driver in the trade-off between the blue and green water. Blue water is concentrated in the autumn-winter season and deciduous trees can increase the relative blue water. The leave phenology and seasonal distribution are determinants in the relative blue water.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
Short summary
Short summary
A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Simon Jones, Lucy Rowland, Peter Cox, Deborah Hemming, Andy Wiltshire, Karina Williams, Nicholas C. Parazoo, Junjie Liu, Antonio C. L. da Costa, Patrick Meir, Maurizio Mencuccini, and Anna B. Harper
Biogeosciences, 17, 3589–3612, https://doi.org/10.5194/bg-17-3589-2020, https://doi.org/10.5194/bg-17-3589-2020, 2020
Short summary
Short summary
Non-structural carbohydrates (NSCs) are an important set of molecules that help plants to grow and respire when photosynthesis is restricted by extreme climate events. In this paper we present a simple model of NSC storage and assess the effect that it has on simulations of vegetation at the ecosystem scale. Our model has the potential to significantly change predictions of plant behaviour in global vegetation models, which would have large implications for predictions of the future climate.
Rafael Poyatos, Oliver Sus, Llorenç Badiella, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Biogeosciences, 15, 2601–2617, https://doi.org/10.5194/bg-15-2601-2018, https://doi.org/10.5194/bg-15-2601-2018, 2018
Short summary
Short summary
Plant traits are characteristics of plants that are easy to measure and that show how plants function. Values of these traits for many species and locations worldwide are available in trait databases, but these are often incomplete. Here we use different statistical methods to fill the gaps in a trait database of Mediterranean and temperate tree species. Combining traits and environmental information provides more plausible gap-filled databases and preserves the observed trait variability.
C. T. Chang, S. Sabaté, D. Sperlich, S. Poblador, F. Sabater, and C. Gracia
Biogeosciences, 11, 6173–6185, https://doi.org/10.5194/bg-11-6173-2014, https://doi.org/10.5194/bg-11-6173-2014, 2014
D. Sperlich, C. T. Chang, J. Peñuelas, C. Gracia, and S. Sabaté
Biogeosciences, 11, 5657–5674, https://doi.org/10.5194/bg-11-5657-2014, https://doi.org/10.5194/bg-11-5657-2014, 2014
E. Doblas-Miranda, P. Rovira, L. Brotons, J. Martínez-Vilalta, J. Retana, M. Pla, and J. Vayreda
Biogeosciences, 10, 8353–8361, https://doi.org/10.5194/bg-10-8353-2013, https://doi.org/10.5194/bg-10-8353-2013, 2013
Related subject area
Biogeosciences
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
A global behavioural model of human fire use and management: WHAM! v1.0
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): calibration-free calculations of water and energy fluxes
biospheremetrics v1.0.1: An R package to calculate two complementary terrestrial biosphere integrity indicators: human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
In-silico calculation of soil pH by SCEPTER v1.0
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Optimal enzyme allocation leads to the constrained enzyme hypothesis: The Soil Enzyme Steady Allocation Model (SESAM v3.1)
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Preprint under review for GMD
Short summary
Short summary
We adapt a fire-behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James Millington
EGUsphere, https://doi.org/10.5194/egusphere-2023-2162, https://doi.org/10.5194/egusphere-2023-2162, 2023
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
EGUsphere, https://doi.org/10.5194/egusphere-2023-1626, https://doi.org/10.5194/egusphere-2023-1626, 2023
Short summary
Short summary
Numerous estimations of water and energy balances heavily depend on empirical equations that necessitate site-specific calibration. This equifinality poses the risk of obtaining 'right answers for wrong reasons.' In this paper, we introduce novel formulations based on first-principles to calculate calibration-free quantities, such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
EGUsphere, https://doi.org/10.5194/egusphere-2023-2503, https://doi.org/10.5194/egusphere-2023-2503, 2023
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract >25 % of the potential pre-industrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of land, water, and fertilizer use, as well as climate change.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Elin Ristorp Aas, Heleen A. de Wit, and Terje Koren Berntsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2069, https://doi.org/10.5194/egusphere-2023-2069, 2023
Short summary
Short summary
By including microbial processes in soil models, we learn about how the soil system interacts with its environment, and responds to climate change. We present a soil process model, MIMICS+, able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also found that when adding nitrogen, the relationship between soil microbes changed notably. Coupling the model to a vegetation model, will allow further investigation of these mechanisms.
Yannek Käber, Florian Hartig, and Harald Bugmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2114, https://doi.org/10.5194/egusphere-2023-2114, 2023
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed with forest inventory data, and climatic effects are challenging to capture.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
EGUsphere, https://doi.org/10.5194/egusphere-2023-2003, https://doi.org/10.5194/egusphere-2023-2003, 2023
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that based on satellite records represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-137, https://doi.org/10.5194/gmd-2023-137, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater pH and bulk soil pH is thus crucial to accurate evaluation of alkalinity required to counteract soil acidification and resulting capture of anthropogenic carbon dioxide through the Enhanced Rock Weathering technique. This has been enabled by the updated reactive-transport SCEPTER code and newly developed framework to simulate soil pH.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
EGUsphere, https://doi.org/10.5194/egusphere-2023-1492, https://doi.org/10.5194/egusphere-2023-1492, 2023
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behavior at low microbial biomass led us formulate the constrained enzyme hypothesis. We now can better describe how microbial mediated linkages of elemental fluxes adapt across decades.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-1287, https://doi.org/10.5194/egusphere-2023-1287, 2023
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predict their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that important questions related to plant-atmosphere interactions can be addressed, such as the effects of rising CO2 and ozone pollution on carbon uptake of the biosphere. The model has been well validated with both ground and satellite observations.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
Short summary
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
Short summary
Short summary
A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
Short summary
Short summary
The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Cited articles
Adams, H. D., Park Williams, A., Xu, C., Rauscher, S. A., Jiang, X., and McDowell, N. G.:
Empirical and process-based approaches to climate-induced forest mortality models, Front. Plant Sci., 4, 1–5, https://doi.org/10.3389/fpls.2013.00438, 2013.
Altava-Ortiz, V. and Barrera-Escoda, A.:
Escenaris Climàtics Regionalitzats a Catalunya (Escat-2020), Servei Meteorologic de Catalunya, Generalitat de Catalunya, Barcelona, 2020.
Ameztegui, A., Rodrigues, M., and Granda, V.:
Uncertainty of biomass stocks in Spanish forests: a comprehensive comparison of allometric equations, Eur. J. For. Res., 141, 395–407, https://doi.org/10.1007/s10342-022-01444-w, 2022.
Anderegg, L. D. L., Berner, L. T., Badgley, G., Sethi, M. L., Law, B. E., and HilleRisLambers, J.:
Within-species patterns challenge our understanding of the leaf economics spectrum, Ecol. Lett., 21, 734–744, https://doi.org/10.1111/ele.12945, 2018.
Anderegg, L. D. L., Griffith, D. M., Cavender-Bares, J., Riley, W. J., Berry, J. A., Dawson, T. E., and Still, C. J.:
Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional, Glob. Change Biol., 28, 2541–2554, https://doi.org/10.1111/gcb.16040, 2022.
Anten, N. P. R. and Bastiaans, L.:
The use of canopy models to analyze light competition among plants, in: Canopy photosynthesis: from basics to application, edited by: Hikosaka, K., Niinemets, Ü., and Anten, N. P. R., Springer, the Netherlands, 379–398, https://doi.org/10.1007/978-94-017-7291-4_14, 2016.
Augustynczik, A. L. D., Gutsch, M., Basile, M., Suckow, F., Lasch, P., Yousefpour, R., Hanewinkel, M.:
Socially optimal forest management and biodiversity conservation in temperate forests under climate change, Ecol. Econ., 169, 106504, https://doi.org/10.1016/j.ecolecon.2019.106504, 2020.
Bartlett, M. K., Scoffoni, C., and Sack, L.:
The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global meta-analysis, Ecol. Lett., 15, 393–405, https://doi.org/10.1111/j.1461-0248.2012.01751.x, 2012.
Berzaghi, F., Wright, I. J., Kramer, K., Oddou-Muratorio, S., Bohn, F. J., Reyer, C. P. O., Sabaté, S., Sanders, T. G. M., and Hartig, F.:
Towards a New Generation of Trait-Flexible Vegetation Models, Trends in Ecology and Evolution, 35, 191–205, https://doi.org/10.1016/j.tree.2019.11.006, 2020.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.:
The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Blanco, J. A., Ameztegui, A., and Rodríguez, F.:
Modelling Forest Ecosystems: a crossroad between scales, techniques and applications, Ecol. Model., 425, 109030, https://doi.org/10.1016/j.ecolmodel.2020.109030, 2020.
Bugmann, H.:
A review of forest gap models, Climatic Change, 51, 259–305, https://doi.org/10.1023/A:1012525626267, 2001.
Bugmann, H. and Seidl, R.:
The evolution, complexity and diversity of models of long-term forest dynamics, J. Ecol., 110, 2288–2307, https://doi.org/10.1111/1365-2745.13989, 2022.
Bugmann, H., Seidl, R., Hartig, F., Bohn, F., Brůna, J., Cailleret, M., François, L., Heinke, J., Henrot, A. J., Hickler, T., Hülsmann, L., Huth, A., Jacquemin, I., Kollas, C., Lasch-Born, P., Lexer, M. J., Merganič, J., Merganičová, K., Mette, T., Miranda, B. R., Nadal-Sala, D., Rammer, W., Rammig, A., Reineking, B., Roedig, E., Sabaté, S., Steinkamp, J., Suckow, F., Vacchiano, G., Wild, J., Xu, C., and Reyer, C. P. O.:
Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale, Ecosphere, 10, e02616, https://doi.org/10.1002/ecs2.2616, 2019.
Bugmann, H. K. M.:
A simplified forest model to study species composition along climate gradients, Ecology, 77, 2055–2074, https://doi.org/10.2307/2265700, 1996.
Buotte, P. C., Koven, C. D., Xu, C., Shuman, J. K., Goulden, M. L., Levis, S., Katz, J., Ding, J., Ma, W., Robbins, Z., and Kueppers, L. M.:
Capturing functional strategies and compositional dynamics in vegetation demographic models, Biogeosciences, 18, 4473–4490, https://doi.org/10.5194/bg-18-4473-2021, 2021.
Burriel, J. A., Gracia, C., Ibàñez, J. J., Mata, T., and Vayreda, J.:
Inventari Ecològic i Forestal de Catalunya, 10 volumes, CREAF, Bellaterra, Spain, 2004.
Cabon, A., Peters, R. L., Fonti, P., Martínez-Vilalta, J., and De Cáceres, M.:
Temperature and water potential co-limit stem cambial activity along a steep elevational gradient, New Phytol., 226, 1325–1340, https://doi.org/10.1111/nph.16456, 2020a.
Cabon, A., Fernández-de-Uña, L., Gea-Izquierdo, G., Meinzer, F. C., Woodruff, D. R., Martínez-Vilalta, J., and De Cáceres, M.:
Water potential control of turgor-driven tracheid enlargement in Scots pine at its xeric distribution edge, New Phytol., 225, 209–221, https://doi.org/10.1111/nph.16146, 2020b.
Cabon, A., Kannenberg, S. A., Arain, A., Babst, F., Baldocchi, D., Belmecheri, S., Delpierre, N., Guerrieri, R., Maxwell, J. T., McKenzie, S., Meinzer, F. C., Moore, D. J. P., Pappas, C., Rocha, A. V., Szejner, P., Ueyama, M., Ulrich, D., Vincke, C., Voelker, S. L., Wei, J., Woodruff, D., and Anderegg, W. R. L.:
Cross-biome synthesis of source versus sink limits to tree growth, Science, 376, 758–761, https://doi.org/10.1126/science.abm4875, 2022.
Caspersen, J. P., Vanderwel, M. C., Cole, W. G., and Purves, D. W.:
How stand productivity results from size- and competition-dependent growth and mortality, PLoS ONE, 6, e28660, https://doi.org/10.1371/journal.pone.0028660, 2011.
Chamberlain, S. and Szocs, E.:
taxize – taxonomic search and retrieval in R, F1000Research, 2, 191, https://f1000research.com/articles/2-191/v2 (last access: 30 May 2023), 2013.
Choat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L., Lens, F., Maherali, H., Martínez-Vilalta, J., Mayr, S., Mencuccini, M., Mitchell, P. J., Nardini, A., Pittermann, J., Pratt, R. B., Sperry, J. S., Westoby, M., Wright, I. J., and Zanne, A. E.:
Global convergence in the vulnerability of forests to drought, Nature, 491, 752–755, https://doi.org/10.1038/nature11688, 2012.
Choat, B., Brodribb, T. J., Brodersen, C. R., Duursma, R. A., López, R., and Medlyn, B. E.:
Triggers of tree mortality under drought, Nature, 558, 531–539, https://doi.org/10.1038/s41586-018-0240-x, 2018.
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.
Chuine, I., Garcia de Cortazar-Atauri, I., Kramer, K., and Hänninen, H.:
Plant Development Models, in: Phenology: An Integrative Environmental Science, edited by: Schwartz, M. D., Springer Science, Dordrecht, the Netherlands, 275–293, https://doi.org/10.1007/978-94-007-6925-0, 2013.
Cochard, H., Pimont, F., Ruffault, J., and Martin-StPaul, N.:
SurEau: a mechanistic model of plant water relations under extreme drought, Ann. For. Sci., 78, https://doi.org/10.1007/s13595-021-01067-y, 2021.
Collalti, A., Tjoelker, M. G., Hoch, G., Mäkelä, A., Guidolotti, G., Heskel, M., Petit, G., Ryan, M. G., Battipaglia, G., Matteucci, G., and Prentice, I. C.:
Plant respiration: Controlled by photosynthesis or biomass?, Glob. Change Biol., 26, 1739–1753, https://doi.org/10.1111/gcb.14857, 2020.
Crous, K. Y., Uddling, J., and De Kauwe, M. G.:
Temperature responses of photosynthesis and respiration in evergreen trees from boreal to tropical latitudes, New Phytol., 234, 353–374, https://doi.org/10.1111/nph.17951, 2022.
Davi, H. and Cailleret, M.:
Assessing drought-driven mortality trees with physiological process-based models, Agr. Forest Meteorol., 232, 279–290, https://doi.org/10.1016/j.agrformet.2016.08.019, 2017.
De Cáceres, M., Martínez-Vilalta, J., Coll, L., Llorens, P., Casals, P., Poyatos, R., Pausas, J. G., and Brotons, L.:
Coupling a water balance model with forest inventory data to predict drought stress: the role of forest structural changes vs. climate changes, Agr. Forest Meteorol., 213, 77–90, https://doi.org/10.1016/j.agrformet.2015.06.012, 2015.
De Cáceres, M., Martin-StPaul, N., Turco, M., Cabon, A., and Granda, V.:
Estimating daily meteorological data and downscaling climate models over landscapes, Environ. Model. Softw., 108, 186–196, https://doi.org/10.1016/j.envsoft.2018.08.003, 2018.
De Cáceres, M., Casals, P., Gabriel, E., and Castro, X.:
Scaling-up individual-level allometric equations to predict stand-level fuel loading in Mediterranean shrublands, Ann. For. Sci., 76, 87, https://doi.org/10.1007/s13595-019-0873-4, 2019.
De Cáceres, M., Mencuccini, M., Martin-StPaul, N., Limousin, J.-M., Coll, L., Poyatos, R., Cabon, A., Granda, V., Forner, A., Valladares, F., and Martínez-Vilalta, J.:
Unravelling the effect of species mixing on water use and drought stress in Mediterranean forests: A modelling approach, Agr. Forest Meteorol., 296, 108233, https://doi.org/10.1016/j.agrformet.2020.108233, 2021.
De Cáceres, M., Granda, V., Cabon, A., and Ameztegui, A.:
emf-creaf/medfate: MEDFATE v.2.9.3, Zenodo [code], https://doi.org/10.5281/zenodo.7695331, 2022.
Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé, J., and François, C.:
Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agr. Forest Meteorol., 149, 938–948, https://doi.org/10.1016/j.agrformet.2008.11.014, 2009.
De Pury, D. G. G. and Farquhar, G. D.:
Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models, Plant Cell Environ., 20, 537–557, https://doi.org/10.1111/j.1365-3040.1997.00094.x, 1997.
de Wergifosse, L., André, F., Goosse, H., Boczon, A., Cecchini, S., Ciceu, A., Collalti, A., Cools, N., D'Andrea, E., De Vos, B., Hamdi, R., Ingerslev, M., Knudsen, M. A., Kowalska, A., Leca, S., Matteucci, G., Nord-Larsen, T., Sanders, T. G., Schmitz, A., Termonia, P., Vanguelova, E., Van Schaeybroeck, B., Verstraeten, A., Vesterdal, L., and Jonard, M.:
Simulating tree growth response to climate change in structurally diverse oak and beech forests, Sci. Total Environ., 806, 150422, https://doi.org/10.1016/j.scitotenv.2021.150422, 2022.
Dietze, M. C., Sala, A., Carbone, M. S., Czimczik, C. I., Mantooth, J. A., Richardson, A. D., and Vargas, R.:
Nonstructural Carbon in Woody Plants, Annu. Rev. Plant Biol., 65, 667–687, https://doi.org/10.1146/annurev-arplant-050213-040054, 2014.
Dijak, W. D., Hanberry, B. B., Fraser, J. S., He, H. S., Wang, W. J., and Thompson, F. R.:
Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change, Landscape Ecol., 32, 1365–1384, https://doi.org/10.1007/s10980-016-0473-8, 2017.
Dixon, G.:
Essential FVS: A User's Guide to the Forest Vegetation Simulator, U. S. Department of Agriculture, Forest Service, Fort Collins, Colorado, 226 pp., https://www.fs.usda.gov/fvs/documents/guides.shtml (last access: 31 May 2023), 2013.
Dufrêne, E., Davi, H., François, C., Le Maire, G., Le Dantec, V., and Granier, A.:
Modelling carbon and water cycles in a beech forest. Part I: Model description and uncertainty analysis on modelled NEE, Ecol. Model., 185, 407–436, https://doi.org/10.1016/j.ecolmodel.2005.01.004, 2005.
Duursma, R. A., Blackman, C. J., Lopéz, R., Martin-StPaul, N. K., Cochard, H., and Medlyn, B. E.:
On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls, New Phytol., 221, 693–705, https://doi.org/10.1111/nph.15395, 2018.
Easterling, M. R., Ellner, S. P., and Dixon, P. M.:
Size-specific sensitivity: Applying a new structured population model, Ecology, 81, 694–708, https://doi.org/10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2, 2000.
Eckes-Shephard, A. H., Tiavlovsky, E., Chen, Y., Fonti, P., and Friend, A. D.:
Direct response of tree growth to soil water and its implications for terrestrial carbon cycle modelling, Glob. Change Biol., 27, 121–135, https://doi.org/10.1111/gcb.15397, 2021.
Fatichi, S., Leuzinger, S., and Körner, C.:
Moving beyond photosynthesis: From carbon source to sink-driven vegetation modeling, New Phytol., 201, 1086–1095, https://doi.org/10.1111/nph.12614, 2014.
Fatichi, S., Pappas, C., Zscheischler, J., and Leuzinger, S.:
Modelling carbon sources and sinks in terrestrial vegetation, New Phytol., 221, 652–668, https://doi.org/10.1111/nph.15451, 2019.
Fer, I., Gardella, A. K., Shiklomanov, A. N., Campbell, E. E., Cowdery, E. M., De Kauwe, M. G., Desai, A., Duveneck, M. J., Fisher, J. B., Haynes, K. D., Hoffman, F. M., Johnston, M. R., Kooper, R., LeBauer, D. S., Mantooth, J., Parton, W. J., Poulter, B., Quaife, T., Raiho, A., Schaefer, K., Serbin, S. P., Simkins, J., Wilcox, K. R., Viskari, T., and Dietze, M. C.:
Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration, Glob. Change Biol., 27, 13–26, https://doi.org/10.1111/gcb.15409, 2021.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D., Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K., Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu, X., Zhang, T., and Moorcroft, P. R.:
Vegetation demographics in Earth System Models: A review of progress and priorities, Glob. Change Biol., 24, 35–54, https://doi.org/10.1111/gcb.13910, 2018.
Forrester, D. I., Mathys, A. S., Stadelmann, G., and Trotsiuk, V.:
Effects of climate on the growth of Swiss uneven-aged forests: Combining >100 years of observations with the 3-PG model, Forest Ecol. Manag., 494, 119271, https://doi.org/10.1016/j.foreco.2021.119271, 2021.
Funk, J. L., Larson, J. E., Ames, G. M., Butterfield, B. J., Cavender-Bares, J., Firn, J., Laughlin, D. C., Sutton-Grier, A. E., Williams, L., and Wright, J.:
Revisiting the Holy Grail: using plant functional traits to understand ecological processes, Biol. Rev., 92, 1156–1173, https://doi.org/10.1111/brv.12275, 2017.
Fyllas, N. M. and Troumbis, A. Y.:
Simulating vegetation shifts in north-eastern Mediterranean mountain forests under climatic change scenarios, Global Ecol. Biogeogr., 18, 64–77, https://doi.org/10.1111/j.1466-8238.2008.00419.x, 2009.
Fyllas, N. M., Phillips, O. L., Kunin, W. E., Matsinos, Y. G., and Troumbis, A. I.:
Development and parameterization of a general forest gap dynamics simulator for the North-eastern Mediterranean Basin (GREek FOrest Species), Ecol. Model., 204, 439–456, https://doi.org/10.1016/j.ecolmodel.2007.02.006, 2007.
Fyllas, N. M., Gloor, E., Mercado, L. M., Sitch, S., Quesada, C. A., Domingues, T. F., Galbraith, D. R., Torre-Lezama, A., Vilanova, E., Ramírez-Angulo, H., Higuchi, N., Neill, D. A., Silveira, M., Ferreira, L., Aymard C., G. A., Malhi, Y., Phillips, O. L., and Lloyd, J.:
Analysing Amazonian forest productivity using a new individual and trait-based model (TFS v.1), Geosci. Model Dev., 7, 1251–1269, https://doi.org/10.5194/gmd-7-1251-2014, 2014.
Fyllas, N. M., Christopoulou, A., Galanidis, A., Michelaki, C. Z., Giannakopoulos, C., Dimitrakopoulos, P. G., Arianoutsou, M., and Gloor, M.:
Predicting species dominance shifts across elevation gradients in mountain forests in Greece under a warmer and drier climate, Reg. Environ. Change, 17, 1165–1177, https://doi.org/10.1007/s10113-016-1093-1, 2017.
García-Callejas, D., Molowny-Horas, R., and Retana, J.:
Projecting the distribution and abundance of Mediterranean tree species under climate change: a demographic approach, J. Plant Ecol., 10, rtw081, https://doi.org/10.1093/jpe/rtw081, 2017.
García-Valdés, R., Estrada, A., Early, R., Lehsten, V., and Morin, X.:
Climate change impacts on long-term forest productivity might be driven by species turnover rather than by changes in tree growth, Global Ecol. Biogeogr., 29, 1360–1372, https://doi.org/10.1111/geb.13112, 2020.
Genuchten, M. V.:
A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980.
Gracia, C. A., Pla, E., Sánchez, A., and Sabaté, S.:
GOTILWA+: Un modelo de crecimiento forestal basado en procesos ecofisiológicos, Cuadernos de la Sociedad Española de Ciencias Forestales, 18, 21–28, 2004.
Granier, A., Bréda, N., Biron, P., and Villette, S.:
A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands, Ecol. Model., 116, 269–283, https://doi.org/10.1016/S0304-3800(98)00205-1, 1999.
González de Andrés, E., Rosas, T., Camarero, J. J., and Martínez-Vilalta, J.:
The intraspecific variation of functional traits modulates drought resilience of European beech and pubescent oak, J. Ecol., 109, 3652–3669, https://doi.org/10.1111/1365-2745.13743, 2021.
Guillemot, J., Francois, C., Hmimina, G., Dufrêne, E., Martin-StPaul, N. K., Soudani, K., Marie, G., Ourcival, J. M., and Delpierre, N.:
Environmental control of carbon allocation matters for modelling forest growth, New Phytol., 214, 180–193, https://doi.org/10.1111/nph.14320, 2017.
Hanbury-Brown, A. R., Ward, R. E., and Kueppers, L. M.:
Forest regeneration within Earth system models: current process representations and ways forward, New Phytol., 235, 20–40, https://doi.org/10.1111/nph.18131, 2022.
Hartig, F., Dyke, J., Hickler, T., Higgins, S. I., O'Hara, R. B., Scheiter, S., and Huth, A.:
Connecting dynamic vegetation models to data - an inverse perspective, J. Biogeogr., 39, 2240–2252, https://doi.org/10.1111/j.1365-2699.2012.02745.x, 2012.
Hasenauer, H.:
Dimensional relationships of open-grown trees in Austria, Forest Ecol. Manag., 96, 197–206, https://doi.org/10.1016/S0378-1127(97)00057-1, 1997.
Hawkes, C.:
Woody plant mortality algorithms: Description, problems and progress, Ecol. Model., 126, 225–248, https://doi.org/10.1016/S0304-3800(00)00267-2, 2000.
Hayat, A., Hacket-Pain, A. J., Pretzsch, H., Rademacher, T. T., and Friend, A. D.:
Modeling Tree Growth Taking into Account Carbon Source and Sink Limitations, Front. Plant Sci., 8, 1–15, https://doi.org/10.3389/fpls.2017.00182, 2017.
Hengl, T., Mendes De Jesus, J., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotí, 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.
Hoshika, Y., Osada, Y., de Marco, A., Peñuelas, J., and Paoletti, E.:
Global diurnal and nocturnal parameters of stomatal conductance in woody plants and major crops, Global Ecol. Biogeogr., 27, 257–275, https://doi.org/10.1111/geb.12681, 2018.
Huang, J., Hammerbacher, A., Weinhold, A., Reichelt, M., Gleixner, G., Behrendt, T., van Dam, N. M., Sala, A., Gershenzon, J., Trumbore, S., and Hartmann, H.:
Eyes on the future – evidence for trade-offs between growth, storage and defense in Norway spruce, New Phytol., 222, 144–158, https://doi.org/10.1111/nph.15522, 2019.
Jones, S., Rowland, L., Cox, P., Hemming, D., Wiltshire, A., Williams, K., Parazoo, N. C., Liu, J., da Costa, A. C. L., Meir, P., Mencuccini, M., and Harper, A. B.:
The impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to drought, Biogeosciences, 17, 3589–3612, https://doi.org/10.5194/bg-17-3589-2020, 2020.
Kattge, J., Bönisch, G., Díaz, S., et al.: TRY plant trait database – enhanced coverage and open access, Glob. Change Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Keane, R., Austin, M., Field, C., and Huth, A.:
Tree mortality in gap models: Application to climate change, Climatic Change, 51, 509–540, 2001.
König, L. A., Mohren, F., Schelhaas, M.-J., Bugmann, H., and Nabuurs, G.-J.:
Tree regeneration in models of forest dynamics – Suitability to assess climate change impacts on European forests, Forest Ecol. Manag., 520, 120390, https://doi.org/10.1016/j.foreco.2022.120390, 2022.
Körner, C.:
Carbon limitation in trees, J. Ecol., 91, 4–17, https://doi.org/10.1046/j.1365-2745.2003.00742.x, 2003.
Körner, C.:
Paradigm shift in plant growth control, Curr. Opin. Plant Biol., 25, 107–114, https://doi.org/10.1016/j.pbi.2015.05.003, 2015.
Korol, R. L., Running, S. W., and Milner, K. S.:
Incorporating intertree competition into an ecosystem model, Can. J. Forest Res., 25, 413–424, https://doi.org/10.1139/x95-046, 1995.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.:
Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014 (data available at: https://www.euro-cordex.net/index.php.en, last access: 30 May 2023).
Kursar, T. A., Engelbrecht, B. M. J., Burke, A., Tyree, M. T., El Omari, B., and Giraldo, J. P.:
Tolerance to low leaf water status of tropical tree seedlings is related to drought performance and distribution, Funct. Ecol., 23, 93–102, https://doi.org/10.1111/j.1365-2435.2008.01483.x, 2009.
Landsberg, J. and Sands, P.:
Chapter 9 – The 3-PG Process-Based Model, Terrestrial Ecology, 4, 241–282, https://doi.org/10.1016/B978-0-12-374460-9.00009-3, 2011.
Landsberg, J. J. and Waring, R. H.:
A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning, Forest Ecol. Manag., 95, 209–228, https://doi.org/10.1016/S0378-1127(97)00026-1, 1997.
Le Roux, X., Lacointe, A., Escobar-Gutiérrez, A., and Le Dizès, S.:
Carbon-based models of individual tree growth: A critical appraisal, Ann. For. Sci., 58, 469–506, https://doi.org/10.1051/forest:2001140, 2001.
Lempereur, M., Martin-Stpaul, N. K., Damesin, C., Joffre, R., Ourcival, J. M., Rocheteau, A., and Rambal, S.:
Growth duration is a better predictor of stem increment than carbon supply in a Mediterranean oak forest: Implications for assessing forest productivity under climate change, New Phytol., 207, 579–590, https://doi.org/10.1111/nph.13400, 2015.
Liu, Q., Peng, C., Schneider, R., Cyr, D., Liu, Z., Zhou, X., and Kneeshaw, D.:
TRIPLEX-Mortality model for simulating drought-induced tree mortality in boreal forests: Model development and evaluation, Ecol. Model., 455, 109652, https://doi.org/10.1016/j.ecolmodel.2021.109652, 2021.
López, R., Cano, F. J., Martin-StPaul, N. K., Cochard, H., and Choat, B.:
Coordination of stem and leaf traits define different strategies to regulate water loss and tolerance ranges to aridity, New Phytol., 230, 497–509, https://doi.org/10.1111/nph.17185, 2021.
Mahnken, M., Cailleret, M., Collalti, A., Trotta, C., Biondo, C., D'Andrea, E., Dalmonech, D., Marano, G., Mäkelä, A., Minunno, F., Peltoniemi, M., Trotsiuk, V., Nadal-Sala, D., Sabaté, S., Vallet, P., Aussenac, R., Cameron, D. R., Bohn, F. J., Grote, R., Augustynczik, A. L. D., Yousefpour, R., Huber, N., Bugmann, H., Merganičová, K., Merganic, J., Valent, P., Lasch-Born, P., Hartig, F., Vega del Valle, I. D., Volkholz, J., Gutsch, M., Matteucci, G., Krejza, J., Ibrom, A., Meesenburg, H., Rötzer, T., van der Maaten-Theunissen, M., van der Maaten, E., and Reyer, C. P. O.:
Accuracy, realism and general applicability of European forest models. Glob. Change Biol., 28, 6921–6943, https://doi.org/10.1111/gcb.16384, 2022.
Mantova, M., Menezes-Silva, P. E., Badel, E., Cochard, H., and Torres-Ruiz, J. M.:
The interplay of hydraulic failure and cell vitality explains tree capacity to recover from drought, Physiol. Plantarum, 172, 247–257, https://doi.org/10.1111/ppl.13331, 2021.
Maréchaux, I. and Chave, J.:
An individual-based forest model to jointly simulate carbon and tree diversity in Amazonia: description and applications, Ecol. Monogr., 87, 632–664, https://doi.org/10.1002/ecm.1271, 2017.
Maréchaux, I., Langerwisch, F., Huth, A., Bugmann, H., Morin, X., Reyer, C. P. O., Seidl, R., Collalti, A., Dantas de Paula, M., Fischer, R., Gutsch, M., Lexer, M. J., Lischke, H., Rammig, A., Rödig, E., Sakschewski, B., Taubert, F., Thonicke, K., Vacchiano, G., and Bohn, F. J.:
Tackling unresolved questions in forest ecology: The past and future role of simulation models, Ecol. Evol., 11, 3746–3770, https://doi.org/10.1002/ece3.7391, 2021.
Martin-StPaul, N., Delzon, S., and Cochard, H.:
Plant resistance to drought depends on timely stomatal closure, Ecol. Lett., 20, 1437–1447, https://doi.org/10.1111/ele.12851, 2017.
Martínez-Vilalta, J., Sala, A., Asensio, D., Galiano, L., Hoch, G., Palacio, S., Piper, F. I., and Lloret, F.:
Dynamics of non-structural carbohydrates in terrestrial plants: a global synthesis, Ecol. Monogr., 86, 495–516, https://doi.org/10.1002/ecm.1231, 2016.
McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb, T., Plaut, J., Sperry, J. S., West, A., Williams, D. G., and Yepez, E. A.:
Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?, New Phytol., 178, 719–39, https://doi.org/10.1111/j.1469-8137.2008.02436.x, 2008.
McDowell, N. G., Beerling, D. J., Breshears, D. D., Fisher, R. A., Raffa, K. F., and Stitt, M.:
The interdependence of mechanisms underlying climate-driven vegetation mortality, Trends in Ecology and Evolution, 26, 523–532, https://doi.org/10.1016/j.tree.2011.06.003, 2011.
McDowell, N. G., Sapes, G., Pivovaroff, A., Adams, H. D., Allen, C. D., Anderegg, W. R. L., Arend, M., Breshears, D. D., Brodribb, T., Choat, B., Cochard, H., De Cáceres, M., De Kauwe, M. G., Grossiord, C., Hammond, W. M., Hartmann, H., Hoch, G., Kahmen, A., Klein, T., Mackay, D. S., Mantova, M., Martínez-Vilalta, J., Medlyn, B. E., Mencuccini, M., Nardini, A., Oliveira, R. S., Sala, A., Tissue, D. T., Torres-Ruiz, J. M., Trowbridge, A. M., Trugman, A. T., Wiley, E., and Xu, C.:
Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit, Nature Reviews Earth & Environment, 0123456789, 41–44, https://doi.org/10.1038/s43017-022-00272-1, 2022.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.:
The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011.
Mencuccini, M., Rosas, T., Rowland, L., Choat, B., Cornelissen, H., Jansen, S., Kramer, K., Lapenis, A., Manzoni, S., Niinemets, Ü., Reich, P., Schrodt, F., Soudzilovskaia, N., Wright, I. H., and Martínez-Vilalta, J.:
Leaf economics and plant hydraulics drive leaf: wood area ratios, New Phytol., 224, 1544–1556, https://doi.org/10.1111/nph.15998, 2019.
Ministerio de Agricultura y Pesca and Alimentación y Medio Ambiente:
Cuarto inventario forestal nacional, Cataluña, Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente, Centro de Publicaciones, Madrid [data set], https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-nacionales/inventario-forestal-nacional/ (last access: 30 May 2023), 2017.
Morán-Ordóñez, A., Ameztegui, A., De Cáceres, M., De-Miguel, S., Lefèvre, F., Brotons, L., and Coll, L.:
Future trade-offs and synergies among ecosystem services in Mediterranean forests under global change scenarios, Ecosyst. Serv., 45, 101174, https://doi.org/10.1016/j.ecoser.2020.101174, 2020.
Morin, X., Bugmann, H., de Coligny, F., Martin-StPaul, N., Cailleret, M., Limousin, J. M., Ourcival, J. M., Prevosto, B., Simioni, G., Toigo, M., Vennetier, M., Catteau, E., and Guillemot, J.:
Beyond forest succession: A gap model to study ecosystem functioning and tree community composition under climate change, Funct. Ecol., 35, 955–975, https://doi.org/10.1111/1365-2435.13760, 2021.
Morris, H., Plavcová, L., Cvecko, P., Fichtler, E., Gillingham, M. A. F., Martínez-Cabrera, H. I., Mcglinn, D. J., Wheeler, E., Zheng, J., Ziemińska, K., and Jansen, S.:
A global analysis of parenchyma tissue fractions in secondary xylem of seed plants, New Phytol., 209, 1553–1565, https://doi.org/10.1111/nph.13737, 2016.
Mouillot, F., Rambal, S., and Lavorel, S.:
A generic process-based SImulator for meditERRanean landscApes (SIERRA): design and validation exercises, Forest Ecol. Manag., 147, 75–97, https://doi.org/10.1016/S0378-1127(00)00432-1, 2001.
Neumann, R. B. and Cardon, Z. G.:
The magnitude of hydraulic redistribution by plant roots: A review and synthesis of empirical and modeling studies, New Phytol., 194, 337–352, https://doi.org/10.1111/j.1469-8137.2012.04088.x, 2012.
Niinemets, Ü. and Valladares, F.:
Tolerance to shade, drought, and waterlogging of temperate Northern Hemisphere trees and shrubs, Ecol. Monogr., 76, 521–547, 2006.
Oddou-Muratorio, S. and Davi, H.:
Simulating local adaptation to climate of forest trees with a Physio-Demo-Genetics model, Evol. Appl., 7, 453–467, https://doi.org/10.1111/eva.12143, 2014.
Palacio, S., Hoch, G., Sala, A., Körner, C., and Millard, P.:
Does carbon storage limit tree growth?, New Phytol., 201, 1096–1100, https://doi.org/10.1111/nph.12602, 2014.
Pavlick, R., Drewry, D. T., Bohn, K., Reu, B., and Kleidon, A.:
The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs, Biogeosciences, 10, 4137–4177, https://doi.org/10.5194/bg-10-4137-2013, 2013.
Penman, H. L.: Natural evaporation from open water, bare soil and grass, P. Roy. Soc. Lond. A Mat., 193, 129–145, 1948.
Planque, B., Aarflot, J. M., Buttay, L., Carroll, J., Fransner, F., Hansen, C., Husson, B., Langangen, Ø., Lindstrøm, U., Pedersen, T., Primicerio, R., Sivel, E., Skogen, M. D., Strombom, E., Stige, L. C., Varpe, Ø., and Yoccoz, N. G.:
A standard protocol for describing the evaluation of ecological models, Ecol. Model., 471, 110059, https://doi.org/10.1016/j.ecolmodel.2022.110059, 2022.
Potkay, A., Hölttä, T., Trugman, A. T., and Fan, Y.:
Turgor-limited predictions of tree growth, height and metabolic scaling over tree lifespans, Tree Physiol., 42, 229–252, https://doi.org/10.1093/treephys/tpab094, 2022.
Poyatos, R., Martínez-Vilalta, J., Cermák, J., Ceulemans, R., Granier, a, Irvine, J., Köstner, B., Lagergren, F., Meiresonne, L., Nadezhdina, N., Zimmermann, R., Llorens, P., and Mencuccini, M.:
Plasticity in hydraulic architecture of Scots pine across Eurasia, Oecologia, 153, 245–59, https://doi.org/10.1007/s00442-007-0740-0, 2007.
Prentice, I. C. and Cowling, S. A.:
Dynamic Global Vegetation Models, in: Encyclopedia of Biodiversity, second edn., Academic Press, 670–689, ISBN 9780123847201, https://doi.org/10.1016/B978-0-12-384719-5.00412-3, 2013.
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.:
Balancing the costs of carbon gain and water transport: Testing a new theoretical framework for plant functional ecology, Ecol. Lett., 17, 82–91, https://doi.org/10.1111/ele.12211, 2014.
Prescott, C. E., Grayston, S. J., Helmisaari, H.-S., Kaštovská, E., Körner, C., Lambers, H., Meier, I. C., Millard, P., and Ostonen, I.:
Surplus Carbon Drives Allocation and Plant–Soil Interactions, Trends in Ecology & Evolution, 35, 1110–1118, https://doi.org/10.1016/j.tree.2020.08.007, 2020.
Price, D., Zimmermann, N., and Van Der Meer, P.:
Regeneration in gap models: Priority issues for studying forest responses to climate change, Climatic Change, 51, 475–508, 2001.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org (last access:
30 May 2023), 2023.
Rasche, L., Fahse, L., Zingg, A., and Bugmann, H.:
Enhancing gap model accuracy by modeling dynamic height growth and dynamic maximum tree height, Ecol. Model., 232, 133–143, https://doi.org/10.1016/j.ecolmodel.2012.03.004, 2012.
Rehschuh, R., Cecilia, A., Zuber, M., Faragó, T., Baumbach, T., Hartmann, H., Jansen, S., Mayr, S., and Ruehr, N.:
Drought-induced xylem embolism limits the recovery of leaf gas exchange in scots pine, Plant Physiol., 184, 852–864, https://doi.org/10.1104/pp.20.00407, 2020.
Reich, P. B., Tjoelker, M. G., Pregitzer, K. S., Wright, I. J., Oleksyn, J., and Machado, J. L.:
Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants, Ecol. Lett., 11, 793–801, https://doi.org/10.1111/j.1461-0248.2008.01185.x, 2008.
Richardson, A. D., Carbone, M. S., Keenan, T. F., Czimczik, C. I., Hollinger, D. Y., Murakami, P., Schaberg, P. G., and Xu, X.:
Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees, New Phytol., 197, 850–861, https://doi.org/10.1111/nph.12042, 2013.
Rosas, T., Mencuccini, M., Barba, J., Cochard, H., Saura-Mas, S., and Martínez-Vilalta, J.:
Adjustments and coordination of hydraulic, leaf and stem traits along a water availability gradient, New Phytol., 223, 632–646, https://doi.org/10.1111/nph.15684, 2019.
Ruffault, J., Pimont, F., Cochard, H., Dupuy, J.-L., and Martin-StPaul, N.:
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level, Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, 2022.
Running, S. W. and Coughlan, J. C.:
A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes, Ecol. Model., 42, 125–154, https://doi.org/10.1016/0304-3800(88)90112-3, 1988.
Sakschewski, B., von Bloh, W., Boit, A., Rammig, A., Kattge, J., Poorter, L., Peñuelas, J., and Thonicke, K.:
Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model, Glob. Change Biol., 21, 2711–2725, https://doi.org/10.1111/gcb.12870, 2015.
Sanchez-Martinez, P., Martínez-Vilalta, J., Dexter, K. G., Segovia, R. A., and Mencuccini, M.:
Adaptation and coordinated evolution of plant hydraulic traits, Ecol. Lett., 23, 1599–1610, https://doi.org/10.1111/ele.13584, 2020.
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.
Scheiter, S., Langan, L., and Higgins, S.:
Next-generation dynamic global vegetation models: learning from community ecology, New Phytol., 198, 957–969, 2013.
Schiestl-Aalto, P., Kulmala, L., Mäkinen, H., Nikinmaa, E., and Mäkelä, A.:
CASSIA - a dynamic model for predicting intra-annual sink demand and interannual growth variation in Scots pine, New Phytol., 206, 647–659, https://doi.org/10.1111/nph.13275, 2015.
Schmitt, S., Maréchaux, I., Chave, J., Fischer, F. J., Piponiot, C., Traissac, S., and Hérault, B.:
Functional diversity improves tropical forest resilience: Insights from a long-term virtual experiment, J. Ecol., 108, 831–843, https://doi.org/10.1111/1365-2745.13320, 2020.
Scrucca, L.:
GA: A Package for Genetic Algorithms in R, J. Stat. Softw., 53, 1–37, https://doi.org/10.18637/jss.v053.i04, 2013.
Serra-Maluquer, X., Mencuccini, M., and Martínez-Vilalta, J.:
Changes in tree resistance, recovery and resilience across three successive extreme droughts in the northeast Iberian Peninsula, Oecologia, 187, 343–354, https://doi.org/10.1007/s00442-018-4118-2, 2018.
Shinozaki, K., Yoda, K., Hozumi, K., and Kira, T.:
A quantitative analysis of plant form – the pipe model theory. I. Basic analysis, Japanese Journal of Ecology, 14, 97–105, 1964.
Siefert, A., Violle, C., Chalmandrier, L., Albert, C. H., Taudiere, A., Fajardo, A., Aarssen, L. W., Baraloto, C., Carlucci, M. B., Cianciaruso, M. V., de L. Dantas, V., de Bello, F., Duarte, L. D. S., Fonseca, C. R., Freschet, G. T., Gaucherand, S., Gross, N., Hikosaka, K., Jackson, B., Jung, V., Kamiyama, C., Katabuchi, M., Kembel, S. W., Kichenin, E., Kraft, N. J. B., Lagerström, A., Bagousse-Pinguet, Y. L., Li, Y., Mason, N., Messier, J., Nakashizuka, T., Overton, J. McC., Peltzer, D. A., Pérez-Ramos, I. M., Pillar, V. D., Prentice, H. C., Richardson, S., Sasaki, T., Schamp, B. S., Schöb, C., Shipley, B., Sundqvist, M., Sykes, M. T., Vandewalle, M., and Wardle, D. A.:
A global meta-analysis of the relative extent of intraspecific trait variation in plant communities, Ecol. Lett., 18, 1406–1419, https://doi.org/10.1111/ele.12508, 2015.
Sperry, J. S. and Love, D. M.:
What plant hydraulics can tell us about responses to climate-change droughts, New Phytol., 207, 14–27, https://doi.org/10.1111/nph.13354, 2015.
Sperry, J. S., Adler, F. R., Campbell, G. S., and Comstock, J. P.:
Limitation of plant water use by rhizosphere and xylem conductance: results from a model, Plant Cell Environ., 21, 347–359, https://doi.org/10.1046/j.1365-3040.1998.00287.x, 1998.
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.
Spicer, R.:
22 – Senescence in Secondary Xylem: Heartwood Formation as an Active Developmental Program, in: Vascular Transport in Plants, in: Physiological Ecology, Vascular Transport in Plants, edited by: Holbrook, N. M. and Zwieniecki, M. A., Academic Press, 457–475, https://doi.org/10.1016/B978-012088457-5/50024-1, 2005.
Stadelmann, G., Temperli, C., Rohner, B., Didion, M., Herold, A., Rösler, E., and Thürig, E.:
Presenting MASSIMO: A Management Scenario Simulation Model to Project Growth, Harvests and Carbon Dynamics of Swiss Forests, Forests, 10, 94, https://doi.org/10.3390/f10020094, 2019.
Tavşanoglu, Ç. and Pausas, J. G.:
A functional trait database for Mediterranean Basin plants, Scientific Data, 5, 1–18, https://doi.org/10.1038/sdata.2018.135, 2018.
Thonicke, K., Billing, M., von Bloh, W., Sakschewski, B., Niinemets, Ü., Peñuelas, J., Cornelissen, J. H. C., Onoda, Y., van Bodegom, P., Schaepman, M. E., Schneider, F. D., and Walz, A.:
Simulating functional diversity of European natural forests along climatic gradients, J. Biogeogr., 47, 1069–1085, https://doi.org/10.1111/jbi.13809, 2020.
Thrippleton, T., Lüscher, F., and Bugmann, H.:
Climate change impacts across a large forest enterprise in the Northern Pre-Alps: dynamic forest modelling as a tool for decision support, Eur. J. For. Res., 139, 483–498, https://doi.org/10.1007/s10342-020-01263-x, 2020.
Tóth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., and Tóth, G.:
New generation of hydraulic pedotransfer functions for Europe, Eur. J. Soil Sci., 66, 226–238, https://doi.org/10.1111/ejss.12192, 2015.
Trasobares, A., Mola-Yudego, B., Aquilué, N., Ramón González-Olabarria, J., Garcia-Gonzalo, J., García-Valdés, R., and De Cáceres, M.:
Nationwide climate-sensitive models for stand dynamics and forest scenario simulation, Forest Ecol. Manag., 505, 119909, https://doi.org/10.1016/j.foreco.2021.119909, 2022.
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J., and Stephenson, N. L.:
Why is Tree Drought Mortality so Hard to Predict?, Trends in Ecology and Evolution, 36, 520–532, https://doi.org/10.1016/j.tree.2021.02.001, 2021.
Vanderwel, M. C., Lyutsarev, V. S., and Purves, D. W.:
Climate-related variation in mortality and recruitment determine regional forest-type distributions, Global Ecol. Biogeogr., 22, 1192–1203, https://doi.org/10.1111/geb.12081, 2013.
Vanoni, M., Cailleret, M., Hülsmann, L., Bugmann, H., and Bigler, C.:
How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?, Forest Ecol. Manag., 433, 606–617, https://doi.org/10.1016/j.foreco.2018.11.042, 2019.
Venturas, M. D., Todd, H. N., Trugman, A. T., and Anderegg, W. R. L.:
Understanding and predicting forest mortality in the western United States using long-term forest inventory data and modeled hydraulic damage, New Phytol., 230, 1896–1910, https://doi.org/10.1111/nph.17043, 2021.
Verheijen, L. M., Brovkin, V., Aerts, R., Bönisch, G., Cornelissen, J. H. C., Kattge, J., Reich, P. B., Wright, I. J., and van Bodegom, P. M.:
Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis, Biogeosciences, 10, 5497–5515, https://doi.org/10.5194/bg-10-5497-2013, 2013.
Verheijen, L. M., Aerts, R., Brovkin, V., Cavender-Bares, J., Cornelissen, J. H. C., Kattge, J., and van Bodegom, P. M.:
Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model, Glob. Change Biol., 21, 3074–3086, https://doi.org/10.1111/gcb.12871, 2015.
Vilà-Cabrera, A., Espelta, J. M., Vayreda, J., and Pino, J.:
“New Forests” from the Twentieth Century are a Relevant Contribution for C Storage in the Iberian Peninsula, Ecosystems, 20, 130–143, https://doi.org/10.1007/s10021-016-0019-6, 2017.
Williams, A. and de Vries, F. T.:
Plant root exudation under drought: implications for ecosystem functioning, New Phytol., 225, 1899–1905, https://doi.org/10.1111/nph.16223, 2020.
Zakharova, L., Meyer, K. M., and Seifan, M.:
Trait-based modelling in ecology: A review of two decades of research, Ecol. Model., 407, 108703, https://doi.org/10.1016/j.ecolmodel.2019.05.008, 2019.
Zanne, A. E., Lopez-Gonzalez, G., Coomes, D. A., Ilic, J., Jansen, S., Lewis, S. L., Miller, R. B., Swenson, N. G., Wiemann, M. C., and Chave, J.: Data from: Towards a worldwide wood economics spectrum, Dryad [data set],
https://doi.org/10.5061/dryad.234, 2009.
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Regional-level applications of dynamic vegetation models are challenging because they need to...
