Lake surface water temperature (LSWT) at Xiashe station from 1967 to 2020; Lake ice depth and lake ice duration at Xiashe station from 1994 to 2020; Runoff at Buha station from 1956 to 2020; Lake level at Xiashe station from 1956 to 2020; Lake area from 1956 to 2020 estimated from the correlation constructed between lake area derived from Landsat images and lake level from gauge measurements in 2001−2020; Air temperature (T) at Gangcha station from 1958 to 2019; Precipitation (P) at Gangcha station from 1958 to 2019
ZHANG Guoqing
This data includes the land cover data of Central Asia, South Asia and Indochina Peninsula in the from 1992 to 2020 with a spatial resolution of 300mLand cover data includes 10 primary categories, which are combined from the secondary categories of the original data. The data source is the surface coverage product CCI-LC of ESA, where the spatial distribution of cropland, built-up land, and water for the land cover data from 1992 to 2020. Combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 500 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), the training sample dataset of land cover interpretation were built from the consistent areas of multiple products. The Google Earth Engine and random forest algorithm were used to correct the cropland, built-up land, and water of temporal CCI-LC data. Using the high resolution images in Google Earth at 2019 and 2020, the accuracy of change areas of cropland, built-up land, and water was validated by the stratified random sampling. A total of 3,600 land parcels were selected from 1,200 land parcels of the three land cover types, indicating that the accuracy of our corrected product increased in the range of 11% to 26% for the change areas compared to the CCI-LC product.
XU Erqi
Land surface temperature (LST) is a key parameter in the study of surface energy balance. It is widely used in the fields of meteorology, climate, hydrology, agriculture and ecology. As an important means to obtain global and regional scale LST information, satellite (thermal infrared) remote sensing is vulnerable to the influence of cloud cover and other atmospheric conditions, resulting in temporal and spatial discontinuity of LST remote sensing products, which greatly limits the application of LST remote sensing products in related research fields. The preparation of this data set is based on the empirical orthogonal function interpolation method, using Terra / Aqua MODIS surface temperature products to reconstruct the lst under ideal clear sky conditions, and then using the cumulative distribution function matching method to fuse era5 land reanalysis data to obtain the lst under all-weather conditions. This method makes full use of the spatio-temporal information of the original MODIS remote sensing products and the cloud impact information in the reanalysis data, alleviates the impact of cloud cover on LST estimation, and finally reconstructs the high-quality global 0.05 ° spatio-temporal continuous ideal clear sky and all-weather LST data set. This data set not only realizes the seamless coverage of space-time, but also has good verification accuracy. The reconstructed ideal clear sky LST data in the experimental areas of 17 land cover types in the world, the average correlation coefficient (R) is 0.971, the bias (bias) is -0.001 K to 0.049 K, and the root mean square error (RMSE) is 1.436 K to 2.688 K. The verification results of the reconstructed all-weather LST data and the measured data of ground stations: the average R is 0.895, the bias is 0.025 K to 2.599 K, and the RMSE is 4.503 K to 7.299 K. The time resolution of this data set is 4 times a day, the spatial resolution is 0.05 °, the time span is 2002-2020, and the spatial range covers the world.
ZHAO Tianjie, YU Pei
Based on gipl1.0 permafrost spatial distribution model, combined with the existing basic data, including climate change, soil types, and vegetation data, the permafrost and seasonal permafrost characteristics of Sichuan Tibet railway are simulated. The data result is 500m spatial resolution grid, including the maximum depth of permafrost and the maximum freezing depth of seasonal permafrost. The results are verified by drilling data. The data date is 2001-20192041-20602081-2100 (20-year average), in which the water body and glacier area are excluded from the calculation range through the mask (null value). The climate data is monthly mean, other data remain unchanged in the process of simulation, and the spatial resolution is 500m. Data sources and "woeldc" lim:https :// www.worldclim.org/ , DEM and vegetation soil: https://data.tpdc.ac.cn/zh-hans/ ”According to the characteristics of different data sources, the authenticity and consistency of the original data are checked and standardized; The permafrost model is used to simulate the permafrost and seasonal frozen soil. The output results are ground temperature and active layer (maximum frozen depth). The simulation results are verified with the borehole ground temperature. Finally, the spatial data set is mapped by ArcGIS. Make digital processing operation standard. In the process of processing, the operators are required to strictly abide by the operation specifications, and the special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation are all in line with the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping. The data can provide necessary data support for the later research on the freezing (thawing) depth of the corridor of Sichuan Tibet project.
YIN Guoan
In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.
LUO Geping
Based on the medium resolution long time series remote sensing image Landsat, the data set obtained six periods of ecosystem type distribution maps of the Qinghai Tibet Plateau in 1990 / 1995 / 2002 / 2005 / 2010 / 2015 through image fusion, remote sensing interpretation and data inversion, and made the original ecological base map of the Qinghai Tibet Plateau in 25 years (1990-2015). According to the area statistics of various ecosystems in the Qinghai Tibet Plateau, the area of woodland and grassland decreased slightly, the area of urban land, rural residential areas and other construction land increased, the area of rivers, lakes and other water bodies increased, and the area of permanent glacier snow decreased from 1990 to 2015. The atlas can be used for the planning, design and management of ecological projects in the Qinghai Tibet Plateau, and can be used as a benchmark for the current situation of the ecosystem, to clarify the temporal and spatial pattern of major ecological projects in the Qinghai Tibet Plateau, and to reveal the change rules and regional differences of the pattern and function of the ecosystem in the Qinghai Tibet Plateau.
ZHAO Hui, WANG Xiaodan
This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.
C.K. Shum
This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CAO Guangzhen
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CHU Duo
"Coupling and Evolution of Hydrological-Ecological-Economic Processes in Heihe River Basin Governance under the Framework of Water Rights" (91125018) Project Data Convergence-MODIS Products-Land Use Data in Northwest China (2000-2010) 1. Data summary: Land Use Data in Northwest China (2000-2010) 2. Data content: Land use data of Shiyanghe River Basin, Heihe River Basin and Shulehe River Basin in Northwest China from 2000 to 2010 obtained by MODIS
WANG Zhongjing
1. Overview of data Based on the Google earth image data in 2012, the land use types of wetland parks were vectorized by visual interpretation method, which provided the data basis for wetland ecosystem service assessment. 2. Data content Land use types include wetland, farmland (corn, vegetables, wheat), water area, forest land, construction land, bare land, etc. Scale: 1: 50,000; Coordinate system: WGS84; Data type: vector polygon; Storage format: Dbf/Shp/Jpeg 3. Space-time range Coverage: Zhangye National Wetland Park; Total area: 46.02 square kilometers.
XU Zhongmin
1. Overview of data This data is based on the latest googleearth remote sensing image data to establish the spatial distribution database of crops in Ganzhou District of Zhangye City. 2. Data content Based on the spatial distribution of maize seed production focused by the project, the land use types in the study area are divided into 14 types (maize seed production land, spring wheat land, vegetable land, greenhouse land, intercropping land, rice land, water area, wetland, forest land, urban and rural industrial and mining residential land, roads, railways and unused land). 3. Space-time range The data range includes 19 villages and towns including Pingshanhu, Shajing, Wujiang River, Jingan, Mingyong, Sanzha, Ganjun, Xindun, Shangqin, Jiantan, Chengguan Town, Liangjiadun, Chang 'an, Dangzhai, Xiaoman, Longqu, Daman, Huazhai and Anyang. The data type is vector polygon and stored in Shape format. The data range covers Ganzhou District.
XU Zhongmin
The distribution map of irrigation area and main and branch canals in Heihe River basin includes the main irrigation area and the distribution of all main and branch canals in Heihe River Basin. The irrigation area mainly includes Luocheng irrigation area, Youlian irrigation area, Liuba irrigation area, Pingchuan irrigation area, liaoquan irrigation area, Liyuan River irrigation area, yannuan irrigation area, Banqiao irrigation area, Shahe irrigation area, Xijun irrigation area, Yingke irrigation area, Daman irrigation area, Maying River irrigation area, shangsan irrigation area, Xinba irrigation area and Hongyazi irrigation area. The distribution map of main and branch canals includes all the main canals and branch canals of these 16 irrigation areas.
XU Maosen, XU Zongxue, HU Litang
The dataset is the distribution map of lakes in Qinghai Lake Basin. The projection is latitude and longitude. The data includes the spatial distribution data and attribute data of the lake. The attribute fields of the lake are: NAME (lake name), CODE (lake code).
WU Lizong
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.
National Basic Geographic Information Center
The data is the river dataset of the north slope of Tianshan River Basin. It is revised according to the topographic map and TM remote sensing image. The scale is 250,000, and the projection is latitude and longitude. The data includes spatial data and attribute data, attribute data fields: HYD_CODE (river code), Name (river name), SHAPE_ leng (river length).The data includes spatial data and attribute data. , SHAPE_leng (river length).
National Basic Geographic Information Center
The dataset is a lake distribution map of the north slope of Tianshan Mountain Basin, with a scale of 250,000. The projection is latitude and longitude. The data includes spatial data and attribute data. The attribute fields of the lake are NAME (name of the lake) and CODE (lake code).
National Basic Geographic Information Center
China's administrative regions are basically divided into three levels: provinces (autonomous regions, municipalities directly under the central government), counties (autonomous counties, cities), townships (nationality townships, towns). In order to meet the needs of user statistics and cartography, we have published 1:1 million national administrative division data sets according to the national basic geographic information center. The administrative division data of Heihe River Basin were prepared. This data reflects the current situation of administrative divisions in Heihe River basin around 2008, including the information of provincial, regional and county-level administrative divisions. Its main attributes (such as area, code of administrative divisions, province (autonomous region), city (region, autonomous prefecture)) come from China's administrative divisions published in 2008.
WU Lizong
This data includes the basic terrain data, soil data, meteorological data, land use / land cover data, etc. needed for SWAT model operation. All maps and relevant point coordinates (meteorological station, hydrological station) adopt the coordinate system of Gauss Kruger projection which is consistent with the basic topographic map of our country. Data content includes: a) The basic topographic data include DEM and river network. The size of DEM grid is 50 * 50m, and the drainage network is manually digitized from 1:100000 topographic map. b) Soil data: including soil physics, soil chemistry and spatial distribution of soil types. The scale of digital soil map is 1:1 million, which is converted into grid format of ESRI, with grid size of 50 * 50m. Each soil profile can be divided into up to 10 layers. The sampling index of soil texture required by the model adopts the American Standard. The parameters are from the second National Soil Census data and related literature. c) Meteorological data: (1) Temperature: the data of daily maximum temperature, daily minimum temperature, wind speed and relative humidity are from the daily observation data of Qilian, Shandan, tole, yeniugou and Zhangye meteorological stations in and around the basin, with the period from 1999 to 2001. (2) Precipitation: the rainfall data comes from five hydrological stations in and around the basin, i.e. OBO (1990-1996), Sunan (1990-2000), Qilian (1990-2000), Yingluoxia (1990-2000), zamashk (1990-2000), Shandan (1999-2001), tole (1999-2001), yeniugou (1999-2001), Zhangye (1999-2001) and Qilian County (1999-2001) Observation data. (3) Wind speed and relative humidity: wind speed and relative humidity come from the daily observation data of 5 meteorological stations in Shandan, tole, yeniugou, Zhangye and Qilian county. The period is from 1999 to 2001. (4) Solar radiation: solar radiation has no corresponding observation data and is generated by model simulation. d) Land use / land cover: 1995 land use data, scale 1:100000. Convert it to grid format of ESRI, with grid size of 50 * 50m. e) Meteorological data simulation tool (weather generator) database: the weather data simulation tool of SWAT model can simulate and calculate the daily meteorological input data required by the model operation according to the monthly statistical data for many years without the actual daily observation data, and can also carry out the interpolation of incomplete observation data. The meteorological data are from the surrounding meteorological stations.
NAN Zhuotong
1. Data overview: this data is the blue and green water data of Heihe River Basin simulated by SWAT model; 2. Data content: data mainly includes blue-green water and green water coefficient of the whole basin and each sub Basin; 3. Spatial and temporal scope: the data time is from 1975 to 2004, and the spatial scope includes 34 sub basins and the whole Heihe River Basin; 4. Data file: the relevant data is placed in the Swat folder, including the sub_basin folder (sub basin distribution map), "blue and green water of the whole Heihe River Basin" folder and "blue and green water of each hydrological response unit of the Heihe River Basin" folder.
LIU Junguo
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