The population dataset of the Heihe River Basin (2000-2009)

This set of data mainly includes the demographic data of 12 counties in 6 prefecture-level cities of Qinghai, Gansu and Inner Mongolia in Heihe River Basin, covering the time period of 2000-2009. The data source is the local statistical yearbook, which mainly includes: Statistical Bureau of Suzhou District. Statistical Yearbook of Suzhou. 2004-2009; Yumen Statistical Bureau. Yumen Statistical Yearbook. 2000-2008; Jinta County Statistical Bureau. Jinta County Statistical Yearbook. 2004-2009; Gaotai Statistical Bureau. Gaotai Statistical Yearbook. 2000-2007; Shandan County Statistical Bureau. Shandan County Statistical Yearbook. 2000-2009; Sunan Yugur Statistical Bureau. Statistical Yearbook of Sunan Yugur Autonomous County. 2004-2009; Minle County Statistical Bureau. Minle County Statistical Yearbook. 2004-2009; Shandan County Statistical Bureau. Shandan County Statistical Yearbook. 2000-2009; Linze County Statistical Bureau. Linze County Statistical Yearbook. 2000-2009; Ejin Banner Statistical Bureau. Ejin Banner Statistical Yearbook. 1990-2005; Qilian County Statistical Bureau. Qilian County National Economic Statistics. 2004-2009; Part of the data of Zhangye City comes from the basic social and economic situation of townships of Zhangye City in 2005. Data of Jiayuguan City is derived from the CNKI statistical data database of China National Knowledge Infrastructure, and only contains some county-level data. Data Content Description: The data mainly includes three population indicators of 12 counties in the basin, including Ganzhou District, Gaotai County, Shandan County, Minle County, Linze County, Sunan Yugur Autonomous County, Jinta County, Sunzhou District and Yumen City, Jiayuguan City, Qilian County, and Ejin Banner. The population indicators are permanent population, agricultural population and non-agricultural population at the end of the year. It is divided into two levels: county level and township level. The statistics currently available are: County level: Ejina Banner: 2006-2009: resident population, agricultural population, non-agricultural population at the end of each year Ganzhou District: 2009: agricultural population, non-agricultural population of the year; Gaotai County: 2009: agricultural population, non-agricultural population of the year; Sunan: 2000-2009: permanent population, agricultural population, non-agricultural population at the end of each year; Minle County: 2009: permanent population, agricultural population, non-agricultural population at the end of the year; Linze: 2009: permanent population, agricultural population, non-agricultural population at the end of the year; Yumen City: 2000-2005: permanent population, agricultural population, non-agricultural population at the end of each year; Township level: Ejin Banner: 2000-2005: permanent population, agricultural population, non-agricultural population at the end of the year; Ganzhou District: 2000-2008: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Gaotai County: 2000-2004, 2006, 2007: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Shandan County: 2000-2007: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Minle County: 2000-2008: permanent population, agricultural population, non-agricultural population at the end of the year; Jinta County: 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Yumen City: 2006-2008: permanent population, agricultural population, non-agricultural population at the end of the year; Suzhou District 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Qilian County: 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Permanent population at the end of the year, agricultural population, non-agricultural population County level township level county level township level county level township level Ejin Banner:2006-2009 2000-2005 2006-2009 2000-2005 2006-2009 2000-2005 Ganzhou District 2000-2009 2009 2000-2008 2009 2000-2008 Gaotai County 2000-2004、 2006、2007、2009 2009 2000-2004、 2006、2007 2009 2000-2004、 2006、2007 Shandan County 2000-2007、2009 2000-2007 2000-2007 Sunan County 2000-2009 2000-2009 2000-2009 Minle County 2009 2000-2008 2009 2000-2008 2009 2000-2008 Linze County 2009 2009 2009 Jinta County 2004-2009 2004-2009 2004-2009 Sunzhou District 2004-2009 2004-2009 2004-2009 Qilian County 2004-2009 2004-2009 2004-2009 Yumen City 2000-2005 2006-2008 2000-2005 2006-2008 2000-2005 2006-2008

0 2020-06-08

Landcover dataset of the Shulehe River Basin (2000)

The data is the Shule River Basin land cover dataset, which is derived from "China's 1: 100,000 Land Use Data Set" in 2000. It is based on Landsat MSS, TM and ETM remote sensing data within three years by satellite remote sensing. 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. The attribute fields include: Area, Perimeter, Code(Land code), Name (land type).

0 2020-06-08

Geo risk index along the " Belt and Road Initiative" (2017)

"One belt, one road" along the lines of risk rating, credit risk rating and Moodie's national sovereignty rating reflects the structure of sovereign risk in every country. The rating of Moodie's national sovereignty is from the highest Aaa to the lowest C level, and there are twenty-one levels. Data source: organized by the author. Data quality is good. The rating level is divided into two parts, including investment level and speculation level. AAA level is the highest, which is the sovereign rating of excellent level. It means the highest credit quality and the lowest credit risk. The interest payment has sufficient guarantee and the principal is safe. The factors that guarantee the repayment of principal and interest are predictable even if they change. The distribution position is stable. C is the lowest rating, indicating that it cannot be used for real investment.

0 2020-06-06

Fertilizer consumption of countries along the Belt and Road(2002-2016)

The data set recorded one belt, one road, 2002-2016 years' fertilizer and pesticide consumption data in 65 countries. Fertilizer and pesticide consumption refers to the amount of plant nutrients and pesticides consumed per unit of cultivated land. Fertilizer products include nitrogen, potassium and phosphate (including phosphate rock powder), and traditional nutrients animal and plant fertilizers are not included. Data source: Food and Agriculture Organization, electronic files and web site. Fertilizer and pesticide are the main sources of agricultural chemical pollution, which pose a serious threat to the agricultural ecological environment and the sustainable development of agricultural economy. The data set reflects one belt, one road, along the line of fertilizer and pesticide use, and can provide data support for the research on agricultural ecological environment and other related research. The data set contains two data tables: fertilizer consumption (kg / ha of cultivated land) and pesticide consumption (kg / ha of cultivated land).

0 2020-06-05

SAM for Gaotai (2012)

The social accounting matrix, also known as the national economy comprehensive matrix or the national economy circulation matrix, uses the matrix method to connect the various accounts of the national economy systematically, represents the statistical index system of the national economy accounting system, and reflects the circulation process of the national economy operation. It uses the matrix form to arrange the national accounts orderly according to the flow and stock, domestic and foreign. The data reflects the balanced value of social accounting matrix in Gaotai County.

0 2020-06-03

Forecast data of water demand in the middle and lower reaches of Heihe River Basin

Water demand in the middle and lower reaches of Heihe River (mainly including water demand for living, livestock, industry, agriculture, tertiary industry, artificial forest and grass ecology in the middle reaches of Heihe River in current year, 2020 and 2030; water demand for living, industry, tertiary industry and ecology in Ejina Banner in the middle reaches of Heihe River in current year, 2020 and 2030)

0 2020-06-03

Global ESA CCI land cover classification map (1992-2015)

The land cover classification product is the second phase product of the ESA Climate Change Initiative (CCI), with a spatial resolution of 300 meters and a temporal coverage of 1992-2015. The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84. The classification of the surface coverage is based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations. When the data are used for scientific research purposes, the ESA CCI Land Cover project should be acknowledged. In addition, the published article should be send to contact@esalandcover-cci.org.

0 2020-06-03

Scheme optimization of "97" water diversion curve under the current engineering conditions of Heihe river basin (1957-2010)

According to the principle of optimization of water diversion scheme and the economic, social and ecological development status of Heihe River Basin, the following three optimization schemes of water diversion scheme are proposed. In Scheme 1, the water consumption in the middle reaches is 630 million m3 in each coming year. In Scheme 2, the water consumption in the middle reaches is 180 million m3 and 60 million m3 in 90% and 75% coming years respectively. In Scheme 3, when the water consumption in Yingluo Gorge is more than 1.9 billion m3, the water consumption in excess of 1.9 billion m3 is distributed by 40% in the middle reaches and 60% in the lower reaches. At the same time, in order to maintain the annual average inflow of 1.58 billion m3 from Yingluo Gorge, 950 million m3 from Zhengyi Gorge, and when the inflow of Yingluo Gorge is less than 1.29 billion m3, 60% of the inflow of less than 1.29 billion m3 will be distributed in the middle reaches and 40% in the lower reaches.

0 2020-06-03

Reservoirs map of the North_Slope_of_Tianshan River Basin (2000)

The data is the reservoir distribution dataset of the north slope of Tianshan River Basin, which is comprehensively prepared by using topographic map and remote sensing image. The scale is 250000, and the projection is latitude and longitude. The data includes spatial data and attribute data, and the attribute field is Name (reservoir name), reflecting the reservoir distribution status of River Basin in the northern foot of Tianshan Mountain around 2000.

0 2020-06-01

1:100,000 landuse dataset of Sichuan Province (1980s)

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.

0 2020-06-01