History Database Of The Global Environment Research Articles

Uncertainties of Global Historical Land Use Datasets in Pasture Reconstruction for the Tibetan Plateau

Global historical land use datasets have been widely used in global or regional environmental change studies. Historical pasture data are essential components of these spatially explicit global datasets, and their uncertainties have not been well evaluated. Using the livestock-based historical pasture dataset for the Tibetan Plateau (TP), we evaluated the uncertainties of these representative global historical land use datasets in pasture reconstruction for the TP over the past 300 years in terms of pasture area estimation and spatial pattern mapping. We found that only the Sustainability and the Global Environment (SAGE) dataset can roughly reflect the temporal and spatial characteristics of historical pasture changes on the TP. The History Database of the Global Environment (HYDE) version 3.2 and the Pongratz Julia (PJ) datasets overestimated pasture area for the TP dramatically, with a maximum area ratio of about 221% and 291%, respectively, and the Kaplan and Krumhardt 2010 (KK10) dataset underestimated pasture area for the TP dramatically, with a minimum area ratio of only 9%. As for the spatial pattern, all these global datasets overestimated the spatial scope of grazing activities obviously. The KK10 dataset unreasonably allocated pasture to forest areas in southeastern Tibet because only climate and soil factors were considered in assessing land suitability for grazing. Using population to estimate pasture area and only using natural factors to allocate pasture area into grids is unsuitable for the TP historical pasture reconstruction. In the future, more information directly related to grazing activities, e.g., the number of livestock and its spatial distribution, and social-cultural factors, including technology and diet, should be used for area estimation and spatial pattern mapping to improve the accuracy of pasture data in these global datasets.

A 1 km global cropland dataset from 10 000 BCE to 2100 CE

Abstract. Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately and systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution and a long time span. In this study, the first 1 km resolution global cropland proportion dataset for 10 000 BCE–2100 CE was produced. With the cropland map initialized in 2010 CE, we first harmonized the cropland demands extracted from the History Database of the Global Environment 3.2 (HYDE 3.2) and the Land-Use Harmonization 2 (LUH2) datasets and then spatially allocated the demands based on the combination of cropland suitability, kernel density, and other constraints. According to our maps, cropland originated from several independent centers and gradually spread to other regions, influenced by some important historical events. The spatial patterns of future cropland change differ in various scenarios due to the different socioeconomic pathways and mitigation levels. The global cropland area generally shows an increasing trend over the past years, from 0×106 km2 in 10 000 BCE to 2.8×106 km2 in 1500 CE, 6.2×106 km2 in 1850 CE, and 16.4×106 km2 in 2010 CE. It then follows diverse trajectories under future scenarios, with the growth rate ranging from 16.4 % to 82.4 % between 2010 CE and 2100 CE. There are large area disparities among different geographical regions. The mapping result coincides well with widely used datasets at present in both distribution pattern and total amount. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The spatiotemporally continuous and conceptually consistent global cropland dataset serves as a more comprehensive alternative for long-term earth system simulations and other precise analyses. The flexible and efficient harmonization and downscaling framework can be applied to specific regions or extended to other land use and cover types through the adjustable parameters and open model structure. The 1 km global cropland maps are available at https://doi.org/10.5281/zenodo.5105689 (Cao et al., 2021a).

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents.

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multitemporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values > 0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

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.

A historical reconstruction of cropland in China from 1900 to 2016

Abstract. A spatially explicit cropland distribution time-series dataset is the basis for the accurate assessment of biogeochemical processes in terrestrial ecosystems and their feedback to the climate system; however, this type of dataset is lacking in China. Existing cropland maps have a coarse resolution, are intermittently covered, or the data are inconsistent. We reconstructed a continuously covered cropland distribution dataset in China spanning from 1900 to 2016 by assimilating multiple data sources. In total, national cropland acreage expanded from 77.72 Mha in 1900 to a peak of 151.00 Mha in 1979, but it consistently decreased thereafter to 134.92 Mha in 2016. The cropland was primarily distributed in three historically cultivated plains in China: the Sichuan Plain, the Northern China Plain, and the Northeast China Plain. Cropland abandonment was approximately 43.12 Mha: it was mainly concentrated in the Northern China Plain and the Sichuan Plain and occurred during the 1990–2010 period. Cropland expansion was over 74.37 Mha: it was primarily found in the southeast, northern central, and northeast regions of China and occurred before 1950. In comparison, the national total and spatial distribution of cropland in the Food and Agriculture Organization (FAO) of the United Nations and the History Database of the Global Environment (HYDE) were distorted during the period from 1960 to 1980 due to the biased signal from the Chinese Agricultural Yearbook. We advocate that newly reconstructed cropland data, in which the bias has been corrected, should be used as the updated data for regional and global assessments, such as greenhouse gas emission accounting studies and food production simulations. The cropland dataset is available via an open-data repository (https://doi.org/10.6084/m9.figshare.13356680; Yu et al., 2020).

Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia

Abstract. Spatially explicit historical land cover datasets are essential not only for simulations of climate and environmental dynamics but also for projections of future land use, food security, climate, and biodiversity. However, widely used global datasets are developed for continental- to global-scale analysis and simulations. Their accuracy depends on the verification of more regional reconstruction results. This study collects cropland area data of each administrative unit (parish/municipality/county) in Scandinavia from multiple sources. The cropland area data are validated, calibrated, interpolated, and allocated into 1 km×1 km grid cells. Then, we develop a dataset with spatially explicit cropland area from 1690 to 1999. Results indicate that the cropland area increased from 1.82×106 ha to 6.71×106 ha from 1690 to 1950 and then decreased to 5.90×106 ha in 1999. Before 1810, cropland cover expanded in southern Scandinavia and remained stable in northern Scandinavia. From 1810 to 1910, northern Scandinavia experienced slight cropland expansion. The cropland area increased rapidly in the southern part of the study area before changing slightly. After 1950, the cropland areas began to decrease in most regions, especially in eastern Scandinavia. When comparing global datasets with this study, although the total Scandinavia cropland area is in agreement among SAGE (Center for Sustainability and the Global Environment), HYDE (History Database of the Global Environment ) 3.2, PJ (Pongratz Julia), and this study, the spatial patterns show considerable differences, except for in Denmark between HYDE 3.2 and this study. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.926591 (Wei et al., 2021).

Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES)

Abstract. Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from −66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Abstract. This paper presents an update and extension of HYDE, the History Database of the Global Environment (HYDE version 3.2). HYDE is an internally consistent combination of historical population estimates and allocation algorithms with time-dependent weighting maps for land use. Categories include cropland, with new distinctions for irrigated and rain-fed crops (other than rice) and irrigated and rain-fed rice. Grazing lands are also provided, divided into more intensively used pasture and less intensively used rangeland, and further specified with respect to conversion of natural vegetation to facilitate global change modellers. Population is represented by maps of total, urban, rural population, population density and built-up area. The period covered is 10 000 before Common Era (BCE) to 2015 Common Era (CE). All data can be downloaded from https://doi.org/10.17026/dans-25g-gez3. We estimate that global population increased from 4.4 million people (we also estimate a lower range < 0.01 and an upper range of 8.9 million) in 10 000 BCE to 7.257 billion in 2015 CE, resulting in a global population density increase from 0.03 persons (or capita, in short cap) km−2 (range 0–0.07) to almost 56 cap km−2 respectively. The urban built-up area evolved from almost zero to roughly 58 Mha in 2015 CE, still only less than 0.5 % of the total land surface of the globe. Cropland occupied approximately less than 1 % of the global land area (13 037 Mha, excluding Antarctica) for a long time period until 1 CE, quite similar to the grazing land area. In the following centuries the share of global cropland slowly grew to 2.2 % in 1700 CE (ca. 293 Mha, uncertainty range 220–367 Mha), 4.4 % in 1850 CE (578 Mha, range 522–637 Mha) and 12.2 % in 2015 CE (ca. 1591 Mha, range 1572–1604 Mha). Cropland can be further divided into rain-fed and irrigated land, and these categories can be further separated into rice and non-rice. Rain-fed croplands were much more common, with 2.2 % in 1700 CE (289 Mha, range 217–361 Mha), 4.2 % (549 Mha, range 496–606 Mha) in 1850 CE and 10.1 % (1316 Mha, range 1298–1325 Mha) in 2015 CE, while irrigated croplands used less than 0.05 % (4.3 Mha, range 3.1–5.5 Mha), 0.2 % (28 Mha, range 25–31 Mha) and 2.1 % (277 Mha, range 273–278 Mha) in 1700, 1850 and 2015 CE, respectively. We estimate the irrigated rice area (paddy) to be 0.1 % (13 Mha, range 9–16 Mha) in 1700 CE, 0.2 % (28 Mha, range 26–31 Mha) in 1850 CE and 0.9 % (118 Mha, range 117–120 Mha) in 2015 CE. The estimates for land used for grazing are much more uncertain. We estimate that the share of grazing land grew from 5.1 % in 1700 CE (667 Mha, range 507–820 Mha) to 9.6 % in 1850 CE (1192 Mha, range 1068–1304 Mha) and 24.9 % in 2015 CE (3241 Mha, range 3211–3270 Mha). To aid the modelling community we have divided land used for grazing into more intensively used pasture, less intensively used converted rangeland and less or unmanaged natural unconverted rangeland. Pasture occupied 1.1 % in 1700 CE (145 Mha, range 79–175 Mha), 1.9 % in 1850 CE (253 Mha, range 218–287 Mha) and 6.0 % (787 Mha, range 779–795 Mha) in 2015 CE, while rangelands usually occupied more space due to their occurrence in more arid regions and thus lower yields to sustain livestock. We estimate converted rangeland at 0.6 % in 1700 CE (82 Mha range 66–93 Mha), 1 % in 1850 CE (129 Mha range 118–136 Mha) and 2.4 % in 2015 CE (310 Mha range 306–312 Mha), while the unconverted natural rangelands occupied approximately 3.4 % in 1700 CE (437 Mha, range 334–533 Mha), 6.2 % in 1850 CE (810 Mha, range 733–881 Mha) and 16.5 % in 2015 CE (2145 Mha, range 2126–2164 Mha).

Soil carbon debt of 12,000 years of human land use

Human appropriation of land for agriculture has greatly altered the terrestrial carbon balance, creating a large but uncertain carbon debt in soils. Estimating the size and spatial distribution of soil organic carbon (SOC) loss due to land use and land cover change has been difficult but is a critical step in understanding whether SOC sequestration can be an effective climate mitigation strategy. In this study, a machine learning-based model was fitted using a global compilation of SOC data and the History Database of the Global Environment (HYDE) land use data in combination with climatic, landform and lithology covariates. Model results compared favorably with a global compilation of paired plot studies. Projection of this model onto a world without agriculture indicated a global carbon debt due to agriculture of 133 Pg C for the top 2 m of soil, with the rate of loss increasing dramatically in the past 200 years. The HYDE classes "grazing" and "cropland" contributed nearly equally to the loss of SOC. There were higher percent SOC losses on cropland but since more than twice as much land is grazed, slightly higher total losses were found from grazing land. Important spatial patterns of SOC loss were found: Hotspots of SOC loss coincided with some major cropping regions as well as semiarid grazing regions, while other major agricultural zones showed small losses and even net gains in SOC. This analysis has demonstrated that there are identifiable regions which can be targeted for SOC restoration efforts.

Spatiotemporal reconstruction of agricultural land cover in Nepal from 1970 to 2010

Agricultural land cover and changes in its extent are directly related to human activities. The rapidly growing population in Nepal has resulted in an increasing trend of agricultural land expansion, and can be used to determine the long-term status of agricultural land cover via reconstruction. This decadal study considered spatially explicit agricultural area, statistical records, population density, soil types, and climate and topography datasets. We collected available datasets and revised them on the basis of a spatiotemporal reconstruction of agricultural land cover. The different datasets were based on 30 × 30 m grid cells used for agricultural land suitability and allocation models. This historical agricultural land reconstruction revealed an increasing trend in agricultural area from 37.03 to 43.88 (103 km2), between 1970 and 2010. In this period, the agricultural land expanded and agriculture became more intensified, especially in the southern and middle parts of the country. A slightly decreasing trend was found in areas surrounding big cities. The evaluation of the results of our reconstruction and a comparison with satellite-based data in 2010 confirmed that this study established an effective methodology. The results of the study were in agreement with the FAO, History Database of the Global Environment 3.1 and 3.2, and the range of differences was found closer. These reconstruction datasets were suitable for use in climatic impact assessments of agricultural land change, as well as carbon cycle and climate modeling studies.

Accuracy comparison of gridded historical cultivated land data in Jiangsu and Anhui provinces

The spatial resolution of source data, the impact factor selection on the grid model and the size of the grid might be the main limitations of global land datasets applied on a regional scale. Quantitative studies of the impacts of rasterization on data accuracy can help improve data resolution and regional data accuracy. Through a case study of cropland data for Jiangsu and Anhui provinces in China, this research compared data accuracy with different data sources, rasterization methods, and grid sizes. First, we investigated the influence of different data sources on gridded data accuracy. The temporal trends of the History Database of the Global Environment (HYDE), Chinese Historical Cropland Data (CHCD), and Suwan Cropland Data (SWCD) datasets were more similar. However, different spatial resolutions of cropland source data in the CHCD and SWCD datasets revealed an average difference of 16.61% when provincial and county data were downscaled to a 10 × 10 km2 grid for comparison. Second, the influence of selection of the potential arable land reclamation rate and temperature factors, as well as the different processing methods for water factors, on accuracy of gridded datasets was investigated. Applying the reclamation rate of potential cropland to grid-processing increased the diversity of spatial distribution but resulted in only a slightly greater standard deviation, which increased by 4.05. Temperature factors only produced relative disparities within 10% and absolute disparities within 2 km2 over more than 90% of grid cells. For the different processing methods for water factors, the HYDE dataset distributed 70% more cropland in grid cells along riverbanks, at the abandoned Yellow River Estuary (located in Binhai County, Yancheng City, Jiangsu Province), and around Hongze Lake, than did the SWCD dataset. Finally, we explored the influence of different grid sizes. Absolute accuracy disparities by unit area for the year 2000 were within 0.1 km2 at a 1 km2 grid size, a 25% improvement over the 10 km2 grid size. Compared to the outcomes of other similar studies, this demonstrates that some model hypotheses and grid-processing methods in international land datasets are truly incongruent with actual land reclamation processes, at least in China. Combining the model-based methods with historical empirical data may be a better way to improve the accuracy of regional scale datasets. Exploring methods for the above aspects improved the accuracy of historical cropland gridded datasets for finer regional scales.

Regional climate model simulations for Europe at 6 and 0.2 k BP: sensitivity to changes in anthropogenic deforestation

Abstract. This study aims to evaluate the direct effects of anthropogenic deforestation on simulated climate at two contrasting periods in the Holocene, ~6 and ~0.2 k BP in Europe. We apply We apply the Rossby Centre regional climate model RCA3, a regional climate model with 50 km spatial resolution, for both time periods, considering three alternative descriptions of the past vegetation: (i) potential natural vegetation (V) simulated by the dynamic vegetation model LPJ-GUESS, (ii) potential vegetation with anthropogenic land use (deforestation) from the HYDE3.1 (History Database of the Global Environment) scenario (V + H3.1), and (iii) potential vegetation with anthropogenic land use from the KK10 scenario (V + KK10). The climate model results show that the simulated effects of deforestation depend on both local/regional climate and vegetation characteristics. At ~6 k BP the extent of simulated deforestation in Europe is generally small, but there are areas where deforestation is large enough to produce significant differences in summer temperatures of 0.5–1 °C. At ~0.2 k BP, extensive deforestation, particularly according to the KK10 model, leads to significant temperature differences in large parts of Europe in both winter and summer. In winter, deforestation leads to lower temperatures because of the differences in albedo between forested and unforested areas, particularly in the snow-covered regions. In summer, deforestation leads to higher temperatures in central and eastern Europe because evapotranspiration from unforested areas is lower than from forests. Summer evaporation is already limited in the southernmost parts of Europe under potential vegetation conditions and, therefore, cannot become much lower. Accordingly, the albedo effect dominates in southern Europe also in summer, which implies that deforestation causes a decrease in temperatures. Differences in summer temperature due to deforestation range from −1 °C in south-western Europe to +1 °C in eastern Europe. The choice of anthropogenic land-cover scenario has a significant influence on the simulated climate, but uncertainties in palaeoclimate proxy data for the two time periods do not allow for a definitive discrimination among climate model results.

Comparison between reconstructions of global anthropogenic land cover change over past two millennia

Three global datasets, the History Database of the Global Environment (HYDE), Kaplan and Krumhardt (KK) and Pongratz of reconstructed anthropogenic land cover change (ALCC) were introduced and compared in this paper. The HYDE dataset was reconstructed by Goldewijk and his colleagues at the National Institute of Public Health and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pongratz and her colleagues at the Max Planck Institute for Meteorology in Germany, covering AD 800–1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the reconstructions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.

Reconstructed cropland in the mid-eleventh century in the traditional agricultural area of China: implications of comparisons among datasets

Reconstructions of historical cropland area and spatial distribution are necessary for studying human effects on the environment due to agricultural development. To understand the current status of reconstructions of cropland area and its spatial distribution in the mid-eleventh century in the traditional agricultural area of China, we compared three available datasets: the historic cropland inventories-based HE dataset, the population-based History Database of the Global Environment (HYDE) dataset, and the PJ dataset. The results indicate that the HYDE and PJ datasets estimated the regional mean cropland area fraction (a ratio of cropland area to total land area, hereafter, CAF) for the study area to be 0.12 and 0.09, respectively, both of which were lower than the HE estimation of 0.18. Moreover, both the HYDE and PJ datasets have a poor ability to capture the spatial distribution of the historical CAF. The HYDE dataset overestimated the cropland area in North China and underestimated the cropland area in the Yangtze River reach. The HYDE dataset also overestimated the cropland area along the great rivers in North China. The PJ dataset underestimated the cropland area in the old agricultural area and overestimated the cropland area in the relatively new agricultural area. These incorrect spatial distributions from the HYDE and PJ datasets mainly resulted from the underestimation of the historical population and an incorrect approach for the spatial allocation of cropland within China. The incorrect approach was mainly derived from a poor understanding of the historic spatial distribution of cropland. Using the expert knowledge of local historians may be an effective method to reduce the uncertainties in the global historic cropland reconstruction.

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

ABSTRACTAim 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.

Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1

This paper describes 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 driving factors of global change. We estimate total and urban/rural population numbers, densities and fractions (including built-up area) for the Holocene, roughly the period 10 000 BC to AD 2000 with a spatial resolution of 5 min longitude/latitude. With a total global population increase from 2 to 6145 million people over that time span, resulting in a global population density increase of < 0.1 cap/km2to almost 46 cap/km2and a urban built-up area evolving from almost zero to 0.5 million km2(still only <0.5% of the total global land surface, but with a huge impact in terms of demands of food, services, building materials, etc.), it is clear that this must have had, and will continue to have, a profound influence on the Earth’s environment and its associated (climate) change. We hope that this data base can contribute to the Earth System Modelers community to gain better insight into long-term global change research.

Répartition mondiale des espaces défrichés à la Pré- et Protohistoire et estimation des rejets de carbone induits

The role of Pre- and Protohistoric anthropogenic land cover changes needs to be quantified i) to establish a baseline for comparison with current human impact on the environment and ii) to separate it from naturally occurring changes in our environment. Results are presented from the simple, adaptation-driven, spatially explicit Global Land Use and technological Evolution Simulator (GLUES) for pre-Bronze age demographic, technological and economic change. Using scaling parameters from the History Database of the Global Environment as well as GLUES-simulated population density and subsistence style, the land requirement for growing crops is estimated. The intrusion of cropland into potentially forested areas is translated into carbon loss due to deforestation with the dynamic global vegetation model VECODE. The land demand in important Prehistoric growth areas - converted from mostly forested areas - led to large-scale regional (country size) deforestation of up to 11% of the potential forest. In total, 29 Gt carbon were lost from global forests between 10 000 BC and 2000 BC and were replaced by crops; this value is consistent with other estimates of Prehistoric deforestation. The generation of realistic (agri-)cultural development trajectories at a regional resolution is a major strength of GLUES. Most of the pre-Bronze age deforestation is simulated in a broad farming belt from Central Europe via India to China. Regional carbon loss is, e.g., 5 Gt in Europe and the Mediterranean, 6 Gt on the Indian subcontinent, 18 Gt in East and Southeast Asia, or 2.3 Gt in subsaharan Africa.

Simulation of the relative impact of land cover and carbon dioxide to climate change from 1700 to 2100

The contribution of land cover change (relative to increased CO2) to climate change at the global scale is investigated using the Global Environmental and Ecological Simulations of Interactive Systems (GENESIS) climate model with an interactive BIOME vegetation model based on simulations at 1700 and 2100. Three 40‐year model equilibrium simulations were performed. The first experiment specified carbon dioxide levels of 280 ppmv without human‐induced land use (1700); the second experiment specified carbon dioxide levels of 690 ppmv without human‐induced land use; the third experiment specified carbon dioxide levels of 690 ppmv and included patterns of extensive human land use (as projected for 2100). Data on the extent of human modification to the land at different times were obtained from the History Database of the Global Environment (HYDE) for 1700 and the Integrated Model to Assess the Global Environment (IMAGE2.2) for 2100. The IMAGE model projects land cover changes in response to demographic and economic activity. The projection based on the A1b scenario of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios was used for this work. As expected, statistically significant (95% level) climate changes in response to carbon dioxide levels were widespread. In contrast, the impact of the land cover change appears to reduce the spatial extent and sometimes magnitude of the changes in temperature due to increased carbon dioxide. Regions where land cover change explained more than 60% of the combined signal due to an increase in CO2 and land cover change are confined to the Northern Hemisphere for near‐surface temperature. Projected land cover changes produced warming (but smaller than that produced by CO2) in some regions and cooling in other regions. The warming, which is about 4 K (seasonal) in some mid to high latitudes, is dominated by physiological factors altering the balance of latent to sensible heating rather than morphological mechanisms. The simulated changes in precipitation are more complex.

Three Centuries of Global Population Growth: A Spatial Referenced Population (Density) Database for 1700–2000

The Earth’s surface has changed considerably over the past centuries. Since the start of the Industrial Revolution in the early 1700s, humans from the “Old World” started to colonize the “New World”. The colonization processes lead to major changes in global land use and land cover. Large parts of the original land cover have been altered (e.g., deforestation), leading to extra emissions of GHG’s to the atmosphere and enhancing global climate change. The spatial and temporal aspects are still not very well known. More and more global integrated environmental assessments concerning global sustainability require long time series of global change indicators, of which population is an important one. This study presents an update of the geo-referenced historical population maps for the period 1700–2000, part of the History Database of the Global Environment (HYDE), which can be used in integrated models of global change and/or global sustainability.

Estimating global land use change over the past 300 years: The HYDE Database

Testing against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate system, these models should aim the simulation of the land use dynamics for long periods, i.e., spanning decades up to a century. Developing such models requires understanding of past and current trends and is therefore strongly data dependent. For this purpose, a history database of the global environment has been developed: HYDE. This paper describes and analyzes parts of HYDE version 2.0, presenting historical population and land use patterns for the past 300 years. Results suggest, among other things, a global increase of cropland area from 265 million ha in 1700 to 1471 million ha in 1990, while the area of pasture has increased more than six fold from 524 to 3451 million ha. In general, the increase of man‐made agricultural land took place at the expense of natural grasslands and to a lesser extent of forests. There are differences between the several regions in the temporal pace of these land use conversions. The temperate/developed regions of Canada, United States, USSR, and Oceania appear to have had their strongest increase during the 19th century, while most of the tropical/developing regions witnessed the largest land use conversions at the end of the last century. Results of this analysis can be used to test integrated models of global change and are available at http://www.rivm.nl/env/int/hyde/.