Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-4977-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4977-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
19 Oct 2020
Model description paper |
| 19 Oct 2020
| 19 Oct 2020
A multi-isotope model for simulating soil organic carbon cycling in eroding landscapes (WATEM_C v1.0)
Zhengang Wang
CORRESPONDING AUTHOR
Guangdong Provincial Key Laboratory of Urbanization
and Geo-simulation, School of Geography and Planning, Sun Yat-Sen
University, Guangzhou 510275, China
Georges Lemaître Center for Earth and Climate Research (TECLIM),
Earth and Life Institute, Université catholique de Louvain, 1348
Louvain-la-Neuve, Belgium
Guangdong Provincial Key Laboratory of Urbanization
and Geo-simulation, School of Geography and Planning, Sun Yat-Sen
University, Guangzhou 510275, China
Kristof Van Oost
Georges Lemaître Center for Earth and Climate Research (TECLIM),
Earth and Life Institute, Université catholique de Louvain, 1348
Louvain-la-Neuve, Belgium
Related authors
Samuel Bouchoms, Zhengang Wang, Veerle Vanacker, and Kristof Van Oost
SOIL, 5, 367–382, https://doi.org/10.5194/soil-5-367-2019, https://doi.org/10.5194/soil-5-367-2019, 2019
Short summary
Short summary
Soil erosion has detrimental effects on soil fertility which can reduce carbon inputs coming from crops to soils. Our study integrated this effect into a model linking soil organic carbon (SOC) dynamics to erosion and crop productivity. When compared to observations, the inclusion of productivity improved SOC loss predictions. Over centuries, ignoring crop productivity evolution in models could result in underestimating SOC loss and overestimating C exchanged with the atmosphere.
Antoine de Clippele, Astrid C. H. Jaeger, Simon Baumgartner, Marijn Bauters, Pascal Boeckx, Clement Botefa, Glenn Bush, Jessica Carilli, Travis W. Drake, Christian Ekamba, Gode Lompoko, Nivens Bey Mukwiele, Kristof Van Oost, Roland A. Werner, Joseph Zambo, Johan Six, and Matti Barthel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3313, https://doi.org/10.5194/egusphere-2024-3313, 2024
Short summary
Short summary
Tropical forest soils as a large terrestrial source of carbon dioxide (CO2) contribute to the GHG budgets. Despite this, carbon flux data from forested wetlands is scarce in tropical Africa. The study presents three years of semi-continuous measurements of surface CO2 fluxes within the Congo Basin. Although no seasonal patterns were evident, our results showed a positive effect of soil temperature and soil moisture, while a quadratic relationship was observed with the water table level.
Kristof Van Oost and Johan Six
Biogeosciences, 20, 635–646, https://doi.org/10.5194/bg-20-635-2023, https://doi.org/10.5194/bg-20-635-2023, 2023
Short summary
Short summary
The direction and magnitude of the net erosion-induced land–atmosphere C exchange have been the topic of a big scientific debate for more than a decade now. Many have assumed that erosion leads to a loss of soil carbon to the atmosphere, whereas others have shown that erosion ultimately leads to a carbon sink. Here, we show that the soil carbon erosion source–sink paradox is reconciled when the broad range of temporal and spatial scales at which the underlying processes operate are considered.
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022, https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture over China for 2000–2020 derived through machine learning trained with in situ measurements of 1789 stations, meteorological forcings, and land surface variables. It contains 10 soil layers with 10 cm intervals up to 100 cm deep. Evaluated by in situ data, the error (ubRMSE) ranges from 0.045 to 0.051, and the correlation (R) range is 0.866-0.893. Compared with ERA5-Land, SMAP-L4, and SoMo.ml, SIMI1.0 has higher accuracy and resolution.
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
Short summary
Short summary
We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
Pengzhi Zhao, Daniel Joseph Fallu, Sara Cucchiaro, Paolo Tarolli, Clive Waddington, David Cockcroft, Lisa Snape, Andreas Lang, Sebastian Doetterl, Antony G. Brown, and Kristof Van Oost
Biogeosciences, 18, 6301–6312, https://doi.org/10.5194/bg-18-6301-2021, https://doi.org/10.5194/bg-18-6301-2021, 2021
Short summary
Short summary
We investigate the factors controlling the soil organic carbon (SOC) stability and temperature sensitivity of abandoned prehistoric agricultural terrace soils. Results suggest that the burial of former topsoil due to terracing provided an SOC stabilization mechanism. Both the soil C : N ratio and SOC mineral protection regulate soil SOC temperature sensitivity. However, which mechanism predominantly controls SOC temperature sensitivity depends on the age of the buried terrace soils.
Laura Summerauer, Philipp Baumann, Leonardo Ramirez-Lopez, Matti Barthel, Marijn Bauters, Benjamin Bukombe, Mario Reichenbach, Pascal Boeckx, Elizabeth Kearsley, Kristof Van Oost, Bernard Vanlauwe, Dieudonné Chiragaga, Aimé Bisimwa Heri-Kazi, Pieter Moonen, Andrew Sila, Keith Shepherd, Basile Bazirake Mujinya, Eric Van Ranst, Geert Baert, Sebastian Doetterl, and Johan Six
SOIL, 7, 693–715, https://doi.org/10.5194/soil-7-693-2021, https://doi.org/10.5194/soil-7-693-2021, 2021
Short summary
Short summary
We present a soil mid-infrared library with over 1800 samples from central Africa in order to facilitate soil analyses of this highly understudied yet critical area. Together with an existing continental library, we demonstrate a regional analysis and geographical extrapolation to predict total carbon and nitrogen. Our results show accurate predictions and highlight the value that the data contribute to existing libraries. Our library is openly available for public use and for expansion.
Lander Van Tricht, Philippe Huybrechts, Jonas Van Breedam, Alexander Vanhulle, Kristof Van Oost, and Harry Zekollari
The Cryosphere, 15, 4445–4464, https://doi.org/10.5194/tc-15-4445-2021, https://doi.org/10.5194/tc-15-4445-2021, 2021
Short summary
Short summary
We conducted innovative research on the use of drones to determine the surface mass balance (SMB) of two glaciers. Considering appropriate spatial scales, we succeeded in determining the SMB in the ablation area with large accuracy. Consequently, we are convinced that our method and the use of drones to monitor the mass balance of a glacier’s ablation area can be an add-on to stake measurements in order to obtain a broader picture of the heterogeneity of the SMB of glaciers.
Sebastian Doetterl, Rodrigue K. Asifiwe, Geert Baert, Fernando Bamba, Marijn Bauters, Pascal Boeckx, Benjamin Bukombe, Georg Cadisch, Matthew Cooper, Landry N. Cizungu, Alison Hoyt, Clovis Kabaseke, Karsten Kalbitz, Laurent Kidinda, Annina Maier, Moritz Mainka, Julia Mayrock, Daniel Muhindo, Basile B. Mujinya, Serge M. Mukotanyi, Leon Nabahungu, Mario Reichenbach, Boris Rewald, Johan Six, Anna Stegmann, Laura Summerauer, Robin Unseld, Bernard Vanlauwe, Kristof Van Oost, Kris Verheyen, Cordula Vogel, Florian Wilken, and Peter Fiener
Earth Syst. Sci. Data, 13, 4133–4153, https://doi.org/10.5194/essd-13-4133-2021, https://doi.org/10.5194/essd-13-4133-2021, 2021
Short summary
Short summary
The African Tropics are hotspots of modern-day land use change and are of great relevance for the global carbon cycle. Here, we present data collected as part of the DFG-funded project TropSOC along topographic, land use, and geochemical gradients in the eastern Congo Basin and the Albertine Rift. Our database contains spatial and temporal data on soil, vegetation, environmental properties, and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020.
Florian Wilken, Peter Fiener, Michael Ketterer, Katrin Meusburger, Daniel Iragi Muhindo, Kristof van Oost, and Sebastian Doetterl
SOIL, 7, 399–414, https://doi.org/10.5194/soil-7-399-2021, https://doi.org/10.5194/soil-7-399-2021, 2021
Short summary
Short summary
This study demonstrates the usability of fallout radionuclides 239Pu and 240Pu as a tool to assess soil degradation processes in tropical Africa, which is particularly valuable in regions with limited infrastructure and challenging monitoring conditions for landscape-scale soil degradation monitoring. The study shows no indication of soil redistribution in forest sites but substantial soil redistribution in cropland (sedimentation >40 cm in 55 years) with high variability.
Simon Baumgartner, Marijn Bauters, Matti Barthel, Travis W. Drake, Landry C. Ntaboba, Basile M. Bazirake, Johan Six, Pascal Boeckx, and Kristof Van Oost
SOIL, 7, 83–94, https://doi.org/10.5194/soil-7-83-2021, https://doi.org/10.5194/soil-7-83-2021, 2021
Short summary
Short summary
We compared stable isotope signatures of soil profiles in different forest ecosystems within the Congo Basin to assess ecosystem-level differences in N cycling, and we examined the local effect of topography on the isotopic signature of soil N. Soil δ15N profiles indicated that the N cycling in in the montane forest is more closed, whereas the lowland forest and Miombo woodland experienced a more open N cycle. Topography only alters soil δ15N values in forests with high erosional forces.
Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, https://doi.org/10.5194/hess-25-1569-2021, 2021
Short summary
Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.
Simon Baumgartner, Matti Barthel, Travis William Drake, Marijn Bauters, Isaac Ahanamungu Makelele, John Kalume Mugula, Laura Summerauer, Nora Gallarotti, Landry Cizungu Ntaboba, Kristof Van Oost, Pascal Boeckx, Sebastian Doetterl, Roland Anton Werner, and Johan Six
Biogeosciences, 17, 6207–6218, https://doi.org/10.5194/bg-17-6207-2020, https://doi.org/10.5194/bg-17-6207-2020, 2020
Short summary
Short summary
Soil respiration is an important carbon flux and key process determining the net ecosystem production of terrestrial ecosystems. The Congo Basin lacks studies quantifying carbon fluxes. We measured soil CO2 fluxes from different forest types in the Congo Basin and were able to show that, even though soil CO2 fluxes are similarly high in lowland and montane forests, the drivers were different: soil moisture in montane forests and C availability in the lowland forests.
Jianxiu Qiu, Wade T. Crow, Jianzhi Dong, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 24, 581–594, https://doi.org/10.5194/hess-24-581-2020, https://doi.org/10.5194/hess-24-581-2020, 2020
Short summary
Short summary
Accurately estimating coupling of evapotranspiration (ET) and soil water content (θ) at different depths is key to investigating land–atmosphere interaction. Here we examine whether the model can accurately represent surface θ (θs) versus ET coupling and vertically integrated θ (θv) versus ET coupling. We find that all models agree with observations that θs contains slightly more information with fPET than θv. In addition, an ET scheme is crucial for accurately estimating coupling of θ and ET.
Samuel Bouchoms, Zhengang Wang, Veerle Vanacker, and Kristof Van Oost
SOIL, 5, 367–382, https://doi.org/10.5194/soil-5-367-2019, https://doi.org/10.5194/soil-5-367-2019, 2019
Short summary
Short summary
Soil erosion has detrimental effects on soil fertility which can reduce carbon inputs coming from crops to soils. Our study integrated this effect into a model linking soil organic carbon (SOC) dynamics to erosion and crop productivity. When compared to observations, the inclusion of productivity improved SOC loss predictions. Over centuries, ignoring crop productivity evolution in models could result in underestimating SOC loss and overestimating C exchanged with the atmosphere.
François Clapuyt, Veerle Vanacker, Marcus Christl, Kristof Van Oost, and Fritz Schlunegger
Solid Earth, 10, 1489–1503, https://doi.org/10.5194/se-10-1489-2019, https://doi.org/10.5194/se-10-1489-2019, 2019
Short summary
Short summary
Using state-of-the-art geomorphic techniques, we quantified a 2-order of magnitude discrepancy between annual, decadal, and millennial sediment fluxes of a landslide-affected mountainous river catchment in the Swiss Alps. Our results illustrate that the impact of a single sediment pulse is strongly attenuated at larger spatial and temporal scales by sediment transport. The accumulation of multiple sediment pulses has rather a measurable impact on the regional pattern of sediment fluxes.
He Zhang, Emilien Aldana-Jague, François Clapuyt, Florian Wilken, Veerle Vanacker, and Kristof Van Oost
Earth Surf. Dynam., 7, 807–827, https://doi.org/10.5194/esurf-7-807-2019, https://doi.org/10.5194/esurf-7-807-2019, 2019
Short summary
Short summary
We evaluated the performance of a drone system to reconstruct 3-D topography. We used a direct georeferencing method to make the pictures have precise coordinates, which also improves the survey efficiency. With both consumer-grade and professional-grade camera and drone setups, we obtained centimetric accuracy, which provides a flexible application in topography remote sensing using drones.
Victoria Naipal, Philippe Ciais, Yilong Wang, Ronny Lauerwald, Bertrand Guenet, and Kristof Van Oost
Biogeosciences, 15, 4459–4480, https://doi.org/10.5194/bg-15-4459-2018, https://doi.org/10.5194/bg-15-4459-2018, 2018
Short summary
Short summary
We seek to better understand the links between soil erosion by rainfall and the global carbon (C) cycle by coupling a soil erosion model to the C cycle of a land surface model. With this modeling approach we evaluate the effects of soil removal on soil C stocks in the presence of climate change and land use change. We find that accelerated soil erosion leads to a potential SOC removal flux of 74 ±18 Pg of C globally over the period AD 1850–2005, with significant impacts on the land C balance.
François Clapuyt, Veerle Vanacker, Fritz Schlunegger, and Kristof Van Oost
Earth Surf. Dynam., 5, 791–806, https://doi.org/10.5194/esurf-5-791-2017, https://doi.org/10.5194/esurf-5-791-2017, 2017
Short summary
Short summary
This work aims at understanding the behaviour of an earth flow located in the Swiss Alps by reconstructing very accurately its topography over a 2-year period. Aerial photos taken from a drone, which are then processed using a computer vision algorithm, were used to derive the topographic datasets. Combination and careful interpretation of high-resolution topographic analyses reveal the internal mechanisms of the earthflow and its complex rotational structure, which is evolving over time.
Florian Wilken, Michael Sommer, Kristof Van Oost, Oliver Bens, and Peter Fiener
SOIL, 3, 83–94, https://doi.org/10.5194/soil-3-83-2017, https://doi.org/10.5194/soil-3-83-2017, 2017
Short summary
Short summary
Model-based analyses of the effect of soil erosion on carbon (C) dynamics are associated with large uncertainties partly resulting from oversimplifications of erosion processes. This study evaluates the need for process-oriented modelling to analyse erosion-induced C fluxes in different catchments. The results underline the importance of a detailed representation of tillage and water erosion processes. For water erosion, grain-size-specific transport is essential to simulate lateral C fluxes.
Gerard Govers, Roel Merckx, Bas van Wesemael, and Kristof Van Oost
SOIL, 3, 45–59, https://doi.org/10.5194/soil-3-45-2017, https://doi.org/10.5194/soil-3-45-2017, 2017
Short summary
Short summary
We discuss pathways towards better soil protection in the 21st century. The efficacy of soil conservation technology is not a fundamental barrier for a more sustainable soil management. However, soil conservation is generally not directly beneficial to the farmer. We believe that the solution of this conundrum is a rapid, smart intensification of agriculture in the Global South. This will reduce the financial burden and will, at the same time, allow more effective conservation.
Florian Wilken, Peter Fiener, and Kristof Van Oost
Earth Surf. Dynam., 5, 113–124, https://doi.org/10.5194/esurf-5-113-2017, https://doi.org/10.5194/esurf-5-113-2017, 2017
Short summary
Short summary
This study presents a model that accounts for preferential erosion and transport of sediment and soil organic carbon in agricultural landscapes. We applied the model to a small catchment in Belgium for a period of 100 years. After a thorough model evaluation, these simulations shows that sediment and carbon export are highly episodic and that the temporal variability is largely influenced by selective erosion and deposition.
Jianlin Zhao, Kristof Van Oost, Longqian Chen, and Gerard Govers
Biogeosciences, 13, 4735–4750, https://doi.org/10.5194/bg-13-4735-2016, https://doi.org/10.5194/bg-13-4735-2016, 2016
Short summary
Short summary
We used a novel approach to reassess erosion rates on the CLP. We found that both current average topsoil erosion rates and the maximum magnitude of the erosion-induced carbon sink are overestimated on the CLP. Although average topsoil losses on the CLP are still high, a major increase in agricultural productivity occurred since 1980. Hence, erosion is currently not a direct threat to agricultural productivity on the CLP but the long-term effects of erosion on soil quality remain important.
Victoria Naipal, Christian Reick, Kristof Van Oost, Thomas Hoffmann, and Julia Pongratz
Earth Surf. Dynam., 4, 407–423, https://doi.org/10.5194/esurf-4-407-2016, https://doi.org/10.5194/esurf-4-407-2016, 2016
Short summary
Short summary
We present a new large-scale coarse-resolution sediment budget model that is compatible with Earth system models and simulates sediment dynamics in floodplains and on hillslopes. We applied this model on the Rhine catchment for the last millennium, and found that the model reproduces the spatial distribution of sediment storage and the scaling relationships as found in observations. We also identified that land use change explains most of the temporal variability in sediment storage.
V. Naipal, C. Reick, J. Pongratz, and K. Van Oost
Geosci. Model Dev., 8, 2893–2913, https://doi.org/10.5194/gmd-8-2893-2015, https://doi.org/10.5194/gmd-8-2893-2015, 2015
Short summary
Short summary
We adjusted the topographical and rainfall erosivity factors that are the triggers of erosion in the Revised Universal Soil Loss Equation (RUSLE) model to make the model better applicable at coarse resolution on a global scale. The adjusted RUSLE model compares much better to current high resolution estimates of soil erosion in the USA and Europe. It therefore provides a basis for estimating past and future global impacts of soil erosion on climate with the use of Earth system models.
F. Wiaux, M. Vanclooster, and K. Van Oost
Biogeosciences, 12, 4637–4649, https://doi.org/10.5194/bg-12-4637-2015, https://doi.org/10.5194/bg-12-4637-2015, 2015
Short summary
Short summary
In this study, we highlight the role of soil physical conditions and gas transfer mechanisms and dynamics in the decomposition and storage of soil organic carbon in subsoil layers. To illustrate it, we measured the time series of soil temperature, moisture and CO2 concentration and calculated CO2 fluxes along 1 m depth soil profiles during 6 months throughout two contrasted soil profiles along a hillslope in the central loess belt of Belgium.
S. Doetterl, J.-T. Cornelis, J. Six, S. Bodé, S. Opfergelt, P. Boeckx, and K. Van Oost
Biogeosciences, 12, 1357–1371, https://doi.org/10.5194/bg-12-1357-2015, https://doi.org/10.5194/bg-12-1357-2015, 2015
Short summary
Short summary
We link the mineralogy of soils affected by erosion and deposition to the distribution of soil carbon fractions, their turnover and microbial activity. We show that the weathering status of soils and their history are controlling the stabilization of carbon with minerals. After burial, aggregated C is preserved more efficiently while non-aggregated C can be released and younger C re-sequestered more easily. Weathering changes the effectiveness of stabilization mechanism limiting this C sink.
E. C. Brevik, A. Cerdà, J. Mataix-Solera, L. Pereg, J. N. Quinton, J. Six, and K. Van Oost
SOIL, 1, 117–129, https://doi.org/10.5194/soil-1-117-2015, https://doi.org/10.5194/soil-1-117-2015, 2015
Short summary
Short summary
This paper provides a brief accounting of some of the many ways that the study of soils can be interdisciplinary, therefore giving examples of the types of papers we hope to see submitted to SOIL.
Z. Wang, K. Van Oost, A. Lang, T. Quine, W. Clymans, R. Merckx, B. Notebaert, and G. Govers
Biogeosciences, 11, 873–883, https://doi.org/10.5194/bg-11-873-2014, https://doi.org/10.5194/bg-11-873-2014, 2014
T. Hoffmann, S. M. Mudd, K. van Oost, G. Verstraeten, G. Erkens, A. Lang, H. Middelkoop, J. Boyle, J. O. Kaplan, J. Willenbring, and R. Aalto
Earth Surf. Dynam., 1, 45–52, https://doi.org/10.5194/esurf-1-45-2013, https://doi.org/10.5194/esurf-1-45-2013, 2013
Related subject area
Biogeosciences
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
China Wildfire Emission (ChinaWED v1) for the period 2012–2022
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Including the Phosphorus cycle into the LPJ-GUESS Dynamic Global Vegetation Model (v4.1, r10994) – Global patterns and temporal trends of N and P primary production limitation
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Process-based Modeling of Solar-induced Chlorophyll Fluorescence with VISIT-SIF version 1.0
The unicellular NUM v.0.91: A trait-based plankton model evaluated in two contrasting biogeographic provinces
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
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
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
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
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-170, https://doi.org/10.5194/gmd-2024-170, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Wildfires release large amounts of greenhouse gases, contributing to global warming. We developed a new model that provides near-real-time estimates of wildfire emissions in China. Our model improves the accuracy of burned area measurements and incorporates advanced data in fuel loads and emission factors. We found that most emissions come from agricultural fires, while emissions from forests and grasslands are decreasing. This model will help reduce the environmental impacts of wildfires.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2592, https://doi.org/10.5194/egusphere-2024-2592, 2024
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they’ve changed from 1901 to 2018. We found that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation has increased, especially in the tropics, while N limitation has decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1914, https://doi.org/10.5194/egusphere-2024-1914, 2024
Short summary
Short summary
Under climate change, the conditions for wildfires to form are becoming more frequent in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a crucial platform for future developments.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
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. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, Luke Smallmann, Susan Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zähle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek El-Madany, Mirco Migliavacca, Marika Honkanen, Yann Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaetan Pique, Amanda Ojasalo, Shaun Quegan, Peter Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
EGUsphere, https://doi.org/10.5194/egusphere-2024-1534, https://doi.org/10.5194/egusphere-2024-1534, 2024
Short summary
Short summary
When it comes to climate change, the land surfaces are where the vast majority of impacts happen. The task of monitoring those across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us see what changes on our lands.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
EGUsphere, https://doi.org/10.5194/egusphere-2024-1542, https://doi.org/10.5194/egusphere-2024-1542, 2024
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical process-based model developed to represent the global SIF observed by GOSAT. Our model simulation reproduced the global distribution and seasonal variations of GOSAT SIF. The model can be utilized to improve photosynthetic process through the combination of biogeochemical modeling and GOSAT SIF.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-53, https://doi.org/10.5194/gmd-2024-53, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We describe and test the size-based NUM model, that define organisms by a single set of parameters, on planktonic unicellular ecosystems in a eutrophic and an oligotrophic site. Results show both sites can be modelled with similar parameters, and a robust performance over a wide range of parameters. The study show that the NUM model is useful for non-experts and applicable for modelling domains with limited ecosystem data. It holds promise for evolutionary scenarios and deep-time climate models.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-1360, https://doi.org/10.5194/egusphere-2024-1360, 2024
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, crucial for understanding their role in the global carbon cycle. By analyzing over 22,000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate the top 10 cm of ocean sediments hold about 171 petagrams of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
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
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater 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
Short summary
Short summary
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
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
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
Short summary
Short summary
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
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 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
Short summary
Short summary
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
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new 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
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
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
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed 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
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Cited articles
Acton, P., Fox, J., Campbell, E., Rowe, H., and Wilkinson, M.: Carbon
isotopes for estimating soil decomposition and physical mixing in
well-drained forest soils, J. Geophys. Res.-Biogeo.,
118, 1532–1545, 2013.
Ahrens, B., Reichstein, M., Borken, W., Muhr, J., Trumbore, S. E., and Wutzler, T.: Bayesian calibration of a soil organic carbon model using Δ14C measurements of soil organic carbon and heterotrophic respiration as joint constraints, Biogeosciences, 11, 2147–2168, https://doi.org/10.5194/bg-11-2147-2014, 2014.
Ahrens, B., Braakhekke, M. C., Guggenberger, G., Schrumpf, M., and
Reichstein, M.: Contribution of sorption, DOC transport and microbial
interactions to the 14C age of a soil organic carbon profile: Insights from
a calibrated process model, Soil Biol. Biochem., 88, 390–402,
2015.
Andren, O. and Katterer, T.: ICBM: The introductory carbon balance model for
exploration of soil carbon balances, Ecol. Appl., 7, 1226–1236,
1997.
Baisden, W. T., Amundson, R., Brenner, D. L., Cook, A. C., Kendall, C., and
Harden, J. W.: A multiisotope C and N modeling analysis of soil organic
matter turnover and transport as a function of soil depth in a California
annual grassland soil chronosequence, Global Biogeochem. Cy., 16,
82-1–82-26, 2002.
Berhe, A. A., Harden, J. W., Torn, M. S., and Harte, J.: Linking soil
organic matter dynamics and erosion-induced terrestrial carbon sequestration
at different landform positions, J. Geophys.
Res.-Biogeo., 113, G04039, https://doi.org/10.1029/2008JG000751, 2008.
Beuselinck, L., Steegen, A., Govers, G., Nachtergaele, J., Takken, I., and
Poesen, J.: Characteristics of sediment deposits formed by intense rainfall
events in small catchments in the Belgian Loam Belt, Geomorphology, 32,
69–82, 2000.
Bouchoms, S., Wang, Z., Vanacker, V., and Van Oost, K.: Evaluating the effects of soil erosion and productivity decline on soil carbon dynamics using a model-based approach, SOIL, 5, 367–382, https://doi.org/10.5194/soil-5-367-2019, 2019.
Cannavo, F.: Sensitivity analysis for volcanic source modeling quality
assessment and model selection, Comput. Geosci., 44, 52–59, 2012.
Chappell, A., Baldock, J., and Sanderman, J.: The global significance of
omitting soil erosion from soil organic carbon cycling schemes, Nat. Clim.
Change, 6, 187–191, 2016.
Coleman, K. and Jenkinson, D. S.: ROTHC-26.3 A model for the turnover of
carbon in soil: Model description and user guide, Lawes Agricultural Trust,
Harpenden, 1995.
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.:
Acceleration of global warming due to carbon-cycle feedbacks in a coupled
climate model, Nature, 408, 184–187, 2000.
Cukier, R. I., Fortuin, C. M., Shuler, K. E., Petschek, A. G., and Schaibly,
J. H.: Study of the sensitivity of coupled reaction systems to uncertainties
in rate coefficients. I. Theory, J. Chem. Phys., 59, 3873–3878, 1973.
Cukier, R. I., Schaibly, J. H., and Shuler, K. E.: Study of the sensitivity
of coupled reaction systems to uncertainties in rate coefficients. III.
Analysis of the approximations, J. Chem. Phys., 63, 1140–1149, 1975.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173, 2006.
Doetterl, S., Berhe, A. A., Nadeu, E., Wang, Z., Sommer, M., and Fiener, P.:
Erosion, deposition and soil carbon: A review of process-level controls,
experimental tools and models to address C cycling in dynamic landscapes,
Earth-Sci. Rev., 154, 102–122, 2016.
Gaspar, L., Navas, A., Walling, D. E., Machín, J., and Gómez
Arozamena, J.: Using 137Cs and 210Pbex to assess soil redistribution on
slopes at different temporal scales, Catena, 102, 46–54, 2013.
Gerwitz, A. and Page, E. R.: An empirical mathematical model to describe
plant root systems, J. Appl. Ecol., 11, 773–781, 1974.
Hairsine, P. B. and Rose, C. W.: Modeling water erosion due to overland flow
using physical principles: 1. Sheet flow, Water Resour. Res., 28, 237–243,
1992a.
Hairsine, P. B. and Rose, C. W.: Modeling water erosion due to overland flow
using physical principles: 2. Rill flow, Water Resour. Res., 28, 245–250,
1992b.
Harden, J. W., Sharpe, J. M., Parton, W. J., Ojima, D. S., Fries, T. L.,
Huntington, T. G., and Dabney, S. M.: Dynamic replacement and loss of soil
carbon on eroding cropland, Global Biogeochem. Cy., 13, 885–901, 1999.
Harris, D., Horwath, W. R., and van Kessel, C.: Acid fumigation of soils to
remove carbonates prior to total organic carbon or carbon-13 isotopic
analysis, Soil Sci. Soc. Am. J., 65, 1853–1856, 2001.
Hua, Q. and Barbetti, M.: Review of tropospheric bomb 14C data for carbon
cycle modeling and age calibration purposes, Radiocarbon, 46, 1273–1298,
2004.
Jagercikova, M., Cornu, S., Bourlès, D., Evrard, O., Hatté, C., and
Balesdent, J.: Quantification of vertical solid matter transfers in soils
during pedogenesis by a multi-tracer approach, J. Soils
Sediments, 17, 408–422, 2017.
Jobbágy, E. G. and Jackson, R. B.: The vertical distribution of soil
organic carbon and its relation to climate and vegetation, Ecol.
Appl., 10, 423–436, 2000.
Johnson, M. O., Mudd, S. M., Pillans, B., Spooner, N. A., Keith Fifield, L.,
Kirkby, M. J., and Gloor, M.: Quantifying the rate and depth dependence of
bioturbation based on optically-stimulated luminescence (OSL) dates and
meteoric 10Be, Earth Surf. Proc. Land., 39, 1188–1196, 2014.
Karlen, I., Olsson, I. U., Kallberg, P., and Kilicci, S.: Absolute determination of the activity of two 14C dating standards, Arkiv Geofysik, 4, 465–471, 1965.
Koven, C. D., Hugelius, G., Lawrence, D. M., and Wieder, W. R.: Higher
climatological temperature sensitivity of soil carbon in cold than warm
climates, Nat. Clim. Change, 7, 817–822, 2017.
Lal, R.: Carbon sequestration, Philos. T. Roy.
Soc. B, 363, 815–830, 2008.
Li, Y., Zhang, Q. W., Reicosky, D. C., Lindstrom, M. J., Bai, L. Y., and Li,
L.: Changes in soil organic carbon induced by tillage and water erosion on a
steep cultivated hillslope in the Chinese Loess Plateau from 1898–1954 and
1954–1998, J. Geophys. Res.-Biogeo., 112, G01021, https://doi.org/10.1029/2005JG000107,
2007.
Liu, S., Bliss, N., Sundquist, E., and Huntington, T. G.: Modeling carbon
dynamics in vegetation and soil under the impact of soil erosion and
deposition, Global Biogeochem. Cy., 17, 1074, https://doi.org/10.1029/2002GB002010, 2003.
Mahowald, N. M., Randerson, J. T., Lindsay, K., Munoz, E., Doney, S. C.,
Lawrence, P., Schlunegger, S., Ward, D. S., Lawrence, D., and Hoffman, F.
M.: Interactions between land use change and carbon cycle feedbacks, Global Biogeochem. Cy., 31, 96–113, 2017.
Maia, S. M. F., Ogle, S. M., Cerri, C. E. P., and Cerri, C. C.: Soil organic
carbon stock change due to land use activity along the agricultural frontier
of the southwestern Amazon, Brazil, between 1970 and 2002, Glob. Change
Biol., 16, 2775–2788, 2010.
Natelhoffer, K. J. and Fry, B.: Controls On Natural Nitrogen-15 And
Carbon-13 Abundances In Forest Soil Organic Matter, Soil Sci. Soc. Am. J.,
52, 1633–1640, 1988.
Nearing, M. A.: A process-based soil erosion model for USDA-water erosion
prediction project technology, T. ASAE, 32,
1587–1593, https://doi.org/10.13031/2013.31195, 1989.
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S.: Analysis of
Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands,
Soil Sci. Soc. Am. J., 51, 1173–1179, 1987.
Quine, T. A., Govers, G., Walling, D. E., Zhang, X., Desmet, P. J. J.,
Zhang, Y., and Vandaele, K.: Erosion processes and landform evolution on
agricultural land – new perspectives from caesium-137 measurements and
topographic-based erosion modelling, Earth Surf. Proc. Land.,
22, 799–816, 1997.
R Development Core Team: R: A Language and Environment for Statistical
Computing, R Foundation for Statistical Computing, Vienna, available at: https://www.R-project.org/ (last access: 15 October 2020), 2011.
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D.
C.: Predicting soil erosion by water: a guide to conservation planning with
the Revised Universal Soil Loss Equation (RUSLE), Agriculture Handbook No.
703, USDA-ARS, Washington, DC, 1997.
Ritchie, J. C. and McHenry, J. R.: Application of Radioactive Fallout
Cesium-137 for Measuring Soil Erosion and Sediment Accumulation Rates and
Patterns: A Review, J. Environ. Qual., 19, 215–233, 1990.
Rosenbloom, N. A., Doney, S. C., and Schimel, D. S.: Geomorphic evolution of
soil texture and organic matter in eroding landscapes, Global Biogeochem.
Cy., 15, 365–381, 2001.
Rosenbloom, N. A., Harden, J. W., Neff, J. C., and Schimel, D. S.:
Geomorphic control of landscape carbon accumulation, J. Geophys.
Res.-Biogeo., 111, G01004, https://doi.org/10.1029/2005JG000077, 2006.
Schiettecatte, W., Gabriels, D., Cornelis, W. M., and Hofman, G.: Enrichment
of organic carbon in sediment transport by interrill and rill erosion
processes, Soil Sci. Soc. Am. J., 72, 50–55, 2008.
Skjemstad, J. O., Spouncer, L. R., Cowie, B., and Swift, R. S.: Calibration
of the Rothamsted organic carbon turnover model (RothC ver. 26.3), using
measurable soil organic carbon pools, Soil Res., 42, 79–88, 2004.
Stuiver, M.: Workshop on 14C data reporting, Radiocarbon, 22, 964–966,
1980.
Stuiver, M. and Polach, H. A.: Reporting of 14C data – discussion,
Radiocarbon, 19, 355–363, 1977.
Takken, I., Govers, G., Steegen, A., Nachtergaele, J., and Guérif, J.:
The prediction of runoff flow directions on tilled fields, J.
Hydrol., 248, 1–13, 2001.
Tans, P. P., De Jong, A. F. M., and Mook, W. G.: Natural atmospheric 14C
variation and the Suess effect, Nature, 280, 826–828, 1979.
Taylor, A., Blake, W. H., Couldrick, L., and Keith-Roach, M. J.: Sorption
behaviour of beryllium-7 and implications for its use as a sediment tracer,
Geoderma, 187–188, 16–23, 2012.
Trumbore, S.: Radiocarbon and soil carbon dynamics, Annu. Rev. Earth
Planet. Sc., 37, 47–66, 2009.
Trumbore, S. E., Vogel, J. S., and Southon, J. R.: AMS 14C measurements
of fractionated soil organic matter: an approach to deciphering the soil
carbon cycle, Radiocarbon, 31, 644–654, 1989.
VandenBygaart, A. J., Kroetsch, D., Gregorich, E. G., and Lobb, D.: Soil C
erosion and burial in cropland, Glob. Change Biol., 18, 1441–1452, 2012.
Van Hemelryck, H., Govers, G., Van Oost, K., and Merckx, R.: Evaluating the
impact of soil redistribution on the in situ mineralization of soil organic
carbon, Earth Surf. Proc. Land., 36, 427–438, 2011.
Van Oost, K., Govers, G., and Desmet, P.: Evaluating the effects of changes
in landscape structure on soil erosion by water and tillage, Landscape
Ecol., 15, 577–589, 2000.
Van Oost, K., Govers, G., and Van Muysen, W.: A process-based conversion
model for caesium-137 derived erosion rates on agricultural land: An
integrated spatial approach, Earth Surf. Proc. Land., 28,
187–207, 2003.
Van Oost, K., Beuselinck, L., Hairsine, P. B., and Govers, G.: Spatial
evaluation of a multi-class sediment transport and deposition model, Earth
Surf. Proc. Land., 29, 1027–1044, 2004.
Van Oost, K., Govers, G., Quine, T. A., Heckrath, G., Olesen, J. E., De
Gryze, S., and Merckx, R.: Landscape-scale modeling of carbon cycling under
the impact of soil redistribution: The role of tillage erosion, Global
Biogeochem. Cy., 19, GB4014, https://doi.org/10.1029/2005GB002471, 2005.
Van Oost, K., Verstraeten, G., Doetterl, S., Notebaert, B., Wiaux, F.,
Broothaerts, N., and Six, J.: Legacy of human-induced C erosion and burial
on soil–atmosphere C exchange, P. Natl. Acad.
Sci. USA, 109, 19492–19497, 2012.
Wang, Z., Govers, G., Steegen, A., Clymans, W., Van den Putte, A., Langhans,
C., Merckx, R., and Van Oost, K.: Catchment-scale carbon redistribution and
delivery by water erosion in an intensively cultivated area, Geomorphology,
124, 65–74, 2010.
Wang, Z., Van Oost, K., Lang, A., Quine, T., Clymans, W., Merckx, R., Notebaert, B., and Govers, G.: The fate of buried organic carbon in colluvial soils: a long-term perspective, Biogeosciences, 11, 873–883, https://doi.org/10.5194/bg-11-873-2014, 2014.
Wang, Z., Doetterl, S., Vanclooster, M., van Wesemael, B., and Van Oost, K.:
Constraining a coupled erosion and soil organic carbon model using
hillslope-scale patterns of carbon stocks and pool composition, J. Geophys. Res.-Biogeo., 120, 452–465, https://doi.org/10.1002/2014JG002768,
2015a.
Wang, Z., Van Oost, K., and Govers, G.: Predicting the long-term fate of
buried organic carbon in colluvial soils, Global Biogeochem. Cy., 29,
65–79, 2015b.
Wang, Z., Qiu, J., and Van Oost, K.: A multi-isotope model for simulating soil organic carbon cycling in eroding landscapes (WATEM_C v1.0), Zenodo, https://doi.org/10.5281/zenodo.3988484, 2020.
Wilken, F., Fiener, P., and Van Oost, K.: Modelling a century of soil redistribution processes and carbon delivery from small watersheds using a multi-class sediment transport model, Earth Surf. Dynam., 5, 113–124, https://doi.org/10.5194/esurf-5-113-2017, 2017.
Yoo, K., Amundson, R., Heimsath, A. M., and Dietrich, W. E.: Erosion of
upland hillslope soil organic carbon: Coupling field measurements with a
sediment transport model, Global Biogeochem. Cy., 19, Gb3003, https://doi.org/10.1029/2004GB002271, 2005.
Zimmermann, M., Leifeld, J., Schmidt, M. W. I., Smith, P., and Fuhrer, J.:
Measured soil organic matter fractions can be related to pools in the RothC
model, Eur. J. Soil Sci., 58, 658–667, 2007.
Short summary
This study developed a spatially distributed carbon cycling model applicable in an eroding landscape. It includes all three carbon isotopes so that it is able to represent the carbon isotopic compositions. The model is able to represent the observations that eroding area is enriched in 13C and depleted of 14C compared to depositional area. Our simulations show that the spatial variability of carbon isotopic properties in an eroding landscape is mainly caused by the soil redistribution.
This study developed a spatially distributed carbon cycling model applicable in an eroding...
