the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Feb 2021
12 Feb 2021
Land-Use Harmonization Datasets for Annual Global Carbon Budgets
- 1Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, U.S.A
- 2Earth Systems Research Center, University of New Hampshire, Durham, NH 03824, U.S.A
- 3PBL Netherlands Environmental Assessment Agency, 2500 GH The Hague, The Netherlands
- 4Department of Geography, University of Exeter, Exeter, EX4 4QE, United Kingdom
- 5NASA Goddard Space Flight Center, Greenbelt, MD, 20771, U.S.A
- 6Department of Geography, Ludwig-Maximilians University, Munich, D-80333, Germany
- 7Copernicus Institute for Sustainable Development, Utrecht University, Utrecht, The Netherlands
- 1Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, U.S.A
- 2Earth Systems Research Center, University of New Hampshire, Durham, NH 03824, U.S.A
- 3PBL Netherlands Environmental Assessment Agency, 2500 GH The Hague, The Netherlands
- 4Department of Geography, University of Exeter, Exeter, EX4 4QE, United Kingdom
- 5NASA Goddard Space Flight Center, Greenbelt, MD, 20771, U.S.A
- 6Department of Geography, Ludwig-Maximilians University, Munich, D-80333, Germany
- 7Copernicus Institute for Sustainable Development, Utrecht University, Utrecht, The Netherlands
Abstract. Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period, and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for CMIP6 simulations, has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual FAO wood harvest data updates for dataset years after 2015 and HYDE gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of one or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. From 1951–2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100,000 km2) and in the year 2009 for cropland (difference of 77,000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time-lags due to the multiple input dataset and model latencies.
Louise Chini et al.
Status: closed
-
RC1: 'Comment on essd-2020-388', Anonymous Referee #1, 07 Apr 2021
General comment:
This paper describes the method for producing the LUH2-GCB datasets, as well as specific details of the recent changes made to the LUH2-GCB2019 dataset. LUH2-GCB2019 dataset corrects cropland and grazing area errors in the underlying input datasets for the globally important region of Brazil, as far back as 1950. This is a very valuable dataset used by both bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the annual Global Carbon Budget (GCB) assessments.
Specific comments:
Sect. 2.3, page 7, line 197-200: For LUH2-GCB2019, a significant update was performed to correct an error in the previously used input data, especially for the country of Brazil. What’s the sources of these new data that used for the correction? Does the updated cropland or grazing land data in Brazil based on FAO or HYDE?
Page 5, line 135-137: there are some minor corrections to the method for determining the spatial patterns of wood harvesting. What specific corrections? Or is there any references information?
Please define acronym FAO, HYDE on first usage. The authors may need to follow this practice throughout the manuscript.
Table 1 needs better presentation, the HYDE simulation years should be given as well.
-
AC1: 'Reply on RC1', Louise Chini, 16 Jun 2021
Thank you for your summary and assessment of our manuscript. Our responses to your specific comments are listed below (along with the original reviewer comments).
1) Sect. 2.3, page 7, line 197-200: For LUH2-GCB2019, a significant update was performed to correct an error in the previously used input data, especially for the country of Brazil. What’s the sources of these new data that used for the correction? Does the updated cropland or grazing land data in Brazil based on FAO or HYDE?
Response: The sources for these corrections are listed later in that same paragraph and include updated FAO and HYDE data, as well as ESA satellite data and data from Brazil’s IGBE. We have added some new wording at the beginning of the paragraph to make these data sources clearer.
2) Page 5, line 135-137: there are some minor corrections to the method for determining the spatial patterns of wood harvesting. What specific corrections? Or is there any references information?
Response: At the time the LUH2-GCB2019 dataset was produced, some small corrections had been made to the original LUH2 wood harvest algorithm. These corrections resulted in slightly different spatial patterns of primary and secondary wood harvest within each country. These spatial pattern changes were a result of correcting a rounding error in the original algorithm that was used to determine how far from existing human activity the wood harvest patterns were likely to extend. Since these corrections were not a main focus of this paper, we have opted to focus the discussion on the changes in spatial patterns of cropland and grazing land, specifically within Brazil, that were updated for the GCB 2019 studies.
3) Please define acronym FAO, HYDE on first usage. The authors may need to follow this practice throughout the manuscript.
Response: Thank you for catching these oversights. We have added definitions at first usage for the acronyms FAO, HYDE, CMIP6, and checked for other instances as well.
4) Table 1 needs better presentation, the HYDE simulation years should be given as well.
Response: The table formatting has been updated to reflect design characteristics of tables in other Copernicus publications. An additional column has also been added to the table to provide the HYDE simulation years.
-
AC1: 'Reply on RC1', Louise Chini, 16 Jun 2021
-
RC2: 'Comment on essd-2020-388', Anonymous Referee #2, 21 May 2021
Louise Chini et al. “Land-Use Harmonization Datasets for Annual Global Carbon Budgets”
GENERAL:
This paper discussed the methodologies used to produce LUH2-GCB updates and extensions, and described specific details of LUH2-GCB2019 dataset and comparisons with previous LUH datasets. As a newest update, LUH2-GCB2019 dataset made some changes compared with previous datasets, especially correcting cropland and grazing areas of Brazil. This paper is very timely and important for users of this LUH2-GCB2019 or previous versions to better select land use data as input for state-of-the-art models in well-organized scientific assessments. I have only minor comments.
My main concern is the capability of LUH2-GCB2019 data capturing regional trends of forest changes. As shown in Figure 5, LUH2-GCB2019 has greater secondary forest areas in mid-high latitudes but lower areas in low latitudes than previous LUH2 v2h dataset over all key time points (2000, 2009 and 2015). These large differences has been related to the changes of wood harvesting used in LUH2-GCB2019, but the changes of spatial patterns on primary forests have not been shown in Figure 5. Please add this information into Figure 5 and further discuss whether this update on secondary forest is better than previous versions especially over China and India, where the recent studies by Chen et al. (2019) showing leading greening tendency. The greenness in China is mainly caused by the afforestation while that in India is likely related to expansion of cropland. Can LUH2-GCB2019 capture those trends?
Minor points:
Some acronyms need to be defined such as FAO, HYDE, BLUE and more at their first appearance.
Figure 5: The unit is fraction or %? Please add it into caption.
Reference
Chen, C. and coauthors: China and India lead in greening of the world through land-use management, Nature Sustainability, 2, 122-129, 2019.
-
AC2: 'Reply on RC2', Louise Chini, 16 Jun 2021
Thank you for your summary and assessment of our manuscript. Our responses to your specific comments are listed below (alongside the orginal reviewer comments):
1) My main concern is the capability of LUH2-GCB2019 data capturing regional trends of forest changes. As shown in Figure 5, LUH2-GCB2019 has greater secondary forest areas in mid-high latitudes but lower areas in low latitudes than previous LUH2 v2h dataset over all key time points (2000, 2009 and 2015). These large differences has been related to the changes of wood harvesting used in LUH2-GCB2019, but the changes of spatial patterns on primary forests have not been shown in Figure 5. Please add this information into Figure 5 and further discuss whether this update on secondary forest is better than previous versions especially over China and India, where the recent studies by Chen et al. (2019) showing leading greening tendency. The greenness in China is mainly caused by the afforestation while that in India is likely related to expansion of cropland. Can LUH2-GCB2019 capture those trends?
Response: Thank you for these helpful suggestions. We have now included primary forest difference maps in Figure 5, along with a brief discussion of the patterns observed in those maps. As noted in the manuscript, the LUH2 datasets provide land-use information rather than land cover information, and as a result are not guaranteed to capture regional patterns of land cover changes as observed in remote sensing data. We do include a simple representation of forested vs. non-forested natural vegetation, but this is also defined by the intended use of the land, i.e. a recently harvested forest that is just beginning to regrow would still be defined as a forest in our dataset, even though it does not yet contain any tree cover. However, to the extent that the land cover changes are already well-represented in the FAO data and HYDE data used by LUH2 as inputs, the LUH2 land-use changes will be consistent with those land cover changes. To that end, the LUH2-GCB2019 dataset does indeed show increased areas of cropland areas in India and China, as well as scattered areas of secondary land increases, when compared with the LUH2 v2h dataset for recent years, as indicated in Chen et al. 2019. We have added a short paragraph about these features of LUH2 to the manuscript, citing the paper you mentioned as an example.
2) Some acronyms need to be defined such as FAO, HYDE, BLUE and more at their first appearance.Response: We have added definitions at first usage for the acronyms FAO, HYDE, BLUE, CMIP6, and checked for other instances as well.
3) Figure 5: The unit is fraction or %? Please add it into caption.
Response: The units are fractions of gridcell. The figure caption has been reworded to hopefully make this clearer to the reader.
4) Reference: Chen, C. and coauthors: China and India lead in greening of the world through land-use management, Nature Sustainability, 2, 122-129, 2019.
Response: We have added this useful paper to our list of references and cited it in the text.
-
AC2: 'Reply on RC2', Louise Chini, 16 Jun 2021
Status: closed
-
RC1: 'Comment on essd-2020-388', Anonymous Referee #1, 07 Apr 2021
General comment:
This paper describes the method for producing the LUH2-GCB datasets, as well as specific details of the recent changes made to the LUH2-GCB2019 dataset. LUH2-GCB2019 dataset corrects cropland and grazing area errors in the underlying input datasets for the globally important region of Brazil, as far back as 1950. This is a very valuable dataset used by both bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the annual Global Carbon Budget (GCB) assessments.
Specific comments:
Sect. 2.3, page 7, line 197-200: For LUH2-GCB2019, a significant update was performed to correct an error in the previously used input data, especially for the country of Brazil. What’s the sources of these new data that used for the correction? Does the updated cropland or grazing land data in Brazil based on FAO or HYDE?
Page 5, line 135-137: there are some minor corrections to the method for determining the spatial patterns of wood harvesting. What specific corrections? Or is there any references information?
Please define acronym FAO, HYDE on first usage. The authors may need to follow this practice throughout the manuscript.
Table 1 needs better presentation, the HYDE simulation years should be given as well.
-
AC1: 'Reply on RC1', Louise Chini, 16 Jun 2021
Thank you for your summary and assessment of our manuscript. Our responses to your specific comments are listed below (along with the original reviewer comments).
1) Sect. 2.3, page 7, line 197-200: For LUH2-GCB2019, a significant update was performed to correct an error in the previously used input data, especially for the country of Brazil. What’s the sources of these new data that used for the correction? Does the updated cropland or grazing land data in Brazil based on FAO or HYDE?
Response: The sources for these corrections are listed later in that same paragraph and include updated FAO and HYDE data, as well as ESA satellite data and data from Brazil’s IGBE. We have added some new wording at the beginning of the paragraph to make these data sources clearer.
2) Page 5, line 135-137: there are some minor corrections to the method for determining the spatial patterns of wood harvesting. What specific corrections? Or is there any references information?
Response: At the time the LUH2-GCB2019 dataset was produced, some small corrections had been made to the original LUH2 wood harvest algorithm. These corrections resulted in slightly different spatial patterns of primary and secondary wood harvest within each country. These spatial pattern changes were a result of correcting a rounding error in the original algorithm that was used to determine how far from existing human activity the wood harvest patterns were likely to extend. Since these corrections were not a main focus of this paper, we have opted to focus the discussion on the changes in spatial patterns of cropland and grazing land, specifically within Brazil, that were updated for the GCB 2019 studies.
3) Please define acronym FAO, HYDE on first usage. The authors may need to follow this practice throughout the manuscript.
Response: Thank you for catching these oversights. We have added definitions at first usage for the acronyms FAO, HYDE, CMIP6, and checked for other instances as well.
4) Table 1 needs better presentation, the HYDE simulation years should be given as well.
Response: The table formatting has been updated to reflect design characteristics of tables in other Copernicus publications. An additional column has also been added to the table to provide the HYDE simulation years.
-
AC1: 'Reply on RC1', Louise Chini, 16 Jun 2021
-
RC2: 'Comment on essd-2020-388', Anonymous Referee #2, 21 May 2021
Louise Chini et al. “Land-Use Harmonization Datasets for Annual Global Carbon Budgets”
GENERAL:
This paper discussed the methodologies used to produce LUH2-GCB updates and extensions, and described specific details of LUH2-GCB2019 dataset and comparisons with previous LUH datasets. As a newest update, LUH2-GCB2019 dataset made some changes compared with previous datasets, especially correcting cropland and grazing areas of Brazil. This paper is very timely and important for users of this LUH2-GCB2019 or previous versions to better select land use data as input for state-of-the-art models in well-organized scientific assessments. I have only minor comments.
My main concern is the capability of LUH2-GCB2019 data capturing regional trends of forest changes. As shown in Figure 5, LUH2-GCB2019 has greater secondary forest areas in mid-high latitudes but lower areas in low latitudes than previous LUH2 v2h dataset over all key time points (2000, 2009 and 2015). These large differences has been related to the changes of wood harvesting used in LUH2-GCB2019, but the changes of spatial patterns on primary forests have not been shown in Figure 5. Please add this information into Figure 5 and further discuss whether this update on secondary forest is better than previous versions especially over China and India, where the recent studies by Chen et al. (2019) showing leading greening tendency. The greenness in China is mainly caused by the afforestation while that in India is likely related to expansion of cropland. Can LUH2-GCB2019 capture those trends?
Minor points:
Some acronyms need to be defined such as FAO, HYDE, BLUE and more at their first appearance.
Figure 5: The unit is fraction or %? Please add it into caption.
Reference
Chen, C. and coauthors: China and India lead in greening of the world through land-use management, Nature Sustainability, 2, 122-129, 2019.
-
AC2: 'Reply on RC2', Louise Chini, 16 Jun 2021
Thank you for your summary and assessment of our manuscript. Our responses to your specific comments are listed below (alongside the orginal reviewer comments):
1) My main concern is the capability of LUH2-GCB2019 data capturing regional trends of forest changes. As shown in Figure 5, LUH2-GCB2019 has greater secondary forest areas in mid-high latitudes but lower areas in low latitudes than previous LUH2 v2h dataset over all key time points (2000, 2009 and 2015). These large differences has been related to the changes of wood harvesting used in LUH2-GCB2019, but the changes of spatial patterns on primary forests have not been shown in Figure 5. Please add this information into Figure 5 and further discuss whether this update on secondary forest is better than previous versions especially over China and India, where the recent studies by Chen et al. (2019) showing leading greening tendency. The greenness in China is mainly caused by the afforestation while that in India is likely related to expansion of cropland. Can LUH2-GCB2019 capture those trends?
Response: Thank you for these helpful suggestions. We have now included primary forest difference maps in Figure 5, along with a brief discussion of the patterns observed in those maps. As noted in the manuscript, the LUH2 datasets provide land-use information rather than land cover information, and as a result are not guaranteed to capture regional patterns of land cover changes as observed in remote sensing data. We do include a simple representation of forested vs. non-forested natural vegetation, but this is also defined by the intended use of the land, i.e. a recently harvested forest that is just beginning to regrow would still be defined as a forest in our dataset, even though it does not yet contain any tree cover. However, to the extent that the land cover changes are already well-represented in the FAO data and HYDE data used by LUH2 as inputs, the LUH2 land-use changes will be consistent with those land cover changes. To that end, the LUH2-GCB2019 dataset does indeed show increased areas of cropland areas in India and China, as well as scattered areas of secondary land increases, when compared with the LUH2 v2h dataset for recent years, as indicated in Chen et al. 2019. We have added a short paragraph about these features of LUH2 to the manuscript, citing the paper you mentioned as an example.
2) Some acronyms need to be defined such as FAO, HYDE, BLUE and more at their first appearance.Response: We have added definitions at first usage for the acronyms FAO, HYDE, BLUE, CMIP6, and checked for other instances as well.
3) Figure 5: The unit is fraction or %? Please add it into caption.
Response: The units are fractions of gridcell. The figure caption has been reworded to hopefully make this clearer to the reader.
4) Reference: Chen, C. and coauthors: China and India lead in greening of the world through land-use management, Nature Sustainability, 2, 122-129, 2019.
Response: We have added this useful paper to our list of references and cited it in the text.
-
AC2: 'Reply on RC2', Louise Chini, 16 Jun 2021
Louise Chini et al.
Data sets
Land-Use Harmonization 2 update for use in Global Carbon Budget 2019 Louise Chini, George Hurtt, Ritvik Sahajpal, Steve Frolking, Kees Klein Goldewijk, Stephen Sitch, Raphael Ganzenmüller, Lei Ma, Lesley Ott, Julia Pongratz, and Benjamin Poulter https://doi.org/10.3334/ORNLDAAC/1851
Model code and software
GLM2 Code (Global Land-use Model 2) for generating LUH2 datasets (Land-Use Harmonization 2) Louise Chini, George Hurtt, Ritvik Sahajpal, and Steve Frolking http://doi.org/10.5281/zenodo.3954113
Louise Chini et al.
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