Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios

Chen, Min; Vernon, Chris R.; Graham, Neal; Hejazi, Mohamad; Huang, Maoyi; Cheng, Yanyan; Calvin, Katherine

doi:10.1038/s41597-020-00669-x

Cited by 135 publications

(154 citation statements)

References 43 publications

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“…At the global scale, only one study was conducted to assess isoprene emission responses to changes in TC by means of satellite land cover datasets. Using the MEGANv2.1 with the Landsat tree cover continuous fields of Sexton et al (2013) and fixed meteorological fields, Chen et al (2018) estimated global isoprene emissions at around 480 Tg yr −1 in 2000. This estimate is higher than our estimate (350 Tg yr −1 in 2001; cf.…”

Section: Comparison To Previous Studiesmentioning

confidence: 99%

“…This disparity is owed to differences in modelling settings and drivers. In particular, Chen et al (2018) used the coupled MEGAN2.1-CLM4.5 along with satellite vegetation data instead of the modelled land cover from CLM4.5, the canopy environmental model from Guenther et al (2012) instead of the MOHYCAN model, and emission factors calculated using three vegetation categories, namely the broad-and needle-leaved trees and non-trees instead of the PFT-dependent emission factors. The decreasing emission trend induced by LULC evaluated for ISOPGFW amounted to 0.33 % (Table 5), which is more than thrice the decreasing trend of 0.1 % yr −1 over 2000-2015 in the study of Chen et al (2018), with significant regional variations.…”

Section: Comparison To Previous Studiesmentioning

confidence: 99%

“…The present study aims to incorporate different satellitebased land cover datasets in the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model (Guenther et al, 2006(Guenther et al, , 2012 for estimating global isoprene emissions and to give a measure of the uncertainty associated with their use. It complements the study of Chen et al (2018) about the impact of LULCCs on isoprene emissions based on remotely sensed LC. The methodology is presented in Sect.…”

Section: Introductionmentioning

confidence: 98%

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Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

Opacka

Müller²,

Stavrakou

et al. 2021

Atmos. Chem. Phys.

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Abstract. Among the biogenic volatile organic compounds (BVOCs) emitted by plant foliage, isoprene is by far the most important in terms of both global emission and atmospheric impact. It is highly reactive in the air, and its degradation favours the generation of ozone (in the presence of NOx) and secondary organic aerosols. A critical aspect of BVOC emission modelling is the representation of land use and land cover (LULC). The current emission inventories are usually based on land cover maps that are either modelled and dynamic or satellite-based and static. In this study, we use the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) model coupled with the canopy model MOHYCAN (Model for Hydrocarbon emissions by the CANopy) to generate and evaluate emission inventories relying on satellite-based LULC maps at annual time steps. To this purpose, we first intercompare the distribution and evolution (2001–2016) of tree coverage from three global satellite-based datasets, MODerate resolution Imaging Spectroradiometer (MODIS), ESA Climate Change Initiative Land Cover (ESA CCI-LC), and the Global Forest Watch (GFW), and from national inventories. Substantial differences are found between the datasets; e.g. the global areal coverage of trees ranges from 30 to 50×106 km2, with trends spanning from −0.26 to +0.03 % yr−1 between 2001 and 2016. At the national level, the increasing trends in forest cover reported by some national inventories (in particular for the US) are contradicted by all remotely sensed datasets. To a great extent, these discrepancies stem from the plurality of definitions of forest used. According to some local censuses, clear cut areas and seedling or young trees are classified as forest, while satellite-based mappings of trees rely on a minimum height. Three inventories of isoprene emissions are generated, differing only in their LULC datasets used as input: (i) the static distribution of the stand-alone version of MEGAN, (ii) the time-dependent MODIS land cover dataset, and (iii) the MODIS dataset modified to match the tree cover distribution from the GFW database. The mean annual isoprene emissions (350–520 Tg yr−1) span a wide range due to differences in tree distributions, especially in isoprene-rich regions. The impact of LULC changes is a mitigating effect ranging from 0.04 to 0.33 % yr−1 on the positive trends (0.94 % yr−1) mainly driven by temperature and solar radiation. This study highlights the uncertainty in spatial distributions of and temporal variability in isoprene associated with remotely sensed LULC datasets. The interannual variability in the emissions is evaluated against spaceborne observations of formaldehyde (HCHO), a major isoprene oxidation product, through simulations using the global chemistry transport model (CTM) IMAGESv2. A high correlation (R > 0.8) is found between the observed and simulated interannual variability in HCHO columns in most forested regions. The implementation of LULC change has little impact on this correlation due to the dominance of meteorology as a driver of short-term interannual variability. Nevertheless, the simulation accounting for the large tree cover declines of the GFW database over several regions, notably Indonesia and Mato Grosso in Brazil, provides the best agreement with the HCHO column trends observed by the Ozone Monitoring Instrument (OMI). Overall, our study indicates that the continuous tree cover fields at fine resolution provided by the GFW database are our preferred choice for constraining LULC (in combination with discrete LULC maps such as those of MODIS) in biogenic isoprene emission models.

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Section: Comparison To Previous Studiesmentioning

confidence: 99%

Section: Comparison To Previous Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

Opacka

Müller²,

Stavrakou

et al. 2021

Atmos. Chem. Phys.

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show abstract

“…A global-scale study related to water scarcity and its regional socioeconomic implications can serve as a testbed for the data fusion development. For example, the effort to collect and harmonize data to support global-scale water scarcity studies which have been conducted using GCAM (Global Change Analysis Model) and the suite of models that provide data to, and downscale from, GCAM [1][2][3][4][5] have exposed the value of this type of analysis and have invoked a flurry of community data requests for those seeking to conduct their own analysis. For global-scale water models, large data sources range from observations or simulations such as, population (density), country/region distribution, water basin and geopolitical region distribution, irrigated areas, livestock density, land area, soil moisture, average temperature, precipitation, and sectoral water withdrawals.…”

Section: Narrativementioning

confidence: 99%

A Self-Evolution Data Fusion Platform for Large-Scale Water Models

2021

Self Cite

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Data acquisition and assimilation enabled by machine learning (ML), artificial intelligence (AI), and advanced methods including experimental/network design/optimization, unsupervised learning (deep learning), leveraging advanced hardware (e.g., edge computing). This development is to enable solving the science questions regarding the human and climatic factors that interact with and drive global water scarcity. Science Challenge:Data-model integration in the context of complex high dimensional hierarchical nonlinear structure. A self-evolution, comprehensive data fusion platform; building data fusion approaches from the ground up into a web-based, globally accessible platform. Rationale:AI's superpower is fueled by data. The successes of applying AI for building integrative predictive models rely on data accessibility, adequacy, and quality, which are generally promoted for data managed in adherence to FAIR (findable, accessible, interoperable, and reusable) guiding principles. Even considering the ability to employ FAIR data, it is still non-trivial to integrate data for scientific discovery (data fusion) given the usually high-dimensional and hierarchical nonlinear cross-dependence structure nature of some domains (e.g., complex large-scale water modeling systems). Recent advances in computational resources, observation and monitoring methods, and sensing tools, bring unprecedented opportunities in data-model integration or data fusion but introduce new challenges that require a systematic approach to provide heuristic value.

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“…LULC is also an important predictor of species’ occurrence and, thus extensively used in ecological studies (Eyringet al 2016; Ruiz-Benito et al 2020; Sobral-Souza et al 2021). There are several LULC datasets available for ecological studies at a global scale under current conditions, such as the Global Land Survey, the 30 Meter Global Land Cover, and the GlobeLand30 (Gutman et al 2013; Pengra et al 2015; Brovelli et al 2015), as well as the near historical period, such as the ESA Climate Change Initiative (1992 to 2015), the Finer Resolution Observation, Monitoring of Global Land Cover (1984 to 2011) (Hollmann et al 2013; Gong et al 2013) and GCAM (2015-2100) (Chen et al 2020). These datasets are usually available in standard Geographic Information System (GIS) formats (e.g.…”

Section: Introductionmentioning

confidence: 99%

Global Land-Use and Land-Cover Data: Historical, Current and Future Scenarios

Vale

Lima‐Ribeiro

Rocha

2021

Preprint

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Land-use land-cover (LULC) data are important predictors of species occurrence and biodiversity threat. Although there are LULC datasets available for ecologists under current conditions, there is a lack of such data under historical and future climatic conditions. This hinders, for example, projecting niche and distribution models under global change scenarios at different times. The Land Use Harmonization Project (LUH2) is a global terrestrial dataset at 0.25° spatial resolution that provides LULC data from 850 to 2300 for 12 LULC state classes. The dataset, however, is compressed in a file format (NetCDF) that is incompatible with most ecological analysis and intractable for most ecologists. Here we selected and transformed the LUH2 data in order to make it more useful for ecological studies. We provide LULC for every year from 850 to 2100, with data from 2015 on provided under two Shared Socioeconomic Pathways (SSP2 and SSP5). We provide two types of file for each year: separate files with continuous values for each of the 12 LULC state classes, and a single categorical file with all state classes combined. To create the categorical layer, we assigned the state with the highest value in a given pixel among the 12 continuous data. The final dataset provides LULC data for 1251 years that will be of interest for macroecology, ecological niche modeling, global change analysis, and other applications in ecology and conservation. We also provide a description of LUH2 prediction of future LULC change through time.

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Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios

Cited by 135 publications

References 43 publications

Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

A Self-Evolution Data Fusion Platform for Large-Scale Water Models

Global Land-Use and Land-Cover Data: Historical, Current and Future Scenarios

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