By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were forecast. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering of the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.
ZHONG Fanglei
Based on the Global 1,000,000 Basic Geographic Data (2010) of the Resource and Environment Science Data Center of the Chinese Academy of Sciences, the railway and highway networks of Arctic countries (USA, Canada, Russia, Norway (including Greenland and the Faroe Islands), Denmark, Sweden, Finland, and Iceland) are extracted via ArcGIS. The data are stored separately by nation. The data format is the .shp format of ArcGIS, and the projection mode is GCS_WGS_1984. The railway network data are from http://www.resdc.cn/data.aspx?DATAID=208, and the highway network data are from http://www.resdc.cn/data.aspx?DATAID=207
YANG Linsheng, WANG Li
Arctic administrative boundary data sets include Arctic_National, Arctic_Provincial, and Arctic_Prefecture vector spatial data sets of arcti-bound countries and Its corresponding name, TYPE related attribute data :(LOCAL_NAME), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (CONTINENT) The data comes from the 1:1,000,000 ADC_WorldMap global data set, which is a comprehensive, up-to-date and seamless geographic digital data. The world map coordinate system is latitude and longitude, WGS84 datum surface, and the arctic data set is the special projection parameter for the arctic (North_Pole_Stereographic).
ADC WorldMap
"Coupling and Evolution of Hydrological-Ecological-Economic Processes in Heihe River Basin Governance under the Framework of Water Rights" (91125018) Project Data Convergence-The documents of the west of Taolai River water conservancy team project plan 1. Data summary: The documents of the west of Taolai River water conservancy team project plan 2. Data content: Taolai River water conservancy team project plan, including the project plan of reservoir irrigation and drainage in the west of the river region
WANG Zhongjing
1) Initial data of community characteristics and main plant biological characteristics of the grass-animal equilibrium stage of the test grassland in 1983; 2) Livestock management data of 4-5 grazing grasslands; 3) Observation data of diversity, productivity and functional group of different grazing grassland communities; 4) Observation data on the height, coverage, biomass, and flower morphology, tillering, and leaf characteristics of main plants in different grazing gradient grasslands 5) Observation data of soil nutrients and litter in different grazing grasslands.
ZHAO Chengzhang
1. The grassland animal husbandry production and management policies in the study area from 1954 to 2012 mainly include: 1) the time series of the formation and evolution of various policies; 2) the key policies related to herdsman's livestock activities and grassland management and utilization. 2. Residents' perception and response to pastoral socio-economic development policies, grassland management systems, ecological compensation policies, ecological restoration projects, and ecological environment status quo.
ZHAO Chengzhang
The data is a dataset of road distribution in Qinghai Lake basin, scale1: 250,000, projection: latitude and longitude, mainly including the spatial distribution and attribute data of main roads in Qinghai Lake basin, attribute fields: code (road code), name (road classification).
National Basic Geographic Information Center
I. Overview This data set contains socio-economic statistics of counties (cities) in the upper reaches of the Yellow River from 2000 to 2005. The data set is divided into basic conditions, comprehensive economics, agriculture, industry and infrastructure, education, health and social security, 4 There are 30 basic categories, including all the socio-economic statistical indicators. Ⅱ. Data processing description The data is stored in excel format, classified by province, with basic socio-economic statistics for each county. Ⅲ. Data content description This data set contains four basic classifications, namely basic situation, comprehensive economy, agriculture, industry and infrastructure, education, health and social security. The basic information includes the administrative area, the number of townships (towns), the number of villagers' committees, the total number of households at the end of the year, the number of rural households, the rural population, the number of employees at the end of the year, the number of rural employees, and the number of agricultural, forestry, animal husbandry and sideline fishermen The total power of agricultural machinery and local telephone users; the total economic categories include: the value added of the primary industry, value added of the secondary industry, revenue within the local fiscal budget, fiscal expenditure, the balance of savings deposits of urban and rural residents, and loans of financial institutions at the end of the year Balance; major categories of agriculture, industry and capital construction include: grain output, cotton output, oil output, total meat output, number of industrial enterprises above designated size, total industrial output value above designated size, and capital investment completed; education, health and social security The major categories include the number of students in ordinary middle schools, the number of students in primary schools, the number of beds in hospitals and health centers, the number of beds in social welfare homes, and the number of beds in social welfare homes. In some remote areas, some data are missing. Ⅳ. Data usage description Through this data set, the socio-economic problems of counties (cities) in the upper reaches of the Yellow River can be analyzed, and the socio-economic driving forces of certain natural processes can be analyzed and researched through this data set.
XUE Xian, DU Heqiang
The dataset is a vector map of administrative boundaries of rivers in the north slope of Tianshan Mountains, with a scale of 250,000, projection: longitude and latitude, data includes spatial data and attribute data, and attribute fields: Name (name of county boundary) and Code (administrative code).
National Basic Geographic Information Center
The data is the road distribution dataset of the river basins at the north slope of the Tianshan Mountains, with a scale of 250000 and a projection of latitude and longitude, including the spatial distribution and attribute data of the main roads in the river basins at the northern foot of the Tianshan Mountains, with attribute fields of code (road code) and Name (road classification).
National Basic Geographic Information Center
The data is the railway map of Qinghai Lake Basin, with a scale of 250,000, projection: latitude and longitude. The data includes spatial data and attribute data. The attribute field is code (railway code).
National Basic Geographic Information Center
The dataset is a vector map of the administrative boundary of Qinghai Lake Basin, with a scale of 250,000 and projection: latitude and longitude. The data includes spatial data and attribute data, mainly including the name and administrative code of the county boundary of Qinghai Lake Basin.
National Basic Geographic Information Center
The data is the railway distribution map of the chaidamu river basin, with a scale of 25,000 and coordinates of longitude and latitude. The data includes spatial data and attribute data. The attribute field is code.
National Basic Geographic Information Center
The data set is the qaidam river basin administrative boundary vector map, scale 250000, projection: longitude and latitude, the data contains spatial data and attribute data, mainly the qaidam river basin county boundary name and administrative code.
WU Lizong
The data is a distribution map of the qaidam river basin, with a scale of 250000 and projected longitude and latitude, including the spatial data and attribute data of the qaidam river basin. The attribute data fields are Area, Perimeter, WRRNM and WRRCD.
National Basic Geographic Information Center
The data is the dataset of the road distribution in the qaidam river basin, scale: 250,000, projection: longitude and latitude, mainly including the spatial distribution and attribute data of the main roads in the qaidam river basin, attribute fields: code (road code), Name (road classification).
National Basic Geographic Information Center
The data is a land cover dataset of the Qinghai Lake Basin, which was derived from the "China 1: 100,000 Land Use Dataset" in 2000. It was constructed based on Landsat MSS, TM and ETM remote sensing data within three years using satellite remote sensing. This data uses a hierarchical land cover classification system, which divides the country into 6 first-class categories (arable land, forest land, grassland, waters, urban and rural areas, industrial and mining, residential land and unused land), and 31 second-class categories. The attribute fields include: Area, Perimeter, Code (Land Code), Name (Land Type).
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
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