Influence of spatially dependent,modeled soil carbon emission factors on life‐cycle greenhouse gas emissions of corn and cellulosic ethanol

Converting land to biofuel feedstock production incurs changes in soil organic carbon (SOC) that can influence biofuel life‐cycle greenhouse gas (GHG) emissions. Estimates of these land use change (LUC) and life‐cycle GHG emissions affect biofuels' attractiveness and eligibility under a number of renewable fuel policies in the USA and abroad. Modeling was used to refine the spatial resolution and depth extent of domestic estimates of SOC change for land (cropland, cropland pasture, grassland, and forest) conversion scenarios to biofuel crops (corn, corn stover, switchgrass, Miscanthus, poplar, and willow) at the county level in the USA. Results show that in most regions, conversions from cropland and cropland pasture to biofuel crops led to neutral or small levels of SOC sequestration, while conversion of grassland and forest generally caused net SOC loss. SOC change results were incorporated into the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation (GREET) model to assess their influence on life‐cycle GHG emissions of corn and cellulosic ethanol. Total LUC GHG emissions (g CO₂eq MJ^?1) were 2.1–9.3 for corn‐, ?0.7 for corn stover‐, ?3.4 to 12.9 for switchgrass‐, and ?20.1 to ?6.2 for Miscanthus ethanol; these varied with SOC modeling assumptions applied. Extending the soil depth from 30 to 100 cm affected spatially explicit SOC change and overall LUC GHG emissions; however, the influence on LUC GHG emission estimates was less significant in corn and corn stover than cellulosic feedstocks. Total life‐cycle GHG emissions (g CO₂eq MJ^?1, 100 cm) were estimated to be 59–66 for corn ethanol, 14 for stover ethanol, 18–26 for switchgrass ethanol, and ?7 to ?0.6 for Miscanthus ethanol. The LUC GHG emissions associated with poplar‐ and willow‐derived ethanol may be higher than that for switchgrass ethanol due to lower biomass yield.     

Temporal patterns of land-use change and carbon storage in China and tropical Asia

Evaluating the annual sources and sinks of carbon from land-use change helps con-strain other terms in the global carbon cycle and may help countries choose how to comply with commitments for reduced emissions. This paper presents the results of recent analyses ofland-use change in China and tropical Asia. The original forest areas are estimated to have cov-ered 546×10~6 ha in tropical Asia and 425×10~6 ha in China. By 1850, 44% of China's forests had been cleared, and another 27% was lost between 1850 and 1980, leaving China with 13% forestcover (29% of the initial forest area). Tropical Asia is estimated to have lost 26% of its initial forestcover before 1850 and another 33% after 1850. The annual emissions of carbon from the two regions re-flect the different histories over the last 150 years, with China's emissions peaking in thelate 1950s (at 0.2-0.5 Pg C·a~(-1)) and tropical Asia's emissions peaking in 1990s (at 1.0 Pg C·a~(-1)). Despite the fact that most deforestation has been for new agricultural land, the majority ofthe lands cleared from forests in China are no longer croplands, but fallow or degraded shrublands.Unlike croplands, the origins of these other lands are poorly documented, and thus add consider-able uncertainty to estimates of flux before the 1980s. Nevertheless, carbon emissions from China seem to have decreased since the 1960s to nearly zero at present. In contrast, emissions of car-bon from tropical Asia were higher in the 1990s than that at any time in the past.     

Temporal patterns of land-use change and carbon storage in China and tropical Asia

Evaluating the annual sources and sinks of carbon from land-use changehelps constrain other terms in the global carbon cycle and may help countries choose how to comply with commitments for reduced emissions. This paper presents the results of recent analyses of land-use change in China and tropical Asia. The original forest areas are estimated to have covered 546×106 ha in tropical Asia and 425×106 ha in China. By 1850, 44% of China's forests had been cleared, and another 27% was lost between 1850 and 1980, leaving China with 13% forest cover (29% of the initial forest area). Tropical Asia is estimated to have lost 26%of its initial forest cover before 1850 and another 33% after 1850. The annual emissions of carbon from the two regions reflect the different histories over the last 150 years, with China's emissions peaking in the late 1950s (at 0.2-0.5 Pg C@a-1) and tropical Asia's emissions peaking in 1990s (at 1.0 Pg C@a-1). Despite the fact that most deforestation has been for new agricultural land, the majority of the lands cleared from forests in China are no longer croplands, but fallow or degraded shrublands. Unlike croplands, the origins of these other lands are poorly documented, and thus add considerable uncertainty to estimates of flux before the 1980s. Nevertheless, carbon emissions from China seem to have decreased since the 1960s to nearly zero at present. In contrast, emissions of carbon from tropical Asia were higher in the 1990s than that at any time in the past.     

Assessing the influence of historic net and gross land changes on the carbon fluxes of Europe

Legacy effects of land cover/use on carbon fluxes require considering both present and past land cover/use change dynamics. To assess past land use dynamics, model‐based reconstructions of historic land cover/use are needed. Most historic reconstructions consider only the net area difference between two time steps (net changes) instead of accounting for all area gains and losses (gross changes). Studies about the impact of gross and net land change accounting methods on the carbon balance are still lacking. In this study, we assessed historic changes in carbon in soils for five land cover/use types and of carbon in above‐ground biomass of forests. The assessment focused on Europe for the period 1950 to 2010 with decadal time steps at 1‐km spatial resolution using a bookkeeping approach. To assess the implications of gross land change data, we also used net land changes for comparison. Main contributors to carbon sequestration between 1950 and 2010 were afforestation and cropland abandonment leading to 14.6 PgC sequestered carbon (of which 7.6 PgC was in forest biomass). Sequestration was highest for old‐growth forest areas. A sequestration dip was reached during the 1970s due to changes in forest management practices. Main contributors to carbon emissions were deforestation (1.7 PgC) and stable cropland areas on peaty soils (0.8 PgC). In total, net fluxes summed up to 203 TgC yr^?1 (98 TgC yr^?1 in forest biomass and 105 TgC yr^?1 in soils). For areas that were subject to land changes in both reconstructions (35% of total area), the differences in carbon fluxes were about 68%. Overall for Europe the difference between accounting for either gross or net land changes led to 7% difference (up to 11% per decade) in carbon fluxes with systematically higher fluxes for gross land change data.     

Long‐term terrestrial carbon dynamics in the Midwestern United States during 1850–2015: Roles of land use and cover change and agricultural management

To meet the increasing food and biofuel demand, the Midwestern United States has become one of the most intensively human‐disturbed hotspots, characterized by widespread cropland expansion and various management practices. However, the role of human activities in the carbon (C) cycling across managed landscape remains far from certain. In this study, based on state‐ and national census, field experiments, and model simulation, we comprehensively examined long‐term carbon storage change in response to land use and cover change (LUCC) and agricultural management in the Midwest from 1850 to 2015. We also quantified estimation uncertainties related to key parameter values. Model estimation showed LUCC led to a reduction of 1.35 Pg (with a range of 1.3–1.4 Pg) in vegetation C pool of the Midwest, yet agricultural management barely affected vegetation C change. In comparison, LUCC reduced SOC by 4.5 Pg (3.1 to 6.2 Pg), while agricultural management practices increased SOC stock by 0.9 Pg. Moreover, we found 45% of the study area was characterized by continuously decreasing SOC caused by LUCC, and SOC in 13% and 31% of the area was fully and partially recovered, respectively, since 1850. Agricultural management was estimated to increase the area of full recovery and partial recovery by 8.5% and 1.1%. Our results imply that LUCC plays an essential role in regional C balance, and more importantly, sustainable land management can be beneficial for strengthening C sequestration of the agroecosystems in the Midwestern US, which may serve as an important contributor to C sinks in the US.     

Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years

We assess the role of changing natural (volcanic, aerosol, insolation) and anthropogenic (CO₂ emissions, land cover) forcings on the global climate system over the last 150 years using an earth system model of intermediate complexity, CLIMBER‐2. We apply several datasets of historical land‐use reconstructions: the cropland dataset by Ramankutty & Foley (1999) (R&F), the HYDE land cover dataset of Klein Goldewijk (2001) , and the land‐use emissions data from Houghton & Hackler (2002) . Comparison between the simulated and observed temporal evolution of atmospheric CO₂ and δ¹³CO₂ are used to evaluate these datasets. To check model uncertainty, CLIMBER‐2 was coupled to the more complex Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. In simulation with R&F dataset, biogeophysical mechanisms due to land cover changes tend to decrease global air temperature by 0.26°C, while biogeochemical mechanisms act to warm the climate by 0.18°C. The net effect on climate is negligible on a global scale, but pronounced over the land in the temperate and high northern latitudes where a cooling due to an increase in land surface albedo offsets the warming due to land‐use CO₂ emissions. Land cover changes led to estimated increases in atmospheric CO₂ of between 22 and 43 ppmv. Over the entire period 1800–2000, simulated δ¹³CO₂ with HYDE compares most favourably with ice core during 1850–1950 and Cape Grim data, indicating preference of earlier land clearance in HYDE over R&F. In relative terms, land cover forcing corresponds to 25–49% of the observed growth in atmospheric CO₂. This contribution declined from 36–60% during 1850–1960 to 4–35% during 1960–2000. CLIMBER‐2‐LPJ simulates the land cover contribution to atmospheric CO₂ growth to decrease from 68% during 1900–1960 to 12% in the 1980s. Overall, our simulations show a decline in the relative role of land cover changes for atmospheric CO₂ increase during the last 150 years.     

Large climate mitigation potential from adding trees to agricultural lands

While improved management of agricultural landscapes is promoted as a promising natural climate solution, available estimates of the mitigation potential are based on coarse assessments of both agricultural extent and aboveground carbon density. Here we combine 30 meter resolution global maps of aboveground woody carbon, tree cover, and cropland extent, as well as a 1 km resolution map of global pasture land, to estimate the current and potential carbon storage of trees in nonforested portions of agricultural lands. We find that global croplands currently store 3.07 Pg of carbon (C) in aboveground woody biomass (i.e., trees) and pasture lands account for an additional 3.86 Pg C across a combined 3.76 billion ha. We then estimate the climate mitigation potential of multiple scenarios of integration and avoided loss of trees in crop and pasture lands based on region‐specific biomass distributions. We evaluate our findings in the context of nationally determined contributions and find that the majority of potential carbon storage from integration and avoided loss of trees in crop and pasture lands is in countries that do not identify agroforestry as a climate mitigation technique.     

Projected changes in mineral soil carbon of European croplands and grasslands, 1990–2080

We present the most comprehensive pan‐European assessment of future changes in cropland and grassland soil organic carbon (SOC) stocks to date, using a dedicated process‐based SOC model and state‐of‐the‐art databases of soil, climate change, land‐use change and technology change. Soil carbon change was calculated using the Rothamsted carbon model on a European 10 × 10′ grid using climate data from four global climate models implementing four Intergovernmental Panel on Climate Change (IPCC) emissions scenarios (SRES). Changes in net primary production (NPP) were calculated by the Lund–Potsdam–Jena model. Land‐use change scenarios, interpreted from the narratives of the IPCC SRES story lines, were used to project changes in cropland and grassland areas. Projections for 1990–2080 are presented for mineral soil only. Climate effects (soil temperature and moisture) will tend to speed decomposition and cause soil carbon stocks to decrease, whereas increases in carbon input because of increasing NPP will slow the loss. Technological improvement may further increase carbon inputs to the soil. Changes in cropland and grassland areas will further affect the total soil carbon stock of European croplands and grasslands. While climate change will be a key driver of change in soil carbon over the 21st Century, changes in technology and land‐use change are estimated to have very significant effects. When incorporating all factors, cropland and grassland soils show a small increase in soil carbon on a per area basis under future climate (1–7 t C ha^?1 for cropland and 3–6 t C ha^?1 for grassland), but when the greatly decreasing area of cropland and grassland are accounted for, total European cropland stocks decline in all scenarios, and grassland stocks decline in all but one scenario. Different trends are seen in different regions. For Europe (the EU25 plus Norway and Switzerland), the cropland SOC stock decreases from 11 Pg in 1990 by 4–6 Pg (39–54%) by 2080, and the grassland SOC stock increases from 6 Pg in 1990 to 1.5 Pg (25%) under the B1 scenario, but decreases to 1–3 Pg (20–44%) under the other scenarios. Uncertainty associated with the land‐use and technology scenarios remains unquantified, but worst‐case quantified uncertainties are 22.5% for croplands and 16% for grasslands, equivalent to potential errors of 2.5 and 1 Pg SOC, respectively. This is equivalent to 42–63% of the predicted SOC stock change for croplands and 33–100% of the predicted SOC stock change for grasslands. Implications for accounting for SOC changes under the Kyoto Protocol are discussed.     

Quantifying uncertainty in estimates of C emissions from above-ground biomass due to historic land-use change to cropping in Australia

Quantifying continental scale carbon emissions from the oxidation of above‐ground plant biomass following land‐use change (LUC) is made difficult by the lack of information on how much biomass was present prior to vegetation clearing and on the timing and location of historical LUC. The considerable spatial variability of vegetation and the uncertainty of this variability leads to difficulties in predicting biomass C density (t_C ha^?1) prior to LUC. The issue of quantifying uncertainties in the estimation of land based sources and sinks of CO₂, and the feasibility of reducing these uncertainties by further sampling, is critical information required by governments world‐wide for public policy development on climate change issues. A quantitative statistical approach is required to calculate confidence intervals (the level of certainty) of estimated cleared above‐ground biomass. In this study, a set of high‐quality observations of steady state above‐ground biomass from relatively undisturbed ecological sites across the Australian continent was combined with vegetation, topographic, climatic and edaphic data sets within a Geographical Information System. A statistical model was developed from the data set of observations to predict potential biomass and the standard error of potential biomass for all 0.05° (approximately 5 × 5 km) land grid cells of the continent. In addition, the spatial autocorrelation of observations and residuals from the statistical model was examined. Finally, total C emissions due to historic LUC to cultivation and cropping were estimated by combining the statistical model with a data set of fractional cropland area per land grid cell, f_Ac (Ramankutty & Foley 1998). Total C emissions from loss of above‐ground biomass due to cropping since European colonization of Australia was estimated to be 757 Mt_C. These estimates are an upper limit because the predicted steady state biomass may be less than the above‐ground biomass immediately prior to LUC because of disturbance. The estimated standard error of total C emissions was calculated from the standard error of predicted biomass, the standard error of f_Ac and the spatial autocorrelation of biomass. However, quantitative estimates of the standard error of f_Ac were unavailable. Thus, two scenarios were developed to examine the effect of error in f_Ac on the error in total C emissions. In the first scenario, in which f_Ac was regarded as accurate (i.e. a coefficient of variation, CV, of f_Ac = 0.0), the 95% confidence interval of the continental C emissions was 379–1135 Mt_C. In the second scenario, a 50% error in estimated cropland area was assumed (a CV of f_Ac = 0.50) and the estimated confidence interval increased to between 350 and 1294 Mt_C. The CV of C emissions for these two scenarios was 25% and 29%. Thus, while accurate maps of land‐use change contribute to decreasing uncertainty in C emissions from LUC, the major source of this uncertainty arises from the prediction accuracy of biomass C density. It is argued that, even with large sample numbers, the high cost of sampling biomass carbon may limit the uncertainty of above‐ground biomass to about a CV of 25%.     

Modelling the role of agriculture for the 20th century global terrestrial carbon balance

In order to better assess the role of agriculture within the global climate‐vegetation system, we present a model of the managed planetary land surface, Lund–Potsdam–Jena managed Land (LPJmL), which simulates biophysical and biogeochemical processes as well as productivity and yield of the most important crops worldwide, using a concept of crop functional types (CFTs). Based on the LPJ‐Dynamic Global Vegetation Model, LPJmL simulates the transient changes in carbon and water cycles due to land use, the specific phenology and seasonal CO₂ fluxes of agricultural‐dominated areas, and the production of crops and grazing land. It uses 13 CFTs (11 arable crops and two managed grass types), with specific parameterizations of phenology connected to leaf area development. Carbon is allocated daily towards four carbon pools, one being the yield‐bearing storage organs. Management (irrigation, treatment of residues, intercropping) can be considered in order to capture their effect on productivity, on soil organic carbon and on carbon extracted from the ecosystem. For transient simulations for the 20th century, a global historical land use data set was developed, providing the annual cover fraction of the 13 CFTs, rain‐fed and/or irrigated, within 0.5° grid cells for the period 1901–2000, using published data on land use, crop distributions and irrigated areas. Several key results are compared with observations. The simulated spatial distribution of sowing dates for temperate cereals is comparable with the reported crop calendars. The simulated seasonal canopy development agrees better with satellite observations when actual cropland distribution is taken into account. Simulated yields for temperate cereals and maize compare well with FAO statistics. Monthly carbon fluxes measured at three agricultural sites also compare well with simulations. Global simulations indicate a ∼24% (respectively ∼10%) reduction in global vegetation (respectively soil) carbon due to agriculture, and 6–9 Pg C of yearly harvested biomass in the 1990s. In contrast to simulations of the potential natural vegetation showing the land biosphere to be an increasing carbon sink during the 20th century, LPJmL simulates a net carbon source until the 1970s (due to land use), and a small sink (mostly due to changing climate and CO₂) after 1970. This is comparable with earlier LPJ simulations using a more simple land use scheme, and within the uncertainty range of estimates in the 1980s and 1990s. The fluxes attributed to land use change compare well with Houghton's estimates on the land use related fluxes until the 1970s, but then they begin to diverge, probably due to the different rates of deforestation considered. The simulated impacts of agriculture on the global water cycle for the 1990s are∼5% (respectively∼20%) reduction in transpiration (respectively interception), and∼44% increase in evaporation. Global runoff, which includes a simple irrigation scheme, is practically not affected.     

The HYDE 3.1 spatially explicit database of human‐induced global land‐use change over the past 12,000 years

Aim This paper presents a tool for long‐term global change studies; it is an update of the History Database of the Global Environment (HYDE) with estimates of some of the underlying demographic and agricultural driving factors. Methods Historical population, cropland and pasture statistics are combined with satellite information and specific allocation algorithms (which change over time) to create spatially explicit maps, which are fully consistent on a 5′ longitude/latitude grid resolution, and cover the period 10,000 bc to ad 2000. Results Cropland occupied roughly less than 1% of the global ice‐free land area for a long time until ad 1000, similar to the area used for pasture. In the centuries that followed, the share of global cropland increased to 2% in ad 1700 (c. 3 million km²) and 11% in ad 2000 (15 million km²), while the share of pasture area grew from 2% in ad 1700 to 24% in ad 2000 (34 million km²) These profound land‐use changes have had, and will continue to have, quite considerable consequences for global biogeochemical cycles, and subsequently global climate change. Main conclusions Some researchers suggest that humans have shifted from living in the Holocene (emergence of agriculture) into the Anthropocene (humans capable of changing the Earth's atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land‐use changes (e.g. as result of the depopulation of Europe due to the Black Death in the 14th century and the aftermath of the colonization of the Americas in the 16th century) we believe that this point might have occurred earlier in time. While there are still many uncertainties and gaps in our knowledge about the importance of land use (change) in the global biogeochemical cycle, we hope that this database can help global (climate) change modellers to close parts of this gap.     

Estimating 20‐year land‐use change and derived CO2 emissions associated with crops,pasture and forestry in Brazil and each of its 27 states

Land‐use change (LUC) in Brazil has important implications on global climate change, ecosystem services and biodiversity, and agricultural expansion plays a critical role in this process. Concerns over these issues have led to the need for estimating the magnitude and impacts associated with that, which are increasingly reported in the environmental assessment of products. Currently, there is an extensive debate on which methods are more appropriate for estimating LUC and related emissions and regionalized estimates are lacking for Brazil, which is a world leader in agricultural production (e.g. food, fibres and bioenergy). We developed a method for estimating scenarios of past 20‐year LUC and derived CO₂ emission rates associated with 64 crops, pasture and forestry in Brazil as whole and in each of its 27 states, based on time‐series statistics and in accordance with most used carbon‐footprinting standards. The scenarios adopted provide a range between minimum and maximum rates of CO₂ emissions from LUC according to different possibilities of land‐use transitions, which can have large impacts in the results. Specificities of Brazil, like multiple cropping and highly heterogeneous carbon stocks, are also addressed. The highest CO₂ emission rates are observed in the Amazon biome states and crops with the highest rates are those that have undergone expansion in this region. Some states and crops showing large agricultural areas have low emissions associated, especially in southern and eastern Brazil. Native carbon stocks and time of agricultural expansion are the most decisive factors to the patterns of emissions. Some implications on LUC estimation methods and standards and on agri‐environmental policies are discussed.     

Doubling protected land area may be inefficient at preserving the extent of undeveloped land and could cause substantial regional shifts in land use

Projection of land use and land-cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land-use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well-defined conservation goals while minimizing impacts to agriculture.     

Carbon Pools and Emissions from Deforestation in Extra-Tropical Forests of Northern Argentina Between 1900 and 2005

We estimated carbon pools and emissions from deforestation in northern Argentine forests between 1900 and 2005, based on forest inventories, deforestation estimates from satellite images and historical data on forests and agriculture. Carbon fluxes were calculated using a book-keeping model. We ran 1000 simulations for a 105-year period with different combinations of values of carbon stocks (Mg C ha⁻¹), soil carbon in the top 0.2 m, and annual deforestation series. The 1000 combinations of parameters were performed as a sensitivity analysis that for each run, randomly selected the values of each variable within a predefined range of values and probability distributions. Using the simulation outputs, we calculated the accumulated C emissions due to deforestation from 1900 to 2005 and the annual emission as the average of the 1000 simulations, and uncertainties of our estimates as the standard deviation. We found that northern Argentine forests contain an estimated 4.54 Pg C (2.312 Pg C in biomass and 2.233 Pg C in soil). Between 1900 and 2005 approximately 30% of the forests were deforested, yielding carbon emissions of 0.945 (SD = 0.270) Pg C. Estimated average annual carbon emissions between 1996 and 2005, mostly from deforestation of the Chaco dry forests, were 20,875 (SD = 6,156) Gg C y⁻¹ (1 Gg = 10⁻⁶ Pg). These values represent the largest source of carbon from land-cover change in the extra-tropical southern hemisphere, between 0.9 and 2.7% of the global carbon emissions from deforestation, and approximately 10% of carbon emissions from the Brazilian Amazon. Deforestation, which has accelerated during the last decades as a result of modern agriculture expansion, represents a major national source of greenhouse gases and the second emission source, after fossil fuel consumption by fixed sources. We conclude that Argentine forests are an important carbon pool and emission source that need more attention for accurate global estimates, and seasonally dry forest deforestation is a key component of the Argentine carbon cycle. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Towards Regional,Error-Bounded Landscape Carbon Storage Estimates for Data-Deficient Areas of the World

Monitoring landscape carbon storage is critical for supporting and validating climate change mitigation policies. These may be aimed at reducing deforestation and degradation, or increasing terrestrial carbon storage at local, regional and global levels. However, due to data-deficiencies, default global carbon storage values for given land cover types such as ‘lowland tropical forest’ are often used, termed ‘Tier 1 type’ analyses by the Intergovernmental Panel on Climate Change (IPCC). Such estimates may be erroneous when used at regional scales. Furthermore uncertainty assessments are rarely provided leading to estimates of land cover change carbon fluxes of unknown precision which may undermine efforts to properly evaluate land cover policies aimed at altering land cover dynamics. Here, we present a repeatable method to estimate carbon storage values and associated 95% confidence intervals (CI) for all five IPCC carbon pools (aboveground live carbon, litter, coarse woody debris, belowground live carbon and soil carbon) for data-deficient regions, using a combination of existing inventory data and systematic literature searches, weighted to ensure the final values are regionally specific. The method meets the IPCC ‘Tier 2’ reporting standard. We use this method to estimate carbon storage over an area of33.9 million hectares of eastern Tanzania, reporting values for 30 land cover types. We estimate that this area stored 6.33 (5.92–6.74) Pg C in the year 2000. Carbon storage estimates for the same study area extracted from five published Africa-wide or global studies show a mean carbon storage value of ∼50% of that reported using our regional values, with four of the five studies reporting lower carbon storage values. This suggests that carbon storage may have been underestimated for this region of Africa. Our study demonstrates the importance of obtaining regionally appropriate carbon storage estimates, and shows how such values can be produced for a relatively low investment.     

The effects of land use and climate change on the carbon cycle of Europe over the past 500 years

The long residence time of carbon in forests and soils means that both the current state and future behavior of the terrestrial biosphere are influenced by past variability in climate and anthropogenic land use. Over the last half‐millennium, European terrestrial ecosystems were affected by the cool temperatures of the Little Ice Age, rising CO₂ concentrations, and human induced deforestation and land abandonment. To quantify the importance of these processes, we performed a series of simulations with the LPJ dynamic vegetation model driven by reconstructed climate, land use, and CO₂ concentrations. Although land use change was the major control on the carbon inventory of Europe over the last 500 years, the current state of the terrestrial biosphere is largely controlled by land use change during the past century. Between 1500 and 2000, climate variability led to temporary sequestration events of up to 3 Pg, whereas increasing atmospheric CO₂ concentrations during the 20th century led to an increase in carbon storage of up to 15 Pg. Anthropogenic land use caused between 25 Pg of carbon emissions and 5 Pg of uptake over the same time period, depending on the historical and spatial pattern of past land use and the timing of the reversal from deforestation to afforestation during the last two centuries. None of the currently existing anthropogenic land use change datasets adequately capture the timing of the forest transition in most European countries as recorded in historical observations. Despite considerable uncertainty, our scenarios indicate that with limited management, extant European forests have the potential to absorb between 5 and 12 Pg of carbon at the present day.     

China's wetland soil organic carbon pool: New estimation on pool size,change, and trajectory

Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R² being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m⁻² in 2000 and 26.57 ± 3.73 kg C m⁻² in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 10³ to 162.8 × 10³ km² in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m⁻² year⁻¹ since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.     

Hydrological Response to Land Cover Changes and Human Activities in Arid Regions Using a Geographic Information System and Remote Sensing

The hydrological response to land cover changes induced by human activities in arid regions has attracted increased research interest in recent decades. The study reported herein assessed the spatial and quantitative changes in surface runoff resulting from land cover change in the Al-Baha region of Saudi Arabia between 1990 and 2000 using an ArcGIS-surface runoff model and predicted land cover and surface runoff depth in 2030 using Markov chain analysis. Land cover maps for 1990 and 2000 were derived from satellite images using ArcGIS 10.1. The findings reveal a 26% decrease in forest and shrubland area, 28% increase in irrigated cropland, 1.5% increase in sparsely vegetated land and 0.5% increase in bare soil between 1990 and 2000. Overall, land cover changes resulted in a significant decrease in runoff depth values in most of the region. The decrease in surface runoff depth ranged from 25-106 mm/year in a 7020-km² area, whereas the increase in such depth reached only 10 mm/year in a 243-km² area. A maximum increase of 73 mm/year was seen in a limited area. The surface runoff depth decreased to the greatest extent in the central region of the study area due to the huge transition in land cover classes associated with the construction of 25 rainwater harvesting dams. The land cover prediction revealed a greater than twofold increase in irrigated cropland during the 2000-2030 period, whereas forest and shrubland are anticipated to occupy just 225 km² of land area by 2030, a significant decrease from the 747 km² they occupied in 2000. Overall, changes in land cover are predicted to result in an annual increase in irrigated cropland and dramatic decline in forest area in the study area over the next few decades. The increase in surface runoff depth is likely to have significant implications for irrigation activities.     

CO2 emissions from land‐use change affected more by nitrogen cycle,than by the choice of land‐cover data

The high uncertainty in land‐based CO₂ fluxes estimates is thought to be mainly due to uncertainty in not only quantifying historical changes among forests, croplands, and grassland, but also due to different processes included in calculation methods. Inclusion of a nitrogen (N) cycle in models is fairly recent and strongly affects carbon (C) fluxes. In this study, for the first time, we use a model with C and N dynamics with three distinct historical reconstructions of land‐use and land‐use change (LULUC) to quantify LULUC emissions and uncertainty that includes the integrated effects of not only climate and CO₂ but also N. The modeled global average emissions including N dynamics for the 1980s, 1990s, and 2000–2005 were 1.8 ± 0.2, 1.7 ± 0.2, and 1.4 ± 0.2 GtC yr^?1, respectively, (mean and range across LULUC data sets). The emissions from tropics were 0.8 ± 0.2, 0.8 ± 0.2, and 0.7 ± 0.3 GtC yr^?1, and the non tropics were 1.1 ± 0.5, 0.9 ± 0.2, and 0.7 ± 0.1 GtC yr^?1. Compared to previous studies that did not include N dynamics, modeled net LULUC emissions were higher, particularly in the non tropics. In the model, N limitation reduces regrowth rates of vegetation in temperate areas resulting in higher net emissions. Our results indicate that exclusion of N dynamics leads to an underestimation of LULUC emissions by around 70% in the non tropics, 10% in the tropics, and 40% globally in the 1990s. The differences due to inclusion/exclusion of the N cycle of 0.1 GtC yr^?1 in the tropics, 0.6 GtC yr^?1 in the non tropics, and 0.7 GtC yr^?1 globally (mean across land‐cover data sets) in the 1990s were greater than differences due to the land‐cover data in the non tropics and globally (0.2 GtC yr^?1). While land‐cover information is improving with satellite and inventory data, this study indicates the importance of accounting for different processes, in particular the N cycle.