This data set contains polygons of glacial lake extent on a near-global scale, averaged over five multi-year periods between 1990 and 2018.

This data set includes the glacial lake inventories in 1990, 2000, 2010, 2015, and 2020: Source: Information of Landsat data used including Path, Row, Sense, Date Date: Date of Landsat acquired Area: Area of glacial lake, with unit of km^2 Region: Hindu Kush, Karakoram, Western Himalaya, Central Himalaya, Eastern Himalaya, Nyainqêntanglha, Hengduan Shan Type: Pro-glacial lakes, Unconnected glacial lake, Supraglacial lake Error: Area uncertainty of glacial lake with unit of km^2 Perimeter: Perimeter of glacial lake with unit of m Elevation: Surface elevation of glacial lake (m a.s.l.) MG_ID1: RGI ID of parent glacier that glacial lake contacted MG_ID2: RGI ID of parent glacier that glacial lake contacted Not_RGI: Parent glacier does not exist in the RGI inventory NewPro: New formed proglacial lake

Plese cite as: Zhang, G., T. Bolch, T. Yao, D. R. Rounce, W. Chen, G. Veh, O. King, S. K. Allen, M. Wang, and W. Wang (2023), Underestimated mass loss from lake-terminating glaciers in the greater Himalaya, Nature Geoscience, doi: 10.1038/s41561-023-01150-1

The VT DEC (Vermont Department of Environmental Conservation) manages an inventory of lake and pond information. The "Lakes and Ponds Inventory" stores the Watershed Management Division's survey and monitoring information used to determines water quality problems and ways to solve them. A project completed by ADS-ANR GIS staff delineated the watersheds and created a web mapping application that allows Lakes Program staff to review, edit, and upload new watershed boundaries for the 800+ lakes in the Lakes Inventory. The layer has editor tracking enabled and filters on “accepted” watersheds.

Due to climate change

The Sierra Lakes Inventory Project (SLIP) was a research endeavor that ran from 1995-2002 and has supported research and management of Sierra Nevada aquatic ecosystems and their terrestrial interfaces. We described the physical characteristics of and surveyed aquatic communities for > 8,000 lentic water bodies in the southern Sierra Nevada, including lakes, ponds, marshes, and meadows. We also created digital map layers for these water bodies when such layers did not exist. The original objective of SLIP was to describe impacts of non-native fish on lake communities, but SLIP data has subsequently enabled study of additional ecological issues, including regional amphibian declines and their impacts on communities, and impacts of non-native fish on terrestrial species. In addition, these data are being used to develop fish removal efforts to restore aquatic ecosystems and recover endangered amphibians. The SLIP data is stored in a relational database that collectively describes water bodies (e.g., depth, elevation, location), surveys (conditions, effort), and communities (including approximately 170 fish, amphibian, reptile, benthic macroinvertebrate, and zooplankton taxa).

The VT DEC (Vermont Department of Environmental Conservation) manages an inventory of lake and pond information. The "Lakes and Ponds Inventory" stores the Water Quality Division's survey and monitoring information used to determines water quality problems and ways to solve them.

The study is towards the preparation of a glacial lake inventory for Arunachal Pradesh and a critical analysis of 40 years temporal change in the lake area along with the long-term change of temperature and precipitation, the characteristics of glacial lake outburst probability and the hazard assessment and the relationship between glacial lakes and climate change. Results showed that there was an obvious increase in the temperature of the basin since 1970. Specifically, in the mountainous area, the significantly increasing temperature in the summer and autumn seasons accelerated the melting rate of glaciers and the formation of a glacial lake. With the use of LANDSAT (1, 2, 4, 5, 7, 8) datasets from 1973-2013, Google earth imagery of various data and Toposheets from SOI the glacial lake inventory was created. In Arunachal Pradesh, the number of glacier lakes increased by 142 between 1970 and 2013. The rate of area change in glacial lakes over the years varies as high as 1000% in the time span of 1970-2013, and almost 370% in the time span of 1980-2013 and 2001-2013. Decade wise analysis shows that the highest number of lakes and higher amount of area change over 40 years is found in the zone 4000-4500 m. Based on remote sensing techniques only with topographic analysis viz. elevation, slope, aspect, the curvature of lakes derived from ASTER DEM data, 33 lakes were considered critical and 28 lakes potentially critical in Arunachal Pradesh after the Hazard assessment of the glacial lake inventory. The evaluation and classification of the outburst probabilities of glacial lakes by remote sensing are challenging and different approaches have been presented in many literatures. The approach present in this study is efficient for analyzing a large number of lakes that need further quantitative assessment. In addition to the rise in temperature and extreme climatic conditions observed since 2000, the glacier lakes associated with the glacier melting are increasing in number and size and to some extent, the potential of glacial lake outburst floods increases. Thereby, persistent attention should be paid to the influences of climatic change in the region and future disaster preparedness by authorities and locals.

The primary purose of the reconnaissance level (RlC) inventory of Kline Lake was to gather information on the presence or absence of fish in the lake, and to gather preliminary data on biophysical attributes of the lake. Kline Lake was included in a secondary level reconnaissance inventory of 34 lakes located in the northern portions of the Kalum, Kispiox, Bulkley and Morice Forest Districts.

Climate change accelerates the extensive retreat of glaciers, leading to the widespread development of glacial lakes. A holistic picture on the spatial distribution of glacial lakes worldwide is crucial for tracking the outburst flood hazards. By employing a semi-automated mapping approach and rigorous quality control, this study inventories global glacial lakes (≥0.01 km2) worldwide (the ice cap/sheet of Antarctic and Greenland excluded). The evaluation result shows that this global glacial lake data has an overall accuracy of 89.37% and 91.42% in number and area, respectively.

The purpose of this survey is to conduct secondary lake inventories in the southern portions of the Morice and Lakes Forest Districts (Prince Rupert Forest Region). The lake was accessed by road from Burns Lake. From Burns Lake, travel northwest on Hwy 16 for approximately 35 km (depends on starting point); turn right; travel 900 m (past house) and turn right onto trail; travel approximately 100m along trail to private dock (prior to entry, request permission from local resident to access driveway and/or dock)

This spatial layer displays lakes that have had full or partial surveys, and contains information regarding the dates of those surveys and an indication of the data collected on each survey date

The purpose of this survey is to conduct secondary lake inventories in the southern portions of the Morice and Lakes Forest Districts (Prince Rupert Forest Region). The lake was accessed by floatplane from Burns Lake. Flying time was approximately 25-30 minutes northwest of Burns Lake. A road (Mitchell Road FSR) runs along the east side of the lake; however, trails leading to the lake were not evident in air photos.

This data set contains lake boundaries, volume changes, and gridded elevations for 124 active subglacial lakes beneath the Antarctic ice sheet. Lakes were identified using laser altimetry data obtained from 2003 to 2009 by NASA's Ice, Cloud, and Land Elevation Satellite (ICESat) mission. The data are provided in Keyhole Markup Language (KML), comma-separated values (CSV), and GEOTiff formats, and are available via FTP.

This dataset is an inventory of all lakes within 1 km (kilometer) of a glacier in Alaska and northwest Canada (Randolph Glacier Inventory (RGI) Region 01) for four different time periods: 1984-1988, 1997-2001, 2007-2011, and 2016-2019. Outlines were created from ~5-year Landsat composites made in Google Earth Engine. Lakes are identified using supervised classification, thresholding, and manual verification. For each lake, area, dam type, topological location, and associated RGI glacier ID (identification) are reported. This time-varying inventory was created to help understand historic changes in ice-marginal lakes in Alaska and the role of dam type and position in characterizing lake trends.

Climate change accelerates the extensive retreat and downwasting of glaciers, leading to the widespread formations and rapid growths of glacial lakes worldwide. Spatial distribution information on glacial lakes is crucial for evidencing the climate change impacts and warning about the hazards of glacial lake outbursts. The existing glacial lake inventories are mostly region or basin specific. Therefore, there is therefore a pressing need to obtain a spatially and temporally constrained dataset of global glacial lakes. Based on a semi-automated approach, this study inventories 117,352 glacial lakes (≥0.01 km2) worldwide, with a net area of 24,755.84 ± 2,971.33 km2. The evaluation result shows that the global glacial lake data have an average overall accuracy of 89.37% and 91.42% in number and area, respectively. The global glacial lakes are widely distributed in different altitudes, ranging from the Earth’s third pole to the coastal zones. Most glacial lakes are distributed in Greenland, High-Mountain Asia (HMA), Alaska, western and northern Canada, and the cordilleras. The number and total area of glacial lakes located at the altitude below 1000 m account for 59.35% and 82.84%, respectively, whereas the lakes spanning more than 3000 m are dominantly observed in HMA. The number of glacial lakes between 0.01–0.1 km2 accounts for 77.24% of the total count but only 11.82% of the total area. The classification of glacial lakes as four groups (non–glacier-fed, ice-uncontacted proglacial, ice-contacted proglacial, and supraglacial lakes) indicates that the ice-uncontacted proglacial lakes dominate the number (67.07%) and area (53.04%) worldwide. This dataset is expected to advance the monitoring of glacial lake expansions and the assessment of glacial hazard risk.

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.

This ice-marginal lake dataset is a series of annual inventories, mapping the extent and presence of lakes across Greenland that share a margin with the Greenland Ice Sheet and/or the surrounding periphery glaciers. The annual inventories provide a comprehensive record of all identified ice marginal lakes, which have been detected using remote sensing techniques. The inventory series was created to better understand the impact of ice-marginal lake change on the future sea level budget and the terrestrial and marine landscapes of Greenland, such as its ecosystems and human activities. The dataset is a complete inventory series of Greenland, with no absent data. Terms of use If the data are presented or used to support results of any kind, please include an acknowledgement and references to the applicable publications: How, P. et al. (2025) "Greenland Ice-Marginal Lake Inventory annual time-series Edition 1". GEUS Dataverse. https://doi.org/10.22008/FK2/MBKW9N How, P. et al. (In Review) "Greenland ice-marginal lake inventory series from 2016 to 2023". Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2025-18 How, P. (In Review) "GrIML: A Python package for investigating Greenland's ice-marginal lakes under a changing climate". J. Open Source Software. https://joss.theoj.org/papers/a2e10775df44b89f26b0ac9dbf8bc9e3 How, P. et al. (2021) "Greenland-wide inventory of ice marginal lakes using a multi-method approach". Sci. Rep. 11, 4481. https://doi.org/10.1038/s41598-021-83509-1 Detailed description The detected lakes are presented as polygon vector features in GeoPackage format (.gpkg), with coordinates provided in the WGS NSIDC Sea Ice Polar Stereographic North (EPSG:3413) projected coordinate system. Ice-marginal lakes were identified using three independent remote sensing methods: 1) multi-temporal backscatter classification from Sentinel-1 synthetic aperture radar imagery; 2) multi-spectral indices classification from Sentinel-2 optical imagery; and 3) sink detection from the ArcticDEM (v3). All data were compiled and filtered in a semi-automated approach, using a modified version of the MEaSUREs GIMP ice mask (https://nsidc.org/data/NSIDC-0714/versions/1) to clip the dataset to within 1 km of the ice margin. Each detected lake was then verified manually. The methodology is open-source and provided at https://github.com/GEUS-Glaciology-and-Climate/GrIML for full reproducibility. Please consult the dataset readme provided with this dataset for further information on the associated metadata. Acknowledgements The inventory series of ice-marginal lakes in Greenland has been produced as part of the European Space Agency (ESA) Living Planet Fellowship project "Examining GReenland’s Ice Marginal Lakes under a changing climate (GrIML)", which is a follow-on effort to the 2017 inventory of ice-marginal lakes created under the European Space Agency (ESA) Climate Change Initiative (CCI) in Option 6 of the Glaciers_cci project (4000109873/14/I-NB). Upkeep and continuation of the inventory series is supported by PROMICE, funded by the Geological Survey of Denmark and Greenland (GEUS) and the Danish Ministry of Climate, Energy and Utilities under the Danish Cooperation for Environment in the Arctic (DANCEA), conducted in collaboration with DTU Space (Technical University of Denmark) and Asiaq Greenland Survey.

In 2019, an invasive plant inventory of priority invasive plant species in priority areas was conducted at Ruby Lake National Wildlife Refuge. Results from this effort will inform the development of invasive plant management objectives, strategies, and serves as a baseline for assessing change in the status of invasive plant distribution or abundance over time. This report holds the data documenting this effort.

Gilmore Lake was surveyed as part of the Burns Lake-Houston small lakes project in which a total of 10 lakes were examined. All of the lakes historically supported rainbow trout sport fisheries, but the fishery has declined over the last five to ten years. This decline has primarily been attributed to poor recruitment of rainbows due to a lack of suitable and accessible spawning habitat. This small lakes project was concerned with the evaluation of rainbow trout spawning habitat for these lakes to give recommendations for possible enhancements of rainbow spawning sites.

In 2013, an invasive plant inventory of priority invasive plant species in priority areas was conducted at Ruby Lake National Wildlife Refuge. Results from this effort will inform the development of invasive plant management objectives, strategies, and serves as a baseline for assessing change in the status of invasive plant distribution or abundance over time.