scholarly journals Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

2017 ◽  
Vol 14 (22) ◽  
pp. 5053-5067 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Regional Scale ◽  
Model Estimate ◽  
Tropical Regions ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Constraint Approach ◽  
The Impact

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

scholarly journals Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

2017 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Large Range ◽  
Regional Scale ◽  
Tropical Regions ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions, and for ecosystem processes affected by LULCC. One drawback of DGVMs however is their large uncertainty. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (EcLUC) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and EcLUC. This method is applicable at global and regional scale. Compared to the large range of EcLUC in the original ensemble (94 to 273 Pg C) during 1901–2012, current biomass observations allow us to derive a new best estimate of 155 ± 50 (1-σ Gaussian error) Pg C. The constrained LULCC emissions are higher than prior DGVM values in tropical regions, but significantly lower in North America. Our approach of constraining cumulative LULCC emissions based on biomass observations reduces the uncertainty of the historical carbon budget, and can also be applied to evaluate the impact of land-based mitigation activities.

scholarly journals Historical CO<sub>2</sub> emissions from land-use and land-cover change and their uncertainty

2020 ◽  
Author(s):  
Thomas Gasser ◽  
Léa Crepin ◽  
Yann Quilcaille ◽  
Richard A. Houghton ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Sink ◽  
Data Sets ◽  
Tropical Regions ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Global Carbon

Abstract. Emissions from land-use and land-cover change are a key component of the global carbon cycle. Models are required to disentangle these emissions and the land carbon sink, however, because only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the Global Carbon Budget remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach relying on a bookkeeping model that embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate global CO2 emissions from land-use and land-cover change were 1.36 ± 0.42 Pg C yr−1 (1-σ range) on average over 2009–2018, and 206 ± 57 Pg C cumulated over 1750–2018. We also estimate that land-cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68 ± 0.57 Pg C yr−1 and 32 ± 23 Pg C over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty following aspects that include the land-use and land-cover change data sets used as input, and the model's biogeochemical parameters. We find the biogeochemical uncertainty dominates our global and regional estimates, with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty, and suggests ways to strengthen the robustness of future Global Carbon Budgets.

scholarly journals Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations

2012 ◽  
Vol 3 (2) ◽  
pp. 597-641 ◽  
Author(s):  
A. J. Pitman ◽  
N. de Noblet-Ducoudré ◽  
F. B. Avila ◽  
L. V. Alexander ◽  
J.-P. Boisier ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Regional Scale ◽  
Extreme Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Rainfall Extremes ◽  
The Impact ◽  
Change In Mean

Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes is amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperature to LULCC is relatively large, we conclude that unless this forcing is included we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.

scholarly journals Estimating the Impact of Land Cover Change on Soil Erosion Using Remote Sensing and GIS Data by USLE Model and Scenario Design

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Anmin Fu ◽  
Yulin Cai ◽  
Tao Sun ◽  
Feng Li
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Land Cover Change ◽  
Regional Scale ◽  
Rainfall Erosivity ◽  
Rainfall Runoff ◽  
Scenario Design ◽  
Gis Data ◽  
The Impact

Great efforts have been made to curb soil erosion and restore the natural environment to Inner Mongolia in China. The purpose of this study is to evaluate the impact of returning farmland to the forest on soil erosion on a regional scale. Considering that rainfall erosivity also has an important impact on soil erosion, the effect of land use and land cover change (LUCC) on soil erosion was evaluated through scenario construction. Firstly, the universal soil loss equation (USLE) model was used to evaluate the actual soil erosion (2001 and 2010). Secondly, two scenarios (scenario 1 and scenario 2) were constructed by assuming that the land cover and rainfall-runoff erosivity are fixed, respectively, and soil erosion under different scenarios was estimated. Finally, the effect of LUCC on soil erosion was evaluated by comparing the soil erosion under actual situations with the hypothetical scenarios. The results show that both land use/cover change and rainfall-runoff erosivity change have significant effects on soil erosion. The land use and land cover change initiated by the ecological restoration projects have obviously reduced the soil erosion in this area. The results also reveal that the method proposed in this paper is helpful to clarify the influencing factors of soil erosion.

scholarly journals Historical CO<sub>2</sub> emissions from land use and land cover change and their uncertainty

2020 ◽  
Vol 17 (15) ◽  
pp. 4075-4101 ◽  
Author(s):  
Thomas Gasser ◽  
Léa Crepin ◽  
Yann Quilcaille ◽  
Richard A. Houghton ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Budget ◽  
Carbon Sink ◽  
Global Carbon Budget ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Global Carbon

Abstract. Emissions from land use and land cover change are a key component of the global carbon cycle. However, models are required to disentangle these emissions from the land carbon sink, as only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the “Global Carbon Budget” remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach that relies on a bookkeeping model, which embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate that the global CO2 emissions from land use and land cover change were 1.36±0.42 PgC yr−1 (1σ range) on average over the 2009–2018 period and reached a cumulative total of 206±57 PgC over the 1750–2018 period. We also estimate that land cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68±0.57 PgC yr−1 and 32±23 PgC over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty, including aspects such as the land use and land cover change data sets used as input and the model's biogeochemical parameters. We find that the biogeochemical uncertainty dominates our global and regional estimates with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty and suggests ways to strengthen the robustness of future Global Carbon Budget estimates.

scholarly journals Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations

2012 ◽  
Vol 3 (2) ◽  
pp. 213-231 ◽  
Author(s):  
A. J. Pitman ◽  
N. de Noblet-Ducoudré ◽  
F. B. Avila ◽  
L. V. Alexander ◽  
J.-P. Boisier ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Climate Models ◽  
Regional Scale ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Rainfall Extremes ◽  
The Impact ◽  
Change In Mean

Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are amplified. While we find some evidence that individual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.

Assessment of land use and land cover datasets for Brazil and impact on C emissions

2021 ◽  
Author(s):  
Thais M. Rosan ◽  
Kees Klein Goldewijk ◽  
Raphael Ganzenmüller ◽  
Michael O'Sullivan ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Dioxide Emissions ◽  
Dynamic Global Vegetation Model ◽  
Global Carbon Budget ◽  
National Estimates ◽  
National Trends ◽  
Global Vegetation ◽  
Land Cover Maps ◽  
The Impact

&lt;p&gt;Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil&amp;#8217;s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&amp;N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil&amp;#8217;s LULCC emissions in agreement with national trends estimates and spatial distribution.&lt;/p&gt;

scholarly journals The Impact of Land Use/Land Cover Change (LULCC) on Water Resources in a Tropical Catchment in Tanzania under Different Climate Change Scenarios

2019 ◽  
Vol 11 (24) ◽  
pp. 7083 ◽  
Author(s):  
Kristian Näschen ◽  
Bernd Diekkrüger ◽  
Mariele Evers ◽  
Britta Höllermann ◽  
Stefanie Steinbach ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
Land Cover ◽  
Water Balance ◽  
Land Cover Change ◽  
Sub Saharan Africa ◽  
Climate Change Scenarios ◽  
Land Use Land Cover ◽  
The Impact

Many parts of sub-Saharan Africa (SSA) are prone to land use and land cover change (LULCC). In many cases, natural systems are converted into agricultural land to feed the growing population. However, despite climate change being a major focus nowadays, the impacts of these conversions on water resources, which are essential for agricultural production, is still often neglected, jeopardizing the sustainability of the socio-ecological system. This study investigates historic land use/land cover (LULC) patterns as well as potential future LULCC and its effect on water quantities in a complex tropical catchment in Tanzania. It then compares the results using two climate change scenarios. The Land Change Modeler (LCM) is used to analyze and to project LULC patterns until 2030 and the Soil and Water Assessment Tool (SWAT) is utilized to simulate the water balance under various LULC conditions. Results show decreasing low flows by 6–8% for the LULC scenarios, whereas high flows increase by up to 84% for the combined LULC and climate change scenarios. The effect of climate change is stronger compared to the effect of LULCC, but also contains higher uncertainties. The effects of LULCC are more distinct, although crop specific effects show diverging effects on water balance components. This study develops a methodology for quantifying the impact of land use and climate change and therefore contributes to the sustainable management of the investigated catchment, as it shows the impact of environmental change on hydrological extremes (low flow and floods) and determines hot spots, which are critical for environmental development.

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