The global annual lake ice phenological dataset includes the freeze-up date, break-up date for 74,245 lakes in the northern hemisphere. The dataset is divided into three parts: 1) current data, obtained from MODIS productions through a DLRM model (with parameters provided), covering the period of 2001 to 2020; 2) historical and 3) future simulation data, obtained from the temperature-based lake-specific models, for the periods of 1861-2005 and 2006-2099, respectively. The historical and future simulations were only performed for 30,063 lakes that meet the model conditions and are presented in the dataset.
WANG Xinchi
This dataset contains in-situ lake level observations at Lumajiangdong Co, Memar Co，Camelot Lake and Jieze Caka on the western Tibetan Plateau. The lake water level was monitored by HOBO water level logger (U20-001-01) or Solist water level logger, which was installed on the lake shore. Lake level data was then calibrated by using the barometer installed near the lake. Then the real water level changes were obtained. The accuracy was less than 0.5 cm. The items of this dataset are as follows: Daily lake level changes at Lumajiangdong Co from 2016 to 2021; Daily lake level changes at Memar Co from 2017 to 2019 and from 2020 to 2021; Daily lake level changes at Luotuo Lake from 2019 to 2020. Daily lake level changes at Jieze Caka Lake from 2019 to 2020. Water level, unit: m.
LEI Yanbin
This data set provides the lake ice phenology of 71 lakes on the Qinghai Tibet Plateau from 2001 to 2020, including the freeze-up start, freeze-up end, break-up start, break-up end, complete freezing duration, and ice cover duration. The data set was extracted from the cloud-gap-filled MODIS daily snow product based on a dynamic changed threshold. Compared with the coarse resolution passive microwave AMSR-E/2 lake ice phenology data set, the average absolute error of the freeze-up start date was 2.33-7.25 days, and the average absolute error of the break-up end date was 1.75-4.67 days. The data can provide a data basis for the relevant research on the response of the Qinghai Tibet Plateau lake system to climate change.
CAI Yu, KE Changqing
Based on Landsat data (kh-9 data in 1976 as auxiliary data), glacial lake data of nearly 40 years (1970s-2018) in the western Nyainqentanglha range were obtained by manual digitization and visual interpretation. The variation characteristics of glacial lake over 0.0036 square kilometers in terms of type, size, elevation and watershed were analyzed in detail. The results show that, between 1976 and 2018, the number of glacial lakes increased by 56% from 192 to 299 and their total area increased by 35% from 6.75 ± 0.13 square kilometers to 9.12 ± 0.13 square kilometers ; the type of glacial lake is changing obviously; the smaller glacial lake is changing faster; the expansion of glacial lake is developing to higher altitude.
LUO Wei, ZHANG Guoqing
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
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 2021, and provided a long and dense lake observations between the 1970s and 2021. We found that the number of lakes, with area larger than 1 k㎡ , has increased to ~1400 in 2021 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.
ZHANG Guoqing
The dataset includes lake ice phenology information of 132 lakes across the Tibetan Plateau (with area larger than 40 km2) from 1978 to 2016 (freeze-up start date, freeze-up end date, break-up start date, break-up end, completely ice-duration and ice duration). The data set uses the combination of model and remote sensing to obtain the phenological information. Firstly, Using the average lake surface temperature extracted by MOD11A2 as calibration data, daily scale long-time series lake surface temperature series was simulated based on an improved lake semi-physical model (air2water). Then the temperature threshold of lake ice phenology was determined by the mod10a1 snow cover product. Compared with the existing research results and data sets, the correlation (R-square) is higher than 0.75. Combined with the advantages of remote sensing and numerical model, this dataset provides support for the analysis of water-air interface exchange, water or heat balance, biochemical processes and their response to climate change of lakes on a large spatio-temporal scale across the Tibetan Plateau.
GUO Linan , WU Yanhong, ZHENG Hongxing, ZHANG Bing , CHI Haojing , FAN Lanxin
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.
ZHANG Yu, ZHANG Guoqing
This dataset consists of four files including (1) Lake ice thickness of 16 large lakes measured by satellite altimeters for 1992-2019 (Altimetric LIT for 16 large lakes.xlsx); (2) Daily lake ice thickness and lake surface snow depth of 1,313 lakes with an area > 50 km2 in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018 (in NetCDF format); (3) Future lake ice thickness and surface snow depth for 2071-2099 modeled by the lake ice model with a modified ice growth module (table S1.xlsx); (4) A lookup table containing lake IDs, names, locations, and areas. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of freshwater ice as well as its response to climate change.
LI Xingdong, LONG Di, HUANG Qi, ZHAO Fanyu
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.
QIU Yubao
This data is mainly the temperature data of the meteorological station set up by the Southeast Tibet station of the Chinese Academy of Sciences in April 2014, located in a ri village, Ranwu Town, Basu County, Changdu City, by the lake in Ranwu, with a geographical location of 96.7699e, 29.4364n and 3920m The model of the instrument probe is hmp155a, the probe is 2m away from the surface, and the underlying surface is alpine meadow. Some original data are missing. It is obtained by correction and interpolation through the flux station also located in the area, the nearby sidaoban meteorological station and the Ranwu station of the Meteorological Bureau. This data is a rare shared data in the region, which can be used as the background basic data of regional climate, rivers, lakes, glaciers, ecology, etc. When using data, the article should reflect the Southeast Tibet station of Chinese Academy of Sciences, and higher precision data can be contacted with the data author.
Luo Lun
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.
Qiu Yubao, Tian Bangsen
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.
QIU Yubao
Lake ice phenology is a seasonal cyclical feature that describes lake ice coverage. The change of lake ice phenology is an important part of carbon, water and energy process study, and one of the sensitive factors of climate change. This dataset is a lake ice phenology based on passive microwave inversion, including lake ice phenology of 200 lakes in the Tibetan Plateau and high latitudes area of the Northern Hemisphere from 2002 to 2018 (including freeze-up start date, freeze-up end date, break-up start date, and break-up end date of the lakes), data of some lakes can date back to 1978. This data is basically consistent with the MODIS monitoring results from the same time with an interpretation error of 2-4 days. Users can use this data to conduct climate change study in the Northern Hemisphere.
QIU Yubao
The medium-resolution MODIS river and lake ice phenology data set in the high latitudes of the northern hemisphere from 2002 to 2019 is based on the Normalized Difference Snow Index (NDSI) data of the Moderate Resolution Imaging Spectroradiometer(MODIS). Daily lake iceextent and coverage under clear-sky conditions was examined byemploying the conventional SNOWMAP algorithm, and thoseunder cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions througha series of steps.The lake ice phenology information obtained in this dataset was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13.
QIU Yubao
This glacial lake inventory receives joint support from International Centre for Integrated Mountain Development (ICIMOD) and United Nations Environment Programme/Regional Resource Centre, Asia and the Pacific (UNEP/RRC-AP). 1. This glacial lake inventory referred to Landsat 4/5 (MSS, TM/1984/1999), Landsat 7 (TM & ETM+), IRS-1C, LISS-III (1995 IRS-1C), (1997 IRS-1D) and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in 2000. 2. Glacial Lake Inventory Coverage: Tista Basin, Sikkim Region 3. Glacial Lake Inventory includes: glacial lake inventory, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 4. Projection parameter: Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Sikkim) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00’00” E Central parallel: 26°00’00” N Scale factor: 0.998786 Standard parallel 1: 23°09’28.17” N Standard parallel 2: 28°49’8.18” N Minimum X Value: 2545172 Maximum X Value: 2645240 Minimum Y Value: 1026436 Maximum Y Value: 1163523 For a detailed data description, please refer to the data file and report.
Samjwal Ratna Bajracharya Samjwal Ratna Bajracharya
This glacial lake inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resource Centre, Asia and The Pacific (UNEP/RRC-AP). 1. The glacial lake inventory adopts the Landsat remote sensing data and reflects the status of glacial lakes in the Pakistan region from 2003 to 2004. 2. In terms of spatial coverage, the glacial lake inventory covers the Swat, Chitral, Gilgit, Hunza, Shigar, Shyok, Upper, Indus, Shingo, Astor and Jhelum river basins in the upper reaches of the Indus River. 3. The glacial lake inventory data include the glacial lake code, glacial lake type, glacial lake area, distance between the glacier and the glacial lake, glaciers related to the glacial lake, etc. For detailed descriptions of the data, please refer to the data file and report.
Samjwal Ratna Bajracharya Samjwal Ratna Bajracharya, Basanta Shrestha, Sharad Prasad Joshi
This glacial lake inventory receives joint support from the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resources Centre for Asia and the Pacific (UNEP/RRC-AP), Cold and Arid Region Environmental and Engineering Research Institute (CAREERI). 9. This glacial lake cataloging uses Landsat (TM and ETM), Aster and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in the Himalayas in 2004. 10. Glacial lake catalogue coverage: the Himalayan region, Pumqu (Arun), Rongxer (Tama Koshi), Poiqu (Bhote-Sun Koshi), Jilongcangbu (Trishuli), Zangbuqin (Budhigandaki), Majiacangbu (Humla Karnali) and others. 11. Glacial Lake cataloging includes glacial lake cataloging, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 12. Data projection information： Projection: Transverse_Mercator False_Easting: 500000.000000 False_Northing: 0.000000 Central_Meridian: 87.000000 Scale_Factor: 0.999600 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: GCS_WGS_1984 Angular Unit: Degree (0.017453292519943299) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_WGS_1984 Spheroid: WGS_1984 Semimajor Axis: 6378137.000000000000000000 Semiminor Axis: 6356752.314245179300000000 Inverse Flattening: 298.257223563000030000 For a detailed data description, please refer to the data file and report.
International Centre for Integrated Mountain Development (ICIMOD)
This glacial lake inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nation Environment Programme/Regional Resources Centre, Asia and The Pacific (UNEP/RRC-AP). 1. The glacial lake inventory uses the remote sensing data of Landsat，reflecting the current status of glacial lakes larger than 0.01 square kilometers in Nepal in 2000. 2. The spatial coverage of the glacial lake inventory： Nepal 3. Contents of the glacial lake inventory: glacial lake code, glacial lake types, glacial lake area, distance between glacial lakes and the glaciers, related glaciers, etc. 4. Data Projection: Grid Zone IIA Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 23°09'28.17"N Standard parallel 2: 28°49'8.18"N Minimum X Value: 1920240 Maximum X Value: 2651760 Minimum Y Value: 914398 Maximum Y Value: 1188720 Grid Zone IIB Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 21°30'00"N Standard parallel 2: 30°00'00"N Minimum X Value: 1823188 Maximum X Value: 2000644 Minimum Y Value: 1306643 Maximum Y Value: 1433476 For a detailed data description, please refer to the data file and report.
Sharad Prasad Joshi, Samjwal Ratna Bajracharya Samjwal Ratna Bajracharya
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.
International Centre for Integrated Mountain Development (ICIMOD)
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