Articles | Volume 9, issue 12
https://doi.org/10.5194/gmd-9-4313-2016
© Author(s) 2016. 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-9-4313-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model evaluation paper
| 
05 Dec 2016
Model evaluation paper |
| 05 Dec 2016
Parameter interactions and sensitivity analysis for modelling carbon heat and water fluxes in a natural peatland, using CoupModel v5
Department of Land and Water Resources Engineering, Royal Institute of Technology, Stockholm, 100 44, Sweden
now at: Institute for Meteorology and Climate Research/Atmospheric Environmental Research (IMK-IFU),
Karlsruhe Institute for Technology, 82467 Garmisch-Partenkirchen, Germany
Mats B. Nilsson
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, 901 83, Sweden
Matthias Peichl
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, 901 83, Sweden
Per-Erik Jansson
Department of Land and Water Resources Engineering, Royal Institute of Technology, Stockholm, 100 44, Sweden
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Hongxing He, Per-Erik Jansson, and Annemieke I. Gärdenäs
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Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important drivers of overstory succession and nutrient cycling. Multi-angle remote sensing enables us to describe surface properties by means that are not possible when using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, our reported method can deliver good retrievals, especially over different forest types with open canopies.
Nataliia Kozii, Kersti Haahti, Pantana Tor-ngern, Jinshu Chi, Eliza Maher Hasselquist, Hjalmar Laudon, Samuli Launiainen, Ram Oren, Matthias Peichl, Jörgen Wallerman, and Niles J. Hasselquist
Hydrol. Earth Syst. Sci., 24, 2999–3014, https://doi.org/10.5194/hess-24-2999-2020, https://doi.org/10.5194/hess-24-2999-2020, 2020
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The hydrologic cycle is one of the greatest natural processes on Earth and strongly influences both regional and global climate as well as ecosystem functioning. Results from this study clearly show the central role trees play in regulating the water cycle of boreal catchments, implying that forest management impacts on stand structure as well as climate change effects on tree growth are likely to have large cascading effects on the way water moves through boreal forested landscapes.
Olli Peltola, Timo Vesala, Yao Gao, Olle Räty, Pavel Alekseychik, Mika Aurela, Bogdan Chojnicki, Ankur R. Desai, Albertus J. Dolman, Eugenie S. Euskirchen, Thomas Friborg, Mathias Göckede, Manuel Helbig, Elyn Humphreys, Robert B. Jackson, Georg Jocher, Fortunat Joos, Janina Klatt, Sara H. Knox, Natalia Kowalska, Lars Kutzbach, Sebastian Lienert, Annalea Lohila, Ivan Mammarella, Daniel F. Nadeau, Mats B. Nilsson, Walter C. Oechel, Matthias Peichl, Thomas Pypker, William Quinton, Janne Rinne, Torsten Sachs, Mateusz Samson, Hans Peter Schmid, Oliver Sonnentag, Christian Wille, Donatella Zona, and Tuula Aalto
Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, https://doi.org/10.5194/essd-11-1263-2019, 2019
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Mousong Wu, Per-Erik Jansson, Jingwei Wu, Xiao Tan, Kang Wang, Peng Chen, and Jiesheng Huang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-466, https://doi.org/10.5194/hess-2018-466, 2018
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Jannis von Buttlar, Jakob Zscheischler, Anja Rammig, Sebastian Sippel, Markus Reichstein, Alexander Knohl, Martin Jung, Olaf Menzer, M. Altaf Arain, Nina Buchmann, Alessandro Cescatti, Damiano Gianelle, Gerard Kiely, Beverly E. Law, Vincenzo Magliulo, Hank Margolis, Harry McCaughey, Lutz Merbold, Mirco Migliavacca, Leonardo Montagnani, Walter Oechel, Marian Pavelka, Matthias Peichl, Serge Rambal, Antonio Raschi, Russell L. Scott, Francesco P. Vaccari, Eva van Gorsel, Andrej Varlagin, Georg Wohlfahrt, and Miguel D. Mahecha
Biogeosciences, 15, 1293–1318, https://doi.org/10.5194/bg-15-1293-2018, https://doi.org/10.5194/bg-15-1293-2018, 2018
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Our work systematically quantifies extreme heat and drought event impacts on gross primary productivity (GPP) and ecosystem respiration globally across a wide range of ecosystems. We show that heat extremes typically increased mainly respiration whereas drought decreased both fluxes. Combined heat and drought extremes had opposing effects offsetting each other for respiration, but there were also strong reductions in GPP and hence the strongest reductions in the ecosystems carbon sink capacity.
Hongxing He, Astrid Meyer, Per-Erik Jansson, Magnus Svensson, Tobias Rütting, and Leif Klemedtsson
Geosci. Model Dev., 11, 725–751, https://doi.org/10.5194/gmd-11-725-2018, https://doi.org/10.5194/gmd-11-725-2018, 2018
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Ectomycorrhizal fungi (ECM) have shown a major impact on forest C and N cycles, but are currently neglected in most ecosystem models. We thus implemented the previously developed ectomycorrhizal fungi model, MYCOFON, into a well-established ecosystem model, CoupModel. This paper describes the key components and features of Coup-MYCOFON. The new version of CoupModel can now simulate C and N fluxes and pools, explicitly accounting for links and feedbacks among plant, soil, and ECM.
Mahdi Nakhavali, Pierre Friedlingstein, Ronny Lauerwald, Jing Tang, Sarah Chadburn, Marta Camino-Serrano, Bertrand Guenet, Anna Harper, David Walmsley, Matthias Peichl, and Bert Gielen
Geosci. Model Dev., 11, 593–609, https://doi.org/10.5194/gmd-11-593-2018, https://doi.org/10.5194/gmd-11-593-2018, 2018
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Chunjing Qiu, Dan Zhu, Philippe Ciais, Bertrand Guenet, Gerhard Krinner, Shushi Peng, Mika Aurela, Christian Bernhofer, Christian Brümmer, Syndonia Bret-Harte, Housen Chu, Jiquan Chen, Ankur R. Desai, Jiří Dušek, Eugénie S. Euskirchen, Krzysztof Fortuniak, Lawrence B. Flanagan, Thomas Friborg, Mateusz Grygoruk, Sébastien Gogo, Thomas Grünwald, Birger U. Hansen, David Holl, Elyn Humphreys, Miriam Hurkuck, Gerard Kiely, Janina Klatt, Lars Kutzbach, Chloé Largeron, Fatima Laggoun-Défarge, Magnus Lund, Peter M. Lafleur, Xuefei Li, Ivan Mammarella, Lutz Merbold, Mats B. Nilsson, Janusz Olejnik, Mikaell Ottosson-Löfvenius, Walter Oechel, Frans-Jan W. Parmentier, Matthias Peichl, Norbert Pirk, Olli Peltola, Włodzimierz Pawlak, Daniel Rasse, Janne Rinne, Gaius Shaver, Hans Peter Schmid, Matteo Sottocornola, Rainer Steinbrecher, Torsten Sachs, Marek Urbaniak, Donatella Zona, and Klaudia Ziemblinska
Geosci. Model Dev., 11, 497–519, https://doi.org/10.5194/gmd-11-497-2018, https://doi.org/10.5194/gmd-11-497-2018, 2018
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Northern peatlands store large amount of soil carbon and are vulnerable to climate change. We implemented peatland hydrological and carbon accumulation processes into the ORCHIDEE land surface model. The model was evaluated against EC measurements from 30 northern peatland sites. The model generally well reproduced the spatial gradient and temporal variations in GPP and NEE at these sites. Water table depth was not well predicted but had only small influence on simulated NEE.
Wenxin Zhang, Per-Erik Jansson, and Bo Elberling
Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-382, https://doi.org/10.5194/bg-2017-382, 2017
Manuscript not accepted for further review
Loise Wandera, Kaniska Mallick, Gerard Kiely, Olivier Roupsard, Matthias Peichl, and Vincenzo Magliulo
Hydrol. Earth Syst. Sci., 21, 197–215, https://doi.org/10.5194/hess-21-197-2017, https://doi.org/10.5194/hess-21-197-2017, 2017
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Upscaling instantaneous to daily evapotranspiration (ETi–ETd) is one of the central challenges in regional vegetation water-use mapping using polar orbiting satellites. Here we developed a robust ETi upscaling for global studies using the ratio between daily and instantaneous global radiation (RSd/RSi). Using data from 126 FLUXNET tower sites, this study demonstrated the RSd/RSi ratio to be the most robust factor explaining ETd/ETi variability across variable sky conditions and multiple biomes.
Mousong Wu, Per-Erik Jansson, Xiao Tan, Jiesheng Huang, and Jingwei Wu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-507, https://doi.org/10.5194/hess-2016-507, 2016
Revised manuscript not accepted
Järvi Järveoja, Matthias Peichl, Martin Maddison, Kaido Soosaar, Kai Vellak, Edgar Karofeld, Alar Teemusk, and Ülo Mander
Biogeosciences, 13, 2637–2651, https://doi.org/10.5194/bg-13-2637-2016, https://doi.org/10.5194/bg-13-2637-2016, 2016
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Restoration is suggested as a strategy to reduce the large greenhouse gas (GHG) emissions from abandoned peat extraction areas. This study investigated GHG fluxes in restored sites with high and low water table level in comparison to a bare peat area. The results show that on the annual scale, both restored sites acted as similar GHG sources 3 years after restoration. However, their net GHG emissions were only half of those from the bare peat area, indicating considerable mitigation potential.
Hongxing He, Per-Erik Jansson, Magnus Svensson, Jesper Björklund, Lasse Tarvainen, Leif Klemedtsson, and Åsa Kasimir
Biogeosciences, 13, 2305–2318, https://doi.org/10.5194/bg-13-2305-2016, https://doi.org/10.5194/bg-13-2305-2016, 2016
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We simulate CO2 and N2O dynamics over a full forest rotation on drained agricultural peatland, using CoupModel. Data used for validation include tree ring-derived biomass data (1966–2011) and measured abiotic and soil emission data (2006–2011). The results show that the C fixed in forest biomass is slightly larger than the soil losses over the full rotation period. However when including N2O and indirect emissions from forest thinning products, the forest system switches to a large GHG source.
S. Osterwalder, J. Fritsche, C. Alewell, M. Schmutz, M. B. Nilsson, G. Jocher, J. Sommar, J. Rinne, and K. Bishop
Atmos. Meas. Tech., 9, 509–524, https://doi.org/10.5194/amt-9-509-2016, https://doi.org/10.5194/amt-9-509-2016, 2016
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Human activities have increased mercury (Hg) cycling between land and atmosphere. To define landscapes as sinks or sources of Hg we have developed an advanced REA system for long-term measurements of gaseous elemental Hg exchange. It was tested in two contrasting environments: above Basel, Switzerland, and a peatland in Sweden. Both landscapes showed net Hg emission (15 and 3 ng m−2 h−1, respectively). The novel system will help to advance our understanding of Hg exchange on an ecosystem scale.
C. Metzger, P.-E. Jansson, A. Lohila, M. Aurela, T. Eickenscheidt, L. Belelli-Marchesini, K. J. Dinsmore, J. Drewer, J. van Huissteden, and M. Drösler
Biogeosciences, 12, 125–146, https://doi.org/10.5194/bg-12-125-2015, https://doi.org/10.5194/bg-12-125-2015, 2015
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To identify site specific differences in CO2-related processes in open peatlands, we calibrated a process oriented model to fit to detailed measurements of carbon fluxes and compared the resulting parameter ranges between the sites. For most processes a common configuration could be applied. Site specific differences were identified for soil respiration coefficients, plant radiation-use efficiencies and plant storage fractions for spring regrowth.
M. Peichl, A. M. Arain, T. R. Moore, J. J. Brodeur, M. Khomik, S. Ullah, N. Restrepo-Coupé, J. McLaren, and M. R. Pejam
Biogeosciences, 11, 5399–5410, https://doi.org/10.5194/bg-11-5399-2014, https://doi.org/10.5194/bg-11-5399-2014, 2014
H. Wu, C. Peng, T. R. Moore, D. Hua, C. Li, Q. Zhu, M. Peichl, M. A. Arain, and Z. Guo
Geosci. Model Dev., 7, 867–881, https://doi.org/10.5194/gmd-7-867-2014, https://doi.org/10.5194/gmd-7-867-2014, 2014
S. H. Wu and P.-E. Jansson
Hydrol. Earth Syst. Sci., 17, 735–749, https://doi.org/10.5194/hess-17-735-2013, https://doi.org/10.5194/hess-17-735-2013, 2013
Related subject area
Biogeosciences
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
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
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
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
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
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
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.
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.
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
Preprint under review 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.
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
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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.
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.
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
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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
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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 >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
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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.
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
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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 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
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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.
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.
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
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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.
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.
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
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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
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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
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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.
Cited articles
Abdalla, M., Hastings, A., Bell, M. J., Smith, J. U., Richards, M., Nilsson, M. B., Peichl, M., Löfvenius, M. O., Lund, M., Helfter, C., Nemitz, E., Sutton, M. A., Aurela, M., Lohila, A., Laurila, T., Dolman, A. J., Belelli-Marchesini, L., Pogson, M., Jones, E., Drewer, J., Drosler, M., and Smith, P.: Simulation of CO2 and Attribution Analysis at Six European Peatland Sites Using the ECOSSE Model, Water Air Soil. Poll., 225, 1–14, https://doi.org/10.1007/s11270-014-2182-8, 2014.
Alexandersson, H., Karlström, C., and Larsson-McCann, S.: Temperaturen och nedercörden i sverige 1961–1990 (Swedish), Temperature and Precipitation in Sweden 1961–1990, Reference Normals Meteorologi 81, Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden, 1991.
Aubinet, M., Grelle, A., Ibrom, A., Rannik, Ü., Moncrieff, J., Foken, T., Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, C., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., and Vesala, T.: Estimates of the Annual Net Carbon and Water Exchange of Forests: The EUROFLUX Methodology, Advances in Ecological Research, 30, 113–175, https://doi.org/10.1016/S0065-2504(08)60018-5, 1999.
Aurela, M.: The timing of snow melt controls the annual CO2 balance in a subarctic fen, Geophys. Res. Lett., 31, L16119, https://doi.org/10.1029/2004GL020315, 2004.
Baird, A. J., Morris, P. J., and Belyea, L. R.: The DigiBog peatland development model 1: Rationale, conceptual model, and hydrological basis, Ecohydrol., 5, 242–255, https://doi.org/10.1002/eco.230, 2012.
Bazin, M. J. and Prosser, J. I.: Physiological models in microbiology, CRC series in mathematical models in microbiology, volume 2, CRC Press, Boca Raton, USA, 1988.
Bellassen, V., Le Maire, G., Dhôte, J. F., Ciais, P., and Viovy, N.: Modelling forest management within a global vegetation model – Part 1: Model structure and general behaviour, Ecol. Model., 221, 2458–2474, https://doi.org/10.1016/j.ecolmodel.2010.07.008, 2010.
Belyea, L. R.: Nonlinear dynamics of peatlands and potential feedbacks on the climate system, in: Carbon Cycling in Northern Peatlands, edited by: Baird, A. J., Belyea, L. R., Comas, X., Reeve, A. S., and Slater, L. D., Geophysical Monograph Series, American Geophysical Union, Washington, D. C., USA, 5–18, 2009.
Ben Touhami, H., Lardy, R., Barra, V., and Bellocchi, G.: Screening parameters in the Pasture Simulation model using the Morris method, Ecol. Model., 266, 42–57, https://doi.org/10.1016/j.ecolmodel.2013.07.005, 2013.
Berglund, E. R. and Mace, A. C.: Seasonal Albedo Variation of Black Spruce and Sphagnum-Sedge Bog Cover Types, J. Appl. Meteorol., 11, 806–812, 1972.
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, https://doi.org/10.1016/s0022-1694(01)00421-8, 2001.
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., and Swenson, S. C.: Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data, J. Geophys. Res., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011.
Bridgham, S. D., Pastor, J., Updegraff, K., Malterer, T. J., Johnson, K., Harth, C., and Chen, J.: Ecosystem control over temperature and energy flux in northern peatlands, Ecol. Appl., 9, 1345–1358, https://doi.org/10.1890/1051-0761(1999)009[1345:ECOTAE]2.0.CO;2, 1999.
Brooks, R. H. and Corey, A. T.: Hydraulic Properties of Porous Media, Hydrology Papers, Colorado State University, Fort Collins, Colorado, USA, 1964.
Carvalhais, N., Reichstein, M., Ciais, P., Collatz, G. J., Mahecha, M. D., Montagnani, L., Papale, D., Rambal, S., and Seixas, J.: Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints, Glob. Change Biol., 16, 2813–2829, https://doi.org/10.1111/j.1365-2486.2010.02173.x, 2010.
Clymo, R. S.: The Limits to Peat Bog Growth, Philos. T. R. Soc. B, 303, 605–654, 1984.
CoupModel: Current version of COUP model for download, available at: http://www.coupmodel.com, last access: 20 October 2015.
Dettmann, U., Bechtold, M., Frahm, E., and Tiemeyer, B.: On the applicability of unimodal and bimodal van Genuchten–Mualem based models to peat and other organic soils under evaporation conditions, J. Hydrol., 515, 103–115, https://doi.org/10.1016/j.jhydrol.2014.04.047, 2014.
Dimitrov, D. D., Grant, R. F., Lafleur, P. M., and Humphreys, E. R.: Modeling the effects of hydrology on ecosystem respiration at Mer Bleue bog, J. Geophys. Res.-Planet., 115, G04043, https://doi.org/10.1029/2010JG001312, 2010.
Drösler, M., Verchot, L. V., Freibauer, A., Pan, G., Evans, C. D., Bourbonniere, R. A., Alm, J. P., Page, S., Agus, F., Hergoualch, K., Couwenberg, J., Jauhiainen, J., Sabiham, S., Wang, C., Srivastava, N., Borgeau-Chavez, L., Hooijer, A., Minkkinen, K., French, N., Strand, T., Sirin, A., Mickler, R., Tansey, K., and Larkin, N.: Drained Inland Organic Soils (chap. 2), in: 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands, Methodological Guidance on Lands with Wet and Drained Soils, and Constructed Wetlands for Wastewater Treatment, edited by: Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Jamsranjav, B., Fukuda, M., and Troxler, T., IPCC, Switzerland, 2.1–2.76, 2013.
Dušek, J., Čížková, H., Stellner, S., Czerný, R., and Květ, J.: Fluctuating water table affects gross ecosystem production and gross radiation use efficiency in a sedge-grass marsh, Hydrobiologia, 692, 57–66, https://doi.org/10.1007/s10750-012-0998-z, 2012.
Esprey, L., Sands, P., and Smith, C.: Understanding 3-PG using a sensitivity analysis, Forest Ecol. Manag., 193, 235–250, https://doi.org/10.1016/j.foreco.2004.01.032, 2004.
Flanagan, L. B. and Johnson, B. G.: Interacting effects of temperature, soil moisture and plant biomass production on ecosystem respiration in a northern temperate grassland, Agr. Forest Meteorol., 130, 237–253, https://doi.org/10.1016/j.agrformet.2005.04.002, 2005.
Friend, A. D., Arneth, A., Kiang, N. Y., Lomas, M., Ogée, J., Rödenbeck, C., Running, S. W., Santaren, J.-D., Sitch, S., Viovy, N., Woodward, I. F., and Zaehle, S.: FLUXNET and modelling the global carbon cycle, Glob. Change Biol., 13, 610–633, 2007.
Frolking, S. E., Roulet, N. T., Moore, T. R., Lafleur, P. M., Bubier, J. L., and Crill, P. M.: Modeling seasonal to annual carbon balance of Mer Bleue Bog, Ontario, Canada, Global Biogeochem. Cy., 16, 4-1–4-21, https://doi.org/10.1029/2001gb001457, 2002.
Frolking, S. E., Roulet, N. T., Tuittila, E., Bubier, J. L., Quillet, A., Talbot, J., and Richard, P. J. H.: A new model of Holocene peatland net primary production, decomposition, water balance, and peat accumulation, Earth Syst. Dynam., 1, 1–21, https://doi.org/10.5194/esd-1-1-2010, 2010.
Giltrap, D. L., Li, C., and Saggar, S.: DNDC: A process-based model of greenhouse gas fluxes from agricultural soils, Agr. Ecosyst. Environ., 136, 292–300, https://doi.org/10.1016/j.agee.2009.06.014, 2010.
Gong, J., Shurpali, N. J., Kellomäki, S., Wang, K., Zhang, C., Salam, M. M. A., and Martikainen, P. J.: High sensitivity of peat moisture content to seasonal climate in a cutaway peatland cultivated with a perennial crop (Phalaris arundinaceae, L.): A modeling study, Agr. Forest Meteorol., 180, 225–235, https://doi.org/10.1016/j.agrformet.2013.06.012, 2013.
Gorham, E.: Northern Peatlands: Role in the Carbon Cycle and Probable Responses to Climatic Warming, Ecol. Appl., 1, 182–195, 1991.
Graham, E. A., Hamilton, M. P., Mishler, B. D., Rundel, P. W., and Hansen, M. H.: Use of a Networked Digital Camera to Estimate Net CO2 Uptake of a Desiccation-Tolerant Moss, Int. J. Plant Sci., 167, 751–758, https://doi.org/10.1086/503786, 2006.
Granberg, G., Grip, H., Löfvenius, M. O., Sundh, I., Svensson, B. H., and Nilsson, M.: A simple model for simulation of water content, soil frost, and soil temperatures in boreal mixed mires, Water Resour. Res., 35, 3771–3782, https://doi.org/10.1029/1999WR900216, 1999.
Grant, R. F., Humphreys, E. R., Lafleur, P. M., and Dimitrov, D. D.: Ecological controls on net ecosystem productivity of a mesic arctic tundra under current and future climates, J. Geophys. Res.-Planet., 116, G01031, https://doi.org/10.1029/2010JG001555, 2011.
He, H., Jansson, P.-E., Svensson, M., Björklund, J., Tarvainen, L., Klemedtsson, L., and Kasimir, Å.: Forests on drained agricultural peatland are potentially large sources of greenhouse gases –insights from a full rotation period simulation, Biogeosciences, 13, 2305–2318, https://doi.org/10.5194/bg-13-2305-2016, 2016.
Hidy, D., Barcza, Z., Haszpra, L., Churkina, G., Pintér, K., and Nagy, Z.: Development of the Biome-BGC model for simulation of managed herbaceous ecosystems, Ecol. Model., 226, 99–119, https://doi.org/10.1016/j.ecolmodel.2011.11.008, 2012.
Hilbert, D. W., Roulet, N., and Moore, T.: Modelling and analysis of peatlands as dynamical systems, J. Ecol., 88, 230–242, https://doi.org/10.1046/j.1365-2745.2000.00438.x, 2000.
Impens, I. and Lemeur, R.: Extinction of net radiation in different crop canopies, Arch. Meteor. Geophy. B, 17, 403–412, https://doi.org/10.1007/BF02243377, 1969.
Ivanov, K. E., Thomson, A., and Ingram, H.: Water movement in mirelands, Academic Press, London, UK, 276 pp., 1981.
Jansson, P.-E.: CoupModel: Model use, Calibration, and Validation, T. ASABE, 55, 1335–1344, 2012.
Jansson, P.-E. and Halldin, S.: Model for the annual water and energy flow in a layered soil, Comparison of forest and energy exchange models, Society for Ecological Modelling, Copenhagen, Denmark, 145–163, 1979.
Jansson, P.-E. and Karlberg, L.: Coupled heat and mass transfer model for soil–plant–atmosphere systems, Royal Institute of Technology, Stockholm, Sweden, 484 pp., available at: https://drive.google.com/file/d/0B0-WJKp0fmYCZ0JVeVgzRWFIbUk/view?pli=1 (last access: 17 November 2015), 2010.
Jarvis, P. G. and Morison, J. I.: The control of transpiration and photosynthesis by the stomata, in: Stomatal Physiology, edited by: Jarvis, P. G. and Mansfield, T. A., Cambridge University Press, Cambridge, UK, 247–279, 1981.
Jonasson, S. and Chapin III, F. S.: Significance of sequential leaf development for nutrient balance of the cotton sedge, Eriophorum vaginatum L, Oecologia, 67, 511–518, https://doi.org/10.1007/BF00790022, 1985.
Keddy, P. A.: Assembly and response rules: two goals for predictive community ecology, J. Veg. Sci., 3, 157–164, https://doi.org/10.2307/3235676, 1992.
Keller, J. K., White, J. R., Bridgham, S. D., and Pastor, J.: Climate change effects on carbon and nitrogen mineralization in peatlands through changes in soil quality, Glob. Change Biol., 10, 1053–1064, https://doi.org/10.1111/j.1529-8817.2003.00785.x, 2004.
Kellner, E.: Surface energy fluxes and control of evapotranspiration from a Swedish Sphagnum mire, Agr. Forest Meteorol., 110, 101–123, https://doi.org/10.1016/S0168-1923(01)00283-0, 2001.
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. Roy. Stat. Soc. B, 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001.
Kim, J. and Verma, S. B.: Surface exchange of water vapour between an open sphagnum fen and the atmosphere, Bound.-Lay. Meteorol., 79, 243–264, https://doi.org/10.1007/BF00119440, 1996.
Kim, Y., Roulet, N., Peng, C., Li, C., Frolking, S., Strachan, L. B., and Tremblay, A.: Multi-Year carbon dioxide flux simulations for mature Canadian black spruce forests and ombrotrophic bogs using Forest-DnDc, Boreal Environ. Res., 19, 417–440, 2014.
Kurbatova, J., Li, C., Tatarinov, F. A., Varlagin, A., Shalukhina, N., and Olchev, A.: Modeling of the carbon dioxide fluxes in European Russia peat bogs, Environ. Res. Lett., 4, 45022, https://doi.org/10.1088/1748-9326/4/4/045022, 2009.
Kurnianto, S., Warren, M., Talbot, J., Kauffman, B., Murdiyarso, D., and Frolking, S.: Carbon accumulation of tropical peatlands over millennia: A modeling approach, Glob. Change Biol., 21, 431–444, https://doi.org/10.1111/gcb.12672, 2014.
Lafleur, P. M., Moore, T. R., Roulet, N. T., and Frolking, S. E.: Ecosystem Respiration in a Cool Temperate Bog Depends on Peat Temperature But Not Water Table, Ecosystems, 8, 619–629, https://doi.org/10.1007/s10021-003-0131-2, 2005.
Laine, A. M., Bubier, J., Riutta, T., Nilsson, M. B., Moore, T. R., Vasander, H., and Tuittila, E.-S.: Abundance and composition of plant biomass as potential controls for mire net ecosytem CO2 exchange, Botany, 90, 63–74, https://doi.org/10.1139/b11-068, 2012.
Lindroth, A., Lund, M., Nilsson, M., Aurela, M., Christensen, T. R., Laurila, T., Rinne, J., Riutta, T., Sagerfors, J., Ström, L., Tuovinen, J.-P., and Vesala, T.: Environmental controls on the CO2 exchange in north European mires, Tellus B, 59, 812–825, https://doi.org/10.1111/j.1600-0889.2007.00310.x, 2007.
Loisel, J., Yu, Z., Beilman, D. W., Camill, P., Alm, J., Amesbury, M. J., Anderson, D., Andersson, S., Bochicchio, C., Barber, K., Belyea, L. R., Bunbury, J., Chambers, F. M., Charman, D. J., Vleeschouwer, F. de, Fialkiewicz-Koziel, B., Finkelstein, S. A., Galka, M., Garneau, M., Hammarlund, D., Hinchcliffe, W., Holmquist, J., Hughes, P., Jones, M. C., Klein, E. S., Kokfelt, U., Korhola, A., Kuhry, P., Lamarre, A., Lamentowicz, M., Large, D., Lavoie, M., MacDonald, G., Magnan, G., Makila, M., Mallon, G., Mathijssen, P., Mauquoy, D., McCarroll, J., Moore, T. R., Nichols, J., O'Reilly, B., Oksanen, P., Packalen, M., Peteet, D., Richard, P. J., Robinson, S., Ronkainen, T., Rundgren, M., Sannel, A. B. K., Tarnocai, C., Thom, T., Tuittila, E.-S., Turetsky, M., Valiranta, M., van der Linden, M., van Geel, B., van Bellen, S., Vitt, D., Zhao, Y., and Zhou, W.: A database and synthesis of northern peatland soil properties and Holocene carbon and nitrogen accumulation, Holocene, 24, 1028–1042, https://doi.org/10.1177/0959683614538073, 2014.
Lund, M., Lafleur, P. M., Roulet, N. T., Anders, L., Christensen, T. R., Aurela, M., Chojnicki, B. H., Lawrence, F. B., Humphreys, E. R., Laurila, T., Oechel, W. C., Olejnik, J., Rinne, J., Schubert, P. E. R., and Nilsson, M. B.: Variability in exchange of CO2 across 12 northern peatland and tundra sites, Glob. Change Biol., 2436–2448, https://doi.org/10.1111/j.1365-2486.2009.02104.x, 2010.
Maljanen, M., Sigurdsson, B. D., Guðmundsson, J., Óskarsson, H., Huttunen, J. T., and Martikainen, P. J.: Greenhouse gas balances of managed peatlands in the Nordic countries – present knowledge and gaps, Biogeosciences, 7, 2711–2738, https://doi.org/10.5194/bg-7-2711-2010, 2010.
Metzger, C., Jansson, P.-E., Lohila, A., Aurela, M., Eickenscheidt, T., Belelli-Marchesini, L., Dinsmore, K. J., Drewer, J., van Huissteden, J., and Drösler, M.: CO2 fluxes and ecosystem dynamics at five European treeless peatlands – merging data and process oriented modeling, Biogeosciences, 12, 125–146, https://doi.org/10.5194/bg-12-125-2015, 2015.
Monteith, J. L.: Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 747–766, 1972.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Binger, R. L., Harmel, R. D., and Veith, T. L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, 2007.
Morris, P. J., Baird, A. J., and Belyea, L. R.: The DigiBog peatland development model 2: Ecohydrological simulations in 2D, Ecohydrol., 5, 256–268, https://doi.org/10.1002/eco.229, 2012.
Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res., 12, 513–522, https://doi.org/10.1029/WR012i003p00513, 1976.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Nilsson, M., Sagerfors, J., Buffam, I., Laudon, H., Eriksson, T., Grelle, A., Klemedtsson, L., Weslien, P. E. R., and Lindroth, A.: Contemporary carbon accumulation in a boreal oligotrophic minerogenic mire – a significant sink after accounting for all C-fluxes, Glob. Change Biol., 14, 2317–2332, https://doi.org/10.1111/j.1365-2486.2008.01654.x, 2008.
Nilson, M., Peich, M., Löfvenius, M. O., and Sagerfors, J.: Flux data and ancillary data of the site SE-Deg for the years 2001–2012, site code: SE-Deg, available at: http://www.europe-fluxdata.eu/, last access: 28 July 2014.
Päivänen, J.: Hydraulic conductivity and water retention in peat soils, Acta For. Fenn., 129, 1–70, 1973.
Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A., and Burlando, P.: Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues, J. Geophys. Res.-Biogeo., 118, 505–528, https://doi.org/10.1002/jgrg.20035, 2013.
Parish, F. (Ed.): Assessment on peatlands, biodiversity and climate change, 203, Wetlands International, Wageningen, the Netherlands, 179 pp., 2008.
Peichl, M., Sagerfors, J., Lindroth, A., Buffam, I., Grelle, A., Klemedtsson, L., Laudon, H., and Nilsson, M. B.: Energy exchange and water budget partitioning in a boreal minerogenic mire, J. Geophys. Res.-Biogeo., 118, 1–13, https://doi.org/10.1029/2012JG002073, 2013.
Peichl, M., Öquist, M., Löfvenius, M. O., Ilstedt, U., Sagerfors, J., Grelle, A., Lindroth, A., and Nilsson, M. B.: A 12-year record reveals pre-growing season temperature and water table level threshold effects on the net carbon dioxide exchange in a boreal fen, Environ. Res. Lett., 9, 55006, https://doi.org/10.1088/1748-9326/9/5/055006, 2014.
Peichl, M., Sonnentag, O., and Nilsson, M.: Bringing Color into the Picture: Using Digital Repeat Photography to Investigate Phenology Controls of the Carbon Dioxide Exchange in a Boreal Mire, Ecosystems, 18, 115–131, https://doi.org/10.1007/s10021-014-9815-z, 2015.
Petrescu, A. M. R., Lohila, A., Tuovinen, J.-P., Baldocchi, D. D., Desai, A. R., Roulet, N. T., Vesala, T., Dolman, A. J., Oechel, W. C., Marcolla, B., Friborg, T., Rinne, J., Matthes, J. H., Merbold, L., Meijide, A., Kiely, G., Sottocornola, M., Sachs, T., Zona, D., Varlagin, A., Lai, D. Y. F., Veenendaal, E., Parmentier, F.-J. W., Skiba, U., Lund, M., Hensen, A., van Huissteden, J., Flanagan, L. B., Shurpali, N. J., Grünwald, T., Humphreys, E. R., Jackowicz-Korczyński, M., Aurela, M. A., Laurila, T., Grüning, C., Corradi, C. A. R., Schrier-Uijl, A. P., Christensen, T. R., Tamstorf, M. P., Mastepanov, M., Martikainen, P. J., Verma, S. B., Bernhofer, C., and Cescatti, A.: The uncertain climate footprint of wetlands under human pressure, P. Natl. Acad. Sci. USA, 112, 4594–4599, https://doi.org/10.1073/pnas.1416267112, 2015.
Prentice, I. C., Bondeau, A., Cramer, W., Harrison, S. P., Hickler, T., Lucht, W., Sitch, S., Smith, B., and Sykes, M. T.: Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change, in: Terrestrial ecosystems in a changing world, edited by: Canadell, J. G., Pataki, D. E., and Pitelka, L., Global change – the IGBP series, Springer, Berlin, New York, 175–192, 2007.
Price, J.: Evaporation from a blanket bog in a foggy coastal environment, Bound.-Lay. Meteorol., 57, 391–406, https://doi.org/10.1007/BF00120056, 1991.
Prihodko, L., Denning, A. S., Hanan, N. P., Baker, I., and Davis, K.: Sensitivity, uncertainty and time dependence of parameters in a complex land surface model, Agr. Forest Meteorol., 148, 268–287, https://doi.org/10.1016/j.agrformet.2007.08.006, 2008.
Quick, W. P., Chaves, M. M., Wendler, R., David, M., Rodrigues, M. L., Passaharinho, J. A., Pereira, J. S., Adcock, M. D., Leegood, R. C., and Stitt, M.: The effect of water stress on photosynthetic carbon metabolism in four species grown under field conditions, Plant Cell Environ., 15, 25–35, https://doi.org/10.1111/j.1365-3040.1992.tb01455.x, 1992.
Quillet, A., Frolking, S., Garneau, M., Talbot, J., and Peng, C.: Assessing the role of parameter interactions in the sensitivity analysis of a model of peatland dynamics, Ecol. Model., 248, 30–40, https://doi.org/10.1016/j.ecolmodel.2012.08.023, 2013.
Ratkowsky, D. A., Olley, J., McMeekin, T. A., and Ball, A.: Relationship between temperature and growth rate of bacterial cultures, J. Bacteriol., 149, 1–5, 1982.
Reich, P. B., Walters, M. B., and Ellsworth, D. S.: Leaf Life-Span in Relation to Leaf, Plant, and Stand Characteristics among Diverse Ecosystems, Ecol. Monogr., 62, 365–392, https://doi.org/10.2307/2937116, 1992.
Reichstein, M., Papale, D., Valentini, R., Aubinet, M., Bernhofer, C., Knohl, A., Laurila, T., Lindroth, A., Moors, E., Pilegaard, K., and Seufert, G.: Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites, Geophys. Res. Lett., 34, L01402, https://doi.org/10.1029/2006GL027880, 2007.
Richards, L. A.: Capillary conduction of liquids through porous mediums, J. Appl. Phys., 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Robroek, B. J. M., Schouten, M. G. C., Limpens, J., Berendse, F., and Poorter, H.: Interactive effects of water table and precipitation on net CO2 assimilation of three co-occurring Sphagnum mosses differing in distribution above the water table, Glob. Change Biol., 15, 680–691, https://doi.org/10.1111/j.1365-2486.2008.01724.x, 2009.
Roulet, N., Hardill, S., and Comer, N.: Continuous measurement of the depth of water table (inundation) in wetlands with fluctuating surfaces, Hydrol. Process., 5, 399–403, https://doi.org/10.1002/hyp.3360050407, 1991.
Ryser, P.: The Importance of Tissue Density for Growth and Life Span of Leaves and Roots: A Comparison of Five Ecologically Contrasting Grasses, Funct. Ecol., 10, 717–723, https://doi.org/10.2307/2390506, 1996.
Sagerfors, J., Lindroth, A., Grelle, A., Klemedtsson, L., Weslien, P., and Nilsson, M.: Annual CO2 exchange between a nutrient-poor, minerotrophic, boreal mire and the atmosphere, J. Geophys. Res.-Biogeo., 113, G01001, https://doi.org/10.1029/2006JG000306, 2008.
Saltelli, A., Tarantola, S., and Campolongo, F.: Sensitivity Analysis as an Ingredient of Modeling, Stat. Sci., 15, 377–395, 2000.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global sensitivity analysis: the primer, John Wiley & Sons, Chichester, UK, https://doi.org/10.1002/9780470725184, 2008.
Sándor, R., Barcza, Z., Hidy, D., Lellei-Kovács, E., Ma, S., and Bellocchi, G.: Modelling of grassland fluxes in Europe: Evaluation of two biogeochemical models, Agr. Ecosyst. Environ., 215, 1–19, https://doi.org/10.1016/j.agee.2015.09.001, 2016.
Santaren, D., Peylin, P., Viovy, N., and Ciais, P.: Optimizing a process-based ecosystem model with eddy-covariance flux measurements: A pine forest in southern France, Global Biogeochem. Cy., 21, GB2013, https://doi.org/10.1029/2006GB002834, 2007.
Santaren, D., Peylin, P., Bacour, C., Ciais, P., and Longdoz, B.: Ecosystem model optimization using in situ flux observations: benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances, Biogeosciences, 11, 7137–7158, https://doi.org/10.5194/bg-11-7137-2014, 2014.
Schulz, K. and Beven, K.: Data-supported robust parameterisations in land surface-atmosphere flux predictions: towards a top-down approach, Hydrol. Process., 17, 2259–2277, https://doi.org/10.1002/hyp.1331, 2003.
Schulze, E.-D.: Biological control of the terrestrial carbon sink, Biogeosciences, 3, 147–166, https://doi.org/10.5194/bg-3-147-2006, 2006.
Shaver, G. R. and Laundre, J.: Exsertion, elongation, and senescence of leaves of Eriophorum vaginatum and Carex bigelowii in Northern Alaska, Glob. Change Biol., 3, 146–157, https://doi.org/10.1111/j.1365-2486.1997.gcb141.x, 1997.
Silvola, J., Alm, J., Ahlholm, U., Nykanen, H., and Martikainen, P. J.: CO2 Fluxes from Peat in Boreal Mires under Varying Temperature and Moisture Conditions, J. Ecol., 84, 219–228, 1996.
Staudt, K., Falge, E., Pyles, R. D., Paw U, K. T., and Foken, T.: Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site, Biogeosciences, 7, 3685–3705, https://doi.org/10.5194/bg-7-3685-2010, 2010.
Steed, J. E., DeWald, L. E., and Kolb, T. E.: Physiological and Growth Responses of Riparian Sedge Transplants to Groundwater Depth, Int. J. Plant Sci., 163, 925–936, https://doi.org/10.1086/342634, 2002.
St-Hilaire, F., Wu, J., Roulet, N. T., Frolking, S., Lafleur, P. M., Humphreys, E. R., and Arora, V.: McGill wetland model: evaluation of a peatland carbon simulator developed for global assessments, Biogeosciences, 7, 3517–3530, https://doi.org/10.5194/bg-7-3517-2010, 2010.
Sulman, B., Desai, A., and Mladenoff, D.: Modeling Soil and Biomass Carbon Responses to Declining Water Table in a Wetland-Rich Landscape, Ecosystems, 16, 491–507, https://doi.org/10.1007/s10021-012-9624-1, 2013.
Tang, J., Zhuang, Q., Shannon, R. D., and White, J. R.: Quantifying wetland methane emissions with process-based models of different complexities, Biogeosciences, 7, 3817–3837, https://doi.org/10.5194/bg-7-3817-2010, 2010.
Tang, J., Miller, P. A., Crill, P. M., Olin, S., and Pilesjö, P.: Investigating the influence of two different flow routing algorithms on soil-water-vegetation interactions using the dynamic ecosystem model LPJ-GUESS, Ecohydrol., 8, 570–583, https://doi.org/10.1002/eco.1526, 2015.
Tatarinov, F. A. and Cienciala, E.: Application of BIOME-BGC model to managed forests, Forest Ecol. Manag., 237, 267–279, https://doi.org/10.1016/j.foreco.2006.09.085, 2006.
Tezara, W., Mitchell, V. J., Driscoll, S. D., and Lawlor, D. W.: Water stress inhibits plant photosynthesis by decreasing coupling factor and ATP, Nature, 401, 914–917, 1999.
Thorsen, M., Refsgaard, J. C., Hansen, S., Pebesma, E., Jensen, J. B., and Kleeschulte, S.: Assessment of uncertainty in simulation of nitrate leaching to aquifers at catchment scale, J. Hydrol., 242, 210–227, https://doi.org/10.1016/S0022-1694(00)00396-6, 2001.
Tian, S., Youssef, M. A., Amatya, D. M., and Vance, E. D.: Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model, Hydrol. Process., 28, 4389–4410, https://doi.org/10.1002/hyp.9948, 2014.
van Huissteden, J., Petrescu, A. M. R., Hendriks, D. M. D., and Rebel, K. T.: Sensitivity analysis of a wetland methane emission model based on temperate and arctic wetland sites, Biogeosciences, 6, 3035–3051, https://doi.org/10.5194/bg-6-3035-2009, 2009.
van Oijen, M., Cameron, D. R., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson, P.-E., Kiese, R., Rahn, K. H., Werner, C., and Yeluripati, J. B.: A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest, Agr. Forest Meteorol., 151, 1609–1621, https://doi.org/10.1016/j.agrformet.2011.06.017, 2011.
Verbeeck, H., Samson, R., Verdonck, F., and Lemeur, R.: Parameter sensitivity and uncertainty of the forest carbon flux model FORUG: A Monte Carlo analysis, Tree Physiol., 26, 807–817, https://doi.org/10.1093/treephys/26.6.807, 2006.
Waddington, J. M., Morris, P. J., Kettridge, N., Granath, G., Thompson, D. K., and Moore, P. A.: Hydrological feedbacks in northern peatlands, Ecohydrol., 8, 113–127, https://doi.org/10.1002/eco.1493, 2015.
Wang, G. and Chen, S.: A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil, Geoderma, 170, 206–216, https://doi.org/10.1016/j.geoderma.2011.11.009, 2012.
Wang, X., He, X., Williams, J. R., Izaurralde, R. C., and Atwood, J. D.: Sensitivity and uncertainty analyses of crop yields and soil organic carbon simulated with EPIC, T. ASABE, 48, 1041–1054, 2005.
Wang, Y.-P., Trudinger, C. M., and Enting, I. G.: A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales, Agr. Forest Meteorol., 149, 1829–1842, https://doi.org/10.1016/j.agrformet.2009.07.009, 2009.
Wania, R., Ross, I., and Prentice, I. C.: Integrating peatlands and permafrost into a dynamic global vegetation model: 1. Evaluation and sensitivity of physical land surface processes, Global Biogeochem. Cy., 23, GB3014, https://doi.org/10.1029/2008GB003412, 2009.
Wania, R., Ross, I., and Prentice, I. C.: Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHyMe v1.3.1, Geosci. Model Dev., 3, 565–584, https://doi.org/10.5194/gmd-3-565-2010, 2010.
Webster, K. L., McLaughlin, J. W., Kim, Y., Packalen, M. S., and Li, C. S.: Modelling carbon dynamics and response to environmental change along a boreal fen nutrient gradient, Ecol. Model., 248, 148–164, https://doi.org/10.1016/j.ecolmodel.2012.10.004, 2013.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Wu, J., Roulet, N. T., Sagerfors, J., and Nilsson, M. B.: Simulation of six years of carbon fluxes for a sedge-dominated oligotrophic minerogenic peatland in Northern Sweden using the McGill Wetland Model (MWM), J. Geophys. Res.-Biogeo., 118, 795–807, https://doi.org/10.1002/jgrg.20045, 2013.
Wu, Y. and Blodau, C.: PEATBOG: a biogeochemical model for analyzing coupled carbon and nitrogen dynamics in northern peatlands, Geosci. Model Dev., 6, 1173–1207, https://doi.org/10.5194/gmd-6-1173-2013, 2013.
Xenakis, G., Ray, D., and Mencuccini, M.: Sensitivity and uncertainty analysis from a coupled 3-PG and soil organic matter decomposition model, Ecol. Model., 219, 1–16, https://doi.org/10.1016/j.ecolmodel.2008.07.020, 2008.
Yeloff, D. and Mauquoy, D.: The influence of vegetation composition on peat humification: Implications for palaeoclimatic studies, Boreas, 35, 662–673, https://doi.org/10.1080/03009480600690860, 2006.
Yordanov, I., Velikova, V., and Tsonev, T.: Plant Responses to Drought, Acclimation, and Stress Tolerance, Photosynthetica, 38, 171–186, https://doi.org/10.1023/A:1007201411474, 2000.
Yu, Z., Turetsky, M. R., Campbell, I. D., and Vitt, D. H.: Modelling long-term peatland dynamics. II. Processes and rates as inferred from litter and peat-core data, Ecol. Model., 145, 159–173, https://doi.org/10.1016/s0304-3800(01)00387-8, 2001.
Yurova, A., Wolf, A., Sagerfors, J., and Nilsson, M.: Variations in net ecosystem exchange of carbon dioxide in a boreal mire: Modeling mechanisms linked to water table position, J. Geophys. Res.-Biogeo., 112, G02025, https://doi.org/10.1029/2006JG000342, 2007.
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics, Global Biogeochem. Cy., 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005.
Zhang, Y., Li, C., Trettin, C. C., Li, H., and Sun, G.: An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems, Global Biogeochem. Cy., 16, 9-1–9-17, https://doi.org/10.1029/2001gb001838, 2002.
Zhao, G., Bryan, B. A., and Song, X.: Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters, Ecol. Model., 279, 1–11, https://doi.org/10.1016/j.ecolmodel.2014.02.003, 2014.
Zhao, W., Hidenori, T., and Zhao, H.: Estimation of vegetative surface albedo in the Kushiro Mire with Landsat TM data, Chinese Geographr. Sci., 7, 278–288, https://doi.org/10.1007/s11769-997-0055-4, 1997.
Zhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., and Zhou, X.: Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model, Geosci. Model Dev., 7, 981–999, https://doi.org/10.5194/gmd-7-981-2014, 2014.
Short summary
Many interactions between various abiotic and biotic processes and their parameters were identified by global sensitivity analysis, revealing strong dependence of a certain model output (e.g. CO2 or heat fluxes, leaf area index, radiation, water table, soil temperature or snow depth) to model set-up and parameterization in many different processes, a limited transferability of parameter values between models, and the importance of ancillary measurements for improving models and thus predictions.
Many interactions between various abiotic and biotic processes and their parameters were...
