Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-3375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Christoph Müller
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Jens Heinke
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Sibyll Schaphoff
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Related authors
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
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.
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.
Mohamed Ayache, Alberte Bondeau, Rémi Pagès, Nicolas Barrier, Sebastian Ostberg, and Melika Baklouti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-342, https://doi.org/10.5194/gmd-2020-342, 2020
Preprint withdrawn
Short summary
Short summary
Land forcing is reported as one of the major sources of uncertainty limiting the capacity of marine biogeochemical models. In this study, we present the first basin-wide simulation at 1/12° of water discharge as well as nitrate (NO3) and phosphate (PO4) release into the Mediterranean from basin-wide agriculture and urbanization, by using the agro-ecosystem model (LPJmL-Med). The model evaluation against observation data, and all implemented processes are described in detail in this manuscript.
Lily-belle Sweet, Christoph Müller, Jonas Jägermeyr, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2025-3006, https://doi.org/10.5194/egusphere-2025-3006, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study presents a method to identify climate drivers of an impact, such as agricultural yield failure, from high-resolution weather data. The approach systematically generates, selects and combines predictors that generalise across different environments. Tested on crop model simulations, the identified drivers are used to create parsimonious models that achieve high predictive performance over long time horizons, offering a more interpretable alternative to black-box models.
Edna Johanna Molina Bacca, Miodrag Stevanović, Benjamin Leon Bodirsky, Jonathan Cornelis Doelman, Louise Parsons Chini, Jan Volkholz, Katja Frieler, Christopher Paul Oliver Reyer, George Hurtt, Florian Humpenöder, Kristine Karstens, Jens Heinke, Christoph Müller, Jan Philipp Dietrich, Hermann Lotze-Campen, Elke Stehfest, and Alexander Popp
Earth Syst. Dynam., 16, 753–801, https://doi.org/10.5194/esd-16-753-2025, https://doi.org/10.5194/esd-16-753-2025, 2025
Short summary
Short summary
Land-use change projections are vital for impact studies. This study compares updated land-use model projections, including CO2 fertilization among other upgrades, from the MAgPIE and IMAGE models under three scenarios, highlighting differences, uncertainty hotspots, and harmonization effects. Key findings include reduced bioenergy crop demand projections and differences in grassland area allocation and sizes, with socioeconomic–climate scenarios' largest effect on variance starting in 2030.
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim G. Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Svenja Szemkus, Sara M. Vallejo-Bernal, Odysseas Vlachopoulos, and Frederik Wolf
Nat. Hazards Earth Syst. Sci., 25, 541–564, https://doi.org/10.5194/nhess-25-541-2025, https://doi.org/10.5194/nhess-25-541-2025, 2025
Short summary
Short summary
Europe frequently experiences compound events, with major impacts. We investigate these events’ interactions, characteristics, and changes over time, focusing on socio-economic impacts in Germany and central Europe. Highlighting 2018’s extreme events, this study reveals impacts on water, agriculture, and forests and stresses the need for impact-focused definitions and better future risk quantification to support adaptation planning.
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.
Felix Jäger, Jonas Schwaab, Yann Quilcaille, Michael Windisch, Jonathan Doelman, Stefan Frank, Mykola Gusti, Petr Havlik, Florian Humpenöder, Andrey Lessa Derci Augustynczik, Christoph Müller, Kanishka Balu Narayan, Ryan Sebastian Padrón, Alexander Popp, Detlef van Vuuren, Michael Wögerer, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 15, 1055–1071, https://doi.org/10.5194/esd-15-1055-2024, https://doi.org/10.5194/esd-15-1055-2024, 2024
Short summary
Short summary
Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.
Markus Drüke, Wolfgang Lucht, Werner von Bloh, Stefan Petri, Boris Sakschewski, Arne Tobian, Sina Loriani, Sibyll Schaphoff, Georg Feulner, and Kirsten Thonicke
Earth Syst. Dynam., 15, 467–483, https://doi.org/10.5194/esd-15-467-2024, https://doi.org/10.5194/esd-15-467-2024, 2024
Short summary
Short summary
The planetary boundary framework characterizes major risks of destabilization of the Earth system. We use the comprehensive Earth system model POEM to study the impact of the interacting boundaries for climate change and land system change. Our study shows the importance of long-term effects on carbon dynamics and climate, as well as the need to investigate both boundaries simultaneously and to generally keep both boundaries within acceptable ranges to avoid a catastrophic scenario for humanity.
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.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
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.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
Short summary
Short summary
The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Kristine Karstens, Benjamin Leon Bodirsky, Jan Philipp Dietrich, Marta Dondini, Jens Heinke, Matthias Kuhnert, Christoph Müller, Susanne Rolinski, Pete Smith, Isabelle Weindl, Hermann Lotze-Campen, and Alexander Popp
Biogeosciences, 19, 5125–5149, https://doi.org/10.5194/bg-19-5125-2022, https://doi.org/10.5194/bg-19-5125-2022, 2022
Short summary
Short summary
Soil organic carbon (SOC) has been depleted by anthropogenic land cover change and agricultural management. While SOC models often simulate detailed biochemical processes, the management decisions are still little investigated at the global scale. We estimate that soils have lost around 26 GtC relative to a counterfactual natural state in 1975. Yet, since 1975, SOC has been increasing again by 4 GtC due to a higher productivity, recycling of crop residues and manure, and no-tillage practices.
Vera Porwollik, Susanne Rolinski, Jens Heinke, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Biogeosciences, 19, 957–977, https://doi.org/10.5194/bg-19-957-2022, https://doi.org/10.5194/bg-19-957-2022, 2022
Short summary
Short summary
The study assesses impacts of grass cover crop cultivation on cropland during main-crop off-season periods applying the global vegetation model LPJmL (V.5.0-tillage-cc). Compared to simulated bare-soil fallowing practices, cover crops led to increased soil carbon content and reduced nitrogen leaching rates on the majority of global cropland. Yield responses of main crops following cover crops vary with location, duration of altered management, crop type, water regime, and tillage practice.
Tobias Herzfeld, Jens Heinke, Susanne Rolinski, and Christoph Müller
Earth Syst. Dynam., 12, 1037–1055, https://doi.org/10.5194/esd-12-1037-2021, https://doi.org/10.5194/esd-12-1037-2021, 2021
Short summary
Short summary
Soil organic carbon sequestration on cropland has been proposed as a climate change mitigation strategy. We simulate different agricultural management practices under climate change scenarios using a global biophysical model. We find that at the global aggregated level, agricultural management practices are not capable of enhancing total carbon storage in the soil, yet for some climate regions, we find that there is potential to enhance the carbon content in cropland soils.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Yvonne Jans, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, https://doi.org/10.5194/hess-25-2027-2021, 2021
Short summary
Short summary
Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656, https://doi.org/10.5194/gmd-14-1639-2021, https://doi.org/10.5194/gmd-14-1639-2021, 2021
Short summary
Short summary
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
Mohamed Ayache, Alberte Bondeau, Rémi Pagès, Nicolas Barrier, Sebastian Ostberg, and Melika Baklouti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-342, https://doi.org/10.5194/gmd-2020-342, 2020
Preprint withdrawn
Short summary
Short summary
Land forcing is reported as one of the major sources of uncertainty limiting the capacity of marine biogeochemical models. In this study, we present the first basin-wide simulation at 1/12° of water discharge as well as nitrate (NO3) and phosphate (PO4) release into the Mediterranean from basin-wide agriculture and urbanization, by using the agro-ecosystem model (LPJmL-Med). The model evaluation against observation data, and all implemented processes are described in detail in this manuscript.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
Short summary
Short summary
Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Femke Lutz, Stephen Del Grosso, Stephen Ogle, Stephen Williams, Sara Minoli, Susanne Rolinski, Jens Heinke, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 13, 3905–3923, https://doi.org/10.5194/gmd-13-3905-2020, https://doi.org/10.5194/gmd-13-3905-2020, 2020
Short summary
Short summary
Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, NOAA Technical Memorandum NESDIS
NGDC-24, National Geophysical Data Center, NOAA, https://doi.org/10.7289/V5C8276M,
2009. a
Anderson, W., You, L., Wood, S., Wood-Sichra, U., and Wu, W.: An analysis of
methodological and spatial differences in global cropping systems models and
maps, Glob. Ecol. Biogeogr., 24, 180–191, https://doi.org/10.1111/geb.12243,
2015. a
Batjes, N.: ISRIC-WISE global data set of derived soil properties on a 0.5 by
0.5 degree grid (Version 3.0), Tech. Rep. Report 2005/08, ISRIC – World Soil
Information, Wageningen, https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/d9eca770-29a4-4d95-bf93-f32e1ab419c3 (last access: 10 June 2023), 2005. a
Batjes, N.: ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global
grid (ver. 1.2), Tech. Rep. Report 2012/01, ISRIC – World Soil Information,
Wageningen, with data set, https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/82f3d6b0-a045-4fe2-b960-6d05bc1f37c0 (last access: 10 June 2023), 2012. a
Batjes, N.: Harmonized soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034, 2016. a
Beames, P., Lehner, B., and Anand, M.: Global Reservoir and Dam (GRanD)
Database Technical Documentation Version 1.3, McGill University, Montreal,
QC, Canada, https://www.globaldamwatch.org/grand (last access: 18 December 2019), 2019. a
Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R. W. A., Heinke,
J., von Bloh, W., and Gerten, D.: Impact of reservoirs on river discharge
and irrigation water supply during the 20th century, Water Resour. Res., 47,
W03509, https://doi.org/10.1029/2009WR008929, 2011. a
Bivand, R., Keitt, T., and Rowlingson, B.: rgdal: Bindings for the “Geospatial”
Data Abstraction Library, R package version
1.4-8,
https://CRAN.R-project.org/package=rgdal (last access: 3 February 2020), 2019. a
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,
Gerten, D., Lotze-Campen, H., Müller, C., Reichstein, M., and Smith,
B.: Modelling the role of agriculture for the 20th century global
terrestrial carbon balance, Glob. Change Biol., 13, 679–706,
https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007. a
Dunnington, D., Pebesma, E., and Rubak, E.: s2: Spherical Geometry Operators
Using the S2 Geometry Library, R package version
1.0.7,
https://CRAN.R-project.org/package=s2 (last access: 26 October 2021), 2021. a
Earthstat Team: Harvested Area and Yield for 175 Crops,
http://www.earthstat.org/harvested-area-yield-175-crops/
(last access: 26 July 2020), 2020. a
Eilander, D., Winsemius, H. C., Van Verseveld, W., Yamazaki, D., Weerts, A.,
and Ward, P. J.: MERIT Hydro IHU, Zenodo [data set], https://doi.org/10.5281/zenodo.5166932, , 2020. a, b, c
Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, 2021. a, b, c
Fader, M., Rost, S., Müller, C., Bondeau, A., and Gerten, D.: Virtual
water content of temperate cereals and maize: Present and potential future
patterns, J. Hydrol., 384, 218–231, https://doi.org/10.1016/j.jhydrol.2009.12.011,
2010. a, b
Fischer, G., Nachtergaele, F. O., Prieler, S., Teixeira, E., Tóth, G.,
van Velthuizen, H., Verelst, L., and Wiberg, D.: Global Agro‐Ecological
Zones (GAEZ v3.0) Model Documentation, IIASA, Laxenburg, Austria and FAO,
Rome, Italy, https://www.gaez.iiasa.ac.at/ (last access: 10 July 2023), 2012. a
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017. a, b, c
Gagolewski, M.: R package stringi: Character string processing facilities,
http://www.gagolewski.com/software/stringi/ (last access: 3 February 2020), 2019. a
Grogan, D., Frolking, S., Wisser, D., Prusevich, A., and Glidden, S.: Global
gridded crop harvested area, production, yield, and monthly physical area
data circa 2015, Sci. Data, 9, 15, https://doi.org/10.1038/s41597-021-01115-2,
2022. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS
monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7,
109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a, b
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS One, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748,
2017. a
Hiebert, J.: udunits2: Udunits-2 Bindings for R, r package
version 0.13,
https://CRAN.R-project.org/package=udunits2 (last access: 4 February 2020), 2016. a
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, R package version
3.0-12,
https://CRAN.R-project.org/package=raster (last access: 5 February 2020), 2020. a
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020 (data available at: https://luh.umd.edu/,
last access: 8 July 2021). a, b, c, d, e
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015, 2015. a, b
Joglekar, A. K. B., Wood-Sichra, U., and Pardey, P. G.: Pixelating crop
production: Consequences of methodological choices, PLOS ONE, 14, 1–16,
https://doi.org/10.1371/journal.pone.0212281, 2019. a
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017 (data available at:
https://www.pbl.nl/en/image/links/hyde, last access: 25 October 2017). a, b, c, d, e
Koirala, S.: Soil Texture Map,
http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html
(last access: 15 March 2021), 2011. a
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019. a
Leff, B., Ramankutty, N., and Foley, J. A.: Geographic distribution of major
crops across the world, Global Biogeochem. Cy., 18, GB1009,
https://doi.org/10.1029/2003GB002108, 2004. a
Lehner, B.: HydroSHEDS Technical Documentation Version 1.2, Conservation
Science Program, World Wildlife Fund US, Washington, D.C., https://hydrosheds.org (last access: 2 March 2022), 2013. a
Lehner, B. and Döll, P.: Development and validation of a global database
of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004 (data available at:
https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database,
last access: 15 August 2018). a, b, c, d
Lehner, B., Verdin, K., and Jarvis, A.: New Global Hydrography Derived From
Spaceborne Elevation Data, Eos, Transactions American Geophysical Union, 89,
93–94, https://doi.org/10.1029/2008EO100001, 2008 (data available at:
https://www.hydrosheds.org, last access: 18 May 2022). a, b, c, d
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete,
B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J.,
Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.:
High-resolution mapping of the world's reservoirs and dams for sustainable
river-flow management, Front. Ecol. Environ., 9, 494–502,
https://doi.org/10.1890/100125, 2011 (data available at:
https://www.globaldamwatch.org/grand, last access: 18 December 2019). a, b, c, d, e
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019. a
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.:
Estimating the volume and age of water stored in global lakes using a
geo-statistical approach, Nat. Commun., 7, 13603,
https://doi.org/10.1038/ncomms13603, 2016. a, b
Microsoft and Weston, S.: doParallel: Foreach Parallel Adaptor for the
“parallel” Package, R package
version 1.0.17,
https://CRAN.R-project.org/package=doParallel (last access: 9 June 2022), 2022. a
Monfreda, C., Ramankutty, N., and Foley, J. a.: Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Global Biogeochem. Cy., 22, GB1022,
https://doi.org/10.1029/2007GB002947 2008 (data available at:
http://www.earthstat.org/, last access: 30 October 2018). a, b, c, d, e, f, g
Mueller, N.: Crop-specific global fertilizer application rates from “Closing
yield gaps through nutrient and water management”, Version 1, Zenodo,
https://doi.org/10.5281/zenodo.5260732, 2012. a, b, c
Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., and
Foley, J. A.: Closing yield gaps through nutrient and water management,
Nature, 490, 254–257, https://doi.org/10.1038/nature11420, 2012. a, b, c, d
NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER
Global Digital Elevation Model V003, distributed by NASA EOSDIS Land
Processes DAAC, https://doi.org/10.5067/ASTER/ASTGTM.003, 2019. a
Natural Earth: Free vector and raster map data @ naturalearthdata.com,
https://www.naturalearthdata.com (last access: 3 July 2020), 2018. a
NOAA National Geophysical Data Center: ETOPO1 1 Arc-Minute Global Relief
Model, NOAA National Centers for Environmental Information,
https://doi.org/10.7289/V5C8276M, 2009. a, b
OpenStreetMap: Land polygons,
https://osmdata.openstreetmap.de/data/land-polygons.html
(last access: 3 July 2020), 2020. a
Ostberg, S.: Code for LandInG v.1.0 sample application at 5 arc-minute and 30
arc-minute resolution, Zenodo [code], https://doi.org/10.5281/zenodo.7802547, 2023. a
Pebesma, E.: Simple Features for R: Standardized Support for Spatial Vector
Data, R J., 10, 439–446, https://doi.org/10.32614/RJ-2018-009, 2018. a
Pebesma, E.: lwgeom: Bindings to Selected “liblwgeom” Functions for Simple
Features, R
package version 0.1-7, https://CRAN.R-project.org/package=lwgeom (last access: 4 February 2020), 2019. a
Pierce, D.: ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format
Data Files, R
package version 1.17, https://CRAN.R-project.org/package=ncdf4 (last access: 3 February 2020), 2019. a
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global monthly
irrigated and rainfed crop areas around the year 2000: A new high-resolution
data set for agricultural and hydrological modeling, Global Biogeochem.
Cy., 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010a. a, b
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000, Version 1.1,
Zenodo [data set], https://doi.org/10.5281/zenodo.7422506,
2010b. a, b, c
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 12 December 2019), 2019. a
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the
planet: 1. Geographic distribution of global agricultural lands in the year
2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952,
2008 (data available at: http://www.earthstat.org/, last access: 31 January 2019). a, b, c
Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J., and Schaphoff, S.:
Agricultural green and blue water consumption and its influence on the
global water system, Water Resour. Res., 44, W09405,
https://doi.org/10.1029/2007WR006331, 2008. a, b, c
Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke, J., and Lucht, W.:
Contribution of permafrost soils to the global carbon budget, Environ. Res.
Lett., 8, 014026, https://doi.org/10.1088/1748-9326/8/1/014026, 2013. a, b
Schaphoff, S., Forkel, M., Müller, C., Knauer, J., von Bloh, W., Gerten, D., Jägermeyr, J., Lucht, W., Rammig, A., Thonicke, K., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation, Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, 2018a. a, b, c, d
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018b. a, b, c, d
Schulzweida, U.: CDO User Guide, Zenodo, https://doi.org/10.5281/zenodo.3539275, 2019. a
Soil Science Division Staff: Soil Survey Manual, USDA Handbook 18,
Government Printing Office, Washington, D.C., https://www.nrcs.usda.gov/resources/guides-and-instructions/soil-survey-manual (last access: 10 June 2023), 2017. a
Takaku, J., Tadono, T., Tsutsui, K., and Ichikawa, M.: Validation of `AW3D'
Global DSM Generated from ALOS PRISM, in: ISPRS Annals of the
Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.
III-4, XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic,
https://doi.org/10.5194/isprsannals-III-4-25-2016
2016 (data available from Japan Aerospace
Exploration Agency (JAXA), https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm, last access: 10 June 2023). a
UNSD: Standard Country or Area Codes for Statistics Use, United Nations,
New York, USA, https://unstats.un.org/unsd/methodology/m49/
(last access: 24 March 2022), 2022. a
USGS EROS: SRTM 1 Arc-Second Global, Earth Resources Observation and Science (EROS) Center, https://doi.org/10.5066/F7PR7TFT, 2014. a
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, 2018. a, b
Vörösmarty, C. J., Fekete, B. M., Meybeck, M., and Lammers, R. B.:
Global system of rivers: Its role in organizing continental land mass and
defining land-to-ocean linkages, Global Biogeochem. Cy., 14, 599–621,
https://doi.org/10.1029/1999GB900092, 2000 (data available at:
https://wsag.unh.edu/Stn-30/stn-30.html, last access: 20 October 2021). a, b, c, d
Waha, K., Dietrich, J. P., Portmann, F. T., Siebert, S., Thornton, P. K.,
Bondeau, A., and Herrero, M.: Multiple cropping systems of the world and the
potential for increasing cropping intensity, Global Environ. Chang., 64,
102131, https://doi.org/10.1016/j.gloenvcha.2020.102131, 2020. a, b
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical,
high-resolution shoreline database, J. Geophys. Res.-Sol.
Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996. a, b
Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F.,
and Tian, D.: The Generic Mapping Tools Version 6, Geochem. Geophy.
Geosy., 20, 5556–5564, https://doi.org/10.1029/2019GC008515, 2019. a
Weston, S.: doMPI: Foreach Parallel Adaptor for the Rmpi Package, r package version
0.2.2,
https://CRAN.R-project.org/package=doMPI (last access: 4 February 2020), 2017. a
Wu, H., Kimball, J. S., Li, H., Huang, M., Leung, L. R., and Adler, R. F.: A
new global river network database for macroscale hydrologic modeling, Water
Resour. Res., 48, 2012WR012313, https://doi.org/10.1029/2012WR012313, 2012. a
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal,
J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of
global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017. a
You, L. and Wood, S.: An entropy approach to spatial disaggregation of
agricultural production, Agr. Syst., 90, 329–347,
https://doi.org/10.1016/j.agsy.2006.01.008, 2006. a
Yu, H.: Rmpi: Parallel Statistical Computing in R, R News, 2, 10–14,
https://cran.r-project.org/doc/Rnews/Rnews_2002-2.pdf (last access: 10 June 2023), 2002. a
Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, 2020.
a, b
Zhang, B., Tian, H., Lu, C., Dangal, S. R. S., Yang, J., and Pan, S.: Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling, Earth Syst. Sci. Data, 9, 667–678, https://doi.org/10.5194/essd-9-667-2017, 2017a. a, b
Zhang, B., Tian, H., Lu, C., Dangal, S. R. S., Yang, J., and Pan,
S.: Manure nitrogen production and application in cropland and rangeland
during 1860 – 2014: A 5-minute gridded global data set for Earth system
modeling, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.871980, 2017b. a, b, c, d, e
Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
We present a new toolbox for generating input datasets for terrestrial ecosystem models from...
Special issue