Uncertainty in plant functional type distributions and its impact on land surface models

Hartley, Andrew James; MacBean, Natasha; Georgievski, Goran; Bontemps, Sophie

doi:10.1016/j.rse.2017.07.037

Cited by 115 publications

(111 citation statements)

References 62 publications

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“…This is because if most of the cropland in this region belongs to class "12", using the corresponding value for class "10" in the cross-walking table (90 % for class "10" vs. 30 % for class "12", Table S1) overestimates cropland areas. Hartley et al (2017) also investigated the uncertainty in simulations of carbon, water and energy fluxes from three LSMs as a result of cross-walking table uncertainty. This study found that the spread in model outputs due to cross-walking uncertainty was higher than uncertainty due to the underlying LC maps (mapping algorithm; Hartley et al, 2017).…”

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

“…Hartley et al (2017) also investigated the uncertainty in simulations of carbon, water and energy fluxes from three LSMs as a result of cross-walking table uncertainty. This study found that the spread in model outputs due to cross-walking uncertainty was higher than uncertainty due to the underlying LC maps (mapping algorithm; Hartley et al, 2017). Despite these uncertainties, satellites provide the only plausible way to derive the global maps of vegetation distribution needed to drive LSMs and validate dynamic global vegetation models.…”

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

“…These satellite land cover products, however, differ in terms of land cover type, spatial resolution, time span, stability and accuracy due to the different sensor designs, classification procedures and validation methods . In order to use satellite land cover (LC) products in LSMs, these maps of LC classes are usually translated into maps of PFTs to drive the carbon dynamics in vegetation and soils (Poulter et al, 2015); however, the cross-walking table between LC classes and PFTs is complicated by subjective decisions related to the interpretation of LC class descriptions, and therefore is a source of uncertainty in model simulations (Hartley et al, 2017). Because LC transitions of opposite directions can happen simultaneously in a 0.5 • × 0.5 • grid cell, which is a typical spatial resolution of LSMs, gross transitions instead of net transitions are gradually implemented in LSMs to more accurately simulate E LUC (Bayer et al, 2017;Shevliakova et al, 2009;Stocker et al, 2014;Wilkenskjeld et al, 2014;Yue et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

MacBean

Ciais

et al. 2018

Earth Syst. Sci. Data

226

119

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Abstract. Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km 2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013 or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992-2015 are 1.5 and 0.9 million km 2 respectively, resulting in a net forest loss of 0.6 million km 2 , mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from

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Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Section: Differences In Total Area Of Forest Cropland and Grasslandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

MacBean

Ciais

et al. 2018

Earth Syst. Sci. Data

226

119

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“…Which LC dataset is used in the model significantly affects the BVOC emission estimates Wang et al, 2011). There are two major sources of uncertainty in the PFTs: the accuracy of the LC map and the cross-walking table used to convert the LC classes to PFTs (Hartley et al, 2017). We used 61 sample points in Beijing and the surrounding area from the Land Cover Validation Dataset by Zhao et al (2014) as primary validation for the LC datasets.…”

Section: Sensitivity Of Bvoc Emissions To Lai and Pftmentioning

confidence: 99%

“…The cross-walking table used in E5 is the default table designed for the global scale. Therefore, two more sensitivity experiments were designed using the "minimum biomass" (minCW) and "maximum biomass" (maxCW) cross-walking tables provided by Hartley et al (2017) for CCI LC to examine the uncertainties associated with the cross-walking table. As shown in Table 6, the area fractions of broadleaf and needleleaf trees were 29.9 and 8.8 % in the maxCW simulation, respectively, which are similar to those of FROM and MODIS (Fig.…”

Section: Sensitivity Of Bvoc Emissions To Lai and Pftmentioning

confidence: 99%

Sensitivity of biogenic volatile organic compound emissions to leaf area index and land cover in Beijing

Wang

Liu

et al. 2018

Atmos. Chem. Phys.

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Abstract. The Beijing area has suffered from severe air pollution in recent years, including ozone pollution in the summer. In addition to the anthropogenic emissions inventory, understanding local ozone pollution requires a reliable biogenic volatile organic compound (BVOC) emission inventory. Forest coverage rose from 20.6 to 35.8 % from 1998 to 2013 in Beijing according to the National Forest Resource Survey (NFRS), and accurate representations of land cover for recent years is crucial for estimating BVOC emissions and their impacts on air quality. In this study, we established a high-resolution BVOC emission inventory in Beijing using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 with three independent leaf area index (LAI) products and three independent land cover products. Various combinations of the Global LAnd Surface Satellite (GLASS), Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD15, and GEOland (GEO) v2 LAI datasets and the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC), MODIS MCD12Q1 plant functional type (PFT) products, and Climate Change Initiative Land Cover (CCI LC) products are used in five model sensitivity experiments (E1-E5), and the experiment using the FROM-GLC with the highest spatial resolution of 30 m and GLASS LAI products was treated as the baseline. These sensitivity calculations were driven by hourly, 3 km meteorological fields from the Weather Research and Forecasting (WRF) model. The following results were obtained: (1) according to the baseline estimate, the total amount of BVOC emissions is 75.9 Gg for the Beijing area, and isoprene, monoterpenes, sesquiterpenes and other VOCs account for 37.6, 14.6, 1.8 and 46 % of the total, respectively. Approximately three-quarters of BVOC emissions occur in the summer. (2) According to the sensitivity experiments, the LAI input does not significantly affect the BVOC emissions. Using MODIS MCD15Q1 and GEO v2 LAI led to slight declines of 2.6 and 1.4 %, respectively, of BVOC emissions in the same area. (3) The spatial distribution of PFTs from different inputs strongly influenced the spatial distribution of BVOC emissions. Furthermore, the cross-walking table for converting land cover classes to PFTs also has a strong impact on BVOC emissions; the sensitivity experiments showed that the estimate of BVOC emissions by CCI LC ranged from 42.1 to 70.2 Gg depending on the cross-walking table used.

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Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

Vittorio

Mao

Shi

et al. 2018

Geophysical Research Letters

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Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

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Uncertainty in plant functional type distributions and its impact on land surface models

Cited by 115 publications

References 62 publications

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015)

Sensitivity of biogenic volatile organic compound emissions to leaf area index and land cover in Beijing

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

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