本数据集基于在龙巴萨巴湖的冰碛坝上安装的自动气象站，记录冰碛坝上的2012年11月11日至2021年3月6日期间的气温数据。在龙巴萨巴湖出水口附近的坝体上安装了5个地温探头 (Campbell 109-L，误差为±0.2°C)，深度分别为10 cm、30 cm、60 cm、100 cm和150 cm，传感器都连接到数据采集仪 (Campbell CR3000-XT)，每10分钟自动记录一次。从定日气象站 (28°38′N， 87°05′E， 4 300 m a.s.l.) 收集了1960~2021年的日平均气温，通过地温-气温线性关系插值出1959-2021年的龙巴萨巴冰碛坝10 cm深处的土壤温度。另外，从CMIP6官方网站（https://esgf-node.llnl.gov/search/cmip6） 获取了2015-2099年SSP1-2.6、SSP2-4.5和SSP5-8.5三种未来情景下的日平均近地面空气温度，选取了其中地温-气温相关系数R2>0.5的8种气候模式换算成冰碛坝10 cm深度处土壤温度。以冰碛坝10 cm深度处的土壤温度作为输入数据，基于COMSOL Multiphysics软件的传热模块，模拟了观测期1959-2021年和未来情景SSP1-2.6，SSP2-4.5和SSP5-8.5下2015-2099年坝体的冻融过程和最大融化深度，并计算了未来情景SSP1-2.6，SSP2-4.5和SSP5-8.5下坝体内部埋藏冰的消融深度。结合PS-InSAR技术获取了坝体表面的形变数据。本次龙巴萨巴冰碛坝温度数据集为龙巴萨巴冰碛坝活动层内的冻融过程研究提供了基础数据。\n
1. 数据集命名\n
Temperature data from Longbasaba automatic meteorological station_2012-2021.xlsx\n
Temperature data from Dingri meteorological station_1959-2021.xlsx\n
Temperature data from CMIP6_2015-2099.xlsx\n
Freeze-thaw process_1959-2021.xlsx\n
Freeze-thaw process of SSP1-2.6_2015-2099.xlsx\n
Freeze-thaw process of SSP2-4.5_2015-2099.xlsx\n
Freeze-thaw process of SSP5-8.5_2015-2099.xlsx\n
Active layer thickness_1959-2020.xlsx\n
Buried ice melting depth of SSP1-2.6_2015-2099\n
Buried ice melting depth of SSP2-4.5_2015-2099\n
Buried ice melting depth of SSP5-8.5_2015-2099\n
Dam_deformationm_deformation.tif\n
2. 属性信息 \n
Time_stamp: 数据的时间戳\n
Ta_Longbsaba: 龙巴萨巴冰碛坝日平均气温 (℃)\n
Ts_10 cm, Ts_30 cm, Ts_60 cm, Ts_100 cm, Ts_150 cm: 龙巴萨巴冰碛坝10 cm, 30 cm, 60 cm, 100 cm, 150 cm处的日平均土壤温度 (℃)\n
Ta_Dingri :定日气象站的日平均气温 (℃) \n
Ta_Longbasba_r: 重建的龙巴萨巴冰碛坝日平均气温 (℃)\n
Ts_10 cm_r:重建的龙巴萨巴冰碛坝10 cm深度处日土壤气温 (℃)\n
Simulated Ts of different depths:不同深度的土壤温度模拟值 (℃)\n
YMTD: 年最大融化深度 (m)\n
BIMD: 埋藏冰融化深度 (m)", "ds_source": "
1. 龙巴萨巴冰碛坝温度数据\n
由安装在龙巴萨巴冰碛坝上的自动气象站记录获取。\n
2. 定日气象站气温数据\n
由中国气象数据网获取，下载地址为http://data.cma.cn/。\n
3. CMIP6未来情景气温数据\n
空间分辨率为100-250 km近地表气温数据，下载地址:https://esgf-node.llnl.gov/search/chttps://esgf-node.llnl.gov/search/cmip6/。\n
4.坝体冻融过程和埋藏冰消融数据\n
基于COMSOL Multiphysics传热模块计算产生。\n
5. 坝体表面形变数据\n
由PS-InSAR监测技术获取。", "ds_process_way": "
1. 龙巴萨巴自动气象站数据处理：\n
由龙巴萨巴自动气象站导出格式为\".data\"格式的原始数据，由excel整理成\".xlsx\"格式的表格数据。\n
2. 定日气象站数据校准:：\n
从中国气象数据网获取定日气象站（站点编号：55664）1959-2021年的气温数据，通过观测时期2012-2021年地温和气温的线性关系，换算成龙巴萨巴冰碛坝10cm深度处的土壤温度。\n
3. 未来情景数据提取\n
从CMIP6官网下载SPP1-2.6，SSP2-4.5，SSP5-8.5三种情景下的各个气候模式数据，用python提取出研究区所在格网的气温数据，从中选取地温-气温相关性高(R2>0.5)的8种气候模式,换算成未来情景下龙巴萨巴冰碛坝10 cm深度处的土壤温度。\n
4.坝体冻融过程模拟\n
基于COMSOL Multiphysics的传热模块，建立起一维的坝体传热模型，输入坝体10 cm深度的土壤温度作为上边界边界条件，下边界条件设置为零通量，时间步长为天，空间步长为1 cm，输出时间和深度的二维温度矩阵。\n
5.年最大融化深度和埋藏冰融化深度\n
利用python语言编写一个提取0℃等温线的程序，夏季0℃等温线的最大深度即为坝体年最大融化深度(YMTD)，埋藏冰融化深度(BIMD)随YMTD增加而增加。在YMTD减少的年份，BITD保持在与前一年相同的数值。\n
6.坝体表面形变数据\n
使用了2017年至2020年的44张Sentinel-1A上升轨道的影像，由GAMMA软件处理得到。", "ds_quality": "
数据精度：\n
(1)龙巴萨巴自动气象站记录的温度数据精度为±0.2 °C；\n
(2)定日气象站的气温数据精度为±0.1°C；\n
(3)未来情景数据的最大空间分辨率为100km；\n
(4)处理后的温度数据保留2位有效数字。\n
(5)坝体冻融过程只保留25 m深度以上10 cm 步长的数据。\n
(6)坝体表面形变数据精度为±0.001 mm。", "ds_acq_start_time": "1959-01-01 00:00:00", "ds_acq_end_time": "2099-12-31 00:00:00", "ds_acq_place": "龙巴萨巴冰碛坝;定日气象站", "ds_acq_lon_east": 88.0, "ds_acq_lat_south": 27.0, "ds_acq_lon_west": 88.0, "ds_acq_lat_north": 27.0, "ds_acq_alt_low": 5520.0, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 336339718, "ds_files_count": 2, "ds_format": ".xlsx,.tif", "ds_space_res": null, "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "2197d695-37fb-4e64-8ae5-41b83fa264d9.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2023-01-10 18:50:09", "last_updated": "2023-06-13 11:25:57", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.db2688.2023", "i18n": { "en": { "title": "Temperature and buried ice melting dataset from 1959-2099 at Longbasaba moraine dam", "ds_format": ".xlsx,.tif", "ds_source": "
1. Temperature data of Longbasaba moraine dam\n
Acquired from the automatic meteorological station records installed on the Longbasaba moraine dam.\n
2. Temperature data of Dingri meteorological station\n
Acquired from the China Meteorological Information Center and downloaded from http://data.cma.cn/.\n
3.Temperature data of CMIP6 future scenario \n
Near-surface air temperature with spatial resolution of 100-250 km , download at:\nhttps://esgf-node.llnl.gov/search/chttps://esgf-node.llnl.gov/searcSpatial resolution of 100-250 km near-surface air temperature nc data, download at:\nhttps://esgf-node.llnl.gov/search/chttps://esgf-node.llnl.gov/search/cmip6/.\n
4.Dam freeze-thaw process and buried ice ablation data\n
Generated based on the COMSOL Multiphysics heat transfer module.\n
5. Dam surface deformation data\n
Acquired by PS-InSAR monitoring technique.", "ds_quality": "
Accuracy of Data：\n
(1) Temperature data accuracy of ±0.2 °C recorded by automatic meteorological stations on the Longbasaba moraine dam;\n
(2) Accuracy of ±0.1 °C for temperature data from DIngri meteorological stations;\n
(3) The maximum spatial resolution of future scenario data is 100km;\n
(4)The processed temperature data is retained as 2 valid digits.\n
(5) Only data for 10 cm steps above 25 m depth are retained for the freeze-thaw process of the dam.\n
(6) The accuracy of the dam surface deformation data is ±0.001 mm.", "ds_ref_way": "", "ds_abstract": "
This dataset is based on an automatic meteorological station installed on the moraine dam of Lake Longbasaba to record air temperature data on the moraine dam for the period November 11, 2012 to March 6, 2021. Five ground temperature probes (Campbell 109-L, ±0.2°C) were installed on the dam near the outlet of Lake Longbasaba at depths of 10 cm, 30 cm, 60 cm, 100 cm, and 150 cm, and the sensors were all connected to a data acquisition instrument (Campbell CR3000-XT) that automatically recorded every 10 minutes. Daily mean air temperatures were collected from the Dingri meteorological station (28°38′N, 87°05′E, 4 300 m a.s.l.) from 1960 to 2021, and the soil temperature at 10 cm depth in the Longbasaba moraine dam was interpolated from 1959 to 2021 by a linear soil-air temperature relationship. In addition, the daily average near-surface air temperatures for three future scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5, were obtained from the official CMIP6 website (https://esgf-node.llnl.gov/search/cmip6) for the years 2015-2099, and eight climate models with soil-air temperature correlation coefficients R2 > 0.5 were selected and converted to soil temperature at 10 cm depth of the dam.Using the soil temperature at 10 cm depth of the moraine dam as input data and based on the heat transfer module of COMSOL Multiphysics software, the freeze-thaw process and maximum thawing depth of the dam for the observed period 1959-2021 and the future scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5 for 2015-2099 were simulated. The melting depth of buried ice inside the dam was also calculated for future scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5. The deformation data of the dam surface were acquired in combination with PS-InSAR technique. This temperature dataset of Longbasaba moraine dam provides the basic data for the study of freeze-thaw processes within the active layer of Longbazaba moraine dam.\n
1. Name of Data\n
Temperature data from Longbasaba automatic meteorological station_2012-2021.xlsx\n
Temperature data from Dingri meteorological station_1959-2021.xlsx\n
Temperature data from CMIP6_2015-2099.xlsx\n
Freeze-thaw process_1959-2021.xlsx\n
Freeze-thaw process of SSP1-2.6_2015-2099.xlsx\n
Freeze-thaw process of SSP2-4.5_2015-2099.xlsx\n
Freeze-thaw process of SSP5-8.5_2015-2099.xlsx\n
Active layer thickness_1959-2020.xlsx\n
Buried ice melting depth of SSP1-2.6_2015-2099\n
Buried ice melting depth of SSP2-4.5_2015-2099\n
Buried ice melting depth of SSP5-8.5_2015-2099\n
Dam_deformationm_deformation.tif\n
2. Data description of attribute items \n
Time_stamp: Timestamp of data\n
Ta_Longbasaba: Mean daily air temperature on the dam of Longbasaba moraine dam (℃)\n
Ts_10 cm, Ts_30 cm, Ts_60 cm, Ts_100 cm, Ts_150 cm: Mean daily soil temperature at 10 cm, 30 cm, 60 cm, 100 cm, 150 cm depth of Longbasaba
Ta_Dingri :Mean daily air temperature at the Dingri meteorological station (℃) \n
Ta_Longbasba_r: Reconstructed Mean daily air temperature of the Longbasba moraine dam (°C)\n
Ts_10 cm_r: Reconstructed Mean daily soil temperature at 10 cm depth of the Longbasba moraine dam (°C)\n
Simulated Ts of different depths: Simulated values of soil temperature at different depths (°C) YMTD: Annual maximum melt depth (m)\n
YMTD: yeayly maximum melt depth (m)\n
BIMD: Buried Ice melting depth (m)
", "ds_time_res": "日", "ds_acq_place": "Longbasaba moraine dam, Dingri meteorological station", "ds_space_res": "", "ds_projection": "", "ds_process_way": "1. Processing of automatic meteorological station data of Longbasaba moraine dam .\n
Export the raw data from Longbasaba automatic weather station in \".data\" format and organize it into table data in \".xlsx\" format by excel.\n
2. Calibration of meteorological data in Dingri meteorological station:.\n
The temperature data of Dingri meteorological station (station ID: 55664) from 1959-2021 were obtained from the China Meteorological Information Center, and converted into soil temperature at 10cm depth of Longbasaba moraine dam by the linear relationship between soil temperature and air temperature in the observed period 2012-2021.\n
3. Future scenario data extraction\n
The data of SPP1-2.6, SSP2-4.5 and SSP5-8.5 were downloaded from the CMIP6 website, and the air temperature data of the grid including the study area were extracted by python, from which eight climate models with high correlation between soil temperature and air temperature (R2>0.5) were selected and converted into soil temperature at 10 cm depth of Longbasaba moraine dam in the future scenario.\n
4. Freeze-thaw process simulation of the dam\n
Based on the heat transfer module of COMSOL Multiphysics, a one-dimensional heat transfer model of the dam was established. The soil temperature at depth of 10 cm of the dam was input as the upper boundary boundary condition, the lower boundary condition was set to zero-flux, the temporal step is day, and the spatial step is 1 cm, and the output is a two-dimensional temperature matrices of time and depth .\n
5. Calculation of Yearly maximum thawing depth and buried ice melting depth\n
A program is written in python language to extract the 0°C isotherm. The maximum depth of 0℃ isotherm in summer is the annual maximum melt depth (YMTD) of the dam, and the Buried ice thaw depth (BIMD) increases with YMTD. During the years in which YMTD decreases, BIMD remains at the same value as that of the previous year. \n
6. Dam surface deformation data\n
44 images of Sentinel-1A ascending orbit from 2017 to 2020 were used, obtained by GAMMA software processing.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "温度数据", "龙巴萨巴", "土壤温度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "龙巴萨巴冰碛坝" ], "ds_time_tags": [ 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099 ], "ds_contributors": [ { "true_name": "王欣", "email": "wangx@hnust.edu.cn", "work_for": "1.湖南科技大学地球科学与空间信息工程学院 2.中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王嘉", "email": "jia_wang@mail.hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "张艳林", "email": "zhangyan102@163.com", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "冉伟杰", "email": "ran.wj@mail.hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "张勇", "email": "yongzhang@hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "魏俊锋", "email": "weijunfeng@hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "刘巧", "email": "liuqiao@imde.ac.cn", "work_for": "中国科学院、水利部成都山地灾害与环境研究所", "country": "中国" }, { "true_name": "雷东钰", "email": "lei.dy@mail.hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王欣", "email": "wangx@hnust.edu.cn", "work_for": "1.湖南科技大学地球科学与空间信息工程学院 2.中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王嘉", "email": "jia_wang@mail.hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "张艳林", "email": "zhangyan102@163.com", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "冉伟杰", "email": "ran.wj@mail.hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "张勇", "email": "yongzhang@hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "魏俊锋", "email": "weijunfeng@hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" }, { "true_name": "刘巧", "email": "liuqiao@imde.ac.cn", "work_for": "中国科学院、水利部成都山地灾害与环境研究所", "country": "中国" }, { "true_name": "雷东钰", "email": "lei.dy@mail.hnust.edu.cn", "work_for": "湖南科技大学地球科学与空间信息工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "王欣", "email": "wangx@hnust.edu.cn", "work_for": "1.湖南科技大学地球科学与空间信息工程学院 2.中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冰川" }