Articles | Volume 7, issue 3
https://doi.org/10.5194/gmd-7-1197-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-1197-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
25 Jun 2014
Methods for assessment of models |
| 25 Jun 2014
Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging
M. Nussbaum
Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
A. Papritz
Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
A. Baltensweiler
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
L. Walthert
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Related authors
Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Madlene Nussbaum, Alice E. Milne, and R. Murray Lark
EGUsphere, https://doi.org/10.5194/egusphere-2022-583, https://doi.org/10.5194/egusphere-2022-583, 2022
Preprint archived
Short summary
Short summary
Our study examines different quantitative methods to predict concentrations of micronutrients in the soil from field samples. However, we emphasize the concerns of stakeholders, who use such information to make decisions, in this case in relation to the study and management of micronutrient deficiency risk in the human population. We propose a framework to think about these concerns then compare common approaches for digital soil mapping within this framework.
Lucie Greiner, Madlene Nussbaum, Andreas Papritz, Stephan Zimmermann, Andreas Gubler, Adrienne Grêt-Regamey, and Armin Keller
SOIL, 4, 123–139, https://doi.org/10.5194/soil-4-123-2018, https://doi.org/10.5194/soil-4-123-2018, 2018
Short summary
Short summary
To maintain the soil resource, spatial information on soil multi-functionality is key. Soil function (SF) maps rate soils potentials to fulfill a certain function, e.g., nutrient regulation. We show how uncertainties in predictions of soil properties generated by digital soil mapping propagate into soil function maps, present possibilities to display this uncertainty information and show that otherwise comparable SF assessment methods differ in their behaviour in view of uncertainty propagation.
Madlene Nussbaum, Kay Spiess, Andri Baltensweiler, Urs Grob, Armin Keller, Lucie Greiner, Michael E. Schaepman, and Andreas Papritz
SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, https://doi.org/10.5194/soil-4-1-2018, 2018
Short summary
Short summary
This paper presents an extensive evaluation of digital soil mapping (DSM) tools. Recently, large sets of environmental covariates (e.g. from analysis of terrain on multiple scales) have become more common for DSM. Many DSM studies, however, only compared DSM methods using less than 30 covariates or tested approaches on few responses. We built DSM models from 300–500 covariates using six approaches that are either popular in DSM or promising for large covariate sets.
Madlene Nussbaum, Lorenz Walthert, Marielle Fraefel, Lucie Greiner, and Andreas Papritz
SOIL, 3, 191–210, https://doi.org/10.5194/soil-3-191-2017, https://doi.org/10.5194/soil-3-191-2017, 2017
Short summary
Short summary
Digital soil mapping (DSM) relates soil property data to environmental data that describe soil-forming factors. With imagery sampled from satellites or terrain analysed at multiple scales, large sets of possible input to DSM are available. We propose a new statistical framework (geoGAM) that selects parsimonious models for DSM and illustrate the application of geoGAM to two study regions. Straightforward interpretation of the modelled effects likely improves end-user acceptance of DSM products.
Florian Zellweger, Eric Sulmoni, Johanna T. Malle, Andri Baltensweiler, Tobias Jonas, Niklaus E. Zimmermann, Christian Ginzler, Dirk Nikolaus Karger, Pieter De Frenne, David Frey, and Clare Webster
Biogeosciences, 21, 605–623, https://doi.org/10.5194/bg-21-605-2024, https://doi.org/10.5194/bg-21-605-2024, 2024
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The microclimatic conditions experienced by organisms living close to the ground are not well represented in currently used climate datasets derived from weather stations. Therefore, we measured and mapped ground microclimate temperatures at 10 m spatial resolution across Switzerland using a novel radiation model. Our results reveal a high variability in microclimates across different habitats and will help to better understand climate and land use impacts on biodiversity and ecosystems.
Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Madlene Nussbaum, Alice E. Milne, and R. Murray Lark
EGUsphere, https://doi.org/10.5194/egusphere-2022-583, https://doi.org/10.5194/egusphere-2022-583, 2022
Preprint archived
Short summary
Short summary
Our study examines different quantitative methods to predict concentrations of micronutrients in the soil from field samples. However, we emphasize the concerns of stakeholders, who use such information to make decisions, in this case in relation to the study and management of micronutrient deficiency risk in the human population. We propose a framework to think about these concerns then compare common approaches for digital soil mapping within this framework.
Lucie Greiner, Madlene Nussbaum, Andreas Papritz, Stephan Zimmermann, Andreas Gubler, Adrienne Grêt-Regamey, and Armin Keller
SOIL, 4, 123–139, https://doi.org/10.5194/soil-4-123-2018, https://doi.org/10.5194/soil-4-123-2018, 2018
Short summary
Short summary
To maintain the soil resource, spatial information on soil multi-functionality is key. Soil function (SF) maps rate soils potentials to fulfill a certain function, e.g., nutrient regulation. We show how uncertainties in predictions of soil properties generated by digital soil mapping propagate into soil function maps, present possibilities to display this uncertainty information and show that otherwise comparable SF assessment methods differ in their behaviour in view of uncertainty propagation.
Madlene Nussbaum, Kay Spiess, Andri Baltensweiler, Urs Grob, Armin Keller, Lucie Greiner, Michael E. Schaepman, and Andreas Papritz
SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, https://doi.org/10.5194/soil-4-1-2018, 2018
Short summary
Short summary
This paper presents an extensive evaluation of digital soil mapping (DSM) tools. Recently, large sets of environmental covariates (e.g. from analysis of terrain on multiple scales) have become more common for DSM. Many DSM studies, however, only compared DSM methods using less than 30 covariates or tested approaches on few responses. We built DSM models from 300–500 covariates using six approaches that are either popular in DSM or promising for large covariate sets.
Madlene Nussbaum, Lorenz Walthert, Marielle Fraefel, Lucie Greiner, and Andreas Papritz
SOIL, 3, 191–210, https://doi.org/10.5194/soil-3-191-2017, https://doi.org/10.5194/soil-3-191-2017, 2017
Short summary
Short summary
Digital soil mapping (DSM) relates soil property data to environmental data that describe soil-forming factors. With imagery sampled from satellites or terrain analysed at multiple scales, large sets of possible input to DSM are available. We propose a new statistical framework (geoGAM) that selects parsimonious models for DSM and illustrate the application of geoGAM to two study regions. Straightforward interpretation of the modelled effects likely improves end-user acceptance of DSM products.
Tessa Sophia van der Voort, Frank Hagedorn, Cameron McIntyre, Claudia Zell, Lorenz Walthert, Patrick Schleppi, Xiaojuan Feng, and Timothy Ian Eglinton
Biogeosciences, 13, 3427–3439, https://doi.org/10.5194/bg-13-3427-2016, https://doi.org/10.5194/bg-13-3427-2016, 2016
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This study explores heterogeneity in 14C content of soil organic matter (SOM) at different spatial scales and across climatic and geologic gradients, which is essential for a better understanding of SOM stability. Results reveal that despite dissimilar environmental conditions, 14C contents in topsoils is relatively uniform and 14C trends with depth are similar. Plot-scale variability is significant. Statistical analysis found a significant correlation of 14C contents (0–5 cm) and temperature.
Related subject area
Biogeosciences
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
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
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
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
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
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
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)
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
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
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
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
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
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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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.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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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 > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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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 behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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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.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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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 by forest inventory data, and climatic effects are challenging to capture.
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
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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
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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
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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
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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.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
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This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
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
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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
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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
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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.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
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
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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
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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
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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
Revised manuscript accepted for GMD
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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
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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
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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.
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
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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
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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.
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
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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
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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
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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.
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
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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.
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
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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.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
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Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
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
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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.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Adams, W. A.: The effect of organic matter on bulk and true densities of some uncultivated podzolic soils, J. Soil Sci., 24, 10–17, https://doi.org/10.1111/j.1365-2389.1973.tb00737.x, 1973.
Arrouays, D., Deslais, W., and Badeau, V.: The carbon content of topsoil and its geographical distribution in France, Soil Use Manage., 17, 7–11, 2001.
Baritz, R., Seufert, G., Montanarella, L., and Ranst, E. V.: Carbon concentrations and stocks in forest soils of Europe, Forest Ecol. Manag., 260, 262–277, https://doi.org/10.1016/j.foreco.2010.03.025, 2010.
BFS: GEOSTAT Benützerhandbuch, Bundesamt für Statistik, Bern, 2001.
Blaser, P., Kernebeek, P., Tebbens, L., van Breemen, N., and Luster, J.: Cryptopodzolic soils in Switzerland, Eur. J. Soil Sci., 48, 411–423, https://doi.org/10.1111/j.1365-2389.1997.tb00207.x, 1997.
Bolliger, J., Hagedorn, F., Leifeld, J., Böhl, J., Zimmermann, S., Soliva, R., and Kienast, F.: Effects of land-use change on carbon stocks in Switzerland, Ecosystems, 11, 895–907, 2008.
Brassel, P. and Lischke, H. (Eds.): Swiss National Forest Inventory: Methods and Models of the Second Assessment, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 2001.
Bundesamt für Umwelt BAFU: GIS-Daten Wald. Sturmschäden, available at: www.bafu.admin.ch/gis/02911/07405 (last access: 18 December 2013), 2010.
Chiti, T., Díaz-Pinés, E., and Rubio, A.: Soil organic carbon stocks of conifers, broadleaf and evergreen broadleaf forests of Spain, Biol. Fert. Soils, 48, 817–826, https://doi.org/10.1007/s00374-012-0676-3, 2012.
Cipar, J., Cooley, T., Lockwood, R., and Grigsby, P.: Distinguishing between coniferous and deciduous forests using hyperspectral imagery, in: Geoscience and Remote Sensing Symposium, 2004 (IGARSS'04) Proceedings, 2004 IEEE International, Vol. 4, 2348–2351, 2004.
Cleveland, W. S.: Robust locally weighted regression and smoothing scatterplots, J. Am. Statist. Assoc., 74, 829–836, 1979.
Cressie, N.: Block kriging for lognormal spatial processes, Math. Geol., 38, 413–443, 2006.
Croux, C. and Dehon, C.: Estimators of the multiple correlation coefficient: local robustness and confidence intervals, Stat. Pap., 44, 315–334, 2003.
De Vos, B., Van Meirvenne, M., Quataert, P., Deckers, J., and Muys, B.: Predictive quality of pedotransfer functions for estimating bulk density of forest soils, Soil Sci. Soc. Am. J., 69, 500–510, 2005.
Diggle, P. J. and Ribeiro Jr., P. J.: Model-Based Geostatistics, Springer, New York, 2007.
ESRI: ArcGIS Desktop: Release 10, ESRI Environmental Systems Research Institute, Redlands, California, USA, available at: http://www.esri.com/ (last access: 18 December 2013), 2010.
Faraway, J. J.: Linear Models with R, Texts in Statistical Science, Vol. 63, Chapman & Hall/CRC, Boca Raton, 2005.
FOEN: Switzerland's Greenhouse Gas Inventory 1990–2010, National inventory report 2012, submission of 13 April 2012 under the United Nations Framework Convention on Climate Change and under the Kyoto Protocol, Federal Office for the Environment FOEN, Climate Division, Bern, 2012a.
FOEN: GIS-Daten Biodiversität, Federal Office for the Environment FOEN, available at: www.bafu.admin.ch/gis/02911/07403 (last access: 18 December 2013), 2012b.
Gabriel, K. R.: Biplot display of multivariate matrices for inspection of data and diagnostics, in: Interpreting Multivariate Data, edited by: Barnett, V., John Wiley & Sons, Chichester, Sheffield, 1981.
Gams, H.: Zur Geschichte, klimatischen Begrenzung und Gliederung der immergrünen Mittelmeerstufe, Veröff. Geobot. Eidgenöss. Tech. Hochsch., Stift. Rübel, Zürich, 12, 163–204, 1935.
Giamboni, M.: SilvaProtect-CH – Phase I, Projektdokumentation, Bundesamt für Umwelt BAFU, 2008.
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts, calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268, 2007.
Gonseth, Y., Wohlgemuth, T., Sansonnens, B., and Buttler, A.: Die biogeographischen Regionen der Schweiz. Erläuterungen und Einteilungsstandard, Umwelt-Materialien Nr. 137, BUWAL, Bundesamt für Umwelt, Wald und Landschaft, 2001.
Gotway, C. A. and Young, L. J.: Combining Incompatible Spatial Data, J. Am. Stat. Assoc., 97, 632–648, 2002.
Grier, C. G. and Running, S. W.: Leaf area of mature northwestern coniferous forests: relation to site water balance, Ecology, 58, 893–899, 1977.
Grimm, R., Behrens, T., Märker, M., and Elsenbeer, H.: Soil organic carbon concentrations and stocks on Barro Colorado Island – digital soil mapping using Random Forests analysis, Geoderma, 146, 102–113, 2008.
Hagedorn, F., Moeri, A., Walthert, L., and Zimmermann, S.: Kohlenstoff in Schweizer Waldböden – bei Klimaerwärmung eine potentielle CO2-Quelle, Schweizerische Zeitschrift für Forstwesen, 161, 530–535, 2010.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning; Data Mining, Inference and Prediction, 2nd Edn., Springer, New York, 2009.
Hengl, T., Heuvelink, G. B. M., and Stein, A.: A generic framework for spatial prediction of soil variables based on regression-kriging, Geoderma, 120, 75–95, 2004.
Honeysett, J. L. and Ratkowsky, D. A.: The use of ignition loss to estimate bulk density of forest soils, J. Soil Sci., 40, 299–308, https://doi.org/10.1111/j.1365-2389.1989.tb01275.x, 1989.
Hotz, M.-C., Weibel, F., Ringgenberg, B., Beyeler, A., Finger, A., Humbel, R., and Sager, J.: Arealstatistik Schweiz: Zahlen – Fakten – Analysen, Bericht, Bundesamt für Statistik (BFS), Neuchâtel, 2005.
IPCC: Good Practice Guidance for Land Use, Land-Use Change and Forestry (IPCC GPG LULUCF), available at: http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.htm (last access: 16 January 2013), 2003.
Jalabert, S. S. M., Martin, M. P., Renaud, J.-P., Boulonne, L., Jolivet, C., Montanarella, L., and Arrouays, D.: Estimating forest soil bulk density using boosted regression modelling, Soil Use Manage., 26, 516–528, 2010.
Jenness, J.: Topographic Position Index (TPI) v. 1.2, available at: http://www.jennessent.com (last access: 31 August 2011), 2006.
Kriegler, F. J., Malila, W. A., Nalepka, R. F., and Richardson, W.: Preprocessing transformations and their effects on multispectral recognition, in: Remote Sensing of the Environment, VI, p. 97, 1969.
Krogh, L., Noergaard, A., Hermansen, M., Greve, M. H., Balstroem, T., and Breuning-Madsen, H.: Preliminary estimates of contemporary soil organic carbon stocks in Denmark using multiple datasets and four scaling-up methods, Agr. Ecosyst. Environ., 96, 19–28, 2003.
Kumar, S., Lal, R., and Liu, D.: A geographically weighted regression kriging approach for mapping soil organic carbon stock, Geoderma, 189–190, 627–634, https://doi.org/10.1016/j.geoderma.2012.05.022, 2012.
Künsch, H. R., Papritz, A., Schwierz, C., and Stahel, W. A.: Robust estimation of the external drift and the variogram of spatial data, in: Proceedings of the ISI 58th World Statistics Congress of the International Statistical Institute, Dublin, https://doi.org/10.3929/ethz-a-009900710, available at: http://e-collection.library.ethz.ch/eserv/eth:7080/eth-7080-01.pdf (last access: 18 December 2013), 2011.
Künsch, H. R., Papritz, A., Schwierz, C., and Stahel, W. A.: Robust geostatistics, in preparation, 2014.
Leifeld, J., Bassin, S., and Fuhrer, J.: Carbon stocks in Swiss agricultural soils predicted by land-use, soil characteristics, and altitude, Agr. Ecosyst. Environ., 105, 255–266, 2005.
Lettens, S., Van Orshoven, J., Van Wesemael, B., and Muys, B.: Soil organic and inorganic carbon contents of landscape units in Belgium derived using data from 1950 to 1970, Soil Use Manage., 20, 40–47, 2004.
Lettens, S., Van Orshoven, J., van Wesemael, B., De Vos, B., and Muys, B.: Stocks and fluxes of soil organic carbon for landscape units in Belgium derived from heterogeneous data sets for 1990 and 2000, Geoderma, 127, 11–23, 2005a.
Lettens, S., Van Orshoven, J., Van Wesemael, B., Muys, B., and Perrin, D.: Soil organic carbon changes in landscape units of Belgium between 1960 and 2000 with reference to 1990, Glob. Change Biol., 11, 2128–2140, 2005b.
Maronna, R. A., Martin, R. D., and Yohai, V. J.: Robust Statistics Theory and Methods, John Wiley & Sons, Chichester, 2006.
Martin, M. P., Wattenbach, M., Smith, P., Meersmans, J., Jolivet, C., Boulonne, L., and Arrouays, D.: Spatial distribution of soil organic carbon stocks in France, Biogeosciences, 8, 1053–1065, https://doi.org/10.5194/bg-8-1053-2011, 2011.
Mathys, L. and Kellenberger, T.: Spot5 RadcorMosaic of Switzerland, Tech. rep., National Point of Contact for Satellite Images NPOC: Swisstopo; Remote Sensing Laboratories, University of Zurich, Zurich, 2009.
Meersmans, J., De Ridder, F., Canters, F., De Baets, S., and Van Molle, M.: A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium), Geoderma, 143, 1–13, 2008.
Meersmans, J., Van Wesemael, B., De Ridder, F., Dotti, M. F., De Baets, S., and Van Molle, M.: Changes in organic carbon distribution with depth in agricultural soils in northern Belgium, 1960–2006, Glob. Change Biol., 15, 2739–2750, 2009.
Meersmans, J., Van Wesemael, B., Goidts, E., Van Molle, M., De Baets, S., and De Ridder, F.: Spatial analysis of soil organic carbon evolution in Belgian croplands and grasslands, 1960–2006, Glob. Change Biol., 17, 466–479, 2011.
Meersmans, J., Martin, M. P., De Ridder, F., Lacarce, E., Wetterlind, J., De Baets, S., Bas, C., Louis, B. P., Orton, T. G., Bispo, A., and Arrouays, D.: A novel soil organic C model using climate, soil type and management data at the national scale in France, Agron. Sustain. Dev., 32, 873–888, 2012a.
Meersmans, J., Martin, M. P., Lacarce, E., De Baets, S., Jolivet, C., Boulonne, L., Lehmann, S., Saby, N. P. A., Bispo, A., and Arrouays, D.: A high resolution map of French soil organic carbon, Agron. Sustain. Dev., 32, 841–851, 2012b.
MeteoSwiss: Mean Monthly and Yearly Mean Norm Values of Precipitation, Temperature and Relative Sunshine Duration (1961–1990), available at: http://www.meteoschweiz.admin.ch/web/en/services/data_portal/gridded_datasets.html (last access: 18 December 2013), 2011.
Minasny, B., McBratney, A., Malone, B., and Wheeler, I.: Digital mapping of soil carbon, Adv. Agron., 118, 1–47, 2013.
Mishra, U., Lal, R., Slater, B., Calhoun, F., Liu, D., and Van Meirvenne, M.: Predicting soil organic carbon stock using profile depth distribution functions and ordinary kriging, Soil Sci. Soc. Am. J., 73, 614–621, 2009.
Mishra, U., Lai, R., Liu, D., and Van Meirvenne, M.: Predicting the spatial variation of the soil organic carbon pool at a regional scale, Soil Sci. Soc. Am. J., 74, 906–914, 2010.
Mishra, U., Torn, M. S., Masanet, E., and Ogle, S. M.: Improving regional soil carbon inventories: combining the IPCC carbon inventory method with regression kriging, Geoderma, 189–190, 288–295, https://doi.org/10.1016/j.geoderma.2012.06.022, 2012.
NABO Nationale Bodenbeobachtung Schweiz: NABODAT, Aufarbeitung Bodendaten, available at: http://www.nabodat.ch/index.php/aufarbeitung-bodendaten32.html (last access: 22 May 2014), 2014.
Nussbaum, M., Papritz, A., Baltensweiler, A., and Walthert, L.: Organic Carbon Stocks of Swiss Forest Soils, Final report, Institute of Terrestrial Ecosystems, ETH Zürich and Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürich and Birmensdorf, available at: http://e-collection.library.ethz.ch/eserv/eth:6027/eth-6027-01.pdf (last access: 18 December 2013), 2012.
Papritz, A.: georob: Robust Geostatistical Analysis of Spatial Data, R Package version 0.1-0, 2013.
Perruchoud, D., Walthert, L., Zimmermann, S., and Lüscher, P.: Contemporay carbon stocks of mineral forest soils in the Swiss Alps, Biogeochemistry, 50, 111–136, 2000.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org/ (last access: 18 December 2013), 2013.
Schmidt, M. W. I., Torn, M. S., Abiven, S., Dittmar, T., Guggenberger, G., Janssens, I. A., Kleber, M., Kögel-Knabner, I., Lehmann, J., Manning, D. A. C., Nannipieri, P., Rasse, D. P., Weiner, S., and Trumbore, S. E.: Persistence of soil organic matter as an ecosystem property, Nature, 478, 49–56, https://doi.org/10.5167/uzh-51257, 2011.
Schroeder, W., Schmidt, G., and Pesch, R.: Regionalising the carbon fixation in forests of North Rhine-Westphalia using inventory data and digital maps, Umweltwissenschaften und Schadstoff-Forschung, 21, 516–526, https://doi.org/10.1007/s12302-009-0091-z, 2009.
Schrumpf, M., Schulze, E. D., Kaiser, K., and Schumacher, J.: How accurately can soil organic carbon stocks and stock changes be quantified by soil inventories?, Biogeosciences, 8, 1193–1212, https://doi.org/10.5194/bg-8-1193-2011, 2011.
Schulp, C., Verburg, P., Kuikman, P., Nabuurs, G.-J., Olivier, J., Vries, W., and Veldkamp, T.: Improving national-scale carbon stock inventories using knowledge on land use history, Environ. Manage., 51, 709–723, https://doi.org/10.1007/s00267-012-9975-6, 2013.
Swiss Federal Statistical Office: Swiss soil suitability map, BFS GEOSTAT, available at: www.bfs.admin.ch/bfs/portal/de/index/dienstleistungen/geostat/datenbeschreibung/digitale_bodeneignungskarte.html (last access: 18 December 2013), 2000a.
Swiss Federal Statistical Office: Tree composition of Swiss forests. BFS GEOSTAT, available at: http://www.bfs.admin.ch/bfs/portal/de/index/dienstleistungen/geostat/datenbeschreibung/waldmischungsgrad.html (last access: 18 December 2013), 2000b.
Swisstopo: Geologische Karte der Schweiz $1:500 000$, available at: http://www.swisstopo.admin.ch/internet/swisstopo/de/home/products/maps/geology/geomaps/gm500.html (last access: 18 December 2013), 2005.
Swisstopo: Switzerland during the Last Glacial Maximum $1:500\;000$, available at: http://www.swisstopo.admin.ch/internet/swisstopo/en/home/products/maps/geology/geomaps/LGM-map500.html (last access: 18 December 2013), 2009.
Swisstopo: Höhenmodelle, available at: http://www.swisstopo.admin.ch/internet/swisstopo/de/home/products/height.html (last access: 18 December 2013), 2011a.
Swisstopo: VECTOR25, available at: http://www.swisstopo.admin.ch/internet/swisstopo/de/home/products/landscape/vector25.html (last access: 18 December 2013), 2011b.
Tarboton, D. G.: A new method for the determination of flow directions and upslope areas in grid digital elevation models, Water Resour. Res., 33, 309–319, https://doi.org/10.1029/96WR03137, 1997.
Walthert, L., Zimmermann, S., Blaser, P., Luster, J., and Lüscher, P.: Waldböden der Schweiz. Band 1. Grundlagen und Region Jura, Eidg. Forschungsanstalt WSL and Hep Verlag, Birmensdorf and Bern, 2004.
Walthert, L., Graf, U., Kammer, A., Luster, J., Pezzotta, D., Zimmermann, S., and Hagedorn, F.: Determination of organic and inorganic carbon, δ13C, and nitrogen in soils containing carbonates after acid fumigation with HCl, J. Plant Nutr. Soil Sc., 173, 207–216, 2010.
Weiss, P., Schieler, K., Schadauer, K., Radunsky, K., and Englisch, M.: Die Kohlenstoffbilanz des Österreichischen Waldes und Betrachtungen zum Kyoto-Protokoll, Tech. rep., Umweltbundesamt/Federal Environment Agency – Austria, Vienna, Austria, 2000.
Wiesmeier, M., Barthold, F., Blank, B., and Kögel-Knabner, I.: Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem, Plant Soil, 340, 7–24, 2011.
Wiesmeier, M., Spörlein, P., Geuss, U., Hangen, E., Haug, S., Reischl, A., Schilling, B., Lützow, M., and Kögel-Knabner, I.: Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth, Glob. Change Biol., 18, 2233–2245, 2012.
Wiesmeier, M., Prietzel, J., Barthold, F., Spörlein, P., Geuss, U., Hangen, E., Reischl, A., Schilling, B., von Lützow, M., and Kögel-Knabner, I.: Storage and drivers of organic carbon in forest soils of southeast Germany (Bavaria) – implications for carbon sequestration, Forest Ecol. Manag., 295, 162–172, https://doi.org/10.1016/j.foreco.2013.01.025, 2013.
Xu, X., Liu, W., Zhang, C., and Kiely, G.: Estimation of soil organic carbon stock and its spatial distribution in the Republic of Ireland, Soil Use Manage., 27, 156–162, 2011.
Zimmermann, N. E.: Calculation of Topographic Position, available at: http://www.wsl.ch/staff/niklaus.zimmermann/programs/aml4_1.html (last access: 18 December 2013), 2000.
Zimmermann, N. E. and Kienast, F.: Predictive mapping of alpine grasslands in Switzerland: species versus community approach, J. Veg. Sci., 10, 469–482, 1999.
