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Institute of Tibetan Plateau Research, CAS

Address:16 Lincui Road, Chaoyang District, Beijing 100101, P.R. China

E-mail: data@itpcas.ac.cn

phone:010-64833041

Source Region of Yellow River - Land Cover and Vegetation Type Ground Verification Point Dataset
  • 2019-07-23
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The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

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Source Region of the Yangtze River - Land Cover and Vegetation Type Ground Verification Point Dataset
  • 2019-07-23
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The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of the Yangtze River (in the south of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

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Hoh Xil - Land Cover and Vegetation Type Ground Verification Point Dataset
  • 2019-07-23
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The dataset is the ground verification point dataset of land cover and vegetation type in the Hoh Xil (in the northwest of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

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The Lakes Larger than 1k㎡ in Tibetan Plateau (V1.0) (1970s, 1990, 2000, 2010)
  • 2019-07-19
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The dataset includes vector map of the lakes larger than 1k㎡ on Tibetan Plateau in 1970s, 1990, 2000, 2010. The lake boundry data was extracted from remote sensing image like Landsat MSS, TM, ETM+, by means of visual interpretation. The data type is vector data, and it's attribute class includes Area (km²). The Projected Coordinate System is Albers Conical Equal Area. It is mainly used in the study of changes in lakes, hydrological and meteorological on the Tibetan Plateau.

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Archaeological site investigation and plant and animal resource utilization in the Tibet Plateau during the Neolithic and Bronze Age
  • 2019-07-06
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By archaeological investigation and excavation in Tibetan Plateau, we discovered 8 Neolithic and Bronze Age sites, including Gaomuxudi, Duojialiang, Shuikou, Qipanshan, Xinzhai, Canxionggasu, Niaodao, Bangga, Baiyangcun and so on. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified animal remains, plant macrofossil, selected some samples for radiocarbon dating and stable carbon and nitrogen isotopes. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Neolithic and Bronze Age.

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A dataset about archaeological site investigation and plant and animal resource utilization in the Tibet Plateau during the Paleolithic
  • 2019-06-21
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By archaeological investigation and excavation in Tibetan Plateau, we discovered 8 Paleolithic sites, including 151, Jiangxigou 1, Jiangxigou 2, Heimahe 1, Xiadawu, Yezere, Niamudi and Lingjiong. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified animal remains, plant macrofossil, selected some samples for radiocarbon dating and stable carbon and nitrogen isotopes. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Paleolithic.

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Population, Urbanization, GDP and Industrial Structure Forecast Scenario Data of the Heihe River Basin (Version 1.0) (2010-2050)
  • 2019-05-30
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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.

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Vulnerability Forecast Scenarios Data Set of Water Resources, Agriculture, and Ecosystem of the Urmuqi River Basin (Version 1.0) ( 2010-2050)
  • 2019-05-30
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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, which was to establish the scenario by setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption. 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 predicted. 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 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.

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Population, Urbanization, GDP and Industrial Structure Forecast Scenario Data of the Urmuqi River Basin (Version 1.0) (2010-2050)
  • 2019-05-30
  • 0
  • 1

Taking 2005 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 is also 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 each industry’s output value was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP, and, therefore, it was adjusted according to the need of the future industrial structure scenarios of the research area.

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Population, Urbanization, GDP and Industrial Structure Predictions for the Aksu River Basin (Version 1.0) (2010-2050)
  • 2019-05-30
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  • 1

Taking 2005 as the base year, the future population scenario was predicted by adopting the logistic model of population. This model not only effectively describes the pattern of changes in population and biomass but is also widely applied in the field of economics. The urbanization rate was predicted using the urbanization logistic model. Based on the observed horizontal pattern of urbanization, a predictive model was established by determining the parameters in the parametric equation by applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data represent the non-agricultural population. The logistic model was used to predict the future gross domestic product of each county (or city), and then the economic development level of each county (or city) in each period (in terms of 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 changes in China and the research area lagged behind the growth in GDP, so the changes were adjusted according to the need for future industrial structure scenarios in the research area.

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