Articles | Volume 17, issue 21
https://doi.org/10.5194/bg-17-5263-2020
© 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/bg-17-5263-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Nov 2020
Research article |
| 05 Nov 2020
| 05 Nov 2020
Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model
Tony W. Carr
CORRESPONDING AUTHOR
University College London, Institute for Sustainable Resources,
London, United Kingdom
Juraj Balkovič
International Institute for Applied Systems Analysis, Ecosystem
Services and Management Program, Laxenburg, Austria
Department of Soil Science, Faculty of Natural Sciences, Comenius
University in Bratislava, Bratislava, Slovak Republic
Paul E. Dodds
University College London, Institute for Sustainable Resources,
London, United Kingdom
Christian Folberth
International Institute for Applied Systems Analysis, Ecosystem
Services and Management Program, Laxenburg, Austria
Emil Fulajtar
International Atomic Energy Agency, Joint FAO/IAEA Division of
Nuclear Techniques in Food and Agriculture, Vienna, Austria
Rastislav Skalsky
International Institute for Applied Systems Analysis, Ecosystem
Services and Management Program, Laxenburg, Austria
National Agricultural and Food Centre, Soil Science and Conservation
Research Institute, Bratislava, Slovak Republic
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Stefano Gianessi, Matteo Polo, Luca Stevanato, Marcello Lunardon, Till Francke, Sascha E. Oswald, Hami Said Ahmed, Arsenio Toloza, Georg Weltin, Gerd Dercon, Emil Fulajtar, Lee Heng, and Gabriele Baroni
Geosci. Instrum. Method. Data Syst., 13, 9–25, https://doi.org/10.5194/gi-13-9-2024, https://doi.org/10.5194/gi-13-9-2024, 2024
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Soil moisture monitoring is important for many applications, from improving weather prediction to supporting agriculture practices. Our capability to measure this variable is still, however, limited. In this study, we show the tests conducted on a new soil moisture sensor at several locations. The results show that the new sensor is a valid and compact alternative to more conventional, non-invasive soil moisture sensors that can pave the way for a wide range of applications.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
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Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Henrique M. D. Goulart, Karin van der Wiel, Christian Folberth, Juraj Balkovic, and Bart van den Hurk
Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, https://doi.org/10.5194/esd-12-1503-2021, 2021
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Agriculture is sensitive to weather conditions and to climate change. We identify the weather conditions linked to soybean failures and explore changes related to climate change. Additionally, we build future versions of a historical extreme season under future climate scenarios. Results show that soybean failures are likely to increase with climate change. Future events with similar physical conditions to the extreme season are not expected to increase, but events with similar impacts are.
Ana Maria Roxana Petrescu, Matthew J. McGrath, Robbie M. Andrew, Philippe Peylin, Glen P. Peters, Philippe Ciais, Gregoire Broquet, Francesco N. Tubiello, Christoph Gerbig, Julia Pongratz, Greet Janssens-Maenhout, Giacomo Grassi, Gert-Jan Nabuurs, Pierre Regnier, Ronny Lauerwald, Matthias Kuhnert, Juraj Balkovič, Mart-Jan Schelhaas, Hugo A. C. Denier van der
Gon, Efisio Solazzo, Chunjing Qiu, Roberto Pilli, Igor B. Konovalov, Richard A. Houghton, Dirk Günther, Lucia Perugini, Monica Crippa, Raphael Ganzenmüller, Ingrid T. Luijkx, Pete Smith, Saqr Munassar, Rona L. Thompson, Giulia Conchedda, Guillaume Monteil, Marko Scholze, Ute Karstens, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2363–2406, https://doi.org/10.5194/essd-13-2363-2021, https://doi.org/10.5194/essd-13-2363-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CO2 fossil emissions and CO2 land fluxes in the EU27+UK. The data integrate recent emission inventories with ecosystem data, land carbon models and regional/global inversions for the European domain, aiming at reconciling CO2 estimates with official country-level UNFCCC national GHG inventories in support to policy and facilitating real-time verification procedures.
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
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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.
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
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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.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Juraj Balkovic, Philippe Ciais, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, Munir Hoffmann, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Nikolay Khabarov, Marian Koch, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Xuhui Wang, Karina Williams, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 2315–2336, https://doi.org/10.5194/gmd-13-2315-2020, https://doi.org/10.5194/gmd-13-2315-2020, 2020
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Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Crop models, which represent plant biology, are necessary tools for this purpose since they allow representing future climate, farmer choices, and new agricultural geographies. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, under the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to evaluate and improve crop models.
Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, Christian Folberth, Michael Glotter, Steven Hoek, Toshichika Iizumi, Roberto C. Izaurralde, Curtis Jones, Nikolay Khabarov, Peter Lawrence, Wenfeng Liu, Stefan Olin, Thomas A. M. Pugh, Deepak K. Ray, Ashwan Reddy, Cynthia Rosenzweig, Alex C. Ruane, Gen Sakurai, Erwin Schmid, Rastislav Skalsky, Carol X. Song, Xuhui Wang, Allard de Wit, and Hong Yang
Geosci. Model Dev., 10, 1403–1422, https://doi.org/10.5194/gmd-10-1403-2017, https://doi.org/10.5194/gmd-10-1403-2017, 2017
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Crop models are increasingly used in climate change impact research and integrated assessments. For the Agricultural Model Intercomparison and Improvement Project (AgMIP), 14 global gridded crop models (GGCMs) have supplied crop yield simulations (1980–2010) for maize, wheat, rice and soybean. We evaluate the performance of these models against observational data at global, national and grid cell level. We propose an open-access benchmark system against which future model versions can be tested.
Christian Folberth, Joshua Elliott, Christoph Müller, Juraj Balkovic, James Chryssanthacopoulos, Roberto C. Izaurralde, Curtis D. Jones, Nikolay Khabarov, Wenfeng Liu, Ashwan Reddy, Erwin Schmid, Rastislav Skalský, Hong Yang, Almut Arneth, Philippe Ciais, Delphine Deryng, Peter J. Lawrence, Stefan Olin, Thomas A. M. Pugh, Alex C. Ruane, and Xuhui Wang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-527, https://doi.org/10.5194/bg-2016-527, 2016
Manuscript not accepted for further review
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Global crop models differ in numerous aspects such as algorithms, parameterization, input data, and management assumptions. This study compares five global crop model frameworks, all based on the same field-scale model, to identify differences induced by the latter three. Results indicate that foremost nutrient supply, soil handling, and crop management induce substantial differences in crop yield estimates whereas crop cultivars primarily result in scaling of yield levels.
K. Frieler, A. Levermann, J. Elliott, J. Heinke, A. Arneth, M. F. P. Bierkens, P. Ciais, D. B. Clark, D. Deryng, P. Döll, P. Falloon, B. Fekete, C. Folberth, A. D. Friend, C. Gellhorn, S. N. Gosling, I. Haddeland, N. Khabarov, M. Lomas, Y. Masaki, K. Nishina, K. Neumann, T. Oki, R. Pavlick, A. C. Ruane, E. Schmid, C. Schmitz, T. Stacke, E. Stehfest, Q. Tang, D. Wisser, V. Huber, F. Piontek, L. Warszawski, J. Schewe, H. Lotze-Campen, and H. J. Schellnhuber
Earth Syst. Dynam., 6, 447–460, https://doi.org/10.5194/esd-6-447-2015, https://doi.org/10.5194/esd-6-447-2015, 2015
M. van der Velde, J. Balkovič, C. Beer, N. Khabarov, M. Kuhnert, M. Obersteiner, R. Skalský, W. Xiong, and P. Smith
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-1561-2014, https://doi.org/10.5194/bgd-11-1561-2014, 2014
Revised manuscript not accepted
Related subject area
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Coupling numerical models of deltaic wetlands with AirSWOT, UAVSAR, and AVIRIS-NG remote sensing data
Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests
Meteorological history of low-forest-greenness events in Europe in 2002–2022
Modelling long-term alluvial-peatland dynamics in temperate river floodplains
Variable particle size distributions reduce the sensitivity of global export flux to climate change
Climate change will cause non-analog vegetation states in Africa and commit vegetation to long-term change
Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections
The capacity of northern peatlands for long-term carbon sequestration
Towards a more complete quantification of the global carbon cycle
Modeling seasonal and vertical habitats of planktonic foraminifera on a global scale
An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data
Sensitivity of woody carbon stocks to bark investment strategy in Neotropical savannas and forests
Modelling past, present and future peatland carbon accumulation across the pan-Arctic region
Biogenic sediments from coastal ecosystems to beach–dune systems: implications for the adaptation of mixed and carbonate beaches to future sea level rise
Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model
Effects of climate change and land management on soil organic carbon dynamics and carbon leaching in northwestern Europe
Quantifying regional, time-varying effects of cropland and pasture on vegetation fire
HESFIRE: a global fire model to explore the role of anthropogenic and weather drivers
Impact of human population density on fire frequency at the global scale
Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art
A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe
Analyzing precipitationsheds to understand the vulnerability of rainfall dependent regions
A new concept for simulation of vegetated land surface dynamics – Part 1: The event driven phenology model
Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology – Part 2: The event driven phenology model
The influence of land cover change in the Asian monsoon region on present-day and mid-Holocene climate
Sensitivity of Holocene atmospheric CO2 and the modern carbon budget to early human land use: analyses with a process-based model
Side effects and accounting aspects of hypothetical large-scale Southern Ocean iron fertilization
Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model
Projected 21st century decrease in marine productivity: a multi-model analysis
Impact of atmospheric and terrestrial CO2 feedbacks on fertilization-induced marine carbon uptake
Luca Cortese, Carmine Donatelli, Xiaohe Zhang, Justin A. Nghiem, Marc Simard, Cathleen E. Jones, Michael Denbina, Cédric G. Fichot, Joshua P. Harringmeyer, and Sergio Fagherazzi
Biogeosciences, 21, 241–260, https://doi.org/10.5194/bg-21-241-2024, https://doi.org/10.5194/bg-21-241-2024, 2024
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This study shows that numerical models in coastal areas can greatly benefit from the spatial information provided by remote sensing. Three Delft3D numerical models in coastal Louisiana are calibrated using airborne SAR and hyperspectral remote sensing products from the recent NASA Delta-X mission. The comparison with the remote sensing allows areas where the models perform better to be spatially verified and yields more representative parameters for the entire area.
Benjamin F. Meyer, Allan Buras, Konstantin Gregor, Lucia S. Layritz, Adriana Principe, Jürgen Kreyling, Anja Rammig, and Christian S. Zang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-139, https://doi.org/10.5194/bg-2023-139, 2023
Revised manuscript accepted for BG
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Late-spring frost (LSF), critically low temperatures when trees have already flushed their leaves, results in freezing damage leaving trees with reduced ability to perform photosynthesis. Forests with a high proportion of susceptible species like European beech are particularly vulnerable. However, this process is rarely included in dynamic vegetation models (DVMs). We show that the effect on simulated productivity and biomass is substantial, warranting more widespread inclusion of LSF in DVMs.
Mauro Hermann, Matthias Röthlisberger, Arthur Gessler, Andreas Rigling, Cornelius Senf, Thomas Wohlgemuth, and Heini Wernli
Biogeosciences, 20, 1155–1180, https://doi.org/10.5194/bg-20-1155-2023, https://doi.org/10.5194/bg-20-1155-2023, 2023
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This study examines the multi-annual meteorological history of low-forest-greenness events in Europe's temperate and Mediterranean biome in 2002–2022. We systematically identify anomalies in temperature, precipitation, and weather systems as event precursors, with noteworthy differences between the two biomes. We also quantify the impact of the most extensive event in 2022 (37 % coverage), underlining the importance of understanding the forest–meteorology interaction in a changing climate.
Ward Swinnen, Nils Broothaerts, and Gert Verstraeten
Biogeosciences, 18, 6181–6212, https://doi.org/10.5194/bg-18-6181-2021, https://doi.org/10.5194/bg-18-6181-2021, 2021
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Here we present a new modelling framework specifically designed to simulate alluvial peat growth, taking into account the river dynamics. The results indicate that alluvial peat growth is strongly determined by the number, spacing and movement of the river channels in the floodplain, rather than by environmental changes or peat properties. As such, the amount of peat that can develop in a floodplain is strongly determined by the characteristics and dynamics of the local river network.
Shirley W. Leung, Thomas Weber, Jacob A. Cram, and Curtis Deutsch
Biogeosciences, 18, 229–250, https://doi.org/10.5194/bg-18-229-2021, https://doi.org/10.5194/bg-18-229-2021, 2021
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A global model is constrained with empirical relationships to quantify how shifts in sinking-particle sizes modulate particulate organic carbon export production changes in a warming ocean. Including the effect of dynamic particle sizes on remineralization reduces the magnitude of predicted 100-year changes in export production by ~14 %. Projections of future export could thus be improved by considering dynamic phytoplankton and particle-size-dependent remineralization depths.
Mirjam Pfeiffer, Dushyant Kumar, Carola Martens, and Simon Scheiter
Biogeosciences, 17, 5829–5847, https://doi.org/10.5194/bg-17-5829-2020, https://doi.org/10.5194/bg-17-5829-2020, 2020
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Lags caused by delayed vegetation response to changing environmental conditions can lead to disequilibrium vegetation states. Awareness of this issue is relevant for ecosystem conservation. We used the aDGVM vegetation model to quantify the difference between transient and equilibrium vegetation states in Africa during the 21st century for two potential climate trajectories. Lag times increased over time and vegetation was non-analog to any equilibrium state due to multi-lag composite states.
Lester Kwiatkowski, Olivier Torres, Laurent Bopp, Olivier Aumont, Matthew Chamberlain, James R. Christian, John P. Dunne, Marion Gehlen, Tatiana Ilyina, Jasmin G. John, Andrew Lenton, Hongmei Li, Nicole S. Lovenduski, James C. Orr, Julien Palmieri, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Charles A. Stock, Alessandro Tagliabue, Yohei Takano, Jerry Tjiputra, Katsuya Toyama, Hiroyuki Tsujino, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, and Tilo Ziehn
Biogeosciences, 17, 3439–3470, https://doi.org/10.5194/bg-17-3439-2020, https://doi.org/10.5194/bg-17-3439-2020, 2020
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We assess 21st century projections of marine biogeochemistry in the CMIP6 Earth system models. These models represent the most up-to-date understanding of climate change. The models generally project greater surface ocean warming, acidification, subsurface deoxygenation, and euphotic nitrate reductions but lesser primary production declines than the previous generation of models. This has major implications for the impact of anthropogenic climate change on marine ecosystems.
Georgii A. Alexandrov, Victor A. Brovkin, Thomas Kleinen, and Zicheng Yu
Biogeosciences, 17, 47–54, https://doi.org/10.5194/bg-17-47-2020, https://doi.org/10.5194/bg-17-47-2020, 2020
Miko U. F. Kirschbaum, Guang Zeng, Fabiano Ximenes, Donna L. Giltrap, and John R. Zeldis
Biogeosciences, 16, 831–846, https://doi.org/10.5194/bg-16-831-2019, https://doi.org/10.5194/bg-16-831-2019, 2019
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Globally, C is added to the atmosphere from fossil fuels and deforestation, balanced by ocean uptake and atmospheric increase. The difference (residual sink) is equated to plant uptake. But this omits cement carbonation; transport to oceans by dust; riverine organic C and volatile organics; and increased C in plastic, bitumen, wood, landfills, and lakes. Their inclusion reduces the residual sink from 3.6 to 2.1 GtC yr-1 and thus the inferred ability of the biosphere to alter human C emissions.
Kerstin Kretschmer, Lukas Jonkers, Michal Kucera, and Michael Schulz
Biogeosciences, 15, 4405–4429, https://doi.org/10.5194/bg-15-4405-2018, https://doi.org/10.5194/bg-15-4405-2018, 2018
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The fossil shells of planktonic foraminifera are widely used to reconstruct past climate conditions. To do so, information about their seasonal and vertical habitat is needed. Here we present an updated version of a planktonic foraminifera model to better understand species-specific habitat dynamics under climate change. This model produces spatially and temporally coherent distribution patterns, which agree well with available observations, and can thus aid the interpretation of proxy records.
Titta Majasalmi, Stephanie Eisner, Rasmus Astrup, Jonas Fridman, and Ryan M. Bright
Biogeosciences, 15, 399–412, https://doi.org/10.5194/bg-15-399-2018, https://doi.org/10.5194/bg-15-399-2018, 2018
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Forest management shapes forest structure and in turn surface–atmosphere interactions. We used Fennoscandian forest maps and inventory data to develop a classification system for forest structure. The classification was integrated with the ESA Climate Change Initiative land cover map to achieve complete surface representation. The result is an improved product for modeling surface–atmosphere exchanges in regions with intensively managed forests.
Anna T. Trugman, David Medvigy, William A. Hoffmann, and Adam F. A. Pellegrini
Biogeosciences, 15, 233–243, https://doi.org/10.5194/bg-15-233-2018, https://doi.org/10.5194/bg-15-233-2018, 2018
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Tree fire tolerance strategies may significantly impact woody carbon stability and the existence of tropical savannas under global climate change. We used a numerical ecosystem model to test the impacts of fire survival strategy under differing fire and rainfall regimes. We found that the high survival rate of large fire-tolerant trees reduced carbon losses with increasing fire frequency, and reduced the range of conditions leading to either complete tree loss or complete grass loss.
Nitin Chaudhary, Paul A. Miller, and Benjamin Smith
Biogeosciences, 14, 4023–4044, https://doi.org/10.5194/bg-14-4023-2017, https://doi.org/10.5194/bg-14-4023-2017, 2017
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We employed an individual- and patch-based dynamic global ecosystem model to quantify long-term C accumulation rates and to assess the effects of historical and projected climate change on peatland C balances across the pan-Arctic. We found that peatlands in Scandinavia, Europe, Russia and central and eastern Canada will become C sources, while Siberia, far eastern Russia, Alaska and western and northern Canada will increase their sink capacity by the end of the 21st century.
Giovanni De Falco, Emanuela Molinaroli, Alessandro Conforti, Simone Simeone, and Renato Tonielli
Biogeosciences, 14, 3191–3205, https://doi.org/10.5194/bg-14-3191-2017, https://doi.org/10.5194/bg-14-3191-2017, 2017
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This study quantifies the contribution of carbonate sediments, produced in seagrass meadows and in photophilic algal communities, to the sediment budget of a beach–dune system. The contribution to the beach sediment budget represents a further ecosystem service provided by seagrass. The dependence of the beach sediment budget on carbonate production associated with coastal ecosystems has implications for the adaptation of carbonate beaches to the seagrass decline and sea level rise.
Nitin Chaudhary, Paul A. Miller, and Benjamin Smith
Biogeosciences, 14, 2571–2596, https://doi.org/10.5194/bg-14-2571-2017, https://doi.org/10.5194/bg-14-2571-2017, 2017
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We incorporated peatland dynamics into
Arcticversion of dynamic vegetation model LPJ-GUESS to understand the long-term evolution of northern peatlands and effects of climate change on peatland carbon balance. We found that the Stordalen mire may be expected to sequester more carbon before 2050 due to milder and wetter climate conditions, a longer growing season and CO2 fertilization effect, turning into a C source after 2050 because of higher decomposition rates in response to warming soils.
Maria Stergiadi, Marcel van der Perk, Ton C. M. de Nijs, and Marc F. P. Bierkens
Biogeosciences, 13, 1519–1536, https://doi.org/10.5194/bg-13-1519-2016, https://doi.org/10.5194/bg-13-1519-2016, 2016
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We modelled the effects of changes in climate and land management on soil organic carbon (SOC) and dissolved organic carbon (DOC) levels in sandy and loamy soils under forest, grassland, and arable land. Climate change causes a decrease in both SOC and DOC for the agricultural systems, whereas for the forest systems, SOC slightly increases. A reduction in fertilizer application leads to a decrease in SOC and DOC levels under arable land but has a negligible effect under grassland.
S. S. Rabin, B. I. Magi, E. Shevliakova, and S. W. Pacala
Biogeosciences, 12, 6591–6604, https://doi.org/10.5194/bg-12-6591-2015, https://doi.org/10.5194/bg-12-6591-2015, 2015
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People worldwide use fire to manage agriculture, but often also suppress fire in the landscape surrounding their fields. Here, we estimate the net result of these effects of cropland and pasture on fire at a regional, monthly level. Pasture is shown, for the first time, to contribute strongly to global patterns of burning. Our results could be used to improve representations of burning in global vegetation and climate models, improving our understanding of how people affect the Earth system.
Y. Le Page, D. Morton, B. Bond-Lamberty, J. M. C. Pereira, and G. Hurtt
Biogeosciences, 12, 887–903, https://doi.org/10.5194/bg-12-887-2015, https://doi.org/10.5194/bg-12-887-2015, 2015
W. Knorr, T. Kaminski, A. Arneth, and U. Weber
Biogeosciences, 11, 1085–1102, https://doi.org/10.5194/bg-11-1085-2014, https://doi.org/10.5194/bg-11-1085-2014, 2014
A. M. Foley, D. Dalmonech, A. D. Friend, F. Aires, A. T. Archibald, P. Bartlein, L. Bopp, J. Chappellaz, P. Cox, N. R. Edwards, G. Feulner, P. Friedlingstein, S. P. Harrison, P. O. Hopcroft, C. D. Jones, J. Kolassa, J. G. Levine, I. C. Prentice, J. Pyle, N. Vázquez Riveiros, E. W. Wolff, and S. Zaehle
Biogeosciences, 10, 8305–8328, https://doi.org/10.5194/bg-10-8305-2013, https://doi.org/10.5194/bg-10-8305-2013, 2013
R. Fuchs, M. Herold, P. H. Verburg, and J. G. P. W. Clevers
Biogeosciences, 10, 1543–1559, https://doi.org/10.5194/bg-10-1543-2013, https://doi.org/10.5194/bg-10-1543-2013, 2013
P. W. Keys, R. J. van der Ent, L. J. Gordon, H. Hoff, R. Nikoli, and H. H. G. Savenije
Biogeosciences, 9, 733–746, https://doi.org/10.5194/bg-9-733-2012, https://doi.org/10.5194/bg-9-733-2012, 2012
V. Kovalskyy and G. M. Henebry
Biogeosciences, 9, 141–159, https://doi.org/10.5194/bg-9-141-2012, https://doi.org/10.5194/bg-9-141-2012, 2012
V. Kovalskyy and G. M. Henebry
Biogeosciences, 9, 161–177, https://doi.org/10.5194/bg-9-161-2012, https://doi.org/10.5194/bg-9-161-2012, 2012
A. Dallmeyer and M. Claussen
Biogeosciences, 8, 1499–1519, https://doi.org/10.5194/bg-8-1499-2011, https://doi.org/10.5194/bg-8-1499-2011, 2011
B. D. Stocker, K. Strassmann, and F. Joos
Biogeosciences, 8, 69–88, https://doi.org/10.5194/bg-8-69-2011, https://doi.org/10.5194/bg-8-69-2011, 2011
A. Oschlies, W. Koeve, W. Rickels, and K. Rehdanz
Biogeosciences, 7, 4017–4035, https://doi.org/10.5194/bg-7-4017-2010, https://doi.org/10.5194/bg-7-4017-2010, 2010
S. Bathiany, M. Claussen, V. Brovkin, T. Raddatz, and V. Gayler
Biogeosciences, 7, 1383–1399, https://doi.org/10.5194/bg-7-1383-2010, https://doi.org/10.5194/bg-7-1383-2010, 2010
M. Steinacher, F. Joos, T. L. Frölicher, L. Bopp, P. Cadule, V. Cocco, S. C. Doney, M. Gehlen, K. Lindsay, J. K. Moore, B. Schneider, and J. Segschneider
Biogeosciences, 7, 979–1005, https://doi.org/10.5194/bg-7-979-2010, https://doi.org/10.5194/bg-7-979-2010, 2010
A. Oschlies
Biogeosciences, 6, 1603–1613, https://doi.org/10.5194/bg-6-1603-2009, https://doi.org/10.5194/bg-6-1603-2009, 2009
Cited articles
Alewell, C., Borrelli, P., Meusburger, K., and Panagos, P.: Using the USLE:
Chances, challenges and limitations of soil erosion modelling, Int. Soil
Water Conserv. Res., 7, 203–225, https://doi.org/10.1016/j.iswcr.2019.05.004, 2019.
Almas, M. and Jamal, T.: Use of RUSLE for Soil Loss Prediction During
Different Growth Periods, Pakistan J. Biol. Sci., 3, 118–121,
https://doi.org/10.3923/pjbs.2000.118.121, 2009.
Auerswald, K., Kainz, M., and Fiener, P.: Soil erosion potential of organic
versus conventional farming evaluated by USLE modelling of cropping
statistics for agricultural districts in Bavaria, Soil Use Manag., 19,
305–311, https://doi.org/10.1079/sum2003212, 2004.
Auerswald, K., Fiener, P., and Dikau, R.: Rates of sheet and rill erosion in
Germany – A meta-analysis, Geomorphology, 111, 182–193,
https://doi.org/10.1016/j.geomorph.2009.04.018, 2009.
Balkovič, J., van der Velde, M., Skalský, R., Xiong, W., Folberth,
C., Khabarov, N., Smirnov, A., Mueller, N. D., and Obersteiner, M.: Global
wheat production potentials and management flexibility under the
representative concentration pathways, Glob. Planet. Change, 122, 107–121,
https://doi.org/10.1016/j.gloplacha.2014.08.010, 2014.
Balkovič, J., Skalský, R., Folberth, C., Khabarov, N., Schmid, E.,
Madaras, M., Obersteiner, M., and van der Velde, M.: Impacts and
Uncertainties of +2 ∘C of Climate Change and Soil Degradation on
European Crop Calorie Supply, Earth's Futur., 6, 373–395,
https://doi.org/10.1002/2017EF000629, 2018.
Benaud, P., Anderson, K., Evans, M., Farrow, L., Glendell, M., James, M.,
Quine, T., Quinton, J., Rawlins, B., Rickson, J., and Brazier, R.:
National-scale geodata describe widespread accelerated soil erosion,
Geoderma, 371, 114378, https://doi.org/10.1016/j.geoderma.2020.114378, 2020.
Boardman, J.: Soil erosion on the South Downs: a review, in Soil Erosion on
Agricultural Land, edited by: Boardman, J., Foster, I. D. L., and
Dearing, J. A., John Wiley & Sons Ltd, Chichester, 87–105, 1990.
Boardman, J.: Soil erosion and flooding on the eastern South Downs, southern
England, 1976–2001, Trans. Inst. Br. Geogr., 28, 176–196,
https://doi.org/10.1111/1475-5661.00086, 2003.
Boardman, J.: Soil erosion science: Reflections on the limitations of
current approaches, Catena, 68, 73–86,
https://doi.org/10.1016/j.catena.2006.03.007, 2006.
Boardman, J. and Evans, R.: The measurement, estimation and monitoring of
soil erosion by runoff at the field scale: Challenges and possibilities with
particular reference to Britain, Prog. Phys. Geogr., 44, 31–49,
https://doi.org/10.1177/0309133319861833, 2020.
Boix-Fayos, C., Martínez-Mena, M., Arnau-Rosalén, E., Calvo-Cases,
A., Castillo, V., and Albaladejo, J.: Measuring soil erosion by field plots:
Understanding the sources of variation, Earth-Sci. Rev., 78,
267–285, https://doi.org/10.1016/j.earscirev.2006.05.005, 2006.
Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C.,
Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V.,
Bagarello, V., Oost, K. Van, Montanarella, L., and Panagos, P.: An assessment
of the global impact of 21st century land use change on soil erosion, Nat.
Commun., 8, 1–13, https://doi.org/10.1038/s41467-017-02142-7, 2017.
Brazier, R.: Quantifying soil erosion by water in the UK: A review of
monitoring and modelling approaches, Prog. Phys. Geogr., 28, 340–365,
https://doi.org/10.1191/0309133304pp415ra, 2004.
Casali, J., Loizu, J., Campo, M. A., De Santisteban, L. M., and
Alvarez-Mozos, J.: Accuracy of methods for field assessment of rill and
ephemeral gully erosion, Catena, 67, 128–138, 2006.
Cerdan, O., Govers, G., Le Bissonnais, Y., Van Oost, K., Poesen, J., Saby,
N., Gobin, A., Vacca, A., Quinton, J., Auerswald, K., Klik, A., Kwaad, F. J.
P. M., Raclot, D., Ionita, I., Rejman, J., Rousseva, S., Muxart, T., Roxo,
M. J., and Dostal, T.: Rates and spatial variations of soil erosion in
Europe: A study based on erosion plot data, Geomorphology, 122,
167–177, https://doi.org/10.1016/j.geomorph.2010.06.011, 2010.
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, https://doi.org/10.1038/nclimate2829, 2016.
Chung, S. W., Gassman, P. W., Kramer, L. A., Williams, J. R., Gu, R. R.,
Chung, S. W., Gassman, P. W., Kramer, L. A., and Williams, J. R.:
Validation of EPIC for Two Watersheds in Southwest Iowa Recommended Citation
Validation of EPIC for Two Watersheds in Southwest Iowa, Iowa State University, Ames, Iowa, USA,
27 pp., 1999.
Cohen, M. J., Shepherd, K. D., and Walsh, M. G.: Empirical reformulation of
the universal soil loss equation for erosion risk assessment in a tropical
watershed, Geoderma, 124, 235–252,
https://doi.org/10.1016/j.geoderma.2004.05.003, 2005.
Den Biggelaar, C., Lal, R., Wiebe, K., Eswaran, H., Breneman, V., and Reich,
P.: The Global Impact Of Soil Erosion On Productivity*, II: Effects On Crop
Yields And Production Over Time, Adv. Agron., 81, 49–95,
https://doi.org/10.1016/S0065-2113(03)81002-7, 2004.
Deng, L., Shangguan, Z., and Li, R.: Effects of the grain-for-green
program on soil erosion in China, Int. J. Sediment Res., 27, 120–127,
https://doi.org/10.1016/S1001-6279(12)60021-3, 2012.
De Ploey, J. and Gabriels, D.: Measuring soil loss and experimental studies,
in Soil Erosion, edited by: Kirkby, M. J. and Morgan, R. P. C.,
Willey, Chichester, 63–108, 1980.
Doetterl, S., Van Oost, K., and Six, J.: Towards constraining the magnitude
of global agricultural sediment and soil organic carbon fluxes, Earth Surf.
Proc. Landforms, 37, 642–655, https://doi.org/10.1002/esp.3198, 2012.
Evans, R.: Finding out about water erosion, Teach. Geogr., 12, 17–20, 1986.
Evans, R.: Some methods of directly assessing water erosion of cultivated
land – a comparison of measurements made in plots and in fields, Prog.
Phys. Geogr., 19, 115–129, 1995.
Evans, R.: An alternative way to assess water erosion of cultivated land –
field-based measurements: An analysis of some results, Appl. Geogr., 22,
187–208, 2002.
Evans, R.: Assessment and monitoring of accelerated water erosion of
cultivated land – when will reality be acknowledged?, Soil Use Manag.,
29, 105–118, https://doi.org/10.1111/sum.12010, 2013.
Evans, R. and Boardman, J.: The new assessment of soil loss by water erosion
in Europe, Panagos P. et al., 2015 Environmental Science & Policy 54,
438-447-A response, Environ. Sci. Policy, 58, 11–15,
https://doi.org/10.1016/j.envsci.2015.12.013, 2016.
Evans, R. and Brazier, R.: Evaluation of modelled spatially distributed
predictions of soil erosion by water versus field-based assessments,
Environ. Sci. Pol., 8, 493–501, 2005.
FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.1), FAO and IIASA, Rome, Italy and Laxenburg, Austria, 38 pp.,
2009.
FAO: AQUASTAT Main Database, available at:
http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en
(last access: 1 July 2020), 2016.
Fick, S. E. and Hijmans, R. .: Worldclim 2: New 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, 2017.
Fischer, F. K., Kistler, M., Brandhuber, R., Maier, H., Treisch, M., and
Auerswald, K.: Validation of official erosion modelling based on
high-resolution radar rain data by aerial photo erosion classification,
Earth Surf. Proc. Landforms, 43, 187–194, https://doi.org/10.1002/esp.4216, 2018.
Folberth, C., Elliott, J., Müller, C., Balkovič, J.,
Chryssanthacopoulos, J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu,
W., Reddy, A., Schmid, E., Skalský, R., Yang, H., Arneth, A., Ciais, P.,
Deryng, D., Lawrence, P. J., Olin, S., Pugh, T. A. M., Ruane, A. C., and
Wang, X.: Parameterization-induced uncertainties and impacts of crop
management harmonization in a global gridded crop model ensemble, PLoS One,
14, e0221862, https://doi.org/10.1371/journal.pone.0221862, 2019.
Fritz, S., See, L., Mccallum, I., You, L., Bun, A., Moltchanova, E.,
Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A.,
Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C.,
Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G.,
Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F.,
Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner,
S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov,
A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M.,
Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and
field size, Glob. Change Biol., 21, 1980–1992, https://doi.org/10.1111/gcb.12838,
2015.
Fu, B. J., Zhao, W. W., Chen, L. D., Zhang, Q. J., Lü, Y. H., Gulinck,
H., and Poesen, J.: Assessment of soil erosion at large watershed scale using
RUSLE and GIS: A case study in the Loess Plateau of China, L. Degrad. Dev.,
16, 73–85, https://doi.org/10.1002/ldr.646, 2005.
Fulajtar, E., Mabit, L., Renschler, C. S., and Lee Zhi Yi, A.: Use of 137Cs
for soil erosion assessment, FAO, Rome, FAO/IAEA, 63 pp., 2017.
García-Ruiz, J. M., Beguería, S., Nadal-Romero, E.,
González-Hidalgo, J. C., Lana-Renault, N., and Sanjuán, Y.: A
meta-analysis of soil erosion rates across the world, Geomorphology, 239,
160–173, https://doi.org/10.1016/j.geomorph.2015.03.008, 2015.
Haile, G. W. and Fetene, M.: Assessment of soil erosion hazard in kilie
catchment, East Shoa, Ethiopia, L. Degrad. Dev., 23, 293–306,
https://doi.org/10.1002/ldr.1082, 2012.
Herweg, K.: The applicability of large-scale geomorphological mapping to
erosion control and soil conservation in a research area in Tuscany,
Z. Geomorphol. Suppl., 68, 175–187, 1988.
Hsieh, Y. P., Grant, K. T., and Bugna, G. C.: A field method for soil erosion
measurements in agricultural and natural lands, J. Soil Water Conserv.,
64, 374–382, https://doi.org/10.2489/jswc.64.6.374, 2009.
Hudson, N. W.: Field measurement of soil erosion and runoff, Food and
Agriculture Organization of the United Nations, available at:
https://books.google.co.uk/books?id=rS1fiFU3rOwC (last access: 2 November 2020), 1993.
IIASA/FAO: Global Agro-ecological Zones (GAEZ v3.0), IIASA, Laxenburg,
Austria and FAO, Rome, Italy, 116 pp., 2012.
Izaurralde, R. C., Williams, J. R., McGill, W. B., Rosenberg, N. J., and
Jakas, M. C. Q.: Simulating soil C dynamics with EPIC: Model description and
testing against long-term data, Ecol. Modell., 192, 362–384,
https://doi.org/10.1016/j.ecolmodel.2005.07.010, 2006.
Jenks, G. F.: The Data Model Concept in Statistical Mapping, Int. Yearb.
Cartogr., 7, 186–190, 1967.
Kaiser, J.: Wounding Earth ' s Fragile Skin, Science, 304,
1616–1618, https://doi.org/10.1126/science.304.5677.1616, 2004.
Kaiser, V. G.: Annual erosion survey of Whitman county, Washington,
1939/40-1975/76, Spokane, WA 99201, 1978.
Karydas, C. G., Sekuloska, T., and Silleos, G. N.: Quantification and
site-specification of the support practice factor when mapping soil erosion
risk associated with olive plantations in the Mediterranean island of Crete,
Environ. Monit. Assess., 149, 19–28, https://doi.org/10.1007/s10661-008-0179-8,
2009.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of
the Köppen-Geiger climate classification updated, Meteorol. Z.,
15, 259–263, https://doi.org/10.1097/00041433-200208000-00008, 2006.
Labrière, N., Locatelli, B., Laumonier, Y., Freycon, V., and Bernoux, M.:
Soil erosion in the humid tropics: A systematic quantitative review, Agr.
Ecosyst. Environ., 203, 127–139, https://doi.org/10.1016/j.agee.2015.01.027, 2015.
Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando,
N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous,
A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. ul
H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Ž., Bungnamei, K.,
Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M.,
McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I.,
and Fritz, S.: Estimating the global distribution of field size using
crowdsourcing, Glob. Change Biol., 25, 174–186, https://doi.org/10.1111/gcb.14492,
2019.
Lobotka, V.: Terraced fields in Slovakia, Agric., 2, 539–549, 1955 (in Slovak: Terasove polia na
Slovensku).
Long, H. L., Heilig, G. K., Wang, J., Li, X. B., Luo, M., Wu, X. Q., and
Zhang, M.: Land use and soil erosion in the upper reaches of the Yangtze
River: Some socio-economic considerations on China's Grain-for-Green
Programme, L. Degrad. Dev., 17, 589–603, https://doi.org/10.1002/ldr.736, 2006.
Loughran, R. J., Elliott, G. L., Campbell, B. L., and Shelly, D. J.:
Estimation of soil erosion from caesium-137 measurements in a small,
cultivated catchment in Australia, Int. J. Radiat. Appl. Instrumentation.
Part, Vol. 39, edited by: Afshar, F. A., Ayoubi, S., and Jalalian, A., 1153–1157,
https://doi.org/10.1016/0883-2889(88)90009-3, 1988.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F.,
Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.,
McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra,
C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., van
Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J.,
Xu, X., Xu, X., and Zhou, T.: Toward more realistic projections of soil
carbon dynamics by Earth system models, Global Biogeochem. Cy., 30,
40–56, https://doi.org/10.1002/2015GB005239, 2016.
Mabit, L., Meusburger, K., Fulajtar, E., and Alewell, C.: The usefulness of
137Cs as a tracer for soil erosion assessment: A critical reply to Parsons
and Foster (2011), Earth-Sci. Rev., 127, 300–307,
https://doi.org/10.1016/j.earscirev.2013.05.008, 2013.
Mabit, L., Chhem-Kieth, S., Dornhofer, P., Toloza, A., Benmansour, M.,
Bernard, C., Fulajtar, E., and Walling, D. E.: 137Cs: A widely used and
validated medium-term soil tracer, in Guidelines for using fallout
radionuclides to assess erosion and effectiveness of soil conservation
strategies, IAEA-TECDOC-1741, IAEA, Vienna, 27–78, 2014.
McCool, D. K., Foster, G. R., Mutchler, C. K., and Meyer, L. D.: Revised
slope length factor for the Universal Soil Loss Equation, Trans. ASAE, 32,
1571–1576, 1989.
McDermid, S. S., Mearns, L. O., and Ruane, A. C.: Representing agriculture in
Earth System Models: Approaches and priorities for development, J. Adv.
Model. Earth Syst., 9, 2230–2265, https://doi.org/10.1002/2016MS000749, 2017.
Meyer, L. D.: Evolution of the Universal Soil Loss Equation, J. Soil Water
Conserv., 39, 99–104, 1984.
Montgomery, D. R.: Soil erosion and agricultural sustainability, P.
Natl. Acad. Sci. USA, 104, 13268–72, https://doi.org/10.1073/pnas.0611508104,
2007.
Morgan, R. P. C.: Soil erosion and conservation, 3rd Edn., Blackwell Science
Ltd., Oxford, 296 pp., 2005.
Mueller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J.,
Ciais, P., Deryng, D., Folberth, C., Glotter, M., Hoek, S., Iizumi, T.,
Izaurralde, R. C., Jones, C., Khabarov, N., Lawrence, P., Liu, W., Olin, S.,
Pugh, T. A. M., Ray, D. K., Reddy, A., Rosenzweig, C., Ruane, A. C.,
Sakurai, G., Schmid, E., Skalsky, R., Song, C. X., Wang, X., De Wit, A., and
Yang, H.: Global gridded crop model evaluation: Benchmarking, skills,
deficiencies and implications, Geosci. Model Dev., 10, 1403–1422,
https://doi.org/10.5194/gmd-10-1403-2017, 2017.
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, 494, 390–390, https://doi.org/10.1038/nature11907, 2012.
Mutchler, C. K., Murphree, C. E., and McGregor, K. C.: Laboratory and Field
Plots for Erosion Research, in: Soil Erosion Research Methods, edited by: R.
Lal, Routledge., p. 352, 1994.
Nearing, M. A., Romkens, M. J. M., Norton, L. D., Stott, D. E., Rhoton, F.
E., Laflen, J. M., Flanagan, D. C., Alonso, C. V., Binger, R. L., Dabney, S.
M., Doering, O. C., Huang, C. H., McGregor, K. C., and Simon, A.:
Measurements and models of soil loss rates, Science, 290,
1300–1301, 2000.
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a
complex environmental model, Environ. Model. Softw., 26, 1515–1525,
https://doi.org/10.1016/j.envsoft.2011.08.010, 2011.
Nyssen, J., Frankl, A., Zenebe, A., Deckers, J., and Poesen, J.: Land
Management in the Northern Ethiopian Highlands: Local and Global
Perspectives; Past, Present and Future, L. Degrad. Dev., 26, 759–764,
https://doi.org/10.1002/ldr.2336, 2015.
Nyssen, J., Tielens, S., Gebreyohannes, T., Araya, T., Teka, K., van de
Wauw, J., Degeyndt, K., Descheemaeker, K., Amare, K., Haile, M., Zenebe, A.,
Munro, N., Walraevens, K., Gebrehiwot, K., Poesen, J., Frankl, A., Tsegay,
A., and Deckers, J.: Understanding spatial patterns of soils for sustainable
agriculture in northern Ethiopia's tropical mountains, PLoS ONE, 14, 1–42, 2019.
Onstad, C. A. and Foster, G. R.: Erosion modeling on a watershed, Trans.
ASAE, 18, 288–292, 1975.
Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E. H., Poesen, J.,
and Alewell, C.: Modelling the effect of support practices (P-factor) on the
reduction of soil erosion by water at European scale, Environ. Sci. Policy,
51, 23–34, https://doi.org/10.1016/j.envsci.2015.03.012, 2015.
Panagos, P., Borrelli, P., Poesen, J., Meusburger, K., Ballabio, C., Lugato,
E., Montanarella, L., and Alewell, C.: Reply to “The new assessment of soil
loss by water erosion in Europe”, Panagos P. et al., 2015 Environ. Sci.
Policy 54, 438-447-A response” by Evans and Boardman [Environ. Sci. Policy
58, 11–15], Environ. Sci. Policy, 59, 53–57,
https://doi.org/10.1016/j.envsci.2016.02.010, 2016.
Panagos, P., Standardi, G., Borrelli, P., Lugato, E., Montanarella, L., and
Bosello, F.: Cost of agricultural productivity loss due to soil erosion in
the European Union: From direct cost evaluation approaches to the use of
macroeconomic models, L. Degrad. Dev., 29, 471–484,
https://doi.org/10.1002/ldr.2879, 2018.
Pannell, D. J., Llewellyn, R. S., and Corbeels, M.: The farm-level economics
of conservation agriculture for resource-poor farmers, Agr. Ecosyst.
Environ., 187, 52–64, https://doi.org/10.1016/j.agee.2013.10.014, 2014.
Parsons, A.: How reliable are our methods for estimating soil erosion by
water?, Sci. Total Environ., 676, 215–221, 2019.
Parsons, A. J. and Foster, I. D. L.: The assumptions of science, A reply to
Mabit et al. (2013), Earth-Sci. Rev., 127, 308–310,
https://doi.org/10.1016/j.earscirev.2013.05.011, 2013.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A.,
Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global
data set of soil, intact regolith, and sedimentary deposit thicknesses for
regional and global land surface modeling, J. Adv. Model. Earth Syst., 8,
41–65, https://doi.org/10.1002/2015MS000526, 2016.
Pimentel, D.: Soil erosion: A food and environmental threat, Environ. Dev.
Sustain., 8, 119–137, https://doi.org/10.1007/s10668-005-1262-8, 2006.
Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D., McNair,
M., Crist, S., Shpritz, L., Fitton, L., Saffouri, R., and Blair, R.:
Environmental and economic costs of soil erosion and conservation benefits,
Science, 267, 1117–1123, https://doi.org/10.1126/science.267.5201.1117,
1995.
Poesen, J., Nachtergaele, J., Verstraeten, G., and Valentin, C.: Gully
erosion and environmental change: Importance and research needs, Catena,
50, 91–133, https://doi.org/10.1016/S0341-8162(02)00143-1, 2003.
Pongratz, J., Dolman, H., Don, A., Erb, K. H., Fuchs, R., Herold, M., Jones,
C., Kuemmerle, T., Luyssaert, S., Meyfroidt, P., and Naudts, K.: Models meet
data: Challenges and opportunities in implementing land management in Earth
system models, Glob. Change Biol., 24, 1470–1487, https://doi.org/10.1111/gcb.13988,
2018.
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, https://doi.org/10.1029/2008GB003435, 2010.
Porwollik, V., Rolinski, S., Heinke, J., and Müller, C.: Generating a
rule-based global gridded tillage dataset, Earth Syst. Sci. Data, 11,
823–843, https://doi.org/10.5194/essd-11-823-2019, 2019.
Rallison, R. E.: Origin and Evolution of the SCS Runoff Equation, in
Proceeding of the Symposium on Watershed Management '80 American Society of
Civil Engineering Boise ID, 1980.
Renard, K., Foster, G., Weesies, G., McCool, D., and Yoder, D.: Predicting
soil erosion by water: a guide to conservation planning with the Revised
Universal Soil Loss Equation (RUSLE), Agric. Handb., 703, 384 pp., 1997.
Romeo, R., Vita, A., Manuelli, S., Zanini, E., Freppaz, M., and Stanchi, S.:
Understanding Mountain Soils: A contribution from mountain areas to the
International Year of Soils 2015, Rome, 157 pp., 2015.
Roose, E.: Land husbandry – Components and strategy. 70 FAO soils bulletin,
Food and Agriculture Organization of the United Nations, Rome, 380 pp., 1996.
Ruane, A. C., Goldberg, R., and Chryssanthacopoulos, J.: Climate forcing
datasets for agricultural modeling: Merged products for gap-filling and
historical climate series estimation, Agr. Forest Meteorol., 200, 233–248,
https://doi.org/10.1016/j.agrformet.2014.09.016, 2015.
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting
dates: An analysis of global patterns, Glob. Ecol. Biogeogr., 19,
607–620, https://doi.org/10.1111/j.1466-8238.2010.00551.x, 2010.
Sadeghi, S. H. R. and Mizuyama, T.: Applicability of the Modified Universal
Soil Loss Equation for prediction of sediment yield in Khanmirza watershed,
Iran, Hydrol. Sci. J., 52, 1068–1075, https://doi.org/10.1623/hysj.52.5.1068, 2007.
Scherer, L. and Pfister, S.: Modelling spatially explicit impacts from
phosphorus emissions in agriculture, Int. J. Life Cycle Assess., 20,
785–795, https://doi.org/10.1007/s11367-015-0880-0, 2015.
Sharpley, A. N. and Williams, J. R.: EPIC – Erosion/Productivity Impact
Calculator: 1. Model Documentation, U.S. Dep. Agric. Tech. Bull., 1768, 235 pp.,
1990.
Skalský, R., Tarasovičová, Z., Balkovič, J., Schmid, E.,
Fuchs, M., Moltchanova, E., Kindermann, G., and Scholtz, P.: GEO-BENE global
database for bio-physical modeling, GEOBENE project, available
at:
http://geo-bene.project-archive.iiasa.ac.at/files/Deliverables/Geo-BeneGlbDb10(DataDescription).pdf (last access: 2 November 2020),
2008.
Sobol, I. M.: On sensitivity estimation for nonlinear mathematical models,
Matem. Mod., 2, 112–118, 1990.
Stroosnijder, L.: Measurement of erosion: Is it possible?, Catena, 64,
162–173, https://doi.org/10.1016/j.catena.2005.08.004, 2005.
Terranova, O., Antronico, L., Coscarelli, R., and Iaquinta, P.: Soil erosion
risk scenarios in the Mediterranean environment using RUSLE and GIS: An
application model for Calabria (southern Italy), Geomorphology, 112,
228–245, https://doi.org/10.1016/j.geomorph.2009.06.009, 2009.
Trimble, S. W. and Crosson, P.: U.S. Soil Erosion Rates–Myth and Reality,
Science, 289, 248–250, https://doi.org/10.1126/science.289.5477.248,
2000.
Turkelboom, F., Poesen, J., and Trébuil, G.: The multiple land
degradation effects caused by land-use intensification in tropical
steeplands: A catchment study from northern Thailand, Catena, 75,
102–116, https://doi.org/10.1016/j.catena.2008.04.012, 2008.
USDA-ARC: Science documentation. Revised Universal Soil Loss Equation,
Version 2 (RUSLE 2), Washington, D.C., 2013.
USGS: USGS 30 ARC-second Global Elevation Data, GTOPO30, https://doi.org/10.5066/F7DF6PQS, 1997.
Våje, P. I., Singh, B. R., and Lal, R.: Soil Erosion and Nutrient Losses
from a Volcanic Ash Soil in Kilimanjaro Region, Tanzania, J. Sustain. Agr.,
26, 23–42, 2005.
Valentin, C., Agus, F., Alamban, R., Boosaner, A., Bricquet, J. P., Chaplot,
V., de Guzman, T., de Rouw, A., Janeau, J. L., Orange, D., Phachomphonh, K.,
Do Duy Phai, Podwojewski, P., Ribolzi, O., Silvera, N., Subagyono, K.,
Thiébaux, J. P., Tran Duc Toan, and Vadari, T.: Runoff and sediment
losses from 27 upland catchments in Southeast Asia: Impact of rapid land use
changes and conservation practices, Agr. Ecosyst. Environ., 128,
225–238, https://doi.org/10.1016/j.agee.2008.06.004, 2008.
Van Oost, K., Quine, T. A., Govers, G., Gryze, S. De, Six, J., Harden, J.
W., Mccarty, G. W., Heckrath, G., Kosmas, C., Giraldez, J. V., and Silva, J.
R. M.: The Impact of Agricultural Soil Erosion on the Global Carbon Cycle,
Science, 318, 626–629, 2007.
Walling, D. E. and Webb, B. W.: Erosion and sediment yield: a global
overview, IAHS Publ. Proc. Reports-Intern Assoc Hydrol. Sci., 236,
3–20,
1996.
Walling, D. E., He, Q., and Zhang, Y.: Conversion Models And Related
Software, in Guidelines for Using Fallout Radionuclides to Assess Erosion
and Effectiveness of Soil Conservation Strategies, IAEA, Vienna, 125–148, 2014.
Watson, A. and Evans, R.: A comparison of estimates of soil erosion made in
the field and from photographs, Soil Till. Res., 19, 17–27, 1991.
Williams, J. R.: Sediment yield prediction with universal equation on using
runoff energy factor, in: Present and prospective technology for predicting
sediment yields and sources, ARS S-40, USDA-ARS,
Washington, DC, 244–252, 1975.
Williams, J. R.: The Erosion-Productivity Impact Calculator (EPIC) Model: A
Case History, Philos. Trans. R. Soc. B, 329, 421–428,
https://doi.org/10.1098/rstb.1990.0184, 1990.
Williams, J. R.: The EPIC model, in: Computer Models of Watershed Hydrology,
edited by: Singh, V. P., Water Resour. Publ., 909–1000, 1995.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall erosion losses,
Agric. Handb.,537, 285–291, https://doi.org/10.1029/TR039i002p00285, 1978.
Zachar, D.: Soil Erosion, Elsevier, Amsterdam, 544 pp., 1982.
Zapata, F.: Handbook for the Assessment of Soil Erosion and Sedimentation
Using Environmental Radionuclides, Springer, Dordrecht, 219 pp., 2002.
Short summary
We generate 30-year mean water erosion estimates in global maize and wheat fields based on daily simulation outputs from an EPIC-based global gridded crop model. Evaluation against field data confirmed the robustness of the outputs for the majority of global cropland and overestimations at locations with steep slopes and strong rainfall. Additionally, we address sensitivities and uncertainties of model inputs to improve water erosion estimates in global agricultural impact studies.
We generate 30-year mean water erosion estimates in global maize and wheat fields based on daily...
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