In this study, an algorithm that combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS) (4 km) is presented to provide a daily cloud-free snow-cover product (500 m), namely Terra-Aqua-IMS (TAI). The overall accuracy of the new TAI is 92.3% as compared with ground stations in all-sky conditions; this value is significantly higher than the 63.1% of the blended MODIS Terra-Aqua product and the 54.6% and 49% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra-Aqua over the Tibetan Plateau (TP) can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with the 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is similar to the 20.6% obtained from the 8-day MODIS Terra product (MOD10A2) but much larger than the 8.1% from the daily blended MODIS Terra-Aqua product due to the cloud blockage.
ZHANG Guoqing
The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121,758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km², with a total area of 1,730.34km² respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.
CHEN Xu, MU Cuicui, JIA Lin, LI Zhilong, FAN Chenyan, MU Mei, PENG Xiaoqing, WU Xiaodong WU Xiaodong
This data is the hydrological data of kuzhan hydrological station in the middle reaches of the Xier river. The station is jointly built by Urumqi Institute of desert meteorology of China Meteorological Administration, Institute of water energy and ecology of Tajik National Academy of Sciences and Tajik hydrometeorological Bureau. The data can be used for scientific research such as water resources assessment and water conservancy projects in Central Asia. Data period: November 2, 2019 to December 5, 2020. Data elements: Hourly velocity (M / s), hourly water level (m) and hourly rainfall (m) Site location: 40 ° 17 ′ 38 ″ n, 69 ° 40 ′ 18 ″ e, 320m 1、 300w-qx River velocity and water level observation instrument （1） Flow rate parameters: 1 power supply voltage 12 (9 ~ 27) V (DC) The working current is 120 (110 ~ 135) MA 3 working temperature (- 40 ~ 85) ℃ 4 measurement range (0.15 ~ 20) m / S The measurement accuracy is ± 0.02m/s The resolution is less than 1 mm The detection range is less than 0.1 ~ 50 m 8 installation height 0.15 ~ 25 m 9 sampling frequency ＜ 20sps （2） Water level parameters: 1 measuring range: 0.5 ~ 20 m The measurement accuracy is ± 3 mm The resolution is less than 1 mm The repeatability was ± 1 mm 2、 SL3-1 tipping bucket rain sensor 1. Water bearing diameter Φ 200mm 2. The measured precipitation intensity is less than 4mm / min 3. Minimum precipitation of 0.1 mm 4. The maximum allowable error is ± 4% mm 3、 Flow velocity, frequency of data acquisition of the observation instrument: the sensor measures the flow velocity and water level data every 5S 4、 Calculation of hourly average velocity: the hourly average velocity and water level data are obtained from the average of all the velocity and water level data measured every 5S within one hour 5、 Description of a large number of values of 0 in water level data: the value of 0 in water level data is caused by power failure and restart of sensor due to insufficient power supply. After restart, the first data is 0, resulting in the hourly average value of 0. On December 5, 2019, the power supply will return to normal after transformation 6、 There are some missing and - 8.191mm abnormal data in rainfall data, which should be eliminated and explained. Data missing 4.10-5.3 data, supplemented, - 8.191mm, similar abnormal data has been marked
HUO Wen, SHANG Huaming
We comprehensively estimated water volume changes for 1132 lakes larger than 1 km2. Overall, the water mass stored in the lakes increased by 169.7±15.1 Gt (3.9±0.4 Gt yr-1) between 1976 and 2019, mainly in the Inner-TP (157.6±11.6 or 3.7±0.3 Gt yr-1). A substantial increase in mass occurred between 1995 and 2019 (214.9±12.7 Gt or 9.0±0.5 Gt yr-1), following a period of decrease (-45.2±8.2 Gt or -2.4±0.4 Gt yr-1) prior to 1995. A slowdown in the rate of water mass increase occurred between 2010 and 2015 (23.1±6.5 Gt or 4.6±1.3 Gt yr-1), followed again by a high value between 2015 and 2019 (65.7±6.7 Gt or 16.4±1.7 Gt yr-1). The increased lake-water mass occurred predominately in glacier-fed lakes (127.1±14.3 Gt) in contrast to non-glacier-fed lakes (42.6±4.9 Gt), and in endorheic lakes (161.9±14.0 Gt) against exorheic lakes (7.8±5.8 Gt) over 1976−2019.
ZHANG Guoqing
The flood distribution data of historical streams and rivers in Qinghai Tibet scientific research area include longitude and latitude, location of occurrence, basic triggering type, date, damage and other attribute information. Data source: survey statistics of disaster investigation department. On the basis of the original data, the necessary data quality control. According to the type description of the original data, the main triggering factors, the location of the occurrence, combined with the 30 meter foundation terrain, the flood type is analyzed and divided. The data can be used as a reference for the analysis of historical flood disasters. The data format is point vector SHP format, which can be directly opened with ArcGIS. The data can be used for flood risk analysis in the corresponding area of the Qinghai Tibet Plateau.
WANG Zhonggen
(1) Data content: daily variation of water level of Kalakuli lake from 2011 to 2019, the coordinates of observation points are 75.03 ° e, 38.43 ° N and 3670m above sea level. (2) Data source and processing method: hobo pressure type automatic water level gauge (u20-001-01) was used, and the recording frequency was 30 minutes. After eliminating the wrong data and abnormal values, the daily change data of water level is obtained by calculation. (3) Data quality description: due to the destruction of the scale in winter, the data is based on the annual observation. Due to the influence of human factors in construction, the data in some periods are missing. () data application prospect: the data can be applied to the research fields of Lake hydrology and hydrological process in high cold region.
XIE Ying
The data set is measured by YSI exo2 water quality multi parameter measuring instrument on the Bank of middle lake of Ranwu lake from April to November every year from 2014 to 2020. The sampling interval is 0.25s-1s. The data is the average value after the instrument is stabilized. The sampling geographic coordinates are: longitude 96.795296, latitude 29.459066, altitude 3925m. The measurement parameters are water temperature, conductivity, dissolved oxygen and turbidity, and the specific parameter unit is indicated in the meter. Data culling part of the obvious outliers, the document is empty, please pay attention to the use. The data will be updated from time to time, and can be used by researchers of water chemistry, Lake microorganism or lake physical and chemical properties in Ranwu Lake Basin.
LUO Lun
This data is the data of the automatic weather station (AWS, Campbell company) set up in Yigong Zangbu basin by the Southeast Tibet alpine environment comprehensive observation and research station of Chinese Academy of Sciences in 2018. The geographic coordinates are 30.1741 n, 94.9334 e, and the altitude is 2282m. The underlying surface is grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), water vapor pressure (kPa) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average value recorded in 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument is tb4, the atmospheric pressure sensor is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Data quality: the quality of the original data is better, less missing. The data station is a meteorological station in the lower altitude of the Qinghai Tibet Plateau, which will be updated from time to time in the future. It can be used by researchers studying climate, hydrology, glaciers, etc.
LUO Lun
This data is from the hydrological station of kafinigan River, a tributary of the upper Amu Darya River. The station is jointly built by Urumqi Institute of desert meteorology of China Meteorological Administration, Institute of water energy and ecology of Tajik National Academy of Sciences and Tajik hydrometeorological Bureau. The data can be used for scientific research such as water resources assessment and water conservancy projects in Central Asia. Data period: November 3, 2019 to December 3, 2020. Data elements: Hourly velocity (M / s), hourly water level (m) and hourly rainfall (m). Site location: 37 ° 36 ′ 01 ″ n, 68 ° 08 ′ 01 ″ e, 420m 1、 300w-qx River velocity and water level observation instrument （1） Flow rate parameters: 1 power supply voltage 12 (9 ~ 27) V (DC) The working current is 120 (110 ~ 135) MA 3 working temperature (- 40 ~ 85) ℃ 4 measurement range (0.15 ~ 20) m / S The measurement accuracy is ± 0.02m/s The resolution is less than 1 mm The detection range is less than 0.1 ~ 50 m 8 installation height 0.15 ~ 25 m 9 sampling frequency ＜ 20sps （2） Water level parameters: 1 measuring range: 0.5 ~ 20 m The measurement accuracy is ± 3 mm The resolution is less than 1 mm The repeatability was ± 1 mm 2、 SL3-1 tipping bucket rain sensor 1. Water bearing diameter Φ 200mm 2. The measured precipitation intensity is less than 4mm / min 3. Minimum precipitation of 0.1 mm 4. The maximum allowable error is ± 4% mm 3、 Flow velocity, frequency of data acquisition of the observation instrument: the sensor measures the flow velocity and water level data every 5S 4、 Calculation of hourly average velocity: the hourly average velocity and water level data are obtained from the average of all the velocity and water level data measured every 5S within one hour 5、 Description of a large number of values of 0 in water level data: the value of 0 in water level data is caused by power failure and restart of sensor due to insufficient power supply. The first data of initial start-up is 0, resulting in the hourly average value of 0. After the power supply transformation on July 26, 2020, the data returned to normal. At the end of September 2020, the power supply began to be insufficient. After the secondary power supply transformation on December 25, 2020, the data returned to normal 6、 Description of water level monitoring (such as line 7358, 2020 / 11 / 3, 16:00, maximum water level 6.7m, minimum water level 0m, how to explain? In addition, the maximum value of the highest water level is 6.7m, which appears many times in the data. It seems that 6.7m is the limit value of the monitoring data. Is this the case? ）: 6.7m is the height from the initial sensor to the bottom of the river bed. The appearance of 6.7m is the abnormal data when the sensor is just started. The sensor is restarted due to the power failure caused by the insufficient power supply of the equipment. This abnormal value appears in the initial start-up. After the power supply transformation on December 25, 2020, the data returns to normal
HUO Wen, SHANG Huaming
The monitoring data set of surface water quality in Xining city of Qinghai Province was collected from July, 2015 to July, 2015. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 15 data tables, which are: surface water quality of Xining City in July 2015, surface water quality of Xining City in November 2015, surface water quality of Xining City in January 2016, and surface water quality of Xining City in February 2016. The data table structure is the same. There are six fields in each data table, such as the monitoring section water quality table of Xining surface water in July 2015 Field 1: serial number Field 2: section name Field 3: executive standard level Field 4: actual water quality grade Field 5: over standard items
Department of Ecology and Environment of Qinghai Province
The data set records the information disclosure form of surface water quality monitoring in Haixi Prefecture of Qinghai Province from January 2019 to June 2020. The data were collected from the ecological environment bureau of Haixi Prefecture. The data set contains 18 data tables, which are respectively the information disclosure table of surface water quality monitoring in January, February, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 of 2019, and the information disclosure table of surface water quality monitoring in January, February, 3, 4, 5 and 6 of 2020. Each data table has 11 fields, such as the information disclosure table of surface water quality monitoring in January 2019 Field 1: serial number Field 2: Region Field 3: water body Field 4: section name Field 5: section level Field 6: monitoring unit Field 7: monitoring frequency Field 8: water quality objectives Field 9: is it up to standard Field 10: over standard factor Field 11: remarks
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
1) These data main included the GPR-surveyed ice thickness of six typical various-sized glaciers in 2016-2018; the GlabTop2-modeled ice thickness of the entire UIB sub-basins, discharge data of the hydrological stations, and related raw & derived data. 2) Data sources and processing methods: We compared the plots and profiles of GPR-surveyed ice bed elevation with the GlabTop2-simulated results and selected the optimal parametric scheme, then simulated the ice thickness of the whole UIB basin and assessed its hydrological effect. These processed results were stored as tables and tif format， 3) Data quality description: The simulated ice thickness has a spatial resolution of 30 m, and has been verified by the GPR-surveyed ice thickness for the MD values were less than 10 m. The maximum error of the GPR-measured data was 230.2 ± 5.4 m, within the quoted glacier error at ± 5%. 4) Synthesizing knowledge of the ice thickness and ice reserves provides critical information for water resources management and regional glacial scientific research, it is also essential for several other fields of glaciology, including hydrological effect, regional climate modeling, and assessment of glacier hazards.
ZHANG Yinsheng
1) Data content (including elements and significance) This data set contains information of flow direction, accumulation of vector river network of Lancang Mekong River Basin. 2) Data sources and processing methods In this data set, the remote sensing stream buring (RSSB) method (Wang et al., 2021) is adopted, and the high-precision elevation model MERIT-DEM and Sentinel-2 optical imagery are fused. 3) Data quality description Validations show that this data set has high spatial accuracy (Wang et al, 2021). 4) Data application achievements and Prospects This data set provides basic information of river networks, which can be used for hydrological model, land surface model, earth system model, as well as for mapping and spatial statistical analysis.
WANG Zifeng
1) Data content (including elements and significance) This data set contains information of flow direction, accumulation of vector river network of Lancang Mekong River Basin. <br><br> 2) Data sources and processing methods In this data set, the remote sensing stream buring (RSSB) method (Wang et al., 2021) is adopted, and the high-precision elevation model MERIT-DEM and Sentinel-2 optical imagery are fused. <br><br> 3) Data quality description Validations show that this data set has high spatial accuracy (Wang et al, 2021). <br><br> 4) Data application achievements and Prospects This data set provides basic information of river networks, which can be used for hydrological model, land surface model, earth system model, as well as for mapping and spatial statistical analysis.
WANG Zifeng
1) Data content (including elements and significance) This data set contains information of flow direction, accumulation of vector river network of Lancang Mekong River Basin. <br><br> 2) Data sources and processing methods In this data set, the remote sensing stream buring (RSSB) method (Wang et al., 2021) is adopted, and the high-precision elevation model MERIT-DEM and Sentinel-2 optical imagery are fused. <br><br> 3) Data quality description Validations show that this data set has high spatial accuracy (Wang et al, 2021). <br><br> 4) Data application achievements and Prospects This data set provides basic information of river networks, which can be used for hydrological model, land surface model, earth system model, as well as for mapping and spatial statistical analysis.
WANG Zifeng
This dataset includes inland water data of five countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan) in the great lakes region of Central Asia, including the distribution of rivers, canals and lakes. The linear and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series produced by the United States, Australia, Canada, and the United Kingdom. The DCW database has not been updated since 1992, and has been provided free of charge since 2006.
XU Xiaofan, TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
Estimate of the thermal state of the ocean is of vital importance to understand the process of air-sea interaction and footprint of climate change in the Indian Ocean. However, the insufficiency and poor coverage of subsurface observation brings quite a large challenge to estimate the subsurface temperature changes accurately. Moreover, surface observation from satellite contains well sample and could also reflect the subsurface information partly. We derived the ‘synthetic profiles’ based on the ‘surface-subsurface regression’ method in order to enrich the subsurface profiles. Then the 3-dimentional gridded temperature dataset are established by combining both the in-situ and synthetic profiles through objective analysis technique. Inter-comparison between the previous released datasets, such as IAP, EN4 and Ishii, this dataset could capture main thermal signal among the Indian Ocean and shows more mesoscale signal because of its higher resolution. This dataset provides monthly mean gridded subsurface temperature estimate among 30E-105E, 45S-30N, ranging from 2005 to 2018. We provide with dataset which has horizontal resolution in quarter degree and 42 vertical levels from surface to 2000-m depth in netCDF format.
WANG Gongjie, ZHAO Liang
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
