scholarly journals Calibration and analysis of the uncertainty in downscaling global land use and land cover projections from GCAM using Demeter (v1.0.0)

2019 ◽  
Vol 12 (5) ◽  
pp. 1753-1764 ◽  
Author(s):  
Min Chen ◽  
Chris R. Vernon ◽  
Maoyi Huang ◽  
Katherine V. Calvin ◽  
Ian P. Kraucunas
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Optimal Parameter ◽  
Earth System ◽  
Parameter Uncertainties ◽  
Selection Threshold ◽  
Global Land ◽  
Parameter Values ◽  
Earth System Models

Abstract. Demeter is a community spatial downscaling model that disaggregates land use and land cover changes projected by integrated human–Earth system models. Demeter has not been intensively calibrated, and we still lack good knowledge about its sensitivity to key parameters and parameter uncertainties. We used long-term global satellite-based land cover records to calibrate key Demeter parameters. The results identified the optimal parameter values and showed that the parameterization substantially improved the model's performance. The parameters of intensification ratio and selection threshold were the most sensitive and needed to be carefully tuned, especially for regional applications. Further, small parameter uncertainties after calibration can be inflated when propagated into future scenarios, suggesting that users should consider the parameterization equifinality to better account for the uncertainties in Demeter-downscaled products. Our study provides a key reference for Demeter users and ultimately contributes to reducing the uncertainties in Earth system model simulations.

scholarly journals Calibration and analysis of the uncertainty in downscaling global land use and land cover projections from GCAM

2019 ◽  
Author(s):  
Min Chen ◽  
Chris R. Vernon ◽  
Maoyi Huang ◽  
Katherine V. Calvin ◽  
Ian P. Kraucunas
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Optimal Parameter ◽  
Earth System ◽  
Parameter Uncertainties ◽  
Selection Threshold ◽  
Global Land ◽  
Parameter Values ◽  
Earth System Models

Abstract. Demeter is a community spatial downscaling model that disaggregates land use and land cover changes projected by integrated human-Earth system models. Demeter has not been intensively calibrated, and we still lack a good knowledge about its sensitivity to key parameters and the parameter uncertainties. We used long-term global satellite-based land cover records to calibrate key Demeter parameters. The results identified the optimal parameter values and showed that the parameterization substantially improved the model’s performance. The parameters of intensification ratio and selection threshold were the most sensitive and needed to be carefully tuned, especially for regional applications. Further, small parameter uncertainties after calibration can be inflated when propagated into future scenarios, suggesting that users should consider the parameterization equifinality to better account for the uncertainties in the Demeter downscaled products. Our study provides a key reference for Demeter users, and ultimately contribute to reducing the uncertainties in Earth system model simulations.

Reconciling global land model estimates and country reporting of anthropogenic land CO2 sources and sinks with the CMIP6 LUMIP NOAA/GFDL LM4.1 simulations

2020 ◽  
Author(s):  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
Richard Houghton ◽  
Louis Verchot
Keyword(s):  
Land Use ◽  
Environmental Changes ◽  
Historical Period ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Earth System ◽  
Sources And Sinks ◽  
Global Land ◽  
Natural Response ◽  
Earth System Models

<p>Global land models, which often served as components Earth system models, and national GHG inventories rely on different methods and produce different estimates of anthropogenic CO<sub>2</sub> emissions and uptakes from land use land cover changes throughout historical period. For example, for 2005 -2014, the sum of the national GHG inventories net emission estimates is 0.1 ± 1.0 GtCO2 yr<sup>–1</sup> while the bookkeeping models is 5.2 ± 2.6 GtCO2 yr<sup>–1</sup> (IPCC SPM 2019).  Previous estimates with the 16 global stand-alone land models produced an estimate of the net land sink of 11.2 ± 2.6 GtCO2 yr<sup>–1</sup> during 2007– 2016 for the natural response of land to human-induced environmental changes such as increasing atmospheric CO<sub>2</sub> concentration, nitrogen deposition, and climate change (IPCC SPM 2019).  However, these 16 models do not provide separate estimates for the managed and unmanaged lands. </p><p> </p><p>Here we use results from simulations with the NOAA/GFDL new land model LM4.1 from the CMIP6 Land Use Model Inercomparison Project (LUMIP) to demonstrate how to reconcile the discrepancy between the inventories and land models estimates of the anthropogenic CO<sub>2 </sub>land emissions by using bookkeeping accounting approach applied to the model results.  In addition, we separate estimates of land fluxes on managed and unmanaged lands. Key features of this model include advanced, second generation dynamic vegetation representation and canopy competition, fire, and land use representation driven by full set of gross transitions from the CMIP6 land use scenarios.  We demonstrate how bookkeeping accounting combined with the LUMIP experiments can enhance understanding of land sector net emission estimates and their applications.</p>

scholarly journals Impact of urban expansion on continental- to regional-scale warming

2022 ◽  
Author(s):  
TC Chakraborty ◽  
Yun Qian
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Surface ◽  
Urban Expansion ◽  
Regional Scale ◽  
Land Use Land Cover ◽  
Earth System ◽  
Earth System Models

Abstract Although the influence of land use/land cover change on climate has become increasingly apparent, cities and other built-up areas are usually ignored when estimating large-scale historical climate change or for future projections since cities cover a small fraction of the terrestrial land surface1,2. As such, ground-based observations of urban near-surface meteorology are rare and most earth system models do not represent historical or future urban land cover3–7. Here, by combining global satellite observations of land surface temperature with historical estimates of built-up area, we demonstrate that the urban temperature signal on continental- to regional-scale warming has become non-negligible, especially for rapidly urbanizing regions in Asia. Consequently, expected urban expansion over the next century suggest further increased urban influence on surface climate under all future climate scenarios. Based on these results, we argue that, in line with other forms of land use/land cover change, urbanization should be explicitly included in future climate change assessments. This would require extensive model development to incorporate urban extent and biophysics in current-generation earth system models to quantify potential urban feedbacks on the climate system at multiple scales.

scholarly journals Mapping past human land use using archaeological data: A new classification for global land use synthesis and data harmonization

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0246662 ◽  
Author(s):  
Kathleen D. Morrison ◽  
Emily Hammer ◽  
Oliver Boles ◽  
Marco Madella ◽  
Nicola Whitehouse ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Classification Scheme ◽  
Earth System ◽  
Land Use Systems ◽  
System Models ◽  
The Past ◽  
The Earth ◽  
Earth System Models ◽  
Past Land Use

In the 12,000 years preceding the Industrial Revolution, human activities led to significant changes in land cover, plant and animal distributions, surface hydrology, and biochemical cycles. Earth system models suggest that this anthropogenic land cover change influenced regional and global climate. However, the representation of past land use in earth system models is currently oversimplified. As a result, there are large uncertainties in the current understanding of the past and current state of the earth system. In order to improve representation of the variety and scale of impacts that past land use had on the earth system, a global effort is underway to aggregate and synthesize archaeological and historical evidence of land use systems. Here we present a simple, hierarchical classification of land use systems designed to be used with archaeological and historical data at a global scale and a schema of codes that identify land use practices common to a range of systems, both implemented in a geospatial database. The classification scheme and database resulted from an extensive process of consultation with researchers worldwide. Our scheme is designed to deliver consistent, empirically robust data for the improvement of land use models, while simultaneously allowing for a comparative, detailed mapping of land use relevant to the needs of historical scholars. To illustrate the benefits of the classification scheme and methods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES) LandCover6k working group, an international project comprised of archaeologists, historians, geographers, paleoecologists, and modelers. Beyond this, the scheme has a wide utility for creating a common language between research and policy communities, linking archaeologists with climate modelers, biodiversity conservation workers and initiatives.

Global Carbon Fluxes Induced by Agriculture-Related Land-Use and Land Cover Change Activities

2021 ◽  
Author(s):  
Atul Jain ◽  
Xiaoming Xu ◽  
Shijie Shu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Flux ◽  
Agricultural Land ◽  
Carbon Fluxes ◽  
Earth System ◽  
Agriculture Land ◽  
Livestock Feed ◽  
Earth System Models

<p>The aim of this study is to estimate the net carbon fluxes from agriculture-related land-use and land cover change (LULCC) activities, which are referred to as emissions from the land due to human activities. These include land use (LU, e.g., farmland for food and feed production, including management) and land cover changes (LCC, e.g., deforestation for and reforestation of agricultural land, and conversion of grasslands and pastureland to agriculture land or vice versa). Agriculture land-use practices could be a source of atmospheric CO2. However, the management of agricultural practices may reduce carbon emissions and increase soil carbon sequestration. Simultaneously, land-cover change activities clear existing ecosystems, their biomass and disturb the soil, generating carbon emissions. Previous earth system models usually have a simple or no representation of land agriculture practices, such as planting crops, fertilization, irrigation, harvesting grains for food and livestock-feed, recovering crop residue for feed and other usages, and grazing, livestock-feed, and manure cycle. This study uses a land surface model with spatially heterogeneous representations of such agricultural land use activities, in addition to land cover change, such as the change from forest to agricultural land. Our study shows the net agricultural land area increase of 0.11 million hectares/yr during 2007-2013, including 2.12 million hectares/yr of other land converted to agricultural land and 2.01 million hectares/yr of agricultural land converted to other lands. The results show that global net carbon flux due to agriculture-related LULCC is 2.26 Pg C/yr (net emission), consisting of 38% due to land-use activities and 62% due to land cover change. South America (22%), North America (19%), and South and Southeast Asia (13%) are the top contributing regions for net carbon flux induced by LULCC. South America has contributed the most flux from land cover change (18%), while North America has generated the most carbon flux due to land-use activities (12%) among all macro geopolitical regions.  By quantifying the carbon fluxes induced by different agriculture activities this study provides a complete estimate of the yearly carbon cycle in the agriculture system at the spatial scale, which may improve the representations of agriculture land use activities in Earth System Models.</p>

scholarly journals Plant functional type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative

2015 ◽  
Vol 8 (1) ◽  
pp. 429-462 ◽  
Author(s):  
B. Poulter ◽  
N. MacBean ◽  
A. Hartley ◽  
I. Khlystova ◽  
O. Arino ◽  
...  
Keyword(s):  
Land Cover ◽  
European Space Agency ◽  
User Needs ◽  
Earth System ◽  
Functional Type ◽  
System Models ◽  
Global Land Cover ◽  
Space Agency ◽  
Global Land ◽  
Earth System Models

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFT). The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.

scholarly journals Soil erosion by snow gliding – a first quantification attempt in a subalpine area in Switzerland

2014 ◽  
Vol 18 (9) ◽  
pp. 3763-3775 ◽  
Author(s):  
K. Meusburger ◽  
G. Leitinger ◽  
L. Mabit ◽  
M. H. Mueller ◽  
A. Walter ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Sediment Yield ◽  
Soil Loss ◽  
Water Erosion ◽  
Erosion Rates ◽  
The Difference ◽  
Snow Gliding

Abstract. Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as a soil erosion agent for four different land use/land cover types in a subalpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide depositions, the fallout radionuclide 137Cs and modelling with the Revised Universal Soil Loss Equation (RUSLE). RUSLE permits the evaluation of soil loss by water erosion, the 137Cs method integrates soil loss due to all erosion agents involved, and the measurement of snow glide deposition sediment yield can be directly related to snow-glide-induced erosion. Further, cumulative snow glide distance was measured for the sites in the winter of 2009/2010 and modelled for the surrounding area and long-term average winter precipitation (1959–2010) with the spatial snow glide model (SSGM). Measured snow glide distance confirmed the presence of snow gliding and ranged from 2 to 189 cm, with lower values on the north-facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected and ongoing land use changes in the Alps. Snow glide erosion estimated from the snow glide depositions was highly variable with values ranging from 0.03 to 22.9 t ha−1 yr−1 in the winter of 2012/2013. For sites affected by snow glide deposition, a mean erosion rate of 8.4 t ha−1 yr−1 was found. The difference in long-term erosion rates determined with RUSLE and 137Cs confirms the constant influence of snow-glide-induced erosion, since a large difference (lower proportion of water erosion compared to total net erosion) was observed for sites with high snow glide rates and vice versa. Moreover, the difference between RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2 = 0.64; p < 0.005) and to the snow deposition sediment yields (R2 = 0.39; p = 0.13). The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding appears to be a crucial and non-negligible process impacting soil erosion patterns and magnitude in subalpine areas with similar topographic and climatic conditions.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

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