"Hydrological ecological economic process coupling and evolution of Heihe River Basin Management under the framework of water rights" (91125018) project data collection 2 - Dunhuang comprehensive plan for rational utilization of water resources and ecological protection (2011-2020) Planning documents mainly include: 1. Current situation and existing problems of regional water resources utilization; 2. Guiding ideology, basic principles and planning objectives; 3. Analysis of economic, social and ecological water demand; 4. Plan for water resources allocation; 5. Construction of water right system; 6. Main engineering measures; 7. Environmental impact arrangement.
The data is the distribution map of 100,000 deserts in the Tarim River Basin. This data uses 2000 TM images as the data source to interpret, extract and revise, and uses remote sensing and geographic information system technology in combination with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code), ashm_id (desert code), of which desert code is as follows: flowing sand 2341010, semi-flowing sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000, saline-alkali land 2343000
WANG Jianhua
The data set is the physiological and ecological parameters of the dominant species of each ecosystem in Heihe River Basin. According to the requirements of tesim model, the data set divides Heihe River basin into seven ecosystems: deciduous broad-leaved forest ecosystem (BRD), evergreen coniferous forest ecosystem (CNF), agricultural field ecosystem (CRP), desert ecosystem (DST), meadow grassland ecosystem (MDS) Shrubbery ecosystem (SHB) and grassland ecosystem (STP). Some of the data in this data set are based on the measured data, some are obtained by reference documents, but after verification, they are applied to the Heihe River Basin. For the data in this data, each parameter of each ecosystem has three values, which are the value in the model, the minimum value and the maximum value of this parameter. The data can provide input parameters for the ecological process model, and the data set is still in further optimization.
PENG Hongchun
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Xiyinghe Station from January 1 to December 31, 2018. The site (101.853E, 37.561N) was located on a alpine meadow in the Menyuan,Qinghai Province. The elevation is 3639 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, and 8 m, towards north), wind speed and direction profile (windsonic; 2, 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, and Ta_8 m; RH_2 m, RH_4 m, and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s/m^2)), soil heat flux (Gs_5 cm, Gs_10cm) (W/m^2), soil temperature (Ts_20 cm, Ts_40 cm) (℃), soil moisture (Ms_20 cm, Ms_40 cm) (%, volumetric water content), soil water potential (SWP_20cm , SWP_40cm)(kpa) , soil conductivity (Ec_20cm, Ec_40cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The meteorological data were missing during Aug. 29 to Oct.18 because of unstable power supply due to battery box flooding; The wind speed and direction profile data were rejected because of sensor failure; The precipitation data were rejected because of program error; The air humidity data before Mar. 2 were rejected due to program error; (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
The compilation basis of frozen soil map includes: (1) frozen soil field survey, exploration and measurement data; (2) aerial photo and satellite image interpretation; (3) topo300 1km resolution ground elevation data; (4) temperature and ground temperature data. Among them, the distribution of permafrost in the Qinghai Tibet Plateau adopts the research results of nanzhuo Tong et al. (2002). Using the measured annual average ground temperature data of 76 boreholes along the Qinghai Tibet highway, regression statistical analysis is carried out to obtain the relationship between the annual average ground temperature and latitude, elevation, and based on this relationship, combined with the gtopo30 elevation data (developed under the leadership of the center for earth resources observation and science and technology, USGS) Global 1 km DEM data) to simulate the annual mean ground temperature distribution over the whole Tibetan Plateau. Taking the annual average ground temperature of 0.5 ℃ as the boundary between permafrost and seasonal permafrost, the boundary between discontinuous Permafrost on the plateau and island Permafrost on the plateau is delimited by referring to the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988); in addition, the division map of Permafrost on the big and small Xing'an Mountains in the Northeast (Guo Dongxin et al., 1981), the distribution map of permafrost and underground ice around the Arctic (b According to rown et al. 1997) and the latest field survey data, the Permafrost Boundary in Northeast China has been revised; the Permafrost Boundary in Northwest mountains mostly uses the boundary defined in the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988). According to the data, the area of permafrost in China is about 1.75 × 106km2, accounting for about 18.25% of China's territory. Among them, alpine permafrost is 0.29 × 106km2, accounting for about 3.03% of China's territory. For more information, please refer to the specification of "1:4 million map of glacial and frozen deserts in China" (Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences, 2006)
WANG Tao
The birds along the Zhamo Highway in Medog and Bome counties are investigated by mist net method and point count method. According to the 400-meter elevation span, elevation transects were set up in the survey area. Four elevation transects are set up in the north slope from Gangcun to Galong Temple in Bome County, from low to high, and nine elevation transects are set up in the south slope from Jiefang Bridge to Galongla in Medog County. So that we can make a breakthrough understanding the formation and maintenance mechanism of bird diversity in this region. The data of bird diversity and distribution will be used to further explore the key scientific issues such as the impact of climate change on bird diversity and adaptation strategies, and the response and protection strategies of bird species diversity under the global climate change.
DONG Feng
The dataset investigated the growth status of plants and leaf morphological indexes of single and conjoined red sand and pearl in the middle and lower reaches of heihe river basin in 2013. The growth indexes were crown width, plant height, and biomass of fine roots and thick roots.Leaf shape indicators are: length, width, thickness, and leaf area, volume, etc.The experimental observation indexes are: leaf nitrogen content, water potential, gas exchange data, chlorophyll fluorescence data. Data include: field observation data and explanatory documents.
SU Peixi
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
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Liancheng Station from January 1 to December 31, 2018. The site (102.833E, 36.681N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2912 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4 m, towards south),infrared temperature sensors (2 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation;-0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m and Ta_8 m; RH_4 m and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5 cm, Gs_10 cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential (SWP_5cm,SWP_10cm)(kpa), soil conductivity (EC_5cm,EC_10cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The soil heat flux data were wrong during Jan.1 to May 30 because of rodent damage; The data during May. 30 to July 6 were missing because the power supply failure; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Linze Station from January 1 to December 31, 2018. The site (100.060° E, 39.237° N) was located on a cropland (maize surface) in the Guzhai Xinghua, which is near Zhangye city, Gansu Province. The elevation is 1400 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation; -0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4m), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_3 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing long wave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5cm, Gs_10cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential(SWP_5cm, SWP_10cm), soil conductivity (Ec_5cm,Ec_10cm) (μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day.The precipitation and the air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
In mid-july 2011, photosynthetic organs (leaves or assimilating branches) of typical desert plants were collected and brought back to the laboratory in a liquid nitrogen tank for determination. The analysis indexes mainly include soluble protein unit: mg/g;Free amino acid unit: g/g;Chlorophyll content unit: mg/g;Superoxide dismutase (SOD) unit: U/g FW;Catalase (CAT) unit: U/(g•min);POD unit: U/(g•min);Proline (Pro) unit: g/g; Soluble sugar unit: g/g;Malondialdehyde (MDA) is given in moles per liter.
SU Peixi
Data for 100000 desert map qaidam river basin, cutting since China 1:100000 desert sand data set, the data of TM images in 2000 data sources, to interpret, extraction, revision, using remote sensing and geographic information system technology combining 1:100000 scale mapping, the desert, sand and gravel gobi for thematic mapping.The desert codes are as follows: mobile sandy land 2341010, semi-mobile sandy land 2341020, semi-fixed sandy land 2341030, gobi desert 2342000, saline alkaline land 2343000.
WANG Jianhua
1) Data content: species list and distribution data of Phrynocephalus and Eremais in Tarim Basin, including class, order, family, genus, species, and detailed distribution information including country, province, city and county; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Tarim Basin from 2008 to 2020, and recording the species composition and distribution range of Phrynocephalus and Eremias in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples information are checked to ensure the quality of distribution data; 4) Data application results and prospects: Through comprehensive analysis of the dataset, the list of species diversity and distribution can provide important data for biodiversity cataloguing in arid central Asia, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.
GUO Xianguang
Shule River Basin is one of the three inland river basins in Hexi corridor. In recent years, with the obvious change of climate and the aggravation of human activities, the shortage of water resources and the problem of ecological environment in Shule River Basin have become increasingly prominent. It is of great significance to study the runoff change of Shule River Basin in the future climate situation for making rational water resources planning and ecological environment protection. The Shule River basin boundary is cut from "China's 1:100000 desert sand data set". Taking the 2000 TM image as the data source, it interprets, extracts, revises, and uses remote sensing and geographic information system technology to combine with the 1:100000 scale mapping requirements to carry out thematic mapping of desert, sand and gravel gobi. Data attribute table: Area (area), perimeter (perimeter), ash_ (sequence code), class (desert code), ash_id (desert code). The desert code is as follows: mobile sand 2341010, semi mobile sand 2341020, semi fixed sand 2341030, Gobi 2342000, salt alkali land 2343000. Collect and sort out the basic, meteorological, topographical and geomorphic data of Shule River Basin, and provide data support for the management of Shule River Basin.
On the basis of physiological and biochemical analysis of photosynthetic organs (leaves or assimilating branches) of typical desert plants in heihe river basin collected in mid-july 2011, some photosynthetic organs of desert plants were collected in mid-july 2012 and put into a liquid nitrogen tank and brought back to the laboratory for determination. Physiological analysis indexes mainly include: soluble protein unit: mg/g;Free amino acid unit: g/g;Chlorophyll content unit: mg/g;Superoxide dismutase (SOD) unit: U/g FW;Catalase (CAT) unit: U/(g•min);POD unit: U/(g•min);Proline (Pro) unit: g/g; Soluble sugar unit: g/g;Malondialdehyde (MDA) is given in moles per liter.
SU Peixi
一. An overview This data set is a 1:100,000 distribution map of China's deserts as the data source, and it is tailored according to the river basin boundary. It mainly reflects the geographical distribution, area size, mobility and fixation degree of deserts, sandy land and gobi in the upper reaches of the Yellow River.The information source of this data set is Landsat TM image in 2000. Using remote sensing and geographic information system technology, according to the requirements of 1:100,000 scale thematic mapping, the thematic mapping of China's deserts, sandlands and gobi was carried out. 二. Data processing instructions This data set takes the 1:100,000 distribution map of China's deserts as the data source and is tailored according to the basin boundary.The information source of this data set is Landsat TM image in 2000. Using remote sensing and geographic information system technology, according to the requirements of 1:100,000 scale thematic mapping, the thematic mapping of China's deserts, sandlands and gobi was carried out.According to the system design requirements and related standards, the input data is standardized and uniformly converted into various data input standard formats. 三. data content description This data set is divided into desert and non-desert category, the non-desert code is 999. The desert is divided into three categories, namely desert (land), gobi and saline-alkali land, and the classification code is 23410, 2342000 and 2343000 respectively.Among them, deserts (land) are divided into four categories, namely mobile desert (land), semi-mobile desert (land), semi-fixed desert (land) and fixed desert (land). The classification codes are 2341010, 2341020, 2341030 and 2341040. 四. Data usage instructions It can make the resources, environment and other related workers understand the desert type, area and distribution in the upper reaches of the Yellow River, and make the classification and evaluation of the wind and sand hazards in ningmeng river section.
XUE Xian, DU Heqiang
By applying supply-demand balance analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, and the results were used to assess the vulnerability of the water resources system in the basin. The IPAT equation was used to establish a future water resource demand scenario, which involved setting various variables, such as the future population growth rate, economic growth rate, and water consumption per unit GDP. 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 Hydro-meteorological Institute, a model of the variation trends of the basin under a changing climate was designed. The glacial melting scenario was used as the model input to construct the runoff scenario in response to climate change. According to the national regulations of the water resource allocation in 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-related land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources in scenarios of climate change, glacial melting and population growth was analysed, 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 in the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, on which basis 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 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 for 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.
YANG Linsheng, ZHONG Fanglei
The data is 100,000 desert distribution map over the north_slope_of_Tianshan River Basin. This data uses 2000 TM image as data source to interpret, extract and revise. Remote sensing and geographic information system technology are combined with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code) and ashm_id (desert code), of which the desert code is as follows: mobile sand 2341010, semi-mobile sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000 and saline-alkali land 2343000.
WANG Jianhua, YAN Changzhen
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