Reconstruction data set of mass balance of seven glaciers in Qinghai Tibet Plateau (1975-2013)

The data set includes the mass balances of Hailuogou Glacier, Parlung No.94 Glacier, Qiyi glacier, Xiaodongkemadi Glacier, Muztagh No.15 Glacier, Meikuang Glacier and NM551 Glacier in the Qinghai Tibet Plateau from 1975 to 2013. Based on several mass balance observations collected from World Glacier Inventory (https://nsidc.org/data/g10002/versions/1) and The Third Pole Environment Database (http://en.tpedatabase.cn/, doi:10.11888/GlaciologyGeocryology.tpe.96.db) by Tandong Yao and the meteorological data obtained from Global Land Assimilation System (GLDAS), the mass balances of the above seven glaciers from 1975 to 2013 are reconstructed by using the glacier material balance calculation formula. This reconstruction data is based on the published glacier material balance data to calibrate the parameters in the glacier material balance formula, and to reconstruct the long-time series material balance by using the glacier material balance formula, in which the parameter calibration results and the reconstruction results of the long-time series data are compared with the relevant research results, demonstrating the rationality of the data results Please refer to the following papers. The data can be used to study the change of water resources in the glacial region, expand the data set of Glacier Mass Balance in the Qinghai Tibet Plateau, and provide reference for the future research of Glacier Mass Balance reconstruction.

0 2020-06-30

Glacier volume dataset of the Qinghai-Tibetan Plateau in 1970s and 2000s

The dataset contains all individual glacial storage (unit: km3) over the Qinghai-Tibetan Plateau in 1970s and 2000s. It is sourced from the resultant data of the paper entitled "Consolidating the Randolph Glacier Inventory and the Glacier Inventory of China over the Qinghai-Tibetan Plateau and Investigating Glacier Changes Since the mid-20th Century". The first draft of this paper has been completed and is planned to be submitted to Earth System Science Data journal. The baseline glacier inventories in 1970s and 2000s are the Randolph Glacier Inventory 4.0 dataset, and the Glacier Inventory of China, respectively. Based on the individual glacial boundaries extracted from the above-mentioned two datasets, the grid-based bedrock elevation dataset (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m), and the glacier surface elevation obtained by a slope-dependent method, the individual glacier volumes in 1970s and 2000s are then calculated. In addition, the calculated results of individual glacier volumes in this study have been compared and verified with the existent results of several glacier volumes, relevant remote sensing datasets, and the global glacier thickness dataset based on the average of multiple glacier model outputs (https://www.research-collection.ethz.ch/handle/20.500.11850/315707, doi:10.3929/ethz-b-000315707), and the errors in the calculations have also been quantified (the error rates of all mountains are within 10%). The established dataset in this study is expected to provide the data basis for the future regional water resources estimation and glacier ablation-involved researches. Moreover, the acquisition of the data also provides a new idea for the future glacier storage estimation.

0 2020-06-28

Meteorological observation data of Kunsha Glacier (2015-2017)

This data set includes the temperature, precipitation, relative humidity, wind speed, wind direction and other daily values in the observation point of Kunsha Glacier. The data is observed from October 3, 2015 to September 19, 2017. It is measured by automatic meteorological station (Onset Company) and a piece of data is recorded every 2 hours. The original data forms a continuous time series after quality control, and the daily mean index data is obtained through calculation. The original data meets the accuracy requirements of China Meteorological Administration (CMA) and the World Meteorological Organization (WMO) for meteorological observation. Quality control includes eliminating the systematic error caused by the missing point data and sensor failure. The data is stored as an excel file.

0 2020-06-24

The Basic datasets of Urumqi river basin in Chinese Cryospheric Information System

Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, and provide parameters and verification data for the development of response and feedback models of permafrost, glacier and snow cover to global changes under GIS framework. On the other hand, the system collates and rescues valuable cryospheric data to provide a scientific, efficient and safe management and analysis tool. Chinese Cryospheric Information System contains three basic databases of different research regions. The basic database of Urumqi river basin is one of three basic databases, which covers the Urumqi river basin in tianshan mountain, east longitude 86-89 °, and north latitude 42-45 °, mainly containing the following data: 1. Cryospheric data.Include: Distribution of glacier no. 1 and glacier no. 2; 2. Natural environment and resources.Include: Terrain digital elevation: elevation, slope, slope direction; Hydrology: current situation of water resource utilization;Surface water; Surface characteristics: vegetation type;Soil type;Land resource evaluation map;Land use status map; 3. Social and economic resources: a change map of human action; Please refer to the documents (in Chinese): "Chinese Cryospheric Information System design. Doc" and "Chinese Cryospheric Information System data dictionary. Doc".

0 2020-06-23

Basic datasets of the Tibetan Plateau in Chinese Cryospheric Information System

Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese Cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic datasets of the Tibet Plateau mainly takes the Tibetan Plateau as the research region, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Information System.doc", "Datasheet of Chinese Cryospheric Information System.DOC", "Database of the Tibetan Plateau.DOC" and "Database of the Tibetan Plateau 2.DOC".

0 2020-06-23

An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

0 2020-06-23

Long-term glacier melt fluctuations of Qiangyong Glacier on the Tibetan Plateau over the past 2500 yr

The source of the data is paper: Zhang, J.F., Xu, B.Q., Turner, F., Zhou, L.P., Gao, P., Lü, X.M., & Nesje, A. (2017). Long-term glacier melt fluctuations over the past 2500 yr in monsoonal high asia revealed by radiocarbon-dated lacustrine pollen concentrates. Geology, 45(4), 359-362. In this paper, the researcher of Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Baiqing Xu, with his postdoctoral fellow, Jifeng Zhang, and collaborators from Peking University and other institutions, propose that the OPE (“old pollen effect”, the offset between the calibrated 14C ages of pollen in lake sediments and the sediment depositional age) as a new indicator of glacier melt intensity and fluctuations by measuring the radiocarbon ages of the sediments of the proglacial lake of Qiangyong Glacier on the southern Tibetan Plateau with multi-methods (bulk organic matter, pollen concentrates and plant residues). This research suggests that hemispheric-scale temperature variations and mid-latitude Westerlies may be the main controllers of the late Holocene glacier variability in monsoonal High Asia. It also shows that the 20th-century glacier melt intensity exceeded that of two historical warm epochs (the Medieval Warm Period, and the Iron/Roman Age Optimum) and is unprecedented at least for the past 2.5 k.y. This data is provided by the author of the paper, it contains long-term glacier melt fluctuations of Qiangyong Glacier over the past 2500 yr reconstructed by the OPE. A 3.06-m-long core (QYL09-4) and a 1.06-m-long parallel gravity core (QY-3) were retrieved by the researchers from the depositional center of Qiangyong Co. Using a new composite extraction procedure, they obtained relatively pure pollen concentrates and plant residue concentrates (PRC; >125 μm) from the finely laminated sediments. Bulk organic matter and the PRC and pollen fractions were used for 14C dating independently. All 14C ages were calibrated with IntCal13 (Reimer et al., 2013). The age-depth model is based on 210Pb and 137Cs ages and five 14C ages of PRC. Only the youngest PRC ages were used for the age-depth model, whereas older ages that produce a stratigraphic reversal and are apparently influenced by redeposited or aquatic plant material were rejected. The deposition model was constructed using the P_Sequence algorithm in Oxcal 4.2 (Bronk Ramsey, 2008). For the calculation of the offset between the calibrated pollen 14C ages and the sediment depositional age, 2σ intervals for interpolated ages according to the deposition model were subtracted from calibrated pollen ages (2σ span), resulting in the age offset between pollen and estimated sediment ages (ΔAgepollen). This data is radiocarbon ages and the calculated ΔAgepollen of core QYL09-4 from a proglacial lake of Qiangyong Glacier. The data contains fields as follows: Lab No. Dating Material Depth (cm) 14C age (yr BP) ∆Agepollen (≥95.4 % yrs) Sediment Age (CE) See attachments for data details: ZhangJF et al. 2017 GEOLOGY_Long-term glacier melt fluctuations over the past 2500 yr on the Tibetan Plateau.pdf.

0 2020-06-16

Inventory dataset of glacial lakes in the Sikkim Region, India (2000)

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.

0 2020-06-09

Inventory of glacial lakes in Pakistan (2003-2004)

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.

0 2020-06-09

Glacier inventory dataset of Nepal (2000)

This glacier 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 glacier inventory uses the remote sensing data of Landsat,reflecting the current status of glaciers in Nepal in 2000. 2、The spatial coverage of the glacier inventory: Nepal 3、Contents of the glacier inventory: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, 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.

0 2020-06-09