Land Use Harmonization Research Articles

Assessing the effects of combined future climate and land use/cover changes on streamflow in the Upper Fen River Basin, China

Study regionTributaries of the Yellow River, Upper Fen River Basin, China. Study focusThe current research analyzed the streamflow changes resulting from interactions between climate and land use/cover (LULC) under Shared Socioeconomic Pathways (SSPs). The study utilized six General Circulation Models (GCMs) from Climate Model Intercomparison Project-6 (CMIP6) and the Land-Use Harmonization 2 (LUH2) dataset as forcing inputs for the Soil and Water Assessment Tool (SWAT) model. This study aims to quantitatively access the alone and combined effects of climate and LULC changes on future streamflow. New hydrologic insights for the regionThe change in future streamflow under the combined effect of climate and LULC exceeded the change observed under the influence of climate or LULC alone in the Fenhe Reservoir Hydrological Station Control Basin (FRCB). The contributions were 72.5 % (SSP126), 73.1 % (SSP245), and 77.2 % (SSP585), respectively. Climate change showed a stronger correlation with future streamflow than LULC changes. Precipitation had the most direct effect on streamflow among the meteorological factors. The expansion of urban land contributes to an increase in streamflow in the source area and southwest tributaries of the FRCB to some extent. The future streamflow will gradually increase in mid-century (2045–2066) under low to high SSP. Adapting effective strategies for the FRCB, can be helpful in water resources planning and management.

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Assessing the land-use harmonization (LUH) 2 dataset in Central Asia for regional climate model projection

Although the land-use harmonization (LUH) datasets have been widely applied in regional climate model (RCM) projections for investigating the role of the land-use forcing in future climate changes, few studies have thoroughly assessed them on local scale, which may bring large uncertainties in the resultant climate information for designing adaption and mitigation measures of climate change. The authors use a local land-use dataset (referred to as Li-LU) as the benchmark to assess the latest version of the LUH datasets, LUH2, in Central Asia (CA) which has undergone extensive land-use changes (LUCs) and might undergo extensive LUCs in the future. The results show that LUH2 has large biases in depicting the historical land-use states in CA for 1995–2015. For instance, the area of grassland (cropland) in LUH2 is about 1.4–1.5 (0.4–0.5) times of that of Li-LU. Moreover, the future LUCs predicted by LUH2 for 2045 (relative to 2005) are much smaller than those of Li-LU and these two datasets generally have opposite signals in changes. In addition, the predicted LUCs of LUH2 do not follow the causal mechanisms [the causal connections between the key drivers (e.g. population, economy, and environment) and land use] behind the LUCs in the past. If the future scenario of LUH2 is used for RCM projection in CA with the historical land-use information from Li-LU, the simulation results could be misleading for understanding the impacts of LUCs on future climate changes there. This study suggests that the LUH datasets should be carefully assessed before using them for regional studies and provides practical notes for selecting the appropriate land-use dataset for RCM projections in other areas around the world.

Effects on early monsoon rainfall in West Africa due to recent deforestation in a convection-permitting ensemble

Abstract. Tropical deforestation can have a significant effect on climate, but research attention has been directed mostly towards Amazonian deforestation. The southern part of West Africa (a region dependent on rain-fed agriculture and vulnerable to droughts and flooding) has seen significant deforestation since the 1950s. Many previous tropical deforestation studies have used idealized and exaggerated deforestation scenarios and parameterized convection models. In this study we estimate for the first time realistic historical deforestation from the Land-Use Harmonization dataset in West Africa and simulate the impacts in a 5 d ensemble forecast in June using a convection-permitting regional climate model. We find that sensible heat flux increases at the expense of latent heat flux in most deforested regions, and rainfall increases by an average of 8.4 % over deforested pixels from 18:00–06:00 UTC, whereas changes are much less pronounced during the day. Over large areas of deforestation approx. 300 km inland (e.g. west Guinea) the roughness-length and thermally enhanced convergence during the afternoon and evening occurs over the deforested areas resulting in increases in rainfall with little impact from reduced daytime humidity. In areas of coastal deforestation (e.g. Côte d'Ivoire), increased winds drive the sea breeze convection inland, resulting in evening rainfall reductions over the deforested area but increases further inland, in line with observations. We suggest our results would not be replicated in parameterized convection models, which are known to struggle with capturing peak convective activity in the late afternoon and long-lived nocturnal rainfall and with reproducing observed surface–rainfall feedbacks.

Effect of land-use legacy on the future carbon sink for the conterminous US

Abstract. Modeling the effects of the terrestrial carbon sink in the future depends upon not just current-day land use and land cover (LULC) but also the legacy of past LULC change (LULCC), which is often not considered. The age distribution of trees in the forest depends upon the history of past disturbances, while the nutrients in the soil depend upon past LULC. Thus, establishing the correct initial state of the vegetation and soil is crucial to model accurately the effect of biogeochemical cycling with environmental change in the future. This study models the effects of LULCC from 1750 to 2014 using the land-use harmonization dataset (LUH2) of land-use transitions with the terrestrial ecosystems model (TEM) for the conterminous US. Modeled LULC include plant functional types (PFTs) of potential vegetation, as well as managed cropland, pastureland, and urban areas. LULCC is treated using a cohort approach, in which a separate cohort occurs every year there is a land-use transition, thereby ensuring proper age structure of forests and regrowth with the correct soil nutrients. From 2000–2014 the modeled net ecosystem productivity (NEP) is 989 TgC yr−1 for the conterminous US but only −15 TgC yr−1 if accounting for carbon lost from land-use transitions and management. The hypothesis is that the initial state of the vegetation and soils significantly affects the future state of the terrestrial carbon sink. In this study, LULC remains constant in the future, with the NCAR CCSM4 RCP8.5 climate used to force the TEM-Hydro model. The following experiments are run from 2015 to 2100, including (a) restarting from existing cohorts in 2014 (RESTART), (b) reinitializing in 2015 based on condensing the cohorts for each PFT into a single cohort (CONDENSED), and (c) restarting from average cohort conditions for each PFT (AVERAGE). The NEP is too low when using condensed cohorts without reinitializing due to a larger increase in heterotrophic respiration (Rh) resulting from the assumption of mature forests. The carbon stocks are larger than using all the cohorts if condensed cohorts are reinitialized due to the assumption of mature, equilibrated forests. Where nitrogen-limited, forest regrowth is enhanced if regrowth starts from more nutrient-rich conditions. Water fluxes are dominated by environmental factors but can be slightly dependent upon the underlying carbon dynamics. It is therefore necessary to account for past disturbances when modeling future changes in carbon dynamics.

Impact of Climate-Driven Land-Use Change on O3 and PM Pollution by Driving BVOC Emissions in China in 2050

This study predicted three future land-use type scenarios in 2050 (including the Shared Socioeconomic Pathway SSP126, SSP585, and carbon scenario) based on the Land-Use Harmonization (LUH2) project and the future evolution of land-use types considering China’s carbon neutrality background. The contribution of land-use changes to terrestrial natural source biogenic volatile organic compounds (BVOCs), as well as O3 and PM concentrations, were determined. Under the SSP126 pathway, meteorological changes would increase BVOC emissions in China by 1.0 TgC in 2050, compared with 2015, while land-use changes would increase them by 1.5–7.1 TgC. The impact of land-use changes on O3 and PM concentrations would be less than 3.6% in 2050 and greater in summer. Regional differences must be considered when calculating future environmental background concentrations of pollutants. Due to more afforestation measures under the SSP126 scenario, the impact of land-use change on pollutants was more obvious under the SSP126 pathway than under the SSP585 pathway. Under the carbon scenario, the increase in PM concentration caused by land-use changes would pose a risk to air quality compliance; thus, it is necessary to consider reducing or offsetting this potential risk through anthropogenic emission control measures.

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Climate change and human activities have profoundly affected the world with extreme precipitation, heat waves, water scarcity, frequent floods and intense droughts. It is acknowledged that climate change will persist and perhaps intensify in the future, and it is thus meaningful to explore the quantitative impacts of these changes on hydrological regimes. The Jiulong River basin serves as an important watershed on the southeast coast of China. However, future hydrological changes under the combined impacts of climate change and land use change have been barely investigated. In this study, the climate outputs from five general circulation models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) were corrected and spatially downscaled by a statistical downscaling method combining quantile mapping and machine learning. The future high-resolution land use maps were projected by the CA–Markov model with land use changes from the Land-Use Harmonization 2 (LUH2) as constraints. The future dynamic vegetation process was projected by the Biome-GBC model, and then, the future hydrological process under four representative concentration pathways and shared socioeconomic pathways (RCP–SSP) combined scenarios was simulated by a distributed hydrological model. Based on the copula method, the flood frequency and corresponding return periods were derived. The results demonstrated that future precipitation and air temperature would continue to rise, and future land use changes would have different developing pathways determined by the designs in various SSP–RCPs. Under the combined impacts of climate and land use change, the total available water resources will increase due to increasing precipitation, and the high flow and low flow will both increase at three stations under the four SSP–RCPs. The annual 1-day maximum discharge is projected to increase by 67–133% in the last decade of the 21st century, and the annual 7-day minimum discharge is projected to increase by 19–39%. The flood frequency analysis showed that the Jiulong River basin would face more frequent floods in the future. By the end of the 21st century, the station-average frequency of a historical 100-year flood will increase by 122% under the most optimistic scenario (SSP126) and increase by 213% under the scenario of greatest regional rivalry (SSP370). We demonstrated that climate change would be the major cause for the increase in future high flows and that land use change would dominate future changes in low flows. Finally, we recommend integrated and sustainable water management systems to tackle future challenges in this coastal basin.

Quantifying the impacts of land cover change on hydrological responses in the Mahanadi river basin in India

Abstract. The objective of this study is to assess the impacts of land cover change on the hydrological responses of the Mahanadi river basin, a large river basin in India. Commonly, such assessments are accomplished by using distributed hydrological models in conjunction with different land use scenarios. However, these models, through their complex interactions among the model parameters to generate hydrological processes, can introduce significant uncertainties to the hydrological projections. Therefore, we seek to further understand the uncertainties associated with model parameterization in those simulated hydrological responses due to different land cover scenarios. We performed a sensitivity-guided model calibration of a physically semi-distributed model, the Variable Infiltration Capacity (VIC) model, within a Monte Carlo framework to generate behavioural models that can yield equally good or acceptable model performances for subcatchments of the Mahanadi river basin. These behavioural models are then used in conjunction with historical and future land cover scenarios from the recently released Land-Use Harmonization version 2 (LUH2) dataset to generate hydrological predictions and related uncertainties from behavioural model parameterization. The LUH2 dataset indicates a noticeable increase in the cropland (23.3 % cover) at the expense of forest (22.65 % cover) by the end of year 2100 compared to the baseline year, 2005. As a response, simulation results indicate a median percent increase in the extreme flows (defined as the 95th percentile or higher river flow magnitude) and mean annual flows in the range of 1.8 % to 11.3 % across the subcatchments. The direct conversion of forested areas to agriculture (of the order of 30 000 km2) reduces the leaf area index, which subsequently reduces the evapotranspiration (ET) and increases surface runoff. Further, the range of behavioural hydrological predictions indicated variation in the magnitudes of extreme flows simulated for the different land cover scenarios; for instance, uncertainty in scenario labelled “Far Future” ranges from 17 to 210 m3 s−1 across subcatchments. This study indicates that the recurrent flood events occurring in the Mahanadi river basin might be influenced by the changes in land use/land cover (LULC) at the catchment scale and suggests that model parameterization represents an uncertainty which should be accounted for in the land use change impact assessment.

Land-use harmonization datasets for annual global carbon budgets

Abstract. Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100 000 km2) and in the year 2009 for cropland (difference of 77 000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies.

Bookkeeping estimates of the net land-use change flux – a sensitivity study with the CMIP6 land-use dataset

Abstract. The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (Bookkeeping of Land Use Emissions, BLUE) based on a LULCC dataset including uncertainty estimates (the Land-Use Harmonization 2 (LUH2) dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land-use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux, while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %; references are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than that from LULCC uncertainty, and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period, the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor of 3) than the other two factors for the net LULCC flux in 2014, and historical LULCC uncertainty is negligible for estimates of future scenarios.

Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6

Abstract. Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

Abstract. Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth’s system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

Terrestrial Vertebrate Biodiversity Loss under Future Global Land Use Change Scenarios

Efficient forward-looking mitigation measures are needed to halt the global biodiversity decline. These require spatially explicit scenarios of expected changes in multiple indicators of biodiversity under future socio-economic and environmental conditions. Here, we link six future (2050 and 2100) global gridded maps (0.25° × 0.25° resolution) available from the land use harmonization (LUH) database, representing alternative concentration pathways (RCP) and shared socio-economic pathways (SSPs), with the countryside species–area relationship model to project the future land use change driven rates of species extinctions and phylogenetic diversity loss (in million years) for mammals, birds, and amphibians in each of the 804 terrestrial ecoregions and 176 countries and compare them with the current (1900–2015) and past (850–1900) rates of biodiversity loss. Future land-use changes are projected to commit an additional 209–818 endemic species and 1190–4402 million years of evolutionary history to extinction by 2100 depending upon the scenario. These estimates are driven by land use change only and would likely be higher once the direct effects of climate change on species are included. Among the three taxa, highest diversity loss is projected for amphibians. We found that the most aggressive climate mitigation scenario (RCP2.6 SSP-1), representing a world shifting towards a radically more sustainable path, including increasing crop yields, reduced meat production, and reduced tropical deforestation coupled with high trade, projects the lowest land use change driven global biodiversity loss. The results show that hotspots of future biodiversity loss differ depending upon the scenario, taxon, and metric considered. Future extinctions could potentially be reduced if habitat preservation is incorporated into national development plans, especially for biodiverse, low-income countries such as Indonesia, Madagascar, Tanzania, Philippines, and The Democratic Republic of Congo that are otherwise projected to suffer a high number of land use change driven extinctions under all scenarios.

Downscaling land-use data to provide global 30″ estimates of five land-use classes.

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km2) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R 2: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R 2 improvement of 0.12 compared with the Land‐use Harmonization data. The downscaling method presented here produced the first global land‐use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land‐use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse‐resolution data for other environmental variables.

Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products

Surface albedo determines radiative forcing and is a key parameter for driving Earth’s climate. Better characterization of surface albedo for individual land cover types can reduce the uncertainty in estimating changes to Earth’s radiation balance due to land cover change. This paper presents albedo look-up maps (LUMs) using a multiscale hierarchical approach based on moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo products and Landsat imagery. Ten years (2001 to 2011) of MODIS BRDF/albedo products were used to generate global albedo climatology. Albedo LUMs of land cover classes defined by the International Geosphere-Biosphere Programme (IGBP) at multiple spatial resolutions were generated. The albedo LUMs included monthly statistics of white-sky (diffuse) and black-sky (direct) albedo for each IGBP class for visible, near-infrared, and shortwave broadband under both snow-free and snow-covered conditions. The albedo LUMs were assessed by using the annual MODIS IGBP land cover map and the projected land use scenarios from the Intergovernmental Panel on Climate Change land-use harmonization project. The comparisons between the reconstructed albedo and the MODIS albedo data product show good agreement. The LUMs provide high temporal and spatial resolution global albedo statistics without gaps for investigating albedo variations under different land cover scenarios and could be used for land surface modeling.

Comparing the influence of net and gross anthropogenic land-use and land-cover changes on the carbon cycle in the MPI-ESM

Abstract. Global vegetation models traditionally treat anthropogenic land-use and land-cover changes (LULCCs) only as the changes in vegetation cover seen from one year to the next (net transitions). This approach ignores subgrid-scale processes such as shifting cultivation which do not affect the net vegetation distribution but which have an impact on the carbon budget. The differences in the carbon stocks feed back on processes like wildfires and desert formation. The simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) all describe LULCCs using the "Land-Use Harmonization Dataset". Though this dataset describes such subgrid-scale processes (gross transitions), some of the CMIP5 models still use the traditional approach. Using JSBACH/CBALANCE – the land carbon component of the Max Planck Institute Earth System Model (MPI-ESM), this study demonstrates how this potentially leads to a severe underestimation of the carbon emissions from LULCCs Using net transitions lowers the average land-use emissions from 1.44 to 0.90 Pg C yr−1 (38%) during the historical period (1850–2005) – a total lowering by 85 Pg C. The difference between the methods is smaller in the RCP scenarios (2006–2100) but in RCP2.6 and RCP8.5 still cumulates to 30–40 Pg C (on average 0.3–0.4 Pg C yr−1 or 13–25%). In RCP4.5 essentially no difference between the methods is found. Results from models using net transitions are furthermore found to be sensitive to model resolution.

Past and future carbon fluxes from land use change, shifting cultivation and wood harvest

Carbon emissions from anthropogenic land use (LU) and land use change (LUC) are quantified with a Dynamic Global Vegetation Model for the past and the 21st century following Representative Concentration Pathways (RCPs). Wood harvesting and parallel abandonment and expansion of agricultural land in areas of shifting cultivation are explicitly simulated (gross LUC) based on the Land Use Harmonization (LUH) dataset and a proposed alternative method that relies on minimum input data and generically accounts for gross LUC. Cumulative global LUC emissions are 72 GtC by 1850 and 243 GtC by 2004 and 27–151 GtC for the next 95 yr following the different RCP scenarios. The alternative method reproduces results based on LUH data with full transition information within <0.1 GtC/yr over the last decades and bears potential for applications in combination with other LU scenarios. In the last decade, shifting cultivation and wood harvest within remaining forests including slash each contributed 19% to the mean annual emissions of 1.2 GtC/yr. These factors, in combination with amplification effects under elevated CO2, contribute substantially to future emissions from LUC in all RCPs.

Global Projections of 21st Century Land-Use Changes in Regions Adjacent to Protected Areas

The conservation efficiency of Protected Areas (PA) is influenced by the health and characteristics of the surrounding landscape matrix. Fragmentation of adjacent lands interrupts ecological flows within PAs and will decrease the ability of species to shift their distribution as climate changes. For five periods across the 21st century, we assessed changes to the extent of primary land, secondary land, pasture and crop land projected to occur within 50 km buffers surrounding IUCN-designated PAs. Four scenarios of land-use were obtained from the Land-Use Harmonization Project, developed for the Intergovernmental Panel on Climate Change's Fifth Assessment Report (AR5). The scenarios project the continued decline of primary lands within buffers surrounding PAs. Substantial losses are projected to occur across buffer regions in the tropical forest biomes of Indo-Malayan and the Temperate Broadleaf forests of the Nearctic. A number of buffer regions are projected to have negligible primary land remaining by 2100, including those in the Afrotropic's Tropical/Subtropical Grassland/Savanna/Shrubland. From 2010–2050, secondary land is projected to increase within most buffer regions, although, as with pasture and crops within tropical and temperate forests, projections from the four land-use scenarios may diverge substantially in magnitude and direction of change. These scenarios demonstrate a range of alternate futures, and show that although effective mitigation strategies may reduce pressure on land surrounding PAs, these areas will contain an increasingly heterogeneous matrix of primary and human-modified landscapes. Successful management of buffer regions will be imperative to ensure effectiveness of PAs and to facilitate climate-induced shifts in species ranges.

Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands

In preparation for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the international community is developing new advanced Earth System Models (ESMs) to assess the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. In addition, four Representative Concentration Pathway (RCP) scenarios of the future (2005–2100) are being provided by four Integrated Assessment Model (IAM) teams to be used as input to the ESMs for future carbon-climate projections (Moss et al. 2010). The diversity of approaches and requirements among IAMs and ESMs for tracking land-use change, along with the dependence of model projections on land-use history, presents a challenge for effectively passing data between these communities and for smoothly transitioning from the historical estimates to future projections. Here, a harmonized set of land-use scenarios are presented that smoothly connects historical reconstructions of land use with future projections, in the format required by ESMs. The land-use harmonization strategy estimates fractional land-use patterns and underlying land-use transitions annually for the time period 1500–2100 at 0.5° × 0.5° resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500–2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the IAM implementations of the RCPs for the period 2005–2100. The computational method integrates these multiple data sources, while minimizing differences at the transition between the historical reconstruction ending conditions and IAM initial conditions, and working to preserve the future changes depicted by the IAMs at the grid cell level. This study for the first time harmonizes land-use history data together with future scenario information from multiple IAMs into a single consistent, spatially gridded, set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system.