Accuracy assessment of global historical cropland datasets based on regional reconstructed historical data—A case study in Northeast China

Historical cropland datasets are fundamental for quantifying the effects of human land use activities on climatic change and the carbon cycle. Two representative global land-use datasets, the Global Land Use Database (termed SAGE dataset) and the Historical Database of the Global Environment (termed HYDE dataset) have been established and used widely. Despite improvement of data quality and methodologies for extracting historical land use information, certain dataset limitations exist that need to be quantified and communicated to users so that they can make informed decisions on whether and how these land-use products should be used. The Cropland data of Northeast China (CNEC) is based on calibrated historical data and a multi-sourced data conversion model, and reconstructs cropland cover change in Northeast China over the last 300 years. Using the CNEC as a reference, we evaluated the accuracy of cropland cover for SAGE and HYDE in Northeast China at spatial scales ranging from the entire Northeast China to provinces and even individual raster grid cells. Neither SAGE nor HYDE reflects real historical land reclamation. Cropland areas in SAGE are overestimated by 20.98 times in 1700 to 1.6 times in 1990. Although HYDE is better, there are significant disagreements in cropland area and distribution between HYDE and CNEC, especially in the 18th and 19th centuries. The proportion of total grid cells whose relative error was greater than 100% was 63.55% in 1700 and 53.27% in 1780. Global cropland dataset errors over Northeast China originate mainly from both the reverse calculation method for historical cropland data based on modern spatial patterns, and modern land-use outputs from satellite data.     

Climate effects of the GlobeLand30 land cover dataset on the Beijing Climate Center climate model simulations

Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution (30 m) global land cover dataset (GlobeLand30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model (BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the GlobeLand30 data in the model. First, the GlobeLand30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type (PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution GlobeLand30 land cover type and area percentage with the coarser model grid resolutions globally. The GlobeLand30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies (lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the GlobeLand30-based data were used in the BCC_CSM atmosphere model. The results suggest that the GlobeLand30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.     

Carbon emissions induced by cropland expansion in Northeast China during the past 300 years

Assessments of the impacts of land use and land cover changes(LUCC) on the terrestrial carbon budget, atmospheric CO2 concentration, and CO2-related climatic change are important to understand the environmental effects of LUCC and provide information about the effects of historical carbon emissions. Using regional land cover reconstructions from historical records, with a bookkeeping model, we estimated the carbon sink changes caused by historical cropland expansion in Northeast China during the past 300 years. The conclusions are as follows:(1) There was a dramatic land reclamation of cropland during the past 300 years in Northeast China. Approximately 26% of the natural land was cultivated, and 38% of the grassland and 20% of the forest and shrubland were converted to cropland.(2) The carbon emission induced by cropland expansion between 1683 and 1980 was 1.06–2.55 Pg C, and the estimation from the moderate scenario was 1.45 Pg C. The carbon emissions of the soil carbon pool was larger than that from the vegetation carbon pool and comprised more than 2/3 of the total carbon emissions.(3) The carbon emissions of the three provinces in Northeast China were different. Heilongjiang Province had the largest carbon emissions, and Jilin Province had the second largest emissions.(4) The primary source of carbon emissions was forest reclamation(taking 60% of the total emissions in the moderate scenario), the secondary source was grassland cultivation(taking 27%), and the tertiary sources were shrubland and wetland reclamation(taking 13%). Examination on the data accuracy revealed that the high-resolution regional land cover data allowed the carbon budget to be evaluated at the county level and improved the precision of the results. The carbon emission estimation in this study was lower than those in previous studies because of the improved land use data quality and various types of land use change considered.     

Global cultivated land mapping at 30 m spatial resolution

Cultivated land is one of the most important types of land cover in the global mapping of land cover, and its variation influences economic development, food security, and ecological environment protection. Existing products of global cultivated land mapping have a low resolution, and high spatial resolution products are in demand. This study uses global remote sensing image datasets in 2000/2010 with a spatial resolution of 30 m (Landsat TM/ETM+, HJ-1), MODIS 250 m NDVI time-serial data, and many types of reference data. An three-layer extraction method based on pixels, objects, and knowledge (POK) was adopted to ease cultivated land extraction in global-scale 30 m images, i.e., cultivated land classification based on pixel-scale multi-feature optimization, cultivated land automatic identification based on objects, and interactive object processing based on information service and priori knowledge. Global 30 m cultivated land mapping was accomplished for the two reference years (2000 and 2010), and statistical analysis was conducted on the data. Results showed that the total cultivated land area was 1.903 billion ha and 1.960 billion ha, respectively. Accuracy assessments showed that overall accuracy of global cultivated land mapping are higher than 92% for both the two reference years. The global cultivated land products in 2000/2010 developed in this research are superior to their international counterparts in terms of spatial resolution and classification accuracy. They also provide significant basic data on global food security, ecological environment supervision, and global change.     

太湖流域上游土地利用变化机制的实证分析--以浙江省安吉县为例

近10年来,太湖流域上游地区经济发展逐步趋于活跃,受此影响,区域人口-资源、环境-发展(PRED)出现了新的特点与趋势。基于上游地区土地利用/覆被变化对流域整体水环境与生态安全格局所具有的重要意义,本文选择安吉县作为典型案例,分析其区域土地利用的基本特征,土地利用变化与景观格局动态的过程,以及驱动因素与机制,由此揭示山区土地利用/覆被变化的一般性规律,为太湖流域上游地区经济发展与土地利用、环境保护相互协调,提供决策借鉴与依据。     

Modeling the climatic effects of the land use/cover change in eastern China

This study aims to quantify the contribution of land use/cover change (LUCC) during the last three decades to climate change conditions in eastern China. The effects of farmland expansion in Northeast China, grassland degradation in Northwest China, and deforestation in South China were simulated using the Weather Research and Forecasting (WRF) model in addition to the latest actual land cover datasets. The simulated results show that when forestland is converted to farmland, the air temperature decreased owing to an increase in surface albedo in Northeast China. The climatic effect of grassland degradation on the Loess Plateau was insignificant because of the negligible difference in albedo between grassland and cropland. In South China, deforestation generally led to a decrease in temperature. Furthermore, the temperature decrease caused by the increase in albedo counteracted the warming effects of the evapotranspiration decrease, so the summer temperature change was not significant in South China. Excluding the effects of urbanization in the North China Plain, the LUCC effects across the entire region of East China presented an overall cooling trend. However, the variation in temperature scale and magnitude was less in summer than that in winter. This result is due mainly to the cooling caused by the increase in albedo offset partly by the increase in temperature caused by the decrease in evaporation in summer. Summer precipitation showed a trend of increasing–decreasing–increasing from southeast to northwest after LUCC, which was induced mainly by the decrease in surface roughness and cyclone circulations appearing northwest of Northeast China, in the middle of the Loess Plateau, and in Yunnan province at 700 hPa after forests were converted into farmland. All results will be instructive for understanding the influence of LUCC on regional climate and future land planning in practice.     

Quantifying the impacts of climate change and land use/cover change on runoff in the lower Connecticut River Basin

Climate change and land use and cover change (LUCC) have had great impacts on watershed hydrological processes. Although previous studies have focused on quantitative assessment of the impacts of climate change and human activities on decreasing run‐off change, few studies have examined regions that have significant increasing run‐off due to both climate variability and land cover change. We show that annual run‐off had a significant increasing trend from 1956 to 2014 in the U.S. lower Connecticut River Basin. Abrupt change point years of annual run‐off for four subbasins are detected by nonparametric Mann–Kendall–Sneyers test and reconfirmed by the double mass curve. We then divide the study period into 2 subperiods at the abrupt change point year in the early 1970s for each subbasin. The Choudhury–Yang equation based on Budyko hypothesis was used to calculate precipitation and potential evapotranspiration, and landscape elasticities of run‐off. The results show that the difference in mean annual run‐off between 2 subperiods for each subbasin ranged from 102 to 165.6 mm. Climate variations were the primary drivers of increasing run‐off in this region. Quantitative contributions of precipitation and potential evapotranspiration in all subbasins are 106.5% and ?3.6% on average, respectively. However, LUCC contributed both positively and negatively to run‐off: ?18.6%, ?13.3%, and 10.1% and 9.9% for 4 subbasins. This may be attributed to historical LUCC occurring after the abrupt change point in each subbasin. Our results provide critical insight on the hydrological dynamics of north‐east tidal river systems to communities and policymakers engaged in water resources management in this region.     

A multi-resolution global land cover dataset through multisource data aggregation

Recent developments of 30 m global land characterization datasets(e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover mapping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC(Finer Resolution Observation and Monitoring-Global Land Cover) and FROM-GLC-seg(Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area(NL-ISA) and MODIS urban extent(MODIS-urban), to produce an improved 30 m global land cover map—FROM-GLC-agg(Aggregation). It was post-processed using additional coarse resolution datasets(i.e., MCD12Q1, GlobCover2009, MOD44 W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion aggregation approaches were employed to create a multi-resolution hierarchy(i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map(at 30 m) and the three maps subsequently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.     

Evaluating the accuracy of Chinese pasture data in global historical land use datasets

Global historical land use datasets are widely used in global environmental change studies; however, uncertainties in the included pasture data have not been evaluated. In this study, using trend, relative difference ratio(RDR), and spatial comparisons, we evaluated the accuracy of China pasture data in the HYDE(versions 3.1 and 3.2), SAGE, and PJ datasets using historical document-based reconstructions, including the land use datasets for China covering 1935–1997(hereafter Ge-dataset)and Northeast China covering the 1700 s–2000(hereafter Ye-dataset), and the satellite-based China's Land-Use/cover Datasets(CLUDs) for 1980–2015. Four important results were obtained.(1) China pasture area in the HYDE, SAGE, and PJ datasets and grassland area in the Ge-dataset, Ye-dataset, and CLUDs show both disparate trends and large differences in absolute values.(2)Spatially, 50.9%, 52.8%, and 63.0% pasture/grassland grids in 2000 had RDRs greater than 60% between HYDE3.1, HYDE3.2,and SAGE datasets, and CLUDs, respectively. The percentage of grids with RDRs less than 20% were 24.9%, 26.7%, and 16.0%,respectively.(3) Based on HYDE3.2, the spatial distribution of pasture in Northeast China over the last 300 year has expanded,which is in contrast to the results from the Ye-dataset, which shows the spatial distribution of grassland shrinking because of human reclamation activities.(4) The large contrasts between the global datasets and Chinese native datasets are due to differences in pasture/grassland definitions, land use practices, and spatial reconstruction methods.     

Global mapping of artificial surfaces at 30-m resolution

Urbanization is expected to accelerate with population growth and economic development at the global scale. The artificial surface is the main land cover form of urbanization. On the one hand, urbanization provides spaces for industry, economic activities and residence. On the other hand, artificial surfaces change the earth surface to a large extent, thus significantly affecting natural processes such as the heat exchange, hydrological processes and ecological balance. Therefore, the global mapping of artificial surfaces is valuable for both natural science and social science. This study produced the global artificial surface maps at 30-m resolution for two base-years using the satellite images acquired around 2000 and 2010. First, we proposed a new definition of “artificial surface” based on patch level with consideration of its geographic meaning and image features at 30-m resolution. Second, pixel-based and object-based image processing techniques were combined for the extraction of artificial surface patches. Finally, human editing and a quality control system were employed to guarantee the quality of global mapping. Independent accuracy assessments show that the user’s accuracy of this product is higher than 80%. It can be concluded that the product is the most reliable one among all the available global datasets of artificial surfaces (or related types). The data can significantly contribute to various research fields, such as urbanization and ecosystem assessment.     

Relationship between nitrogen deposition and LUCC and its impact on terrestrial ecosystem carbon budgets in China

Increased nitrogen(N) deposition and land-use and land-cover change(LUCC) have influenced the terrestrial ecosystem carbon budget in China over the past few decades.However,the coupling effects of N deposition and LUCC on the carbon cycle remain unclear.This study first evaluated the effects of LUCC on N deposition based on estimated N deposition data from NO_2 column remote sensing data and the GlobeLand30 LUCC dataset,and then assessed the coupling effects of N deposition and LUCC on carbon budgets in China based on a terrestrial ecosystem process-based model.The results showed that the average rate of increase in N deposition in China was 0.35 Tg N yr~(-1)(Tg = 10~(12) g),which caused net primary production(NPP) and net ecosystem production(NEP) to rise by 92.2 Tg C yr~(-1) and 46.9 Tg C yr~(-1),respectively.The effects of LUCC reduced N deposition by 0.21 GgNyr~(-1)(Gg= 10~9g).The land changed from forest to cropland had the greatest rate of increase in N deposition among all types of land-cover change.Changes from cropland to forest slowed the rate of N deposition increase the most.Generally,the change in N deposition resulting from LUCC reduced NPP and NEP by 0.7 and 0.4 Gg C yr~(-1),respectively.Compared with the total effects of N deposition on NPP and NEP,N deposition changes caused by LUCC had a limited aggregate effect on the C budget.     

Climate change analysis on historical watershed‐scale precipitation by means of long‐term dynamical downscaling

A methodology based on long‐term dynamical downscaling to analyse climate change effects on watershed‐scale precipitation during a historical period is proposed in this study. The reliability and applicability of the methodology were investigated based on the long‐term dynamical downscaling results. For an application of the proposed methodology, two study watersheds in Northern California were selected: the Upper Feather River watershed and the Yuba River watershed. Then, precipitation was reconstructed at 3‐km spatial resolution and hourly intervals over the study watersheds for 141 water years from 1 October 1871 to 30 September 2012 by dynamically downscaling a long‐term atmospheric reanalysis dataset, 20th century global reanalysis version 2 by means of a regional climate model. The reconstructed precipitation was compared against observed precipitation, in order to assess the applicability of the proposed methodology for the reconstruction of watershed‐scale precipitation and to validate this methodology. The validation shows that the reconstructed precipitation is in good agreement with observation data. Moreover, the differences between the reconstructed precipitation and the corresponding observations do not significantly change through the historical period. After the validation, climate change analysis was conducted based on the reconstructed precipitation. Through this analysis, it was found that basin‐average precipitation has increased significantly over both of the study watersheds during the historical period. An upward trend in monthly basin‐average precipitation is not significant in wet months except February while it is significant in dry months of the year. Furthermore, peak values of basin‐average precipitation are also on an upward trend over the study watersheds. The upward trend in peak basin‐average precipitation is more significant during a shorter duration. Copyright © 2016 John Wiley & Sons, Ltd.     

High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China’s HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.     

Simulation of urban expansion based on cellular automata and maximum entropy model

Urban expansion is a hot topic in land use/land cover change(LUCC) researches. In this paper, maximum entropy model and cellular automata(CA) model are coupled into a new CA model(Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps.Firstly, Maxent-CA model was built by using remote sensing data of China in 2000(basic data) and spatial variables(such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples,neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio(sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution.Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes.     

太湖流域LUCC对水文过程的影响

基于1971年枯水年、1989年丰水年、2000年平水年3类典型代表年的逐日降雨量、逐日蒸发量以及不同时期地表覆盖遥感分类数据,以城市化快速发展地表覆盖变化明显的太湖流域为研究区域,利用太湖流域河网水量模型进行了土地利用/覆被变化的水文响应研究,分析了太湖流域1990 2000年与2000 2006年间的土地利用/覆被变化及其对水位过程的影响.不仅有利于对城市化地区水文特征变化规律深入了解,也为典型城市化地区防洪减灾提供科学可靠的依据.研究表明,太湖流域城镇化进程的加快引起了土地利用/覆被变化的主要表现是水田、水域等面积向城镇面积转化,城镇化进程加快,2000 2006年期间的城镇化速度大于1990 2000年间;下垫面的变化对太湖流域水文过程产生了明显的影响,随着城市化进程地表覆盖的变化,水位有整体升高的趋势,并且增幅加大,与城镇化速率变化趋势相一致,城镇化程度高的地方水位上升更为明显;降雨量也是水位过程的影响因素之一.     

Sensitivity of surface air temperature change to land use/cover types in China

Using CRU high resolution grid observational temperature and ERA40 reanalysis surface air temperature data during 1960–1999, we investigated the sensitivity of surface air temperature change to land use/cover types in China by subtracting the reanalysis from the observed surface air temperature (observation minus reanalysis, OMR). The results show that there is a stable and systemic impact of land use/cover types on surface air temperature. The surface warming of each land use/cover type reacted differently to global warming. The OMR trends of unused land (⩾0.17 °C/decade), mainly comprised by sandy land, Gobi and bare rock gravel land, are obviously larger than those of the other land use/cover types. The OMR over grassland, farmland and construction land shows a moderate decadal warmingabout 0.12°C/decade, 0.10°C/decade, 0.12°C/decade, respectively. Woodland areas do not show a significant warming trend (0.06°C/decade). The overall assessment indicates that the surface warming is larger for areas that are barren and anthropogenically developed. The better the vegetation cover, the smaller the OMR warming trend. Responses of surface air temperature to land use/cover types with similar physical and chemical properties and biological processes have no significant difference. The surface air temperature would not react significantly until the intensity of land cover changes reach a certain degree. Within the same land use/cover type, areas in eastern China with intensive human activities exhibit larger warming trend. The results provide observational evidence for modeling research on the impact of land use/cover change on regional climate. Thus, projecting further surface climate of China in regional scale should not only take greenhouse gas increase into account, but also consider the impact of land use/cover types and land cover change. Supported by National Basic Research Program of China (Grant No. 2005CB422006), National Natural Science Foundation of China (Grant Nos. 90202012, 40771206)     

Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River,Northwest China

Climate change and land use/cover change (LUCC) are two factors that produce major impacts on hydrological processes. Understanding and quantifying their respective influence is of great importance for water resources management and socioeconomic activities as well as policy and planning for sustainable development. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated and validated in upper stream of the Heihe River in Northwest China. The reliability of the SWAT model was corroborated in terms of the Nash–Sutcliffe efficiency (NSE), the correlation coefficient (R), and the relative bias error (BIAS). The findings proposed a new method employing statistical separation procedures using a physically based modeling system for identifying the individual impacts of climate change and LUCC on hydrology processes, in particular on the aspects of runoff and evapotranspiration (ET). The results confirmed that SWAT was a powerful and accurate model for diagnosis of a key challenge facing the Heihe River Basin. The model assessment metrics, NSE, R, and BIAS, in the data were 0.91%, 0.95%, and 1.14%, respectively, for the calibration period and 0.90%, 0.96%, and ?0.15%, respectively, for the validation period. An assessment of climate change possibility showed that precipitation, runoff, and air temperature exhibited upward trends with a rate of 15.7 mm, 6.1 mm, and 0.38 °C per decade for the 1980 to 2010 period, respectively. Evaluation of LUCC showed that the changes in growth of vegetation, including forestland, grassland, and the shrub area have increased gradually while the barren area has decreased. The integrated effects of LUCC and climate change increased runoff and ET values by 3.2% and 6.6% of the total runoff and ET, respectively. Climate change outweighed the impact of LUCC, thus showing respective increases in runoff and ET of about 107.3% and 81.2% of the total changes. The LUCC influence appeared to be modest by comparison and showed about ?7.3% and 18.8% changes relative to the totals, respectively. The increase in runoff caused by climate change factors is more than the offsetting decreases resulting from LUCC. The outcomes of this study show that the climate factors accounted for the notable effects more significantly than LUCC on hydrological processes in the upper stream of the Heihe River.     

AntarcticaLC2000: The new Antarctic land cover database for the year 2000

Antarctica plays a key role in global energy balance and sea level change. It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differences of land cover has usually been overlooked. Land cover in Antarctica is one of the most important drivers of changes in the Earth system. Detailed land cover information over the Antarctic region is necessary as spatial resolution improves in land process models. However, there is a lack of complete Antarctic land cover dataset derived from a consistent data source. To fill this data gap, we have produced a database named Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. A total of 4067 validation sample units were collected through visual interpretation in a stratified random sampling manner. An overall accuracy of 92.3% and the Kappa coefficient of 0.836 were achieved. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73268.81 km² (0.537%), 225937.26 km² (1.656%), and 13345460.41 km² (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality. These indicate that AntarcticaLC2000, the new land cover dataset for Antarctica entirely derived from satellite data, is a reliable product for a broad spectrum of applications.     

Projected 21st century climate change on snow conditions over Shasta Dam watershed by means of dynamical downscaling

Snow is an important component of the Earth's climate system and is particularly vulnerable to global warming. It has been suggested that warmer temperatures may cause significant declines in snow water content and snow cover duration. In this study, snowfall and snowmelt were projected by means of a regional climate model that was coupled to a physically based snow model over Shasta Dam watershed to assess changes in snow water content and snow cover duration during the 21st century. This physically based snow model requires both physical data and future climate projections. These physical data include topography, soils, vegetation, and land use/land cover, which were collected from associated organizations. The future climate projections were dynamically downscaled by means of the regional climate model under 4 emission scenarios simulated by 2 general circulation models (fifth‐generation of the ECHAM general circulation model and the third‐generation atmospheric general circulation model). The downscaled future projections were bias corrected before projecting snowfall and snowmelt processes over Shasta Dam watershed during 2010–2099. This study's results agree with those of previous studies that projected snow water equivalent is decreasing by 50–80% whereas the fraction of precipitation falling as snowfall is decreasing by 15% to 20%. The obtained projection results show that future snow water content will change in both time and space. Furthermore, the results confirm that physical data such as topography, land cover, and atmospheric–hydrologic data are instrumental in the studies on the impact of climate change on the water resources of a region.     

Spatial patterns of vegetation and climate in the North China Plain during the Last Glacial Maximum and Holocene climatic optimum

Reconstructing the spatial patterns of regional climate and vegetation during specific intervals in the past is important for assessing the possible responses of the ecological environment under future global warming scenarios. In this study, we reconstructed the history of regional vegetation and climate based on six radiocarbon-dated pollen records from the North China Plain. Combining the results with existing pollen records, we reconstruct the paleoenvironment of the North China Plain during the Last Glacial Maximum(LGM) and the Holocene Climatic Optimum(HCO). The results show that changes in the regional vegetation since the LGM were primarily determined by climatic conditions, the geomorphic landscape and by human activity.During the LGM, the climate was cold and dry; mixed broadleaf-coniferous forest and deciduous-evergreen broadleaf forest developed in the southern mountains, and cold-resistant coniferous forest and mixed broadleaf-coniferous forest were present in the northern mountains. The forest cover was relatively low, with mesophytic and hygrophilous meadow occupying the southern part of the plain, and temperate grassland and desert steppe were distributed in the north; Chenopodiaceae-dominated halophytes grew on the exposed continental shelf of the Bohai Sea and Yellow Sea. During the HCO, the climate was warm and wet;deciduous broadleaf forest and deciduous-evergreen broadleaf forest, with subtropical species, developed in the southern mountains, and deciduous broadleaf forest with thermophilic species was present in northern mountains. Although the degree of forest cover was greater than during the LGM, the vegetation of the plain area was still dominated by herbs, while halophytes had migrated inland due to sea level rise. In addition, the expansion of human activities, especially the intensification of cultivation,had a significant influence on the natural vegetation. Our results provide data and a scientific basis for paleoclimate modelling and regional carbon cycle assessment in north China, with implications for predicting changes in the ecological environment under future global warming scenarios.