Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6919-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6919-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Sep 2022
Model description paper |
| 13 Sep 2022
| 13 Sep 2022
Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland
Chengyong Wu
CORRESPONDING AUTHOR
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Academy of Plateau Science and Sustainability, Xining, 810008, China
Kelong Chen
MOE Key Laboratory of Tibetan Plateau Land Surface Processes and
Ecological Conservation/Qinghai Province Key Laboratory of Physical
Geography and Environmental Processes, Xining 810008, China
Academy of Plateau Science and Sustainability, Xining, 810008, China
Chongyi E
MOE Key Laboratory of Tibetan Plateau Land Surface Processes and
Ecological Conservation/Qinghai Province Key Laboratory of Physical
Geography and Environmental Processes, Xining 810008, China
Academy of Plateau Science and Sustainability, Xining, 810008, China
Xiaoni You
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Dongcai He
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Liangbai Hu
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Baokang Liu
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Runke Wang
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Yaya Shi
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Chengxiu Li
School of Resources and Environmental Engineering, Tianshui Normal
University, Tianshui 741001, China
Fumei Liu
MOE Key Laboratory of Tibetan Plateau Land Surface Processes and
Ecological Conservation/Qinghai Province Key Laboratory of Physical
Geography and Environmental Processes, Xining 810008, China
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The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
bottom-upbiophysical land use model capable of identifying broad pathways towards climate neutrality in the agriculture, forestry, and other land use (AFOLU) sector. The model is intended to bridge the gap between hindsight representations of national emissions and much larger globally integrated assessment models.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
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In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
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This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
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Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
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Short summary
The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as meteorology, soil, and remote sensing (RS) has notable disadvantages. We drove the CASA using RS data and conducted a case study of the Qinghai Lake basin alpine grassland. The simulated result is similar to published and measured net primary productivity (NPP). It may provide a reference for simulating vegetation NPP to satisfy the requirements of accounting carbon stocks and other applications.
The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as...
