This glacial lake inventory receives joint support from the International Centre for Integrated Mountain Development (ICIMOD) and United Nations Environment Programme/Regional Resource Centre, Asia and the Pacific (UNEP/RRC-AP). 5. This glacial lake inventory referred to Landsat 4/5 (MSS and TM), SPOT(XS), IRS-1C/1D(LISS-III) and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in 2004. 6. Glacial Lake Inventory Coverage: Yamuna basin, Ravi basin, Chenab basin, Satluj River Basin and others. 7. The Glacial Lake Inventory includes glacial lake inventory, glacial lake type, glacial lake width, glacial lake orientation, glacial lake length from the glacier and other attributes. 8. Projection parameter: Projection: Albers Equal Area Conic Ellipsoid: WGS 84 Datum: WGS 1984 False easting: 0.0000000 False northing: 0.0000000 Central meridian: 82° 30’E Central parallel: 0° 0’ N Latitude of first parallel: 20° N Latitude of second parallel: 35° N For a detailed data description, please refer to the data file and report.
The data set integrated glacier inventory data and 426 Landsat TM/ETM+/OLI images, and adopted manual visual interpretation to extract glacial lake boundaries within a 10-km buffer from glacier terminals using ArcGIS and ENVI software, normalized difference water index maps, and Google Earth images. It was established that 26,089 and 28,953 glacial lakes in HMA, with sizes of 0.0054–5.83 km2, covered a combined area of 1692.74 ± 231.44 and 1955.94 ± 259.68 km2 in 1990 and 2018, respectively.The current glacial lake inventory provided fundamental data for water resource evaluation, assessment of glacial lake outburst floods, and glacier hydrology research in the mountain cryosphere region
There are three types of glacial lakes: supraglacial lakes, lakes attached to the end of the glacier and lakes not attached to the end of the glacier. Based on this classification, the following properties are studied: the variation in the number and area of glacial lakes in different basins in the Third Pole region, the changes in extent in terms of size and area, distance from glaciers, the differences in area changes between lakes with and without the supply of glacial melt water runoff, the characteristics of changes in the glacial lake area with respect to elevation, etc. Data source: Landsat TM/ETM+ 1990, 2000, 2010. The data were visually interpreted, which included checking and editing by comparing the original image with Google Earth images when the area was greater than 0.003 square kilometres. The data were applied to glacial lake changes and glacial lake outburst flood assessments in the Third Pole region. Data type: Vector data. Projected Coordinate System: Albers Conical Equal Area.
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.
Lake ice is an important parameter of the cryosphere, its change is closely related to the climate parameters such as temperature and precipitation, and can directly reflect the climate change, so it is an important indicator of the regional climate parameter change. However, because the research area is often located in the area with poor natural environment and few population, large-scale field observation is difficult to carry out, so sentinel 1 satellite data is used. The spatial resolution of 10 m and the temporal resolution of better than 30 days are used to monitor the changes of different types of lake ice, which fills the observation gap. Hmrf algorithm is used to classify different types of lake ice. Through time series analysis of the distribution of different types of lake ice in three polar regions with a part area of more than 25km2, a lake ice type data set is formed. The distribution of different types of lake ice in these lakes can be obtained. The data includes the serial number of the processed lake, the year in which it is located and the serial number in the time series, vector and other information. The data set includes the algorithm used, sentinel-1 satellite data used, imaging time, polar area, lake ice type and other information. Users can determine the changes of different types of lake ice in the time series according to the vector file.
The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2018, and provided a long and dense lake observations between the 1970s and 2018. We found that the number of lakes, with area larger than 1 km2, has increased to ~1400 in 2018 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
There are many lakes on the Tibetan Plateau. The phenology and duration of lake ice age in this area is very sensitive to regional and global climate change, so it is used as a key indicator of climate change research, especially the comparative study of environmental changes in the Earth's three poles. However, due to its harsh natural environment and sparse population, it lacked routine field measurements of lake ice phenology. Using the Moderate-resolution Imaging Spectroradiometer (MODIS) to normalize the Different Snow Index (NDSI) data, the lake ice was monitored at a resolution of 500 meters to fill the observation gap. The traditional snow map algorithm was used to detect the daily ice volume and coverage extent of lakes under sunny condition. The spatial and temporal continuity of lake surface conditions was applied to re-determine the daily ice volume and coverage extent of lakes under cloud cover condition through a series of steps. Time series analysis was performed on 308 lakes larger than 3 k㎡ to determine effective record of lake ice extent and coverage, then to form a daily lake ice extent and coverage data set. And furthermore, four lake ice phenological parameters: freeze-up start ( FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) can be obtained from 216 lakes of the data set, and two parameters: FUS and BUE can be obtained from the other 92 lakes.
River ice is the main component of the cryosphere, and the freezing of rivers in the polar region has a significant impact on the Arctic shipping and transportation industry. With the construction of "ice silk road" between China and Russia, monitoring the change of river ice in Erqis river basin can provide theoretical basis for river navigation. The sparse distribution of hydrological stations in the Arctic limits the study of river ice. The limited available data of hydrological stations show that the trend of river ice rupture is ahead of schedule, but the specific climate mechanism driving this trend is very complex. Therefore, optical data with high temporal resolution (such as MODIS products) are suitable for monitoring river ice phenology and mapping river ice cover range, which is helpful to understand the process of river ice rupture. Based on MODIS and passive microwave data, a method of monitoring river ice in Erqis River Basin by using different remote sensing data is realized in this study, in order to analyze the phenological parameters of river ice such as the time of river closure, the time of river closure, the speed of river opening, the speed of river closure and the duration of freezing period. At the same time, it is helpful to understand the response of river ice breaking process to Arctic climate warming.